CN117279164B - Lighting lamp control method and system based on data prediction - Google Patents
Lighting lamp control method and system based on data prediction Download PDFInfo
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Abstract
The invention discloses a lighting lamp control method and a lighting lamp control system based on data prediction, wherein the method comprises the following steps: acquiring historical brightness adjustment operation of a target user on a target lighting lamp in a historical time period and corresponding lighting lamp sensing parameters; establishing a mathematical relationship model corresponding to the target user according to the historical brightness adjustment operation and the corresponding lighting lamp sensing parameters; determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameters and the mathematical relationship model; and when the current display brightness is higher than the energy-saving brightness range corresponding to the current display brightness preference, determining a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference. Therefore, the invention can simultaneously give consideration to the energy-saving lighting of the lighting lamp for controlling and taking care of the brightness preference experience of the user.
Description
Technical Field
The invention relates to the technical field of lamp data processing, in particular to a lamp control method and system based on data prediction.
Background
With the rising of the lighting lamp in the home market and the development of data processing technology, more and more intelligent lighting lamps are also researched and developed, and the intelligent lighting lamps have data processing capability and can provide more intelligent lighting services for users.
However, in the prior art, because of the diversified functional components of the intelligent lighting lamp, power consumption is generally high, and how to realize energy-saving control of the intelligent lighting lamp is a research and development focus, but the existing energy-saving control of the lighting lamp is still generally simply in the selection of a luminescent material or the control of the luminescent power, and the control effect is improved by not considering the combination of algorithm advantages and the historical operation record of a user. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lighting lamp control method and a lighting lamp control system based on data prediction, which can simultaneously give consideration to the energy-saving lighting of a lighting lamp for controlling and caring the brightness preference experience of a user, and improve the user experience while achieving energy-saving control.
In order to solve the technical problem, a first aspect of the present invention discloses a lighting lamp control method based on data prediction, the method comprising:
Acquiring historical brightness adjustment operation of a target user on a target lighting lamp in a historical time period and corresponding lighting lamp sensing parameters;
establishing a mathematical relationship model among the time point corresponding to the target user, the lighting lamp sensing parameters and the brightness preference according to the historical brightness adjustment operation and the corresponding lighting lamp sensing parameters;
when the target lighting lamp works, determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameters and the mathematical relationship model;
when the current display brightness is higher than an energy-saving brightness range corresponding to the current display brightness preference, determining a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference; the power change instruction is used for adjusting the current display brightness to the energy-saving brightness range.
As an optional implementation manner, in the first aspect of the present invention, the historical brightness adjustment operation includes an adjustment operation and an operation parameter, and the adjustment operation is a brightness increasing operation or a brightness decreasing operation; the operation parameters comprise at least one of operation time, operation repetition times and brightness change degree control; and/or the illumination lamp sensing parameters comprise at least one of temperature sensing parameters, image sensing parameters, ambient light brightness sensing parameters and position sensing parameters of the target illumination lamp.
In a first aspect of the present invention, the establishing a mathematical relationship model between the time point, the lighting lamp sensing parameter and the brightness preference corresponding to the target user according to the historical brightness adjustment operation and the corresponding lighting lamp sensing parameter includes:
determining each historical brightness adjustment operation and a corresponding operation time point as a first training data set;
training according to the first training data set to obtain a first operation prediction neural network model capable of predicting brightness adjustment operation according to a time point;
determining each historical brightness adjustment operation and corresponding lighting lamp sensing parameters as a second training data set;
training according to the second training data set to obtain a second operation prediction neural network model capable of adjusting operation according to sensing prediction brightness predicted by the sensing parameters of the lighting lamp;
fitting to obtain a polynomial relation model among the operation time points, the illumination lamp sensing parameters and the brightness adjustment operation according to each historical brightness adjustment operation, the corresponding operation time points and the illumination lamp sensing parameters based on a polynomial fitting algorithm;
and determining the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model as mathematical relation models among the time points, the lighting lamp sensing parameters and the brightness preferences corresponding to the target user.
In an optional implementation manner, in a first aspect of the present invention, the determining, according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameter, and the mathematical relationship model, the current display brightness preference corresponding to the target user includes:
inputting the current time point into the first operation prediction neural network model to obtain a first prediction brightness operation corresponding to the target user and a corresponding first prediction probability;
inputting current lighting lamp sensing parameters into the second operation prediction neural network model to obtain a second prediction brightness operation corresponding to the target user and a second prediction probability corresponding to the target user;
inputting the current time point and the current lighting lamp sensing parameters into the polynomial relation model to obtain a third predicted brightness operation corresponding to the target user;
determining a final predicted brightness operation corresponding to the target user according to the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability and the third predicted brightness operation;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the final predicted brightness operation.
As an optional implementation manner, in a first aspect of the present invention, the determining, according to the first predicted luminance operation, the first predicted probability, the second predicted luminance operation, the second predicted probability, and the third predicted luminance operation, a final predicted luminance operation corresponding to the target user includes:
calculating a first operation similarity between the first predicted luminance operation and the second predicted luminance operation;
calculating a second operation similarity between the first predicted luminance operation and the third predicted luminance operation;
calculating a third operation similarity between the second predicted luminance operation and the third predicted luminance operation;
determining priorities corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity and the third operation similarity;
and determining the operation with the highest priority among the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation as the final predicted brightness operation corresponding to the target user.
As an optional implementation manner, in a first aspect of the present invention, the determining priorities corresponding to the first predicted luminance operation, the second predicted luminance operation, and the third predicted luminance operation according to the first predicted probability, the second predicted probability, the first operation similarity, the second operation similarity, and the third operation similarity includes:
calculating a first average value of the first operation similarity and the second operation similarity and a product of the first average value and a first weight to obtain a priority corresponding to the first predicted brightness operation; the first weight is proportional to the first predictive probability;
calculating a second average value of the first operation similarity and the third operation similarity and a product of the second average value and a second weight to obtain a priority corresponding to the second predicted brightness operation; the second weight is proportional to the second predictive probability;
calculating a third average value of the second operation similarity and the third operation similarity and a product of the third average value and a third weight to obtain a priority corresponding to the third predicted brightness operation; the third weight is greater than 1 when the third average value is higher than the first average value and the second average value, and is less than 1 otherwise; the absolute value of the third weight is in direct proportion to the average value difference value; the average value difference value is a difference value between the third average value and a fourth average value, and the fourth average value is an average value of the first average value and the second average value.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the current display brightness of the target lighting lamp and the final predicted brightness operation, a current display brightness preference corresponding to the target user includes:
determining a predicted brightness change parameter corresponding to the final predicted brightness operation according to the final predicted brightness operation and a preset corresponding relation model of operation and brightness change;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the predicted brightness change parameter.
The second aspect of the invention discloses a lighting lamp control system based on data prediction, which comprises:
the acquisition module is used for acquiring historical brightness adjustment operation of a target user on a target lighting lamp in a historical time period and corresponding lighting lamp sensing parameters;
the establishing module is used for establishing a mathematical relation model among the time point corresponding to the target user, the lighting lamp sensing parameter and the brightness preference according to the historical brightness adjusting operation and the corresponding lighting lamp sensing parameter;
the determining module is used for determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameters and the mathematical relationship model when the target lighting lamp works;
The adjusting module is used for determining a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference when the current display brightness is higher than an energy-saving brightness range corresponding to the current display brightness preference; the power change instruction is used for adjusting the current display brightness to the energy-saving brightness range.
As an alternative embodiment, in the second aspect of the present invention, the historical brightness adjustment operation includes an adjustment operation and an operation parameter, the adjustment operation being a brightness increase operation or a brightness decrease operation; the operation parameters comprise at least one of operation time, operation repetition times and brightness change degree control; and/or the illumination lamp sensing parameters comprise at least one of temperature sensing parameters, image sensing parameters, ambient light brightness sensing parameters and position sensing parameters of the target illumination lamp.
In a second aspect of the present invention, the specific manner of establishing the mathematical relationship model between the time point corresponding to the target user, the lighting lamp sensing parameter and the brightness preference according to the historical brightness adjustment operation and the corresponding lighting lamp sensing parameter includes:
Determining each historical brightness adjustment operation and a corresponding operation time point as a first training data set;
training according to the first training data set to obtain a first operation prediction neural network model capable of predicting brightness adjustment operation according to a time point;
determining each historical brightness adjustment operation and corresponding lighting lamp sensing parameters as a second training data set;
training according to the second training data set to obtain a second operation prediction neural network model capable of adjusting operation according to sensing prediction brightness predicted by the sensing parameters of the lighting lamp;
fitting to obtain a polynomial relation model among the operation time points, the illumination lamp sensing parameters and the brightness adjustment operation according to each historical brightness adjustment operation, the corresponding operation time points and the illumination lamp sensing parameters based on a polynomial fitting algorithm;
and determining the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model as mathematical relation models among the time points, the lighting lamp sensing parameters and the brightness preferences corresponding to the target user.
In a second aspect of the present invention, as an optional implementation manner, the determining module determines, according to a current time point, a current lighting lamp sensing parameter, and the mathematical relationship model, a specific manner of the current display brightness preference corresponding to the target user, where the determining module includes:
Inputting the current time point into the first operation prediction neural network model to obtain a first prediction brightness operation corresponding to the target user and a corresponding first prediction probability;
inputting current lighting lamp sensing parameters into the second operation prediction neural network model to obtain a second prediction brightness operation corresponding to the target user and a second prediction probability corresponding to the target user;
inputting the current time point and the current lighting lamp sensing parameters into the polynomial relation model to obtain a third predicted brightness operation corresponding to the target user;
determining a final predicted brightness operation corresponding to the target user according to the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability and the third predicted brightness operation;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the final predicted brightness operation.
As an optional implementation manner, in the second aspect of the present invention, the determining module determines, according to the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability, and the third predicted brightness operation, a specific manner of a final predicted brightness operation corresponding to the target user, where the determining module includes:
Calculating a first operation similarity between the first predicted luminance operation and the second predicted luminance operation;
calculating a second operation similarity between the first predicted luminance operation and the third predicted luminance operation;
calculating a third operation similarity between the second predicted luminance operation and the third predicted luminance operation;
determining priorities corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity and the third operation similarity;
and determining the operation with the highest priority among the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation as the final predicted brightness operation corresponding to the target user.
As an optional implementation manner, in the second aspect of the present invention, the determining module determines, according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity, and the third operation similarity, a specific manner of priorities corresponding to the first prediction luminance operation, the second prediction luminance operation, and the third prediction luminance operation, respectively, includes:
Calculating a first average value of the first operation similarity and the second operation similarity and a product of the first average value and a first weight to obtain a priority corresponding to the first predicted brightness operation; the first weight is proportional to the first predictive probability;
calculating a second average value of the first operation similarity and the third operation similarity and a product of the second average value and a second weight to obtain a priority corresponding to the second predicted brightness operation; the second weight is proportional to the second predictive probability;
calculating a third average value of the second operation similarity and the third operation similarity and a product of the third average value and a third weight to obtain a priority corresponding to the third predicted brightness operation; the third weight is greater than 1 when the third average value is higher than the first average value and the second average value, and is less than 1 otherwise; the absolute value of the third weight is in direct proportion to the average value difference value; the average value difference value is a difference value between the third average value and a fourth average value, and the fourth average value is an average value of the first average value and the second average value.
In a second aspect of the present invention, as an optional implementation manner, the determining module determines, according to the current display brightness of the target lighting lamp and the final predicted brightness operation, a specific manner of the current display brightness preference corresponding to the target user, where the specific manner includes:
determining a predicted brightness change parameter corresponding to the final predicted brightness operation according to the final predicted brightness operation and a preset corresponding relation model of operation and brightness change;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the predicted brightness change parameter.
In a third aspect, the present invention discloses another illumination lamp control system based on data prediction, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to execute some or all of the steps in the data prediction based lighting lamp control method disclosed in the first aspect of the present invention.
A fourth aspect of the present invention discloses a computer storage medium storing computer instructions for performing part or all of the steps of the data prediction-based illumination lamp control method disclosed in the first aspect of the present invention when the computer instructions are called.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, a prediction mathematical model corresponding to a user can be established according to the historical brightness adjustment operation of a target user and the sensing parameters of the lighting lamp, the brightness preference of the user is predicted in the process of real-time working of the lighting lamp, and the brightness reduction adjustment is realized when the brightness of the lighting lamp is too high, so that the energy-saving lighting of the lighting lamp can be simultaneously considered for controlling and caring the brightness preference experience of the user, and the user experience is improved while the energy-saving control is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a lighting lamp control method based on data prediction according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of an illumination lamp control system based on data prediction according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of another lighting lamp control system based on data prediction according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a data prediction-based illumination lamp control method and system, which can establish a prediction mathematical model corresponding to a user according to historical brightness adjustment operation of a target user and sensing parameters of an illumination lamp, predict brightness preference of the user in the process of real-time work of the illumination lamp, and realize brightness reduction adjustment when the illumination lamp brightness is too high, so that energy-saving illumination of the illumination lamp can be simultaneously considered for controlling and caring for brightness preference experience of the user, and the user experience is improved while energy-saving control is achieved. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of an illumination lamp control method based on data prediction according to an embodiment of the invention. The method described in fig. 1 may be applied to a corresponding data processing device, a data processing terminal, and a data processing server, where the server may be a local server or a cloud server, and the embodiment of the present invention is not limited to the method shown in fig. 1, and the method for controlling an illumination lamp based on data prediction may include the following operations:
101. And acquiring historical brightness adjustment operation of the target user on the target lighting lamp in a historical time period and corresponding lighting lamp sensing parameters.
Specifically, the historical brightness adjustment operation includes an adjustment operation and an operation parameter.
Specifically, the adjustment operation is a luminance increasing operation or a luminance decreasing operation.
Specifically, the operation parameter includes at least one of operation time, the number of operation iterations, and the degree of control brightness change.
Specifically, the illumination lamp sensing parameters include at least one of a temperature sensing parameter, an image sensing parameter, an ambient light brightness sensing parameter, and a position sensing parameter of the target illumination lamp.
102. And establishing a mathematical relationship model among the time point corresponding to the target user, the lighting lamp sensing parameters and the brightness preference according to the historical brightness adjustment operation and the corresponding lighting lamp sensing parameters.
103. When the target lighting lamp works, the current display brightness preference corresponding to the target user is determined according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameters and the mathematical relationship model.
104. And when the current display brightness is higher than the energy-saving brightness range corresponding to the current display brightness preference, determining a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference.
Specifically, the power change instruction is used for adjusting the current display brightness to the energy-saving brightness range.
Specifically, the energy-saving brightness range is a brightness value interval corresponding to the brightness value of the current display brightness preference, the size of the interval can be determined by an operator according to the energy-saving requirement of different lighting lamps or the brightness requirement of a user, and generally, the difference between the upper limit of the interval and the brightness value of the current display brightness preference is smaller than the difference between the lower limit of the interval and the brightness value, so that the energy-saving brightness range is a brightness interval with a middle value smaller than the current display brightness preference.
Optionally, the power change instruction may be determined according to a preset correspondence between the brightness difference and the change instruction, and the power change instruction may be determined according to a difference between any brightness value in the current display brightness and the energy-saving brightness range, and the correspondence.
Therefore, the method described by the embodiment of the invention can establish a prediction mathematical model corresponding to the user according to the historical brightness adjustment operation of the target user and the sensing parameters of the lighting lamp, predict the brightness preference of the user in the process of real-time work of the lighting lamp, and realize the adjustment of brightness reduction when the brightness of the lighting lamp is too high, so that the energy-saving lighting of the lighting lamp can be simultaneously considered for controlling and caring the brightness preference experience of the user, and the user experience is improved while the energy-saving control is achieved.
As an optional embodiment, in the step, according to the historical brightness adjustment operation and the corresponding lighting lamp sensing parameters, a mathematical relationship model between the time point corresponding to the target user, the lighting lamp sensing parameters and the brightness preference is established, which includes:
determining each historical brightness adjustment operation and a corresponding operation time point as a first training data set;
training according to a first training data set to obtain a first operation prediction neural network model capable of predicting brightness adjustment operation according to a time point;
determining each historical brightness adjustment operation and corresponding lighting lamp sensing parameters as a second training data set;
training according to a second training data set to obtain a second operation prediction neural network model capable of adjusting operation according to sensing prediction brightness predicted by the sensing parameters of the lighting lamp;
fitting according to each historical brightness adjustment operation and corresponding operation time points and illumination lamp sensing parameters based on a polynomial fitting algorithm to obtain a polynomial relation model among the operation time points, the illumination lamp sensing parameters and the brightness adjustment operation;
and determining the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model as mathematical relation models among the time points corresponding to the target user, the lighting lamp sensing parameters and the brightness preference.
Optionally, the neural network model in the invention can be a neural network prediction algorithm model with a CNN structure, a RNN structure or an LTSM structure, and an operator can select and test a corresponding model architecture according to actual data scenes and data characteristics.
Optionally, the fitting to obtain the polynomial relation model may be implemented by a dynamic programming algorithm and a least square method, in the fitted polynomial relation model, the operation time point, the lighting lamp sensing parameter and the brightness adjustment operation are respectively represented by at least one parameter value, and the polynomial relation model is formed by a polynomial mathematical relation between the parameter values.
Through the embodiment, the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model can be obtained through training of the plurality of training data sets, so that the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model are used as mathematical relation models of target users to realize subsequent operation prediction and verification screening, accurate user brightness preference can be obtained through prediction, and brightness preference experience of users can be effectively cared.
As an optional embodiment, in the step, determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameter and the mathematical relationship model includes:
Inputting the current time point into a first operation prediction neural network model to obtain a first prediction brightness operation corresponding to a target user and a corresponding first prediction probability;
inputting the current lighting lamp sensing parameters into a second operation prediction neural network model to obtain a second prediction brightness operation corresponding to the target user and a corresponding second prediction probability;
inputting the current time point and the current lighting lamp sensing parameters into a polynomial relation model to obtain a third predicted brightness operation corresponding to the target user;
determining a final predicted brightness operation corresponding to the target user according to the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability and the third predicted brightness operation;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the final predicted brightness operation.
Through the embodiment, the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability and the third predicted brightness operation can be predicted through the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model, so that the accurate final predicted brightness operation of the user can be determined, the accurate user brightness preference can be obtained through the prediction, and the brightness preference experience of the user can be effectively cared.
As an alternative embodiment, in the step, determining the final predicted luminance operation corresponding to the target user according to the first predicted luminance operation, the first predicted probability, the second predicted luminance operation, the second predicted probability, and the third predicted luminance operation includes:
calculating a first operation similarity between the first predicted luminance operation and the second predicted luminance operation;
calculating a second operation similarity between the first predicted luminance operation and the third predicted luminance operation;
calculating a third operation similarity between the second predicted luminance operation and the third predicted luminance operation;
determining priorities corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation respectively according to the first predicted probability, the second predicted probability, the first operation similarity, the second operation similarity and the third operation similarity;
and determining the operation with the highest priority in the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation as the final predicted brightness operation corresponding to the target user.
Through the embodiment, the first operation similarity, the second operation similarity and the third operation similarity can be calculated to determine the accurate priority corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation respectively, so that the accurate final predicted brightness operation of the user can be determined later, the accurate user brightness preference can be obtained through prediction, and the brightness preference experience of the user can be effectively attended to.
As an optional embodiment, in the step, determining priorities corresponding to the first predicted luminance operation, the second predicted luminance operation, and the third predicted luminance operation according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity, and the third operation similarity, respectively, includes:
calculating a first average value of the first operation similarity and the second operation similarity and a product of the first average value and the first weight to obtain a priority corresponding to the first predicted brightness operation; the first weight is proportional to the first prediction probability;
calculating a second average value of the first operation similarity and the third operation similarity and a product of the second average value and the second weight to obtain a priority corresponding to the second predicted brightness operation; the second weight is proportional to the second predictive probability;
calculating a third average value of the second operation similarity and the third operation similarity and a product of the third average value and a third weight to obtain a priority corresponding to a third predicted brightness operation; the third weight is greater than 1 when the third average value is higher than the first average value and the second average value, and is less than 1 otherwise; the absolute value of the third weight is proportional to the average value difference; the average value difference is the difference between the third average value and a fourth average value, and the fourth average value is the average value of the first average value and the second average value.
Specifically, the third weight is determined by the fact that the reliability of the prediction of the polynomial fitting mathematical model is low under the condition of more parameters, so that if the prediction result is far different from the prediction results of other two neural networks, the reliability of the prediction result is effectively reduced, and the reduction degree is also in direct proportion to the difference degree.
Through the embodiment, the accurate priorities corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation can be calculated through the average value calculation and the weight determination rule, the calculated priorities can effectively represent the credibility and the matching degree corresponding to different predicted operations, the accurate final predicted brightness operation of the user can be determined later, accurate user brightness preference can be obtained through prediction, and the brightness preference experience of the user can be effectively attended to.
As an optional embodiment, in the step, determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the final predicted brightness operation includes:
determining a predicted brightness change parameter corresponding to the final predicted brightness operation according to the final predicted brightness operation and a preset corresponding relation model of operation and brightness change;
And determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the predicted brightness change parameter.
Alternatively, the corresponding relation model of the operation and the brightness change may be a corresponding rule between specific parameters preset by an operator according to experience or experimental results, or may be a neural network prediction model obtained by training a training data set including a plurality of training brightness operations and corresponding brightness change labels, so as to be used for predicting a predicted brightness change parameter corresponding to the final predicted brightness operation.
Through the embodiment, the predicted brightness change parameters corresponding to the final predicted brightness operation can be determined through the preset corresponding relation model of the operation and the brightness change, and the accurate user brightness preference is obtained through the determination, so that the subsequent brightness adjustment is carried out while the user brightness preference experience is taken care of while the effective energy-saving management and control is carried out.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an illumination lamp control system based on data prediction according to an embodiment of the present invention. The system described in fig. 2 may be applied to a corresponding data processing device, a data processing terminal, and a data processing server, where the server may be a local server or a cloud server, and embodiments of the present invention are not limited. As shown in fig. 2, the system may include:
An obtaining module 201, configured to obtain a historical brightness adjustment operation of a target user on a target lighting lamp and corresponding lighting lamp sensing parameters in a historical time period;
the establishing module 202 is configured to establish a mathematical relationship model between a time point corresponding to the target user, the illumination lamp sensing parameter and the brightness preference according to the historical brightness adjustment operation and the corresponding illumination lamp sensing parameter;
the determining module 203 is configured to determine, when the target lighting lamp works, a current display brightness preference corresponding to the target user according to a current display brightness of the target lighting lamp, a current time point, a current lighting lamp sensing parameter and a mathematical relationship model;
the adjusting module 204 is configured to determine a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference when the current display brightness is higher than the energy-saving brightness range corresponding to the current display brightness preference; the power change instruction is used for adjusting the current display brightness to the energy-saving brightness range.
As an alternative embodiment, the historical brightness adjustment operation includes an adjustment operation and an operation parameter, the adjustment operation being a brightness increase operation or a brightness decrease operation; the operation parameters include at least one of operation time, operation repetition number and control brightness variation degree; and/or the illumination lamp sensing parameters include at least one of a temperature sensing parameter, an image sensing parameter, an ambient light brightness sensing parameter, and a position sensing parameter of the target illumination lamp.
As an alternative embodiment, the specific manner of establishing the mathematical relationship model between the time point corresponding to the target user, the illumination lamp sensing parameter and the brightness preference by the establishing module 202 according to the historical brightness adjustment operation and the corresponding illumination lamp sensing parameter includes:
determining each historical brightness adjustment operation and a corresponding operation time point as a first training data set;
training according to a first training data set to obtain a first operation prediction neural network model capable of predicting brightness adjustment operation according to a time point;
determining each historical brightness adjustment operation and corresponding lighting lamp sensing parameters as a second training data set;
training according to a second training data set to obtain a second operation prediction neural network model capable of adjusting operation according to sensing prediction brightness predicted by the sensing parameters of the lighting lamp;
fitting according to each historical brightness adjustment operation and corresponding operation time points and illumination lamp sensing parameters based on a polynomial fitting algorithm to obtain a polynomial relation model among the operation time points, the illumination lamp sensing parameters and the brightness adjustment operation;
and determining the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model as mathematical relation models among the time points corresponding to the target user, the lighting lamp sensing parameters and the brightness preference.
As an optional embodiment, the determining module 203 determines, according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameter and the mathematical relationship model, a specific manner of the current display brightness preference corresponding to the target user, including:
inputting the current time point into a first operation prediction neural network model to obtain a first prediction brightness operation corresponding to a target user and a corresponding first prediction probability;
inputting the current lighting lamp sensing parameters into a second operation prediction neural network model to obtain a second prediction brightness operation corresponding to the target user and a corresponding second prediction probability;
inputting the current time point and the current lighting lamp sensing parameters into a polynomial relation model to obtain a third predicted brightness operation corresponding to the target user;
determining a final predicted brightness operation corresponding to the target user according to the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability and the third predicted brightness operation;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the final predicted brightness operation.
As an alternative embodiment, the determining module 203 determines a specific manner of the final predicted luminance operation corresponding to the target user according to the first predicted luminance operation, the first predicted probability, the second predicted luminance operation, the second predicted probability, and the third predicted luminance operation, including:
calculating a first operation similarity between the first predicted luminance operation and the second predicted luminance operation;
calculating a second operation similarity between the first predicted luminance operation and the third predicted luminance operation;
calculating a third operation similarity between the second predicted luminance operation and the third predicted luminance operation;
determining priorities corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation respectively according to the first predicted probability, the second predicted probability, the first operation similarity, the second operation similarity and the third operation similarity;
and determining the operation with the highest priority in the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation as the final predicted brightness operation corresponding to the target user.
As an alternative embodiment, the determining module 203 determines, according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity, and the third operation similarity, a specific manner of the priorities corresponding to the first prediction luminance operation, the second prediction luminance operation, and the third prediction luminance operation, respectively, including:
Calculating a first average value of the first operation similarity and the second operation similarity and a product of the first average value and the first weight to obtain a priority corresponding to the first predicted brightness operation; the first weight is proportional to the first prediction probability;
calculating a second average value of the first operation similarity and the third operation similarity and a product of the second average value and the second weight to obtain a priority corresponding to the second predicted brightness operation; the second weight is proportional to the second predictive probability;
calculating a third average value of the second operation similarity and the third operation similarity and a product of the third average value and a third weight to obtain a priority corresponding to a third predicted brightness operation; the third weight is greater than 1 when the third average value is higher than the first average value and the second average value, and is less than 1 otherwise; the absolute value of the third weight is proportional to the average value difference; the average value difference is the difference between the third average value and a fourth average value, and the fourth average value is the average value of the first average value and the second average value.
As an optional embodiment, the determining module 203 determines, according to the current display brightness of the target lighting lamp and the final predicted brightness operation, a specific manner of the current display brightness preference corresponding to the target user, including:
Determining a predicted brightness change parameter corresponding to the final predicted brightness operation according to the final predicted brightness operation and a preset corresponding relation model of operation and brightness change;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the predicted brightness change parameter.
The details and technical effects of the modules in the embodiment of the present invention may refer to the description in the first embodiment, and are not described herein.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another lighting lamp control system based on data prediction according to an embodiment of the present invention. As shown in fig. 3, the system may include:
a memory 301 storing executable program code;
a processor 302 coupled with the memory 301;
the processor 302 invokes the executable program code stored in the memory 301 to perform some or all of the steps in the data prediction based illumination lamp control method disclosed in the embodiment of the present invention.
Example IV
The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing part or all of the steps in the illumination lamp control method based on data prediction disclosed in the embodiment of the invention when the computer instructions are called.
The system embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a lighting lamp control method and a lighting lamp control system based on data prediction, which are disclosed by the embodiment of the invention, are only used for illustrating the technical scheme of the invention, and are not limited by the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A method for controlling an illumination lamp based on data prediction, the method comprising:
acquiring historical brightness adjustment operation of a target user on a target lighting lamp in a historical time period and corresponding lighting lamp sensing parameters;
determining each historical brightness adjustment operation and a corresponding operation time point as a first training data set;
training according to the first training data set to obtain a first operation prediction neural network model capable of predicting brightness adjustment operation according to a time point;
Determining each historical brightness adjustment operation and corresponding lighting lamp sensing parameters as a second training data set;
training according to the second training data set to obtain a second operation prediction neural network model capable of adjusting operation according to sensing prediction brightness predicted by the sensing parameters of the lighting lamp;
fitting to obtain a polynomial relation model among the operation time points, the illumination lamp sensing parameters and the brightness adjustment operation according to each historical brightness adjustment operation, the corresponding operation time points and the illumination lamp sensing parameters based on a polynomial fitting algorithm;
determining the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model as mathematical relation models among time points, lighting lamp sensing parameters and brightness preferences corresponding to the target user;
when the target lighting lamp works, determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameters and the mathematical relationship model;
when the current display brightness is higher than an energy-saving brightness range corresponding to the current display brightness preference, determining a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference; the power change instruction is used for adjusting the current display brightness to the energy-saving brightness range.
2. The data prediction based illumination lamp control method according to claim 1, wherein the historical brightness adjustment operation includes an adjustment operation and an operation parameter, the adjustment operation being a brightness increase operation or a brightness decrease operation; the operation parameters comprise at least one of operation time, operation repetition times and brightness change degree control; and/or the illumination lamp sensing parameters comprise at least one of temperature sensing parameters, image sensing parameters, ambient light brightness sensing parameters and position sensing parameters of the target illumination lamp.
3. The method for controlling an illumination lamp based on data prediction according to claim 1, wherein determining the current display brightness preference corresponding to the target user according to the current display brightness of the target illumination lamp, the current time point, the current illumination lamp sensing parameter and the mathematical relationship model comprises:
inputting the current time point into the first operation prediction neural network model to obtain a first prediction brightness operation corresponding to the target user and a corresponding first prediction probability;
inputting current lighting lamp sensing parameters into the second operation prediction neural network model to obtain a second prediction brightness operation corresponding to the target user and a second prediction probability corresponding to the target user;
Inputting the current time point and the current lighting lamp sensing parameters into the polynomial relation model to obtain a third predicted brightness operation corresponding to the target user;
determining a final predicted brightness operation corresponding to the target user according to the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability and the third predicted brightness operation;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the final predicted brightness operation.
4. The method of claim 3, wherein determining a final predicted brightness operation corresponding to the target user based on the first predicted brightness operation, the first predicted probability, the second predicted brightness operation, the second predicted probability, and the third predicted brightness operation comprises:
calculating a first operation similarity between the first predicted luminance operation and the second predicted luminance operation;
calculating a second operation similarity between the first predicted luminance operation and the third predicted luminance operation;
Calculating a third operation similarity between the second predicted luminance operation and the third predicted luminance operation;
determining priorities corresponding to the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity and the third operation similarity;
and determining the operation with the highest priority among the first predicted brightness operation, the second predicted brightness operation and the third predicted brightness operation as the final predicted brightness operation corresponding to the target user.
5. The method of claim 4, wherein determining priorities of the first predicted luminance operation, the second predicted luminance operation, and the third predicted luminance operation according to the first prediction probability, the second prediction probability, the first operation similarity, the second operation similarity, and the third operation similarity, respectively, comprises:
calculating a first average value of the first operation similarity and the second operation similarity and a product of the first average value and a first weight to obtain a priority corresponding to the first predicted brightness operation; the first weight is proportional to the first predictive probability;
Calculating a second average value of the first operation similarity and the third operation similarity and a product of the second average value and a second weight to obtain a priority corresponding to the second predicted brightness operation; the second weight is proportional to the second predictive probability;
calculating a third average value of the second operation similarity and the third operation similarity and a product of the third average value and a third weight to obtain a priority corresponding to the third predicted brightness operation; the third weight is greater than 1 when the third average value is higher than the first average value and the second average value, and is less than 1 otherwise; the absolute value of the third weight is in direct proportion to the average value difference value; the average value difference value is a difference value between the third average value and a fourth average value, and the fourth average value is an average value of the first average value and the second average value.
6. The method for controlling a lighting lamp based on data prediction according to claim 3, wherein said determining a current display brightness preference corresponding to the target user based on the current display brightness of the target lighting lamp and the final predicted brightness operation comprises:
Determining a predicted brightness change parameter corresponding to the final predicted brightness operation according to the final predicted brightness operation and a preset corresponding relation model of operation and brightness change;
and determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp and the predicted brightness change parameter.
7. A data prediction based lighting lamp control system, the system comprising:
the acquisition module is used for acquiring historical brightness adjustment operation of a target user on a target lighting lamp in a historical time period and corresponding lighting lamp sensing parameters;
the establishing module is used for establishing a mathematical relation model among the time point corresponding to the target user, the lighting lamp sensing parameter and the brightness preference according to the historical brightness adjusting operation and the corresponding lighting lamp sensing parameter, and specifically comprises the following steps:
determining each historical brightness adjustment operation and a corresponding operation time point as a first training data set;
training according to the first training data set to obtain a first operation prediction neural network model capable of predicting brightness adjustment operation according to a time point;
determining each historical brightness adjustment operation and corresponding lighting lamp sensing parameters as a second training data set;
Training according to the second training data set to obtain a second operation prediction neural network model capable of adjusting operation according to sensing prediction brightness predicted by the sensing parameters of the lighting lamp;
fitting to obtain a polynomial relation model among the operation time points, the illumination lamp sensing parameters and the brightness adjustment operation according to each historical brightness adjustment operation, the corresponding operation time points and the illumination lamp sensing parameters based on a polynomial fitting algorithm;
determining the first operation prediction neural network model, the second operation prediction neural network model and the polynomial relation model as mathematical relation models among time points, lighting lamp sensing parameters and brightness preferences corresponding to the target user;
the determining module is used for determining the current display brightness preference corresponding to the target user according to the current display brightness of the target lighting lamp, the current time point, the current lighting lamp sensing parameters and the mathematical relationship model when the target lighting lamp works;
the adjusting module is used for determining a power change instruction of the target lighting lamp according to the current display brightness of the target lighting lamp and the current display brightness preference when the current display brightness is higher than an energy-saving brightness range corresponding to the current display brightness preference; the power change instruction is used for adjusting the current display brightness to the energy-saving brightness range.
8. A data prediction based lighting lamp control system, the system comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the data prediction based illumination lamp control method of any one of claims 1-6.
9. A computer storage medium storing computer instructions for performing the data prediction based illumination lamp control method according to any one of claims 1 to 6 when called.
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