A low-power AOV data acquisition and transmission system

Abstract

The present invention relates to the technical field of data transmission, and specifically to a low-power AOV data acquisition and transmission system. The system includes a data capture and rejection module, an image data analysis module, a local data writing module, and a data synchronization and transmission module. In the present invention, by periodically starting the camera and automatically capturing images within a preset interval, the energy consumption is effectively reduced and the device operation time is extended. By analyzing the pixel changes of consecutive images and rejecting duplicate images, the storage of invalid data is reduced, and the storage efficiency and data processing speed are improved. According to the evaluation results of the volatility of the image content, the data acquisition interval is adjusted, making the data acquisition process more refined, capable of reducing resource consumption without sacrificing data quality. By evaluating the performance indicators of different communication networks and selecting the best data transmission route, the communication energy consumption is significantly reduced, ensuring the economy and environmental friendliness of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and particularly to a low-power AOV data acquisition and transmission system. Background Art

[0002] The technical field of data transmission involves various methods and systems for effectively moving and managing data between computing devices. The technology can be implemented through wired or wireless means, including fiber optic communication, satellite transmission, wireless local area network, and cellular network technology. The technology focuses on improving transmission speed, enhancing data integrity, reducing error rates, improving the energy efficiency of the system, optimizing data transmission efficiency, reducing latency, and enhancing encryption and security during data transmission.

[0003] Among them, the low-power AOV data acquisition and transmission system is a system specifically designed to optimize the acquisition and transmission efficiency of audio, video, and other operation data between devices. By reducing the energy consumption during data transmission, the system can extend the running time of the device, and is particularly suitable for mobile devices and remote monitoring devices powered by batteries or other limited energy sources, which have high requirements for real-time data transmission and efficient energy consumption management. By integrating coding technology and energy efficiency management strategies, the system can significantly reduce power consumption while ensuring data quality, improving the sustainability and economic benefits of the overall system.

[0004] There are obvious deficiencies in traditional data transmission and energy efficiency management. In traditional systems, data acquisition fails to be dynamically adjusted according to actual needs, resulting in the generation of excessive invalid data, increasing the burden of storage and processing. The lack of effective data preprocessing and compression mechanisms keeps the data storage cost high. In terms of data transmission, the characteristics of different communication networks are not effectively evaluated and utilized, often resulting in excessive communication energy consumption and resource waste. The data security and integrity guarantee measures are not balanced with the data transmission efficiency, so that the efficiency of the system is affected while ensuring data security. It limits the ability of the data transmission system to handle large-scale data streams and high-performance requirements, and hinders the development and application of the technology. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose a low-power AOV data acquisition and transmission system.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A low-power AOV data acquisition and transmission system, the system includes:

[0007] Based on the target data acquisition environment, the data capture and duplicate image elimination module regularly activates the camera at a preset data acquisition interval to capture image data of the target area, writes the captured image data into the flash, puts the camera into sleep mode, evaluates the degree of image change by analyzing the pixel changes between two consecutive images, eliminates duplicate images, and obtains the result of duplicate image elimination;

[0008] Based on the result of duplicate image elimination, the image data analysis module evaluates the duplicate image elimination frequency by counting the data of eliminated duplicate images over a period of time, and evaluates the volatility of the image content based on the degree of image change and the number of non-duplicate images over a period of time, obtaining the result of image data analysis;

[0009] Based on the result of image data analysis, the local data writing module adjusts the preset data acquisition interval according to the duplicate image elimination frequency and the volatility of the image content, performs image data acquisition at the adjusted data acquisition interval, and compresses the data in the flash after it is full, and writes the compressed data into the TF card, obtaining the result of local content writing;

[0010] Based on the result of local content writing, the data synchronization and transmission module extracts the status data of each communication method, analyzes the estimated transmission time of each communication method according to the size of the compressed data, combines the communication energy consumption per unit time of each communication method, selects the communication method with the lowest energy consumption for real-time data transmission, and transmits the data to the cloud server for backup, obtaining the result of data transmission.

[0011] The improvement of the present invention is that the steps for obtaining the result of duplicate image elimination are as follows:

[0012] Based on the target data acquisition environment, the camera is regularly activated at a preset data acquisition interval to capture images of the target area, the image data is written into the flash, and the acquisition timestamp of the image is recorded, obtaining the basic image data set;

[0013] Based on the basic image data set, analyze the pixel changes between two consecutive image data, compare the pixel differences between the two images, and use the formula:

[0014] ;

[0015] Calculate the image change index;

[0016] where, is the image change index, is the gray value of the th pixel point in the current image, is the gray value of the th pixel point in the previous image, is the total number of pixels in the image;

[0017] Based on the image change index, compare it with a preset change threshold, eliminate the images with a change index lower than the preset change threshold, and record the timestamps corresponding to the duplicate images to obtain the result of duplicate image elimination.

[0018] The improvement of the present invention is that the step of evaluating the duplicate image elimination frequency is:

[0019] Based on the result of duplicate image elimination, according to a preset statistical period, count the number of eliminated duplicate images within a period of time to obtain the statistical result of elimination data;

[0020] Based on the statistical result of elimination data, according to the length of the statistical period, through the formula:

[0021] ;

[0022] Calculate the duplicate image elimination frequency to obtain the analysis result of elimination frequency;

[0023] Wherein, is the duplicate image elimination frequency, is the total number of duplicate images within the statistical period, is the time length of the statistical period.

[0024] The improvement of the present invention is that the step of obtaining the image data analysis result is:

[0025] Based on the result of duplicate image elimination, count the number of remaining images after eliminating duplicate images within a period of time to obtain the non-duplicate image statistical information;

[0026] Based on the non-duplicate image statistical information, extract the image change index of each non-duplicate image, calculate the average value of the image change index to obtain the volatility correlation data;

[0027] Based on the volatility correlation data, through the formula:

[0028] ;

[0029] Calculate the volatility index of the image content and integrate the duplicate image elimination frequency within the current statistical period to obtain the image data analysis result;

[0030] Wherein, is the volatility index of the image content, is the average value of the change indexes of non-duplicate images within the statistical period, is the total number of non-duplicate images within the statistical period, is the logarithmic adjustment coefficient.

[0031] The improvement of the present invention is that the step of adjusting the preset data acquisition interval is as follows:

[0032] Based on the analysis result of the image data, extract the duplicate image rejection frequency and the volatility index of the image content within the current statistical period, and obtain the preset data acquisition interval to obtain acquisition interval correlation information;

[0033] Based on the acquisition interval correlation information, through the formula:

[0034] ;

[0035] Calculate the adjusted data acquisition interval;

[0036] Wherein, is the duplicate image rejection frequency, is the volatility index of the image content, and are influence coefficients, is the preset data acquisition interval, is the longest tolerable data acquisition interval, is the adjusted data acquisition interval;

[0037] Based on the adjusted data acquisition interval, set the adjusted data acquisition interval as the image acquisition interval of the camera, and perform image acquisition according to the adjusted data acquisition interval to obtain an interval adjustment result.

[0038] The improvement of the present invention is that the step of obtaining the local content writing result is as follows:

[0039] Based on the interval adjustment result, according to the adjusted data acquisition interval, perform the data acquisition and rejection process, and write the captured image data into the flash to obtain image data storage information;

[0040] Based on the image data storage information, monitor the flash storage space. When it is detected that the flash is full, start the data compression program to compress the image data stored in the flash to obtain compressed image data;

[0041] Based on the compressed image data, write the compressed image data into the TF card to obtain a local content writing result.

[0042] The improvement of the present invention is that the step of analyzing the expected transmission time of each communication method is as follows:

[0043] Based on the local content writing result, detect the status data of each communication method, including network latency, packet loss rate, and bandwidth data, to obtain a network status data set;

[0044] Based on the network status data set, according to the compressed total data size, through the formula:

[0045] ;

[0046] Calculate the expected data transmission time for each communication method to obtain the transmission time analysis result:

[0047] Wherein, is the communication delay, is the communication packet loss rate, is the communication bandwidth, is the compressed total data size, is the communication fluctuation influence coefficient, is the expected data transmission time.

[0048] The improvement of the present invention is that the steps for obtaining the data transmission result are:

[0049] Based on the transmission time analysis result, according to the data transmission records of each communication method, extract the communication energy consumption per unit time, startup energy consumption, and waiting energy consumption parameters of each communication method to obtain communication selection association data;

[0050] Based on the communication selection association data, through the formula:

[0051] ;

[0052] Calculate the total transmission energy consumption of each communication method;

[0053] Wherein, is the total transmission energy consumption of the communication method, is the communication energy consumption per unit time, is the expected data transmission time, is the startup energy consumption of the communication method, is the communication waiting time, is the waiting energy consumption per unit time;

[0054] Based on the total transmission energy consumption of each communication method, according to the magnitude of the total transmission energy consumption, select the communication method with the lowest total energy consumption, and transmit the data to the cloud server for backup to obtain the data transmission result.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are:

[0056] In the present invention, by starting the camera at regular intervals and automatically capturing images within a preset interval, the energy consumption is effectively reduced and the device operation time is extended. By analyzing the pixel changes of consecutive images and eliminating duplicate images, the storage of invalid data is reduced, and the storage efficiency and data processing speed are improved. According to the evaluation result of the volatility of the image content, the data acquisition interval is adjusted, making the data acquisition process more refined, and the resource consumption can be reduced without sacrificing data quality. By compressing the data stored in the flash and transferring it to the TF card, the data storage structure is optimized, the storage space is saved, and the flexibility of data management is improved. By evaluating the performance indicators of different communication networks and selecting the best data transmission route, the communication energy consumption is significantly reduced, ensuring the economy and environmental friendliness of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is the system flowchart of the present invention;

[0058] Figure 2 is the flowchart of obtaining the result of eliminating duplicate images of the present invention;

[0059] Figure 3 is the flowchart of obtaining the evaluation frequency of eliminating duplicate images of the present invention;

[0060] Figure 4 is the flowchart of obtaining the result of image data analysis of the present invention;

[0061] Figure 5 is the flowchart of adjusting the preset data acquisition interval of the present invention;

[0062] Figure 6 is the flowchart of obtaining the result of writing local content of the present invention;

[0063] Figure 7 is the flowchart of analyzing the expected transmission time of each communication method of the present invention;

[0064] Figure 8 is the flowchart of obtaining the data transmission result of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0065] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0066] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0067] Please refer to Figure 1 , the present invention provides a technical solution: a low-power AOV data acquisition and transmission system, the system includes:

[0068] Based on the target data acquisition environment, the data capture and rejection module regularly activates the camera at a preset data acquisition interval, captures the image data of the target area, writes the captured image data into the flash, puts the camera into sleep mode, evaluates the degree of image change by analyzing the pixel changes between two consecutive images, rejects duplicate images, and records the timestamps corresponding to the duplicate images to obtain the result of rejecting duplicate images;

[0069] Based on the result of rejecting duplicate images, the image data analysis module evaluates the frequency of rejecting duplicate images by counting the rejection data of duplicate images within a period of time, and evaluates the volatility of the image content according to the degree of image change and the number of non-duplicate images within a period of time to obtain the result of image data analysis;

[0070] Based on the result of image data analysis, the local data writing module adjusts the preset data acquisition interval according to the frequency of rejecting duplicate images and the volatility of the image content, performs image data acquisition at the adjusted data acquisition interval, and compresses the data in the flash after the flash is full, and writes the compressed data into the TF card to obtain the result of writing local content;

[0071] Based on the result of writing local content, the data synchronization and transmission module extracts the status data of each communication method, including network latency, packet loss rate, and bandwidth parameters, analyzes the expected transmission time of each communication method according to the size of the compressed data, and selects the communication method with the lowest energy consumption for real-time data transmission according to the expected transmission time and combines the communication energy consumption per unit time of each communication method, and transmits the data to the cloud server for backup to obtain the result of data transmission.

[0072] The duplicate image elimination results include valid image indices, the total number of eliminated images, and the corresponding timestamp records. The image data analysis results include the statistical distribution of image change degrees, the storage frequency of non-duplicate images, and the quantitative metrics of image content volatility. The local content writing results include the compression ratio, compression efficiency, and data integrity verification results of the data inside the flash. The data transmission results include the selected type of communication method, the actual transmission time, the energy consumption records, the storage status of the data in the cloud, and the backup integrity.

[0073] Please refer to Figure 2 , and the steps to obtain the duplicate image elimination results are as follows:

[0074] Based on the target data acquisition environment, at a preset data acquisition interval, the camera is started regularly to capture images of the target area. The image data is written into the flash, and the acquisition timestamp of the image is recorded to obtain a basic image dataset.

[0075] According to the preset data acquisition interval, the camera is started regularly to capture images of the target area in the environment. The target area is captured regularly, and the image data will be written into the flash device. Each time an image is captured, the acquisition timestamp of the image is recorded. During the process of recording the timestamp, time synchronization is performed according to the internal clock or through the Network Time Protocol service to ensure the accuracy of the timestamp. For example, if the set acquisition interval is every 5 seconds, the camera is activated every 5 seconds to capture an image of the area and write this image along with its acquisition time into the flash. This operation ensures the timeliness and integrity of the data. During the process of generating the basic image dataset, each image data undergoes preliminary format conversion processing, which includes converting the original image data from the original format of the camera to a more storage-efficient format, such as JPEG or PNG. Each step of the image data processing should be recorded in detail for subsequent analysis and invocation.

[0076] Based on the basic image dataset, analyze the pixel changes between two consecutive image data, compare the pixel differences between the two images, and through the formula:

[0077] ;

[0078] Calculate the image change index;

[0079] Among them, is the image change index, is the gray value of the th pixel point in the current image, is the gray value of the th pixel point in the previous image, is the total number of pixels in the image;

[0080] Formula:

[0081] ;

[0082] The advantage of the formula is that by analyzing the pixel changes between two consecutive images, it can effectively detect changes in the image content, which helps subsequent image processing processes such as duplicate image elimination or anomaly detection.

[0083] Detailed explanation of the formula and the derivation process of formula calculation:

[0084] In this formula, represents the image change index, which is used to measure the degree of change between two consecutive images. is the grayscale value of the th pixel in the current image. is the grayscale value of the th pixel in the previous image. represents the total number of pixels in the image. Let the total number of pixels be (to slow down the calculation demonstration). Assume the following specific grayscale value data: , ; , ; , ; , ; According to the formula, calculate the absolute value of the grayscale value difference between each pair of pixels, sum them up, and then divide by the total number of pixels to obtain the image change index .

[0085] Calculate the absolute value of the grayscale difference of each pair of pixels: ; ; ; .

[0086] Calculate the sum of the differences:

[0087] ;

[0088] Divide the sum by the total number of pixels to calculate the image change index :

[0089] ;

[0090] The value of the image change index is 3.25. The value represents the average degree of change in the grayscale values of this set of images. Based on this result, the changes in the image content can be further analyzed, such as whether duplicate or unimportant image data needs to be eliminated, so as to perform effective data management and processing.

[0091] Based on the image change index, compare it with a preset change threshold, eliminate the images whose change index is lower than the preset change threshold, and record the timestamps corresponding to the duplicate images to obtain the result of eliminating duplicate images;

[0092] Eliminate the images whose change index is lower than the preset change threshold, automatically filter out the duplicate or insignificantly changed images. The images are considered as duplicate content that requires no processing. Eliminating the images can significantly reduce the storage requirements and improve the efficiency of subsequent processing steps. For example, if 3 is set as the threshold, when the detected image change index is lower than this threshold, the relevant images will not be stored or transmitted, thus saving resources. The timestamps recorded in the elimination result enable tracking of the specific time points of each elimination, which is very important for future data auditing or backtracking. This process realizes the optimized management of data, ensuring that only necessary and useful image data is retained for processing.

[0093] Please refer to Figure 3 , the steps for evaluating the frequency of eliminating duplicate images are as follows:

[0094] Based on the result of eliminating duplicate images, according to the preset statistical period, count the number of eliminated duplicate images within a period of time to obtain the statistical result of elimination data;

[0095] Count the number of eliminated duplicate images within a period of time. Through statistics, it can provide a quantitative view to show the number of images automatically eliminated due to content duplication or insignificant changes within a given time period. For example, during a monitoring period, the system may automatically eliminate dozens to hundreds of images per hour, depending on the activity level in the monitored area and the trigger sensitivity of the camera. If the activity in the monitored area is relatively stable, such as in a warehouse, the number of duplicate images within an hour is dozens. Statistics not only help evaluate the effective use of storage resources but also reflect the suitability of the camera monitoring settings. Whether it is necessary to adjust the trigger parameters of the camera or optimize the image processing algorithm to avoid the generation of too much invalid data.

[0096] Based on the statistical result of elimination data, according to the length of the statistical period, through the formula:

[0097] ;

[0098] Calculate the frequency of eliminating duplicate images to obtain the analysis result of elimination frequency;

[0099] Among them, is the frequency of eliminating duplicate images, is the total number of duplicate images within the statistical period, is the time length of the statistical period;

[0100] Formula:

[0101] ;

[0102] The advantage of the formula is that by calculating the frequency of duplicate image rejection within a given time period, the efficiency of the monitoring system can be quantitatively analyzed, reflecting the consistency and change patterns of activities within the monitored area, which provides data support for optimizing the monitoring settings and adjusting the camera configuration.

[0103] Detailed explanation of the formula and the derivation process of the formula calculation:

[0104] In this formula, represents the frequency of duplicate image rejection, which is a unit of measurement used to measure the average number of images rejected per unit time. is the total number of duplicate images within the statistical period, and the data is automatically recorded and calculated by image analysis software. For example, within one hour, the system may automatically reject 100 duplicate images. is the length of the statistical period, in days. If the statistical period is one hour, then 1 hour. Assuming that 100 images are rejected within one hour, the process of calculating the frequency of duplicate image rejection is as follows: images / hour. The result shows that during monitoring, 100 duplicate images are automatically rejected per hour. This data provides an intuitive expression of the efficiency of the monitoring system and can be used for further system optimization and parameter adjustment.

[0105] Please refer to Figure 4 for the steps to obtain the results of image data analysis:

[0106] Based on the results of duplicate image rejection, count the number of images retained after rejection of duplicate images over a period of time to obtain non-duplicate image statistical information;

[0107] Based on the results of duplicate image rejection, by analyzing the data of duplicate images rejected within a specific time period, count the number of remaining non-duplicate images. The statistical process involves data classification and counting. For example, database queries or data analysis software can be used. For example, during a one-day monitoring period, duplicate images are marked and rejected daily, and the number of remaining non-duplicate images is automatically counted by the database. The statistical results show that 3000 non-duplicate images are retained in one day, which provides accurate basic data for subsequent image analysis, obtains non-duplicate image statistical information, and these information reflect the efficiency of the image acquisition system and the quality of image data, providing key baseline data for the next step of analysis.

[0108] Based on the non-duplicate image statistical information, extract the image change index of each non-duplicate image and calculate the average value of the image change index to obtain volatility correlation data;

[0109] Based on non-repetitive image statistical information, the image change index of each image is extracted through specialized image analysis software or scripts, and the average value of the change index is calculated to evaluate the overall variability of the image dataset. This calculation can be achieved through programming. For example, using the Python programming language combined with the NumPy library to perform arithmetic mean operations on the change indices of all non-repetitive images. Suppose the average change index obtained within a one-day monitoring period is 0.5. This result helps to understand the degree of change in the image set and obtain volatility correlation data.

[0110] Based on the volatility correlation data, through the formula:

[0111] ;

[0112] Calculate the volatility index of the image content and integrate the repetition image rejection frequency within the current statistical period to obtain the image data analysis result;

[0113] Among them, is the volatility index of the image content, is the average value of the change indices of non-repetitive images within the statistical period, is the total number of non-repetitive images within the statistical period, is the logarithmic adjustment coefficient;

[0114] Formula:

[0115] ;

[0116] The benefit of the formula is that by combining the average value of the change indices of non-repetitive images and the logarithmic adjustment of the image quantity, the volatility of the image content can be quantified, thus more precisely describing the dynamic characteristics of the image dataset.

[0117] Detailed explanation of the formula and the derivation process of the formula calculation:

[0118] In this formula, represents the average value of the change indices of non-repetitive images within the statistical period, such as 0.5 calculated above, is the total number of non-repetitive images, for example, 3000, is an adjustment coefficient used to adjust the influence of the logarithmic function, set to 0.03. This coefficient can be adjusted according to the actual data distribution to reflect the characteristics of different datasets. By substituting these parameter values into the formula, the calculation process is as follows:

[0119] Calculate the logarithmic term , getting approximately 1.959, and then calculate 8.

[0120] The result shows that the volatility index of the image content is 0.98, providing a quantitative basis for subsequent image processing and analysis decisions.

[0121] Please refer to Figure 5 , the steps for adjusting the preset data acquisition interval are as follows:

[0122] Based on the image data analysis results, extract the duplicate image rejection frequency and the volatility index of the image content within the current statistical period, and obtain the preset data acquisition interval to get the acquisition interval correlation information;

[0123] Extract the duplicate image rejection frequency and the volatility index of the image content within the current statistical period. Obtain data through analysis tools or database queries, export the daily image analysis records. The statistical period can be one hour, one day, one week, or one month. According to the data within the period, extract the rejection frequency of duplicate images. It is necessary to obtain the average value of the volatility indices of all non-duplicate images within this period. If the volatility index of each image is known, calculate the average of the values to obtain the period average volatility index. The information obtained is the acquisition interval correlation information, and this data is used for subsequent data acquisition interval adjustment analysis.

[0124] Based on the acquisition interval correlation information, through the formula:

[0125] ;

[0126] Calculate the adjusted data acquisition interval;

[0127] Among them, is the duplicate image rejection frequency, is the volatility index of the image content, and are influence coefficients, is the preset data acquisition interval, is the longest tolerable data acquisition interval, is the adjusted data acquisition interval;

[0128] Formula:

[0129] ;

[0130] The benefit of the formula is that by dynamically adjusting the data acquisition interval, the efficiency of data collection can be optimized, the storage of irrelevant data can be reduced, and at the same time, important events are ensured not to be missed. This adjustment method is flexibly adjusted according to the actual operation of the system and the changes in image data, thereby improving the overall performance of the system.

[0131] Detailed explanation of the formula and the derivation process of formula calculation: represents the adjusted data acquisition interval, is the longest tolerable acquisition interval, set to 30 minutes, is the preset data acquisition interval, such as 5 minutes, is the frequency of repeated image rejection, for example 0.3, and are adjustment coefficients, set to 2 and 0.5 respectively, adjusted according to actual needs. The coefficients reflect the influence degree of the rejection frequency and the volatility index on the acquisition interval, is the volatility index of the image content, for example 0.5. By substituting these parameters, the calculation process is as follows:

[0132] Calculate the denominator part:

[0133] ;

[0134] Calculate the overall fraction:

[0135] minutes;

[0136] Because minutes, so minutes;

[0137] The results show that considering the high repeated image rejection rate and the low volatility index, the adjusted data acquisition interval is significantly reduced to respond to the rapidly changing image data requirements.

[0138] Based on the adjusted data acquisition interval, set the adjusted data acquisition interval as the image acquisition interval of the camera, and perform image acquisition according to the adjusted data acquisition interval to obtain the interval adjustment result;

[0139] Set the adjusted data acquisition interval as the image acquisition interval of the camera. By programming to adjust the camera settings or configuration file, ensure that the camera operates according to the new acquisition interval. For example, set the trigger time interval of the camera to 2.4 minutes, which can be achieved by accessing the camera's API or modifying its configuration file, by writing a script to update the camera settings or directly adjusting in the device's user interface. The adjusted settings ensure that the image acquisition frequency is synchronized with the actual image change rate, optimize the storage resources and processing time, improve the efficiency of the monitoring system, and also ensure the data quality, thus achieving efficient and accurate image data acquisition in practical applications.

[0140] Please refer to Figure 6 for the steps to obtain the local content writing result:

[0141] Based on the interval adjustment result, according to the adjusted data acquisition interval, perform the data acquisition and rejection process, and write the captured image data into the flash to obtain the image data storage information;

[0142] Data acquisition and elimination processes are carried out according to the adjusted data acquisition interval, and the captured image data is written into the flash. The camera performs data acquisition according to the new acquisition interval, which is dynamically adjusted based on the previous image analysis results. For example, if the adjusted interval is set to once every 2.4 minutes, the camera is activated every 2.4 minutes to capture images and write the image data into the flash storage device. This operation not only involves image capture but also includes the process of transferring image data from the camera buffer to the storage device. After each data write, the stored image data information is updated, which is crucial for subsequent image management and analysis, helping the system administrator monitor the usage of storage space and ensure data security and integrity. For example, the system records the storage timestamp and storage location of each image, and this information is used to track the status of each data block, thereby optimizing the storage management strategy.

[0143] Based on the information of the image data stored, monitor the flash storage space. When it is detected that the flash is full, start the data compression program to compress the image data stored in the flash to obtain the compressed image data.

[0144] Continuously monitor the capacity of the storage device. Once the storage space approaches its capacity limit, such as reaching a 95% usage rate, automatically trigger the compression program, which compresses the image data stored in the flash to free up storage space. The compression algorithm may include JPEG or other image compression technologies. These technologies can effectively reduce the data size of each image while maintaining the usability of the image. For example, an image with an original size of 2MB may be reduced to 1MB after compression. This not only saves storage space but also improves the efficiency of data management. The compression process needs to ensure data integrity and recoverability for future analysis or recovery operations.

[0145] Based on the compressed image data, write the compressed image data into the TF card to obtain the local content write result.

[0146] Write the compressed image data into the TF card to obtain the local content write result. This process involves transferring the processed data from the system memory to an external storage medium, namely the TF card, to ensure long-term data preservation and convenient portability. For example, after the compression process is completed, the system transfers this data from the internal flash storage to the TF card. This process is carried out through a high-speed data interface such as USB or SDIO to ensure the speed and efficiency of data transmission. The use of the TF card enables data to be easily transferred between different devices and provides convenience for data backup. The management of this process includes ensuring the verification of data integrity and optimizing the write speed of the TF card to meet the application requirements in different scenarios.

[0147] Please refer toFigure 7 , the steps for analyzing the expected transmission time of each communication method are as follows:

[0148] Based on the local content writing result, detect the status data of each communication method, including network latency, packet loss rate, and bandwidth data, to obtain a network status data set;

[0149] Detect the status data of each communication method. Detect the real-time status of each communication channel through a network monitoring tool. For example, use network performance monitoring software such as Wireshark or PRTG to monitor the network parameters of various communication methods, such as Wi-Fi, Ethernet, 3G / 4G, etc., and record the latency data of each channel, usually in milliseconds, the packet loss rate is displayed as a percentage, and the bandwidth data is measured in Mbps. The data is summarized into a network status data set to provide a basis for subsequent data transmission decisions. For example, in a sampling, an Ethernet connection may show a latency of 20 ms, a packet loss rate of 0.5%, and a bandwidth of 100 Mbps. These detailed metrics help optimize the data transmission path and adjust the network configuration to ensure the efficiency and reliability of data transmission.

[0150] Based on the network status data set, according to the total compressed data size, through the formula:

[0151] ;

[0152] Calculate the expected data transmission time of each communication method to obtain the transmission time analysis result:

[0153] Among them, is the communication latency, is the communication packet loss rate, is the communication bandwidth, is the total compressed data size, is the communication fluctuation impact coefficient, is the expected data transmission time;

[0154] Formula:

[0155] ;

[0156] The advantage of the formula is that it provides a method to estimate the data transmission time under different network states, which is particularly important for data-intensive applications such as real-time video monitoring or remote data backup, and can help system administrators and network engineers optimize network resources and improve the efficiency of data transmission.

[0157] Detailed explanation of the formula and the derivation process of formula calculation:

[0158] In this formula, represents the expected data transmission time, is the total compressed data size, such as 500MB, is the communication bandwidth. Assume the network bandwidth is 100Mbps, is the communication packet loss rate, such as 0.5%, is the communication fluctuation influence coefficient, which may be set to 1.05 according to network stability, is the communication delay, set to 50ms. When calculating the effective bandwidth, consider the packet loss rate:

[0159] Mbps;

[0160] Calculate the main body time of data transmission:

[0161] seconds;

[0162] Finally, add the communication delay:

[0163] seconds;

[0164] The results show that under the current network conditions, it is expected to take about 5.28 seconds to transmit 500MB of data. The calculation results have practical application value for evaluating data transmission strategies and predicting network loads.

[0165] Please refer to Figure 8 , the steps to obtain the data transmission results are as follows:

[0166] Based on the transmission time analysis results, according to the data transmission records of each communication method, extract the communication energy consumption per unit time, start-up energy consumption, and waiting energy consumption parameters of each communication method to obtain communication selection correlation data;

[0167] Extract the communication energy consumption per unit time, start-up energy consumption, and waiting energy consumption parameters of each communication method. The energy consumption parameters of each communication method are obtained from the comprehensive monitoring records. For example, through real-time energy consumption monitoring devices or software, collect the energy consumption of each communication module during data transmission. The communication energy consumption per unit time is usually measured in watts per hour. The start-up energy consumption includes the energy consumption of the device from the sleep or off state to the fully operational state. The waiting energy consumption refers to the energy consumption of the device in the standby state but not actually transmitting data. The data is recorded and analyzed through an energy consumption monitoring system such as a power quality analyzer or dedicated software. For example, in one sampling, the communication energy consumption per unit time of Wi-Fi communication may be recorded as 0.3 watts / second, the start-up energy consumption is 2 watts, and the waiting energy consumption is 0.05 watts / second. The data is crucial for selecting the most effective communication method to optimize energy consumption.

[0168] Based on the communication selection correlation data, through the formula:

[0169] ;

[0170] Calculate the total transmission energy consumption of each communication method;

[0171] Among them, is the total transmission energy consumption of the communication method, is the communication energy consumption per unit time, is the estimated data transmission time, is the startup energy consumption of the communication method, is the communication waiting time, is the waiting energy consumption per unit time;

[0172] Formula:

[0173] ;

[0174] The advantage of the formula is that it can accurately calculate the total energy consumption in different communication states, which helps to select the most energy-efficient one among multiple communication methods, thereby reducing energy consumption and increasing system efficiency.

[0175] Detailed explanation of the formula and the derivation process of formula calculation:

[0176] In this formula, represents the total transmission energy consumption, is the communication energy consumption per unit time, such as 0.3 watts / second, is the estimated data transmission time, which was calculated to be approximately 5.28 seconds previously, is the startup energy consumption, such as 2 watts, is the communication waiting time, set to 10 seconds, is the waiting energy consumption per unit time, such as 0.05 watts / second.

[0177] Calculate the total energy consumption of startup and transmission:

[0178] Watts;

[0179] Add the startup energy consumption:

[0180] Watts;

[0181] Calculate the waiting energy consumption:

[0182] Watts;

[0183] Final total energy consumption:

[0184] Watts;

[0185] The results show that under the given parameters and conditions, the total energy consumption of selecting this communication method for data transmission is extremely low, proving its superiority in energy efficiency and providing a scientific basis for decision-making.

[0186] Based on the total transmission energy consumption of each communication method, according to the magnitude of the total transmission energy consumption, select the communication method with the lowest total energy consumption, and transmit the data to the cloud server for backup to obtain the data transmission result;

[0187] Evaluate the total transmission energy consumption of each communication method, such as the aforementioned Wi-Fi, Ethernet, 4G and other communication methods. By comparing the energy consumption data of each communication method, select the one with the lowest energy consumption for data transmission. For example, if the Ethernet method provides the lowest energy consumption efficiency, it will be preferentially used. During the data transmission process, the transmission speed and stability will be monitored to ensure the secure and efficient transmission of data to the cloud server. In addition, data encryption, transmission protocol selection and network security settings involved in the backup operation are also essential links to ensure the security and integrity of data during transmission. For example, use the TLS / SSL protocol to encrypt the data, optimize the transmission path through load balancing technology, and record the transmission results, including the time when the transmission is completed, the amount of data transmitted, and any possible errors or abnormal information. The data provides necessary records and evidence for subsequent operation and maintenance and auditing.

[0188] The above is only the preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A low-power AOV data acquisition and transmission system, characterized in that, The system includes: Based on the target data acquisition environment, the data capture and duplicate image removal module starts the camera at regular intervals according to the preset data acquisition interval, captures the image data of the target area, writes the captured image data into the flash, puts the camera into sleep mode, evaluates the degree of image change by analyzing the pixel changes between two consecutive images, removes duplicate images, and obtains the result of duplicate image removal; Based on the result of duplicate image removal, the image data analysis module evaluates the duplicate image removal frequency by counting the data of removed duplicate images within a certain period of time, and evaluates the volatility of the image content according to the degree of image change and the number of non-duplicate images by counting the data of the degree of image change within a certain period of time, and obtains the result of image data analysis; Based on the result of image data analysis, the local data writing module adjusts the preset data acquisition interval according to the duplicate image removal frequency and the volatility of the image content, performs image data acquisition according to the adjusted data acquisition interval, compresses the data in the flash after the flash is full, and writes the compressed data into the TF card to obtain the result of local content writing; Based on the result of local content writing, the data synchronization and transmission module extracts the status data of each communication method, analyzes the expected transmission time of each communication method according to the size of the compressed data, combines the communication energy consumption per unit time of each communication method, selects the communication method with the lowest energy consumption for real-time data transmission, and transmits the data to the cloud server for backup to obtain the result of data transmission.

2. The low-power AOV data acquisition and transmission system according to claim 1, wherein The steps for obtaining the result of duplicate image removal are as follows: Based on the target data acquisition environment, start the camera at regular intervals according to the preset data acquisition interval, capture the images of the target area, write the image data into the flash, and record the acquisition timestamp of the images to obtain the basic image dataset; Based on the basic image dataset, analyze the pixel changes between two consecutive image data, compare the pixel differences between the two images, and use the formula: ； Calculate the image change index; Among them, is the image change index, is the gray value of the -th pixel in the current image, is the gray value of the -th pixel in the previous image, is the total number of pixels in the image; Based on the image change index, compare it with the preset change threshold, remove the images with the change index lower than the preset change threshold, and record the timestamps corresponding to the duplicate images to obtain the result of duplicate image removal.

3. The low-power AOV data acquisition and transmission system according to claim 1, characterized in that, The steps for evaluating the duplicate image removal frequency are as follows: Based on the result of duplicate image removal, count the number of removed duplicate images within a certain period of time according to the preset statistical period to obtain the statistical result of the removed data; Based on the statistical result of the removed data, calculate the duplicate image removal frequency according to the length of the statistical period by using the formula: ； to obtain the analysis result of the removal frequency; wherein, is the duplicate image rejection frequency, is the total number of duplicate images within the statistical period, is the time length of the statistical period.

4. The low-power AOV data acquisition and transmission system according to claim 3, characterized in that The steps for obtaining the result of image data analysis are as follows: Based on the result of duplicate image removal, count the number of remaining images after removing duplicate images within a certain period of time to obtain the statistical information of non-duplicate images; Based on the statistical information of non-duplicate images, extract the image change index of each non-duplicate image, calculate the average value of the image change index to obtain the volatility correlation data; Based on the volatility correlation data, calculate the volatility index of the image content by using the formula: ； and integrate the duplicate image removal frequency within the current statistical period to obtain the result of image data analysis; Among them, is the volatility index of the image content, is the average value of the non-repetitive image change index within the statistical period, is the total number of non-repetitive images within the statistical period, is the logarithmic adjustment coefficient.

5. The low-power AOV data acquisition and transmission system according to claim 1, characterized in that The steps for adjusting the preset data acquisition interval are as follows: Based on the analysis result of the image data, extract the duplicate image rejection frequency and the volatility index of the image content within the current statistical period, and obtain the preset data acquisition interval to obtain acquisition interval correlation information; Based on the acquisition interval correlation information, through the formula: ； Calculate the adjusted data acquisition interval; Among them, is the frequency of duplicate image removal, is the volatility index of the image content, and are influence coefficients, is the preset data acquisition interval, is the longest tolerable data acquisition interval, is the adjusted data acquisition interval; Based on the adjusted data acquisition interval, set the adjusted data acquisition interval as the image acquisition interval of the camera, and perform image acquisition according to the adjusted data acquisition interval to obtain an interval adjustment result.

6. The low-power AOV data acquisition and transmission system according to claim 5, wherein The steps for obtaining the local content writing result are as follows: Based on the interval adjustment result, perform data acquisition and rejection processes according to the adjusted data acquisition interval, and write the captured image data into the flash to obtain image data storage information; Based on the image data storage information, monitor the flash storage space. When it is detected that the flash is full, start the data compression program to compress the image data stored in the flash to obtain compressed image data; Based on the compressed image data, write the compressed image data into the TF card to obtain the local content writing result.

7. The low-power AOV data acquisition and transmission system according to claim 1, characterized in that, The steps for analyzing the expected transmission time of each communication method are as follows: Based on the local content writing result, detect the status data of each communication method, including network latency, packet loss rate, and bandwidth data, to obtain a network status data set; Based on the network status data set, according to the total size of the compressed data, through the formula: ； Calculate the expected data transmission time of each communication method to obtain a transmission time analysis result: Among them, is the communication delay, is the communication packet loss rate, is the communication bandwidth, is the total size of the compressed data, is the communication fluctuation influence coefficient, is the estimated data transmission time.

8. The low-power AOV data acquisition and transmission system according to claim 7, characterized in that The steps for obtaining the data transmission result are as follows: Based on the transmission time analysis result, extract the unit time communication energy consumption, startup energy consumption, and waiting energy consumption parameters of each communication method according to the data transmission records of each communication method to obtain communication selection correlation data; Based on the communication selection correlation data, through the formula: ； Calculate the total transmission energy consumption of each communication method; Among them, is the total transmission energy consumption of the communication method, is the communication energy consumption per unit time, is the expected data transmission time, is the startup energy consumption of the communication method, is the communication waiting time, is the waiting energy consumption per unit time; Based on the total transmission energy consumption of each communication method, select the communication method with the lowest total energy consumption according to the size of the total transmission energy consumption, and transmit the data to the cloud server for backup to obtain the data transmission result.

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