CN117279139B - LED intelligent control method, device and equipment based on data perception - Google Patents
LED intelligent control method, device and equipment based on data perception Download PDFInfo
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- H05B45/00—Circuit arrangements for operating light-emitting diodes [LED]
- H05B45/10—Controlling the intensity of the light
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Abstract
The invention relates to an intelligent LED control method, device and equipment based on data perception, which belong to the technical field of LED control. According to the invention, the luminous parameters of the LEDs are predicted by fusing the external factors and the influence of the LEDs, so that the luminous parameters of the LEDs in corresponding scenes can be simulated, the control precision of the related control parameters of the LEDs is improved, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of LED control, in particular to an LED intelligent control method, device and equipment based on data perception.
Background
With the continuous development of the internet of things technology and the continuous upgrade of the intelligent lighting electrical technology, lighting electrical control starts to be interacted and fused with a software system gradually, and has positive influence on the development of the whole intelligent lighting field. However, the light emitting parameters of the LED are related to various factors, such as performance degradation of the LED itself and influence of external factors on the light emitting characteristics of the LED, which are not considered in the current technology, so that the related control parameters of the LED are not accurate enough, and the use experience of the user is reduced.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an LED intelligent control method, device and equipment based on data perception.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides an LED intelligent control method based on data perception, which comprises the following steps:
constructing a data sensing network, and acquiring sensing data information of a target area through the data sensing network;
acquiring internal factor data and external factor data influencing LED luminous parameters through big data, and predicting real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
Predicting real-time luminous parameters of the LEDs under the influence of external factor data based on the external factor data and the perception data of the target area, and acquiring the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
and generating relevant regulation and control parameters according to the luminous parameters of the LEDs in the preset area, and regulating and controlling the LEDs through the relevant regulation and control parameters.
Further, in the method, a data sensing network is constructed, and sensing data information of a target area is acquired through the data sensing network, which specifically comprises the following steps:
setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
counting the information transmission rate of the information transmission equipment, acquiring an average information transmission rate, setting an average information transmission rate threshold value, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold value;
when the average information transmission rate is smaller than the average information transmission rate threshold value, the number information of the information transmission devices is uniformly increased according to iteration algebra, and new number information of the information transmission devices is generated;
And calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than the average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
Further, in the method, the method predicts the real-time lighting parameters of the LED under the influence of the internal factor data according to the internal factor data, and specifically comprises the following steps:
acquiring variation characteristic data of LED luminous parameters under various internal factor data through big data, and constructing a characteristic matrix according to the variation characteristic data of the LED luminous parameters under various internal factor data;
constructing an LED luminous parameter prediction model based on a deep learning network, introducing a singular value decomposition algorithm, decomposing a feature matrix through the singular value decomposition algorithm, and obtaining a feature vector matrix composed of feature vectors;
inputting a feature vector matrix formed by feature vectors into an LED luminous parameter prediction model for coding learning, and storing model parameters and outputting the LED luminous parameter prediction model after the LED luminous parameter prediction model meets preset requirements;
And predicting real-time luminous parameters of the LEDs under the influence of the internal factor data by using an LED luminous parameter prediction model.
Further, in the method, the real-time lighting parameters of the LED under the influence of the external factor data are predicted based on the external factor data and the perception data of the target area, and specifically include:
acquiring variation characteristic data of LED luminous parameters under various external factor data through big data, constructing an LED luminous parameter knowledge graph, introducing a graph neural network, and taking the LED luminous parameters as a first graph node;
using each external factor data as a second graph node, constructing a topology structure diagram according to the first graph node and the second graph node through directional edge description, and obtaining an adjacent matrix corresponding to the topology structure diagram;
inputting the adjacency matrix into the LED luminous parameter knowledge graph for storage, inputting the perception data of the target area into the LED luminous parameter knowledge graph, and carrying out data matching through a traversal algorithm;
and after the data are matched, acquiring real-time luminous parameters of the LED under the influence of external factor data.
Further, in the method, by simulating the light emitting characteristic of the LED, the light emitting parameters of the LED in the preset area are obtained, which specifically includes:
Constructing a virtual scene, acquiring light-emitting parameter data of the LEDs in different areas under the current scene, and inputting the light-emitting parameter data of the LEDs in different areas under the current scene into the virtual scene to acquire a simulation model of the LEDs in the virtual scene;
fusing the real-time luminous parameters of the LEDs under the influence of the internal factor data and the real-time luminous parameters of the LEDs under the influence of the external factor data to generate real-time luminous parameter data of the LEDs;
correcting a simulation model of the LED in the virtual scene according to the real-time luminous parameter data of the LED to generate a real-time luminous parameter simulation model of the LED;
acquiring the luminous parameters of the LEDs in a preset area through a real-time LED real-time luminous parameter simulation model, and outputting the luminous parameters of the LEDs in the preset area.
Further, in the method, the generation of the relevant regulation parameters according to the light emitting parameters of the LED in the preset area specifically includes:
presetting first threshold information and second threshold range information of LED luminous parameters, and judging whether the luminous parameters of the LEDs in a preset area are larger than the first threshold information of the LED luminous parameters or not;
when the light-emitting parameter of the LED in the preset area is larger than the first threshold value information of the light-emitting parameter of the LED, further judging whether the light-emitting parameter of the LED in the preset area is in the second threshold value range information or not;
When the luminous parameters of the LEDs in the preset area are not in the second threshold range information, generating relevant regulation and control parameters according to the second threshold range information and the luminous parameters of the LEDs in the preset area;
when the luminous parameters of the LEDs in the preset area are not larger than the first threshold value information of the luminous parameters of the LEDs, corresponding LED installation positions are obtained, and related early warning information is generated according to the corresponding LED installation positions.
The second aspect of the present invention provides an LED intelligent control device based on data perception, the device comprising a memory and a processor, the memory comprising an LED intelligent control method program based on data perception, the LED intelligent control method program based on data perception realizing the following steps when executed by the processor:
constructing a data sensing network, and acquiring sensing data information of a target area through the data sensing network;
acquiring internal factor data and external factor data influencing LED luminous parameters through big data, and predicting real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
predicting real-time luminous parameters of the LEDs under the influence of external factor data based on the external factor data and the perception data of the target area, and acquiring the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
And generating relevant regulation and control parameters according to the luminous parameters of the LEDs in the preset area, and regulating and controlling the LEDs through the relevant regulation and control parameters.
Further, in the device, a data sensing network is constructed, and sensing data information of a target area is acquired through the data sensing network, which specifically comprises:
setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
counting the information transmission rate of the information transmission equipment, acquiring an average information transmission rate, setting an average information transmission rate threshold value, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold value;
when the average information transmission rate is smaller than the average information transmission rate threshold value, the number information of the information transmission devices is uniformly increased according to iteration algebra, and new number information of the information transmission devices is generated;
and calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than the average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
Further, in the device, the generation of the relevant regulation and control parameters according to the light emitting parameters of the LED in the preset area specifically includes:
presetting first threshold information and second threshold range information of LED luminous parameters, and judging whether the luminous parameters of the LEDs in a preset area are larger than the first threshold information of the LED luminous parameters or not;
when the light-emitting parameter of the LED in the preset area is larger than the first threshold value information of the light-emitting parameter of the LED, further judging whether the light-emitting parameter of the LED in the preset area is in the second threshold value range information or not;
when the luminous parameters of the LEDs in the preset area are not in the second threshold range information, generating relevant regulation and control parameters according to the second threshold range information and the luminous parameters of the LEDs in the preset area;
when the luminous parameters of the LEDs in the preset area are not larger than the first threshold value information of the luminous parameters of the LEDs, corresponding LED installation positions are obtained, and related early warning information is generated according to the corresponding LED installation positions.
A third aspect of the present invention provides an LED intelligent control device based on data awareness, comprising:
the acquisition module is used for constructing a data perception network and acquiring perception data information of a target area through the data perception network;
the first analysis module acquires internal factor data and external factor data influencing the LED luminous parameters through big data, and predicts the real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
The second analysis module predicts the real-time luminous parameters of the LEDs under the influence of the external factor data based on the external factor data and the perception data of the target area, and obtains the luminous parameters of the LEDs in the preset area by simulating the luminous characteristics of the LEDs;
and the regulation and control module generates relevant regulation and control parameters according to the luminous parameters of the LEDs in the preset area, and regulates and controls the LEDs through the relevant regulation and control parameters.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the invention, a data sensing network is constructed, sensing data information of a target area is obtained through the data sensing network, internal factor data and external factor data which influence the luminous parameters of the LED are obtained through big data, real-time luminous parameters of the LED under the influence of the internal factor data are predicted according to the internal factor data, further real-time luminous parameters of the LED under the influence of the external factor data are predicted based on the external factor data and the sensing data of the target area, luminous parameters of the LED in a preset area are obtained through simulating the luminous characteristics of the LED, and finally relevant regulation parameters are generated according to the luminous parameters of the LED in the preset area, and regulation is performed according to the relevant regulation parameters. According to the invention, the luminous parameters of the LEDs are predicted by fusing the external factors and the influence of the LEDs, so that the luminous parameters of the LEDs in corresponding scenes can be simulated, the control precision of the related control parameters of the LEDs is improved, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a data-aware based LED intelligent control method;
FIG. 2 shows a portion of a method flowchart of an LED intelligent control method based on data awareness;
FIG. 3 shows a schematic diagram of a data-aware based LED intelligent control device;
fig. 4 shows a schematic diagram of a data-aware based LED intelligent control device.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides an LED intelligent control method based on data sensing, which includes the following steps:
s102, constructing a data sensing network, and acquiring sensing data information of a target area through the data sensing network;
s104, acquiring internal factor data and external factor data influencing LED luminous parameters through big data, and predicting real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
s106, predicting real-time luminous parameters of the LEDs under the influence of external factor data based on the external factor data and the perception data of the target area, and acquiring the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
s108, generating relevant regulation and control parameters according to the luminous parameters of the LEDs in the preset area, and regulating and controlling the LEDs through the relevant regulation and control parameters.
The invention predicts the luminous parameters of the LED by fusing the external factors and the influence of the inside of the LED, so that the luminous parameters of the LED in the corresponding scene can be simulated, the control precision of the related control parameters of the LED is improved, and the user experience is improved.
As shown in fig. 2, it should be noted that, constructing a data sensing network, and acquiring sensing data information of a target area through the data sensing network specifically includes:
S202, setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
s204, counting the information transmission rate of the information transmission equipment, acquiring an average information transmission rate, setting an average information transmission rate threshold, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold;
s206, when the average information transmission rate is smaller than the average information transmission rate threshold, uniformly increasing the number information of the information transmission devices according to iteration algebra, and generating new number information of the information transmission devices;
and S208, calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than an average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
It should be noted that, the environment sensing device includes, but is not limited to, a temperature sensor, a humidity sensor, a haze sensor, and the information transmission device includes an antenna, an information transmitting base station, an information receiving base station, and the like. When the number of the environment sensing devices in the target area is continuously increased, the MIMO network is also determined when the number of the information transmission devices is determined, and when each environment sensing device is continuously increased, the environment sensing device occupies an information transmission channel during information transmission, so that the information transmission rate is reduced, the number of the information transmission devices in the data sensing network can be adjusted through an easy particle swarm algorithm, sensing data of the environment sensing devices can be timely acquired, and the luminous parameters of the LEDs can be timely regulated and controlled according to the sensing data of the environment sensing devices.
Further, in the method, the method predicts the real-time lighting parameters of the LED under the influence of the internal factor data according to the internal factor data, and specifically comprises the following steps:
acquiring variation characteristic data of LED luminous parameters under various internal factor data through big data, and constructing a characteristic matrix according to the variation characteristic data of the LED luminous parameters under various internal factor data;
constructing an LED luminous parameter prediction model based on a deep learning network, introducing a singular value decomposition algorithm, decomposing a feature matrix through the singular value decomposition algorithm, and obtaining a feature vector matrix composed of feature vectors;
inputting a feature vector matrix formed by feature vectors into an LED luminous parameter prediction model for coding learning, and storing model parameters and outputting the LED luminous parameter prediction model after the LED luminous parameter prediction model meets preset requirements;
and predicting real-time luminous parameters of the LEDs under the influence of the internal factor data by using an LED luminous parameter prediction model.
The internal factor data is the data of the luminous intensity value of the LED, the temperature value variation parameter caused by the luminous for a certain time, the luminous power parameter and the like, wherein the luminous parameter is changed due to the performance degradation of the internal parts of the LED. The feature matrix is decomposed through a singular value decomposition algorithm, so that the calculation complexity of the model can be reduced, and the prediction speed of the model can be improved. Deep learning networks include deep neural networks, recurrent neural networks, and the like.
Further, in the method, the real-time lighting parameters of the LED under the influence of the external factor data are predicted based on the external factor data and the perception data of the target area, and specifically include:
acquiring variation characteristic data of LED luminous parameters under various external factor data through big data, constructing an LED luminous parameter knowledge graph, introducing a graph neural network, and taking the LED luminous parameters as a first graph node;
using each external factor data as a second graph node, constructing a topology structure diagram according to the first graph node and the second graph node through directional edge description, and obtaining an adjacent matrix corresponding to the topology structure diagram;
inputting the adjacency matrix into the LED luminous parameter knowledge graph for storage, inputting the perception data of the target area into the LED luminous parameter knowledge graph, and carrying out data matching through a traversal algorithm;
and after the data are matched, acquiring real-time luminous parameters of the LED under the influence of external factor data.
The external factor data includes data such as temperature, humidity, haze, etc., and the light emission parameters of the LEDs are different when the external factor data includes data such as temperature, humidity, haze, etc., and the light intensity of the LEDs is lower when the external factor data is higher than a predetermined temperature. The haze can also influence the transmission of illumination, and part of illumination can be shielded by the haze, so that the illumination intensity of a preset area does not meet the preset requirement. By introducing the graph neural network, the luminous parameters and the external factors can be correlated, and the efficiency of data query can be improved.
Further, in the method, by simulating the light emitting characteristic of the LED, the light emitting parameters of the LED in the preset area are obtained, which specifically includes:
constructing a virtual scene, acquiring light-emitting parameter data of the LEDs in different areas under the current scene, and inputting the light-emitting parameter data of the LEDs in different areas under the current scene into the virtual scene to acquire a simulation model of the LEDs in the virtual scene;
fusing the real-time luminous parameters of the LEDs under the influence of the internal factor data and the real-time luminous parameters of the LEDs under the influence of the external factor data to generate real-time luminous parameter data of the LEDs;
correcting a simulation model of the LED in the virtual scene according to the real-time luminous parameter data of the LED to generate a real-time luminous parameter simulation model of the LED;
acquiring the luminous parameters of the LEDs in a preset area through a real-time LED real-time luminous parameter simulation model, and outputting the luminous parameters of the LEDs in the preset area.
The virtual scene is constructed by virtual reality technology, three-dimensional modeling technology and the like, so that the illumination intensity parameters of the LEDs in the preset area are simulated under the conditions of various external factors and internal factors, and a real-time LED real-time luminous parameter simulation model is generated, so that the illumination intensity of the preset area, such as the illumination intensity of the outdoor LED street lamp in the preset road area, the illumination intensity of the indoor LED in the preset area and the like, is obtained.
Further, in the method, the generation of the relevant regulation parameters according to the light emitting parameters of the LED in the preset area specifically includes:
presetting first threshold information and second threshold range information of LED luminous parameters, and judging whether the luminous parameters of the LEDs in a preset area are larger than the first threshold information of the LED luminous parameters or not;
when the light-emitting parameter of the LED in the preset area is larger than the first threshold value information of the light-emitting parameter of the LED, further judging whether the light-emitting parameter of the LED in the preset area is in the second threshold value range information or not;
when the luminous parameters of the LEDs in the preset area are not in the second threshold range information, generating relevant regulation and control parameters according to the second threshold range information and the luminous parameters of the LEDs in the preset area;
when the luminous parameters of the LEDs in the preset area are not larger than the first threshold value information of the luminous parameters of the LEDs, corresponding LED installation positions are obtained, and related early warning information is generated according to the corresponding LED installation positions.
When the luminous parameter of the LED in the preset area is larger than the first threshold value information of the luminous parameter of the LED, the corresponding LED is indicated to work normally, and the LED lamp is not in fault or poor in luminous efficiency; otherwise, the LED is abnormally operated, and there is a malfunction or an LED lamp with poor luminous efficiency. When the light emitting parameters of the LEDs in the preset area are not in the second threshold range information, the fact that the adjustment is possibly needed due to the influence of external factors and the light emitting characteristics of the LEDs is indicated, when the difference between the second threshold range information and the light emitting parameters of the LEDs in the preset area is larger than a preset difference value, relevant regulation and control parameters are generated according to the second threshold range information and the light emitting parameters of the LEDs in the preset area, and when the difference between the second threshold range information and the light emitting parameters of the LEDs in the preset area is not larger than the preset difference value, the fact that the current light emitting parameters meet requirements is indicated, and adjustment is not needed.
In addition, the method can further comprise the following steps:
acquiring performance degradation characteristic data of each environment sensing device under each external factor through big data, constructing a Bayesian network, and inputting the performance degradation characteristic data of the environment sensing device under each external factor into the Bayesian network;
acquiring the predicted time information of each environment sensing device with abnormal performance through the Bayesian grid, carrying out early warning according to the predicted time information of the environment sensing device with abnormal performance, and simultaneously acquiring environment sensing data acquired by the environment sensing device within the predicted time information of the environment sensing device with abnormal performance;
taking environment sensing data acquired by the environment sensing equipment within the predicted time information of abnormal performance as abnormal data, and simultaneously acquiring geographic position information of the abnormal data;
and acquiring the LED lamp with the geographic position information of the abnormal data closest to the information, acquiring the environment sensing data acquired by the LED lamp closest to the information, and taking the environment sensing data acquired by the LED lamp closest to the information as push information of the abnormal environment sensing equipment.
When the environment sensing device is in the predicted time information of the abnormal performance, the collected data are abnormal data, so that the environment sensing data collected by the LED lamp closest to the environment sensing device are used as push information of the abnormal environment sensing device, and further the luminous parameters of the LED are regulated and controlled according to the push information of the abnormal environment sensing device.
In addition, the method can further comprise the following steps:
initializing an installation position node of the information transmission equipment, randomly selecting an installation node of the environment sensing equipment, calculating a distance value from the installation position node of the information transmission equipment to the installation node of the environment sensing equipment, and calculating an information transmission energy consumption value according to the distance value;
acquiring the correlation between the information transmission energy consumption value and the distance value according to the information transmission energy consumption value from the installation position node of the transmission equipment to the installation node of the environment sensing equipment;
calculating the information transmission energy consumption value from the installation node of each environment sensing device to the installation position node of the information transmission device according to the correlation between the information transmission energy consumption value and the distance value, and counting the information transmission energy consumption value to obtain the total energy consumption value;
presetting a total energy consumption threshold, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, adjusting the installation position node of the information transmission equipment according to the genetic algebra when the total energy consumption value is larger than the total energy consumption threshold until the total energy consumption value is not larger than the total energy consumption threshold, and outputting the installation position node of the information transmission equipment.
The method can optimize the installation position node of the information transmission equipment, reduce the energy consumption value during information transmission, and save more energy and reduce emission compared with the prior art.
As shown in fig. 3, the second aspect of the present invention provides an LED intelligent control device based on data perception, which includes a memory 41 and a processor 42, wherein the memory 41 includes an LED intelligent control method program based on data perception, and when the LED intelligent control method program based on data perception is executed by the processor 42, the following steps are implemented:
constructing a data sensing network, and acquiring sensing data information of a target area through the data sensing network;
acquiring internal factor data and external factor data influencing LED luminous parameters through big data, and predicting real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
predicting real-time luminous parameters of the LEDs under the influence of external factor data based on the external factor data and the perception data of the target area, and acquiring the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
and generating relevant regulation and control parameters according to the luminous parameters of the LEDs in the preset area, and regulating and controlling the LEDs through the relevant regulation and control parameters.
It should be noted that, the device predicts the luminous parameters of the LED by fusing the external factors and the influence of the inside of the LED, so that the luminous parameters of the LED in the corresponding scene can be simulated, the control precision of the relevant control parameters of the LED is improved, and the user experience is improved.
Further, in the device, a data sensing network is constructed, and sensing data information of a target area is acquired through the data sensing network, which specifically comprises:
setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
counting the information transmission rate of the information transmission equipment, acquiring an average information transmission rate, setting an average information transmission rate threshold value, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold value;
when the average information transmission rate is smaller than the average information transmission rate threshold value, the number information of the information transmission devices is uniformly increased according to iteration algebra, and new number information of the information transmission devices is generated;
and calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than the average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
It should be noted that, the environment sensing device includes, but is not limited to, a temperature sensor, a humidity sensor, a haze sensor, and the information transmission device includes an antenna, an information transmitting base station, an information receiving base station, and the like. When the number of the environment sensing devices in the target area is continuously increased, the MIMO network is also determined when the number of the information transmission devices is determined, and when each environment sensing device is continuously increased, the environment sensing device occupies an information transmission channel during information transmission, so that the information transmission rate is reduced, the number of the information transmission devices in the data sensing network can be adjusted through an easy particle swarm algorithm, sensing data of the environment sensing devices can be timely acquired, and the luminous parameters of the LEDs can be timely regulated and controlled according to the sensing data of the environment sensing devices.
Further, in the device, the generation of the relevant regulation and control parameters according to the light emitting parameters of the LED in the preset area specifically includes:
presetting first threshold information and second threshold range information of LED luminous parameters, and judging whether the luminous parameters of the LEDs in a preset area are larger than the first threshold information of the LED luminous parameters or not;
when the light-emitting parameter of the LED in the preset area is larger than the first threshold value information of the light-emitting parameter of the LED, further judging whether the light-emitting parameter of the LED in the preset area is in the second threshold value range information or not;
When the luminous parameters of the LEDs in the preset area are not in the second threshold range information, generating relevant regulation and control parameters according to the second threshold range information and the luminous parameters of the LEDs in the preset area;
when the luminous parameters of the LEDs in the preset area are not larger than the first threshold value information of the luminous parameters of the LEDs, corresponding LED installation positions are obtained, and related early warning information is generated according to the corresponding LED installation positions.
As shown in fig. 4, a third aspect of the present invention provides an LED intelligent control device based on data perception, including:
the acquisition module 10 constructs a data perception network, and acquires perception data information of a target area through the data perception network;
the first analysis module 20 acquires internal factor data and external factor data affecting the LED lighting parameters through big data, and predicts the real-time lighting parameters of the LED under the influence of the internal factor data according to the internal factor data;
the second analysis module 30 predicts the real-time light emitting parameters of the LED under the influence of the external factor data based on the external factor data and the perception data of the target area, and obtains the light emitting parameters of the LED in the preset area by simulating the light emitting characteristics of the LED;
the regulation and control module 40 generates relevant regulation and control parameters according to the luminous parameters of the LEDs in the preset area, and regulates and controls the LEDs through the relevant regulation and control parameters.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The LED intelligent control method based on data perception is characterized by comprising the following steps of:
constructing a data perception network, and acquiring perception data information of a target area through the data perception network;
acquiring internal factor data and external factor data influencing LED luminous parameters through big data, and predicting real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
predicting real-time luminous parameters of the LEDs under the influence of the external factor data based on the external factor data and the perception data of the target area, and acquiring the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
generating relevant regulation and control parameters according to the luminous parameters of the LEDs in a preset area, and regulating and controlling the LEDs through the relevant regulation and control parameters;
Constructing a data perception network, and acquiring perception data information of a target area through the data perception network, wherein the method specifically comprises the following steps of:
setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
counting the information transmission rate of the information transmission equipment, obtaining an average information transmission rate, setting an average information transmission rate threshold, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold;
when the average information transmission rate is smaller than an average information transmission rate threshold value, the number information of the information transmission devices is uniformly increased according to the iteration algebra, and new number information of the information transmission devices is generated;
and calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than the average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
2. The intelligent control method for the LED based on data perception according to claim 1, wherein the method for predicting the real-time lighting parameters of the LED under the influence of the internal factor data according to the internal factor data specifically comprises the following steps:
acquiring variation characteristic data of LED luminous parameters under various internal factor data through big data, and constructing a characteristic matrix according to the variation characteristic data of the LED luminous parameters under various internal factor data;
constructing an LED luminous parameter prediction model based on a deep learning network, introducing a singular value decomposition algorithm, decomposing the feature matrix through the singular value decomposition algorithm, and obtaining a feature vector matrix composed of feature vectors;
inputting the feature vector matrix formed by the feature vectors into the LED luminous parameter prediction model for coding learning, and storing model parameters and outputting the LED luminous parameter prediction model after the LED luminous parameter prediction model meets the preset requirements;
and predicting real-time luminous parameters of the LEDs under the influence of the internal factor data by using the LED luminous parameter prediction model.
3. The method for intelligently controlling the LED based on the data sensing according to claim 1, wherein the real-time lighting parameters of the LED under the influence of the external factor data are predicted based on the external factor data and the sensing data of the target area, specifically comprising:
Acquiring variation characteristic data of LED luminous parameters under various external factor data through big data, constructing an LED luminous parameter knowledge graph, introducing a graph neural network, and taking the LED luminous parameters as a first graph node;
constructing a topology structure diagram according to the first graph node and the second graph node through directional edge description by taking each external factor data as a second graph node, and acquiring an adjacent matrix corresponding to the topology structure diagram;
inputting the adjacency matrix into the LED luminous parameter knowledge graph for storage, inputting the perception data of the target area into the LED luminous parameter knowledge graph, and carrying out data matching through a traversal algorithm;
and after the data are matched, acquiring real-time luminous parameters of the LED under the influence of external factor data.
4. The intelligent control method for the LED based on data perception according to claim 1, wherein the method is characterized in that the luminous parameters of the LED in the preset area are obtained by simulating the luminous characteristics of the LED, and specifically comprises the following steps:
constructing a virtual scene, acquiring light-emitting parameter data of an LED in different areas under the current scene, and inputting the light-emitting parameter data of the LED in different areas under the current scene into the virtual scene to acquire a simulation model of the LED in the virtual scene;
Fusing the real-time luminous parameters of the LEDs under the influence of the internal factor data and the real-time luminous parameters of the LEDs under the influence of the external factor data to generate real-time luminous parameter data of the LEDs;
correcting a simulation model of the LED in a virtual scene according to the real-time luminous parameter data of the LED to generate a real-time luminous parameter simulation model of the LED;
and acquiring the light emitting parameters of the LEDs in a preset area through the real-time LED real-time light emitting parameter simulation model, and outputting the light emitting parameters of the LEDs in the preset area.
5. The intelligent control method of the LED based on data sensing according to claim 1, wherein the generating relevant regulation parameters according to the light emitting parameters of the LED in the preset area specifically comprises:
presetting first threshold information and second threshold range information of LED luminous parameters, and judging whether the luminous parameters of the LEDs in a preset area are larger than the first threshold information of the LED luminous parameters or not;
when the luminous parameter of the LED in the preset area is larger than the first threshold value information of the luminous parameter of the LED, further judging whether the luminous parameter of the LED in the preset area is in the second threshold value range information or not;
when the luminous parameters of the LEDs in the preset area are not in the second threshold range information, generating relevant regulation and control parameters according to the second threshold range information and the luminous parameters of the LEDs in the preset area;
When the luminous parameters of the LEDs in the preset area are not larger than the first threshold information of the luminous parameters of the LEDs, corresponding LED installation positions are obtained, and related early warning information is generated according to the corresponding LED installation positions.
6. The LED intelligent control device based on data perception is characterized by comprising a memory and a processor, wherein the memory comprises an LED intelligent control method program based on data perception, and when the LED intelligent control method program based on data perception is executed by the processor, the following steps are realized:
constructing a data perception network, and acquiring perception data information of a target area through the data perception network;
acquiring internal factor data and external factor data influencing LED luminous parameters through big data, and predicting real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
predicting real-time luminous parameters of the LEDs under the influence of the external factor data based on the external factor data and the perception data of the target area, and acquiring the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
generating relevant regulation and control parameters according to the luminous parameters of the LEDs in a preset area, and regulating and controlling the LEDs through the relevant regulation and control parameters;
Constructing a data perception network, and acquiring perception data information of a target area through the data perception network, wherein the method specifically comprises the following steps of:
setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
counting the information transmission rate of the information transmission equipment, obtaining an average information transmission rate, setting an average information transmission rate threshold, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold;
when the average information transmission rate is smaller than an average information transmission rate threshold value, the number information of the information transmission devices is uniformly increased according to the iteration algebra, and new number information of the information transmission devices is generated;
and calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than the average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
7. The data-aware-based intelligent LED controller of claim 6, wherein the generating of the relevant control parameters according to the light emitting parameters of the LED in the preset area comprises:
presetting first threshold information and second threshold range information of LED luminous parameters, and judging whether the luminous parameters of the LEDs in a preset area are larger than the first threshold information of the LED luminous parameters or not;
when the luminous parameter of the LED in the preset area is larger than the first threshold value information of the luminous parameter of the LED, further judging whether the luminous parameter of the LED in the preset area is in the second threshold value range information or not;
when the luminous parameters of the LEDs in the preset area are not in the second threshold range information, generating relevant regulation and control parameters according to the second threshold range information and the luminous parameters of the LEDs in the preset area;
when the luminous parameters of the LEDs in the preset area are not larger than the first threshold information of the luminous parameters of the LEDs, corresponding LED installation positions are obtained, and related early warning information is generated according to the corresponding LED installation positions.
8. LED intelligent control equipment based on data perception, its characterized in that includes:
the acquisition module is used for constructing a data perception network and acquiring perception data information of a target area through the data perception network;
The first analysis module acquires internal factor data and external factor data influencing LED luminous parameters through big data, and predicts real-time luminous parameters of the LED under the influence of the internal factor data according to the internal factor data;
the second analysis module predicts the real-time luminous parameters of the LEDs under the influence of the external factor data based on the external factor data and the perception data of the target area, and obtains the luminous parameters of the LEDs in a preset area by simulating the luminous characteristics of the LEDs;
the regulation and control module generates relevant regulation and control parameters according to the luminous parameters of the LEDs in a preset area, and regulates and controls the LEDs according to the relevant regulation and control parameters;
constructing a data perception network, and acquiring perception data information of a target area through the data perception network, wherein the method specifically comprises the following steps of:
setting a plurality of environment sensing devices and information transmission devices in a target area, initializing the quantity information of the information transmission devices in a data sensing network, and acquiring the information transmission rate of each information transmission device by carrying out information transmission rate simulation on the environment sensing devices;
counting the information transmission rate of the information transmission equipment, obtaining an average information transmission rate, setting an average information transmission rate threshold, integrating a particle swarm algorithm, setting iteration algebra, and judging whether the average information transmission rate is smaller than the average information transmission rate threshold;
When the average information transmission rate is smaller than an average information transmission rate threshold value, the number information of the information transmission devices is uniformly increased according to the iteration algebra, and new number information of the information transmission devices is generated;
and calculating an average information transmission rate according to the number information of the new information transmission devices, stopping iteration when the average information transmission rate is larger than the average information transmission rate threshold value, outputting the number information of the new information transmission devices, and constructing a data perception network according to the number information of the new information transmission devices.
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