Intelligent building data management system

Abstract

The invention relates to a smart building data management system, which comprises a first cloud computing node, a second cloud computing node, a model mapping mechanism, a compression analysis mechanism and a wireless transmission mechanism. The intelligent building data management system is reliable in logic and intelligent in design.

Description

Technical Field

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

[0002] Smart building engineering is also known as weak current system engineering, which mainly refers to communication automation (CA), building automation (BA), office automation (OA), fire automation (FA) and security automation (SA), referred to 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, broadcasting system engineering, conference system engineering, video on demand system engineering, intelligent community property management system engineering, video conferencing system engineering, large-screen display system engineering, intelligent lighting, sound 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 on site in smart building projects, strict requirements need to be placed on the transmission accuracy and efficiency of the on-site monitoring images of the smart building projects to ensure effective and reliable reproduction of the images at the remote receiving end. At the same time, a large number of system components result in a large amount of transmitted image data, and the compression ratio of data transmission is given higher priority. However, the existing technology lacks a solution mechanism that takes into account both the transmission effect and the transmission compression ratio of the on-site images of smart building projects. Summary of the invention

[0004] In order to solve the technical problems in the prior art, the present invention provides a smart 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 the surrounding image blocks, and wirelessly transmit the intra-frame coding compression ratio obtained by intelligently analyzing the current image block using the surrounding image blocks to a remote cloud computing management node, thereby providing valuable reference information for the selection of the latest intra-frame coding compression ratio to ensure the image compression effect, and achieving a balance between the transmission effect and the transmission compression ratio of the on-site images of the smart building project.

[0005] According to the present invention, a smart building data management system is provided, the system comprising: A first cloud computing node is used to parse multiple pieces of relevant information of a current image block to be intra-coded from an on-site imaging image of a smart building, wherein the multiple pieces of relevant information of the current image block are a signal-to-noise ratio, a contrast, a vertical coordinate value and a horizontal coordinate value of each foreground pixel point of the current image block, and a mean square error of pixel values ​​of the current image block; The second cloud computing node is used to analyze multiple pieces of relevant information of surrounding image blocks of the current image block, wherein the multiple pieces of relevant information of the surrounding image blocks are signal-to-noise ratio, contrast, vertical coordinate value and horizontal coordinate value of each foreground pixel point of the surrounding image blocks, and mean square error of pixel values ​​of the surrounding image blocks; A model mapping mechanism, used to perform each learning action on the deep residual network to obtain a deep residual network after completing each learning action and output it as a deep residual network model, wherein the number of learning actions completed by the deep residual network is monotonically positively correlated with the number of pixels 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 use 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 pixels and the 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 coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block; A wireless transmission mechanism, connected to the compression analysis mechanism, for wirelessly transmitting the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using surrounding image blocks obtained by intelligent analysis to a remote cloud computing management node; Among them, the deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-frame coded, and multiple related information of the surrounding image blocks of the current image block. The intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks includes: the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks is a ratio obtained by dividing the amount of data corresponding to the current image block by the amount of data of the data stream obtained after performing intra-frame coding on the current image block; Wherein, parsing multiple pieces of relevant information of the current image block to be intra-coded, 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 of the current image block, and the mean square error of the pixel value of the current image block, including: the mean square error of the pixel value of the current image block is the mean square error of each pixel value corresponding to each pixel point of the current image block; Among them, multiple related information of the surrounding image blocks of the current image block are analyzed, and the multiple related information of the surrounding image blocks are the signal-to-noise ratio, contrast, vertical coordinate value and horizontal coordinate value of each foreground pixel point of the surrounding image blocks 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 have the same pixel points as those occupied by the current image block.

[0006] It can be seen that the present invention has at least the following three beneficial technical effects: Technical effect A: A first cloud computing node is used to parse multiple related information of a current image block to be intra-frame encoded from an on-site imaging image of a smart building, wherein the multiple related 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 of the current image block, and the mean square error of the pixel value of the current image block; and a second cloud computing node is used to parse multiple related information of surrounding image blocks of the current image block, wherein the multiple related 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 of the surrounding image blocks, and the mean square error of the pixel value of the surrounding image blocks, thereby obtaining multiple basic data required for subsequent intra-frame encoding compression ratio intelligent analysis operations; Technical effect B: A model mapping mechanism is introduced to perform each learning action on the deep residual network to obtain a deep residual network after completing 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 pixels occupied by the current image block, thereby being used for an artificial intelligence model required for subsequent intelligent analysis operations of intra-frame coding compression ratios; Technical effect C: A targetedly designed deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-frame encoded, and multiple related information of the surrounding image blocks of the current image block. The intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks obtained by the intelligent analysis is wirelessly transmitted to a remote cloud computing management node, so as to predict the intra-frame coding compression ratio before using the surrounding image blocks to perform intra-frame coding on the current image block, thereby providing a quick and convenient processing mechanism for obtaining a satisfactory intra-frame coding compression ratio.

[0007] The smart building data management system of the present invention has reliable logic and intelligent design. 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 the surrounding image blocks, and wirelessly transmitting the intra-frame coding compression ratio obtained by the intelligent analysis to the remote cloud computing management node, valuable reference information is provided for the selection of the latest intra-frame coding compression ratio to ensure the image compression effect of the on-site picture of the smart building project. DETAILED DESCRIPTION

[0008] The implementation scheme of the smart building data management system of the present invention will be described in detail below.

[0009] The smart building data management system according to the first embodiment of the present invention comprises: A first cloud computing node is used to parse multiple pieces of relevant information of a current image block to be intra-coded from an on-site imaging image of a smart building, wherein the multiple pieces of relevant information of the current image block are a signal-to-noise ratio, a contrast, a vertical coordinate value and a horizontal coordinate value of each foreground pixel point of the current image block, and a mean square error of pixel values ​​of the current image block; For example, a first cloud computing node is used to parse multiple related information of a current image block derived from an on-site imaging image of a smart building to be encoded in an intra-frame, wherein the multiple related information of the current image block is a signal-to-noise ratio, a contrast, a vertical coordinate value and a horizontal coordinate value of each foreground pixel point of the current image block, and a mean square error of the pixel value of the current image block. The first cloud computing node can be implemented by using a cloud storage network element, and is used to parse multiple related information of a current image block derived from an on-site imaging image of a smart building to be encoded in an intra-frame, wherein the multiple related information of the current image block is a signal-to-noise ratio, a contrast, a vertical coordinate value and a horizontal coordinate value of each foreground pixel point of the current image block, and a mean square error of the pixel value of the current image block; The second cloud computing node is used to analyze multiple pieces of relevant information of surrounding image blocks of the current image block, wherein the multiple pieces of relevant information of the surrounding image blocks are signal-to-noise ratio, contrast, vertical coordinate value and horizontal coordinate value of each foreground pixel point of the surrounding image blocks, and mean square error of pixel values ​​of the surrounding image blocks; A model mapping mechanism, used to perform each learning action on the deep residual network to obtain a deep residual network after completing each learning action and output it as a deep residual network model, wherein the number of learning actions completed by the deep residual network is monotonically positively correlated with the number of pixels 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 use 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 pixels and the 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 coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block; A wireless transmission mechanism, connected to the compression analysis mechanism, for wirelessly transmitting the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using surrounding image blocks obtained by intelligent analysis to a remote cloud computing management node; Among them, the deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-frame coded, and multiple related information of the surrounding image blocks of the current image block. The intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks includes: the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks is a ratio obtained by dividing the amount of data corresponding to the current image block by the amount of data of the data stream obtained after performing intra-frame coding on the current image block; Wherein, parsing multiple pieces of relevant information of the current image block to be intra-coded, 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 of the current image block, and the mean square error of the pixel value of the current image block, including: the mean square error of the pixel value of the current image block is the mean square error of each pixel value corresponding to each pixel point of the current image block; Wherein, parsing multiple pieces of relevant information of surrounding image blocks of the current image block, wherein 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 of the surrounding image blocks, 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 have the same pixel points as the current image block; Among them, the model mapping mechanism is used to perform each learning action on the deep residual network to obtain a deep residual network after completing each learning action and output it as a deep residual network model, and the number of learning actions completed by the deep residual network is monotonically positively associated with the number of pixels occupied by the current image block, including: using an information conversion formula to express the information conversion relationship of the monotonically positive correlation between the number of learning actions completed by the deep residual network and the number of pixels occupied by the current image block; Among them, the information conversion relationship in which the number of learning actions completed by the deep residual network and the number of pixels occupied by the current image block are monotonically positively correlated using an information conversion formula includes: in the information conversion formula, the number of pixels occupied by the current image block is input information, and the number of learning actions completed by the deep residual network corresponding to the number of pixels occupied by the current image block is output information; And wherein, the deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-coded, and multiple related information of the surrounding image blocks of the current image block, and also includes: inputting the number of pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-coded, and multiple related information of the surrounding image blocks of the current image block in parallel into the deep residual network model.

[0010] Different from the first embodiment of the present invention, the smart building data management system according to the second embodiment of the present invention may further include the following components: An instant acquisition device is connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, respectively, and is used to measure the instant throughput data of each of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node; 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 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, including: the instant acquisition device includes a plurality of throughput measurement units, which are respectively connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, so as to complete the respective 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; The instant acquisition device includes a plurality of throughput measurement units, which are used to respectively connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node to complete the respective 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, including: the plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to respectively connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node to complete the respective 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; The plurality of throughput measurement units are a plurality of throughput sensing circuits, which are used to respectively connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node to respectively measure the instantaneous throughput data of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node, including: the plurality of throughput sensing circuits have the same structure; Among them, the multiple throughput measurement units are multiple throughput sensing circuits, which are used to connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively, so as to complete the respective measurements 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. It also includes: the multiple throughput sensing circuits have the same throughput measurement upper limit value and throughput measurement lower limit value.

[0011] Different from the first embodiment of the present invention, the smart building data management system according to the third embodiment of the present invention may further include the following components: a directional processing component, arranged near the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node and connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively, for providing 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; 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 the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node with data configuration services based on wired transmission, including: the directional processing component is connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node through different wired configuration units; And wherein, a 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 also includes: the internal structures of 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.

[0012] In addition, in the smart building data management system, the deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-frame encoded, and multiple related information of the surrounding image blocks of the current image block. The method also 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.

[0013] Although the present invention has been described in accordance with the presently preferred embodiments, it should be understood that the disclosed contents cannot be interpreted solely by the above limited description. After reading the above description, various substitutions and modifications to the present invention will undoubtedly be obvious to those of ordinary skill in the art. Therefore, it is intended that the appended claims be interpreted as covering all substitutions and modifications that are within the spirit and scope of the "essence" of the present invention.

Claims

1. A smart building data management system, characterized in that: The system comprises: A first cloud computing node is used to parse multiple pieces of relevant information of a current image block to be intra-coded from an on-site imaging image of a smart building, wherein the multiple pieces of relevant information of the current image block are a signal-to-noise ratio, a contrast, a vertical coordinate value and a horizontal coordinate value of each foreground pixel point of the current image block, and a mean square error of pixel values ​​of the current image block; The second cloud computing node is used to analyze multiple pieces of relevant information of surrounding image blocks of the current image block, wherein the multiple pieces of relevant information of the surrounding image blocks are signal-to-noise ratio, contrast, vertical coordinate value and horizontal coordinate value of each foreground pixel point of the surrounding image blocks, and mean square error of pixel values ​​of the surrounding image blocks; A model mapping mechanism, used to perform each learning action on the deep residual network to obtain a deep residual network after completing each learning action and output it as a deep residual network model, wherein the number of learning actions completed by the deep residual network is monotonically positively correlated with the number of pixels 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 use 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 pixels and the 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 coded, and multiple pieces of relevant information of the surrounding image blocks of the current image block; A wireless transmission mechanism, connected to the compression analysis mechanism, for wirelessly transmitting the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using surrounding image blocks obtained by intelligent analysis to a remote cloud computing management node; Among them, the deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-frame coded, and multiple related information of the surrounding image blocks of the current image block. The intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks includes: the intra-frame coding compression ratio obtained by performing intra-frame coding on the current image block using the surrounding image blocks is a ratio obtained by dividing the amount of data corresponding to the current image block by the amount of data of the data stream obtained after performing intra-frame coding on the current image block; Wherein, parsing multiple pieces of relevant information of the current image block to be intra-coded, 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 of the current image block, and the mean square error of the pixel value of the current image block, including: the mean square error of the pixel value of the current image block is the mean square error of each pixel value corresponding to each pixel point of the current image block; Among them, multiple related information of the surrounding image blocks of the current image block are analyzed, and the multiple related information of the surrounding image blocks are the signal-to-noise ratio, contrast, vertical coordinate value and horizontal coordinate value of each foreground pixel point of the surrounding image blocks 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 have the same pixel points as those occupied by the current image block.

2. The smart building data management system according to claim 1, characterized in that: A model mapping mechanism, used for performing each learning action on the deep residual network to obtain a deep residual network after completing each learning action and outputting it as a deep residual network model, wherein the number of learning actions completed by the deep residual network is monotonically positively correlated with the number of pixels occupied by the current image block, including: using an information conversion formula to represent the information conversion relationship of the monotonically positive correlation between the number of learning actions completed by the deep residual network and the number of pixels occupied by the current image block; Among them, the information conversion relationship that uses the information conversion formula to express the monotonically positive correlation between the number of learning actions completed by the deep residual network and the number of pixels occupied by the current image block includes: in the information conversion formula, the number of pixels 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 pixels occupied by the current image block is the output information.

3. The smart building data management system according to claim 2, characterized in that: The deep residual network model 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 pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-coded, and multiple related information of the surrounding image blocks of the current image block. The method also includes: inputting the number of pixels and the signal-to-noise ratio of the image where the current image block is located, multiple related information of the current image block to be intra-coded, and multiple related information of the surrounding image blocks of the current image block into the deep residual network model in parallel.

4. The smart building data management system according to claim 3, characterized in that: The system further comprises: An instant acquisition device is connected to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node, respectively, and is used to measure the instant throughput data of each of the compression analysis mechanism, the model mapping mechanism, the first cloud computing node, and the second cloud computing node; 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 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. The instant acquisition device includes multiple throughput measurement units, which are used to respectively connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node to complete the respective measurements 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.

5. The smart building data management system according to claim 4, characterized in that: The instant acquisition device includes multiple throughput measurement units, which are used to connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively 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, including: the multiple throughput measurement units are multiple throughput sensing circuits, which are used to connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively 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.

6. The smart building data management system according to claim 5, characterized in that: The multiple throughput measurement units are multiple throughput sensing circuits, which are used to respectively connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node to complete the respective measurements 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, including: the structures of the multiple throughput sensing circuits are the same.

7. The smart building data management system according to claim 6, characterized in that: The multiple throughput measurement units are multiple throughput sensing circuits, which are used to connect to the compression analysis mechanism, the model mapping mechanism, the first cloud computing node and the second cloud computing node respectively to complete the respective measurements 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. It also includes: the multiple throughput sensing circuits have the same throughput measurement upper limit value and throughput measurement lower limit value.

8. The smart building data management system according to claim 4, characterized in that: The system further comprises: A 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 smart building data management system according to claim 8, characterized in that: A 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 is connected to 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 smart building data management system according to claim 8, characterized in that: A 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. It also includes: the internal structures of 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.