Network camera data storage system and method based on wireless transmission

Abstract

The invention discloses a network camera data storage system and method based on wireless transmission, and belongs to the technical field of wireless transmission, and the system comprises a wireless link sensing module which is used for monitoring original parameters of each wireless transmission path in real time, calculating the original parameters, generating a real-time quality score of each wireless transmission path, and sending the real-time quality score to a server; outputting a link quality spectrum; the data processing module is used for acquiring original video data acquired by the network camera and outputting coded video data; the energy consumption decision module is used for generating an equipment working mode instruction; and the path execution module is used for selecting a main transmission path and an auxiliary transmission path from the link quality spectrum and transmitting the coded video data to a preset rear-end storage system through the selected path, and the method comprises the steps A1-A5. The main transmission path and the auxiliary transmission path are intelligently selected through the link quality spectrum, the long-term working stability of the system in a complex wireless environment is improved, and the service life of the system in the complex wireless environment is prolonged.

Description

A Data Storage System and Method for Network Cameras Based on Wireless Transmission Technical Field

[0001] This invention relates to the field of wireless transmission technology, and in particular to a network camera data storage system and method based on wireless transmission. Background Technology

[0002] A network camera is a monitoring device that integrates image acquisition, signal processing, and network transmission functions. It acquires on-site video information through its built-in image sensor and transmits the digitized video stream data to a remote storage system or monitoring center via wired or wireless networks, enabling real-time monitoring, image recording, and post-event traceability. It is widely used in security monitoring, intelligent transportation, industrial inspection, and other fields.

[0003] Traditional wireless transmission-based network camera data storage systems typically employ a simple data transmission strategy. Their working principle involves the device determining the data transmission path from multiple available wireless transmission paths based on a preset fixed order or a random selection mechanism, and continuously transmitting the acquired raw video data with fixed encoding parameters and transmission power. This approach lacks adaptability to dynamically changing wireless link states and the characteristics of the data itself.

[0004] Existing technologies suffer from two significant drawbacks: First, the lack of a real-time perception and comprehensive quality assessment mechanism for multi-dimensional raw parameters such as signal strength, available bandwidth, transmission jitter, and bit error rate makes it impossible to effectively prevent data packets from being transmitted on degraded links. This leads to stuttering, screen tearing, or even interruption of video streams in high-interference wireless environments, severely impacting the continuity and reliability of monitoring footage. Second, the system lacks an adaptive power management mechanism that correlates with real-time network congestion levels and remaining device battery power. It cannot dynamically adjust encoding parameters and transmission power based on data frame priority and network status, resulting in low energy efficiency. For battery-powered network cameras, this significantly shortens their battery life and affects the long-term stability and lifespan of the equipment due to continuous high-power operation. Therefore, there is an urgent need to provide a wireless transmission-based network camera data storage system and method to address these issues. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the following shortcomings of the prior art: First, due to the lack of a real-time perception and comprehensive quality assessment mechanism for multi-dimensional raw parameters such as signal strength, available bandwidth, transmission jitter, and bit error rate, it is impossible to effectively prevent data packets from being transmitted on links with degraded quality. This leads to video stream stuttering, screen tearing, or even interruption in high-interference wireless environments, seriously affecting the continuity and reliability of the monitoring screen. Second, the system lacks an adaptive power management mechanism that is related to the real-time network congestion level and the remaining power of the device. It cannot dynamically adjust the encoding parameters and transmission power according to the priority of data frames and network status, resulting in low energy efficiency of the device. For battery-powered network cameras, this significantly shortens their battery life and affects the stability and lifespan of the device due to continuous high-power operation. The invention provides a network camera data storage system and method based on wireless transmission.

[0006] To solve the above-mentioned technical problems, one technical solution adopted by the present invention is: to provide a network camera data storage system based on wireless transmission, including an intelligent sensing system integrated into the network camera, wherein the intelligent sensing system includes:

[0007] The wireless link sensing module monitors the raw parameters of each wireless transmission path in real time, calculates the raw parameters, generates a real-time quality score for each wireless transmission path, and outputs a link quality spectrum containing all wireless transmission paths and their corresponding quality scores.

[0008] The data processing module acquires the raw video data collected by the network camera. When a wireless transmission path to be evaluated is identified from the link quality spectrum that meets the preset preference conditions and the quality score of the wireless transmission path to be evaluated is lower than the preset reliability threshold, the module outputs the encoded video data and marks the transmission priority identifiers for various data frames in the encoded video data.

[0009] The energy consumption decision module monitors the remaining battery power of the network camera and the network congestion level obtained from the link quality spectrum analysis. Based on the remaining battery power and the network congestion level, it calculates an energy adaptive coefficient, matches the preset energy-saving strategy mapping table according to the energy adaptive coefficient, and generates a device working mode instruction.

[0010] The path execution module selects a wireless transmission path from the link quality spectrum whose quality score meets the preset preferred conditions as the main transmission path, and selects another wireless transmission path with the next lower quality score as the auxiliary transmission path. According to the transmission priority identifier, it allocates high-priority data frames to the main transmission path and low-priority data frames to the auxiliary transmission path, and transmits the encoded video data to the preset back-end storage system via the selected path.

[0011] The present invention is further configured such that the original parameters in the wireless link sensing module include signal strength, available bandwidth, transmission jitter, and bit error rate;

[0012] The method for generating the real-time quality score for each wireless transmission path is as follows:

[0013] S1. The signal strength, available bandwidth, transmission jitter, and bit error rate of each wireless transmission path are collected and converted to the same preset scale range to form signal scale value, bandwidth scale value, stability scale value, and bit error rate scale value. Based on the network stability requirements defined in the current application scenario, preset signal impact weight, bandwidth impact weight, stability impact weight, and bit error rate impact weight are configured for the signal scale value, bandwidth scale value, stability scale value, and bit error rate scale value.

[0014] S2. Multiply the signal scale value, bandwidth scale value, stability scale value, and bit error rate scale value by preset signal influence weight, bandwidth influence weight, stability influence weight, and bit error rate influence weight respectively to obtain signal weighted value, bandwidth weighted value, stability weighted value, and bit error rate weighted value. Add the signal weighted value, bandwidth weighted value, and stability weighted value and then subtract the bit error rate weighted value to obtain the initial score for each wireless transmission path.

[0015] S3. Normalize the initial score to obtain the final quality score.

[0016] The present invention is further configured such that the method for outputting the link quality spectrum in the wireless link sensing module is:

[0017] S4. Sort all wireless transmission paths based on the quality score to generate a path quality sequence, and mark the wireless transmission paths in the path quality sequence whose quality score is lower than a preset effective threshold to form an effective path set and a path set to be optimized.

[0018] S5. Pack the identification information of each wireless transmission path in the effective path set, the quality score and the original parameters into a path description unit, arrange all the path description units in descending order according to the quality score, add a timestamp, and integrate to generate a link quality spectrum.

[0019] The present invention is further configured such that the output step of the encoded video data in the data processing module includes: adjusting the encoding resolution, frame rate, and keyframe generation interval of the original video data according to the difference between the quality score and the reliability threshold, and outputting encoded video data adapted to the current network state. The specific steps are as follows:

[0020] Q1. Calculate the difference between the quality score and the reliability threshold of the wireless transmission path to be evaluated, and determine the video compression level based on the magnitude of the difference. The video compression level is negatively correlated with the magnitude of the difference.

[0021] Q2. Based on the video compression level, obtain the corresponding encoding resolution baseline value, frame rate baseline value, and keyframe interval baseline value from the preset encoding parameter mapping table. Simultaneously, extract the statistical value of the rate of change of the quality score of all wireless transmission paths within a preset time window from the link quality spectrum as the overall network fluctuation index. Based on the magnitude of the overall network fluctuation index, scale the encoding resolution baseline value, the frame rate baseline value, and the keyframe interval baseline value proportionally to generate dynamically adjusted encoding parameters. The proportional scaling adjustment includes: calculating a scaling factor with the overall network fluctuation index as input; multiplying the encoding resolution baseline value and the frame rate baseline value by the scaling factor to obtain preliminary resolution and frame rate values, respectively; dividing the keyframe interval baseline value by the scaling factor to obtain a preliminary interval value; and finally constraining the preliminary resolution value, the preliminary frame rate value, and the preliminary interval value within the preset feasible range of corresponding parameters to generate adjusted encoding parameters.

[0022] Q3. Use the adjusted encoding parameters to encode the original video data in real time to generate encoded video data.

[0023] The present invention is further configured such that: the specific steps in step Q1 for determining the video compression level based on the magnitude of the difference are as follows:

[0024] Q101. Based on the historical transmission data recorded in the link quality spectrum, analyze the correlation between the difference between the quality score and the reliability threshold and the video smoothness index. When the rate of change of the video smoothness index exceeds the preset change threshold, record the specific difference at this time as the interval division benchmark. Divide the range formed by the continuous difference into multiple amplitude intervals according to the interval division benchmark, and configure a preset compression level parameter for each amplitude interval.

[0025] Q102. Based on the calculated difference in the actual compression level parameters, determine the range in which it falls, and use the compression level parameters corresponding to the range as the video compression level.

[0026] The present invention is further configured such that: the remaining battery power of the network camera in the energy consumption decision module is monitored in real time by a power monitoring unit integrated inside the network camera;

[0027] The steps for analyzing network congestion are as follows:

[0028] R1. Extract the quality score sequence of all wireless transmission paths within a preset time window from the link quality spectrum, calculate the rate of change of the quality score of each wireless transmission path within the preset time window, and calculate the average and variance of the rate of change of all wireless transmission paths as a network stability index.

[0029] R2. By combining the average and variance of the network stability index with the trend of the proportion of the effective path set in the total number of paths, a mathematical relationship is constructed between the product of the average and variance and the reciprocal of the trend of the proportion, and the comprehensive congestion index is calculated.

[0030] R3. Perform linear normalization on the comprehensive congestion index to obtain the network congestion level value.

[0031] The present invention is further configured such that the calculation method for the energy adaptive coefficient in the energy consumption decision module is as follows:

[0032] R4. Calculate the ratio of the remaining battery power to the preset baseline power value to obtain the power ratio factor. At the same time, calculate the difference between the network congestion level value and the preset baseline congestion value to obtain the congestion offset. Multiply the power ratio factor and the congestion offset to obtain the energy consumption adjustment factor.

[0033] R5. Add the energy consumption adjustment factor to the preset energy benchmark coefficient to obtain the energy adaptive coefficient;

[0034] The energy-saving strategy mapping table specifically includes the mapping relationship between the energy adaptive coefficients with different numerical ranges and the corresponding device operating mode instructions. The device operating mode instructions include specific configuration combinations of image acquisition parameters, event listening levels, and communication power levels.

[0035] The present invention is further configured such that the method for selecting the main transmission path in the path execution module is as follows:

[0036] T1. Select all wireless transmission paths whose quality scores meet the preset preference conditions from the link quality spectrum to form a candidate path set. When the candidate path set contains only one wireless transmission path, the wireless transmission path is determined as the main transmission path. When the candidate path set contains multiple wireless transmission paths, proceed to step T2.

[0037] T2. Obtain the size of the data to be transmitted from the original video data, combine it with the average transmission rate record of each candidate wireless transmission path in the same historical time period, calculate the estimated transmission time of each candidate path, and multiply the inverse of the estimated transmission time by the quality score to obtain the transmission performance score.

[0038] T3. Sort the transmission performance scores of all candidate wireless transmission paths in descending order, select the wireless transmission path with the highest ranking as the main transmission path, and select the wireless transmission path with the second highest ranking as the auxiliary transmission path.

[0039] The present invention is further configured such that: when the path execution module allocates low-priority data frames to the auxiliary transmission path, it simultaneously sets the final transmission power of the main transmission path and the auxiliary transmission path according to the communication power level in the device working mode instruction;

[0040] The pre-defined backend storage system is a distributed storage array deployed in a remote monitoring center, equipped with a data receiving interface and a storage management unit. It is used to receive and integrate the encoded video data transmitted through the main transmission path and the auxiliary transmission path, and to classify and store it.

[0041] The data storage method for network cameras based on wireless transmission includes the following steps:

[0042] A1. Monitor the raw parameters of each available wireless transmission path in real time, calculate the raw parameters, generate a real-time quality score for each wireless transmission path, and output the link quality spectrum.

[0043] A2. Obtain the raw video data collected by the network camera. When a wireless transmission path to be evaluated that meets the preset preference conditions is identified from the link quality spectrum, and the quality score of the wireless transmission path to be evaluated is lower than the preset reliability threshold, output the encoded video data and mark the transmission priority identifier for each type of data frame in the encoded video data.

[0044] A3. Continuously monitor the remaining battery power of the network camera and the network congestion level obtained from the link quality spectrum analysis. Calculate the energy adaptive coefficient based on the remaining battery power and the network congestion level. Match the preset energy-saving strategy mapping table according to the energy adaptive coefficient and generate device working mode instructions.

[0045] A4. Select a wireless transmission path from the link quality spectrum whose quality score meets the preset preferred conditions as the main transmission path, and select another wireless transmission path with the next lower quality score as the auxiliary transmission path. According to the transmission priority identifier, allocate high-priority data frames to the main transmission path and low-priority data frames to the auxiliary transmission path. Transmit the encoded video data to the preset back-end storage system via the selected path to complete the high-reliability and energy-efficient optimized transmission of video data to the storage device.

[0046] The beneficial effects of this invention are as follows:

[0047] 1. This invention uses a wireless link sensing module to perform real-time sensing and comprehensive quality assessment of multi-dimensional raw parameters such as signal strength, available bandwidth, transmission jitter and bit error rate, and generates a link quality spectrum. This effectively avoids data packets being transmitted on links with degraded quality, thereby ensuring the continuity and reliability of the monitoring screen and significantly reducing the occurrence of video stream stuttering, screen tearing and interruption.

[0048] 2. This invention combines real-time network congestion levels with the remaining power of the device through an energy consumption decision module, dynamically calculates the energy adaptive coefficient and matches energy-saving strategies, and generates working mode instructions that include image acquisition parameters and communication power levels. This achieves adaptive power consumption management, improves the energy efficiency of the device, and effectively extends the battery life of battery-powered devices.

[0049] 3. This invention uses a path execution module to intelligently select primary and secondary transmission paths based on the link quality spectrum, and dynamically allocates transmission tasks and adjusts transmission power according to data frame priority and network status. Combined with a seamless redundancy switching mechanism, it improves the long-term working stability and lifespan of the system in complex wireless environments. Attached Figure Description

[0050] Figure 1 is a system flowchart of the present invention;

[0051] Figure 2 is a flowchart of the method of the present invention. Detailed Implementation

[0052] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0053] Please refer to Figures 1 and 2. The wireless transmission-based network camera data storage system includes an intelligent sensing system integrated within the network camera. The intelligent sensing system includes:

[0054] The wireless link sensing module monitors the raw parameters of each available wireless transmission path in real time, calculates the raw parameters, generates a real-time quality score for each wireless transmission path, and outputs a link quality spectrum containing all wireless transmission paths and their corresponding quality scores.

[0055] The data processing module acquires the raw video data collected by the network camera. When a wireless transmission path to be evaluated that meets the preset optimization conditions is identified from the link quality spectrum, and the quality score of the wireless transmission path to be evaluated is lower than the preset reliability threshold, the encoded video data is output, and the transmission priority identifier is marked for various data frames in the encoded video data.

[0056] The energy consumption decision module continuously monitors the remaining battery power of the network camera and the network congestion level obtained from the link quality spectrum analysis. Based on the remaining battery power and the network congestion level, it calculates the energy adaptive coefficient, matches the preset energy-saving strategy mapping table according to the energy adaptive coefficient, and generates a device working mode instruction that includes image acquisition parameters, event listening level and communication power level.

[0057] The path execution module receives the link quality spectrum, encoded video data with transmission priority identifiers, and device operating mode instructions. It selects a wireless transmission path from the link quality spectrum that meets the preset optimization conditions as the primary transmission path, and selects another wireless transmission path with the next lower quality score as the secondary transmission path. Based on the transmission priority identifiers, it allocates high-priority data frames to the primary transmission path and low-priority data frames to the secondary transmission path. The encoded video data is then transmitted to the preset back-end storage system via the selected path, completing the highly reliable and energy-efficient optimized transmission of video data to the storage device.

[0058] The intelligent sensing system consists of a wireless link sensing module, a data processing module, an energy consumption decision module, and a path execution module connected in sequence.

[0059] The system uses a wireless link sensing module to evaluate path quality in real time and dynamically adjusts power consumption in conjunction with energy consumption decisions to achieve highly reliable video transmission and energy efficiency optimization. The data processing module uses adaptive encoding to reduce bandwidth requirements, and the path execution module intelligently selects dual paths to allocate priority data, significantly improving the continuity of monitoring images, device battery life, and adaptability to complex wireless environments.

[0060] The raw parameters in the wireless link sensing module include signal strength, available bandwidth, transmission jitter, and bit error rate. These raw parameters are monitored in real time by a multi-dimensional sensor array integrated into the network camera. This sensor array synchronously collects the signal strength, available bandwidth, transmission jitter, and bit error rate of each available wireless transmission path and transmits the monitoring data to the wireless link sensing module in real time for subsequent calculations.

[0061] The method for generating real-time quality scores for each wireless transmission path is as follows:

[0062] S1. The signal strength, available bandwidth, transmission jitter and bit error rate of each wireless transmission path are collected and converted to the same preset scale range to form signal scale value, bandwidth scale value, stability scale value and bit error rate scale value. Based on the network stability requirements defined in the current application scenario, preset signal impact weight, bandwidth impact weight, stability impact weight and bit error rate impact weight are configured for the signal scale value, bandwidth scale value, stability scale value and bit error rate scale value.

[0063] The preset same scale range is achieved through a linear normalization method, which maps signal strength to a signal scale value of 0-100, available bandwidth to a bandwidth scale value of 0-100, transmission jitter to a stability scale value of 0-100, and bit error rate to a bit error rate scale value of 0-100, ensuring that all parameters are comparable.

[0064] The specific steps for configuring the preset signal impact weight, bandwidth impact weight, stability impact weight, and bit error impact weight are as follows: Based on the network stability requirement level defined in the current application scenario, the requirement level is quantified into a numerical value, and the impact weight value of each parameter is calculated through a weight allocation algorithm. The higher the network stability requirement level, the larger the configured values ​​of the stability impact weight and bit error impact weight.

[0065] S2. Multiply the signal scale value, bandwidth scale value, stability scale value, and bit error scale value by the preset signal influence weight, bandwidth influence weight, stability influence weight, and bit error influence weight, respectively, to obtain the signal weighted value, bandwidth weighted value, stability weighted value, and bit error weighted value. Add the signal weighted value, bandwidth weighted value, and stability weighted value together and subtract the bit error weighted value to obtain the initial score for each wireless transmission path.

[0066] S3. Normalize the initial score to map it to the standard score range of 0-100 points to obtain the final quality score.

[0067] The normalization process uses a linear transformation formula: Quality Score = (Initial Score - Minimum Initial Score) / (Maximum Initial Score - Minimum Initial Score) × 100, which ensures that the quality scores of different wireless transmission paths are comparable and facilitates subsequent path selection decisions.

[0068] The method for outputting the link quality spectrum in the wireless link sensing module is as follows:

[0069] S4. Sort all wireless transmission paths based on quality scores to generate a path quality sequence, and mark wireless transmission paths in the path quality sequence whose quality scores are lower than a preset effective threshold to form a set of effective paths and a set of paths to be optimized.

[0070] The preset effective threshold is determined by analyzing the correspondence between quality score and video transmission success rate in historical transmission data. The quality score value corresponding to the abrupt change point of the video transmission success rate curve is selected as the optimal threshold. This threshold ensures that all wireless transmission paths in the effective path set can meet the minimum video transmission reliability requirements.

[0071] S5. Package the identification information, quality score and original parameters of each wireless transmission path in the effective path set into a path description unit, arrange all path description units in descending order of quality score, add timestamps, and integrate to generate a link quality spectrum.

[0072] The output steps of the encoded video data in the data processing module include: adjusting the encoding resolution, frame rate, and keyframe generation interval of the original video data according to the difference between the quality score and the reliability threshold, and outputting encoded video data adapted to the current network status. The specific steps are as follows:

[0073] Q1. Calculate the difference between the quality score and the reliability threshold of the wireless transmission path to be evaluated, and determine the video compression level based on the magnitude of the difference. The video compression level is negatively correlated with the magnitude of the difference.

[0074] The specific steps in step Q1 for determining the video compression level based on the magnitude of the difference are as follows:

[0075] Q101. Based on the historical transmission data recorded in the link quality spectrum, analyze the correlation between the difference between the quality score and the reliability threshold and the video smoothness index. When the rate of change of the video smoothness index exceeds the preset change threshold, record the specific difference at this time as the interval division benchmark. According to the interval division benchmark, divide the range formed by the continuous difference into multiple amplitude intervals with clear boundary values, and configure a preset compression level parameter for each amplitude interval. The compression level parameter is determined based on the minimum data compression strength required to ensure smooth video transmission within the amplitude interval.

[0076] The boundary value is determined as follows: Based on the historical transmission data recorded in the link quality spectrum, the correlation between the difference between the quality score and the reliability threshold and the video smoothness index is analyzed. When the rate of change of the video smoothness index exceeds the preset change threshold, the specific difference amplitude at this time is recorded as the interval division benchmark. Based on the benchmark, the continuous difference amplitude range is divided into multiple amplitude intervals, and a corresponding compression level parameter is configured for each amplitude interval. The interval boundary value is determined by statistically analyzing the critical point of the sudden change of the video smoothness index in historical data, ensuring that the boundary value can effectively distinguish the impact level of different network states on video quality.

[0077] Q102. Based on the calculated difference in the actual compression level parameters, determine the range in which it falls, and use the compression level parameter corresponding to the range as the video compression level.

[0078] Q2. Based on the video compression level, obtain the corresponding encoding resolution baseline value, frame rate baseline value, and keyframe interval baseline value from the preset encoding parameter mapping table. At the same time, extract the statistical value of the rate of change of the quality score of all wireless transmission paths within a preset time window from the link quality spectrum as the overall network fluctuation index. Based on the magnitude of the overall network fluctuation index, scale the encoding resolution baseline value, frame rate baseline value, and keyframe interval baseline value proportionally to generate dynamically adjusted encoding parameters. The proportional scaling adjustment includes: calculating the scaling factor with the overall network fluctuation index as input; multiplying the encoding resolution baseline value and frame rate baseline value by the scaling factor to obtain the initial resolution value and the initial frame rate value, respectively; dividing the keyframe interval baseline value by the scaling factor to obtain the initial interval value; and finally constraining the initial resolution value, the initial frame rate value, and the initial interval value within the preset feasible range of the corresponding parameters to generate the adjusted encoding parameters.

[0079] The preset encoding parameter mapping table is constructed by establishing a correspondence between video compression levels and encoding parameters. The mapping table contains the resolution baseline value, frame rate baseline value, and key frame interval baseline value corresponding to different compression levels. The baseline values ​​are generated based on historical encoding efficiency statistics. The construction of the mapping table needs to consider the balance between bandwidth requirements and image quality loss under different compression levels, and the initial values ​​of encoding parameters adapted to the current compression level are quickly obtained by looking up the table.

[0080] The optimal value of the preset time window is determined by analyzing the network fluctuation cycle characteristics. The typical value is 100-500 milliseconds. This value needs to cover the coherence time of a typical wireless channel to ensure that short-term fluctuation characteristics of network quality can be captured. At the same time, it avoids introducing too much irrelevant historical data due to an excessively long window or insufficient statistical significance due to an excessively short window. Its specific value can be dynamically adjusted through the correlation analysis between historical transmission success rate and window length.

[0081] The scaling factor is calculated as follows: First, extract the statistical value of the rate of change of quality score of all wireless transmission paths within a preset time window from the link quality spectrum as the overall network fluctuation index. After normalizing the fluctuation index to the range of 0-1, it is mapped to the scaling factor through a nonlinear function. The nonlinear function adopts an S-shaped curve to ensure that the scaling factor is close to 1 when the fluctuation is small in order to keep the parameter stable, and the scaling factor deviates significantly from 1 when the fluctuation is large in order to achieve adaptive adjustment.

[0082] The feasible range of the preset corresponding parameters is determined according to the technical specifications of the video coding standard and the requirements of the application scenario. The feasible range of the coding resolution is set to 1 / 8 of the original resolution to the full resolution, the feasible range of the frame rate is set to 1fps to the original frame rate, and the feasible range of the key frame interval is set to 1 frame to 10 seconds. These ranges must simultaneously meet the working limitations of the video encoder and the visual experience requirements of the end user.

[0083] Q3. Use the adjusted encoding parameters to encode the original video data in real time to generate encoded video data.

[0084] The specific content of real-time encoding is as follows: using dynamically adjusted encoding parameters to perform frame-level compression processing on the original video data, including spatial downsampling based on resolution parameters, temporal frame reduction based on frame rate parameters, setting frame type structure based on key frame interval parameters, and adding a standard encapsulation header to the output data stream to generate encoded video data that meets transmission requirements. The entire process must be completed within a single frame processing time limit to ensure real-time performance.

[0085] Example 1

[0086] When this system is implemented in a certain type of network camera, its intelligent sensing system's wireless link sensing module monitors the raw parameters (signal strength -65dBm / -85dBm / -70dBm, available bandwidth 80Mbps / 20Mbps / 40Mbps, transmission jitter 3ms / 15ms / 8ms, bit error rate 1e-6 / 1e-4 / 1e-5) of three available wireless transmission paths (Wi-Fi 5G, 4G, and backup Wi-Fi 2.4G) in real time through a built-in sensor array.

[0087] First, the parameters are linearly normalized to the scale range of 0-100 to obtain signal scale values ​​(72 / 30 / 58), bandwidth scale values ​​(80 / 20 / 40), stability scale values ​​(85 / 20 / 60), and bit error scale values ​​(90 / 40 / 70), and the influence weights of signal / bandwidth / stability / bit error are configured as (0.3 / 0.3 / 0.2 / 0.2).

[0088] The signal weighting value (21.6 / 9 / 17.4), bandwidth weighting value (24 / 6 / 12), stability weighting value (17 / 4 / 12), and bit error weighting value (18 / 8 / 14) were calculated by weighting. The initial score (44.6 / 11 / 27.4) was obtained by adding the first three values ​​and subtracting the bit error weighting value. After normalization, the final quality score (89 / 22 / 55) was generated. The link quality spectrum was generated by sorting the paths based on the score. The path with the highest score (89 points) was selected as the main transmission path, and the second highest path (55 points) was selected as the auxiliary path.

[0089] Calculate the difference between the quality score and the reliability threshold (60 points) (29 points). Determine the video compression level (high) based on the difference. Obtain the baseline value (1080p resolution, 30fps frame rate, 2-second keyframe interval) from the encoding parameter mapping table. Calculate the scaling factor (0.85) by combining the statistical value of the quality score change rate within a 100ms time window (fluctuation index 0.15). Adjust the baseline value to obtain the encoding parameters (918p resolution, 25fps frame rate, 2.35-second keyframe interval).

[0090] This embodiment dynamically selects the optimal transmission path through a multi-parameter fusion quality scoring mechanism, and combines adaptive coding and dual-path priority allocation to significantly reduce video packet loss rate and stuttering in complex wireless environments.

[0091] Among them, the remaining battery power of the network camera in the energy consumption decision module is monitored in real time by the power monitoring unit integrated inside the network camera. The power monitoring unit transmits the monitored remaining battery power data to the energy consumption decision module for energy adaptive coefficient calculation.

[0092] The steps for analyzing network congestion are as follows:

[0093] R1. Extract the quality score sequence of all wireless transmission paths within a preset time window from the link quality spectrum, calculate the rate of change of the quality score of each wireless transmission path within the preset time window, and calculate the average and variance of the rate of change of all wireless transmission paths as a network stability index.

[0094] The steps for calculating the rate of change are as follows: extract the quality score sequence of each wireless transmission path within a preset time window from the link quality spectrum, calculate the ratio of the absolute value of the difference between quality scores at adjacent time points to the time interval, obtain the rate of change of quality score for each path, and calculate the average and variance of the rate of change of all paths as a network stability indicator. The time interval is determined according to the data sampling frequency to ensure that the rate of change calculation can accurately reflect the dynamic fluctuation characteristics of the quality score.

[0095] R2. By combining the average and variance in the network stability index with the trend of the proportion of the effective path set in the total number of paths, the comprehensive congestion index is calculated by constructing a mathematical relationship between the product of the average and variance and the reciprocal of the trend of the proportion.

[0096] The total number of paths is the sum of the set of valid paths and the set of paths to be optimized.

[0097] R3. Perform linear normalization on the comprehensive congestion index, mapping it to a numerical range of 0 to 1 to obtain the network congestion level value.

[0098] The calculation method for the energy adaptive coefficient in the energy consumption decision module is as follows:

[0099] R4. Calculate the power ratio factor by comparing the remaining battery power with the preset baseline power value. At the same time, calculate the congestion offset by comparing the network congestion level with the preset baseline congestion value. Multiply the power ratio factor and the congestion offset to obtain the energy consumption adjustment factor.

[0100] The preset baseline battery level is set based on the statistical characteristics of the average battery consumption rate and quality score change rate under typical working modes of network cameras. Usually, 70% of the device's full battery level is taken as the baseline value. This value is optimized and determined based on the correlation analysis between battery level and network stability indicators in historical battery usage data, ensuring that the baseline battery level can effectively adapt to the energy adaptive coefficient calculation requirements when network fluctuations are large.

[0101] The preset baseline congestion value is set based on the average rate of change and variance distribution of historical network stability indicators. By analyzing the correspondence between congestion level and quality score change rate under normal network conditions, the median congestion level corresponding to the minimum variance of the rate of change is selected as the baseline congestion value to provide a stable reference benchmark in network congestion level calculation.

[0102] R5. Add the energy consumption adjustment factor to the preset energy baseline coefficient to obtain the energy adaptive coefficient;

[0103] The preset energy baseline coefficient is determined by the typical energy consumption impact of the comprehensive baseline power value and the baseline congestion value. It is usually set to 0.5. This coefficient is derived from the regression analysis of the historical energy consumption data of the equipment under the baseline conditions. It serves as the basic offset of the energy consumption adjustment factor to ensure that the energy adaptive coefficient fluctuates reasonably within the range of 0 to 1, thereby optimizing the matching accuracy of the energy-saving strategy mapping table.

[0104] The energy-saving strategy mapping table specifically includes the mapping relationship between energy adaptive coefficients with different numerical ranges and the corresponding device operating mode instructions. The device operating mode instructions include specific configuration combinations of image acquisition parameters, event listening levels, and communication power levels.

[0105] The method for selecting the main transmission path in the path execution module is as follows:

[0106] T1. Select all wireless transmission paths whose quality scores meet the preset preference conditions from the link quality spectrum to form a candidate path set. When the candidate path set contains only one wireless transmission path, the wireless transmission path is determined as the main transmission path. When the candidate path set contains multiple wireless transmission paths, proceed to step T2.

[0107] T2. Obtain the size of the raw video data to be transmitted, combine it with the average transmission rate record of each candidate wireless transmission path in the same historical period, calculate the estimated transmission time of each candidate path, and multiply the inverse of the estimated transmission time by the quality score to obtain the transmission performance score.

[0108] T3. Sort the transmission performance scores of all candidate wireless transmission paths in descending order, select the wireless transmission path with the highest ranking as the primary transmission path, and select the wireless transmission path with the second highest ranking as the secondary transmission path.

[0109] When the main transmission path is interrupted due to link quality deterioration or external interference, the path execution module first detects that the quality score of the main transmission path is lower than the preset effective threshold or that continuous transmission has timed out based on the real-time updated link quality spectrum, and then triggers the path switching mechanism. The module promotes the auxiliary transmission path to the new main transmission path, and at the same time selects the available path with the highest quality score from the candidate path set as the new auxiliary transmission path, and dynamically adjusts the transmission power of the new path according to the communication power level in the device working mode instruction. Subsequently, the module reallocates high-priority data frames to the new main transmission path and low-priority data frames to the new auxiliary transmission path according to the transmission priority identifier, ensuring that the encoded video data continues to be transmitted to the preset back-end storage system through the updated path combination, realizing seamless redundancy switching of the transmission link.

[0110] In the path execution module, when allocating low-priority data frames to the auxiliary transmission path, the final transmission power of the main transmission path and the auxiliary transmission path is set according to the communication power level in the device working mode instruction.

[0111] The pre-defined backend storage system is a distributed storage array deployed in a remote monitoring center, equipped with a data receiving interface and a storage management unit. It is used to receive and integrate encoded video data transmitted through the main transmission path and auxiliary transmission path, and to classify and store it.

[0112] The data storage method for network cameras based on wireless transmission includes the following steps:

[0113] A1. Monitor the raw parameters of each available wireless transmission path in real time, calculate the raw parameters, generate a real-time quality score for each wireless transmission path, and output the link quality spectrum.

[0114] A2. Acquire the raw video data collected by the network camera. When a wireless transmission path to be evaluated that meets the preset optimization conditions is identified from the link quality spectrum, and the quality score of the wireless transmission path to be evaluated is lower than the preset reliability threshold, output the encoded video data and mark the transmission priority identifier for each type of data frame in the encoded video data.

[0115] A3. Continuously monitor the remaining battery power of the network camera and the network congestion level obtained from the link quality spectrum analysis. Calculate the energy adaptation coefficient based on the remaining battery power and the network congestion level. Match the preset energy-saving strategy mapping table according to the energy adaptation coefficient and generate the device working mode instruction.

[0116] A4. Select a wireless transmission path from the link quality spectrum that meets the preset optimization conditions as the main transmission path, and select another wireless transmission path with the next lower quality score as the auxiliary transmission path. According to the transmission priority identifier, allocate high-priority data frames to the main transmission path and low-priority data frames to the auxiliary transmission path. Transmit the encoded video data to the preset back-end storage system through the selected path to complete the high-reliability and energy-efficient optimized transmission of video data to the storage device.

[0117] Example 2

[0118] In Example 2, the energy consumption decision module monitors the remaining battery power (80%) of the network camera and the network congestion level (0.4) obtained from the link quality spectrum analysis in real time. Based on the ratio of the remaining battery power to the preset baseline power value (70%), it calculates the power ratio factor (80 / 70≈1.14). At the same time, it calculates the congestion offset (0.4-0.5=-0.1) by the difference between the network congestion level value and the preset baseline congestion value (0.5). The power ratio factor is multiplied by the congestion offset to obtain the energy consumption adjustment factor (1.14×(-0.1)=-0.114). The energy consumption adjustment factor is added to the preset energy baseline coefficient (0.5) to obtain the energy adaptive coefficient (-0.114+0.5=0.386). Based on the energy adaptive coefficient, it matches the energy-saving strategy mapping table to generate the device working mode instruction (including image acquisition parameters 1080p, event listening level medium, and communication power level medium).

[0119] The path execution module selects wireless transmission paths from the link quality spectrum that meet the preset optimization conditions to form a candidate path set (main path 89 points, auxiliary path 55 points). When the quality score of the main path drops to 50 points due to link deterioration (below the effective threshold of 60 points), the path switching mechanism is triggered, the auxiliary path is promoted to the new main path, and the path with the highest quality score (70 points) in the candidate set is reselected as the new auxiliary path. The final transmission power of the new path is set according to the communication power level in the device working mode instruction. According to the transmission priority identifier, high-priority data frames are allocated to the new main path and low-priority data frames are allocated to the new auxiliary path to ensure that the encoded video data is continuously transmitted to the preset back-end storage system.

[0120] This embodiment dynamically optimizes device power consumption through the energy consumption decision module and intelligently switches transmission paths through the path execution module, significantly reducing the impact of wireless environment fluctuations on video streams, improving transmission reliability by more than 30%, and extending battery life by 20%, achieving a balance between high energy efficiency and high reliability.

[0121] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A network camera data storage system based on wireless transmission, characterized in that: The system includes an intelligent sensing system integrated into a network camera. This system comprises: a wireless link sensing module, which monitors the raw parameters of each wireless transmission path in real time, calculates the raw parameters, generates a real-time quality score for each wireless transmission path, and outputs a link quality spectrum containing all wireless transmission paths and their corresponding quality scores; a data processing module, which acquires raw video data collected by the network camera, and when a wireless transmission path to be evaluated is identified from the link quality spectrum whose quality score meets preset optimization conditions, and whose quality score is lower than a preset reliability threshold, outputs encoded video data and marks various data frames in the encoded video data with transmission priority identifiers; and an energy consumption decision module, which monitors... The system measures the remaining battery power of the network camera and the network congestion level obtained from the link quality spectrum analysis. Based on the remaining battery power and the network congestion level, it calculates an energy adaptive coefficient and matches it with a preset energy-saving strategy mapping table to generate a device operating mode instruction. The path execution module selects a wireless transmission path from the link quality spectrum whose quality score meets the preset preference conditions as the primary transmission path and selects another wireless transmission path with the next lower quality score as the secondary transmission path. According to the transmission priority identifier, it allocates high-priority data frames to the primary transmission path and low-priority data frames to the secondary transmission path. The encoded video data is then transmitted to a preset back-end storage system via the selected path.

2. The network camera data storage system based on wireless transmission according to claim 1, characterized in that: The original parameters in the wireless link sensing module include signal strength, available bandwidth, transmission jitter, and bit error rate. The method for generating the real-time quality score for each wireless transmission path is as follows: S1. Convert the collected signal strength, available bandwidth, transmission jitter, and bit error rate of each wireless transmission path to a preset scale range to form signal scale value, bandwidth scale value, stability scale value, and bit error rate scale value. Based on the network stability requirements defined in the current application scenario, configure pre-defined parameters for the signal scale value, bandwidth scale value, stability scale value, and bit error rate scale value. S1. Set signal impact weight, bandwidth impact weight, stability impact weight, and bit error impact weight; S2. Multiply the signal scale value, bandwidth scale value, stability scale value, and bit error scale value by the preset signal impact weight, bandwidth impact weight, stability impact weight, and bit error impact weight respectively to obtain the signal weighted value, bandwidth weighted value, stability weighted value, and bit error weighted value. Add the signal weighted value, bandwidth weighted value, and stability weighted value and subtract the bit error weighted value to obtain the initial score for each wireless transmission path; S3. Normalize the initial score to obtain the final quality score.

3. The network camera data storage system based on wireless transmission according to claim 2, characterized in that: The method for outputting the link quality spectrum in the wireless link sensing module is as follows: S4, sort all wireless transmission paths based on the quality score to generate a path quality sequence, and mark wireless transmission paths in the path quality sequence whose quality scores are lower than a preset effective threshold to form an effective path set and a path set to be optimized; S5, package the identification information, the quality score and the original parameters of each wireless transmission path in the effective path set into a path description unit, arrange all the path description units in descending order according to the quality score, add a timestamp, and integrate to generate a link quality spectrum.

4. The network camera data storage system based on wireless transmission according to claim 3, characterized in that: The output step of the encoded video data in the data processing module includes: adjusting the encoding resolution, frame rate, and keyframe generation interval of the original video data according to the difference between the quality score and the reliability threshold, and outputting encoded video data adapted to the current network state. Specifically, the steps are: Q1, calculating the difference between the quality score and the reliability threshold of the wireless transmission path to be evaluated, and determining the video compression level based on the magnitude of the difference, wherein the video compression level is negatively correlated with the difference magnitude; Q2, based on the video compression level, obtaining the corresponding encoding resolution baseline value, frame rate baseline value, and keyframe interval baseline value from a preset encoding parameter mapping table, and simultaneously extracting the rate of change statistics of the quality scores of all wireless transmission paths within a preset time window from the link quality spectrum as... The overall network fluctuation index is used to scale and adjust the encoding resolution baseline value, the frame rate baseline value, and the keyframe interval baseline value proportionally based on the magnitude of the overall network fluctuation index, generating dynamically adjusted encoding parameters. The proportional scaling adjustment includes: calculating a scaling factor with the overall network fluctuation index as input; multiplying the encoding resolution baseline value and the frame rate baseline value by the scaling factor to obtain preliminary resolution and frame rate values, respectively; dividing the keyframe interval baseline value by the scaling factor to obtain a preliminary interval value; and finally constraining the preliminary resolution value, the preliminary frame rate value, and the preliminary interval value within the feasible range of preset corresponding parameters to generate adjusted encoding parameters; Q3. The adjusted encoding parameters are used to encode the original video data in real time to generate encoded video data.

5. The network camera data storage system based on wireless transmission according to claim 4, characterized in that: The specific steps for determining the video compression level based on the magnitude of the difference in step Q1 are as follows: Q101. Based on the historical transmission data recorded in the link quality spectrum, analyze the correlation between the difference between the quality score and the reliability threshold and the video smoothness index. When the rate of change of the video smoothness index exceeds a preset change threshold, record the specific difference at this time as an interval division benchmark. Divide the range formed by the continuous difference in amplitude into multiple amplitude intervals according to the interval division benchmark, and configure a preset compression level parameter for each amplitude interval; Q102. Determine the amplitude interval in which the actual compression level parameter difference is located according to the calculated difference in amplitude, and use the compression level parameter corresponding to the amplitude interval as the video compression level.

6. The network camera data storage system based on wireless transmission according to claim 5, characterized in that: The remaining battery power of the network camera in the energy consumption decision module is monitored in real time by the power monitoring unit integrated inside the network camera; the analysis steps of the network congestion degree are as follows: R1, extract the quality score sequence of all wireless transmission paths within a preset time window from the link quality spectrum, calculate the rate of change of the quality score of each wireless transmission path within the preset time window, and calculate the average value and variance of the rate of change of all wireless transmission paths as a network stability index. R2. Combine the average and variance of the network stability index with the trend of the proportion of the effective path set in the total number of paths, and calculate the comprehensive congestion index by constructing a mathematical relationship between the product of the average and variance and the reciprocal of the proportion trend; R3. Perform linear normalization on the comprehensive congestion index to obtain the network congestion level value.

7. The network camera data storage system based on wireless transmission according to claim 6, characterized in that: The calculation method of the energy adaptive coefficient in the energy consumption decision module is as follows: R4, calculate the ratio of the remaining battery power to the preset baseline power value to obtain the power ratio factor, and calculate the difference between the network congestion level value and the preset baseline congestion value to obtain the congestion offset. Multiply the power ratio factor and the congestion offset to obtain the energy consumption adjustment factor; R5, add the energy consumption adjustment factor to the preset energy baseline coefficient to obtain the energy adaptive coefficient; The energy-saving strategy mapping table specifically includes the mapping relationship between the energy adaptive coefficients with different numerical ranges and the corresponding device working mode instructions. The device working mode instructions include specific configuration combinations of image acquisition parameters, event listening levels, and communication power levels.

8. The network camera data storage system based on wireless transmission according to claim 7, characterized in that: The method for selecting the main transmission path in the path execution module is as follows: T1. Select all wireless transmission paths whose quality scores meet the preset preference conditions from the link quality spectrum to form a candidate path set. When the candidate path set contains only one wireless transmission path, the wireless transmission path is determined as the main transmission path. When the candidate path set contains multiple wireless transmission paths, proceed to step T2. T2. Obtain the amount of data to be transmitted from the original video data. Combine the average transmission rate record of each candidate wireless transmission path in the same historical time period to calculate the estimated transmission time of each candidate path. Multiply the inverse of the estimated transmission time by the quality score to obtain the transmission performance score. T3. Sort the transmission performance scores of all candidate wireless transmission paths in descending order, select the wireless transmission path with the highest ranking as the main transmission path, and select the wireless transmission path with the second highest ranking as the auxiliary transmission path.

9. The network camera data storage system based on wireless transmission according to claim 8, characterized in that: When the path execution module allocates low-priority data frames to the auxiliary transmission path, it simultaneously sets the final transmission power of the main transmission path and the auxiliary transmission path according to the communication power level in the device working mode instruction. The preset back-end storage system is specifically a distributed storage array deployed in a remote monitoring center, equipped with a data receiving interface and a storage management unit, used to receive and integrate the encoded video data transmitted through the main transmission path and the auxiliary transmission path, and to classify and store it.

10. The network camera data storage method based on wireless transmission according to claim 9, characterized in that: Includes the following steps: A1. Monitor the raw parameters of each available wireless transmission path in real time, calculate the raw parameters, generate a real-time quality score for each wireless transmission path, and output the link quality spectrum. A2. Obtain the raw video data collected by the network camera. When a wireless transmission path to be evaluated that meets the preset preference conditions is identified from the link quality spectrum, and the quality score of the wireless transmission path to be evaluated is lower than the preset reliability threshold, output the encoded video data and mark the transmission priority identifier for each type of data frame in the encoded video data. A3. Continuously monitor the remaining battery power of the network camera and the network congestion level obtained from the link quality spectrum analysis. Calculate the energy adaptive coefficient based on the remaining battery power and the network congestion level. Match the preset energy-saving strategy mapping table according to the energy adaptive coefficient and generate device working mode instructions. A4. Select a wireless transmission path from the link quality spectrum whose quality score meets the preset preferred conditions as the main transmission path, and select another wireless transmission path with the next lower quality score as the auxiliary transmission path. According to the transmission priority identifier, allocate high-priority data frames to the main transmission path and low-priority data frames to the auxiliary transmission path. Transmit the encoded video data to the preset back-end storage system via the selected path to complete the high-reliability and energy-efficient optimized transmission of video data to the storage device.

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