Smart Home Environment Data Judgment System

Abstract

The present invention relates to a smart home environment data judgment system, comprising: an imaging processing mechanism installed in the kitchen working environment to perform optoelectronic induction processing on the kitchen working environment during the kitchen operation time interval, for obtaining and outputting corresponding instant imaging signals; a judgment execution device for intelligently judging the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment and multiple visualization information of the instant imaging signal by using a feedforward neural network after multiple trainings. Through the present invention, an artificial intelligence model with a customized structure design can be used to intelligently analyze the image processing type passed by the instant imaging signal in the imaging processing mechanism based on various specifically screened basic data, thereby providing a reliable basis for identifying the content authenticity of the instant imaging signal from the kitchen working environment.

Description

Technical Field

[0001] The present invention relates to the field of smart home, and particularly to a system for judging smart home environment data. Background Art

[0002] Building a smart city is also an objective requirement for transforming the urban development mode and improving the urban development quality. By building a smart city, various types of information such as urban economy, culture, public resources, management services, citizen life, and ecological environment can be transmitted, integrated, exchanged, and used in a timely manner, improving the interconnection, comprehensive perception, and information utilization capabilities of things, things and people, and people and people, thereby greatly improving the capabilities of public management and services and significantly enhancing the material and cultural living standards of the people. Building a smart city will make urban development more comprehensive and sustainable, and will make urban life healthier and more beautiful.

[0003] In a smart city, the kitchen is a monitoring scenario that appears frequently and requires key attention. However, for the purposeful or unpurposeful reasons of the kitchen operator, the monitoring images finally obtained by the monitoring end of the smart city are often not the original images sensed by the image sensing component, but the processed images after image processing. At this time, it is very crucial to understand the specific type of image processing. In the prior art, there is a lack of a targeted identification mechanism for such image processing types, resulting in the inability to provide a reliable basis for identifying the authenticity of the content of the real-time imaging signal from the kitchen working environment. Summary of the Invention

[0004] To solve the technical problems in related fields, the present invention provides a smart home environment data judgment system. By using an artificial intelligence model with a customized structure to intelligently analyze the types of image processing that the instant imaging signal undergoes in the imaging processing mechanism. The customization of the structure of the artificial intelligence model lies in that the artificial intelligence model is a feedforward neural network after multiple trainings, and the number of trainings of the feedforward neural network is proportional to the total number of pixels of the instant imaging signal. Thus, artificial intelligence models with different structures are customized for different imaging processing mechanisms. The floor area, the number of devices, the number of staff, and the distance from the farthest position to the environment analysis mechanism in the kitchen working environment are collected as multiple environmental contents of the kitchen working environment. Image signal analysis is performed on the instant imaging signal to obtain the respective imaging depth-of-field values corresponding to each pixel of the instant imaging signal and the respective brightness values corresponding to each pixel of the instant imaging signal, so as to obtain a number of full and comprehensive basic information for performing intelligent analysis processing. A feedforward neural network after multiple trainings is also used to intelligently judge the type number of the image processing type that the instant imaging signal undergoes based on the multiple environmental contents of the kitchen working environment, the respective imaging depth-of-field values corresponding to each pixel of the instant imaging signal, and the respective brightness values corresponding to each pixel of the instant imaging signal, thereby providing a reliable basis for identifying the authenticity of the content of the instant imaging signal from the kitchen working environment.

[0005] According to the present invention, there is provided a smart home environment data judgment system, the system comprising:

[0006] An imaging processing mechanism, installed in the kitchen working environment to perform optoelectronic induction processing on the kitchen working environment during the kitchen operation time interval, for obtaining and outputting a corresponding instant imaging signal;

[0007] An environment analysis mechanism, for collecting the floor area, the number of devices, the number of staff, and the distance from the farthest position to the environment analysis mechanism in the kitchen working environment, and outputting the floor area, the number of devices, the number of staff, and the distance from the farthest position to the environment analysis mechanism in the kitchen working environment as multiple environmental contents of the kitchen working environment;

[0008] A numerical extraction device, respectively connected to the imaging processing mechanism and the environment analysis mechanism, for performing image signal analysis on the instant imaging signal to obtain the respective imaging depth-of-field values corresponding to each pixel of the instant imaging signal and the respective brightness values corresponding to each pixel of the instant imaging signal;

[0009] A judgment execution device, which is respectively connected to the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism, is configured to use a feedforward neural network after multiple trainings to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, each imaging depth value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal;

[0010] Among them, using a feedforward neural network after multiple trainings to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, each imaging depth value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal includes: after the original image corresponding to the instant imaging signal is subjected to image processing corresponding to the image processing type with the type number obtained by intelligent judgment, the instant imaging signal is obtained;

[0011] Among them, using a feedforward neural network after multiple trainings to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, each imaging depth value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal further includes: the number of times of training of the feedforward neural network is proportional to the total number of pixel points of the instant imaging signal.

[0012] Thus, the present invention has at least the following three important inventive concepts:

[0013] The first one: using an artificial intelligence model with a customized structure to intelligently analyze the image processing type passed by the instant imaging signal in the imaging processing mechanism. The customization of the structure of the artificial intelligence model lies in that the artificial intelligence model is a feedforward neural network after multiple trainings and the number of times of training of the feedforward neural network is proportional to the total number of pixel points of the instant imaging signal, so as to customize artificial intelligence models with different structures for different imaging processing mechanisms:

[0014] The second one: collecting the floor area, the number of devices, the number of staff in the kitchen working environment, and the distance from the farthest position to the environment analysis mechanism as multiple environmental contents of the kitchen working environment, and performing image signal analysis on the instant imaging signal to obtain each imaging depth value corresponding to each pixel point of the instant imaging signal and each brightness value corresponding to each pixel point of the instant imaging signal, so as to obtain a number of sufficient and comprehensive basic information for performing intelligent analysis processing;

[0015] Third: The feedforward neural network after multiple trainings is used to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, each imaging depth value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal, so as to provide a reliable basis for the identification of the content authenticity of the instant imaging signal from the kitchen working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The embodiments of the present invention will be described below in conjunction with the drawings, where:

[0017] Figure 1 It is a schematic internal structure diagram of the intelligent home environment data judgment system shown according to the first embodiment of the present invention.

[0018] Figure 2 It is a schematic internal structure diagram of the intelligent home environment data judgment system shown according to the second embodiment of the present invention.

[0019] Figure 3 It is a schematic internal structure diagram of the intelligent home environment data judgment system shown according to the third embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The embodiments of the intelligent home environment data judgment system of the present invention will be described in detail below with reference to the drawings.

[0021] Figure 1 It is a schematic internal structure diagram of the intelligent home environment data judgment system shown according to the first embodiment of the present invention, and the system includes:

[0022] An imaging processing mechanism, installed in the kitchen working environment to perform optoelectronic induction processing on the kitchen working environment during the kitchen operation time interval, and used to obtain and output a corresponding instant imaging signal;

[0023] An environmental analysis mechanism, used to collect the floor area, the number of equipment, the number of staff in the kitchen working environment, and the distance value from the farthest position from the environmental analysis mechanism to the environmental analysis mechanism, and use the floor area, the number of equipment, the number of staff in the kitchen working environment, and the distance value from the farthest position from the environmental analysis mechanism to the environmental analysis mechanism as multiple environmental contents of the kitchen working environment and output them;

[0024] Specifically, an environment analysis mechanism is used to collect the floor area, the number of devices, the number of staff members in the kitchen working environment, and the distance value from the farthest position from the environment analysis mechanism to the environment analysis mechanism, and use the floor area, the number of devices, the number of staff members in the kitchen working environment, and the distance value from the farthest position from the environment analysis mechanism to the environment analysis mechanism as multiple environmental contents of the kitchen working environment. The output includes: selecting and using multiple acquisition components to respectively collect the floor area, the number of devices, the number of staff members in the kitchen working environment, and the distance value from the farthest position from the environment analysis mechanism to the environment analysis mechanism;

[0025] A numerical value extraction device, which is respectively connected to the imaging processing mechanism and the environment analysis mechanism, and is used to perform image signal analysis on the instant imaging signal to obtain respective imaging depth-of-field numerical values corresponding to each pixel point of the instant imaging signal and respective brightness numerical values corresponding to each pixel point of the instant imaging signal;

[0026] A judgment execution device, which is respectively connected to the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism, and is used to use a feedforward neural network after multiple trainings to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, respective imaging depth-of-field numerical values corresponding to each pixel point of the instant imaging signal, and respective brightness numerical values corresponding to each pixel point of the instant imaging signal;

[0027] Among them, using a feedforward neural network after multiple trainings to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, respective imaging depth-of-field numerical values corresponding to each pixel point of the instant imaging signal, and respective brightness numerical values corresponding to each pixel point of the instant imaging signal includes: after the original image corresponding to the instant imaging signal undergoes image processing corresponding to the image processing type with the type number obtained by intelligent judgment, the instant imaging signal is obtained;

[0028] Among them, using a feedforward neural network after multiple trainings to intelligently judge the type number of the image processing type passed by the instant imaging signal based on multiple environmental contents of the kitchen working environment, respective imaging depth-of-field numerical values corresponding to each pixel point of the instant imaging signal, and respective brightness numerical values corresponding to each pixel point of the instant imaging signal further includes: the number of times of feedforward neural network training is proportional to the total number of pixel points of the instant imaging signal;

[0029] Among them, the type number of the image processing type through which the instant imaging signal passes is intelligently determined by using a feedforward neural network after multiple trainings based on multiple environmental contents of the kitchen working environment, each imaging depth-of-field value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal, and further includes: the image processing type corresponding to the type number obtained by intelligent determination is one of an image sharpening processing type, an image smoothing processing type, an image filtering processing type, an image interpolation processing type, an image enhancement processing type, and a distortion correction processing type.

[0030] Figure 2 It is a schematic internal structure diagram of the intelligent home environment data judgment system shown in the second embodiment of the present invention.

[0031] Unlike Figure 1 the Figure 2 intelligent home environment data judgment system in

[0032] The positioning server device is respectively connected to the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environmental analysis mechanism, and is used to respectively provide the current instant positioning data of each of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environmental analysis mechanism;

[0033] Among them, the positioning server device is respectively connected to the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environmental analysis mechanism, and is used to respectively provide the current instant positioning data of each of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environmental analysis mechanism, including: the positioning server device includes a plurality of positioning service units, which are used to be respectively connected to the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environmental analysis mechanism to complete the respective supply of the current instant positioning data of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environmental analysis mechanism;

[0034] Among them, the positioning server device includes a plurality of positioning service units, which are respectively connected to the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism to complete the separate supply of the current real-time positioning data of the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism respectively, including: the plurality of positioning service units are a plurality of positioning sensors, which are respectively connected to the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism to complete the separate supply of the current real-time positioning data of the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism respectively;

[0035] Among them, the plurality of positioning service units are a plurality of positioning sensors, which are respectively connected to the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism to complete the separate supply of the current real-time positioning data of the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism respectively, including: the structures of the plurality of positioning service units are the same;

[0036] And among them, the plurality of positioning service units are a plurality of positioning sensors, which are respectively connected to the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism to complete the separate supply of the current real-time positioning data of the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism respectively, further including: the plurality of positioning sensors have the same positioning range.

[0037] Figure 3 It is a schematic internal structure diagram of the intelligent home environment data judgment system shown in the third embodiment of the present invention.

[0038] Unlike Figure 1 the intelligent home environment data judgment system in Figure 3 may further include the following components:

[0039] A user control interface, which is respectively connected to the plurality of positioning service units of the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism, and is used to synchronously control the current working modes of the positioning service units of the judgment execution device, the numerical extraction device, the imaging processing mechanism, and the environmental analysis mechanism respectively;

[0040] Among them, the user control interface is respectively connected to a plurality of positioning service units of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism, and is used to synchronously control the current working modes of the positioning service units of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism respectively, including: the current working modes of the positioning service units of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism are sleep working modes or running working modes;

[0041] And among them, the user control interface is respectively connected to a plurality of positioning service units of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism, and is used to synchronously control the current working modes of the positioning service units of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism respectively, including: a plurality of positioning service units of the judgment execution device, the numerical value extraction device, the imaging processing mechanism, and the environment analysis mechanism are all based on the Beidou positioning mechanism.

[0042] In addition, in the smart home environment data judgment system, the type number of the image processing type through which the instant imaging signal passes is intelligently judged by using a feedforward neural network after multiple trainings based on multiple environmental contents of the kitchen working environment, each imaging depth value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal, and further includes: synchronously inputting the multiple environmental contents of the kitchen working environment, each imaging depth value corresponding to each pixel point of the instant imaging signal, and each brightness value corresponding to each pixel point of the instant imaging signal into the feedforward neural network after multiple trainings, and executing the feedforward neural network after multiple trainings to obtain the type number of the image processing type through which the instant imaging signal passes output by the feedforward neural network after multiple trainings.

[0043] By using the smart home environment data judgment system of the present invention, aiming at the technical problem that in the prior art, the image processing type through which the kitchen monitoring picture passes cannot be determined at the monitoring end of the smart city, the image processing type through which the instant imaging signal passes in the imaging processing mechanism is intelligently analyzed by using an artificial intelligence model with a customized structure based on various specifically screened basic data, so as to provide a reliable basis for identifying the content authenticity of the instant imaging signal from the kitchen working environment, and solve the above technical problem.

[0044] The embodiments described above are only used to clearly understand the principle of the present disclosure. Without substantially departing from the present disclosure, many changes and modifications can be made. All the modifications and changes described herein are included in the scope of the present disclosure.

Claims

1. A smart home environment data judgment system, characterized in that: The system comprises: An imaging processing mechanism is installed in the kitchen working environment to perform photoelectric sensing processing on the kitchen working environment within the kitchen operation time interval, so as to obtain and output corresponding instant imaging signals; An artificial intelligence model with customized structure is used to intelligently analyze the image processing types that the instant imaging signal undergoes in the imaging processing mechanism; The environment analysis unit is used to collect the floor space, number of equipment, number of staff and distance values ​​from the farthest position of the environment analysis unit to the environment analysis unit of the kitchen working environment, and output the floor space, number of equipment, number of staff and distance values ​​from the farthest position of the environment analysis unit to the environment analysis unit as multiple environmental contents of the kitchen working environment; Among them, the structural customization of the artificial intelligence model lies in that the artificial intelligence model is a feedforward neural network that has been trained for many times and the number of times the feedforward neural network has been trained is proportional to the total number of pixels of the instant imaging signal, so that artificial intelligence models with different structures are customized for different imaging processing agencies, and the floor area, number of equipment, number of staff and distance values ​​from the farthest position of the environmental analysis agency to the environmental analysis agency of the kitchen working environment are collected as multiple environmental contents of the kitchen working environment, and image signal analysis is performed on the instant imaging signal to obtain each imaging depth value corresponding to each pixel of the instant imaging signal and each brightness value corresponding to each pixel of the instant imaging signal, so as to obtain multiple basic information for performing intelligent analysis and processing, and a feedforward neural network that has been trained for many times is used to intelligently judge the type number of the image processing type that the instant imaging signal has undergone based on the multiple environmental contents of the kitchen working environment, the imaging depth values ​​corresponding to each pixel of the instant imaging signal and the brightness values ​​corresponding to each pixel of the instant imaging signal; A value extraction device is connected to the imaging processing mechanism and the environment analysis mechanism respectively, and is used to perform image signal analysis on the instant imaging signal to obtain each imaging depth value corresponding to each pixel point of the instant imaging signal and each brightness value corresponding to each pixel point of the instant imaging signal; The judgment execution device is connected to the value extraction device, the imaging processing mechanism and the environment analysis mechanism respectively, and is used to use a feedforward neural network that has been trained multiple times to intelligently judge the type number of the image processing type that the instant imaging signal has undergone based on multiple environmental contents of the kitchen working environment, the imaging depth values ​​corresponding to each pixel point of the instant imaging signal, and the brightness values ​​corresponding to each pixel point of the instant imaging signal; Wherein, the original image corresponding to the instant imaging signal is subjected to image processing corresponding to the image processing type of the type number obtained by intelligent judgment, thereby obtaining the instant imaging signal; The number of times the feedforward neural network is trained is proportional to the total number of pixels of the instant imaging signal.

2. The smart home environment data determination system according to claim 1, characterized in that: The image processing type of the type number obtained by intelligent judgment is one of an image sharpening processing type, an image smoothing processing type, an image filtering processing type, an image interpolation processing type, an image enhancement processing type, and a distortion correction processing type.

3. The smart home environment data determination system as claimed in claim 2, characterized in that: The system further comprises: A positioning server device, connected to the judgment execution device, the value extraction device, the imaging processing mechanism and the environmental analysis mechanism, respectively, for providing the judgment execution device, the value extraction device, the imaging processing mechanism and the environmental analysis mechanism with respective current instant positioning data; Among them, the positioning server device includes multiple positioning service units, which are used to connect with the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism respectively, so as to complete the separate supply of current and real-time positioning data to the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism.

4. The smart home environment data determination system as claimed in claim 3, characterized in that: The multiple positioning service units are multiple positioning sensors, which are used to connect to the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism respectively, so as to complete the respective supply of current and real-time positioning data to the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism.

5. The smart home environment data determination system as claimed in claim 4, characterized in that: The multiple positioning service units have the same structure.

6. The smart home environment data determination system as claimed in claim 5, characterized in that: The plurality of positioning sensors have the same positioning range.

7. The smart home environment data determination system as claimed in claim 2, characterized in that: The system further comprises: The user control interface is respectively connected to the multiple positioning service units of the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism, and is used to synchronously control the current working mode of the respective positioning service units of the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism.

8. The smart home environment data determination system as claimed in claim 7, characterized in that: The current working mode of the respective positioning service units of the judgment execution device, the numerical extraction device, the imaging processing mechanism and the environmental analysis mechanism is a sleep working mode or a running working mode.

9. The smart home environment data determination system according to claim 7, characterized in that: The judgment execution device, the numerical extraction device, the imaging processing mechanism and the multiple positioning service units of the environment analysis mechanism are all based on the Beidou positioning mechanism.

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