An explosion-proof video conference system and a video conference implementation method

Abstract

The present invention relates to the field of television image communication technology, and in particular to an explosion-proof conference television system and a conference television implementation method. The method comprises the following steps: acquiring the original conference image stream through multi-source image acquisition, and performing displacement compensation and multi-resolution fusion, generating a standardized image stream, simulating conference communication transmission and inspecting the image feature quality, deducing the equipment load and analyzing the bearing capacity of the explosion-proof conference television, obtaining a bearing capacity change curve, screening out the extreme load point according to the curve, performing load reduction segmentation, generating a low-load image stream, performing path redistribution on the low-load image stream, forming a path allocation matrix, and realizing real-time transmission to ensure the effective operation of the explosion-proof conference television. The present invention realizes a safer and more efficient explosion-proof conference television implementation method.

Description

Technical Field

[0001] The present invention relates to the technical field of television image communication, and in particular to an explosion-proof television conference system and a television conference implementation method. Background Art

[0002] Traditional conference video communication systems mainly use wired networks or single wireless networks to transmit video data. To a certain extent, such systems can no longer meet the needs of large-scale conferences or important conferences that require high-reliability transmission. In particular, video conferences are held in chemical companies and energy companies that operate in explosive gases. Due to the potential safety hazards of explosions, it is difficult for traditional systems to ensure the safety of the transmission process. At the same time, as the scale of video conferences continues to expand, the load on individual devices is also increasing. High-load operation can easily cause overheating for electronic equipment, which will cause certain safety issues for the equipment and user systems. Especially when used in situations that require high protection, the failure of traditional single equipment will bring greater safety risks. In the process of transmitting video streams, traditional conference communication systems perform consistent processing for different devices, and do not perform personalized optimization for the different tolerances and operating states of individual devices, which greatly reduces resource utilization efficiency. Summary of the invention

[0003] Based on this, it is necessary to provide an explosion-proof video conference system and a video conference implementation method to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for realizing explosion-proof conference television includes the following steps:

[0005] Step S1: performing multi-source image acquisition on the conference scene to obtain an original conference image stream; performing displacement compensation processing on the original conference image stream to generate a calibrated image stream; performing multi-resolution fusion processing on the calibrated image stream to obtain a standardized image stream;

[0006] Step S2: performing queue transmission simulation on the standardized image stream to generate conference communication simulation data; performing feature quality inspection on the conference communication simulation data to generate image feature quality;

[0007] Step S3: Performing equipment load deduction on the standardized image stream according to the image feature quality to obtain equipment load data; performing machine bearing capacity analysis on the explosion-proof conference television based on the equipment load data to obtain a bearing capacity change curve;

[0008] Step S4: screening the equipment load data for limit points according to the bearing capacity change curve to generate limit load points; performing load reduction segmentation on the standardized image stream according to the limit load points to generate a low-load image stream;

[0009] Step S5: reallocate the paths of the low-load image streams to obtain a path allocation matrix; perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

[0010] The present invention achieves comprehensive coverage of conference scenes through multi-source image acquisition, ensures the quality and integrity of the original conference image stream, eliminates motion blur and jitter in the image through displacement compensation processing, enhances the usability of the image, and improves the details and clarity of the image through multi-resolution fusion processing. The generated standardized image stream is more suitable for subsequent processing and analysis, ensuring the accurate transmission and display of image information. In the queuing transmission simulation stage, the generated conference communication simulation data undergoes feature quality inspection to ensure that the image features in the transmission meet the preset standards, provides high-quality image streams, lays a solid foundation for subsequent equipment load deduction, and ensures the stability and reliability of image transmission during the meeting. Equipment load deduction is performed based on the image feature quality, and the generated equipment load data provides a basis for the bearing capacity analysis of explosion-proof conference televisions. The bearing capacity change curve intuitively shows the performance of the equipment under different loads, helps identify the safe operating range of the equipment, and ensures the stability and safety of the equipment under high load conditions. Limit point screening and load reduction segmentation ensure that the equipment can still work effectively under high load conditions. The generated low-load image stream reduces the operating pressure of the equipment, improves the service life and reliability of the explosion-proof conference television, provides a good foundation for real-time transmission, and ensures that the equipment can continue to play a role in emergency or special situations. The path allocation matrix formed by path redistribution optimizes the transmission path of low-load image streams, improves the efficiency and real-time performance of data transmission, ensures the stability of conference links, and provides an efficient and reliable explosion-proof conference television implementation solution, enabling conference scenes to proceed smoothly in complex environments and ensuring the security and effectiveness of information exchange.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: using a conference television camera to perform multi-source image acquisition on the conference scene to obtain an original conference image stream; extracting feature points from the original conference image stream to obtain a feature point data set;

[0013] Step S12: Calculate the relative position relationship of the conference video camera based on the feature point data set to obtain a space mapping matrix;

[0014] Step S13: performing spatial geometric transformation on the original conference image stream according to the spatial mapping matrix to generate position correction data; performing edge compensation processing on the original conference image stream based on the position correction data to obtain a calibrated image stream;

[0015] Step S14: performing multi-level scaling on the calibration image stream to obtain a multi-resolution version image stream;

[0016] Step S15: performing cross-resolution feature registration on the multi-resolution version image stream to generate image registration parameters;

[0017] Step S16: Perform progressive feature fusion on the multi-version images of the resolution according to the image registration parameters to obtain a standardized image stream.

[0018] The present invention uses a conference television camera to perform multi-source image acquisition, ensuring that the original conference image stream obtained can fully reflect all details of the conference scene, and forms a feature point data set through feature point extraction, laying the foundation for subsequent spatial relationship calculation, enhancing the processing ability of image data, and effectively improving the accuracy of subsequent processing. Relative position relationship calculation is performed based on the feature point data set, and the generated spatial mapping matrix provides the precise position relationship between the camera and the conference scene, ensuring that the subsequent geometric transformation can accurately reflect the actual spatial layout, improving the accuracy and reliability of image processing, and providing a guarantee for achieving high-quality image effects. The original conference image stream is subjected to spatial geometric transformation according to the spatial mapping matrix, and the generated position correction data ensures the accurate correspondence between the object and the scene in the image. The edge compensation processing further improves the image quality. The obtained calibrated image stream eliminates unnecessary distortion while retaining details, providing high-quality input for subsequent processing. The calibrated image stream is scaled at multiple levels, and the generated resolution multi-version image stream can meet different display requirements and transmission conditions, ensuring that clear visual effects can be provided on various devices, enhancing the adaptability of the image, and improving the visual experience of the conference scene. The image registration parameters generated by cross-resolution feature registration of multi-version image streams can effectively align image features of different resolutions, ensure accurate mapping between versions, improve the quality of image fusion, and provide a solid foundation for subsequent feature fusion. The multi-version images of different resolutions are progressively fused according to the image registration parameters. The resulting standardized image stream retains the advantages of each resolution while integrating multi-view information, providing high-quality, unified and stable image output, ensuring that the explosion-proof conference TV can achieve excellent visual effects and stable performance in various application scenarios.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S21: Arranging the transmission sequence of the standardized image stream to obtain an image transmission sequence; performing bandwidth allocation calculation on the image transmission sequence based on preset transmission bandwidth data to obtain a bandwidth allocation table;

[0021] Step S22: sorting the standardized image streams in queue according to the bandwidth allocation table to obtain a transmission queue table; calculating the channel capacity of the transmission queue table to obtain a channel capacity parameter;

[0022] Step S23: performing transmission simulation on the standardized image stream based on the channel capacity parameter to generate conference communication simulation data;

[0023] Step S24: performing frame rate statistical analysis on the conference communication simulation data to obtain frame rate statistical data; performing feature quality evaluation on the conference communication simulation data according to the frame rate statistical data to generate image feature quality.

[0024] The present invention arranges the transmission sequence of the standardized image stream, and the generated image transmission sequence ensures the orderly transmission of image data, optimizes the processing efficiency of the data stream, and improves the timeliness of information transmission during the meeting. By performing bandwidth allocation calculation based on the preset transmission bandwidth data, the bandwidth allocation table obtained improves the utilization rate of network resources and ensures that high-quality image transmission can be achieved even when the network bandwidth is limited. The standardized image stream is queued according to the bandwidth allocation table, and the generated transmission queue table effectively manages the transmission order of the data, reduces delays and congestion. The channel capacity calculation provides important capacity parameters for subsequent data transmission, ensures the stability of conference communications, and improves the overall communication quality. Transmission simulation is performed based on the channel capacity parameters, and the generated conference communication simulation data reflects the transmission effect in the real environment, provides data support for subsequent performance evaluation, ensures effective conference communications in complex network environments, and improves the adaptability of the system. The frame rate statistics of conference communication simulation data are analyzed, and the obtained frame rate statistics provide an objective evaluation of image fluency, ensuring that high frame rates can be maintained in actual transmission, improving the visual experience. The feature quality of conference communication simulation data is evaluated based on the frame rate statistics, and the generated image feature quality indicators provide a basis for system optimization, so that the explosion-proof conference television can maintain high-quality image output under different conditions, ensuring clear and accurate information exchange.

[0025] Preferably, step S3 comprises the following steps:

[0026] Step S31: performing data volume statistics on the standardized image stream according to the image feature quality to obtain the image data scale; performing resource demand calculation on the image data scale to obtain a resource demand set;

[0027] Step S32: performing demand response simulation on the resource demand set to generate simulated response demand data; performing load deduction on the standardized image flow based on the simulated response demand data to obtain equipment load data;

[0028] Step S33: acquiring TV physical characteristic data; performing long-term load heating simulation on the TV physical characteristic data based on the equipment load data to generate simulated TV heating data;

[0029] Step S34: Calculate the load capacity of the explosion-proof conference television according to the simulated television heating data to obtain load capacity change data; perform curve fitting processing on the load capacity change data to obtain a bearing capacity change curve.

[0030] The present invention performs data volume statistics on standardized image streams according to the quality of image features, and the scale of the generated image data provides a clear understanding of transmission and storage requirements. The resource demand calculation ensures that the system can reasonably allocate the required resources under different network environments. The obtained resource demand set provides an important basis for subsequent resource optimization and scheduling, ensuring that the explosion-proof conference television can still maintain stable operation under high load conditions. The resource demand set is simulated for demand response, and the generated simulated response demand data can truly reflect the changes in the equipment's demand for resources. Based on this data, the load is deduced for the standardized image stream, and the obtained equipment load data provides a basis for subsequent load capacity analysis, ensuring that the equipment can effectively respond to sudden changes under dynamic load environments, and improving the flexibility and adaptability of the system. Acquiring television physical property data provides the necessary physical basis for load capacity analysis, and combining the equipment load data to perform long-term load heating simulation, the generated simulated television heating data accurately reflects the heating conditions of the equipment under different working conditions, ensuring that there is a scientific basis for the thermal management of the equipment, and providing protection for the safe operation of the explosion-proof conference television. The load capacity is calculated based on the simulated TV heating data. The obtained load capacity change data provides a quantitative analysis of the stability of the equipment under different environmental conditions. The curve fitting process ensures that the change trend of the load capacity is clearly visible. The obtained load capacity change curve provides an important reference for the reliability design of the explosion-proof conference TV, ensuring that the equipment can operate effectively under various conditions of use and guaranteeing the smooth progress of the meeting.

[0031] Preferably, step S32 includes the following steps:

[0032] Step S321: Calculate the processor occupancy of the resource requirement set according to the preset video conferencing parameters to obtain occupancy distribution data;

[0033] Step S322: performing resource competition simulation on the occupancy distribution data to obtain simulated response demand data;

[0034] Step S323: performing computational load calculation on the simulated response demand data to generate computational load data; performing data flow analysis on the standardized image stream to obtain flow distribution data;

[0035] Step S324: performing load matching processing on the computational load data and the flow distribution data to generate a load matching table; performing peak estimation on the load matching table to obtain peak estimation data;

[0036] Step S325: perform load aggregation processing on the peak estimation data to obtain a basic load set; perform time series integration on the basic load set to generate equipment load data.

[0037] The present invention calculates the processor occupancy of the resource demand set according to the preset conference television parameters, and the generated occupancy distribution data can accurately reflect the resource usage of the device under different working conditions, provide a basis for subsequent resource allocation, ensure that the performance of the device under high load conditions can meet the conference requirements, and improve the stability and reliability of the meeting. The occupancy distribution data is simulated for resource competition, and the obtained simulated response demand data truly depicts the resource competition that the system will encounter in actual operation, providing data support for optimizing resource allocation, ensuring that the device can effectively manage resource allocation during multi-tasking, and reducing the risk of performance degradation caused by resource competition. The simulated response demand data is calculated for computing load, and the generated computing load data provides a clear quantitative indicator for the processing capacity of the device. By performing data flow analysis on the standardized image stream, the obtained flow distribution data provides detailed flow information for real-time transmission requirements, ensuring that the device can maintain an efficient operating state when processing data. The load matching process is performed on the computing load data and the flow distribution data. The generated load matching table ensures the reasonable load distribution of the equipment under different operating conditions. The peak estimation data obtained by performing peak estimation on the load matching table provides a scientific basis for the equipment's operating capacity under high load conditions, ensuring that the equipment can operate stably under peak conditions. The load aggregation process is performed on the peak estimation data. The basic load set obtained provides comprehensive data support for subsequent load analysis. By performing time series integration on the basic load set, the generated equipment load data ensures a comprehensive understanding of the equipment's operating status, allowing the explosion-proof conference television to maintain efficient and stable performance in various usage scenarios.

[0038] Preferably, step S33 includes the following steps:

[0039] Step S331: acquiring TV physical characteristic data; performing power conversion calculation on the TV physical characteristic data to obtain a power distribution table; locating heat sources on the power distribution table to generate load heating position data;

[0040] Step S332: performing heat conduction simulation on the load heating position data to obtain a temperature rise conduction path; performing hot spot superposition analysis on the temperature rise conduction path to generate a hot spot distribution set;

[0041] Step S333: performing time series conversion on the hotspot distribution set to obtain time series thermal data; performing cumulative temperature rise calculation on the time series thermal data to generate temperature rise curve data; performing heat dissipation compensation inference on the temperature rise curve data to generate cumulative heat dissipation compensation heat value;

[0042] Step S334: Perform load heating simulation on the accumulated heat dissipation compensation heat value to generate simulated TV heating data.

[0043] The present invention provides a basis for subsequent power conversion calculations by acquiring the physical property data of the television. The generated power distribution table accurately reflects the power demand of the device under different working conditions, provides a basis for locating the heat source, and the load heating position data ensures that the specific area of ​​heating can be clearly identified, thereby improving the pertinence and effectiveness of subsequent thermal management. The load heating position data is subjected to heat conduction simulation, and the obtained temperature rise conduction path provides a clear perspective for understanding the propagation of heat inside the device. By performing hot spot superposition analysis on the temperature rise conduction path, the generated hot spot distribution set can accurately identify the high temperature area generated by the device during operation, providing important data support for optimizing the heat dissipation design. The hot spot distribution set is subjected to time series conversion, and the obtained time series thermal data provides detailed information on the temperature change law. By performing cumulative temperature rise calculation on the time series thermal data, the generated temperature rise curve data can intuitively reflect the temperature change trend of the device during operation, and the heat dissipation compensation inference provides a scientific basis for the temperature management of the device under different load conditions. The load heating simulation is performed on the cumulative heat dissipation compensation heat value, and the generated simulated TV heating data ensures that the heating situation can be accurately predicted during the actual operation of the equipment, providing comprehensive theoretical support for the heat dissipation design and thermal management of the explosion-proof conference TV, ensuring that the equipment can still maintain stable operation under high load conditions and guaranteeing the smooth progress of the meeting.

[0044] Preferably, step S4 comprises the following steps:

[0045] Step S41: splitting the bearing capacity change curve to obtain a segmented change curve; identifying inflection points of the segmented change curve to generate a curve inflection point distribution;

[0046] Step S42: performing load safety boundary mapping on the equipment load data based on the curve inflection point distribution to generate a boundary range set; performing critical calculation on the boundary range set to obtain a limit load point;

[0047] Step S43: performing overload calculation on the standardized image stream according to the limit load point to generate a load difference result; formulating segmentation and reorganization rules for the standardized image stream according to the load difference result to generate a data segmentation rule;

[0048] Step S44: based on the data segmentation rule, the standardized image stream is cut and reorganized to generate a low-load image stream.

[0049] The present invention splits the bearing capacity change curve to generate a segmented change curve that can refine the performance of the equipment under different load conditions. The curve inflection point distribution generated by inflection point identification provides key data for subsequent load safety assessment, ensuring that the turning points of the equipment under different working conditions can be accurately identified, and providing a basis for optimizing load management. The load safety boundary of the equipment load data is mapped based on the curve inflection point distribution, and the generated boundary range set provides a clear boundary for the safe operation of the equipment. The limit load point obtained by critical calculation of the boundary range set ensures the safety of the equipment under high load conditions, preventing equipment damage or performance degradation caused by overload. The overload calculation of the standardized image stream is performed according to the limit load point, and the generated load difference result provides a quantitative basis for optimizing the transmission of the image stream. The segmentation and reorganization rules formulated for the load difference result ensure that the stability and clarity of the image stream can be maintained under the condition of overload, thereby improving the adaptability and flexibility of the system. The standardized image stream is cut and reorganized based on data segmentation rules, and the generated low-load image stream effectively reduces the pressure of data transmission and processing, ensuring that the equipment can process image information efficiently and stably during operation, optimizing the performance of explosion-proof conference television, and ensuring the quality of conference communications in various complex environments.

[0050] Preferably, step S43 includes the following steps:

[0051] Step S431: Calculate the transmission load of the standardized image stream to obtain an image transmission load set;

[0052] Step S432: numerically compare the image transmission load set and the limit load point to generate a limit comparison difference; calculate the limit degree of the limit comparison difference to obtain the limit degree data;

[0053] Step S433: performing a difference evaluation on the standardized image flow and the extreme load point according to the over-limit degree data to generate a load difference result;

[0054] Step S434: performing a balance demand analysis on the load difference result to obtain a balance demand parameter; performing a resource allocation calculation on the balance demand parameter to generate resource allocation data;

[0055] Step S435: Formulate segmentation rules for the standardized image stream based on the resource allocation data to generate data segmentation rules.

[0056] The present invention calculates the transmission load of the standardized image stream, and the generated image transmission load set provides quantitative data for subsequent load management, ensures that the transmission demand in each time period can be accurately evaluated, improves the responsiveness of the system under high load conditions, and lays a foundation for the stability of conference communication. The image transmission load set and the extreme load point are numerically compared, and the generated extreme comparison difference can clearly reflect the gap between the current load and the equipment bearing capacity, providing a basis for the calculation of the over-limit degree, ensuring that the load change can be monitored in real time during the operation of the equipment, and reducing the risk of equipment failure due to excessive load. The standardized image stream and the extreme load point are evaluated for difference according to the over-limit degree data, and the generated load difference results provide important information for subsequent load adjustment, ensuring that the uneven load can be identified in time in actual operation, and providing data support for optimizing resource allocation. The load difference results are analyzed for the balance demand, and the obtained balance demand parameters provide a scientific basis for the system in resource allocation. The resource allocation data generated by the resource allocation calculation of the balance demand parameters ensures that the system resources can be reasonably allocated under different load conditions, thereby improving the overall operation efficiency. Based on the resource allocation data, the standardized image stream segmentation rules are formulated. The generated data segmentation rules provide clear guidance for the subsequent image stream processing, ensuring flexible response under load fluctuations, enhancing the adaptability and stability of the explosion-proof conference television, and effectively ensuring the smooth progress of the meeting.

[0057] Preferably, step S5 comprises the following steps:

[0058] Step S51: performing transmission network topology connection on the low-load image stream to obtain a network connection diagram; performing conflict detection on the network connection diagram to generate a conflict detection diagram;

[0059] Step S52: performing path redistribution on the network connection graph based on the conflict detection graph to obtain a path allocation matrix;

[0060] Step S53: Design the transmission timing of the path allocation matrix to obtain transmission timing parameters; build a conference link for the path allocation matrix based on the transmission timing parameters to generate a conference transmission link;

[0061] Step S54: Based on the conference transmission link, the low-load image stream is transmitted in real time to implement the explosion-proof conference television implementation method.

[0062] The present invention performs transmission network topology connection on low-load image streams, and the generated network connection diagram clearly shows the connection relationship between various devices, ensuring that the location of each node can be effectively identified during data transmission, providing an intuitive basis for subsequent conflict detection, and improving the efficiency and accuracy of network management. The network connection diagram is subjected to conflict detection, and the generated conflict detection diagram can timely identify potential conflict points in the network, providing a basis for subsequent path redistribution, ensuring that conflicts in data transmission can be effectively avoided in a complex network environment, and improving the stability and reliability of the network. The network connection diagram is subjected to path redistribution based on the conflict detection diagram, and the obtained path allocation matrix provides optimized path selection for data transmission, ensuring that network bandwidth can be reasonably allocated under high load conditions, reducing transmission delays, and improving the fluency of conference communications. The path allocation matrix is ​​subjected to transmission timing design, and the generated transmission timing parameters ensure that the sending time of each data packet can be reasonably arranged during the data transmission process. By constructing a conference link for the path allocation matrix based on the transmission timing parameters, the generated conference transmission link provides stable support for real-time transmission, and optimizes the overall transmission efficiency of the conference. Based on the conference transmission link, the conference link is used to transmit low-load image streams in real time, ensuring that the explosion-proof conference television can stably and quickly transmit high-quality image data in various complex environments, providing users with a clear and smooth conference experience, and ensuring the smooth progress of the meeting and the effective exchange of information.

[0063] The present invention also provides an explosion-proof television conference system for executing the above-mentioned explosion-proof television conference implementation method, and the explosion-proof television conference system comprises:

[0064] The data calibration module is used to collect multi-source images of the conference scene to obtain the original conference image stream; perform displacement compensation processing on the original conference image stream to generate a calibration image stream; perform multi-resolution fusion processing on the calibration image stream to obtain a standardized image stream;

[0065] The quality detection module is used to simulate the queue transmission of the standardized image stream to generate conference communication simulation data; perform feature quality inspection on the conference communication simulation data to generate image feature quality;

[0066] The load analysis module is used to deduce the equipment load of the standardized image stream according to the image feature quality to obtain the equipment load data; based on the equipment load data, the machine bearing capacity analysis of the explosion-proof conference TV is performed to obtain the bearing capacity change curve;

[0067] The load reduction module is used to screen the equipment load data for limit points according to the bearing capacity change curve to generate the limit load points; load reduction segmentation of the standardized image stream is performed according to the limit load points to generate the low-load image stream;

[0068] The image transmission module is used to reallocate the paths of low-load image streams to obtain a path allocation matrix; and to perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

[0069] The present invention uses a data calibration module to perform multi-source image acquisition on the conference scene to ensure that the original conference image stream obtained has high quality and integrity. The displacement compensation process effectively eliminates jitter and blur in the image. The generated calibration image stream is processed by multi-resolution fusion to provide a clear standardized image stream, which lays a solid foundation for subsequent image analysis and processing. The quality detection module performs queue transmission simulation on the standardized image stream, and the generated conference communication simulation data provides real data support for subsequent performance evaluation. By performing feature quality inspection on the simulated data, the image feature quality index obtained ensures that high-quality image output can be maintained during the transmission process, thereby improving the overall visual effect of the conference. The load analysis module deduces the equipment load through the image feature quality, and the obtained equipment load data provides an important basis for the subsequent bearing capacity analysis. The bearing capacity change curve intuitively shows the performance of the equipment under different loads, ensuring that the equipment can operate safely and stably under high load conditions, reducing the risk of failure. The load reduction module screens the extreme load points according to the bearing capacity change curve, and the generated extreme load points ensure that the operating status of the equipment can be effectively managed under high load conditions. By load reduction segmentation of the standardized image stream, the generated low-load image stream helps to optimize data transmission and reduce the operating pressure of the equipment. The image transmission module reallocates the paths of low-load image streams, and the generated path allocation matrix provides optimized path selection for real-time transmission, ensuring efficient and stable transmission of image data under different network conditions, providing guarantee for the smooth operation of explosion-proof conference television, and improving the communication efficiency of the meeting and the accuracy of information transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of the steps of a method for implementing an explosion-proof video conferencing system;

[0071] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0072] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0073] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve this, please refer to Figures 1 to 3 , a method for realizing explosion-proof conference television, comprising the following steps:

[0078] Step S1: performing multi-source image acquisition on the conference scene to obtain an original conference image stream; performing displacement compensation processing on the original conference image stream to generate a calibrated image stream; performing multi-resolution fusion processing on the calibrated image stream to obtain a standardized image stream;

[0079] Step S2: performing queue transmission simulation on the standardized image stream to generate conference communication simulation data; performing feature quality inspection on the conference communication simulation data to generate image feature quality;

[0080] Step S3: Performing equipment load deduction on the standardized image stream according to the image feature quality to obtain equipment load data; performing machine bearing capacity analysis on the explosion-proof conference television based on the equipment load data to obtain a bearing capacity change curve;

[0081] Step S4: screening the equipment load data for limit points according to the bearing capacity change curve to generate limit load points; performing load reduction segmentation on the standardized image stream according to the limit load points to generate a low-load image stream;

[0082] Step S5: reallocate the paths of the low-load image streams to obtain a path allocation matrix; perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

[0083] The present invention achieves comprehensive coverage of conference scenes through multi-source image acquisition, ensures the quality and integrity of the original conference image stream, eliminates motion blur and jitter in the image through displacement compensation processing, enhances the usability of the image, and improves the details and clarity of the image through multi-resolution fusion processing. The generated standardized image stream is more suitable for subsequent processing and analysis, ensuring the accurate transmission and display of image information. In the queuing transmission simulation stage, the generated conference communication simulation data undergoes feature quality inspection to ensure that the image features in the transmission meet the preset standards, provides high-quality image streams, lays a solid foundation for subsequent equipment load deduction, and ensures the stability and reliability of image transmission during the meeting. Equipment load deduction is performed based on the image feature quality, and the generated equipment load data provides a basis for the bearing capacity analysis of explosion-proof conference televisions. The bearing capacity change curve intuitively shows the performance of the equipment under different loads, helps identify the safe operating range of the equipment, and ensures the stability and safety of the equipment under high load conditions. Limit point screening and load reduction segmentation ensure that the equipment can still work effectively under high load conditions. The generated low-load image stream reduces the operating pressure of the equipment, improves the service life and reliability of the explosion-proof conference television, provides a good foundation for real-time transmission, and ensures that the equipment can continue to play a role in emergency or special situations. The path allocation matrix formed by path redistribution optimizes the transmission path of low-load image streams, improves the efficiency and real-time performance of data transmission, ensures the stability of conference links, and provides an efficient and reliable explosion-proof conference television implementation solution, enabling conference scenes to proceed smoothly in complex environments and ensuring the security and effectiveness of information exchange.

[0084] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for implementing an explosion-proof video conference according to the present invention. In this example, the method for implementing an explosion-proof video conference includes the following steps:

[0085] Step S1: performing multi-source image acquisition on the conference scene to obtain an original conference image stream; performing displacement compensation processing on the original conference image stream to generate a calibrated image stream; performing multi-resolution fusion processing on the calibrated image stream to obtain a standardized image stream;

[0086] In this embodiment, multi-source image acquisition is performed on the conference scene, and a high-resolution camera and multiple cameras are used to synchronously shoot the conference scene from different angles to ensure that the acquired original conference image stream has a rich amount of information. Subsequently, the original conference image stream is subjected to displacement compensation processing, and the optical flow method (Optical Flow) or feature point tracking algorithm is used to correct the jitter and displacement in the image by analyzing the motion vector between images to generate a calibration image stream. Then, the calibration image stream is subjected to multi-resolution fusion processing, and images of different resolutions are integrated using wavelet transform (Wavelet Transform) or pyramid fusion technology to ensure that the details and colors of each image are retained, and finally a standardized image stream is obtained to provide high-quality input data for subsequent image processing.

[0087] Step S2: performing queue transmission simulation on the standardized image stream to generate conference communication simulation data; performing feature quality inspection on the conference communication simulation data to generate image feature quality;

[0088] In this embodiment, a queue transmission simulation is performed on the standardized image stream, a network simulator (such as ns-3) is used to establish a transmission model, the standardized image stream is queued in chronological order, the network transmission under different conditions is simulated, and conference communication simulation data is generated. Then, the feature quality of the conference communication simulation data is checked, and the simulation data is analyzed using image quality evaluation indicators such as the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The clarity, contrast and color reproduction of the image are evaluated, and an image feature quality report is generated to ensure that the subsequently processed images meet the visual standards and ensure the transmission quality.

[0089] Step S3: Performing equipment load deduction on the standardized image stream according to the image feature quality to obtain equipment load data; performing machine bearing capacity analysis on the explosion-proof conference television based on the equipment load data to obtain a bearing capacity change curve;

[0090] In this embodiment, the equipment load is deduced for the standardized image stream according to the image feature quality. First, the feature data in the image stream are counted, and a machine learning algorithm (such as support vector machine SVM) is used to analyze the relationship between the image features and the equipment load to generate equipment load data. Then, based on the equipment load data, the machine bearing capacity of the explosion-proof conference television is analyzed, and the finite element analysis (FEA) technology is used to simulate the equipment to obtain a bearing capacity change curve. The curve clearly shows the performance of the equipment under different loads. These data provide a scientific basis for the safe operation of the equipment.

[0091] Step S4: screening the equipment load data for limit points according to the bearing capacity change curve to generate limit load points; performing load reduction segmentation on the standardized image stream according to the limit load points to generate a low-load image stream;

[0092] In this embodiment, the device load data is screened for limit points according to the bearing capacity change curve, and a statistical analysis method (such as a peak detection algorithm) is applied to identify the limit load point of the device when the load changes. Then, the standardized image stream is load-reduced and segmented according to the limit load point, and an adaptive threshold segmentation method (Adaptive Thresholding) is used to segment the standardized image stream into a low-load image stream, thereby ensuring that the burden on the device can be reduced during data transmission, so that the device remains stable during operation, thereby improving the work efficiency of the device.

[0093] Step S5: reallocate the paths of the low-load image streams to obtain a path allocation matrix; perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

[0094] In this embodiment, the path of the low-load image stream is reallocated. First, the network topology is analyzed, and the transmission path of the current network is identified by using a network traffic analysis tool (such as Wireshark), and a path allocation matrix is ​​generated. The path is adjusted by an optimization algorithm (such as the Dijkstra algorithm) to ensure the optimization of the data flow. Then, the low-load image stream is linked to a real-time transmission conference according to the path allocation matrix, and the low-load image stream is sent to each conference terminal through a real-time transmission protocol (such as RTSP, Real-Time Streaming Protocol) to achieve efficient operation of the explosion-proof conference television, ensure stable transmission of conference content in a complex network environment, and ensure the smooth progress of the meeting.

[0095] Preferably, step S1 comprises the following steps:

[0096] Step S11: using a conference television camera to perform multi-source image acquisition on the conference scene to obtain an original conference image stream; extracting feature points from the original conference image stream to obtain a feature point data set;

[0097] Step S12: Calculate the relative position relationship of the conference video camera based on the feature point data set to obtain a space mapping matrix;

[0098] Step S13: performing spatial geometric transformation on the original conference image stream according to the spatial mapping matrix to generate position correction data; performing edge compensation processing on the original conference image stream based on the position correction data to obtain a calibrated image stream;

[0099] Step S14: performing multi-level scaling on the calibration image stream to obtain a multi-resolution version image stream;

[0100] Step S15: performing cross-resolution feature registration on the multi-resolution version image stream to generate image registration parameters;

[0101] Step S16: Perform progressive feature fusion on the multi-version images of the resolution according to the image registration parameters to obtain a standardized image stream.

[0102] In this embodiment, a conference TV camera is used for multi-source image acquisition, and multiple high-definition cameras are set at different locations in the conference room to ensure full coverage. Synchronous acquisition technology is used to start each camera at the same time through a controller (such as a camera console) to obtain the original conference image stream, and then feature points are extracted from the original conference image stream. The feature extraction algorithm is used to analyze each frame of the image, identify key feature points and record their coordinates and descriptors, and generate a feature point data set to ensure that the data set can reflect the structure and characteristics of the conference scene and provide basic data for subsequent processing. Relative position relationship calculation is performed based on the feature point data set. First, the feature points of each camera are matched, and RANSAC (random sampling consensus algorithm) is used to remove abnormal matching points. Then, the triangulation method is used to calculate the relative position relationship between each camera, and a spatial mapping matrix is ​​generated. The matrix contains the spatial coordinate information of each camera relative to other cameras. According to the spatial mapping matrix, the original conference image stream is spatially geometrically transformed. Affine transformation or perspective transformation methods are used to adjust the image streams collected by each camera so that they are aligned in the same coordinate system to generate position correction data. Then, the original conference image stream is edge compensated based on the position correction data. Image processing techniques (such as bilinear interpolation or edge extension) are used to repair the image edges, making the calibration image stream more visually coherent and natural, ensuring that the edges of each image are clear and distortion-free. The calibration image stream is scaled at multiple levels. The image pyramid scaling technology is used to process the calibration image stream at different resolutions to generate multiple image versions with different resolutions. Image scaling algorithms (such as bicubic interpolation) are used to ensure the quality of images with different resolutions, ensuring that important detail information is still retained at lower resolutions, forming a multi-resolution image stream, and providing rich image data for subsequent feature registration. Perform cross-resolution feature registration on multi-version image streams, use feature matching algorithms (such as ORB, Oriented FAST and Rotated BRIEF) to match feature points of images of different resolutions, generate image registration parameters, and achieve alignment between images of different resolutions by calculating the transformation matrix (Transformation Matrix), ensuring that image features at multiple resolutions can be accurately matched, forming a unified set of registration parameters, and providing a basis for subsequent feature fusion.According to the image registration parameters, the features of multi-version images with different resolutions are progressively fused. The weighted averaging technology is used to weight the features according to the quality and resolution of each image, and the feature information of different versions of images is superimposed layer by layer to generate a standardized image stream to ensure that the final output image has high visual quality and consistency. The image fusion algorithm (such as the image mixing model) is used to optimize the image synthesis effect to ensure that the image reaches the best state in all details and overall visual performance.

[0103] Preferably, step S2 comprises the following steps:

[0104] Step S21: Arranging the transmission sequence of the standardized image stream to obtain an image transmission sequence; performing bandwidth allocation calculation on the image transmission sequence based on preset transmission bandwidth data to obtain a bandwidth allocation table;

[0105] Step S22: sorting the standardized image streams in queue according to the bandwidth allocation table to obtain a transmission queue table; calculating the channel capacity of the transmission queue table to obtain a channel capacity parameter;

[0106] Step S23: performing transmission simulation on the standardized image stream based on the channel capacity parameter to generate conference communication simulation data;

[0107] Step S24: performing frame rate statistical analysis on the conference communication simulation data to obtain frame rate statistical data; performing feature quality evaluation on the conference communication simulation data according to the frame rate statistical data to generate image feature quality.

[0108] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0109] Step S21: Arranging the transmission sequence of the standardized image stream to obtain an image transmission sequence; performing bandwidth allocation calculation on the image transmission sequence based on preset transmission bandwidth data to obtain a bandwidth allocation table;

[0110] In this embodiment, the transmission sequence of the standardized image stream is arranged. First, the structure of the transmission sequence is defined, the transmission timestamp and priority of each frame of the image are specified, and the image stream is sorted using a scheduling algorithm (such as a polling algorithm Round Robin) to generate an image transmission sequence. Next, based on preset transmission bandwidth data, a bandwidth allocation algorithm (such as a dynamic bandwidth allocation algorithm) is used to calculate the bandwidth allocation of the image transmission sequence to ensure that the transmission bandwidth of each image frame is reasonably allocated under limited network bandwidth, and a bandwidth allocation table is generated. The bandwidth allocation table includes a predetermined transmission rate of each frame of the image and its corresponding bandwidth requirement, so as to provide data support for subsequent transmission optimization.

[0111] Step S22: sorting the standardized image streams in queue according to the bandwidth allocation table to obtain a transmission queue table; calculating the channel capacity of the transmission queue table to obtain a channel capacity parameter;

[0112] In this embodiment, the standardized image streams are queued according to the bandwidth allocation table, and the queue management system (such as the priority queue) is used to sort the image transmission tasks to ensure that high-priority image frames are processed first during transmission, and a transmission queue table is generated. Then, the channel capacity of the transmission queue table is calculated, and the network capacity calculation model (such as the Shannon formula) is applied to evaluate the available channel capacity in the current network environment to obtain the channel capacity parameter. The channel capacity parameter indicates the maximum data transmission rate that the network can support under given bandwidth conditions, which provides an important basis for subsequent transmission simulation.

[0113] Step S23: performing transmission simulation on the standardized image stream based on the channel capacity parameter to generate conference communication simulation data;

[0114] In this embodiment, the transmission of the standardized image stream is simulated based on the channel capacity parameters, a network simulation tool (such as ns-3 or OMNeT++) is used to establish a transmission simulation environment, the network topology is configured, the channel capacity parameters are set, the data stream is simulated and transmitted, and conference communication simulation data is generated. During the simulation, the transmission delay, packet loss rate and bandwidth usage of each frame of the image are recorded to ensure that the simulation results truly reflect the communication characteristics in the actual network environment and provide basic data for subsequent performance analysis.

[0115] Step S24: performing frame rate statistical analysis on the conference communication simulation data to obtain frame rate statistical data; performing feature quality evaluation on the conference communication simulation data according to the frame rate statistical data to generate image feature quality.

[0116] In this embodiment, a frame rate statistical analysis is performed on the conference communication simulation data. First, the timestamp and image frame information in the simulation data are extracted, and the number of image frames successfully transmitted per second is calculated to obtain frame rate statistical data. Then, based on the frame rate statistical data, a feature quality assessment is performed on the conference communication simulation data. An image quality assessment algorithm (such as PSNR and SSIM) is used to analyze each frame of the image to generate an image feature quality report. The report includes the image clarity, stability, and color reproduction, ensuring that the communication quality can be accurately assessed in actual applications, providing a basis for subsequent optimization.

[0117] Preferably, step S3 comprises the following steps:

[0118] Step S31: performing data volume statistics on the standardized image stream according to the image feature quality to obtain the image data scale; performing resource demand calculation on the image data scale to obtain a resource demand set;

[0119] Step S32: performing demand response simulation on the resource demand set to generate simulated response demand data; performing load deduction on the standardized image flow based on the simulated response demand data to obtain equipment load data;

[0120] Step S33: acquiring TV physical characteristic data; performing long-term load heating simulation on the TV physical characteristic data based on the equipment load data to generate simulated TV heating data;

[0121] Step S34: Calculate the load capacity of the explosion-proof conference television according to the simulated television heating data to obtain load capacity change data; perform curve fitting processing on the load capacity change data to obtain a bearing capacity change curve.

[0122] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0123] Step S31: performing data volume statistics on the standardized image stream according to the image feature quality to obtain the image data scale; performing resource demand calculation on the image data scale to obtain a resource demand set;

[0124] In this embodiment, data volume statistics are performed on the standardized image stream according to the image feature quality. First, the resolution, color depth and number of frames of each frame in the standardized image stream are extracted, and the size of each frame is calculated using an image data statistics tool (such as OpenCV). The size of each frame is multiplied by the total number of frames to obtain the image data scale. Subsequently, resource demand calculations are performed on the image data scale. Based on the transmission and processing requirements of the image stream, the required storage space, computing power and network bandwidth are estimated. A resource demand analysis model (such as a demand forecasting model) is used to generate a resource demand set, which includes storage requirements, processing power requirements and network bandwidth requirements, providing a detailed quantitative basis for subsequent resource allocation.

[0125] Step S32: performing demand response simulation on the resource demand set to generate simulated response demand data; performing load deduction on the standardized image flow based on the simulated response demand data to obtain equipment load data;

[0126] In this embodiment, a demand response simulation is performed on the resource demand set, and a resource demand response model is constructed using simulation software (such as MATLAB or Simulink) to simulate resource demand changes under different load conditions and generate simulated response demand data. Then, based on the simulated response demand data, a load deduction is performed on the standardized image stream, and a load analysis tool (such as load analysis software) is used to simulate and calculate the equipment load to obtain equipment load data. The equipment load data reflects the operating load of the equipment under different transmission conditions and image feature qualities, ensuring that the load changes of the equipment can be accurately evaluated in actual applications, providing important data support for subsequent thermal management and heating simulation.

[0127] Step S33: acquiring TV physical characteristic data; performing long-term load heating simulation on the TV physical characteristic data based on the equipment load data to generate simulated TV heating data;

[0128] In this embodiment, the physical characteristic data of the television is obtained, and the physical characteristics of the television are measured using data acquisition instruments (such as temperature sensors and power meters) to obtain parameters such as the power consumption, heat dissipation capacity and operating temperature of the device. Then, based on the device load data, a long-term load heating simulation is performed on the physical characteristic data of the television. The heating conditions of the television under different loads are modeled using thermal simulation software (such as ANSYS Thermal or COMSOL Multiphysics). The device load data is input for long-term operation simulation to generate simulated TV heating data. The simulation data includes the temperature change curve and heat distribution of the device under different loads, which provides a necessary basis for subsequent load capacity calculation.

[0129] Step S34: Calculate the load capacity of the explosion-proof conference television according to the simulated television heating data to obtain load capacity change data; perform curve fitting processing on the load capacity change data to obtain a bearing capacity change curve.

[0130] In this embodiment, the load capacity of the explosion-proof conference television is calculated based on the simulated television heating data. First, the simulated television heating data is analyzed, and the temperature data of the equipment under different load states is extracted. The thermal balance of the television is calculated by thermodynamic formulas to obtain the load capacity change data. Subsequently, the load capacity change data is subjected to curve fitting processing. A curve fitting tool (such as the SciPy library in Python) is used to analyze the data to generate a bearing capacity change curve. The curve clearly shows the change in the carrying capacity of the equipment under different loads, ensuring that the equipment can operate safely and stably under high load conditions, and providing a scientific basis for subsequent equipment optimization and improvement.

[0131] Preferably, step S32 includes the following steps:

[0132] Step S321: Calculate the processor occupancy of the resource requirement set according to the preset video conferencing parameters to obtain occupancy distribution data;

[0133] Step S322: performing resource competition simulation on the occupancy distribution data to obtain simulated response demand data;

[0134] Step S323: performing computational load calculation on the simulated response demand data to generate computational load data; performing data flow analysis on the standardized image stream to obtain flow distribution data;

[0135] Step S324: performing load matching processing on the computational load data and the flow distribution data to generate a load matching table; performing peak estimation on the load matching table to obtain peak estimation data;

[0136] Step S325: perform load aggregation processing on the peak estimation data to obtain a basic load set; perform time series integration on the basic load set to generate equipment load data.

[0137] In this embodiment, according to the preset conference video parameters, the processor occupancy calculation is performed on the resource demand set. First, the processor performance indicators are defined, including the main frequency, the number of cores and the cache size. The processor is benchmarked using a performance analysis tool (such as Intel VTune or AMD uProf) to determine the computing resources required for each image processing task. Then, each image processing task in the standardized image stream is matched with the processor performance. By analyzing the complexity of each task, the occupancy rate of each task on the processor is calculated, and occupancy distribution data is generated. The data includes the occupancy rate of each image processing task and the overall processor occupancy, ensuring that the operating status of the processor can be effectively evaluated under high load conditions. Resource competition simulation is performed on the occupancy distribution data, and a resource competition model is constructed using a simulation tool (such as AnyLogic or Simul8) to simulate the competition of multiple image processing tasks on the processor. The occupancy distribution data is input, the priority and execution time of the task are set, and multiple rounds of simulation are performed to observe the allocation and competition status of resources under different load conditions, and simulated response demand data is generated. The data includes the response time and resource occupancy of each task under different conditions, ensuring that the resource competition characteristics of the processor in actual applications can be truly reflected, providing a basis for subsequent calculation of computing load. The computational load is calculated for the simulated response demand data. First, the response time and occupancy rate of each task are analyzed. The computational load of each task on the processor per unit time is calculated through a mathematical model (such as a queuing theory model) to obtain the computational load data. Then, the data flow of the standardized image stream is analyzed. The data transmission in the system is monitored using a traffic analysis tool (such as Wireshark). The transmission rate, delay and packet loss rate of the image stream are extracted to generate traffic distribution data. The traffic distribution data includes the traffic changes in each time period to ensure that the subsequent load matching processing has comprehensive data support. The computational load data and traffic distribution data are load matched. First, the computational load data are compared with the traffic distribution data to identify the correlation between the two. The corresponding computational load and traffic data are matched through a load matching algorithm (such as a weighted matching algorithm) to generate a load matching table. The load matching table lists the computational load and the corresponding traffic situation in each time period. Then, the load matching table is peak estimated. The peak load under high load conditions is determined using statistical methods (such as the quantile method) to obtain peak estimation data. The peak estimation data provides a performance estimate of the system under extreme conditions, laying the foundation for subsequent load aggregation processing.The peak estimation data is load aggregated. First, the peak loads in each time period are aggregated to calculate the maximum load of the overall system and generate a basic load set. The basic load set contains the load boundaries of the system under different working conditions. Then, the basic load set is time-series integrated and time-series analysis tools (such as the pandas library in R or Python) are used to organize and integrate the load data. The load is weighted averaged based on time to generate equipment load data. The equipment load data reflects the load conditions of the equipment in each time period under preset conditions, providing a quantitative basis for subsequent load analysis and optimization.

[0138] Preferably, step S33 includes the following steps:

[0139] Step S331: acquiring TV physical characteristic data; performing power conversion calculation on the TV physical characteristic data to obtain a power distribution table; locating heat sources on the power distribution table to generate load heating position data;

[0140] Step S332: performing heat conduction simulation on the load heating position data to obtain a temperature rise conduction path; performing hot spot superposition analysis on the temperature rise conduction path to generate a hot spot distribution set;

[0141] Step S333: performing time series conversion on the hotspot distribution set to obtain time series thermal data; performing cumulative temperature rise calculation on the time series thermal data to generate temperature rise curve data; performing heat dissipation compensation inference on the temperature rise curve data to generate cumulative heat dissipation compensation heat value;

[0142] Step S334: Perform load heating simulation on the accumulated heat dissipation compensation heat value to generate simulated TV heating data.

[0143] In this embodiment, the physical characteristic data of the television is obtained. First, the power consumption, heat dissipation capacity and operating temperature of the television are measured using measuring instruments (such as a power meter and a temperature probe), and the power consumption data of the device under different working conditions are recorded. Then, the physical characteristic data of the television is subjected to power conversion calculation. The measured power data is converted into a standardized format using a power conversion formula (such as P=VI, where P is power, V is voltage, and I is current). Subsequently, a power distribution table is generated, which lists the power consumption under different working modes. Next, the power distribution table is used to locate the heat source. A thermal imager (such as a FLIR thermal imager) is used to scan the television to identify the location of the heat source and generate load heating position data. The data includes the specific location of each heat source and the corresponding power value, providing a basis for subsequent heat conduction analysis. Perform heat conduction simulation on the load heating position data. First, use heat conduction simulation software (such as ANSYS Thermal or COMSOL Multiphysics) to establish a heat conduction model of the TV, input the load heating position data, set material properties and boundary conditions, and perform heat conduction analysis to obtain the temperature rise conduction path. The path clearly shows how heat propagates inside the device. Then perform hotspot superposition analysis on the temperature rise conduction path. Use the superposition principle to integrate the thermal effects of each heat source and generate a hotspot distribution set. The hotspot distribution set includes the location, temperature and heat distribution of each hotspot, ensuring that the subsequent sequential thermal analysis can accurately reflect the thermal distribution characteristics of the equipment during operation. The hotspot distribution set is converted into a time series. First, the time interval of the time series is defined according to the working cycle of the equipment and the temperature change law, and the hotspot distribution data is converted into time series thermal data. The time series analysis tools (such as the pandas library in R or Python) are used to organize the data. Then, the cumulative temperature rise is calculated for the time series thermal data. The cumulative sum algorithm (Cumulative Sum) is used to calculate the temperature change at each time point to generate the temperature rise curve data. The temperature rise curve data shows the temperature change of the equipment during the entire working cycle. Then, the heat dissipation compensation is inferred for the temperature rise curve data. According to the heat dissipation characteristics of the equipment and environmental conditions, the heat dissipation at each time point is calculated to generate the cumulative heat dissipation compensation heat value to ensure that the temperature change of the equipment can be reasonably predicted under high load conditions. To simulate the load heating of the accumulated heat dissipation compensation heat value, first use thermal simulation software (such as MATLAB or Simulink) to build a load heating model, input the accumulated heat dissipation compensation heat value, simulate the heating of the TV under different load conditions, perform long-term operation simulation, and generate simulated TV heating data. The simulation data includes temperature change curves, heat distribution and heat dissipation efficiency under different working conditions, ensuring that the simulation results truly reflect the heating characteristics of the TV under high load, and ultimately provide a scientific basis for subsequent equipment optimization and thermal management strategies.

[0144] Preferably, step S4 comprises the following steps:

[0145] Step S41: splitting the bearing capacity change curve to obtain a segmented change curve; identifying inflection points of the segmented change curve to generate a curve inflection point distribution;

[0146] Step S42: performing load safety boundary mapping on the equipment load data based on the curve inflection point distribution to generate a boundary range set; performing critical calculation on the boundary range set to obtain a limit load point;

[0147] Step S43: performing overload calculation on the standardized image stream according to the limit load point to generate a load difference result; formulating segmentation and reorganization rules for the standardized image stream according to the load difference result to generate a data segmentation rule;

[0148] Step S44: based on the data segmentation rule, the standardized image stream is cut and reorganized to generate a low-load image stream.

[0149] In this embodiment, the bearing capacity change curve is split. First, a data analysis tool (such as Python's NumPy library or MATLAB) is used to process the bearing capacity change curve, and the curve is decomposed into multiple continuous line segments using linear interpolation to generate a segmented change curve. Then, the inflection point of the segmented change curve is identified, and an inflection point detection algorithm (such as the second-order derivative method) is used to analyze the slope change of the curve, identify the inflection point position, and then generate a curve inflection point distribution. The curve inflection point distribution lists in detail the coordinates of each inflection point and the corresponding bearing capacity value, providing key data for subsequent load safety boundary mapping, ensuring that the bearing capacity change range can be accurately identified under high load conditions. Based on the distribution of curve inflection points, the load safety boundary mapping of equipment load data is performed. First, the coordinates of the curve inflection points are associated with the equipment load data. The inflection point positions are mapped to the equipment load data using a mapping algorithm (such as interpolation mapping) to generate a boundary range set. The boundary range set contains the safe load range corresponding to each inflection point. Then, the boundary range set is critically calculated, and the limit load point of each inflection point is determined using a critical value analysis method (such as the maximum and minimum method) to obtain the limit load point. The limit load point is the maximum load capacity of the equipment under specific conditions, which provides an important reference for subsequent overload calculations. According to the limit load point, the overload calculation of the standardized image stream is performed. First, the load characteristics of the standardized image stream are analyzed, and the load analysis tool (such as load calculation software) is used to compare the actual load with the limit load point to generate a load difference result. The load difference result contains the amplitude and influencing factors of the overload in detail. Then, according to the load difference result, the standardized image stream is segmented and reorganized to formulate rules, and a data segmentation algorithm (such as a threshold-based segmentation algorithm) is used to generate a data segmentation rule. The data segmentation rule clarifies how to effectively segment the image stream under overload conditions to reduce the load and maintain data integrity. Based on the data segmentation rules, the standardized image stream is cut and reorganized. First, the standardized image stream is segmented according to the established segmentation rules, and each image block is extracted and reorganized using image processing tools (such as OpenCV) to generate a low-load image stream. Each image block in the low-load image stream is optimized to ensure that the load on the device is reduced while maintaining the visual quality. The final low-load image stream can be transmitted smoothly under high load conditions, providing a guarantee for the stable operation of the explosion-proof conference television.

[0150] Preferably, step S43 includes the following steps:

[0151] Step S431: Calculate the transmission load of the standardized image stream to obtain an image transmission load set;

[0152] Step S432: numerically compare the image transmission load set and the limit load point to generate a limit comparison difference; calculate the limit degree of the limit comparison difference to obtain the limit degree data;

[0153] Step S433: performing a difference evaluation on the standardized image flow and the extreme load point according to the over-limit degree data to generate a load difference result;

[0154] Step S434: performing a balance demand analysis on the load difference result to obtain a balance demand parameter; performing a resource allocation calculation on the balance demand parameter to generate resource allocation data;

[0155] Step S435: Formulate segmentation rules for the standardized image stream based on the resource allocation data to generate data segmentation rules.

[0156] In this embodiment, the transmission load calculation is performed on the standardized image stream. First, the calculation model of the transmission load is defined, including the size of each frame of the image, the transmission frequency and the network bandwidth. The load calculation software (such as NetworX or Wireshark) is used to analyze each frame of the image, calculate the total transmission volume within a specific time, and generate an image transmission load set. The load set contains the transmission load data in different time periods, including the peak load and the average load, so as to ensure that the transmission load characteristics of the image stream can be accurately evaluated in practical applications, and provide necessary data support for the subsequent overload calculation. The load data in the image transmission load set are compared with the limit load points one by one, and the limit comparison difference is generated using a statistical analysis tool (such as the pandas library in R or Python). The difference indicates the difference between the actual load and the limit load. Then, the overload degree of the limit comparison difference is calculated, and the threshold analysis method (Threshold Analysis) is used to calculate the overload degree data. The overload degree data will reflect the load overload situation in different time periods, ensuring that the overload problem can be effectively identified under high load conditions, and providing a basis for the subsequent load difference evaluation. Define the difference evaluation index, use similarity measurement methods (such as cosine similarity or Euclidean distance) to analyze the over-limit degree data and the load data of the image flow, and generate load difference results. The load difference results include the load difference value and over-limit status in each time period to ensure that the load changes can be fully reflected and provide detailed data support for subsequent balance demand analysis. Use demand forecasting models (such as linear regression models) to analyze load difference data, identify the causes of load imbalance, and generate balance demand parameters. The balance demand parameters include the difference between load demand and actual supply in each time period. Then, perform resource allocation calculations on the balance demand parameters, use resource allocation algorithms (such as optimal allocation algorithms) to calculate the resource allocation plans required under different load conditions, and generate resource allocation data. The resource allocation data lists the optimal resource configuration plans under various load conditions to ensure that the equipment can operate stably under high load conditions. Analyze the resource requirements in the resource allocation data, combine the load characteristics, formulate segmentation rules, use segmentation algorithms (such as dynamic segmentation algorithms) to segment the standardized image stream into multiple data blocks that adapt to different load conditions, and generate data segmentation rules. The data segmentation rules clarify how to adjust the structure of the image stream under different load conditions to ensure that the system can still run efficiently under high load conditions. The final generated segmentation rules will provide guidance for subsequent data transmission and processing.

[0157] Preferably, step S5 comprises the following steps:

[0158] Step S51: performing transmission network topology connection on the low-load image stream to obtain a network connection diagram; performing conflict detection on the network connection diagram to generate a conflict detection diagram;

[0159] Step S52: performing path redistribution on the network connection graph based on the conflict detection graph to obtain a path allocation matrix;

[0160] Step S53: Design the transmission timing of the path allocation matrix to obtain transmission timing parameters; build a conference link for the path allocation matrix based on the transmission timing parameters to generate a conference transmission link;

[0161] Step S54: Based on the conference transmission link, the low-load image stream is transmitted in real time to implement the explosion-proof conference television implementation method.

[0162] In this embodiment, a network topology design tool (such as Cisco Packet Tracer or GNS3) is used to construct a network connection diagram, which includes various network nodes and connection lines. The nodes represent conference televisions, switches and routers, and the connection lines represent data transmission paths. Then, the network connection diagram is subjected to conflict detection, and a conflict detection algorithm (such as CSMA / CD algorithm) is used to analyze the conflict points in the network to generate a conflict detection diagram. The conflict detection diagram clearly lists the data packet conflicts and bandwidth competition in the network, providing a basis for subsequent path reallocation, ensuring that network conflict problems can be effectively identified and resolved in practical applications. The conflict nodes in the conflict detection diagram are analyzed, and the network connection diagram is replanned using a path optimization algorithm (such as Dijkstra algorithm) to ensure that conflict points are avoided during the reallocation process, and a path allocation matrix is ​​generated. The path allocation matrix lists in detail the optimized connection relationship between each node, including transmission delay and bandwidth allocation, ensuring that efficient data transmission can be achieved under network load conditions, and providing data support for subsequent transmission timing design. According to the network topology and the connection of the path allocation matrix, define the time window for data transmission, use the timing analysis tool (such as MATLAB or Simulink) to generate the transmission timing parameters, which include the transmission start time, transmission duration and corresponding node delay of each data packet, build the conference link for the path allocation matrix based on the transmission timing parameters, use the link construction tool (such as OpenFlow or SDN controller) to establish the conference transmission link, generate the conference transmission link, which includes the connection status of each node and its data transmission path, and ensure that the conference communication is carried out smoothly within the predefined time. Configure the network settings of the conference TV to ensure that the parameters of the device and the conference transmission link are consistent, use the real-time transmission protocol (such as RTP or RTSP) to set up data transmission, and then start the real-time transmission of the low-load image stream, execute the explosion-proof conference TV implementation method, ensure that each conference participant can receive and transmit the image stream data in real time, monitor the network status of the system and the transmission of the data packet, use the network monitoring tool (such as Wireshark) for real-time tracking, and adjust the transmission parameters in time to ensure the smooth progress of the meeting, and finally achieve efficient and secure conference communication.

[0163] The present invention also provides an explosion-proof video conference system for executing the above-mentioned explosion-proof video conference implementation method, and the explosion-proof video conference system comprises:

[0164] The data calibration module is used to collect multi-source images of the conference scene to obtain the original conference image stream; perform displacement compensation processing on the original conference image stream to generate a calibration image stream; perform multi-resolution fusion processing on the calibration image stream to obtain a standardized image stream;

[0165] The quality detection module is used to simulate the queue transmission of the standardized image stream to generate conference communication simulation data; perform feature quality inspection on the conference communication simulation data to generate image feature quality;

[0166] The load analysis module is used to deduce the equipment load of the standardized image stream according to the image feature quality to obtain the equipment load data; based on the equipment load data, the machine bearing capacity analysis of the explosion-proof conference TV is performed to obtain the bearing capacity change curve;

[0167] The load reduction module is used to screen the equipment load data for limit points according to the bearing capacity change curve to generate the limit load points; load reduction segmentation of the standardized image stream is performed according to the limit load points to generate the low-load image stream;

[0168] The image transmission module is used to reallocate the paths of low-load image streams to obtain a path allocation matrix; and to perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

[0169] The present invention uses a data calibration module to perform multi-source image acquisition on the conference scene to ensure that the original conference image stream obtained has high quality and integrity. The displacement compensation process effectively eliminates jitter and blur in the image. The generated calibration image stream is processed by multi-resolution fusion to provide a clear standardized image stream, which lays a solid foundation for subsequent image analysis and processing. The quality detection module performs queue transmission simulation on the standardized image stream, and the generated conference communication simulation data provides real data support for subsequent performance evaluation. By performing feature quality inspection on the simulated data, the image feature quality index obtained ensures that high-quality image output can be maintained during the transmission process, thereby improving the overall visual effect of the conference. The load analysis module deduces the equipment load through the image feature quality, and the obtained equipment load data provides an important basis for the subsequent bearing capacity analysis. The bearing capacity change curve intuitively shows the performance of the equipment under different loads, ensuring that the equipment can operate safely and stably under high load conditions, reducing the risk of failure. The load reduction module screens the extreme load points according to the bearing capacity change curve, and the generated extreme load points ensure that the operating status of the equipment can be effectively managed under high load conditions. By load reduction segmentation of the standardized image stream, the generated low-load image stream helps to optimize data transmission and reduce the operating pressure of the equipment. The image transmission module reallocates the paths of low-load image streams, and the generated path allocation matrix provides optimized path selection for real-time transmission, ensuring efficient and stable transmission of image data under different network conditions, providing guarantee for the smooth operation of explosion-proof conference television, and improving the communication efficiency of the meeting and the accuracy of information transmission.

[0170] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0171] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for realizing explosion-proof video conferencing, characterized in that: The following steps are involved: Step S1: performing multi-source image acquisition on the conference scene to obtain an original conference image stream; performing displacement compensation processing on the original conference image stream to generate a calibrated image stream; performing multi-resolution fusion processing on the calibrated image stream to obtain a standardized image stream; Step S2: performing queue transmission simulation on the standardized image stream to generate conference communication simulation data; performing feature quality inspection on the conference communication simulation data to generate image feature quality; Step S3: Perform equipment load deduction on the standardized image stream according to the image feature quality to obtain equipment load data; perform machine bearing capacity analysis on the explosion-proof conference television based on the equipment load data to obtain a bearing capacity change curve; wherein step S3 includes the following steps: Step S31: performing data volume statistics on the standardized image stream according to the image feature quality to obtain the image data scale; performing resource demand calculation on the image data scale to obtain a resource demand set; Step S32: performing demand response simulation on the resource demand set to generate simulated response demand data; performing load deduction on the standardized image flow based on the simulated response demand data to obtain equipment load data; Step S33: acquiring TV physical characteristic data; performing long-term load heating simulation on the TV physical characteristic data based on the equipment load data to generate simulated TV heating data; Step S34: Calculate the load capacity of the explosion-proof conference TV according to the simulated TV heating data to obtain load capacity change data; perform curve fitting processing on the load capacity change data to obtain a bearing capacity change curve; Step S4: screening the equipment load data for limit points according to the bearing capacity change curve to generate limit load points; performing load reduction segmentation on the standardized image stream according to the limit load points to generate a low-load image stream; Step S5: reallocate the paths of the low-load image streams to obtain a path allocation matrix; perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

2. The method for realizing explosion-proof video conferencing according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using a conference television camera to perform multi-source image acquisition on the conference scene to obtain an original conference image stream; extracting feature points from the original conference image stream to obtain a feature point data set; Step S12: Calculate the relative position relationship of the conference video camera based on the feature point data set to obtain a space mapping matrix; Step S13: performing spatial geometric transformation on the original conference image stream according to the spatial mapping matrix to generate position correction data; performing edge compensation processing on the original conference image stream based on the position correction data to obtain a calibrated image stream; Step S14: performing multi-level scaling on the calibration image stream to obtain a multi-resolution version image stream; Step S15: performing cross-resolution feature registration on the multi-resolution version image stream to generate image registration parameters; Step S16: Perform progressive feature fusion on the multi-version images of the resolution according to the image registration parameters to obtain a standardized image stream.

3. The method for realizing explosion-proof video conferencing according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Arranging the transmission sequence of the standardized image stream to obtain an image transmission sequence; performing bandwidth allocation calculation on the image transmission sequence based on preset transmission bandwidth data to obtain a bandwidth allocation table; Step S22: sorting the standardized image streams in queue according to the bandwidth allocation table to obtain a transmission queue table; calculating the channel capacity of the transmission queue table to obtain a channel capacity parameter; Step S23: performing transmission simulation on the standardized image stream based on the channel capacity parameter to generate conference communication simulation data; Step S24: performing frame rate statistical analysis on the conference communication simulation data to obtain frame rate statistical data; performing feature quality evaluation on the conference communication simulation data according to the frame rate statistical data to generate image feature quality.

4. The method for realizing explosion-proof video conferencing according to claim 1, characterized in that: Step S32 includes the following steps: Step S321: Calculate the processor occupancy of the resource requirement set according to the preset video conferencing parameters to obtain occupancy distribution data; Step S322: performing resource competition simulation on the occupancy distribution data to obtain simulated response demand data; Step S323: performing computational load calculation on the simulated response demand data to generate computational load data; performing data flow analysis on the standardized image stream to obtain flow distribution data; Step S324: performing load matching processing on the computational load data and the flow distribution data to generate a load matching table; performing peak estimation on the load matching table to obtain peak estimation data; Step S325: perform load aggregation processing on the peak estimation data to obtain a basic load set; perform time series integration on the basic load set to generate equipment load data.

5. The method for realizing explosion-proof video conferencing according to claim 1, characterized in that: Step S33 includes the following steps: Step S331: acquiring TV physical characteristic data; performing power conversion calculation on the TV physical characteristic data to obtain a power distribution table; locating heat sources on the power distribution table to generate load heating position data; Step S332: performing heat conduction simulation on the load heating position data to obtain a temperature rise conduction path; performing hot spot superposition analysis on the temperature rise conduction path to generate a hot spot distribution set; Step S333: performing time series conversion on the hotspot distribution set to obtain time series thermal data; performing cumulative temperature rise calculation on the time series thermal data to generate temperature rise curve data; performing heat dissipation compensation inference on the temperature rise curve data to generate cumulative heat dissipation compensation heat value; Step S334: Perform load heating simulation on the accumulated heat dissipation compensation heat value to generate simulated TV heating data.

6. The method for realizing explosion-proof video conferencing according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: splitting the bearing capacity change curve to obtain a segmented change curve; identifying inflection points of the segmented change curve to generate a curve inflection point distribution; Step S42: performing load safety boundary mapping on the equipment load data based on the curve inflection point distribution to generate a boundary range set; performing critical calculation on the boundary range set to obtain a limit load point; Step S43: performing overload calculation on the standardized image stream according to the limit load point to generate a load difference result; formulating segmentation and reorganization rules for the standardized image stream according to the load difference result to generate a data segmentation rule; Step S44: based on the data segmentation rule, the standardized image stream is cut and reorganized to generate a low-load image stream.

7. The method for realizing explosion-proof video conferencing according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: Calculate the transmission load of the standardized image stream to obtain an image transmission load set; Step S432: numerically compare the image transmission load set and the limit load point to generate a limit comparison difference; calculate the limit degree of the limit comparison difference to obtain the limit degree data; Step S433: performing a difference evaluation on the standardized image flow and the extreme load point according to the over-limit degree data to generate a load difference result; Step S434: performing a balance demand analysis on the load difference result to obtain a balance demand parameter; performing a resource allocation calculation on the balance demand parameter to generate resource allocation data; Step S435: Formulate segmentation rules for the standardized image stream based on the resource allocation data to generate data segmentation rules.

8. The method for realizing explosion-proof video conferencing according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing transmission network topology connection on the low-load image stream to obtain a network connection diagram; performing conflict detection on the network connection diagram to generate a conflict detection diagram; Step S52: performing path redistribution on the network connection graph based on the conflict detection graph to obtain a path allocation matrix; Step S53: Design the transmission timing of the path allocation matrix to obtain transmission timing parameters; build a conference link for the path allocation matrix based on the transmission timing parameters to generate a conference transmission link; Step S54: Based on the conference transmission link, the low-load image stream is transmitted in real time to implement the explosion-proof conference television implementation method.

9. An explosion-proof video conference system, characterized in that: Used to execute the explosion-proof video conferencing implementation method as claimed in claim 1, the explosion-proof video conferencing system comprises: The data calibration module is used to collect multi-source images of the conference scene to obtain the original conference image stream; perform displacement compensation processing on the original conference image stream to generate a calibration image stream; perform multi-resolution fusion processing on the calibration image stream to obtain a standardized image stream; The quality detection module is used to simulate the queue transmission of the standardized image stream to generate conference communication simulation data; perform feature quality inspection on the conference communication simulation data to generate image feature quality; The load analysis module is used to deduce the equipment load of the standardized image stream according to the image feature quality to obtain the equipment load data; based on the equipment load data, the machine bearing capacity analysis of the explosion-proof conference TV is performed to obtain the bearing capacity change curve; The load reduction module is used to screen the equipment load data for limit points according to the bearing capacity change curve to generate the limit load points; load reduction segmentation of the standardized image stream according to the limit load points to generate the low-load image stream; The image transmission module is used to reallocate the paths of low-load image streams to obtain a path allocation matrix; and to perform real-time transmission conference links on the low-load image streams according to the path allocation matrix to implement the explosion-proof conference television implementation method.

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