A method and system for transmitting image data information during skin care process

Abstract

This application proposes a method and system for transmitting image data information during skin care, relating to the field of image data information transmission technology. The method includes acquiring skin images and performing recognition processing on the skin images to obtain suspected artifact regions, confirmed true texture regions, and smooth regions in the skin images; monitoring the uplink status of the wireless network connected to the skin care device, wherein the uplink status includes instantaneous upload rate and data packet confirmation latency; obtaining the wireless network status based on the uplink status; generating a congestion signal when the wireless network is in a congested state; determining the compression ratio for suspected artifact regions based on the congestion signal; generating a recovery signal when the wireless network returns to a non-congested state; and restoring the compression ratio for suspected artifact regions based on the recovery signal. This application can ensure the accuracy of remote diagnosis and timely adjustment of care plans, improving the efficiency and reliability of remote skin care.

Description

Technical Field

[0001] This application relates to the field of image data information transmission technology, and in particular to a method and system for transmitting image data information during skin care. Background Technology

[0002] In related technologies, the core of modern remote skincare image transmission methods is to divide skin images into high-frequency texture areas (key details such as pores and fine lines, with low compression ratios) and low-frequency smooth areas (such as skin tone, with high compression ratios) to balance transmission quality and efficiency. However, in practical applications, skincare products containing micron-level optical elements can cause problems. When the ultrathin film layer formed by these skincare products is illuminated by the device, it produces nonlinear optical effects, causing subtle brightness variations resembling textures in what should be low-frequency smooth areas on the image sensor—an optical artifact. Because the device's frequency analysis module is trained on standard skin data, it cannot distinguish these artifacts from real high-frequency textures, mistakenly classifying them as high-frequency areas that need to be finely preserved. Subsequently, the system processes these areas using a low compression ratio strategy, leading to a surge in single-frame image data and generating significant redundancy. Furthermore, home wireless networks have limited uplink bandwidth and are not optimized for continuous high-bitrate uploads, causing the uplink to quickly saturate. Ultimately, the amount of data far exceeded the network's carrying capacity, causing severe congestion, packet loss, and retransmission. Images received remotely were stuttering, delayed, and fragmented, making it impossible for doctors or AI to accurately assess the skin condition. The original transmission method failed to realize its advantages and instead became a bottleneck, significantly reducing the efficiency and reliability of remote care. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for transmitting image data information during skin care, aiming to ensure the accuracy of remote diagnosis and timely adjustment of care plans, thereby improving the efficiency and reliability of remote skin care.

[0004] In a first aspect, embodiments of this application provide a method for transmitting image data information during a skin care process, applied to a skin care device, comprising:

[0005] Acquire skin images and perform recognition processing on the skin images to obtain suspected artifact regions, confirmed real texture regions, and smooth regions in the skin images;

[0006] Monitor the uplink status of the wireless network to which the skin care device is connected, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment latency;

[0007] The status of the wireless network is obtained based on the uplink status;

[0008] When the wireless network is in a congested state, a congestion signal is generated.

[0009] Based on the congestion signal, determine the compression ratio for the suspected artifact region;

[0010] When the wireless network returns to a non-congested state, a recovery signal is generated;

[0011] Based on the recovery signal, the compression ratio of the suspected artifact region is restored.

[0012] According to some embodiments of this application, determining the compression ratio of the suspected artifact region based on the congestion signal includes:

[0013] Based on the network congestion level indicated by the congestion signal, determine the compression ratio for the suspected artifact region;

[0014] Alternatively, the compression ratio of the suspected artifact region can be determined based on the network congestion level and the regularity or repetition of the suspected artifact region.

[0015] According to some embodiments of this application, the monitoring of the uplink status of the wireless network to which the skin care device is connected includes, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment latency, and further includes:

[0016] Monitor the retransmission rate and effective throughput of the wireless network;

[0017] Based on the retransmission rate and the effective throughput, the existence state of the underlying transmission efficiency loss of the wireless network is obtained.

[0018] If the state of existence is present, increase the compression ratio of the suspected artifact region.

[0019] According to some embodiments of this application, determining the compression ratio for the suspected artifact region based on the network congestion level indicated by the congestion signal includes:

[0020] Obtain the changes in the network congestion level and the instantaneous congestion level;

[0021] The congestion level smoothing index is obtained by calculating the changes in the network congestion level.

[0022] The target compression ratio for the suspected artifact region is determined based on the instantaneous congestion level and the congestion level smoothing index.

[0023] The target compression ratio is compared with the preset compression ratio to obtain a first comparison result;

[0024] Based on the first comparison result, the compression ratio of the suspected artifact region is adjusted.

[0025] According to some embodiments of this application, determining the target compression ratio for the suspected artifact region based on the instantaneous congestion level and the congestion level smoothing index includes:

[0026] The compression ratio of the congestion level smoothing index is obtained by weighting the high compression ratio corresponding to the instantaneous congestion level and the low compression ratio corresponding to the congestion level smoothing index.

[0027] Based on the compression ratio of the congestion level smoothing index, the weight of the congestion level smoothing index in the calculation of the preset buffer compression ratio is determined, and the target buffer compression ratio is obtained.

[0028] The target buffer compression ratio is compared with the current compression ratio applied to the suspected artifact region to obtain a second comparison result;

[0029] Based on the second comparison result, the target compression ratio for the suspected artifact region is determined.

[0030] According to some embodiments of this application, determining the weight of the congestion level smoothing index in the calculation of the preset buffer compression ratio based on the compression ratio of the congestion level smoothing index, and obtaining the target buffer compression ratio, includes:

[0031] The weight of the congestion level smoothing index is determined based on the preset target range in which the value of the congestion level smoothing index falls.

[0032] The target buffer compression ratio is obtained based on the weight of the congestion level smoothing index.

[0033] According to some embodiments of this application, determining the weight of the congestion level smoothing index based on the preset target interval in which the value of the congestion level smoothing index falls includes:

[0034] When the value of the congestion level smoothing index is near the boundary of the preset target interval, the weight corresponding to the current interval is maintained within the preset time window.

[0035] When the value of the congestion level smoothing index stably enters the first preset interval and remains there for the first preset time, or crosses multiple intervals, the weight is switched according to the preset rate.

[0036] According to some embodiments of this application, the boundary of the preset target interval is obtained through the following steps:

[0037] Obtain the statistical distribution characteristics of the congestion level smoothing index;

[0038] Based on the statistical distribution characteristics of the congestion level smoothing index, the width and location of the interval boundary are obtained;

[0039] The range of the interval boundary is obtained based on the long-term average and standard deviation of the congestion level smoothing index;

[0040] The interval boundary of the preset target interval is obtained based on the width and position of the interval boundary and the range of the interval boundary.

[0041] According to some embodiments of this application, obtaining the preset time window includes:

[0042] Monitor the trend of the congestion level smoothing index value within an initial preset time window;

[0043] When the trend of change shows a worsening trend, the length of the initial preset time window is shortened to obtain the preset time window;

[0044] When the trend of change shows an improving trend, the length of the initial preset time window is extended to obtain the preset time window.

[0045] Secondly, embodiments of this application provide an image data information transmission system for a skin care process, comprising:

[0046] The acquisition module is used to acquire skin images and perform recognition processing on the skin images to obtain suspected artifact regions, confirmed real texture regions, and smooth regions in the skin images;

[0047] The monitoring module is used to monitor the uplink status of the wireless network to which the skin care device is connected, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment delay;

[0048] The status acquisition module is used to obtain the status of the wireless network based on the uplink status;

[0049] A congestion signal generation module is used to generate a congestion signal when the wireless network is in a congested state.

[0050] The determination module is used to determine the compression ratio of the suspected artifact region based on the congestion signal;

[0051] A recovery signal generation module is used to generate a recovery signal when the wireless network recovers to a non-congested state;

[0052] The recovery module is used to restore the compression ratio of the suspected artifact region based on the recovery signal.

[0053] According to the technical solution of the embodiments of this application, it has at least the following beneficial effects: The image data information transmission method in the skin care process disclosed in this application, by intelligently identifying suspected artifact areas, confirming real texture areas and smooth areas in skin images, and combining real-time monitoring of the uplink status of the wireless network, dynamically adjusts the compression ratio of suspected artifact areas, effectively solving the problem of image recognition errors caused by the optical effects of skin care products in the prior art, which leads to a surge in data volume and network congestion, significantly reduces the data transmission load, effectively alleviates the pressure on the uplink of the home wireless network, thereby overcoming the defects of image stuttering, delay and image fragmentation in the remote skin care process, ensuring the accuracy of remote diagnosis and timely adjustment of the care plan, and greatly improving the efficiency and reliability of remote skin care.

[0054] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0055] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0056] Figure 1 A schematic flowchart illustrating an image data transmission method during a skin care process, provided in one embodiment of this application;

[0057] Figure 2 This is a schematic diagram of an image data information transmission system for a skin care process provided in one embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0060] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0061] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0062] Based on the above, this application proposes a method and system for transmitting image data information during skin care, aiming to ensure the accuracy of remote diagnosis and timely adjustment of care plans, thereby improving the efficiency and reliability of remote skin care.

[0063] The image data transmission method for the skin care process provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the image data transmission method for the skin care process, but is not limited to the above forms.

[0064] This application can be applied to numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices. It should be noted that in various specific embodiments of this invention, when processing is required based on data related to the characteristics of an object (e.g., user attributes or sets of attribute information), permission or consent from the corresponding object is obtained first, and the collection, use, and processing of this data comply with relevant laws and standards. Furthermore, when the embodiments of the present invention need to obtain the attribute information of an object, they will obtain the separate permission or separate consent of the corresponding object through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or separate consent of the corresponding object, they will then obtain the relevant data of the object necessary for the embodiments of the present invention to operate normally.

[0065] See Figure 1 , Figure 1 This is a flowchart illustrating an image data transmission method during a skin care process according to an embodiment of this application. The image data transmission method during a skin care process provided in this embodiment includes, but is not limited to, steps S110 to S170, which will be described in detail below.

[0066] Step S110: Acquire skin images and perform recognition processing on the skin images to obtain suspected artifact areas, confirmed real texture areas, and smooth areas in the skin images;

[0067] Step S120: Monitor the uplink status of the wireless network to which the skin care device is connected, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment delay;

[0068] Step S130: Obtain the status of the wireless network based on the uplink status;

[0069] Step S140: When the wireless network is in a congested state, generate a congestion signal;

[0070] Step S150: Determine the compression ratio for suspected artifact regions based on congestion signals;

[0071] Step S160: When the wireless network returns to a non-congested state, a recovery signal is generated;

[0072] Step S170: Based on the recovery signal, restore the compression ratio of the suspected artifact region.

[0073] It should be noted that "skin care device" refers to a device used to acquire and preliminarily process skin images, such as handheld skin analyzers, smart mirrors, or beauty devices with integrated image acquisition functions. This device typically includes an image sensor, processing unit, and wireless communication module. "Skin image" refers to an image of the skin surface acquired by the skin care device, containing visual information such as skin texture, color, blemishes, and pores. "Identified true texture areas" refer to high-frequency texture areas in the skin image that are clearly formed by the skin's physiological structure (such as pores, fine lines, and blemishes). These areas carry crucial information about the skin's health and require high-fidelity transmission. "Smooth areas" refer to areas in the skin image where information changes slowly and at low frequency, such as large areas of skin tone or shine. These areas can typically be transmitted with a high compression ratio. "Wireless network uplink status" refers to the performance indicators of the wireless network connection when the skin care device transmits data to a server or cloud, including instantaneous upload rate and packet acknowledgment latency. "Instantaneous upload rate" refers to the actual speed at which data is uploaded from the skin care device to the network at a given moment. "Packet acknowledgment latency" refers to the time elapsed from when a data packet is sent from the sender to when it is acknowledged by the receiver. A "congestion signal" is an indication signal generated by the system when the wireless network is determined to be congested. A "recovery signal" is an indication signal generated by the system when the wireless network recovers from congestion to non-congestion.

[0074] First, the skin care device acquires skin images using its built-in image sensor. These images are then sent to an image processing module for identification. This processing is based on frequency domain analysis, texture feature extraction, or deep learning models. Fourier transform or wavelet transform can be used to analyze the image's frequency, identifying high-frequency components as textured regions and low-frequency components as smooth regions. For identifying suspected artifact regions, the brightness and color gradient change patterns of local image areas are analyzed and matched against a pre-defined optical artifact feature library. Alternatively, a deep learning model can be trained to distinguish between frequency changes caused by genuine skin texture and similar texture-like frequency changes caused by external optical effects. For example, this model can be trained on a large number of real skin images and skin images containing optical artifacts to learn the features that distinguish between these two types of regions. The image processing module can first perform preliminary high- and low-frequency segmentation, and then perform secondary analysis on regions initially identified as high-frequency but exhibiting specific regularities or repetitions to identify suspected artifact regions. The skin care device continuously monitors the uplink performance of its connected wireless network. Instantaneous upload rate can be obtained by measuring the amount of data successfully uploaded per unit time, for example, by counting the number of bytes sent within a fixed time interval. Packet acknowledgment latency can be calculated by sending a test packet and recording its sending time, then waiting for and recording the acknowledgment time upon receiving the packet; the difference between the two is the latency. This monitoring data can be obtained through the device's built-in network module or the API interface provided by the operating system. The device can periodically send small packets to a preset server and measure the round-trip time (RTT) to estimate the packet acknowledgment latency. Simultaneously, instantaneous upload rate can be obtained by monitoring the send buffer status and actual sending rate of the network interface.

[0075] In one embodiment, the system comprehensively analyzes monitored metrics such as instantaneous upload rate and packet acknowledgment delay to determine whether the current wireless network is in a congested or non-congested state. An upload rate threshold and a delay threshold can be set. When the instantaneous upload rate is consistently below the preset threshold and the packet acknowledgment delay is consistently above the preset threshold, the network is determined to be congested. More complex algorithms based on moving averages or exponentially weighted moving averages can be used to smooth instantaneous data, reducing the impact of short-term fluctuations on the judgment. When the instantaneous upload rate is below a certain percentage of its long-term average for multiple consecutive sampling periods, and the packet acknowledgment delay is above a certain percentage of its long-term average for multiple consecutive sampling periods, the network is determined to be congested. Then, when the system determines that the wireless network is congested, it immediately generates a congestion signal. This congestion signal can be a simple Boolean value (e.g., true indicates congestion, false indicates non-congestion) or a structure containing more information, such as indicating the level or duration of congestion. This signal is passed to the subsequent compression ratio determination module. Subsequently, the system dynamically adjusts the compression ratio for suspected artifact areas based on the received congestion signal. When a congestion signal indicates network congestion, the system can increase the compression ratio for suspected artifact areas to reduce data volume. Specifically, a compression ratio lookup table can be pre-defined, corresponding to different compression ratios based on congestion levels (if the congestion signal includes level information). Mild congestion corresponds to a medium compression ratio, and severe congestion to a high compression ratio. An adaptive algorithm can be used to gradually increase the compression ratio based on the severity of congestion until network conditions improve. A compression method based on quantization step size adjustment can be employed, increasing the quantization step size when the network is congested, thereby improving the compression ratio. Finally, when the system detects that the wireless network has recovered from a congested state to a non-congested state, a recovery signal is generated. The recovery signal indicates that network conditions have improved, and the system will restore the compression ratio for suspected artifact areas to a normal or lower level based on this signal to ensure that image details are preserved as much as possible when network conditions permit. For example, it can restore to a preset default compression ratio, or dynamically adjust to a suitable lower compression ratio based on the network performance in the current non-congested state.

[0076] It should be noted that network congestion level is a quantitative assessment of the current congestion level of the wireless network. It can be divided into different levels, such as mild congestion, moderate congestion, and severe congestion, based on uplink parameters such as instantaneous upload rate and packet acknowledgment latency. Different congestion levels correspond to different compression requirements; higher levels typically require higher compression ratios. Furthermore, the regularity or repetition of suspected artifact regions refers to the degree of repetition in the arrangement of image pixels or texture features within that region. For example, for suspected artifact regions with highly repetitive or regular patterns, their data redundancy is high. Even with a high compression ratio, the original information may be well recovered after decompression, or the distortion may have a minimal impact on visual perception. Conversely, for irregular or low-repetition regions, excessive compression may lead to obvious visual artifacts. Therefore, when determining the compression ratio, the characteristics of the suspected artifact region itself should be considered.

[0077] In one embodiment, assume a skin care device is uploading skin image data via a wireless network. When the uplink condition of the wireless network is detected to deteriorate, generating a congestion signal, the system first assesses the current network congestion level. For example, if the instantaneous upload rate drops sharply and the packet acknowledgment delay increases significantly, it is judged as a severe congestion level. At this time, the system determines a higher compression ratio based on this severe congestion level, for example, setting the compression ratio of suspected artifact regions to 80%. Furthermore, if the system can also analyze the image content of suspected artifact regions, for example, one suspected artifact region is identified as having a highly repetitive grid-like texture, while another suspected artifact region appears as irregular spots, under the severe congestion level, the system can apply a higher compression ratio (e.g., 90%) to the highly repetitive grid-like texture region because it is not sensitive to compression distortion; while applying a relatively lower compression ratio (e.g., 70%) to the irregular spot region to avoid excessive distortion. In this way, even under network congestion, key image information can be preserved as much as possible while ensuring transmission efficiency, or more efficient data transmission can be achieved without affecting visual perception.

[0078] It's important to note that monitoring the retransmission rate of a wireless network refers to the continuous tracking and recording of the proportion of data packets that fail to be received during data transmission due to various reasons (such as signal interference, network congestion, receiver errors, etc.) and require retransmission by the sender. The retransmission rate is one of the key indicators for measuring network reliability and transmission efficiency. Effective throughput can be understood as the amount of valid data actually successfully transmitted by the network per unit time, excluding retransmitted data, protocol overhead, and other invalid data. Monitoring these two indicators allows for a deeper understanding of the network's actual transmission performance. Specifically, determining the existence of underlying transmission efficiency losses in a wireless network based on the retransmission rate and effective throughput involves analyzing the values ​​of retransmission rate and effective throughput to determine whether there is a decrease in transmission efficiency caused by underlying network problems (such as physical layer errors, link layer congestion, or low protocol efficiency). When the retransmission rate exceeds a preset threshold, or the effective throughput is significantly lower than the theoretical maximum value, it can be determined that underlying transmission efficiency losses exist.

[0079] In one embodiment, a skin care device is uploading skin image data via a wireless network. At a certain moment, the instantaneous upload rate and data packet acknowledgment delay are both within the normal range. According to the method described above, the system may not determine that it is in a congested state and therefore will not adjust the compression ratio. However, due to occasional interference in the wireless environment, the retransmission rate of data packets suddenly increases to 20%, while the effective throughput decreases by 30%. This application will monitor the anomalies in the retransmission rate and effective throughput and determine that there is an underlying transmission efficiency loss in the wireless network. When this state is detected, the system will immediately increase the compression ratio of suspected artifact regions in the currently transmitted skin image, for example, from the default 1:5 to 1:10. In this way, even if the network surface indicators are normal, but the actual transmission efficiency is impaired, the system can proactively reduce the amount of data transmitted, thereby effectively reducing the number of retransmissions, improving the success rate of data packet transmission, and ensuring that the skin image data can be stably and efficiently transmitted to the backend system for analysis and processing.

[0080] It should be noted that obtaining changes in network congestion levels and instantaneous congestion levels refers to the system continuously monitoring and recording the uplink status of the wireless network to obtain historical network congestion data over a period of time, and acquiring the current instantaneous congestion level in real time. Changes in network congestion levels can be understood as the fluctuations or trends in network congestion levels over a certain time window. Furthermore, the congestion level smoothing index aims to smooth historical network congestion data, eliminating the impact of instantaneous fluctuations and providing a more stable congestion indicator that better reflects long-term trends. The smoothing index can be calculated using moving averages, exponentially weighted moving averages, or other statistical filtering algorithms. Its purpose is to reduce the system's sensitivity to short-term network fluctuations and avoid excessively frequent compression ratio adjustments. The determination of the target compression ratio comprehensively considers both the immediate network condition (instantaneous congestion level) and the long-term trend (congestion level smoothing index), ensuring that compression ratio adjustments respond to current network pressure while maintaining a certain degree of stability. When the instantaneous congestion level is high but the congestion level smoothing index is low, it may indicate that the network congestion is temporary. In this case, the adjustment range of the target compression ratio can be relatively conservative. Conversely, if both are high, it may be necessary to more aggressively increase the compression ratio.

[0081] In one embodiment, the system monitors the instantaneous upload rate and packet acknowledgment latency of the wireless network once per second and calculates the instantaneous congestion level accordingly. To obtain the congestion level smoothing index, an Exponentially Weighted Moving Average (EWMA) algorithm with a 60-second time window can be used, where the most recent instantaneous congestion level is given a higher weight (e.g., 0.2), while historical data gradually diminishes its influence. When the instantaneous congestion level suddenly spikes from a low level (e.g., 20%) to a high level (e.g., 80%) at a certain moment, the congestion level smoothing index may still be at a low to medium level (e.g., 30%) because it is calculated based on data from the past 60 seconds. In this case, when determining the target compression ratio for suspected artifact areas, the system will comprehensively consider the 80% instantaneous congestion level and the 30% congestion level smoothing index. For example, a weighted average approach can be used, assigning a weight of 0.4 to the instantaneous congestion level and a weight of 0.6 to the congestion level smoothing index, thereby obtaining a relatively moderate target compression ratio instead of directly using the high compression ratio corresponding to the 80% congestion level. The target compression ratio is then compared to a preset compression ratio. For example, the preset compression ratio could be set to a maximum of 1:10 and a minimum of 1:2. If the calculated target compression ratio is 1:12, exceeding the maximum allowable compression ratio, the first comparison result will indicate that the target compression ratio is too high, and the system will adjust the actual applied compression ratio to 1:10. Conversely, if the target compression ratio is 1:1, lower than the minimum allowable compression ratio, the system may adjust it to 1:2. Even with brief and drastic network fluctuations, the compression ratio adjustment remains stable and reasonable, avoiding overreaction and ensuring the continuity and quality of skin image data transmission.

[0082] It should be noted that the weight of the congestion level smoothing index in the preset buffer compression ratio calculation can be determined based on the compression ratio obtained from the congestion level smoothing index. The preset buffer compression ratio calculation aims to provide a smooth transition of the compression ratio target, avoiding drastic jumps in the compression ratio. The higher the compression ratio of the congestion level smoothing index, the greater its weight in the buffer calculation, and vice versa, to ensure that the buffering effect matches the actual network conditions. Thus, a target buffer compression ratio can be obtained, which represents the expected compression ratio after smoothing under the current network conditions. This target buffer compression ratio is compared with the current compression ratio applied to suspected artifact areas, yielding a second comparison result. This comparison aims to evaluate the difference between the current compression ratio and the expected buffer target. Based on the second comparison result, the final target compression ratio for suspected artifact areas can be determined. If the target buffer compression ratio is higher than the current compression ratio, the compression ratio can be gradually increased; if it is lower than the current compression ratio, the compression ratio can be gradually decreased. This gradual adjustment method effectively avoids abrupt changes in the compression ratio, thereby ensuring the stability of data transmission.

[0083] In one embodiment, assuming the instantaneous congestion level indicates moderate network congestion, the corresponding high compression ratio is set to 5:1, while the congestion level smoothing index indicates a slow improvement in network congestion, with a corresponding low compression ratio set to 2:1. In this case, these two compression ratios can be weighted and averaged, with the instantaneous congestion level assigned a weight of 0.7 and the congestion level smoothing index assigned a weight of 0.3, resulting in a compression ratio of (5 * 0.7 + 2 * 0.3) = 3.5 + 0.6 = 4.1:1. Next, based on this 4.1:1 compression ratio, its weight in the calculation of the preset buffer compression ratio is determined. If 4.1:1 falls within a certain medium compression range, it is assigned a weight of 0.6 to calculate the target buffer compression ratio. Assuming the current compression ratio applied to suspected artifact regions is 3:1, and the calculated target buffer compression ratio is 3.8:1. By comparing the two results, the system will gradually adjust the compression ratio from 3:1 to 3.8:1 in increments of 0.2, rather than jumping it immediately. This ensures the smoothness of the compression ratio adjustment and avoids impacting transmission stability.

[0084] It's important to note that the preset target interval divides the possible range of the congestion level smoothing index into multiple discrete, pre-defined numerical intervals. Each interval can correspond to one or a set of specific weight values, used to weight the compression ratio of the congestion level smoothing index in the buffer compression ratio calculation. When the value of the congestion level smoothing index is in a lower interval, a smaller weight may be assigned, indicating that the current network condition is relatively stable, and the compression ratio adjustment should be more conservative; conversely, when its value is in a higher interval, a larger weight may be assigned, indicating that the network congestion is more severe or fluctuates more, requiring a more aggressive adjustment of the compression ratio. The weight of the congestion level smoothing index in the buffer compression ratio calculation is determined by selecting or calculating the corresponding weight value based on which preset target interval the current value of the congestion level smoothing index falls into. This weight value will be used for weighted averaging to more accurately reflect the impact of the congestion level smoothing index on the final target buffer compression ratio. Therefore, by combining the compression ratio of the congestion level smoothing index with this weight, a more reasonable target buffer compression ratio can be obtained, thereby guiding the compression ratio adjustment for suspected artifact areas.

[0085] In one embodiment, the congestion level smoothing index is assumed to range from 0 to 100. It can be divided into three preset target intervals: The first interval ([0, 30)) represents good or slightly congested network conditions. When the congestion level smoothing index falls within this interval, its weight in the buffer compression ratio calculation is set to 0.2 to maintain a low compression ratio and prioritize image quality. The second interval ([30, 70)) represents moderately congested or fluctuating network conditions. When the congestion level smoothing index falls within this interval, its weight in the buffer compression ratio calculation is set to 0.5 to moderately increase the compression ratio and balance transmission efficiency and image quality. The third interval ([70, 100]) represents severely congested or continuously deteriorating network conditions. When the congestion level smoothing index falls within this interval, its weight in the buffer compression ratio calculation is set to 0.8 to significantly increase the compression ratio and prioritize smooth data transmission. Once the compression ratio of the congestion level smoothing index is calculated, it is dynamically weighted and averaged according to the preset target interval in which its value falls, ultimately yielding a target buffer compression ratio that better reflects the current network conditions. For example, if the compression ratio of the congestion level smoothing index is X, and the smoothing index value is 25 (in the first interval), then its weight in the buffer compression ratio calculation is 0.2; if the smoothing index value is 60 (in the second interval), then the weight is 0.5. This interval-based weight determination method allows the system to flexibly and accurately adjust the compression strategy based on the actual degree and trend of network congestion.

[0086] It should be noted that when the congestion level smoothing index fluctuates near the boundary of the preset target interval, the system maintains the weight corresponding to the current interval within a preset time window to avoid frequent weight switching due to minor fluctuations. "Near the interval boundary" can be understood as within a certain tolerance range on both sides of the interval boundary, which can be set as the interval boundary value plus or minus a small threshold. The "preset time window" refers to a preset duration during which, even if the congestion level smoothing index slightly crosses the interval boundary, its corresponding weight will not change immediately, thus providing a certain degree of buffering and stability.

[0087] In one embodiment, it is assumed that the preset target interval is divided into three levels: low congestion (weight 0.2), medium congestion (weight 0.5), and high congestion (weight 0.8), with the boundary between low and medium congestion being X1, and the boundary between medium and high congestion being X2. When the value of the congestion level smoothing index fluctuates around X1, for example, within the range of X1-0.05 to X1+0.05, and the current weight is 0.2, and this fluctuation continues within a preset time window (e.g., 5 seconds), the system will continue to maintain the weight at 0.2 instead of immediately switching to 0.5. This effectively avoids frequent weight switching caused by instantaneous fluctuations. Furthermore, when the value of the congestion level smoothing index steadily rises from around X1 and continuously and steadily enters the medium congestion interval (e.g., the value is greater than X1+0.1) for a first preset time (e.g., 10 seconds), the system will activate the weight switching mechanism. At this point, the weight will not immediately jump from 0.2 to 0.5, but will be adjusted gradually at a preset rate (e.g., increasing by 0.05 per second). After 6 seconds ((0.5-0.2) / 0.05 = 6), the weight will smoothly reach 0.5. When the congestion level smoothing index jumps directly from the low congestion range to the high congestion range (i.e., crossing the medium congestion range), the system will also gradually adjust the weight according to the preset rate to ensure the smoothness of the adjustment process. This mechanism makes the adjustment of the compression ratio more intelligent and stable, thereby improving the overall performance and user experience of skin image data transmission.

[0088] It's important to note that obtaining the statistical distribution characteristics of the congestion level smoothing index involves the system continuously collecting historical data on the index and performing statistical analysis, such as calculating its frequency distribution, probability density function, and cumulative distribution function. This allows us to understand the frequency and trend of the congestion level smoothing index across different numerical ranges. Furthermore, based on the statistical distribution characteristics of the congestion level smoothing index, the width and location of the interval boundaries can be determined. The initial location of the boundaries and the numerical range they cover can be determined based on the peaks, troughs, inflection points, or specific percentiles of the distribution. The width of the interval boundaries can be understood as the size of the area occupied by each preset target interval on the numerical axis, while the location refers to the center point or starting point of that interval. Additionally, the range of the interval boundaries can be obtained based on the long-term mean and standard deviation of the congestion level smoothing index. The long-term mean provides the central trend of the congestion level smoothing index, while the standard deviation reflects its volatility or dispersion. By combining these global statistics, a reasonable total range of interval boundaries that can cover most real-world scenarios can be set, thus avoiding boundaries that are too narrow or too broad. Based on the width and location of the obtained interval boundaries, as well as the range of the interval boundaries, the specific interval boundaries of the preset target intervals can be comprehensively determined. This means that, under the constraint of the global statistical range, by combining local distribution characteristics, each preset target interval can be accurately located and divided, so that each interval can better represent the specific state of the congestion level smoothing index.

[0089] It is worth noting that the solution in this application, through in-depth statistical analysis of the congestion level smoothing index, including its statistical distribution characteristics, long-term average, and standard deviation, can dynamically and accurately determine the boundary of the preset target interval. This means the interval boundary is no longer a statically preset fixed value, but can adaptively adjust according to the actual behavior pattern of the congestion level smoothing index. Therefore, when the value of the congestion level smoothing index fluctuates near the interval boundary, the system can more accurately determine its network state and maintain or switch weights accordingly, thus avoiding misjudgments or delayed responses caused by improper boundary settings.

[0090] It should be noted that monitoring the trend of the congestion level smoothing index within the initial preset time window involves continuous observation and analysis of the index value. The congestion level smoothing index reflects the stability and development direction of network congestion. By monitoring it within an initial preset time window, it can be determined whether the current network condition is deteriorating, improving, or remaining stable. The initial preset time window can be a fixed preset duration, such as 5 seconds, 10 seconds, or an empirical value determined based on historical data. The trend can be determined by calculating the moving average or rate of change of the congestion level smoothing index, or by comparing it with a preset threshold. When the trend shows a deteriorating trend, such as a continuous rise in the congestion level smoothing index value or a sharp increase in a short period of time, it indicates that network congestion is worsening. In this case, to respond more quickly to network deterioration, the length of the initial preset time window is shortened, resulting in a preset time window used for subsequent weight maintenance. Shortening the time window allows the system to detect network deterioration more quickly and adjust the compression ratio of image data in a timely manner to avoid data transmission delays or interruptions. Conversely, when the trend shows improvement, the congestion level smoothing index continuously decreases or remains at a low level, indicating that network congestion is easing or stabilizing. At this time, to avoid oversensitivity to network conditions and frequent adjustments, the initial preset time window is extended, resulting in a preset time window used for subsequent weight maintenance. Extending the time window helps the system maintain stability for a longer period when network conditions improve, avoiding frequent adjustments to the compression ratio due to short-term fluctuations, thereby improving data transmission smoothness. The solution in this application dynamically adjusts the length of the preset time window, enabling the system to adaptively adjust its response speed according to the changing trend of the network congestion level smoothing index. When network conditions worsen, shortening the time window can accelerate the system's response to congestion and allow for timely compression measures; when network conditions improve or stabilize, extending the time window can increase system stability and avoid unnecessary frequent adjustments. This achieves a more flexible and intelligent adaptation to changes in the network environment.

[0091] In one embodiment, a skin care device is transmitting skin image data via a wireless network. Initially, a preset time window for monitoring the congestion level smoothing index is set to 10 seconds. If the system detects that the congestion level smoothing index has been continuously rising over the past 5 seconds, and the increase exceeds a preset threshold (e.g., an increase of 0.1 per second), this is considered a deteriorating trend. In this case, the system shortens the preset time window from 10 seconds to 5 seconds. This means that subsequent weight maintenance decisions will be evaluated based on a shorter time window, thereby accelerating the response to network deterioration, such as immediately increasing the image compression ratio to reduce transmission load. On the other hand, if the system detects that the congestion level smoothing index has been continuously falling over the past 5 seconds, and the decrease exceeds a preset threshold (e.g., a decrease of 0.05 per second), this is considered an improving trend. In this case, the system extends the preset time window from 10 seconds to 15 seconds. This means that subsequent weight maintenance decisions will be evaluated based on a longer time window, thereby avoiding premature reduction of the compression ratio due to temporary network improvement, ensuring the stability and continuity of network conditions, and maintaining high-quality image transmission.

[0092] See Figure 2 , Figure 2 This is a schematic diagram of an image data transmission system for a skin care process according to an embodiment of this application. The image data transmission system 200 for a skin care process includes:

[0093] The acquisition module 210 is used to acquire skin images and perform recognition processing on the skin images to obtain suspected artifact areas, confirmed real texture areas, and smooth areas in the skin images;

[0094] The monitoring module 220 is used to monitor the uplink status of the wireless network to which the skin care device is connected, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment delay;

[0095] The status acquisition module 230 is used to obtain the status of the wireless network based on the uplink status;

[0096] The congestion signal generation module 240 is used to generate a congestion signal when the wireless network is in a congested state.

[0097] The determination module 250 is used to determine the compression ratio of suspected artifact regions based on congestion signals;

[0098] The recovery signal generation module 260 is used to generate a recovery signal when the wireless network recovers to a non-congested state;

[0099] Recovery module 270 is used to restore the compression ratio of suspected artifact regions based on the recovery signal.

[0100] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0101] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0102] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for transmitting image data information during a skin care process, applied to skin care equipment, characterized in that, include: Acquire skin images and perform recognition processing on the skin images to obtain suspected artifact regions, confirmed real texture regions, and smooth regions in the skin images; Monitor the uplink status of the wireless network to which the skin care device is connected, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment latency; The status of the wireless network is obtained based on the uplink status; When the wireless network is in a congested state, a congestion signal is generated. Based on the congestion signal, determine the compression ratio for the suspected artifact region; When the wireless network returns to a non-congested state, a recovery signal is generated; Based on the recovery signal, restore the compression ratio of the suspected artifact region; The identification process includes performing preliminary high- and low-frequency segmentation on the skin image, and performing secondary analysis on the regions that have been initially segmented into high-frequency regions but have regularity or repetition, in order to identify the suspected artifact regions. The step of determining the compression ratio for the suspected artifact region based on the congestion signal includes: Based on the network congestion level indicated by the congestion signal, determine the compression ratio for the suspected artifact region; Alternatively, the compression ratio of the suspected artifact regions can be determined based on the network congestion level and the regularity or repetition of the suspected artifact regions. Determining the compression ratio for the suspected artifact region based on the network congestion level indicated by the congestion signal includes: Obtain the changes in the network congestion level and the instantaneous congestion level; The congestion level smoothing index is obtained by calculating the changes in the network congestion level. The target compression ratio for the suspected artifact region is determined based on the instantaneous congestion level and the congestion level smoothing index. The target compression ratio is compared with the preset compression ratio to obtain a first comparison result; Based on the first comparison result, adjust the compression ratio of the suspected artifact region; The step of determining the target compression ratio for the suspected artifact region based on the instantaneous congestion level and the congestion level smoothing index includes: The compression ratio of the congestion level smoothing index is obtained by weighting the high compression ratio corresponding to the instantaneous congestion level and the low compression ratio corresponding to the congestion level smoothing index. Based on the compression ratio of the congestion level smoothing index, the weight of the congestion level smoothing index in the calculation of the preset buffer compression ratio is determined, and the target buffer compression ratio is obtained. The target buffer compression ratio is compared with the current compression ratio applied to the suspected artifact region to obtain a second comparison result; Based on the second comparison result, the target compression ratio for the suspected artifact region is determined; The step of determining the weight of the congestion level smoothing index in the calculation of the preset buffer compression ratio based on the compression ratio of the congestion level smoothing index, and obtaining the target buffer compression ratio, includes: The weight of the congestion level smoothing index is determined based on the preset target range in which the value of the congestion level smoothing index falls. The target buffer compression ratio is obtained based on the smoothing exponential weight of the congestion level. The step of determining the weight of the congestion level smoothing index based on the preset target interval in which the value of the congestion level smoothing index falls includes: When the value of the congestion level smoothing index is near the boundary of the preset target interval, the weight corresponding to the current interval is maintained within the preset time window. When the value of the congestion level smoothing index stably enters the first preset interval and remains there for the first preset time, or crosses multiple intervals, the weight is switched according to the preset rate.

2. The method according to claim 1, characterized in that, The monitoring of the uplink status of the wireless network to which the skin care device is connected includes, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment latency, and further includes: Monitor the retransmission rate and effective throughput of the wireless network; Based on the retransmission rate and the effective throughput, the existence state of the underlying transmission efficiency loss of the wireless network is obtained. If the state of existence is present, increase the compression ratio of the suspected artifact region.

3. The method according to claim 1, characterized in that, The boundary of the preset target interval is obtained through the following steps: Obtain the statistical distribution characteristics of the congestion level smoothing index; Based on the statistical distribution characteristics of the congestion level smoothing index, the width and location of the interval boundary are obtained; The range of the interval boundary is obtained based on the long-term average and standard deviation of the congestion level smoothing index; The interval boundary of the preset target interval is obtained based on the width and position of the interval boundary and the range of the interval boundary.

4. The method according to claim 1, characterized in that, Obtaining the preset time window includes: Monitor the trend of the congestion level smoothing index value within an initial preset time window; When the trend of change shows a worsening trend, the length of the initial preset time window is shortened to obtain the preset time window; When the trend of change shows an improving trend, the length of the initial preset time window is extended to obtain the preset time window.

5. An image data information transmission system for a skin care process, applied to skin care equipment, characterized in that, include: The acquisition module is used to acquire skin images and perform recognition processing on the skin images to obtain suspected artifact regions, confirmed real texture regions, and smooth regions in the skin images; The monitoring module is used to monitor the uplink status of the wireless network to which the skin care device is connected, wherein the uplink status includes instantaneous upload rate and data packet acknowledgment delay; The status acquisition module is used to obtain the status of the wireless network based on the uplink status; A congestion signal generation module is used to generate a congestion signal when the wireless network is in a congested state. The determination module is used to determine the compression ratio of the suspected artifact region based on the congestion signal; A recovery signal generation module is used to generate a recovery signal when the wireless network recovers to a non-congested state; The recovery module is used to recover the compression ratio of the suspected artifact region based on the recovery signal; The identification process includes performing preliminary high- and low-frequency segmentation on the skin image, and performing secondary analysis on the regions that have been initially segmented into high-frequency regions but have regularity or repetition, in order to identify the suspected artifact regions. The step of determining the compression ratio for the suspected artifact region based on the congestion signal includes: Based on the network congestion level indicated by the congestion signal, determine the compression ratio for the suspected artifact region; Alternatively, the compression ratio of the suspected artifact regions can be determined based on the network congestion level and the regularity or repetition of the suspected artifact regions. Determining the compression ratio for the suspected artifact region based on the network congestion level indicated by the congestion signal includes: Obtain the changes in the network congestion level and the instantaneous congestion level; The congestion level smoothing index is obtained by calculating the changes in the network congestion level. The target compression ratio for the suspected artifact region is determined based on the instantaneous congestion level and the congestion level smoothing index. The target compression ratio is compared with the preset compression ratio to obtain a first comparison result; Based on the first comparison result, adjust the compression ratio of the suspected artifact region; The step of determining the target compression ratio for the suspected artifact region based on the instantaneous congestion level and the congestion level smoothing index includes: The compression ratio of the congestion level smoothing index is obtained by weighting the high compression ratio corresponding to the instantaneous congestion level and the low compression ratio corresponding to the congestion level smoothing index. Based on the compression ratio of the congestion level smoothing index, the weight of the congestion level smoothing index in the calculation of the preset buffer compression ratio is determined, and the target buffer compression ratio is obtained. The target buffer compression ratio is compared with the current compression ratio applied to the suspected artifact region to obtain a second comparison result; Based on the second comparison result, the target compression ratio for the suspected artifact region is determined; The step of determining the weight of the congestion level smoothing index in the calculation of the preset buffer compression ratio based on the compression ratio of the congestion level smoothing index, and obtaining the target buffer compression ratio, includes: The weight of the congestion level smoothing index is determined based on the preset target range in which the value of the congestion level smoothing index falls. The target buffer compression ratio is obtained based on the smoothing exponential weight of the congestion level. The step of determining the weight of the congestion level smoothing index based on the preset target interval in which the value of the congestion level smoothing index falls includes: When the value of the congestion level smoothing index is near the boundary of the preset target interval, the weight corresponding to the current interval is maintained within the preset time window. When the value of the congestion level smoothing index stably enters the first preset interval and remains there for the first preset time, or crosses multiple intervals, the weight is switched according to the preset rate.

Images