Electric energy meter power factor management system

Abstract

The invention relates to an electric energy meter power factor management system. The system comprises a content analysis device which is used for synchronously inputting each working current, each rated power and each resistance value corresponding to each load device managed by the current electric energy meter and multiple configuration parameters of the current electric energy meter into a factor analysis model, obtaining a current electric energy meter power factor lower limit value and a current electric energy meter power factor upper limit value; and the change detection device is used for triggering the content analysis device to execute the primary factor analysis model when the number and / or type of each load device managed by the current electric energy meter are / is changed. Through the system, the power factor of the electric energy meter can be directly analyzed by introducing a factor analysis model with a customized structural design and multiple items of basic data screened in a targeted manner, so that the detection of various indirect data by merging excessive detection components is not needed, and the detection efficiency of the power factor of the electric energy meter is improved.

Description

Technical Field

[0001] The present invention relates to the field of electric energy meters, and more specifically, to a power factor management system for electric energy meters. Background Art

[0002] The electric energy meter is an instrument used to measure electric energy, also known as an electric meter, a fire meter, or a kilowatt-hour meter. It refers to an instrument that measures various electrical quantities, commonly known as an electric meter or a fire meter. The earliest electric energy meter was made in 1881 based on the principle of electrolysis. Although each of these electric energy meters weighed dozens of kilograms, was very heavy, and had no guarantee of accuracy, it was still regarded as a major invention in the scientific and technological community at the time and was highly valued and praised by people. It was quickly adopted in engineering. With the development of science and technology, the discovery and application of alternating current has put forward new requirements for the development of electric energy meters. Through the efforts of scientists, the induction electric energy meter was born. Because the induction electric energy meter has a series of advantages such as simple structure, safe operation, low price, durability, easy maintenance and mass production, it has developed rapidly.

[0003] With the development of artificial intelligence technology, as one of the main types of power equipment, the requirements for intelligent transformation of electricity meters are getting higher and higher. The power equipment manager expects to directly obtain various indirect data at the electricity meter end, rather than detecting various indirect data by incorporating too many detection components. For example, whenever the number and / or type of each load equipment managed by the electricity meter changes, the power equipment manager expects to directly obtain the power factor range of the electricity meter limited by the power factor lower limit threshold and the power factor upper limit threshold through the intelligent analysis mode. Obviously, there is a lack of targeted intelligent data analysis solutions in the existing technology. Summary of the invention

[0004] In order to solve the technical problems in the prior art, the present invention provides an electric energy meter power factor management system, which introduces a step-by-step learning mechanism to perform each learning action on a deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and outputs the deep feedforward neural network after the step-by-step learning as a factor analysis model. The number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter, thereby designing artificial intelligence models with different structures for performing power factor analysis of electric energy meters for different management states of different electric energy meters, and selectively screening multiple basic data for the artificial intelligence model for performing power factor analysis of electric energy meters, including The working currents, rated powers and resistance values ​​corresponding to the load devices currently managed by the electric energy meter, as well as the working frequency, operating voltage and rated power of the current electric energy meter are respectively known, thereby ensuring the reliability and stability of the analysis results. A change detection device is also introduced to trigger the content analysis device to execute a factor analysis model when it detects that the number and / or type of the load devices currently managed by the electric energy meter have changed. Specifically, the change detection device determines whether the number and / or type of the load devices currently managed by the electric energy meter have changed based on the value change of the total resistance value of the load devices currently managed by the electric energy meter, thereby avoiding false operation of the factor analysis operation.

[0005] According to the present invention, there is provided an electric energy meter power factor management system, the system comprising: A step-by-step learning mechanism, used to perform each learning action on the deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and output the deep feedforward neural network after the step-by-step learning as a factor analysis model, wherein the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices managed by the current electric energy meter; The information input mechanism is used to obtain the respective working currents, rated powers and resistance values ​​corresponding to the respective load devices currently managed by the electric energy meter; A parameter acquisition mechanism, used to output the current operating frequency, operating voltage and rated power of the electric energy meter as multiple configuration parameters of the current electric energy meter; The content analysis device is connected to the step-by-step learning mechanism, the information input mechanism and the parameter acquisition mechanism respectively, and is used to synchronously input the respective working currents, the respective rated powers and the respective resistance values ​​corresponding to the respective load devices managed by the current electric energy meter and the multiple configuration parameters of the current electric energy meter into the factor analysis model, so as to execute the factor analysis model and obtain the lower limit value of the power factor of the current electric energy meter and the upper limit value of the power factor of the current electric energy meter output by the factor analysis model; A change detection device, connected to the content analysis device, for triggering the content analysis device to execute a factor analysis model when detecting a change in the number and / or type of each load device managed by the current electric energy meter; The change detection device is connected to the content analysis device and is used to trigger the content analysis device to execute a factor analysis model when detecting that the number and / or type of each load device currently managed by the electric energy meter has changed. The change detection device determines whether the number and / or type of each load device currently managed by the electric energy meter has changed based on the value change of the total resistance value of each load device currently managed by the electric energy meter; wherein each learning action is performed on the deep feedforward neural network to perform step-by-step learning of the deep feedforward neural network, and the deep feedforward neural network after the step-by-step learning is output as a factor analysis model, and the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter, including: using a forward correlation function to represent a numerical mapping relationship of the positive correlation between the number of learning actions performed by the deep feedforward neural network and the number of load devices currently managed by the electric energy meter; Among them, the working currents, rated powers and resistance values ​​corresponding to the load devices managed by the current electric energy meter and multiple configuration parameters of the current electric energy meter are synchronously input into the factor analysis model to execute the factor analysis model, and the current electric energy meter power factor lower limit value and the current electric energy meter power factor upper limit value output by the factor analysis model are obtained, including: the current electric energy meter power factor lower limit value and the current electric energy meter power factor upper limit value limit the current electric energy meter power factor range.

[0006] The present invention needs to have at least the following three important invention points: Invention point A: A step-by-step learning mechanism is introduced to perform each learning action on the deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and the deep feedforward neural network after the step-by-step learning is output as a factor analysis model. The number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter, so that artificial intelligence models with different structures for performing power factor analysis of electric energy meters are designed for different management states of different electric energy meters; Invention point B: The artificial intelligence model for performing power factor analysis of the electric energy meter selectively selects multiple basic data, including the respective working currents, the respective rated powers, the respective resistance values ​​corresponding to the respective load devices managed by the current electric energy meter, and the current working frequency, operating voltage and rated power of the electric energy meter, thereby ensuring the reliability and stability of the analysis results; Invention point C: A change detection device is introduced to trigger the content analysis device to execute a factor analysis model when it detects that the quantity and / or type of each load device currently managed by the electric energy meter has changed. Specifically, the change detection device determines whether the quantity and / or type of each load device currently managed by the electric energy meter has changed based on the numerical change of the total resistance value of each load device currently managed by the electric energy meter, thereby avoiding false operation of the factor analysis operation.

[0007] The power factor management system of the electric energy meter of the present invention is stable in operation and intelligent in control. Since the factor analysis model with customized structural design and multiple basic data screened in a targeted manner can be introduced to directly analyze the power factor of the electric energy meter, there is no need to incorporate too many detection components to detect various indirect data, thereby improving the detection efficiency of the power factor of the electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS Those skilled in the art may better understand the numerous advantages of the present invention by referring to the accompanying drawings, in which: Figure 1 It is a structural schematic diagram of an electric energy meter power factor management system according to the primary embodiment of the present invention.

[0009] Figure 2 It is a structural schematic diagram of an electric energy meter power factor management system according to a secondary embodiment of the present invention.

[0010] Figure 3 FIG. 4 is a structural diagram of an electric energy meter power factor management system according to another embodiment of the present invention. DETAILED DESCRIPTION

[0011] Figure 1 1 is a schematic diagram of a power factor management system for an electric energy meter according to a primary embodiment of the present invention, the system comprising: A step-by-step learning mechanism, used to perform each learning action on the deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and output the deep feedforward neural network after the step-by-step learning as a factor analysis model, wherein the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices managed by the current electric energy meter; Specifically, the step-by-step learning mechanism is used to perform each learning action on the deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and output the deep feedforward neural network after the step-by-step learning as a factor analysis model, and the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter, including: selecting an ASIC chip to implement the step-by-step learning mechanism, which is used to perform each learning action on the deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and output the deep feedforward neural network after the step-by-step learning as a factor analysis model, and the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter; The information input mechanism is used to obtain the respective working currents, rated powers and resistance values ​​corresponding to the respective load devices currently managed by the electric energy meter; A parameter acquisition mechanism, used to output the current operating frequency, operating voltage and rated power of the electric energy meter as multiple configuration parameters of the current electric energy meter; The content analysis device is connected to the step-by-step learning mechanism, the information input mechanism and the parameter acquisition mechanism respectively, and is used to synchronously input the respective working currents, the respective rated powers and the respective resistance values ​​corresponding to the respective load devices managed by the current electric energy meter and the multiple configuration parameters of the current electric energy meter into the factor analysis model, so as to execute the factor analysis model and obtain the lower limit value of the power factor of the current electric energy meter and the upper limit value of the power factor of the current electric energy meter output by the factor analysis model; A change detection device, connected to the content analysis device, for triggering the content analysis device to execute a factor analysis model when detecting a change in the number and / or type of each load device managed by the current electric energy meter; The change detection device is connected to the content analysis device and is used to trigger the content analysis device to execute a factor analysis model when detecting that the number and / or type of each load device currently managed by the electric energy meter has changed. The change detection device determines whether the number and / or type of each load device currently managed by the electric energy meter has changed based on the value change of the total resistance value of each load device currently managed by the electric energy meter; wherein each learning action is performed on the deep feedforward neural network to perform step-by-step learning of the deep feedforward neural network, and the deep feedforward neural network after the step-by-step learning is output as a factor analysis model, and the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter, including: using a forward correlation function to represent a numerical mapping relationship of the positive correlation between the number of learning actions performed by the deep feedforward neural network and the number of load devices currently managed by the electric energy meter; Wherein, each portion of the working current, each portion of the rated power and each portion of the resistance value corresponding to each load device managed by the current electric energy meter and multiple configuration parameters of the current electric energy meter are synchronously input into the factor analysis model to execute the factor analysis model, and the current electric energy meter power factor lower limit value and the current electric energy meter power factor upper limit value output by the factor analysis model are obtained, including: the current electric energy meter power factor lower limit value and the current electric energy meter power factor upper limit value limit the current electric energy meter power factor range; Wherein, the change detection device determines whether the quantity and / or type of each load device currently managed by the electric energy meter changes based on the value change of the total resistance value of each load device currently managed by the electric energy meter, including: when the value change of the total resistance value of each load device currently managed by the electric energy meter is greater than or equal to the set group value threshold, it is determined that the quantity and / or type of each load device currently managed by the electric energy meter changes; Among them, the change detection device judges whether the quantity and / or type of each load device managed by the current electric energy meter has changed based on the numerical change of the total resistance value of each load device managed by the current electric energy meter, and also includes: when the numerical change of the total resistance value of each load device managed by the current electric energy meter is less than the set group value threshold, it is judged that the quantity and / or type of each load device managed by the current electric energy meter has not changed.

[0012] Figure 2 It is a structural schematic diagram of an electric energy meter power factor management system according to a secondary embodiment of the present invention.

[0013] and Figure 1 different, Figure 2 The power factor management system of the energy meter in can also include the following components: A wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, respectively, and is used to receive various configuration information of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in a time-sharing manner, so as to realize real-time configuration of the current working mode of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism; Wherein, the wireless transceiver is respectively connected with the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, and is used to receive the respective configuration information of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in time-sharing, so as to realize the real-time configuration of the current working mode of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively. The wireless transceiver is respectively connected with the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism through a wireless communication link; The wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a time division duplex communication link, including: the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism have different IP address data; The wireless transceiver is connected to the content analysis device, the step-by-step learning mechanism, the information input mechanism and the parameter collection mechanism respectively through wireless communication links, including: the wireless transceiver is connected to the content analysis device, the step-by-step learning mechanism, the information input mechanism and the parameter collection mechanism respectively through time division duplex communication links; Among them, the wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a wireless communication link, including: the wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a frequency division duplex communication link.

[0014] Figure 3 FIG. 4 is a structural diagram of an electric energy meter power factor management system according to another embodiment of the present invention.

[0015] and Figure 1 different, Figure 3 The power factor management system of the energy meter in can also include the following components: An HDMI transmission device, connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism, respectively, for providing data input operation and data output operation for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism in a time-sharing manner; Wherein, the HDMI transmission device is respectively connected with the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, and is used to provide data input operation and data output operation for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in time sharing, including: the HDMI transmission device includes a plurality of HDMI transmission interfaces, which are respectively connected with the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, and are used to provide data input operation and data output operation for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in time sharing; Wherein, the HDMI transmission device comprises a plurality of HDMI transmission interfaces, which are respectively connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism, and are used to provide data input operation and data output operation for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism in a time-sharing manner, including: the internal structures of the plurality of HDMI transmission interfaces are the same; And wherein, the HDMI transmission device includes multiple HDMI transmission interfaces, which are used to be connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism respectively, and are used to provide data input operations and data output operations for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism in a time-sharing manner, including: the multiple HDMI transmission interfaces use the same pulse generating circuit.

[0016] In addition, in the power factor management system of the electric energy meter, the numerical mapping relationship of the positive correlation between the number of learning actions performed by the deep feedforward neural network and the number of load devices currently managed by the electric energy meter using a forward correlation function includes: the number of load devices currently managed by the electric energy meter is the input parameter of the forward correlation function, and the number of learning actions performed by the deep feedforward neural network is the output parameter of the forward correlation function; And wherein, the number of load devices currently managed by the electric energy meter is the input parameter of the forward correlation function, and the number of learning actions performed by the deep feedforward neural network is the output parameter of the forward correlation function, including: the fewer the number of load devices currently managed by the electric energy meter, the fewer the number of learning actions performed by the deep feedforward neural network.

[0017] It will be appreciated by those skilled in the art that various modifications and changes may be made without departing from the scope and spirit of the present invention. It should therefore be understood that the above embodiments are for illustration only and are not intended to be limiting. Because the scope of the present invention is defined by the claims rather than the foregoing description, any changes and modifications that fall within the scope and boundaries of the claims or the equivalents of these scopes and boundaries are subject to the claims.

Claims

1. A power factor management system for an electric energy meter, characterized in that: The system comprises: A step-by-step learning mechanism, used to perform each learning action on the deep feedforward neural network to perform step-by-step learning on the deep feedforward neural network, and output the deep feedforward neural network after the step-by-step learning as a factor analysis model, wherein the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices managed by the current electric energy meter; An information input mechanism is used to obtain the respective working currents, respective rated powers and respective resistance values ​​corresponding to the respective load devices currently managed by the electric energy meter; A parameter collection mechanism, used to output the current operating frequency, operating voltage and rated power of the electric energy meter as multiple configuration parameters of the current electric energy meter; The content analysis device is connected to the step-by-step learning mechanism, the information input mechanism and the parameter acquisition mechanism respectively, and is used to synchronously input the respective working currents, the respective rated powers and the respective resistance values ​​corresponding to the respective load devices managed by the current electric energy meter and the multiple configuration parameters of the current electric energy meter into the factor analysis model, so as to execute the factor analysis model and obtain the lower limit value of the power factor of the current electric energy meter and the upper limit value of the power factor of the current electric energy meter output by the factor analysis model; A change detection device, connected to the content analysis device, for triggering the content analysis device to execute a factor analysis model when detecting a change in the number and / or type of each load device managed by the current electric energy meter; The change detection device is connected to the content analysis device and is used to trigger the content analysis device to execute a factor analysis model when detecting that the number and / or type of each load device currently managed by the electric energy meter has changed. The change detection device determines whether the number and / or type of each load device currently managed by the electric energy meter has changed based on the value change of the total resistance value of each load device currently managed by the electric energy meter; wherein each learning action is performed on the deep feedforward neural network to perform step-by-step learning of the deep feedforward neural network, and the deep feedforward neural network after the step-by-step learning is output as a factor analysis model, and the number of learning actions performed by the deep feedforward neural network is positively correlated with the number of load devices currently managed by the electric energy meter, including: using a forward correlation function to represent a numerical mapping relationship of the positive correlation between the number of learning actions performed by the deep feedforward neural network and the number of load devices currently managed by the electric energy meter; Among them, the working currents, rated powers and resistance values ​​corresponding to the load devices managed by the current electric energy meter and multiple configuration parameters of the current electric energy meter are synchronously input into the factor analysis model to execute the factor analysis model, and the current electric energy meter power factor lower limit value and the current electric energy meter power factor upper limit value output by the factor analysis model are obtained, including: the current electric energy meter power factor lower limit value and the current electric energy meter power factor upper limit value limit the current electric energy meter power factor range.

2. The power factor management system of an electric energy meter according to claim 1, characterized in that: The change detection device determines whether the number and / or type of each load device currently managed by the electric energy meter changes based on the value change of the total resistance value of each load device currently managed by the electric energy meter, including: when the value change of the total resistance value of each load device currently managed by the electric energy meter is greater than or equal to the set group value threshold, it is determined that the number and / or type of each load device currently managed by the electric energy meter changes; Among them, the change detection device judges whether the quantity and / or type of each load device managed by the current electric energy meter has changed based on the numerical change of the total resistance value of each load device managed by the current electric energy meter, and also includes: when the numerical change of the total resistance value of each load device managed by the current electric energy meter is less than the set group value threshold, it is judged that the quantity and / or type of each load device managed by the current electric energy meter has not changed.

3. The power factor management system of an electric energy meter as claimed in claim 2, characterized in that: The system further comprises: A wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, respectively, and is used to receive various configuration information of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in a time-sharing manner, so as to realize real-time configuration of the current working mode of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism; Among them, the wireless transceiver device is respectively connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, and is used to receive the configuration information of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in a time-sharing manner, so as to realize the real-time configuration of the current working mode of the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism. The wireless transceiver device is respectively connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism through a wireless communication link.

4. The power factor management system of an electric energy meter as claimed in claim 3, characterized in that: The wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a time division duplex communication link, including: the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism have different IP address data.

5. The power factor management system of an electric energy meter as claimed in claim 4, characterized in that: The wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a wireless communication link, including: the wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a time division duplex communication link.

6. The power factor management system of an electric energy meter as claimed in claim 4, characterized in that: The wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a wireless communication link, including: the wireless transceiver device is connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism respectively through a frequency division duplex communication link.

7. The power factor management system of an electric energy meter as claimed in claim 2, characterized in that: The system further comprises: An HDMI transmission device, connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism, respectively, for providing data input operation and data output operation for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism in a time-sharing manner; Among them, the HDMI transmission device is respectively connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, and is used to provide data input operations and data output operations for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in a time-sharing manner. The HDMI transmission device includes multiple HDMI transmission interfaces, which are respectively connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism, and are used to provide data input operations and data output operations for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter collection mechanism in a time-sharing manner.

8. The power factor management system of an electric energy meter as claimed in claim 7, characterized in that: The HDMI transmission device includes multiple HDMI transmission interfaces, which are used to be connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism respectively, and are used to provide data input operations and data output operations for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism in a time-sharing manner, including: the internal structures of the multiple HDMI transmission interfaces are the same.

9. The power factor management system of an electric energy meter according to claim 7, characterized in that: The HDMI transmission device includes multiple HDMI transmission interfaces, which are used to be connected to the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism respectively, and are used to provide data input operations and data output operations for the content analysis device, the step-by-step learning mechanism, the information entry mechanism and the parameter acquisition mechanism in a time-sharing manner, including: the multiple HDMI transmission interfaces use the same pulse generating circuit.

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