An intelligent venue management system based on AI digital technology

Abstract

The application discloses an intelligent stadium management system based on AI digital technology, and relates to a computing processing system, which comprises a video acquisition module, an AI processing module, a data storage and analysis module and an alarm and response module.The video acquisition module is used for acquiring video data of each camera in the stadium.The AI processing module is used for processing the acquired video data, realizing intelligent identification and seamless splicing of moving pictures of people at the edges of two cameras.The data storage and analysis module is used for storing the processed video data and performing data analysis to obtain results.The alarm and response module is used for risk assessment according to the results and executing danger report based on the risk assessment results.The application realizes intelligent identification and seamless splicing of moving pictures of people at the edges of two cameras by introducing AI algorithm to process monitoring video, thereby eliminating monitoring dead angles and improving the safety management level of the stadium.

Description

TECHNICAL FIELD

[0001] The application relates to a computing processing system, in particular to an intelligent venue management system based on AI digital technology. BACKGROUND

[0002] With the promotion of wisdom and intelligence, data security problems are increasingly valued. In the intelligent venue (park), data security problems involve the attention and value of multiple subjects such as asset holders, operators, resident enterprises, job and residence crowds and visitors. In order to solve the above problems, the following prior art is provided to solve them:

[0003] Referring to Chinese patent CN111784282A, a smart venue comprehensive management platform is disclosed, which is characterized in that the platform includes a business full-process covering core platform that can be online edited and customized business rules and processes, a big data platform that collects and analyzes data of the management platform, and an IOT platform that uniformly manages hardware devices used by B-end enterprises. The core platform, the big data platform that collects and analyzes data of the management platform, and the IOT platform are all interactive. 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 application, a smart venue comprehensive management platform is provided, which combines data collection and data analysis, and formulates feasible operation and management strategies and sales strategies based on data analysis results.

[0004] Referring to Chinese patent CN117459556A, a competitive sports smart venue operation service system is disclosed, which includes 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, machine 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 a device early warning system, an indoor environment system, a public security system, a public service system, etc. The command center is a visual operation and maintenance security command center. The present application combines national event characteristics to develop application functions with characteristics, realizes staggered construction, differential application, complementary advantages, and forms a complete and unified whole. The system establishes a perfect preplan response mechanism in function application, which is beneficial to managers to make intuitive and rapid judgments and decisive decisions, realizes uninterrupted service, and has strong fault tolerance, automatic detection, and fault automatic alarm functions.

[0005] In the prior art including the above patent, and now running this sports venue, limited to the construction level at the time of construction. Therefore, it is necessary to carry out intelligent venue reconstruction in order to meet the needs of the current intelligent venue, and the monitoring camera is a common monitoring system. Due to the large coverage area of the venue, and the monitoring area includes the stand and the main sports site, so the number of monitoring cameras is large, and in the process of data collection, the moving picture of the person at the edge of the two cameras will inevitably be formed, so as to form a monitoring dead angle. How to collect the person at the edge of the two cameras is expected to be well solved. SUMMARY

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

[0007] In order to achieve the above purpose, the present application provides the following technical scheme: an intelligent venue management system based on AI digital technology, comprising:

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

[0009] An AI processing module for processing the acquired video data to realize intelligent identification and seamless splicing of the moving picture of the person at the edge of the 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 risk assessment based on the results, and executing risk report based on the risk assessment results.

[0012] As a preferred, the video acquisition module is composed of a plurality of camera groups, and the acquisition range of each camera is Wherein: 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] The first data composed of simultaneous period photos collected by each camera position, and each photo has a corresponding camera position identifier Sn;

[0015] The coordinates of the overlapping part of the effective monitoring range of the adjacent cameras are extracted, and the overlapping part is marked in the photo under the corresponding identifier Sn to obtain the second data.

[0016] As a preferred, the marking of the overlapping part in the second data includes the following steps:

[0017] S01, create multiple photo processing channels about the same period under the cloud disk;

[0018] S02, establish an xy-axis coordinate between the adjacent two sides of the photo, and establish a mask grid;

[0019] S03, extracting the pixel features of the photo based on the feature extraction algorithm, and obtaining the coordinate position of the pixel features on the mask grid;

[0020] S04, obtaining the identification Sn of the current photo, extracting the photos of identification Sn-1 and Sn+1, and splicing the pixel features at the edge of the mask grid to obtain the combined features.

[0021] As preferred, the AI processing module comprises:

[0022] The target detection submodule analyzes the first data and the second data contained in the video data to obtain a moving object that can be identified;

[0023] The target tracking submodule extracts features from pictures captured by multiple cameras based on the determined moving object to obtain dynamic parameters of the moving object.

[0024] The image stitching submodule performs processing on objects that cannot be identified by the target detection submodule, and pre-processes the objects that cannot be identified under consecutive frames to obtain clear feature dynamic parameters.

[0025] As preferred, the image stitching submodule pre-processes the objects that cannot be identified under consecutive frames, comprising:

[0026] S11, converting the photos of consecutive frames into grayscale images and performing noise reduction through Gaussian filtering and mean filtering;

[0027] S12, using a background subtraction algorithm to separate the foreground and background of the noise-reduced photos, and extracting the blurred objects in motion obtained under consecutive frames;

[0028] S13, using YOLO algorithm to calculate the speed of the blurred objects in motion, performing photo scaling, obtaining the displacement speed of the blurred objects in motion on the picture, and then performing pixel backward processing on the consecutive frame photos to obtain the backward pixel features of the consecutive frames;

[0029] S14, comparing the backward pixel features of multiple photos to determine whether they can be identified by the target detection submodule, and if not, returning to step S11.

[0030] As preferred, the data storage and analysis module comprises:

[0031] The motion trajectory analysis submodule extracts the limbs of the moving object based on a feature extraction algorithm and forms a limb movement trajectory reference line in a three-point marking manner.

[0032] The cloud database matches the obtained limb movement trajectory reference line with a plurality of trajectory reference lines in stock, and if a match is obtained, the judgment language of the corresponding trajectory reference line is fed back as a result, and if a match cannot be obtained, no feedback is provided.

[0033] As a preferred, the alarm and response module comprises:

[0034] The preset voice prompt submodule matches the obtained result with the corresponding risk level and selects the voice recorded in advance corresponding to the risk to play back;

[0035] The security notification submodule matches the obtained result with the corresponding risk level to prompt the security personnel in the form of a short message, and the short message contains coordinates.

[0036] In the above technical solution, the intelligent venue management system based on AI digital technology provided by the present application has the following beneficial effects: efficient, intelligent and safe operation of the venue is realized. 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 make it can adapt to the needs of different scales and types of venues, and has wide application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The module diagram provided for embodiment 1 of the present application;

[0038] Figure 2 The submodule diagram of the AI processing module provided for embodiment 1 of the present application;

[0039] Figure 3 The submodule diagram of the data storage and analysis module provided for embodiment 1 of the present application;

[0040] Figure 4 The submodule diagram of the alarm and response module provided for embodiment 1 of the present application. DETAILED DESCRIPTION

[0041] In order to make the technical solution of the present application clearer, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0042] Embodiment 1

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

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

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

[0046] The data storage and analysis module is configured to store the processed video data and perform data analysis to obtain a result.

[0047] The alarm and response module is configured to perform risk assessment based on the result and execute a risk report based on the risk assessment result.

[0048] In the above technology, by integrating the four modules of video acquisition, AI processing, data storage and analysis, and alarm and response, comprehensive and intelligent monitoring and management of the venue is realized. The system can acquire video data in real time, use AI technology for intelligent recognition and processing, monitor and analyze the personnel flow and abnormal behavior in the venue in real time, and issue an alarm in a timely manner based on the risk assessment result, thereby providing strong support for the safety management of the venue

[0049] Further, the video acquisition module in the above embodiment is composed of a plurality of camera groups, and the acquisition range of each camera is wherein 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 is composed of simultaneous period photos acquired by each camera position, and each photo has a corresponding camera position identifier Sn.

[0052] The coordinate values of the overlapping part of the effective monitoring ranges of adjacent cameras are extracted, and the overlapping part is marked in the photo under the corresponding identifier Sn to obtain the second data.

[0053] Further, the marking of the overlapping part in the second data includes the following steps:

[0054] S01, create a plurality of photo processing channels about the same period under the cloud disk;

[0055] S02, establish an xy-axis coordinate on the adjacent two sides of the photo and establish a mask grid.

[0056] S03, extract the pixel features of the photo based on a feature extraction algorithm and obtain the coordinate position of the pixel features on the mask grid.

[0057] S04, obtaining the identification Sn of the current photo, extracting the photos of the identifications Sn-1 and Sn+1, and splicing the pixel features at the edges of the mask grid to obtain the combined features.

[0058] Specifically, the video acquisition module composed of multiple camera groups realizes omnidirectional and dead-angle-free video monitoring in the venue. The collection range of each camera is accurately calculated to ensure the coverage and accuracy of the monitoring. Moreover, the overlapping parts of the effective monitoring ranges of adjacent cameras are extracted and marked in the photos, providing a reliable basis for subsequent image stitching and processing. This function effectively solves the monitoring blind area problem between multiple cameras and improves the continuity and completeness of the video data.

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

[0060] The target detection submodule analyzes the first data and the second data in the video data to obtain a moving object that can be recognized;

[0061] The target tracking submodule extracts features from the pictures collected by the multiple cameras based on the determined moving object to obtain dynamic parameters of the moving object.

[0062] The image stitching submodule performs processing on the object that cannot be recognized by the target detection submodule, and pre-processes the object that cannot be recognized under consecutive frames to obtain clear feature dynamic parameters.

[0063] Secondly, the image stitching submodule pre-processes the object that cannot be recognized under consecutive frames, including:

[0064] S11, converting the consecutive frame photos into grayscale images and performing noise reduction through Gaussian filtering and mean filtering;

[0065] S12, using a background subtraction algorithm to separate the foreground and background of the noise-reduced photos, and extracting the blurred object in motion obtained under consecutive frames;

[0066] S13, using the YOLO algorithm to calculate the speed of the blurred object in motion, performing proportional conversion on the photos to obtain the displacement speed of the blurred object on the photos, and then performing pixel backward shift processing on the consecutive frame photos to obtain the backward shift pixel features of the consecutive frames;

[0067] S14, comparing the multiple photo backward shift pixel features to determine whether they can be recognized by the target detection submodule, and if not, returning to step S11.

[0068] Specifically, the intelligent recognition two camera edge moving picture of the person, and carry out seamless splicing, realize the continuous monitoring of the personnel flow in the venue. This function effectively avoids the monitoring blind area caused by camera switching, improves the continuity and accuracy of monitoring. And can accurately identify the moving objects in the video, while 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 objects that cannot be identified by the target detection sub-module, through continuous frame pixel feature extraction and post-processing, clear feature dynamic parameters are obtained, improving the recognition ability and accuracy of the system.

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

[0070] The motion trajectory analysis sub-module extracts the limbs of the moving object based on the feature extraction algorithm, and forms a four-limb movement trajectory reference line using a three-point identification method.

[0071] The cloud database matches the obtained four-limb movement trajectory reference line with a plurality of trajectory reference lines stored in the library, if a match is obtained, the judgment language of the corresponding trajectory reference line is fed back, if it cannot be matched, it is not fed back.

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

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

[0074] The preset voice prompt sub-module matches the obtained result with the corresponding risk level, and selects the voice recorded in advance corresponding to the risk to broadcast;

[0075] The security notification sub-module matches the obtained result with the corresponding risk level to prompt the security personnel in the form of a short message, and the short message contains coordinates.

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

[0077] In summary, the embodiment one realizes the efficiency, intelligence and security of the venue operation. 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 make it can adapt to the needs of different scale and type of venues, has wide application prospect.

[0078] Embodiment 2

[0079] The embodiment of the application provides a non-transitory computer readable storage medium, the non-transitory computer readable storage medium stores at least one instruction or at least one program, the at least one instruction or at least one program is loaded and executed by a processor to realize the steps of:

[0080] A video acquisition module is configured to acquire video data of each camera in the venue.

[0081] An AI processing module is configured to process the acquired video data, to realize intelligent recognition and seamless splicing of moving pictures of edge characters of two cameras.

[0082] A data storage and analysis module is configured to store the processed video data and perform data analysis to obtain a result.

[0083] An alarm and response module is configured to perform risk assessment according to the result, and execute risk report based on the risk assessment result.

[0084] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of the methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. 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. As an illustration but 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by 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 above-described functions.

[0086] Embodiment 3

[0087] The embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to realize the steps of:

[0088] The video acquisition module is configured to acquire video data of each camera in the venue.

[0089] The AI processing module is configured to process the acquired video data, and realize intelligent recognition and seamless splicing of moving pictures of edge people of the two cameras.

[0090] The data storage and analysis module is configured to store the processed video data and perform data analysis to obtain a result.

[0091] The alarm and response module is configured to perform risk assessment according to the result, and perform risk report based on the risk assessment result.

[0092] The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it cannot be understood as the limitation of the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An intelligent venue management system based on AI digital technology, characterized in that, The application relates to a risk monitoring system for a venue, comprising: a video acquisition module for acquiring video data of each camera in the venue; an AI processing module for processing the acquired video data to realize intelligent identification and seamless splicing of moving pictures of edge figures of two cameras; a data storage and analysis module for storing the processed video data and performing data analysis to obtain results; an alarm and response module for risk assessment based on the results and execution of risk reporting based on the risk assessment results; The video acquisition module is composed of multiple camera groups, and the acquisition range of each camera is wherein: 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. the video data comprises: first data composed of photos of the same period acquired by each camera position, and each photo has a corresponding camera position identifier Sn; coordinates of an overlapping part of the effective monitoring range of adjacent cameras are extracted, and the overlapping part is marked in the photo under the corresponding identifier Sn to obtain second data; the AI processing module comprises: a target detection submodule for analyzing the video data containing the first data and the second data to obtain a moving object that can be identified; a target tracking submodule for performing feature extraction on pictures collected by multiple cameras based on the determined moving object to obtain dynamic parameters of the moving object; an image splicing submodule for processing objects that cannot be identified by the target detection submodule, and obtaining clear feature dynamic parameters by preprocessing the objects that cannot be identified under continuous frames; the data storage and analysis module comprises: a motion trajectory analysis submodule for extracting limbs of the moving object based on a feature extraction algorithm and forming a four-limb motion trajectory reference line in a three-point identification manner; a cloud database for matching the obtained four-limb motion trajectory reference line with multiple trajectory reference lines stored in the database, and if a match is obtained, the judgment language of the corresponding trajectory reference line is fed back as a result, and if a match cannot be obtained, no feedback is given. 2.The intelligent venue management system based on AI digital technology of claim 1, wherein The marking of the overlapping part in the second data comprises the following steps: S01, creating multiple photo processing channels about the same period under the cloud disk; S02, establishing an xy-axis coordinate at 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 the coordinate position of the pixel features on the mask grid; S04, obtaining the identifier Sn of the current photo, extracting the photos of identifiers Sn-1 and Sn+1, and splicing the pixel features at the edge of the mask grid to obtain combined features. 3.The intelligent venue management system based on AI digital technology of claim 2, wherein, The preprocessing of the objects that cannot be identified under continuous frames by the image splicing submodule comprises: S11, converting the photos of continuous frames into gray-scale images and performing noise reduction through Gaussian filtering and mean filtering; S12, separating the foreground and background of the photos after noise reduction by using a background subtraction algorithm, and extracting the blurred objects in motion under continuous frames; S13, using a YOLO algorithm to determine the speed of the blurred objects in motion, performing proportional conversion of the photos, obtaining the displacement speed of the blurred objects in motion on the picture, and then performing pixel backward shift processing on the photos of continuous frames to obtain the backward shift pixel features of continuous frames. S14, compare the pixel features of the plurality of photos, judge whether the target can be recognized by the target detection submodule, if not, return to step S11.

4. The intelligent venue management system based on AI digital technology according to claim 1, characterized in that, The alarm and response module comprises: A preset voice prompt submodule matches the obtained result with the corresponding risk level and selects the voice recorded in advance corresponding to the risk to broadcast; A security notification submodule matches the obtained result with the corresponding risk level and prompts a security personnel in the form of a short message, wherein the short message contains coordinates.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the AI digital technology-based intelligent venue management system of any one of claims 1 to 4.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the AI digital technology-based intelligent venue management system of any one of claims 1 to 4.

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