Bandwidth real-time stream storage method and system for million WIFI bulb camera

Abstract

The invention relates to the field of modern security and protection, and provides a bandwidth real-time stream storage method for a million WIFI bulb camera, which comprises the following steps of: firstly, monitoring a network bandwidth condition in real time, collecting and extracting key data values, analyzing a bandwidth use trend to carry out performance evaluation to obtain a cascade and a performance index, and analyzing image transmission quality and bandwidth requirements based on the cascade and the performance index; the method comprises the following steps: determining a storage path, calculating storage efficiency, optimizing shooting parameters, calculating a bandwidth saving rate, extracting multi-bandwidth characteristics, querying bandwidth fluctuation distribution and fluctuation labels, and finally generating a bandwidth real-time stream storage scheme. The storage efficiency of the video stream can be improved.

Description

Technical Field

[0001] The present invention relates to the field of modern security, and in particular to a bandwidth real-time stream storage method and system for a million WIFI light bulb cameras. Background Art

[0002] In the field of modern security, with the continuous advancement of technology and people's growing demand for security, efficient monitoring equipment has become crucial. Million WIFI light bulb cameras, as a new type of intelligent monitoring equipment, play an important role in homes, businesses and other places.

[0003] At present, traditional storage methods are often relatively simple, mainly relying on fixed storage devices and limited bandwidth allocation, and are unable to efficiently and in real time store large amounts of video stream data. This method not only has limited storage capacity, but is also prone to freezes and data loss when facing highly concurrent video streams, affecting the reliability of monitoring. In addition, when processing complex video stream data, existing methods lack intelligent bandwidth allocation and storage management methods, making it difficult to fully utilize limited bandwidth resources to meet monitoring needs in different scenarios. Therefore, a bandwidth real-time stream storage method based on million WIFI light bulb cameras is needed to improve the storage efficiency of video streams. Summary of the invention

[0004] The present invention provides a bandwidth real-time stream storage method and system for a million WIFI light bulb camera, the main purpose of which is to improve the storage efficiency of video streams.

[0005] To achieve the above purpose, the present invention provides a bandwidth real-time stream storage method for a million WIFI light bulb cameras, comprising:

[0006] Monitor the network bandwidth corresponding to the million WIFI light bulb cameras in real time, collect bandwidth usage data corresponding to the network bandwidth, and extract key data values ​​from the bandwidth usage data;

[0007] Analyze the bandwidth usage trend corresponding to the key data value, perform performance evaluation on the bandwidth usage trend to obtain performance evaluation data, perform gradient division on the performance evaluation data to obtain performance evaluation levels, and calculate the performance index corresponding to the performance evaluation levels;

[0008] Based on the performance index, analyze the image transmission quality corresponding to the million WIFI light bulb camera, perform bandwidth demand analysis on the image transmission quality, obtain a bandwidth demand analysis value, query the bandwidth allocation strategy corresponding to the bandwidth demand analysis value, and mark the key allocation area corresponding to the bandwidth allocation strategy;

[0009] Based on the key allocation area, determine the storage path corresponding to the million WIFI light bulb camera, analyze the path transmission rate corresponding to the storage path, calculate the storage efficiency corresponding to the storage path based on the path transmission rate, optimize the shooting parameters corresponding to the million WIFI light bulb camera based on the storage efficiency, and calculate the bandwidth saving rate corresponding to the shooting parameters;

[0010] Based on the bandwidth saving rate, multiple bandwidth features corresponding to the million WIFI light bulb cameras are extracted; based on the multiple bandwidth features, the bandwidth fluctuation distribution inside the million WIFI light bulb cameras is queried; the fluctuation labels corresponding to the bandwidth fluctuation distribution are analyzed; based on the fluctuation labels, a bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras is generated.

[0011] Optionally, the collecting bandwidth usage data corresponding to the network bandwidth situation includes:

[0012] Analyzing initial traffic data in the network bandwidth situation;

[0013] Querying the flow rate change pattern corresponding to the initial flow rate data;

[0014] Extracting a sudden flow segment in the flow variation law;

[0015] Based on the traffic mutation segment, determining a bandwidth fluctuation point in the network bandwidth situation;

[0016] Based on the bandwidth fluctuation point, bandwidth usage data corresponding to the network bandwidth situation is collected.

[0017] Optionally, the step of performing gradient division on the performance evaluation data to obtain performance evaluation levels includes:

[0018] Analyzing the degree of difference between the performance evaluation data and the preset performance standard;

[0019] Based on the degree of difference, the performance evaluation data is classified and sorted to obtain a sorting result;

[0020] Determine the performance range of the data in the sorting result;

[0021] Based on the performance interval range, determining a gradient rule corresponding to the performance evaluation data;

[0022] Based on the gradient rule, the performance evaluation data is gradient divided to obtain performance evaluation levels.

[0023] Optionally, the calculating the performance index corresponding to the performance evaluation level includes:

[0024] The performance index corresponding to the performance evaluation level is calculated using the following formula:

[0025]

[0026] Wherein, PI represents the performance index corresponding to the performance evaluation level, n represents the total number of sub-levels of the performance evaluation level, i represents the number index corresponding to the sub-level, and w i represents the weight coefficient corresponding to the i-th sub-level, S i represents the evaluation value corresponding to the i-th sub-level, t 1 and t 2 They respectively represent the evaluation start time and the evaluation end time corresponding to the evaluation time of the performance evaluation level, and B(t) represents the dynamic performance index at time t.

[0027] Optionally, performing bandwidth requirement analysis on the image transmission quality to obtain a bandwidth requirement analysis value includes:

[0028] Analyzing quality parameter indicators corresponding to the image transmission quality;

[0029] Based on the quality parameter indicator, determining a bandwidth requirement range corresponding to the image transmission quality;

[0030] Based on the bandwidth requirement range, filtering bandwidth interval data corresponding to the image transmission quality;

[0031] Querying bandwidth mapping requirements corresponding to the bandwidth interval data;

[0032] Based on the bandwidth mapping requirement, a bandwidth requirement analysis is performed on the image transmission quality to obtain a bandwidth requirement analysis value.

[0033] Optionally, the calculating the storage efficiency corresponding to the storage path based on the path transmission rate includes:

[0034] The storage efficiency corresponding to the storage path is calculated using the following formula:

[0035]

[0036] Wherein, EV represents the storage efficiency corresponding to the storage path, t' 2 and t' 1 They represent the start time and end time of calculating storage efficiency, T'(t') represents the path transmission rate at time t', n' represents the number of data blocks corresponding to the storage path, i' represents the number index of data blocks, and V' i' represents the volume of the i'th data block, P' represents the stability index corresponding to the storage path, and Q' represents the interference factor index corresponding to the storage path.

[0037] Optionally, the optimizing the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency includes:

[0038] Analyze the correlation characteristics between the storage efficiency and the amount of image data in the million WIFI light bulb cameras;

[0039] Based on the correlation characteristics, identifying the change characteristics corresponding to the amount of image data in the million WIFI light bulb cameras;

[0040] Conducting an in-depth analysis on the change characteristics to obtain detailed analysis results;

[0041] Extracting key influencing factors from the detailed analysis results;

[0042] Based on the key influencing factors, the shooting parameters corresponding to the million WIFI light bulb cameras are optimized.

[0043] Optionally, the calculating the bandwidth saving rate corresponding to the shooting parameter includes:

[0044] The bandwidth saving rate corresponding to the shooting parameters is calculated using the following formula:

[0045]

[0046] Wherein, BV represents the bandwidth saving rate corresponding to the shooting parameters, R 0 represents the average bandwidth usage corresponding to the original shooting parameters, R p represents the average bandwidth occupancy corresponding to the optimized shooting parameters, m represents the number of shooting time periods corresponding to the shooting parameters, j represents the number index corresponding to the shooting time period, and F j represents the reduction in bandwidth occupancy in the jth shooting time period, and Mt represents the adjustment coefficient corresponding to the shooting parameter.

[0047] Optionally, extracting multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate includes:

[0048] Conducting an in-depth analysis on the bandwidth saving rate to obtain an analysis result;

[0049] Querying bandwidth related data in the analysis result;

[0050] Based on the bandwidth-related data, determine the feature extraction direction corresponding to the million WIFI light bulb cameras;

[0051] Analyzing potential feature performance corresponding to the feature extraction direction;

[0052] Based on the potential feature representation, multi-bandwidth features corresponding to the million WIFI light bulb cameras are extracted.

[0053] Optionally, in order to solve the above problem, the present invention provides a bandwidth real-time stream storage system for a million WIFI light bulb cameras, the system comprising:

[0054] A data extraction module is used to monitor the network bandwidth corresponding to the million WIFI light bulb cameras in real time, collect bandwidth usage data corresponding to the network bandwidth, and extract key data values ​​from the bandwidth usage data;

[0055] An index calculation module, used for analyzing the bandwidth usage trend corresponding to the key data value, performing performance evaluation on the bandwidth usage trend to obtain performance evaluation data, performing gradient division on the performance evaluation data to obtain performance evaluation levels, and calculating the performance index corresponding to the performance evaluation levels;

[0056] A region marking module is used to analyze the image transmission quality corresponding to the million WIFI light bulb camera based on the performance index, perform bandwidth demand analysis on the image transmission quality, obtain a bandwidth demand analysis value, query a bandwidth allocation strategy corresponding to the bandwidth demand analysis value, and mark a key allocation area corresponding to the bandwidth allocation strategy;

[0057] A saving rate calculation module, used for determining the storage path corresponding to the million WIFI light bulb cameras based on the key allocation area, analyzing the path transmission rate corresponding to the storage path, calculating the storage efficiency corresponding to the storage path based on the path transmission rate, optimizing the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency, and calculating the bandwidth saving rate corresponding to the shooting parameters;

[0058] The solution generation module is used to extract the multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate, query the bandwidth fluctuation distribution inside the million WIFI light bulb cameras based on the multi-bandwidth features, analyze the fluctuation labels corresponding to the bandwidth fluctuation distribution, and generate the bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras based on the fluctuation labels.

[0059] Firstly, by real-time monitoring of the network bandwidth corresponding to the million WIFI light bulb cameras, the present invention can timely understand the operation status of the current network and ensure the stability of the video stream transmission of the camera. In the case of limited bandwidth, it can also issue an early warning so as to take corresponding measures, thereby better planning and managing network resources, avoiding freezes and data loss caused by insufficient bandwidth, and ensuring the reliability of monitoring. At the same time, by analyzing the bandwidth usage trend corresponding to the key data value, the present invention can predict the changes in network bandwidth demand in advance, which is helpful to optimize resource allocation and reasonably arrange the working mode of the camera. When the network bandwidth is relatively abundant, high-definition video transmission or data backup is performed, and when the bandwidth is tight, the video quality is appropriately reduced or the data transmission volume is reduced, thereby improving the utilization efficiency of network resources. Based on the performance index, the present invention analyzes the image transmission quality corresponding to the million WIFI light bulb cameras, which is helpful to timely discover potential problems, such as whether there is image blur, freeze or Signal interruption and other situations, so as to take targeted measures for optimization and improvement. Secondly, through the analysis of performance index, it can provide strong data support for the debugging and optimization of the equipment. The present invention determines the storage path corresponding to the million WIFI light bulb cameras based on the key allocation area, which can reduce the delay and packet loss rate in the data transmission process, and can transmit the images captured by the camera to the storage device faster, which helps to optimize the allocation of storage resources, thereby improving the overall performance and scalability of the storage system. Further, the present invention extracts the multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate, which helps to make more precise adjustments to the camera's shooting parameters according to actual conditions to achieve the best bandwidth utilization efficiency. It can determine whether it is necessary to adjust the shooting parameters such as resolution and frame rate by analyzing the multi-bandwidth features, so as to quickly locate the problem and take corresponding measures to solve it, so as to avoid the problem from expanding and affecting the normal operation of the entire network. Therefore, the bandwidth real-time stream storage method and system of the million WIFI light bulb cameras proposed by the present invention can improve the storage efficiency of the video stream. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of a flow chart of a bandwidth real-time stream storage method for a million WIFI light bulb cameras provided by an embodiment of the present invention;

[0061] Figure 2 A schematic diagram of a module for implementing a bandwidth real-time stream storage system for a million WIFI light bulb cameras provided in an embodiment of the present invention.

[0062] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0064] The embodiment of the present application provides a bandwidth real-time stream storage method for a million WIFI light bulb cameras. The execution subject of the bandwidth real-time stream storage method for a million WIFI light bulb cameras includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the bandwidth real-time stream storage method for a million WIFI light bulb cameras can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0065] Embodiment 1:

[0066] Reference Figure 1 As shown, it is a flow chart of a method for storing bandwidth real-time stream of a million WIFI light bulb cameras provided by an embodiment of the present invention. In this embodiment, the method for storing bandwidth real-time stream of a million WIFI light bulb cameras includes:

[0067] S1. Real-time monitoring of the network bandwidth corresponding to the million WIFI light bulb cameras, collecting bandwidth usage data corresponding to the network bandwidth, and extracting key data values ​​from the bandwidth usage data.

[0068] The present invention can timely understand the operation status of the current network by real-time monitoring of the network bandwidth corresponding to the million WIFI light bulb cameras, ensure the stability of the video stream transmission of the camera, and can also give early warning when the bandwidth is limited so as to take corresponding measures, thereby better planning and managing network resources, avoiding freezes and data loss caused by insufficient bandwidth, and ensuring the reliability of monitoring.

[0069] Among them, the million WIFI light bulb camera refers to an intelligent camera with shooting pixels reaching the million level, WIFI connection function and an appearance similar to a light bulb. This camera can usually be installed in various places to provide both lighting function and video surveillance; the network bandwidth situation refers to the transmission capacity of the network channel that the camera relies on when transmitting video data, which includes the network's uplink bandwidth and downlink bandwidth. The uplink bandwidth determines the speed at which the camera transmits the captured video data to a storage device or a monitoring platform, and the downlink bandwidth affects the smoothness and clarity of the user when viewing the video. Optionally, the real-time monitoring of the network bandwidth corresponding to the million WIFI light bulb camera can be achieved through a traffic analysis algorithm, such as: using the NetFlow algorithm to collect the generated video stream traffic information, including the source IP address, destination IP address, port number, protocol type, and traffic size, etc., so as to infer the usage of the network bandwidth.

[0070] Furthermore, the present invention helps to rationally plan network resources and ensure stable and smooth video transmission of the camera by collecting bandwidth usage data corresponding to the network bandwidth situation. It can analyze the camera usage pattern and network traffic trend so as to predict possible network congestion in advance and take corresponding preventive measures, such as adjusting the camera settings, increasing the network bandwidth or optimizing the network architecture.

[0071] Among them, the bandwidth usage data refers to the detailed data on the actual network bandwidth occupied by the million WIFI light bulb cameras obtained after analyzing and processing the network bandwidth situation, including bandwidth usage at different time points, average bandwidth usage value, peak bandwidth and other information.

[0072] As an embodiment of the present invention, the collecting of bandwidth usage data corresponding to the network bandwidth situation includes: analyzing initial traffic data in the network bandwidth situation; querying the traffic change pattern corresponding to the initial traffic data; extracting the traffic mutation segment in the traffic change pattern; based on the traffic mutation segment, determining the bandwidth fluctuation point in the network bandwidth situation; based on the bandwidth fluctuation point, collecting the bandwidth usage data corresponding to the network bandwidth situation.

[0073] Among them, the initial traffic data refers to the original data volume information transmitted by the million WIFI light bulb cameras through the network within a specific time period, including the upstream and downstream traffic values, etc., which reflects the most basic network data transmission situation of the camera without further analysis and processing; the traffic change law refers to the trend and pattern presented by the initial traffic data over time, such as the increase and decrease of traffic in different time periods, periodic changes, whether there is a sudden increase or decrease, etc.; the traffic mutation segment refers to the time period in the traffic change law when the traffic data shows obvious and large-scale rapid changes, which is the interval of rapid traffic changes caused by certain specific operations of the camera (such as adjusting the resolution, starting or stopping recording, etc.) or sudden changes in the network environment; the bandwidth fluctuation point refers to the specific location or time point of bandwidth instability caused by factors such as traffic mutation segments in the network bandwidth situation.

[0074] Further, the analysis of the initial traffic data in the network bandwidth situation can be implemented through a network traffic analysis tool, such as: using Wireshark to capture network data packets, and determining the traffic data generated by the million WIFI light bulb cameras by analyzing the data packets; the query of the traffic change law corresponding to the initial traffic data can be implemented through a data visualization tool, such as: using the Tableau tool to display the initial traffic data in the form of a chart, and intuitively observe the traffic change law over time; the extraction of the traffic mutation segment in the traffic change law can be implemented by an anomaly detection algorithm, such as: using the isolation forest algorithm to detect anomalies different from the normal traffic pattern, so as to determine the traffic mutation segment; the determination of the bandwidth fluctuation point in the network bandwidth situation can be implemented by a network performance monitoring tool, such as: using the Nagios tool to monitor the network bandwidth usage in real time, and issue an alarm when the bandwidth fluctuates greatly, so as to determine the bandwidth fluctuation point; the collection of the bandwidth usage data corresponding to the network bandwidth situation can be implemented through a programming interface, such as: the API provided by the network device, and by calling these interfaces, the real-time usage data of the network bandwidth can be obtained.

[0075] By extracting key data values ​​from the bandwidth usage data, the present invention can intuitively grasp the peak value of the network bandwidth, thereby determining the transmission bottleneck of the camera in a specific period of time, so as to prepare countermeasures in advance, such as adjusting shooting parameters or increasing network bandwidth resources, thereby facilitating timely troubleshooting and repair, and ensuring the stable operation of the monitoring system.

[0076] Among them, the key data values ​​refer to key values ​​for evaluating network bandwidth status and camera performance, which are screened out from bandwidth usage data. These key data values ​​usually include peak bandwidth in bandwidth usage data, that is, the maximum bandwidth occupied by the camera during data transmission, which can help determine whether the network can withstand the load of the camera in certain special circumstances (such as high-definition video transmission, high frame rate recording), and avoid video freeze or loss caused by network congestion. Optionally, the extraction of key data values ​​in the bandwidth usage data can be achieved through data value extraction tools, such as SQL and other tools.

[0077] S2. Analyze the bandwidth usage trend corresponding to the key data value, perform performance evaluation on the bandwidth usage trend to obtain performance evaluation data, perform gradient division on the performance evaluation data to obtain performance evaluation levels, and calculate the performance index corresponding to the performance evaluation levels.

[0078] By analyzing the bandwidth usage trend corresponding to the key data value, the present invention can predict the change of network bandwidth demand in advance, which is helpful to optimize resource allocation and reasonably arrange the working mode of the camera. When the network bandwidth is relatively abundant, high-definition video transmission or data backup is performed, and when the bandwidth is tight, the video quality is appropriately reduced or the data transmission volume is reduced, thereby improving the utilization efficiency of network resources.

[0079] The bandwidth usage trend refers to the change direction and pattern of the network bandwidth usage of the million WIFI light bulb cameras over a period of time, which may include an increase, decrease or stability in bandwidth usage over time, and may also reflect the fluctuation of bandwidth usage in different time periods and scenarios. Optionally, the analysis of the bandwidth usage trend corresponding to the key data value can be achieved through data visualization tools, such as: plotting the key data values ​​(such as bandwidth usage, bandwidth peak, etc.) into a line graph according to the time series using tools such as Excel and Tableau, and observing the direction of the line in the line graph to intuitively analyze the bandwidth usage trend.

[0080] Furthermore, the present invention obtains performance evaluation data by performing performance evaluation on the bandwidth usage trend, and can clearly know whether the network bandwidth can stably support the operation of the camera and its adaptability in different time periods. It can also judge whether it is necessary to increase the network bandwidth, upgrade the network equipment or reallocate network resources based on these data to better meet the needs of subsequent camera shooting.

[0081] The performance evaluation data refers to a series of data sets that can reflect network performance-related characteristics and indicators obtained through comprehensive analysis and quantitative evaluation of bandwidth usage trends, including bandwidth utilization data, bandwidth stability indicators, network delay data and other data. Optionally, the performance evaluation of the bandwidth usage trend can be achieved through network performance testing tools, such as: Iperf, Netperf and other tools.

[0082] Furthermore, the present invention obtains performance evaluation levels by gradient division of the performance evaluation data, which can clearly show the performance of the network at different levels, such as distinguishing different levels of performance states such as excellent, good, medium and poor, and can quickly locate the current position of the network bandwidth, so as to take targeted measures to optimize or adjust it.

[0083] Among them, the performance evaluation ladder refers to the different levels or layers obtained by dividing the performance evaluation data according to the gradient rule. For example, it can be divided into different levels such as "level one (high performance)", "level two (medium performance)", and "level three (low performance)".

[0084] As an embodiment of the present invention, the gradient division of the performance evaluation data to obtain performance evaluation levels includes: analyzing the degree of difference between the performance evaluation data and a preset performance standard; based on the degree of difference, classifying and sorting the performance evaluation data to obtain a sorting result; determining a performance interval range of the data in the sorting result; based on the performance interval range, determining a gradient rule corresponding to the performance evaluation data; based on the gradient rule, gradient division of the performance evaluation data to obtain performance evaluation levels.

[0085] Among them, the preset performance standard refers to a set of performance indicator thresholds or ranges pre-set according to the normal operation requirements of the million WIFI light bulb cameras, network service quality requirements and relevant technical specifications, which may include standards in multiple aspects such as the reasonable range of bandwidth utilization, the maximum acceptable value of network delay, and the upper limit of packet loss rate; the degree of difference refers to the quantitative expression of the deviation amount or degree of deviation between the performance evaluation data and the preset performance standard. For example, if the preset bandwidth utilization standard is 60%, and the bandwidth utilization actually measured is 75%, then the degree of difference is 15% (75%-60%); the sorting result refers to the order obtained by arranging the performance evaluation data according to the degree of difference between the performance evaluation data and the preset performance standard. For example, for a set of bandwidth utilization performance evaluation data, after sorting according to the degree of difference from the preset standard, the order of bandwidth utilization data at each time point or in different scenarios is obtained; the performance interval range refers to dividing the performance evaluation data into several continuous intervals according to the sorting result, and each interval represents a performance level range. For example, for bandwidth utilization The performance evaluation data can be divided into different performance ranges such as "excellent" (bandwidth utilization between 40% and 50%), "good" (between 50% and 60%), "average" (between 60% and 70%), "poor" (between 70% and 80%), and "extremely poor" (above 80%) according to the sorting results and actual conditions; the gradient rule refers to a rule or method for dividing the performance evaluation data into different performance levels according to a certain order and step size. For example, the gradient rule can stipulate that one performance level is increased every time the difference degree increases by a certain proportion (such as 10%), or the levels are divided according to the boundary value of the performance interval range, so that each level represents a relatively clear performance state.

[0086] Further, the analysis of the difference between the performance evaluation data and the preset performance standard can be achieved by a difference calculation method, such as: for each performance evaluation indicator (such as bandwidth utilization, delay, etc.), the corresponding preset standard value is subtracted from the actual evaluation data to obtain the absolute difference degree; the classification and sorting of the performance evaluation data can be achieved by a bubble sorting method, such as: comparing two adjacent data, exchanging them if the order is not correct, and finally achieving sorting after multiple traversals; the determination of the performance interval range of the data in the sorting result can be achieved by an equidistant division method, such as: first determining the minimum and maximum values ​​of the data, and then dividing the interval according to a certain interval distance; the determination of the gradient rule corresponding to the performance evaluation data can be achieved by a rule formulation method, such as: formulating a gradient rule by considering the impact of network performance on the video transmission quality, storage and other business functions of the million WIFI light bulb cameras; the gradient division of the performance evaluation data can be achieved by a lookup table method, such as: creating a lookup table, establishing a mapping relationship between different performance interval ranges or difference degree conditions and corresponding gradient levels, and determining the gradient level to which each performance evaluation data belongs by looking up this table.

[0087] By calculating the performance index corresponding to the performance evaluation level, the present invention can intuitively compare the performance advantages and disadvantages between different levels, and no longer rely solely on relatively vague level divisions to judge network conditions. For example, through the performance index, it can be clearly seen that the performance corresponding to the first-level performance level is several times that of the second-level level, or the specific value of the performance gap between two similar levels, thereby accurately evaluating the degree of change in network performance.

[0088] The performance index refers to a quantitative value used to comprehensively evaluate the overall performance level represented by the performance evaluation ladder. The performance index can help users intuitively understand the quality of network performance and provide an important basis for network optimization and resource allocation.

[0089] As an embodiment of the present invention, the calculating the performance index corresponding to the performance evaluation level includes:

[0090] The performance index corresponding to the performance evaluation level is calculated using the following formula:

[0091]

[0092] Wherein, PI represents the performance index corresponding to the performance evaluation level, n represents the total number of sub-levels of the performance evaluation level, i represents the number index corresponding to the sub-level, and w i represents the weight coefficient corresponding to the i-th sub-level, S i represents the evaluation value corresponding to the i-th sub-level, t 1 and t2 They respectively represent the evaluation start time and the evaluation end time corresponding to the evaluation time of the performance evaluation level, and B(t) represents the dynamic performance index at time t.

[0093] In detail, the sub-level refers to a further subdivision of the performance evaluation level. For example, when evaluating the network performance of the million WIFI light bulb cameras, the performance evaluation level can be divided into three levels: high, medium and low, and the sub-levels can include bandwidth utilization sub-level, delay sub-level, packet loss rate sub-level, etc.; the weight coefficient refers to the weight used to measure the relative importance of different sub-levels when calculating the performance index. For example, if the bandwidth utilization has a greater impact on the network performance, then the weight coefficient of the bandwidth utilization sub-level may be relatively high; the evaluation value refers to the quantitative evaluation result of the performance of each sub-level, reflecting the specific performance of each sub-level under the current network state; the evaluation time refers to the time period for performance evaluation, and the evaluation start time t 1 and the evaluation termination time t 2 The scope of this time period is determined; the dynamic performance indicator refers to a performance indicator that changes with time and is used to reflect the dynamic changes in network performance in the time dimension. For example, the usage of network bandwidth, the transmission rate of data packets, etc. may all be dynamic performance indicators.

[0094] S3. Based on the performance index, analyze the image transmission quality corresponding to the million WIFI light bulb cameras, perform bandwidth demand analysis on the image transmission quality to obtain a bandwidth demand analysis value, query the bandwidth allocation strategy corresponding to the bandwidth demand analysis value, and mark the key allocation area corresponding to the bandwidth allocation strategy.

[0095] The present invention analyzes the image transmission quality corresponding to the million WIFI light bulb cameras based on the performance index, which helps to timely discover potential problems, such as whether there is image blur, freeze or signal interruption, so as to take targeted measures for optimization and improvement. Secondly, through the analysis of the performance index, strong data support can be provided for the debugging and optimization of the equipment.

[0096] Among them, the image transmission quality refers to the comprehensive performance of the image captured by the million WIFI light bulb cameras in terms of clarity, smoothness, color accuracy, real-time and stability during the network transmission process. It specifically covers the clarity of the image when it is collected from the camera end and transmitted to the receiving end (such as mobile phones, computers and other display devices) through the WIFI network, that is, whether the image details are clearly distinguishable, and whether there are blurs, mosaics and other conditions that affect the viewing effect. Optionally, the analysis of the image transmission quality corresponding to the million WIFI light bulb cameras can be achieved through an image quality evaluation algorithm, such as using algorithms such as PSNR to compare the mean square error (MSE) between the original image and the transmitted image to calculate the image quality score.

[0097] Furthermore, by performing a bandwidth demand analysis on the image transmission quality to obtain a bandwidth demand analysis value, the present invention can reasonably plan the network architecture to ensure that the network infrastructure can meet the image transmission requirements of the million WIFI light bulb cameras, avoid problems such as image transmission freeze, delay or even loss due to insufficient bandwidth, thereby ensuring the smoothness and stability of image transmission.

[0098] The bandwidth requirement analysis value refers to a specific bandwidth value obtained by performing bandwidth requirement analysis on image transmission quality. This value represents the exact bandwidth required to achieve stable and high-quality image transmission under specific image transmission quality requirements.

[0099] As an embodiment of the present invention, the bandwidth requirement analysis of the image transmission quality to obtain a bandwidth requirement analysis value includes: analyzing a quality parameter indicator corresponding to the image transmission quality; determining a bandwidth requirement range corresponding to the image transmission quality based on the quality parameter indicator; screening bandwidth interval data corresponding to the image transmission quality based on the bandwidth requirement range; querying a bandwidth mapping requirement corresponding to the bandwidth interval data; and performing a bandwidth requirement analysis on the image transmission quality based on the bandwidth mapping requirement to obtain a bandwidth requirement analysis value.

[0100] The quality parameter index refers to a specific parameter used to measure the image transmission quality. It may include image resolution, frame rate, color depth, compression ratio, etc. For example, high-resolution images usually require more bandwidth for transmission, and high-frame-rate videos will also increase bandwidth requirements; the bandwidth requirement range refers to a rough bandwidth requirement interval determined according to the quality parameter index of image transmission quality, and this range represents the minimum and maximum values ​​of the required bandwidth under specific image transmission quality requirements; the bandwidth interval data refers to a data set of specific bandwidth value intervals related to image transmission quality that are further screened out after determining the bandwidth requirement range. For example, if the bandwidth requirement range is [10Mbps-20Mbps], then the bandwidth interval data may include specific bandwidth values ​​that meet the image transmission quality in different network environments and usage scenarios, such as 12Mbps, 15Mbps, 18Mbps, etc.; the bandwidth mapping requirement refers to the requirement to correspond and associate image transmission quality with specific bandwidth requirements, which may include determining how changes in image transmission quality affect changes in bandwidth requirements, and how to adjust image transmission quality according to different bandwidth conditions to achieve the best transmission effect.

[0101] Furthermore, the analysis of the quality parameter index corresponding to the image transmission quality can be achieved through image analysis tools, such as Adobe Photoshop, GIMP and other tools; the determination of the bandwidth requirement range corresponding to the image transmission quality can be achieved through a machine learning algorithm, such as using a regression algorithm to train a large amount of image transmission data and the corresponding bandwidth usage, and establish a model between image quality parameters and bandwidth requirements, so as to predict the bandwidth requirement range under different image qualities; the screening of bandwidth interval data corresponding to the image transmission quality can be achieved through a clustering algorithm, such as using a K-Means clustering algorithm to cluster the bandwidth data, and determine the cluster to which it belongs according to the requirements of the image transmission quality, so as to screen out the corresponding bandwidth interval data; the query of the bandwidth mapping requirement corresponding to the bandwidth interval data can be achieved through a data analysis tool, such as Excel, Tableau and other tools; the bandwidth requirement analysis of the image transmission quality can be achieved through an optimization algorithm, such as using a genetic algorithm, a particle swarm optimization and other algorithms, taking the image transmission quality and bandwidth requirements as optimization goals, and searching for the optimal bandwidth allocation solution through an algorithm.

[0102] The present invention queries the bandwidth allocation strategy corresponding to the bandwidth demand analysis value, marks the key allocation area corresponding to the bandwidth allocation strategy, and scientifically allocates limited network bandwidth resources according to actual needs, thereby avoiding problems such as image transmission jamming and blurring due to unreasonable bandwidth allocation. Secondly, marking the key allocation area corresponding to the bandwidth allocation strategy helps to more accurately manage and optimize the network.

[0103] The bandwidth allocation strategy refers to a method and scheme for reasonably allocating limited network bandwidth resources in order to meet the network requirements of different devices or applications. It may include allocating bandwidth according to the priority of the device, such as allocating more bandwidth to the image transmission task of the high-priority million WIFI bulb camera; or dynamically allocating according to different time stages, such as reducing the bandwidth of non-critical applications during peak hours to ensure the smoothness of image transmission of critical devices. The bandwidth allocation strategy may also involve the use of traffic shaping technology, bandwidth limitation technology and other means to achieve effective management and allocation of network bandwidth; the key allocation area refers to a specific area or part in the network that plays a key role in affecting the image transmission quality and needs to focus on allocating bandwidth resources, including certain key nodes in the network, such as core routers, switches and other devices; it may also be certain specific network links, which carry a large amount of image transmission data and have a high demand for bandwidth. The key allocation area may also be certain specific device groups, such as the area where a group of high-resolution million WIFI bulb cameras are located. These cameras have a large demand for bandwidth and are therefore determined as key allocation areas. Optionally, the bandwidth allocation strategy corresponding to the query bandwidth demand analysis value can be implemented through a network management tool, such as: SolarWinds, PRTG Network Monitor and other tools; the marking of the key allocation area corresponding to the bandwidth allocation strategy can be achieved through a traffic analysis method, such as: by analyzing the flow direction and size of network traffic, determining which areas have a greater demand for bandwidth, and thus marking them as key allocation areas.

[0104] S4. Based on the key allocation area, determine the storage path corresponding to the million WIFI light bulb cameras, analyze the path transmission rate corresponding to the storage path, calculate the storage efficiency corresponding to the storage path based on the path transmission rate, optimize the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency, and calculate the bandwidth saving rate corresponding to the shooting parameters.

[0105] The present invention determines the storage path corresponding to the million WIFI light bulb cameras based on the key allocation area, which can reduce the delay and packet loss rate in the data transmission process, and can transmit the images taken by the camera to the storage device more quickly, which helps to optimize the allocation of storage resources, thereby improving the overall performance and scalability of the storage system.

[0106] Among them, the storage path refers to the specific location and method of saving the image data captured by the million WIFI light bulb cameras in the storage device, which may include the physical address of the storage device (such as the hard disk drive letter, the IP address of the network storage device, etc.), the folder structure and the file naming rules and other information. Optionally, the determination of the storage path corresponding to the million WIFI light bulb cameras can be achieved through a policy-based storage allocation method, such as: formulating a storage allocation strategy to determine the storage path according to factors such as the importance of the million WIFI light bulb cameras, the size of the image data, and the storage period.

[0107] Furthermore, by analyzing the path transmission rate corresponding to the storage path, the present invention can be quickly saved to the storage device, avoiding the risk of data backlog and loss due to slow transmission, and ensuring the stable transmission of image data. Moreover, according to the transmission rate performance of different storage paths, the storage location of the image data can be reasonably allocated to achieve optimal configuration of resources.

[0108] Among them, the path transmission rate refers to the transmission speed when data is transmitted from the million WIFI bulb camera to the storage device or read from the storage device on a specific storage path, which is usually measured in units such as bits per second (bps), kilobits per second (Kbps), and megabits per second (Mbps), which includes network bandwidth, read and write performance of storage devices, data transmission protocols, processing capabilities of intermediate devices, etc. Optionally, the analysis of the path transmission rate corresponding to the storage path can be achieved through a file transfer test method, such as: selecting a larger file, transferring it from the million WIFI bulb camera to the storage device through the storage path, recording the transmission time and file size, and then calculating the transmission rate.

[0109] Furthermore, the present invention calculates the storage efficiency corresponding to the storage path based on the path transmission rate, and can accurately understand the actual effect of the image data storage of the million WIFI light bulb cameras under a specific storage path, which helps to optimize the allocation of storage resources and can reasonably adjust the storage location of the image data to ensure that the storage of the image data is not affected.

[0110] The storage efficiency refers to an indicator that measures the effectiveness and performance of a storage path in storing data within a specific time period, and it comprehensively considers multiple aspects such as path transmission rate, data block size, time, and stability and interference factors of the storage path.

[0111] As an embodiment of the present invention, the calculating the storage efficiency corresponding to the storage path based on the path transmission rate includes:

[0112] The storage efficiency corresponding to the storage path is calculated using the following formula:

[0113]

[0114] Wherein, EV represents the storage efficiency corresponding to the storage path, t' 2 and t' 1 They represent the start time and end time of calculating storage efficiency, T'(t') represents the path transmission rate at time t', n' represents the number of data blocks corresponding to the storage path, i' represents the number index of data blocks, and V' i' represents the volume of the i'th data block, P' represents the stability index corresponding to the storage path, and Q' represents the interference factor index corresponding to the storage path.

[0115] In detail, the start time refers to the start time of calculating the storage efficiency, at which point in time the data transmission and storage conditions on the storage path are recorded and analyzed to determine the storage efficiency within a period of time; the end time refers to the end time of calculating the storage efficiency, and together with the start time, constitutes a time period for calculating the storage efficiency of the storage path within this time period; the path transmission rate refers to the speed of data transmission on the storage path, that is, the rate at which data is transmitted from the source (such as a million WIFI light bulb cameras) to the storage device at a specific time point; the data block refers to the smaller storage unit into which the image data is divided; the volume size refers to the size of the storage space occupied by each data block; the stability index refers to a measure of the stability of the storage path, and multiple factors can be considered, such as the fluctuation of the path transmission rate, the reliability of the storage device, the stability of the network connection, etc.; the interference factor index refers to a comprehensive measure of various interference factors that affect the performance of the storage path, and these interference factors may include network interference, storage device failure, software errors, etc.

[0116] Furthermore, the present invention optimizes the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency, so that the million WIFI light bulb cameras can better adapt to the requirements of storage efficiency without reducing image quality, and by adjusting shooting parameters, such as reducing resolution, adjusting frame rate, etc., the size of image data can be reduced, thereby improving storage efficiency and ensuring that image data can be stored more quickly and stably.

[0117] The shooting parameters refer to the parameters that can be adjusted by the million WIFI light bulb camera during the shooting process, including but not limited to resolution, frame rate, exposure, contrast, color saturation, etc.

[0118] As an embodiment of the present invention, optimizing the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency includes: analyzing the correlation characteristics between the storage efficiency and the amount of image data in the million WIFI light bulb cameras; identifying the change characteristics corresponding to the amount of image data in the million WIFI light bulb cameras based on the correlation characteristics; performing in-depth analysis on the change characteristics to obtain detailed analysis results; extracting key influencing factors in the detailed analysis results; and optimizing the shooting parameters corresponding to the million WIFI light bulb cameras based on the key influencing factors.

[0119] The image data volume refers to the size of data occupied by the images taken by the million WIFI light bulb cameras during storage or transmission, which is usually measured in units such as bytes, kilobytes (KB), and megabytes (MB), depending on factors such as image resolution, color depth, and compression method; the correlation characteristic refers to the relationship and influence between storage efficiency and the image data volume in the million WIFI light bulb cameras. For example, storage efficiency may decrease with the increase of image data volume, or within a certain range, storage efficiency and image data volume present a certain specific functional relationship; the change characteristic refers to the difference between the storage efficiency and the image data volume of the million WIFI light bulb cameras under different storage efficiencies. The changing trends and characteristics of the amount of image data in the machine, for example, when the storage efficiency decreases, the amount of image data may show characteristics such as slower growth and increased fluctuations; the detailed analysis results refer to the specific conclusions and information obtained after an in-depth analysis of the changing characteristics of the storage efficiency and the amount of image data, which include explanations of the causes of the changes, the interaction between different factors, possible optimization directions, etc.; the key influencing factors refer to the factors extracted from the detailed analysis results that have an important impact on the relationship between storage efficiency and image data volume, which may include shooting parameters such as the camera's resolution setting, frame rate, compression algorithm, and external factors such as the network environment and storage device performance.

[0120] Furthermore, the analysis of the correlation characteristics between the storage efficiency and the image data volume in the million WIFI light bulb cameras can be achieved through an experimental comparison method, such as: by changing the shooting parameters of the million WIFI light bulb cameras, observing the changes in the image data volume and the corresponding storage efficiency changes under different parameters, so as to analyze the correlation characteristics between the two; the identification of the change characteristics corresponding to the image data volume in the million WIFI light bulb cameras can be achieved through a trend analysis method, such as: analyzing the historical data of the image data volume, calculating the growth rate, change amplitude and other indicators of the data volume to identify the change characteristics; the in-depth analysis of the change characteristics can be achieved through text analysis tools, such as: NVivo, MAXQDA and other tools; the extraction of key influencing factors in the detailed analysis results can be achieved through a principal component analysis method, such as: by performing principal component analysis on multiple variables in the detailed analysis results, extracting the main components, i.e., key influencing factors; the optimization of the shooting parameters corresponding to the million WIFI light bulb cameras can be achieved through a particle swarm optimization algorithm, such as: the shooting parameters can be used as the position of the particles, and the storage efficiency can be used as the fitness function, and the particle swarm optimization algorithm can be used for optimization.

[0121] The present invention calculates the bandwidth saving rate corresponding to the shooting parameters, which helps to better balance the relationship between image quality and bandwidth occupancy under limited network bandwidth conditions, can significantly reduce the demand for bandwidth, can avoid network congestion, improve the stability and fluency of the entire network, and can reduce the demand for higher bandwidth network services, thereby improving the stability and fluency of the entire network.

[0122] The bandwidth saving rate refers to an indicator for measuring the proportion of network bandwidth saved after adjusting the shooting parameters, which reflects the degree of optimization of network bandwidth resources in the process from using the original shooting parameters to optimizing the shooting parameters.

[0123] As an embodiment of the present invention, the calculating the bandwidth saving rate corresponding to the shooting parameter includes:

[0124] The bandwidth saving rate corresponding to the shooting parameters is calculated using the following formula:

[0125]

[0126] Wherein, BV represents the bandwidth saving rate corresponding to the shooting parameters, R 0 represents the average bandwidth usage corresponding to the original shooting parameters, R p represents the average bandwidth occupancy corresponding to the optimized shooting parameters, m represents the number of shooting time periods corresponding to the shooting parameters, j represents the number index corresponding to the shooting time period, and F jrepresents the reduction in bandwidth occupancy in the jth shooting time period, and Mt represents the adjustment coefficient corresponding to the shooting parameter.

[0127] In detail, the original shooting parameters refer to the shooting settings used by the million WIFI light bulb camera before optimization, including but not limited to parameters such as resolution, frame rate, and image quality; the optimized shooting parameters refer to the shooting settings after adjustment and optimization, and the parameters are obtained by adjusting the original shooting parameters by analyzing factors such as storage efficiency and network conditions; the shooting time period refers to dividing time into different intervals, which is used to analyze and calculate the impact of shooting parameters on bandwidth occupancy in different time periods; the bandwidth occupancy reduction refers to the reduction in network bandwidth occupancy caused by the use of optimized shooting parameters in a specific shooting time period; the adjustment coefficient refers to the second item in the bandwidth saving rate calculation formula used to adjust the bandwidth saving rate to balance the impact of different factors on the bandwidth saving rate. The size of the adjustment coefficient can be set according to actual conditions, such as the stability of the network environment, the complexity of the shooting scene, and other factors.

[0128] S5. Based on the bandwidth saving rate, extract the multi-bandwidth features corresponding to the million WIFI light bulb cameras; based on the multi-bandwidth features, query the bandwidth fluctuation distribution inside the million WIFI light bulb cameras; analyze the fluctuation labels corresponding to the bandwidth fluctuation distribution; based on the fluctuation labels, generate the bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras.

[0129] The present invention extracts the multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate, which helps to make more precise adjustments to the camera's shooting parameters according to actual conditions to achieve optimal bandwidth utilization efficiency. It can determine whether it is necessary to adjust shooting parameters such as resolution and frame rate by analyzing the multi-bandwidth features, so as to quickly locate the problem and take corresponding measures to solve it, thereby avoiding the problem from expanding and affecting the normal operation of the entire network.

[0130] Among them, the multi-bandwidth characteristics refer to the various characteristics related to bandwidth usage exhibited by the million WIFI light bulb cameras under different conditions. These characteristics may include bandwidth occupancy patterns under different shooting parameters, the law of bandwidth changes over time, bandwidth adaptability under different network environments, etc.

[0131] As an embodiment of the present invention, extracting multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate includes: performing an in-depth analysis on the bandwidth saving rate to obtain an analysis result; querying bandwidth-related data in the analysis result; determining a feature extraction direction corresponding to the million WIFI light bulb cameras based on the bandwidth-related data; analyzing potential feature performance corresponding to the feature extraction direction; and extracting multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the potential feature performance.

[0132] Among them, the analysis result refers to the conclusion and information obtained after an in-depth analysis of the bandwidth saving rate, which may include the changing trend of the bandwidth saving rate, influencing factors, and the relationship with other parameters; the bandwidth-related data refers to the data related to the bandwidth usage of the million WIFI light bulb cameras, which may include information such as bandwidth occupancy, transmission rate, and data flow under different shooting parameters; the feature extraction direction refers to the direction and focus for extracting multi-bandwidth features determined according to the bandwidth-related data, which may indicate which aspects of the features are most critical for understanding the bandwidth usage of the million WIFI light bulb cameras; the potential feature expression refers to the feature expression form predicted to appear after analyzing the feature extraction direction. For example, if the feature extraction direction is to analyze the bandwidth changes under different resolutions, then the potential feature expression may include a linear relationship, a nonlinear relationship, or a specific threshold effect between resolution and bandwidth occupancy.

[0133] Furthermore, the in-depth analysis of the bandwidth saving rate can be achieved through data analysis tools, such as Excel, SPSS and other tools; the query of bandwidth-related data in the analysis results can be achieved through data mining algorithms, such as mining bandwidth-related data from a large amount of network data; the determination of the feature extraction direction corresponding to the million WIFI light bulb cameras can be achieved through a machine learning platform, such as TensorFlow, PyTorch, etc.; the analysis of potential feature performance corresponding to the feature extraction direction can be achieved through a simulation method, such as by establishing a model, simulating bandwidth usage under different conditions, and analyzing potential feature performance; the extraction of multi-bandwidth features corresponding to the million WIFI light bulb cameras can be achieved through feature extraction tools, such as OpenCV, Scikit-image and other tools.

[0134] Based on the multi-bandwidth features, the present invention queries the bandwidth fluctuation distribution inside the million WIFI light bulb cameras, analyzes the fluctuation labels corresponding to the bandwidth fluctuation distribution, and can accurately identify which factors will cause unstable changes in bandwidth. It can further analyze whether it is caused by network congestion, camera settings itself or other external factors, so as to take corresponding measures to solve the problem.

[0135] Among them, the bandwidth fluctuation distribution refers to the distribution state of the fluctuation of the network bandwidth used by the million WIFI light bulb cameras during operation as time or different conditions change, including information on the amplitude, frequency, trend, etc. of the bandwidth fluctuation; the fluctuation label refers to a specific identifier assigned to facilitate the classification and management of different bandwidth fluctuation situations. For example, labels such as "slight fluctuation", "moderate fluctuation", "severe fluctuation" can be set, or labels such as "fluctuation caused by network congestion" and "fluctuation caused by equipment failure" can be set according to the cause of the fluctuation. Optionally, the query of the bandwidth fluctuation distribution inside the million WIFI light bulb cameras can be achieved through network performance monitoring tools, such as PRTG, SolarWinds and other tools, which can monitor network traffic and bandwidth usage in real time, generate detailed reports and charts, and help understand the bandwidth fluctuation distribution; the analysis of the fluctuation label corresponding to the bandwidth fluctuation distribution can be achieved through a clustering algorithm, such as using the K-Means clustering algorithm to cluster data with similar bandwidth fluctuation characteristics into one category, and then assign a label to each cluster.

[0136] The present invention generates a bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras based on the fluctuation tag, and can make targeted storage strategy adjustments according to different bandwidth fluctuation conditions to ensure efficient storage under various network conditions. It can automatically adjust to a storage method that saves more bandwidth to avoid storage failure or data loss due to network instability.

[0137] The bandwidth real-time stream storage solution refers to a combination of a series of storage strategies and methods formulated for the real-time video stream data generated by the million WIFI light bulb cameras according to the bandwidth fluctuation situation. The solution includes determining the storage format, selecting the storage device, the storage location distribution, the data compression method, the setting of the cache mechanism and other aspects. For example, the storage format may be dynamically adjusted between high-definition and low-bandwidth formats according to the bandwidth fluctuation label; the storage device can be a local hard disk, a network storage device or a cloud storage service, which is selected according to different bandwidth and cost requirements; the storage location distribution can consider distributed storage to improve reliability and access speed; the data compression method will be optimized according to the bandwidth situation to minimize the bandwidth occupied by storage while ensuring the image quality; the cache mechanism is used to cope with sudden bandwidth fluctuations and ensure the continuous storage of the video stream. Optionally, the bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras can be generated by a solution generation tool, such as EMC Isilon, Qumulo Core and other tools.

[0138] Firstly, by real-time monitoring of the network bandwidth corresponding to the million WIFI light bulb cameras, the present invention can timely understand the operation status of the current network and ensure the stability of the video stream transmission of the camera. In the case of limited bandwidth, it can also issue an early warning so as to take corresponding measures, thereby better planning and managing network resources, avoiding freezes and data loss caused by insufficient bandwidth, and ensuring the reliability of monitoring. At the same time, by analyzing the bandwidth usage trend corresponding to the key data value, the present invention can predict the changes in network bandwidth demand in advance, which is helpful to optimize resource allocation and reasonably arrange the working mode of the camera. When the network bandwidth is relatively abundant, high-definition video transmission or data backup is performed, and when the bandwidth is tight, the video quality is appropriately reduced or the data transmission volume is reduced, thereby improving the utilization efficiency of network resources. Based on the performance index, the present invention analyzes the image transmission quality corresponding to the million WIFI light bulb cameras, which is helpful to timely discover potential problems, such as whether there is image blur, freeze or Signal interruption and other situations, so as to take targeted measures for optimization and improvement. Secondly, through the analysis of performance index, it can provide strong data support for the debugging and optimization of the equipment. The present invention determines the storage path corresponding to the million WIFI light bulb cameras based on the key allocation area, which can reduce the delay and packet loss rate in the data transmission process, and can transmit the images captured by the camera to the storage device faster, which helps to optimize the allocation of storage resources, thereby improving the overall performance and scalability of the storage system. Further, the present invention extracts the multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate, which helps to make more precise adjustments to the camera's shooting parameters according to actual conditions to achieve the best bandwidth utilization efficiency. It can determine whether it is necessary to adjust the shooting parameters such as resolution and frame rate by analyzing the multi-bandwidth features, so as to quickly locate the problem and take corresponding measures to solve it, so as to avoid the problem from expanding and affecting the normal operation of the entire network. Therefore, the bandwidth real-time stream storage method and system of the million WIFI light bulb cameras proposed by the present invention can improve the storage efficiency of the video stream.

[0139] Embodiment 2:

[0140] like Figure 2 FIG. 1 is a module diagram of a bandwidth real-time stream storage system for a million WIFI light bulb cameras provided by an embodiment of the present invention.

[0141] The bandwidth real-time stream storage system 200 of a million WIFI light bulb camera of the present invention can be installed in an electronic device. According to the functions to be implemented, the bandwidth real-time stream storage system 200 of a million WIFI light bulb camera can include a data extraction module 201, an index calculation module 202, an area marking module 203, a saving rate calculation module 204 and a solution generation module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0142] In this embodiment, the functions of each module / unit are as follows:

[0143] The data extraction module 201 is used to monitor the network bandwidth corresponding to the million WIFI light bulb cameras in real time, collect bandwidth usage data corresponding to the network bandwidth, and extract key data values ​​from the bandwidth usage data;

[0144] An index calculation module 202 is used to analyze the bandwidth usage trend corresponding to the key data value, perform performance evaluation on the bandwidth usage trend to obtain performance evaluation data, perform gradient division on the performance evaluation data to obtain performance evaluation levels, and calculate the performance index corresponding to the performance evaluation level;

[0145] The area marking module 203 is used to analyze the image transmission quality corresponding to the million WIFI light bulb camera based on the performance index, perform bandwidth demand analysis on the image transmission quality, obtain a bandwidth demand analysis value, query the bandwidth allocation strategy corresponding to the bandwidth demand analysis value, and mark the key allocation area corresponding to the bandwidth allocation strategy;

[0146] The saving rate calculation module 204 is used to determine the storage path corresponding to the million WIFI light bulb camera based on the key allocation area, analyze the path transmission rate corresponding to the storage path, calculate the storage efficiency corresponding to the storage path based on the path transmission rate, optimize the shooting parameters corresponding to the million WIFI light bulb camera based on the storage efficiency, and calculate the bandwidth saving rate corresponding to the shooting parameters;

[0147] The solution generation module 205 is used to extract the multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate, query the bandwidth fluctuation distribution inside the million WIFI light bulb cameras based on the multi-bandwidth features, analyze the fluctuation labels corresponding to the bandwidth fluctuation distribution, and generate the bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras based on the fluctuation labels.

[0148] In detail, the modules described in the bandwidth real-time stream storage system 200 of a million WIFI light bulb cameras described in an embodiment of the present invention adopt the same technical means as the bandwidth real-time stream storage method of a million WIFI light bulb cameras described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0149] 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 bandwidth real-time stream storage method for a million WIFI light bulb cameras, characterized in that: The method comprises: Monitor the network bandwidth corresponding to the million WIFI light bulb cameras in real time, collect bandwidth usage data corresponding to the network bandwidth, and extract key data values ​​from the bandwidth usage data; Analyze the bandwidth usage trend corresponding to the key data value, perform performance evaluation on the bandwidth usage trend to obtain performance evaluation data, perform gradient division on the performance evaluation data to obtain performance evaluation levels, and calculate the performance index corresponding to the performance evaluation levels; Based on the performance index, analyze the image transmission quality corresponding to the million WIFI light bulb camera, perform bandwidth demand analysis on the image transmission quality, obtain a bandwidth demand analysis value, query the bandwidth allocation strategy corresponding to the bandwidth demand analysis value, and mark the key allocation area corresponding to the bandwidth allocation strategy; Based on the key allocation area, determine the storage path corresponding to the million WIFI light bulb camera, analyze the path transmission rate corresponding to the storage path, calculate the storage efficiency corresponding to the storage path based on the path transmission rate, optimize the shooting parameters corresponding to the million WIFI light bulb camera based on the storage efficiency, and calculate the bandwidth saving rate corresponding to the shooting parameters; Based on the bandwidth saving rate, multiple bandwidth features corresponding to the million WIFI light bulb cameras are extracted; based on the multiple bandwidth features, the bandwidth fluctuation distribution inside the million WIFI light bulb cameras is queried; the fluctuation labels corresponding to the bandwidth fluctuation distribution are analyzed; based on the fluctuation labels, a bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras is generated.

2. A bandwidth real-time stream storage method for a million WIFI light bulb camera as claimed in claim 1, characterized in that: The collecting of bandwidth usage data corresponding to the network bandwidth situation includes: Analyzing initial traffic data in the network bandwidth situation; Querying the flow rate change pattern corresponding to the initial flow rate data; Extracting a sudden flow segment in the flow variation law; Based on the traffic mutation segment, determining a bandwidth fluctuation point in the network bandwidth situation; Based on the bandwidth fluctuation point, bandwidth usage data corresponding to the network bandwidth situation is collected.

3. A bandwidth real-time stream storage method for a million WIFI light bulb camera as claimed in claim 2, characterized in that: The step of performing gradient division on the performance evaluation data to obtain performance evaluation levels includes: Analyzing the degree of difference between the performance evaluation data and the preset performance standard; Based on the degree of difference, the performance evaluation data is classified and sorted to obtain a sorting result; Determine the performance range of the data in the sorting result; Based on the performance interval range, determining a gradient rule corresponding to the performance evaluation data; Based on the gradient rule, the performance evaluation data is gradient divided to obtain performance evaluation levels.

4. The bandwidth real-time stream storage method of a million WIFI light bulb camera as claimed in claim 1, characterized in that: The calculating the performance index corresponding to the performance evaluation level includes: The performance index corresponding to the performance evaluation level is calculated using the following formula: Wherein, PI represents the performance index corresponding to the performance evaluation level, n represents the total number of sub-levels of the performance evaluation level, i represents the number index corresponding to the sub-level, and w i represents the weight coefficient corresponding to the i-th sub-level, S i represents the evaluation value corresponding to the i-th sub-level, t1 and t2 respectively represent the evaluation start time and evaluation end time corresponding to the evaluation time of the performance evaluation level, and B(t) represents the dynamic performance index at time t.

5. The bandwidth real-time stream storage method of a million WIFI light bulb camera as claimed in claim 1, characterized in that: The performing bandwidth requirement analysis on the image transmission quality to obtain a bandwidth requirement analysis value includes: Analyzing quality parameter indicators corresponding to the image transmission quality; Based on the quality parameter indicator, determining a bandwidth requirement range corresponding to the image transmission quality; Based on the bandwidth requirement range, filtering bandwidth interval data corresponding to the image transmission quality; Querying bandwidth mapping requirements corresponding to the bandwidth interval data; Based on the bandwidth mapping requirement, a bandwidth requirement analysis is performed on the image transmission quality to obtain a bandwidth requirement analysis value.

6. The bandwidth real-time stream storage method of a million WIFI light bulb camera as claimed in claim 1, characterized in that: The calculating the storage efficiency corresponding to the storage path based on the path transmission rate includes: The storage efficiency corresponding to the storage path is calculated using the following formula: Wherein, EV represents the storage efficiency corresponding to the storage path, t'2 and t'1 represent the start time and end time of calculating the storage efficiency respectively, T'(t') represents the path transmission rate at time t', n' represents the number of data blocks corresponding to the storage path, i' represents the number index of data blocks, V' i' represents the volume of the i'th data block, P' represents the stability index corresponding to the storage path, and Q' represents the interference factor index corresponding to the storage path.

7. The bandwidth real-time stream storage method of a million WIFI light bulb camera as claimed in claim 1, characterized in that: The optimizing the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency includes: Analyze the correlation characteristics between the storage efficiency and the amount of image data in the million WIFI light bulb cameras; Based on the correlation characteristics, identifying the change characteristics corresponding to the amount of image data in the million WIFI light bulb cameras; Conducting an in-depth analysis on the change characteristics to obtain detailed analysis results; Extracting key influencing factors from the detailed analysis results; Based on the key influencing factors, the shooting parameters corresponding to the million WIFI light bulb cameras are optimized.

8. The bandwidth real-time stream storage method of a million WIFI light bulb camera as claimed in claim 1, characterized in that: The calculating the bandwidth saving rate corresponding to the shooting parameter includes: The bandwidth saving rate corresponding to the shooting parameters is calculated using the following formula: Wherein, BV represents the bandwidth saving rate corresponding to the shooting parameters, R0 represents the average bandwidth occupancy corresponding to the original shooting parameters, and R p represents the average bandwidth occupancy corresponding to the optimized shooting parameters, m represents the number of shooting time periods corresponding to the shooting parameters, j represents the number index corresponding to the shooting time period, and F j represents the reduction in bandwidth occupancy in the jth shooting time period, and Mt represents the adjustment coefficient corresponding to the shooting parameter.

9. The bandwidth real-time stream storage method of a million WIFI light bulb camera as claimed in claim 1, characterized in that: The extracting of multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate includes: Conducting an in-depth analysis on the bandwidth saving rate to obtain an analysis result; Querying bandwidth related data in the analysis result; Based on the bandwidth-related data, determine the feature extraction direction corresponding to the million WIFI light bulb cameras; Analyzing potential feature performance corresponding to the feature extraction direction; Based on the potential feature representation, multi-bandwidth features corresponding to the million WIFI light bulb cameras are extracted.

10. A bandwidth real-time stream storage system for millions of WIFI light bulb cameras, characterized in that: A method for storing bandwidth real-time streams of a million WIFI light bulb cameras according to any one of claims 1 to 9, the system comprising: A data extraction module is used to monitor the network bandwidth corresponding to the million WIFI light bulb cameras in real time, collect bandwidth usage data corresponding to the network bandwidth, and extract key data values ​​from the bandwidth usage data; An index calculation module, used for analyzing the bandwidth usage trend corresponding to the key data value, performing performance evaluation on the bandwidth usage trend to obtain performance evaluation data, performing gradient division on the performance evaluation data to obtain performance evaluation levels, and calculating the performance index corresponding to the performance evaluation levels; A region marking module is used to analyze the image transmission quality corresponding to the million WIFI light bulb camera based on the performance index, perform bandwidth demand analysis on the image transmission quality, obtain a bandwidth demand analysis value, query a bandwidth allocation strategy corresponding to the bandwidth demand analysis value, and mark a key allocation area corresponding to the bandwidth allocation strategy; A saving rate calculation module, used for determining the storage path corresponding to the million WIFI light bulb cameras based on the key allocation area, analyzing the path transmission rate corresponding to the storage path, calculating the storage efficiency corresponding to the storage path based on the path transmission rate, optimizing the shooting parameters corresponding to the million WIFI light bulb cameras based on the storage efficiency, and calculating the bandwidth saving rate corresponding to the shooting parameters; The solution generation module is used to extract the multi-bandwidth features corresponding to the million WIFI light bulb cameras based on the bandwidth saving rate, query the bandwidth fluctuation distribution inside the million WIFI light bulb cameras based on the multi-bandwidth features, analyze the fluctuation labels corresponding to the bandwidth fluctuation distribution, and generate the bandwidth real-time stream storage solution corresponding to the million WIFI light bulb cameras based on the fluctuation labels.

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