Intelligent building data processing system

Abstract

The invention relates to a smart building data processing system. The system comprises a first big data analysis network element, a second big data analysis network element, a numerical value identification mechanism, an information identification mechanism, an object selection mechanism and a compression processing mechanism. The system can complete automatic and intelligent screening of data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart buildings, and more particularly, to a smart building data processing system. BACKGROUND

[0002] A smart building refers to a building environment that is optimized for users by combining the structure, system, service, and management of the building according to the needs of the users, thereby providing a high-efficiency, comfortable, and convenient personalized building environment. A smart building is a product of the integration of modern science and technology. Its technical foundation is mainly composed of modern building technology, modern computer technology, modern communication technology, and modern control technology. A smart building site refers to a site where construction operations are carried out in a smart building mode. Various new technologies are beginning to be applied in smart buildings.

[0003] For example, CN119150410A discloses a structure simulation optimization method and system for a smart building, relating to the technical field of intelligent buildings, which includes: collecting and obtaining structure monitoring data streams of a target smart building to obtain a building structure space simulation model; obtaining building structure optimization targets according to smart building design requirements and user demand information, and obtaining building structure effect fitness functions through the building structure optimization targets, performing parameter optimization analysis to obtain a structure parameter optimization threshold, performing iterative optimization to determine building structure optimization parameters, and performing structure optimization on the target smart building. This technology solves the technical problem of unreasonable building structure design in the prior art, which affects the safety and reliability of the building, and achieves the technical effect of obtaining reasonable structure optimization, which helps to improve the safety and reliability of the building.

[0004] CN119126718A discloses a smart building integrated management system and method, and electronic equipment, relating to the field of automation, which includes: a device Agent layer, a regional Agent layer, a coordination management Agent layer, and a user interface Agent layer are connected through Ethernet and field bus; the device Agent layer includes Agents corresponding to different devices; the regional Agent layer includes emergency transaction Agents, security Agents, energy saving Agents, and environment Agents; the coordination management Agent layer includes control decision Agents and a blackboard area; the control decision Agents register information of all Agents, analyze information on the blackboard area, and monitor and coordinate the operation of all Agents; the blackboard area includes information of all Agents and publishes information through a preset language; the user interface Agent layer includes information interaction with management personnel and improvement based on the interaction process. This technology realizes comprehensive integrated management of various devices, regions, and functions in the building, effectively improving the overall coordination and operation efficiency of the system.

[0005] CN119065404A discloses a green intelligent building control method, system and intelligent terminal, relates to green building technology field, its method includes: obtaining the weather condition of the preset building position;According to the weather condition and the building position to determine the solar elevation angle;According to the solar elevation angle and the building position to determine the irradiation glass window;According to the irradiation glass window and the solar elevation angle to determine the shading area;According to the shading area and the preset sunshade area to determine the sunshade quantity and the sunshade position of the preset sunshade device;Based on the sunshade quantity and the sunshade position to control the sunshade device to move from the preset reset position and shade the irradiation glass window. SUMMARY

[0006] In order to solve the technical problems in the related art, the present application provides a kind of intelligent building data processing system, can be divided into blocks and reference image block around each image block in reference image block, corresponding overall covariance closest to the overall covariance corresponding to target image block image block as priority image block output, and using priority image block to target image block It is executed based on the compression processing of interframe coding, wherein, when the absolute value of the difference of two overall covariances corresponding to two image blocks respectively is minimum, it is judged that the two overall covariances corresponding to two image blocks respectively are closest, so that the data analysis mechanism based on big data analysis network element is used to complete the automation, intelligent screening of the best interframe coding reference block, improve the quality and efficiency of interframe coding.

[0007] The present application at least needs to have the following three important invention points: Firstly: for the current image of real-time collected image belonging to intelligent building site, a plurality of big data analysis network elements are used to obtain the respective hue component value, luminance component value and saturation component value corresponding to each pixel point of the target image block to be interframe coded in the current image in HSB color space, and the image block in the interframe coding reference frame of the current image which is at the same position as the target image block is taken as a reference image block, the respective hue component value, luminance component value and saturation component value corresponding to each pixel point of the reference image block in HSB color space are obtained, and the respective hue component value, luminance component value and saturation component value corresponding to each pixel point of each image block around the reference image block in HSB color space are obtained, thereby providing sufficient and comprehensive basic data for subsequent numerical identification; Secondly, the covariance of the hue component value, the brightness component value and the saturation component value of the target image block is calculated based on the respective hue component value, the respective brightness component value and the respective saturation component value of each pixel point of the target image block, and the covariance is output as the overall covariance corresponding to the target image block, and the covariance of the hue component value, the brightness component value and the saturation component value of the reference image block is calculated based on the respective hue component value, the respective brightness component value and the respective saturation component value of each pixel point of the reference image block, and the covariance is output as the overall covariance corresponding to the reference image block, and the covariance of the hue component value, the brightness component value and the saturation component value of each image block around the reference image block is calculated based on the respective hue component value, the respective brightness component value and the respective saturation component value of each pixel point of each image block around the reference image block, and the covariance is output as the overall covariance corresponding to each image block around the reference image block; Finally, a target selection mechanism is introduced to output the image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block from the reference image block and the image blocks around the reference image block as a priority image block, and to perform compression processing based on inter-frame coding on the target image block by using the priority image block, wherein when the absolute value of the difference between the two overall covariances corresponding to two image blocks is the smallest, it is determined that the two overall covariances corresponding to the two image blocks are closest, so that the automatic and intelligent selection of the best inter-frame coding reference block is completed by using the data analysis mechanism based on the big data analysis network element, and the quality and efficiency of inter-frame coding are improved.

[0008] According to the present application, a smart building data processing system is provided, which comprises: A first big data analysis network element is configured to obtain the respective hue component value, the respective brightness component value and the respective saturation component value of each pixel point of a target image block to be inter-frame coded in a current image in the HSB color space, wherein the current image is a real-time collected image of a smart building construction site. A second big data analysis network element is configured to obtain the respective hue component value, the respective brightness component value and the respective saturation component value of each pixel point of an image block in the same position as the target image block in an inter-frame coding reference frame of the current image in the HSB color space, and to obtain the respective hue component value, the respective brightness component value and the respective saturation component value of each pixel point of each image block around the reference image block in the HSB color space. The numerical identification mechanism is connected with the first big data analysis network element, and is configured to calculate the covariance of hue component values, brightness component values and saturation component values of the target image block based on the respective hue component values, brightness component values and saturation component values of each pixel point of the target image block, and output the covariance as the overall covariance corresponding to the target image block. The information identification mechanism is connected with the second big data analysis network element, and is configured to calculate the covariance of hue component values, brightness component values and saturation component values of the reference image block based on the respective hue component values, brightness component values and saturation component values of each pixel point of the reference image block, and output the covariance as the overall covariance corresponding to the reference image block, and is further configured to calculate the covariance of hue component values, brightness component values and saturation component values of each image block around the reference image block based on the respective hue component values, brightness component values and saturation component values of each pixel point of each image block around the reference image block, and output the covariance as the overall covariance corresponding to each image block around the reference image block. The object selection mechanism is connected with the numerical identification mechanism and the information identification mechanism, respectively, and is configured to output, as a priority image block, an image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block from among the reference image block and each image block around the reference image block. The compression processing mechanism is connected with the object selection mechanism, and is configured to perform compression processing based on inter-frame coding on the target image block by using the priority image block. The object selection mechanism is connected with the numerical identification mechanism and the information identification mechanism, respectively, and is configured to output, as a priority image block, an image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block from among the reference image block and each image block around the reference image block, including: when the absolute value of the difference between the two overall covariances corresponding to the two image blocks is the smallest, determining that the two overall covariances corresponding to the two image blocks are closest.

[0009] The intelligent building data processing system of the present application is stable in operation and intelligent in control. The image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block is output as a priority image block, and compression processing based on inter-frame coding is performed on the target image block by using the priority image block, so that the automatic and intelligent screening of the best inter-frame coding reference block is completed. DETAILED DESCRIPTION

[0010] Since the smart building is a high-precision building system, the collection and transmission of the site monitoring picture of the smart building site also requires high precision. For example, when transmitting the site monitoring picture of the smart building site in the inter-frame coding mode, the most suitable image block that matches the target image block to be inter-frame coded needs to be selected for inter-frame coding. Obviously, there is a lack of such an image processing mechanism in the prior art.

[0011] Embodiment 1

[0012] The smart building data processing system according to the present application comprises: The first big data analysis network element is configured to obtain respective hue component values, respective brightness component values and respective saturation component values corresponding to each pixel point of a target image block to be inter-frame coded in a current image in the HSB color space, the current image being a real-time collection image of a smart building site. For example, the first big data analysis network element is configured to obtain respective hue component values, respective brightness component values and respective saturation component values corresponding to each pixel point of a target image block to be inter-frame coded in a current image in the HSB color space, the current image being a real-time collection image of a smart building site, wherein the value of the hue component value, the brightness component value and the saturation component value corresponding to each pixel point in the HSB color space is between 0 and 255. The second big data analysis network element is configured to obtain respective hue component values, respective brightness component values and respective saturation component values corresponding to each pixel point of a reference image block in the inter-frame coding reference frame of the current image, the reference image block being located at the same position as the target image block, and obtain respective hue component values, respective brightness component values and respective saturation component values corresponding to each pixel point of each image block around the reference image block in the HSB color space. The numerical discrimination mechanism is connected to the first big data analysis network element and is configured to calculate the covariance of the hue component value, the brightness component value and the saturation component value of the target image block based on the respective hue component values, the respective brightness component values and the respective saturation component values of each pixel point of the target image block, and output the covariance as the overall covariance corresponding to the target image block. The information identifying mechanism is connected with the second big data analysis network element, and is configured to calculate the covariance of hue component values, brightness component values and saturation component values of the reference image block based on the respective hue component values, brightness component values and saturation component values of each pixel point of the reference image block, and output the covariance as the overall covariance corresponding to the reference image block, and is further configured to calculate the covariance of hue component values, brightness component values and saturation component values of each image block around the reference image block based on the respective hue component values, brightness component values and saturation component values of each pixel point of each image block around the reference image block, and output the covariance as the overall covariance corresponding to each image block around the reference image block. The object selecting mechanism is connected with the value discriminating mechanism and the information identifying mechanism respectively, and is configured to output, as a priority image block, an image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block among the reference image block and each image block around the reference image block. The compression processing mechanism is connected with the object selecting mechanism, and is configured to perform compression processing based on inter-frame coding on the target image block by using the priority image block. The object selecting mechanism is connected with the value discriminating mechanism and the information identifying mechanism respectively, and is configured to output, as a priority image block, an image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block among the reference image block and each image block around the reference image block, including that when the absolute value of the difference between the two overall covariances corresponding to the two image blocks is the smallest, it is determined that the two overall covariances corresponding to the two image blocks are closest. The value discriminating mechanism is connected with the first big data analysis network element, and is configured to calculate the covariance of hue component values, brightness component values and saturation component values of the target image block based on the respective hue component values, brightness component values and saturation component values of each pixel point of the target image block, and output the covariance as the overall covariance corresponding to the target image block, including that the value discriminating mechanism comprises a value receiving device, a covariance analyzing device and a value output device, and the value receiving device, the covariance analyzing device and the value output device are connected in sequence. The object selecting mechanism is connected with the value discriminating mechanism and the information identifying mechanism respectively, and is configured to output, as a priority image block, an image block corresponding to the overall covariance closest to the overall covariance corresponding to the target image block among the reference image block and each image block around the reference image block, and further including that the object selecting mechanism is implemented by using an editable logic device.

[0013] Embodiment 2

[0014] The smart building data processing system according to Embodiment 2 of the present application can further comprise the following components: a FLASH storage mechanism connected with the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, for providing data storage service for the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively; In the embodiment, the FLASH storage mechanism is connected with the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, for providing data storage service for the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, which includes that the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device are connected with the FLASH storage mechanism respectively through different data channels. In the embodiment, the FLASH storage mechanism is connected with the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, for providing data storage service for the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, which includes that the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device use the same waveform generating component.

[0015] Embodiment 3

[0016] The smart building data processing system according to Embodiment 3 of the present application can further comprise the following components: a real-time display device connected with the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, for displaying the state information of the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device simultaneously; In the embodiment, the real-time display device is connected with the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device respectively, for displaying the state information of the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device simultaneously, which includes that the state information of the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device displayed simultaneously includes the sleep state or the working state of the object selection mechanism, the numerical value receiving device, the covariance analysis device and the numerical value output device. The real-time display device is connected to the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device. The real-time display device is a liquid crystal display screen or a real-time display device. The real-time display device is connected to the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device. The real-time display device is composed of multiple LED display units arranged in a rectangular matrix pattern. The real-time display device is connected to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device. It also includes the fact that the multiple LED display units arranged in a rectangular array pattern have the same structure.

[0017] In addition, in the smart building data processing system, the first big data analysis network element is used to obtain the values ​​of each hue component, each brightness component, and each saturation component of each pixel in the target image block to be inter-frame encoded in the current image under the HSB color space. This includes the fact that the value of any one of the hue component, brightness component, and saturation component values ​​of each pixel under the HSB color space is between 0 and 255.

[0018] While the foregoing disclosure relates to preferred embodiments of the invention, it should be understood that the details presented above are for illustrative purposes only. Those skilled in the art can make various changes and modifications to the invention without departing from its spirit and scope.

Claims

1. A smart building data processing system, characterized in that, The system includes: The first big data analysis network element is used to obtain the values ​​of each hue component, each brightness component, and each saturation component of each pixel in the target image block to be inter-frame encoded in the current image in the HSB color space. The current image is a real-time captured image of a smart construction site. The second big data analysis network element is used to take the image block in the inter-frame coding reference frame of the current image that is in the same position as the target image block as the reference image block, and to obtain the hue component value, brightness component value and saturation component value of each pixel of the reference image block in the HSB color space, and to obtain the hue component value, brightness component value and saturation component value of each pixel of each image block around the reference image block in the HSB color space. The numerical identification mechanism is connected to the first big data analysis network element and is used to calculate the covariance of the three component values ​​of the hue component, brightness component and saturation component of the target image block based on the hue component value, brightness component value and saturation component value corresponding to each pixel point of the target image block, and output it as the overall covariance corresponding to the target image block. The information identification mechanism, connected to the second big data analysis network element, is used to calculate the covariance of the three component values ​​of the hue component, brightness component, and saturation component of the reference image block based on the hue component values, brightness component values, and saturation component values ​​corresponding to each pixel point of the reference image block, and outputs it as the overall covariance corresponding to the reference image block. It is also used to calculate the covariance of the three component values ​​of the hue component, brightness component, and saturation component of each image block surrounding the reference image block based on the hue component values, brightness component values, and saturation component values ​​corresponding to each pixel point of each image block surrounding the reference image block, and output it as the overall covariance corresponding to each image block surrounding the reference image block. The object selection mechanism is connected to the numerical discrimination mechanism and the information recognition mechanism respectively, and is used to output the image block whose overall covariance is closest to that of the target image block as the priority image block. A compression processing mechanism, connected to the object selection mechanism, is used to perform inter-frame coding-based compression processing on target image blocks using priority image block segmentation; The object selection mechanism, which is connected to the numerical identification mechanism and the information recognition mechanism respectively, is used to output the image block whose overall covariance is closest to that of the target image block as the priority image block. This includes determining that the two overall covariances corresponding to the two image blocks are closest when the absolute value of the difference between the two overall covariances corresponding to the two image blocks is the smallest.

2. The intelligent building data processing system as described in claim 1, characterized in that: The numerical identification mechanism, connected to the first big data analysis network element, is used to calculate the covariance of the three component values ​​of the target image block based on the hue component value, brightness component value, and saturation component value corresponding to each pixel point of the target image block, and output the overall covariance corresponding to the target image block as the output. The numerical identification mechanism includes a numerical receiving device, a covariance analysis device, and a numerical output device, which are connected in sequence. The object selection mechanism, which is connected to the numerical identification mechanism and the information recognition mechanism respectively, is used to output the image block with the highest overall covariance of the target image block as the priority image block. The mechanism also includes: implementing the object selection mechanism using a programmable logic device.

3. The intelligent building data processing system as described in claim 2, characterized in that, The system also includes: The FLASH storage mechanism is connected to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device respectively, and is used to provide data storage services to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device in a time-division manner. The FLASH storage mechanism is connected to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, respectively, and is used to provide data storage services to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device in a time-division manner. This includes the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device being connected to the FLASH storage mechanism using different data channels.

4. The intelligent building data processing system as described in claim 3, characterized in that: The FLASH storage mechanism is connected to the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device respectively, and is used to provide data storage services to the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device in a time-division manner. This includes the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device using the same waveform generation component.

5. The intelligent building data processing system as described in claim 2, characterized in that, The system also includes: A real-time display device is connected to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device.

6. The intelligent building data processing system as described in claim 5, characterized in that: A real-time display device is connected to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, including: the simultaneously displayed status information of the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, including the sleep state or working state of the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device.

7. The intelligent building data processing system as described in claim 6, characterized in that: A real-time display device, connected to the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device respectively, for simultaneously displaying various status information of the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device, further includes: the real-time display device is a liquid crystal display screen or a real-time display device.

8. The intelligent building data processing system as described in claim 7, characterized in that: The real-time display device is connected to the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the value receiving device, the covariance analysis device, and the value output device. The real-time display device is composed of multiple LED display units arranged in a rectangular array pattern.

9. The intelligent building data processing system as described in claim 8, characterized in that: The real-time display device is connected to the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device, respectively, and is used to simultaneously display various status information of the object selection mechanism, the numerical receiving device, the covariance analysis device, and the numerical output device. It also includes the fact that the multiple LED display units arranged in a rectangular array pattern have the same structure.