Intelligent venue management system based on AI digital technology

Abstract

The invention discloses an intelligent venue management system based on an AI digital technology, and relates to a calculation processing system, and the system comprises a video collection module which is used for collecting the video data of each camera in a venue; the AI processing module is used for processing the collected video data to realize intelligent identification and seamless splicing of figure moving pictures at the edges of the two cameras; the data storage and analysis module is used for storing the processed video data and performing data analysis to obtain a result; and the alarm and response module is used for carrying out risk assessment according to the result and executing dangerous case report based on the risk assessment result. According to the invention, the AI algorithm is introduced to process the monitoring video, and intelligent identification and seamless splicing of moving pictures of figures at the edges of the two cameras are realized, so that monitoring dead angles are eliminated, and the safety management level of the venue is improved.

Description

Technical Field

[0001] The present invention relates to a computing and processing system, and more particularly to an intelligent venue management system based on AI digital technology. Background Art

[0002] With the advancement of wisdom and intelligence, data security issues have attracted increasing attention. In intelligent venues (parks), data security issues involve the concerns and attention of multiple entities such as asset holders, operators, resident enterprises, working and living populations, and visitors. To address the above issues, the following existing technologies are provided for solution:

[0003] Referring to Chinese Patent, Publication No.: CN111784282A, a disclosed integrated management platform for a smart venue, characterized in that the platform includes a core platform that covers the entire business process and can online edit and customize business rules and processes, a big data platform that performs data analysis after collecting various data of the management platform, and an IOT platform that uniformly manages the hardware devices used by B-end enterprises. The core platform, the big data platform that collects and analyzes various data of the management platform, and the IOT platform all interact with each other, and the core platform, the big data platform, and the IOT platform all interact with an access end application platform; the access end application platform includes a multi-entry consumption self-service terminal for C-end users. In the present invention, an integrated management platform for a smart venue is provided that combines data collection and data analysis, and formulates feasible operation management strategies and sales strategies based on the data analysis results.

[0004] Referring to Chinese Patent, Publication No.: CN117459556A, a disclosed operation service system for a competitive sports smart venue, including a command center, an application system layer, a platform layer, a storage center, a network layer, and an infrastructure layer. The infrastructure layer includes event equipment, computer room equipment, sensing equipment, security equipment, etc. The platform layer includes an infrastructure common platform, a venue digital twin platform, and a venue operation big data platform. The system application layer includes an equipment warning system, an indoor environment system, a public security system, a public service system, etc. The command center is a security command center for visual operation and maintenance. The present invention develops characteristic application functions in combination with the characteristics of national events, realizes staggered construction, differential application, complementary advantages, and forms a complete and unified whole. The system establishes a perfect pre-plan response mechanism in terms of function application, which is conducive to intuitive and rapid judgment and decisive decision-making by managers, realizes uninterrupted service, and has strong fault tolerance, automatic detection, and automatic fault alarm functions.

[0005] In the prior art including the above patents, the stadiums in operation now are limited by the construction level at the time of construction. Therefore, it is necessary to carry out the transformation of intelligent stadiums to meet the current needs of intelligent stadiums. The monitoring camera is a common monitoring system. Since the stadium has a large coverage area and the monitoring area includes the stands and the main sports field, a large number of cameras need to be installed. Inevitably, there will be moving pictures of people at the edges of two cameras during the data collection process, resulting in monitoring blind spots. How to solve the problem of collecting images of people at the edges of two cameras is expected to be well solved. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent stadium management system based on AI digital technology to solve the above problems.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: An intelligent stadium management system based on AI digital technology, comprising:

[0008] A video acquisition module for acquiring video data of each camera in the stadium;

[0009] An AI processing module for processing the acquired video data to realize intelligent recognition and seamless splicing of moving pictures of people at the edges of two cameras;

[0010] A data storage and analysis module for storing the processed video data and performing data analysis to obtain results;

[0011] An alarm and response module for performing risk assessment according to the results and reporting dangerous situations based on the risk assessment results.

[0012] Preferably, the video acquisition module is composed of multiple camera groups, and the acquisition range of each camera is , where: r is the effective monitoring range of the camera, wI is the width of the image sensor in the monitoring camera, and f is the focal length of the monitoring camera;

[0013] The video data includes:

[0014] First data composed of simultaneous photos collected by each camera position, and each photo is marked with the corresponding camera position identifier Sn;

[0015] Extract the coordinate values of the overlapping part of the effective monitoring ranges of adjacent cameras, and mark the overlapping part in the photos under the corresponding identifier Sn to obtain the second data.

[0016] Preferably, obtaining the mark of the overlapping part in the second data includes the following steps:

[0017] S01. Create multiple photo processing channels for the same time period under the cloud disk;

[0018] S02. Establish xy-axis coordinates on two adjacent sides of the photo and create a mask grid;

[0019] S03. Extract the pixel features of the photo based on the feature extraction algorithm and obtain the coordinate positions of the pixel features on the mask grid;

[0020] S04. Obtain the identifier Sn of the current photo, extract the photos with identifiers Sn-1 and Sn+1, and splice the pixel features at the edge of the mask grid to obtain the combined features.

[0021] Preferably, the AI processing module includes:

[0022] A target detection sub-module that analyzes the first data and the second data contained in the video data to obtain a movable object that can be identified;

[0023] A target tracking sub-module that extracts features from the pictures collected by multiple cameras based on the determined movable object to obtain the dynamic parameters of the captured movable object.

[0024] An image stitching sub-module that processes the objects that cannot be identified by the target detection sub-module, and obtains clear feature dynamic parameters through preprocessing of the objects that cannot be identified in consecutive frames.

[0025] Preferably, the preprocessing of the objects that cannot be identified in consecutive frames by the image stitching sub-module includes:

[0026] S11. Convert the photos in consecutive frames into grayscale images and perform noise reduction through Gaussian filtering and mean filtering;

[0027] S12. Use the background subtraction algorithm to separate the foreground and background of the denoised photos, and extract the blurred objects in motion obtained in consecutive frames;

[0028] S13. Adopt the YOLO algorithm to calculate the speed of the blurred objects in motion, perform equal-proportion conversion of the photos, obtain the displacement speed of the blurred objects in motion on the pictures, and then perform pixel backward movement processing on the consecutive-frame photos to obtain the backward-moved pixel features of consecutive frames;

[0029] S14. Compare the backward-moved pixel features of multiple photos to determine whether they can be identified by the target detection sub-module. If not, return to step S11.

[0030] Preferably, the data storage and analysis module includes:

[0031] The motion trajectory analysis sub-module extracts the limbs of the moving object based on the feature extraction algorithm and forms a reference line for the limb movement trajectory in the way of three-point identification;

[0032] The cloud database matches the obtained reference line of the limb movement trajectory with multiple reference lines in the inventory. If a match is obtained, the judgment language of the corresponding reference line is fed back as a result. If no match can be made, no feedback is given.

[0033] Preferably, the alarm and response module includes:

[0034] The preset voice prompt sub-module matches the obtained result with the corresponding risk level and selects the pre-recorded voice corresponding to the risk for broadcast;

[0035] The security notice sub-module matches the obtained result with the corresponding risk level and prompts the security personnel in the form of a text message, and the text message includes coordinates.

[0036] In the above technical solution, an intelligent venue management system based on AI digital technology provided by the present invention has the following beneficial effects: realizing the high-efficiency, intelligence and safety of venue operation. It not only improves the management and operation efficiency, reduces the transformation cost and maintenance cost, but also improves the user experience and the safety guarantee level of the venue. At the same time, the scalability and flexibility of the system enable it to adapt to the needs of venues of different scales and types, and have a wide range of application prospects. Description of the Drawings

[0037] Figure 1 It is the module diagram provided by Embodiment 1 of the present invention;

[0038] Figure 2 It is the sub-module diagram of the AI processing module provided by Embodiment 1 of the present invention;

[0039] Figure 3 It is the sub-module diagram of the data storage and analysis module provided by Embodiment 1 of the present invention;

[0040] Figure 4 It is the sub-module diagram of the alarm and response module provided by Embodiment 1 of the present invention. Detailed Description of the Invention

[0041] To make the technical solution of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and specific embodiments.

[0042] Embodiment 1

[0043] An intelligent venue management system based on AI digital technology includes:

[0044] The video acquisition module is used to acquire the video data of each camera in the venue;

[0045] The AI processing module is used to process the acquired video data to achieve intelligent recognition and seamless splicing of the moving pictures of the edge figures of two cameras;

[0046] The data storage and analysis module is used to store the processed video data and perform data analysis to obtain results;

[0047] The alarm and response module is used to perform risk assessment based on the results and execute danger reporting based on the risk assessment results.

[0048] In the above technology, by integrating four major modules of video acquisition, AI processing, data storage and analysis, and alarm and response, all-round and intelligent monitoring and management of the venue are realized. The system can collect video data in real time, use AI technology for intelligent recognition and processing, monitor and analyze the personnel flow, abnormal behaviors, etc. in the venue in real time, and issue alarms in a timely manner according to the risk assessment results, providing strong support for the safety management of the venue.

[0049] Furthermore, the video acquisition module in the above embodiment is composed of multiple camera groups, and the acquisition range of each camera is , where: r is the effective monitoring range of the camera, w I is the width of the image sensor in the monitoring camera, and f is the focal length of the monitoring camera;

[0050] The video data includes:

[0051] The first data composed of the simultaneous photos collected by each camera position, and each photo is marked with the corresponding camera position identifier Sn;

[0052] Extract the coordinate values of the overlapping part of the effective monitoring ranges of adjacent cameras, and mark the overlapping part in the photos under the corresponding identifier Sn to obtain the second data.

[0053] Furthermore, obtaining the marks of the overlapping part in the second data includes the following steps:

[0054] S01. Create multiple photo processing channels for the same time period under the cloud disk;

[0055] S02. Establish xy-axis coordinates on two adjacent sides of the photo and establish a mask grid;

[0056] S03. Extract the pixel features of the photo based on the feature extraction algorithm and obtain the coordinate positions of the pixel features on the mask grid;

[0057] S04. Obtain the identifier Sn of the current photo, extract the photos with identifiers Sn-1 and Sn+1, and splice the pixel features at the edge of the mask grid to obtain the combined features.

[0058] Specifically, through the video acquisition module composed of multiple camera groups, all-round and non-blind-spot video monitoring in the venue is achieved. The acquisition range of each camera is accurately calculated to ensure the coverage rate and accuracy of monitoring. And by extracting the overlapping parts of the effective monitoring ranges of adjacent cameras and marking them in the photos, a reliable basis is provided for subsequent image splicing and processing. This function effectively solves the problem of monitoring blind spots between multiple cameras and improves the continuity and integrity of video data.

[0059] Furthermore, the AI processing module in the above embodiments includes:

[0060] The target detection sub-module analyzes the first data and the second data included in the video data to obtain a movable object that can be identified.

[0061] The target tracking sub-module extracts features from the pictures collected by multiple cameras based on the determined movable object to obtain the dynamic parameters for capturing the movable object.

[0062] The image splicing sub-module processes the objects that cannot be identified by the target detection sub-module, and through preprocessing of the objects that cannot be identified in consecutive frames, clear feature dynamic parameters are obtained.

[0063] Secondly, the preprocessing of the objects that cannot be identified in consecutive frames by the image splicing sub-module includes:

[0064] S11. Convert the consecutive frame photos into grayscale images and perform noise reduction through Gaussian filtering and mean filtering.

[0065] S12. Use the background subtraction algorithm to separate the foreground and background of the noise-reduced photos and extract the blurred objects in motion obtained in consecutive frames.

[0066] S13. Adopt the YOLO algorithm to calculate the speed of the blurred objects in motion, perform proportional conversion of the photos, obtain the displacement speed of the blurred objects in motion on the picture, and then perform pixel backward movement processing on the consecutive frame photos to obtain the backward pixel features of the consecutive frames.

[0067] S14. Compare the backward pixel features of multiple photos to determine whether they can be identified by the target detection sub-module. If not, return to step S11.

[0068] Specifically, it can intelligently identify the moving images of people at the edges of two cameras and seamlessly stitch them together, achieving continuous monitoring of the personnel flow in the venue. This function effectively avoids the monitoring blind spots caused by camera switching, improving the continuity and accuracy of monitoring. Moreover, it can accurately identify the moving objects in the video, and the target tracking sub-module can track the dynamic parameters of these objects in real time, providing data support for subsequent behavior analysis and trajectory prediction. Secondly, it can also preprocess the objects that cannot be recognized by the target detection sub-module. Through pixel feature extraction and backward processing of consecutive frames, clear dynamic feature parameters are obtained, improving the recognition ability and accuracy of the system.

[0069] Furthermore, the data storage and analysis module in the above embodiment includes:

[0070] A motion trajectory analysis sub-module that extracts the limbs of the moving object based on the feature extraction algorithm and forms a reference line for the limb movement trajectory in the way of three-point identification;

[0071] A cloud database that matches the obtained reference line of the limb movement trajectory with multiple reference lines stored in the inventory. If a match is obtained, the judgment language of the corresponding reference line is fed back as a result. If no match can be found, no feedback is given.

[0072] Specifically, the above embodiment can extract the limb features of the moving object and form a reference line for the limb movement trajectory, providing a basis for behavior analysis and trajectory matching. This function helps to identify abnormal behaviors and timely discover potential safety hazards. And by matching the obtained reference line of the limb movement trajectory with multiple reference lines in the cloud database, the system can quickly judge whether there are abnormal behaviors and give corresponding result feedback. This function improves the intelligence level and response speed of the system.

[0073] Furthermore, the alarm and response module in the above embodiment includes:

[0074] A preset voice prompt sub-module that matches the obtained result with the corresponding risk level and selects the pre-recorded voice corresponding to the risk for broadcast;

[0075] A security notice sub-module that matches the obtained result with the corresponding risk level and prompts the security personnel in the form of a text message, and the text message includes coordinates.

[0076] Specifically, the preset voice prompt sub-module can play corresponding voice alerts according to the risk assessment results, timely reminding the venue management personnel and security personnel to pay attention to abnormal situations. This function helps to quickly respond to and handle emergencies, ensuring the safety inside the venue. The security notice sub-module can notify the security personnel in the form of text messages, including the risk level and coordinate information, providing convenience for quickly locating and handling abnormal situations. This function effectively improves the response speed and handling efficiency of the security personnel.

[0077] In summary, the first embodiment realizes the high-efficiency, intelligence and security of venue operation. It not only improves the management and operation efficiency, reduces the transformation cost and maintenance cost, but also enhances the user experience and the security guarantee level of the venue. At the same time, the scalability and flexibility of the system enable it to adapt to the needs of venues of different scales and types, with broad application prospects.

[0078] Embodiment 2

[0079] The embodiment of the present invention provides a non-transitory computer-readable storage medium, in which at least one instruction or at least one program segment is stored, and at least one instruction or at least one program segment is loaded and executed by a processor to implement the steps:

[0080] A video acquisition module, for acquiring video data of each camera in the venue;

[0081] An AI processing module, for processing the acquired video data to realize the intelligent recognition and seamless splicing of the moving pictures of the marginal figures of two cameras;

[0082] A data storage and analysis module, for storing the processed video data and performing data analysis to obtain results;

[0083] An alarm and response module, for performing risk assessment according to the results and reporting the dangerous situation based on the risk assessment results.

[0084] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database or other media used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0085] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0086] Embodiment 3

[0087] An embodiment of the present invention provides an electronic device, including a processor and a memory. At least one instruction or at least one program segment is stored in the memory. The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the steps:

[0088] A video acquisition module, configured to acquire video data of each camera in the venue;

[0089] An AI processing module, configured to process the acquired video data to implement intelligent recognition and seamless splicing of the moving pictures of the people at the edges of two cameras;

[0090] A data storage and analysis module, configured to store the processed video data and perform data analysis to obtain results;

[0091] An alarm and response module is used to perform risk assessment based on the results and execute danger reporting based on the risk assessment results.

[0092] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. An intelligent venue management system based on AI digital technology, characterized in that: include: Video acquisition module, used to collect video data from various cameras in the venue; AI processing module, used to process the collected video data, and realize intelligent recognition and seamless splicing of moving images of people at the edges of two cameras; The data storage and analysis module is used to store the processed video data and perform data analysis to obtain the results; The alarm and response module is used to perform risk assessment according to the results and to perform risk reporting based on the risk assessment results.

2. According to claim 1, the intelligent venue management system based on AI digital technology is characterized in that: The video acquisition module is composed of multiple camera groups, and the acquisition range of each camera is , where: r is the effective monitoring range of the camera, wI is the width of the image sensor in the surveillance camera, and f is the focal length of the surveillance camera; The video data includes: First data consisting of photos collected by each camera position at the same time, and each photo has a corresponding camera position identifier Sn; The coordinate values ​​of the overlapping parts of the effective monitoring ranges of adjacent cameras are extracted, and the overlapping parts are marked in the photo under the corresponding identifier Sn to obtain the second data.

3. According to claim 2, the intelligent venue management system based on AI digital technology is characterized in that: Acquiring the mark of the overlapping part from the second data comprises the following steps: S01. Create multiple photo processing channels for the same period of time in the cloud disk; S02, establishing xy axis coordinates on two adjacent sides of the photo and establishing a mask grid; S03, extracting pixel features of the photo based on a feature extraction algorithm, and obtaining coordinate positions of the pixel features on the mask grid; S04, obtaining the identifier Sn of the current photo, extracting the photos with identifiers Sn-1 and Sn+1, and splicing the pixel features at the edge of the mask grid to obtain the combined features.

4. According to claim 1, the intelligent venue management system based on AI digital technology is characterized in that: The AI ​​processing module includes: The target detection submodule analyzes the first data and the second data contained in the video data to obtain a moving object that is sufficiently identifiable; The target tracking submodule extracts features from the images captured by multiple cameras based on the determined moving object to obtain dynamic parameters of the moving object. The image stitching submodule processes the objects that cannot be identified by the target detection submodule, and pre-processes the unidentifiable objects under continuous frames to obtain clear feature dynamic parameters.

5. According to claim 4, the intelligent venue management system based on AI digital technology is characterized in that: The image stitching submodule pre-processes the unrecognizable objects in the continuous frames, including: S11, converting the photos of the continuous frames into grayscale images, and performing noise reduction by Gaussian filtering and mean filtering; S12, using a background subtraction algorithm to separate the foreground and background of the photo after noise reduction, and extracting blurred objects in motion obtained in continuous frames; S13, using the YOLO algorithm to calculate the speed of the moving blurred object, performing a proportional conversion on the photo, obtaining the displacement speed of the moving blurred object on the photo, and then performing pixel back-shift processing on the consecutive frame photos to obtain the back-shifted pixel features of the consecutive frames; S14, comparing the backward pixel features of multiple photos to determine whether they can be recognized by the target detection submodule, if not, returning to step S11.

6. According to claim 1, the intelligent venue management system based on AI digital technology is characterized in that: The data storage and analysis module includes: The motion trajectory analysis submodule extracts the limbs of the moving object based on the feature extraction algorithm, and forms the reference line of the limb movement trajectory by using three-point identification; The cloud database matches the acquired limb movement trajectory reference lines with multiple trajectory reference lines in stock. If a match is obtained, the judgment language of the corresponding trajectory reference line will be fed back as a result. If a match cannot be found, no feedback will be given.

7. According to claim 1, the intelligent venue management system based on AI digital technology is characterized in that: The alarm and response module comprises: A preset voice prompt submodule is used to match the obtained result with the corresponding risk level, and to select the voice recorded in advance for the corresponding risk to broadcast; The security notification submodule matches the obtained result with the corresponding risk level and prompts the security personnel in the form of a text message, wherein the text message includes the coordinates.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the intelligent venue management system based on AI digital technology as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent venue management system based on AI digital technology as described in any one of claims 1 to 7 is implemented.

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