Intelligent Building Data Management System

Abstract

The present invention relates to a smart building data management system, including a first cloud computing node, a second cloud computing node, a model mapping mechanism, a compression analysis mechanism, and a wireless transmission mechanism. The smart building data management system of the present invention is logically reliable and intelligently designed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent buildings, and more specifically, to an intelligent building data management system. Background Art

[0002] Intelligent building engineering, also known as low-voltage system engineering, mainly refers to communication automation (CA), building automation (BA), office automation (OA), fire automation (FA), and security automation (SA), abbreviated as 5A. The systems included are: computer management system engineering, building equipment automatic control system engineering, communication system engineering, security monitoring and anti-theft alarm system engineering, satellite and shared television system engineering, garage management system engineering, integrated wiring system engineering, computer network system engineering, broadcast system engineering, conference system engineering, video-on-demand system engineering, intelligent community property management system engineering, videoconference system engineering, large-screen display system engineering, intelligent lighting and audio control system engineering, fire alarm system engineering, computer room engineering, one-card system engineering, etc.

[0003] Since a large number of system components are arranged at the intelligent building engineering site, strict requirements need to be imposed on the transmission accuracy and efficiency of the on-site monitoring images of the intelligent building project to ensure effective and reliable reproduction at the remote image receiving end. At the same time, the large number of system components result in a large amount of transmitted picture data, and the requirement for the compression ratio of data transmission is more prioritized. However, there is a lack of a solution mechanism in the prior art that takes into account both the transmission effect and the transmission compression ratio of the on-site images of the intelligent building project. Summary of the Invention

[0004] To solve the technical problems in the prior art, the present invention provides an intelligent building data management system, which can use an artificial intelligence model to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using surrounding image blocks, and wirelessly transmit the intra-frame coding compression ratio obtained by intelligently analyzing the use of surrounding image blocks to perform intra-frame coding on the current image block to a remote cloud computing management node, thereby providing valuable reference information for the selection of the nearest intra-frame coding compression ratio to ensure the image compression effect, and achieving the balance between the transmission effect and the transmission compression ratio of the on-site images of the intelligent building project.

[0005] According to the present invention, an intelligent building data management system is provided, and the system includes:

[0006] A first cloud computing node for analyzing a plurality of relevant information of a current image block of an on-site imaging image of an intelligent building that is to be intra-frame coded, where the plurality of relevant information of the current image block is the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block;

[0007] A second cloud computing node, configured to analyze multiple pieces of relevant information of surrounding image blocks of the current image block, where the multiple pieces of relevant information of the surrounding image blocks are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the surrounding image blocks;

[0008] A model mapping mechanism, configured to perform each learning action on the deep residual network to obtain the deep residual network after each learning action and output it as a deep residual network model, where the number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block;

[0009] A compression analysis mechanism, connected to the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, and configured to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points of the image where the current image block is located, the signal-to-noise ratio, multiple pieces of relevant information of the current image block to be intra-frame coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block by using the deep residual network model;

[0010] A wireless transmission mechanism, connected to the compression analysis mechanism, and configured to wirelessly transmit the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks, which is intelligently analyzed, to a remote cloud computing management node;

[0011] Wherein, intelligently analyzing the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points of the image where the current image block is located, the signal-to-noise ratio, multiple pieces of relevant information of the current image block to be intra-frame coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block by using the deep residual network model includes: the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks is the ratio obtained by dividing the data volume corresponding to the current image block by the data volume of the data stream obtained after performing intra-frame coding on the current image block;

[0012] Wherein, analyzing multiple pieces of relevant information of the current image block to be intra-frame coded, where the multiple pieces of relevant information of the current image block are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block includes: the mean square error of pixel values of the current image block is the mean square error of each portion of pixel values corresponding to each pixel point of the current image block;

[0013] Among them, parsing multiple relevant information of the surrounding image blocks of the current image block, where the multiple relevant information of the surrounding image blocks is the signal-to-noise ratio, contrast ratio, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of the pixel values of the surrounding image blocks, including: the surrounding image blocks are located near the current image block and occupy the same pixel points as the current image block.

[0014] It can be seen that the present invention has at least the following three beneficial technical effects:

[0015] Technical effect A: Using the first cloud computing node to parse multiple relevant information of the current image block of the on-site imaging image of the smart building source to be intra-frame encoded, where the multiple relevant information of the current image block is the signal-to-noise ratio, contrast ratio, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of the pixel values of the current image block, and using the second cloud computing node to parse multiple relevant information of the surrounding image blocks of the current image block, where the multiple relevant information of the surrounding image blocks is the signal-to-noise ratio, contrast ratio, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of the pixel values of the surrounding image blocks, so as to obtain multiple basic data required for subsequent intelligent analysis operations of the intra-frame encoding compression ratio;

[0016] Technical effect B: Introducing a model mapping mechanism to perform each learning action on the deep residual network to obtain the deep residual network after each learning action and output it as a deep residual network model. The number of learning actions completed by the deep residual network is monotonically positively correlated with the number of pixel points occupied by the current image block, so as to obtain an artificial intelligence model required for subsequent intelligent analysis operations of the intra-frame encoding compression ratio;

[0017] Technical effect C: Using a specifically designed deep residual network model to intelligently analyze the intra-frame encoding compression ratio obtained by performing intra-frame encoding on the current image block using the surrounding image blocks according to the number of pixel points in the image where the current image block is located, the signal-to-noise ratio, multiple relevant information of the current image block to be intra-frame encoded, and multiple relevant information of the surrounding image blocks of the current image block, and wirelessly transmit the intelligently analyzed intra-frame encoding compression ratio obtained by performing intra-frame encoding on the current image block using the surrounding image blocks to the remote cloud computing management node, so as to predict the intra-frame encoding compression ratio before performing intra-frame encoding on the current image block using the surrounding image blocks, thereby providing a fast and convenient processing mechanism for obtaining a satisfactory intra-frame encoding compression ratio.

[0018] The intelligent building data management system of the present invention is logically reliable and intelligently designed. By using an artificial intelligence model to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using surrounding image blocks, and wirelessly transmitting the intra-frame coding compression ratio obtained by intelligently analyzing the use of surrounding image blocks to perform intra-frame coding on the current image block to a remote cloud computing management node, it provides valuable reference information for the selection of the nearest intra-frame coding compression ratio to ensure the image compression effect of the on-site picture of the intelligent building project. Detailed implementation manners

[0019] The implementation manners of the intelligent building data management system of the present invention will be described in detail below.

[0020] The intelligent building data management system according to the first embodiment of the present invention includes:

[0021] A first cloud computing node, configured to analyze multiple pieces of relevant information of a current image block of an on-site imaging image of an intelligent building to be intra-frame coded, where the multiple pieces of relevant information of the current image block are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block;

[0022] Exemplarily, a first cloud computing node, configured to analyze multiple pieces of relevant information of a current image block of an on-site imaging image of an intelligent building to be intra-frame coded, where the multiple pieces of relevant information of the current image block are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block includes: The first cloud computing node for analyzing multiple pieces of relevant information of the current image block of the on-site imaging image of the intelligent building to be intra-frame coded can be implemented by a cloud storage network element, where the multiple pieces of relevant information of the current image block are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block;

[0023] A second cloud computing node, configured to analyze multiple pieces of relevant information of surrounding image blocks of the current image block, where the multiple pieces of relevant information of the surrounding image blocks are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the surrounding image blocks;

[0024] A model mapping mechanism, configured to perform each learning action on a deep residual network to obtain the deep residual network after each learning action and output it as a deep residual network model, where the number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block;

[0025] A compression analysis mechanism, which is respectively connected to the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple relevant information of the current image block to be intra-frame coded, and multiple relevant information of the surrounding image blocks of the current image block by using the deep residual network model;

[0026] A wireless transmission mechanism, which is connected to the compression analysis mechanism, and is used to wirelessly transmit the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks, which is intelligently analyzed, to a remote cloud computing management node;

[0027] Among them, intelligently analyzing the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple relevant information of the current image block to be intra-frame coded, and multiple relevant information of the surrounding image blocks of the current image block by using the deep residual network model includes: the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks is the ratio obtained by dividing the data volume corresponding to the current image block by the data volume of the data stream obtained after performing intra-frame coding on the current image block;

[0028] Among them, analyzing multiple relevant information of the current image block to be intra-frame coded, and the multiple relevant information of the current image block is the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block, including: the mean square error of pixel values of the current image block is the mean square error of each copy of pixel values corresponding to each pixel point of the current image block;

[0029] Among them, analyzing multiple relevant information of the surrounding image blocks of the current image block, and the multiple relevant information of the surrounding image blocks is the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the surrounding image block, including: the surrounding image block is located near the current image block and occupies the same number of pixel points as the current image block;

[0030] Among them, the model mapping mechanism is used to perform various learning actions on the deep residual network to obtain the deep residual network after each learning action and output it as the deep residual network model. The number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block, including: representing the information conversion relationship in which the number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block using an information conversion formula;

[0031] Among them, representing the information conversion relationship in which the number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block using an information conversion formula includes: in the information conversion formula, the number of pixel points occupied by the current image block is the input information, and the number of learning actions completed by the deep residual network corresponding to the number of pixel points occupied by the current image block is the output information;

[0032] And among them, using the deep residual network model to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple relevant information of the current image block to be intra-frame coded, and multiple relevant information of the surrounding image blocks of the current image block further includes: parallelly inputting the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple relevant information of the current image block to be intra-frame coded, and multiple relevant information of the surrounding image blocks of the current image block into the deep residual network model.

[0033] Different from the first embodiment of the present invention, the intelligent building data management system according to the second embodiment of the present invention may further include the following components:

[0034] An instant acquisition device, which is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to respectively measure the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node;

[0035] Among them, the instant acquisition device is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to measure the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively. The instant acquisition device includes a plurality of throughput measurement units, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively.

[0036] Among them, the instant acquisition device includes a plurality of throughput measurement units, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively. The plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively.

[0037] Among them, the plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively. The structures of the plurality of throughput sensing circuits are the same.

[0038] Among them, the plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively. It further includes that the plurality of throughput sensing circuits have the same throughput measurement upper limit value and throughput measurement lower limit value.

[0039] Different from the first embodiment of the present invention, the intelligent building data management system according to the third embodiment of the present invention may further include the following components:

[0040] The directional processing component is arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to provide data configuration services based on wired transmission for the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively;

[0041] Among them, the directional processing component is arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to provide data configuration services based on wired transmission for the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, including: the directional processing component connects the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node through different wired configuration units;

[0042] And among them, the directional processing component is arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to provide data configuration services based on wired transmission for the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, and further includes: the internal structures of the different wired configuration units connecting the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node are the same.

[0043] In addition, in the intelligent building data management system, using the deep residual network model to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple relevant information of the current image block to be intra-frame encoded, and multiple relevant information of the surrounding image blocks of the current image block further includes: executing the deep residual network model to obtain the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks output by the deep residual network model.

[0044] Although the present invention has been described in terms of the presently preferred embodiments, it is to be understood that the disclosed subject matter should not be limited to the described embodiments only. After reading the above description, various substitutions and modifications to the present invention will no doubt be obvious to those of ordinary skill in the art. Accordingly, it is intended that the appended claims be construed to cover all such substitutions and modifications as fall within the spirit and scope of the "substance" of the present invention.

Claims

1. An intelligent building data management system, characterized in that, The system includes: A first cloud computing node, configured to analyze multiple pieces of relevant information of a current image block of an on-site imaging image from a smart building to be intra-frame encoded, where the multiple pieces of relevant information of the current image block are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block; A second cloud computing node, configured to analyze multiple pieces of relevant information of surrounding image blocks of the current image block, where the multiple pieces of relevant information of the surrounding image blocks are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the surrounding image blocks; A model mapping mechanism, configured to perform each learning action on a deep residual network to obtain the deep residual network after each learning action and output it as a deep residual network model, where the number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block; A compression analysis mechanism, connected to the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, and configured to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple pieces of relevant information of the current image block to be intra-frame encoded, and multiple pieces of relevant information of the surrounding image blocks of the current image block by using the deep residual network model; A wireless transmission mechanism, connected to the compression analysis mechanism, and configured to wirelessly transmit the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks, which is intelligently analyzed, to a remote cloud computing management node; Among them, intelligently analyzing the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points and signal-to-noise ratio of the image where the current image block is located, multiple pieces of relevant information of the current image block to be intra-frame encoded, and multiple pieces of relevant information of the surrounding image blocks of the current image block by using the deep residual network model includes: the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks is the ratio obtained by dividing the data volume corresponding to the current image block by the data volume of the data stream obtained after performing intra-frame coding on the current image block; Among them, analyzing multiple pieces of relevant information of the current image block to be intra-frame encoded, where the multiple pieces of relevant information of the current image block are the signal-to-noise ratio, contrast, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of pixel values of the current image block includes: the mean square error of pixel values of the current image block is the mean square error of each portion of pixel values corresponding to each pixel point of the current image block; Among them, parsing multiple pieces of relevant information of the surrounding image blocks of the current image block, where the multiple pieces of relevant information of the surrounding image blocks are the signal-to-noise ratio, contrast ratio, vertical coordinate values and horizontal coordinate values of each foreground pixel point, and the mean square error of the pixel values of the surrounding image blocks, including: the surrounding image blocks are located near the current image block and occupy the same pixel points as the current image block.

2. The intelligent building data management system according to claim 1, characterized in that: A model mapping mechanism, used to perform each learning action on the deep residual network to obtain the deep residual network after each learning action and output it as a deep residual network model. The number of learning actions completed by the deep residual network is monotonically and positively correlated with the number of pixel points occupied by the current image block, including: using an information conversion formula to represent the information conversion relationship between the number of learning actions completed by the deep residual network and the number of pixel points occupied by the current image block being monotonically and positively correlated; Among them, using an information conversion formula to represent the information conversion relationship between the number of learning actions completed by the deep residual network and the number of pixel points occupied by the current image block being monotonically and positively correlated includes: in the information conversion formula, the number of pixel points occupied by the current image block is the input information, and the number of learning actions completed by the deep residual network corresponding to the number of pixel points occupied by the current image block is the output information.

3. The intelligent building data management system according to claim 2, characterized in that: Using the deep residual network model to intelligently analyze the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks according to the number of pixel points, signal-to-noise ratio, multiple pieces of relevant information of the current image block to be intra-frame coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block, further including: parallelly inputting the number of pixel points, signal-to-noise ratio, multiple pieces of relevant information of the current image block to be intra-frame coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block of the image where the current image block is located into the deep residual network model.

4. The intelligent building data management system according to claim 3, wherein, The system further includes: An instant acquisition device, respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and used to measure the respective instant throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node; Among them, the real-time acquisition device is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, and is used to respectively measure the real-time throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, including: the real-time acquisition device includes a plurality of throughput measurement units, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the real-time throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively.

5. The intelligent building data management system according to claim 4, characterized in that: The real-time acquisition device includes a plurality of throughput measurement units, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the real-time throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, including: the plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the real-time throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively.

6. The intelligent building data management system according to claim 5, characterized in that: The plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the real-time throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, including: the structures of the plurality of throughput sensing circuits are the same.

7. The intelligent building data management system according to claim 6, characterized in that: The plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to be respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node to complete the separate measurement of the real-time throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node respectively, and further include: the plurality of throughput sensing circuits have the same throughput measurement upper limit value and throughput measurement lower limit value.

8. The intelligent building data management system according to claim 4, wherein The system further includes: The directional processing component is arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, and is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, and is used to provide data configuration services based on wired transmission for the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively.

9. The intelligent building data management system according to claim 8, wherein: The directional processing component is arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, and is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, and is used to provide data configuration services based on wired transmission for the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively, including: the directional processing component connects the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node through different wired configuration units.

10. The intelligent building data management system according to claim 8, wherein: The directional processing component is arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, and is respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, and is used to provide data configuration services based on wired transmission for the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively, further including: the internal structures of the different wired configuration units connecting the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node are the same.