Cloud-based image product customized data management system

Abstract

This invention relates to the field of cloud computing and intelligent image data processing technology, specifically to a customized data management system for image products based on a cloud platform. It comprises a cloud platform data management center, semantic monitoring, feature drift analysis, transmission strategy, model matching, and data reconstruction units. The semantic monitoring unit calculates a semantic entropy evaluation value and identifies the chaotic state of model understanding through adversarial operations between image feature complexity and model confidence. It identifies the image to be reconstructed and determines the signal. If it is a drift risk signal, the feature drift unit verifies atypical features and outputs intervention or calibration instructions to prevent blind reconstruction beyond cognitive boundaries. If it is a steady-state signal, the transmission strategy unit analyzes throughput requirements, the model matching unit quantifies reconstruction decisions, and outputs a highly compressed or high-fidelity reconstructed signal. This invention addresses the problem of semantic illusions easily generated by generative AI when reconstructing complex textures; the semantic monitoring unit calculates a semantic entropy evaluation value.

Description

Technical Field

[0001] This invention relates to the field of cloud computing and intelligent image data processing technology, specifically to a customized data management system for image products based on a cloud platform. Background Technology

[0002] In high-precision medical diagnosis or industrial detection scenarios across regions, edge node devices continuously collect image data containing complex textures and key features, and need to transmit it to the cloud in real time for archiving or analysis. Since such scenarios are usually in communication environments with low bandwidth, high latency or drastic network fluctuations, directly transmitting raw high-fidelity data can easily cause transmission blockage, increased packet loss rate and response timeout, which cannot meet the real-time requirements.

[0003] To address transmission bottlenecks, existing solutions often employ high compression ratio coding or introduce generative models for image reconstruction to reduce data volume. However, when faced with rare lesions or atypical industrial defects, these solutions are prone to feature drift or semantic illusions due to a lack of monitoring of the model's cognitive boundaries. This results in false textures or loss of key details in the reconstructed images. Furthermore, existing systems generally lack a joint evaluation mechanism for network throughput and model reliability, often resorting to blind reconstruction strategies. They fail to dynamically adjust the transmission mode based on actual network load and model confidence, leading to wasted computing resources or risks in medical diagnosis.

[0004] Therefore, how to establish a dynamic balance mechanism that takes into account both transmission efficiency and semantic reliability between limited network resources and the demand for high-precision data, and avoid misdiagnosis or missed detection caused by excessive model association, has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a cloud-based image product customized data management system. Specifically, the technical solution of this invention includes:

[0006] The cloud platform includes a data management center, a semantic monitoring unit, a feature drift analysis unit, a transmission strategy unit, a model matching unit, and a data reconstruction unit.

[0007] The cloud platform data management center is configured to retrieve image request information from each edge node and send the image request information to the semantic monitoring unit for single-point semantic fidelity monitoring and analysis, to obtain regular image data and image data to be reconstructed, to perform discrimination processing on the semantic entropy evaluation value of the image data to be reconstructed, and to obtain steady-state semantic signals or drift risk signals.

[0008] When a drift risk signal is generated, the feature drift analysis unit is configured to perform atypical feature verification feedback analysis on the feature vector distribution information of the acquired image data to be reconstructed, and obtain intervention instructions or adaptive calibration instructions.

[0009] When generating a steady-state semantic signal, the transmission strategy unit is configured to perform throughput demand coefficient acquisition and analysis on the transmission environment information of the acquired image data to be reconstructed, process the obtained bandwidth evaluation value and load evaluation value to obtain the throughput demand coefficient, and the model matching unit is configured to perform reconstruction decision quantitative matching analysis on the model reliability information of the acquired image data to be reconstructed, compare and analyze the obtained strategy evaluation index to obtain a high-compression reconstruction signal or a high-fidelity reconstruction signal.

[0010] The preferred single-point semantic fidelity monitoring and analysis process is as follows:

[0011] The cloud platform's operating cycle is collected and set as a time threshold. Each edge node connected to the cloud platform is set as a source node. Image request information of each source node within the time threshold is obtained. The image request information includes feature reconstruction request code.

[0012] Detect whether the source node generates image request information: If the source node generates image request information, generate a reconstruction request instruction and set the image data of the source node corresponding to the reconstruction request instruction as the image data to be reconstructed; if the source node does not generate image request information, generate a direct save instruction and set the image data of the source node corresponding to the direct save instruction as regular image data.

[0013] Preferably, a preset feature complexity value of the image data to be reconstructed within a time threshold is obtained, and the confidence score value of the current generative model reconstruction of the image data to be reconstructed within the time threshold is obtained, and the confidence interval value of the current generative model reconstruction is set as the fidelity maintenance value.

[0014] The difference between the preset feature complexity value and the fidelity maintenance value is set as the semantic entropy evaluation value.

[0015] The semantic entropy evaluation value is compared and analyzed with the preset semantic entropy threshold: if the semantic entropy evaluation value is greater than the preset semantic entropy threshold, a drift risk signal is generated; if the semantic entropy evaluation value is less than or equal to the preset semantic entropy threshold, a steady-state semantic signal is generated.

[0016] The preferred non-typical feature verification feedback analysis process is as follows:

[0017] Obtain the vector version information of the feature extraction model in the image data to be reconstructed within the time threshold. The vector version information includes the vector feature code and the drift risk value. Extract the characters of the vector feature code and set the string composed of the extracted characters of the vector feature code as the feature information string.

[0018] At the same time, obtain the latest benchmark feature code from the cloud benchmark library, and set the string composed of the characters extracted from the latest benchmark feature code as the benchmark feature string;

[0019] The feature information string is compared and analyzed with the benchmark feature string: if the feature information string is consistent with the benchmark feature string, a benchmark consistency signal is generated; if the feature information string is inconsistent with the benchmark feature string, a verification instruction is generated.

[0020] Preferably, when a verification instruction is generated, the drift risk value is compared and analyzed with a preset drift risk value threshold: if the drift risk value is greater than the preset drift risk value threshold, an intervention instruction is generated; if the drift risk value is less than or equal to the preset drift risk value threshold, an adaptive calibration instruction is generated.

[0021] The drift risk value represents the product of the total number of unseen features in the current feature vector of the image data to be reconstructed and the feature drift response delay time after data normalization. The feature drift response delay time represents the time between the moment the drift feature is generated and the moment the model recognizes it.

[0022] Preferably, the throughput demand coefficient acquisition and analysis process is as follows:

[0023] Acquire the transmission environment information of the image data to be reconstructed within the time threshold. The transmission environment information includes bandwidth assessment value and load assessment value.

[0024] The bandwidth assessment value and load assessment value are compared and analyzed with the preset bandwidth assessment value threshold and preset load assessment value threshold, respectively. The number of parameters in the bandwidth assessment value and load assessment value that are greater than or equal to the corresponding preset threshold is counted, and the number of parameters is set as the throughput demand coefficient.

[0025] Preferably, the bandwidth assessment value is equal to the product of the normalized value of the link instability score of the image data to be reconstructed and the normalized value of the packet loss rate. The link instability score represents the proportion of time that the transmission link of the image data to be reconstructed is disconnected or unstable, and the packet loss rate represents the proportion of the number of lost data packets to the total number of transmissions.

[0026] The load assessment value is equal to the product of the normalized number of queue waiting requests for the image data to be reconstructed exceeding the preset threshold and the normalized value of the node latency test value of the image data to be reconstructed. The node latency test value represents the portion of the round-trip latency of the image data to be reconstructed that exceeds the preset threshold.

[0027] Preferably, the quantitative matching analysis process for reconstructing decisions is as follows:

[0028] Obtain model reliability information of the image data to be reconstructed within the time threshold. The model reliability information includes the model risk level. The value obtained by multiplying the throughput demand coefficient by the value corresponding to the model reliability level is set as the strategy evaluation index.

[0029] The strategy evaluation index is compared with the preset strategy evaluation index threshold: if the strategy evaluation index is greater than the preset strategy evaluation index threshold, a high-compression reconstruction signal is generated; if the strategy evaluation index is less than or equal to the preset strategy evaluation index threshold, a high-fidelity reconstruction signal is generated.

[0030] The analysis process for the model risk level is as follows: obtain the total number of historical hallucinations generated, the total number of parameter distribution shifts, and the number of lagging model update versions of the image data to be reconstructed within the time threshold. Then, perform reverse mapping safety scoring on the values ​​corresponding to the total number of historical hallucinations generated, the total number of parameter distribution shifts, and the number of lagging model update versions, and set them as the model reliability level.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This invention addresses the problem of semantic illusions that generative AI is prone to when reconstructing complex textures. The system calculates semantic entropy evaluation values ​​through a semantic monitoring unit. This mechanism innovatively performs adversarial operations on the complexity of image features and the confidence of the model, which can accurately identify whether the model's understanding of the current image is chaotic. High-risk data in a certain state is pre-judged to determine whether it is suitable for AI reconstruction before data transmission. For complex features that exceed the cognitive boundaries of the model, the system generates a drift risk signal to prevent blind reconstruction, effectively avoiding medical misdiagnosis or missed detection of key industrial defects caused by AI over-association.

[0033] 2. This invention addresses rare lesions or atypical features that edge nodes may encounter. The system performs atypical feature verification through a feature drift analysis unit. By comparing the feature information string at the edge with the baseline feature string in the cloud and calculating the drift risk value by combining the total number of unseen features and the response delay duration, the system can distinguish between occasional unknown features and systematic model biases. Depending on the degree of risk, the system automatically triggers adaptive calibration or manual intervention. This hierarchical processing mechanism not only prevents system downtime caused by occasional noise points but also decisively blocks systemic failures that may lead to major accidents.

[0034] 3. This invention addresses the problem of severe network fluctuations in cross-regional scenarios. The system deeply analyzes transmission environment information through a transmission strategy unit. Unlike traditional single bandwidth speed measurement, this system comprehensively considers link stability, packet loss rate, queue waiting request count, and node latency test values ​​to calculate the throughput demand coefficient. This coefficient can accurately quantify the network environment's capacity to carry AI-reconstructed data streams and accurately identify soft fault states where physical connections exist but data transmission quality is poor, providing a precise decision-making basis for subsequent transmission strategy selection.

[0035] 4. To address the risks of blindly pursuing compression rates under limited resources, this invention employs a model matching unit to perform quantitative matching analysis for reconstruction decisions. The system jointly evaluates the severity of the network environment and the security level of the model, generating a strategy evaluation index. This mechanism constructs a security circuit breaker logic: high-compression reconstruction is only permitted under the dual conditions of extremely poor network environment and extremely reliable model. If the model has historical illusions or update lag risks, the system will force a downgrade to high-fidelity transmission even if network bandwidth is insufficient. This dynamic balancing strategy ensures that transmission efficiency is maximized without sacrificing core diagnostic security. Attached Figure Description

[0036] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0037] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0039] Example 1:

[0040] Please see Figure 1 A cloud-based image product customized data management system, including a cloud platform data management center, semantic monitoring unit, feature drift analysis unit, transmission strategy unit, model matching unit, and data reconstruction unit;

[0041] The cloud platform data management center is configured to retrieve image request information from each edge node and send the image request information to the semantic monitoring unit for single-point semantic fidelity monitoring and analysis, to obtain regular image data and image data to be reconstructed, to perform discrimination processing on the semantic entropy evaluation value of the image data to be reconstructed, and to obtain steady-state semantic signals or drift risk signals.

[0042] When a drift risk signal is generated, the feature drift analysis unit is configured to perform atypical feature verification feedback analysis on the feature vector distribution information of the acquired image data to be reconstructed, and obtain intervention instructions or adaptive calibration instructions.

[0043] When generating a steady-state semantic signal, the transmission strategy unit is configured to perform throughput demand coefficient acquisition and analysis on the transmission environment information of the acquired image data to be reconstructed, process the obtained bandwidth evaluation value and load evaluation value to obtain the throughput demand coefficient, and the model matching unit is configured to perform reconstruction decision quantitative matching analysis on the model reliability information of the acquired image data to be reconstructed, compare and analyze the obtained strategy evaluation index to obtain a high-compression reconstruction signal or a high-fidelity reconstruction signal.

[0044] This embodiment provides a system that is mainly applied to high-precision medical or industrial image transmission scenarios in cross-regional and low-bandwidth environments; the cloud platform data management center, as the core scheduling hub of the system, is configured to retrieve image request information from various edge nodes, such as remote medical stations and deep-sea exploration terminals, and send the image request information to the semantic monitoring unit;

[0045] The semantic monitoring unit is used to address the risks of blind reconstruction. It performs single-point semantic fidelity monitoring and analysis on the received information. This analysis aims to determine in advance whether the current data is suitable for reconstruction using generative AI before data transmission. After processing, regular image data, i.e., data that does not need or is not suitable for AI reconstruction, and image data to be reconstructed, i.e., data that needs to be compressed and transmitted by AI, are obtained. The unit further performs discriminative processing on the semantic entropy evaluation value of the image data to be reconstructed in order to assess whether the AI ​​model's understanding of the data is in a chaotic state.

[0046] When the system generates a drift risk signal, the feature drift analysis unit is triggered. This unit is configured to perform atypical feature verification feedback analysis on the feature vector distribution information of the acquired image data to be reconstructed. Its core purpose is to distinguish between systematic deviations and occasional unknown features, thereby outputting intervention instructions or adaptive calibration instructions. When the system generates a steady-state semantic signal, the transmission strategy unit is triggered. This unit is configured to perform throughput demand coefficient acquisition analysis on the transmission environment information of the acquired image data to be reconstructed.

[0047] By comprehensively processing the bandwidth assessment value and the load assessment value, the throughput demand coefficient is obtained, thereby quantifying the current network environment's ability to carry AI reconstruction data streams; the model matching unit is configured to perform reconstruction decision quantification matching analysis on the model reliability information of the collected image data to be reconstructed, and by comparing and analyzing the obtained strategy evaluation index, a high-compression reconstruction signal or a high-fidelity reconstruction signal is finally output, and the data reconstruction unit performs the final image generation or transmission task based on the above signal;

[0048] This embodiment constructs a safety valve for generative AI-reconstructed images by introducing semantic monitoring and feature drift analysis mechanisms. While pursuing compression rate, the system can perceive the risk of semantic illusion in the model in real time. When feature drift is detected, such as rare lesion texture, the system can automatically downgrade or calibrate, avoiding semantic misdiagnosis or missed detection of key defects caused by AI over-association in low bandwidth environment, and achieving a dynamic balance between image data transmission efficiency and semantic credibility.

[0049] Example 2:

[0050] The single-point semantic fidelity monitoring and analysis process is as follows: collect the cloud platform's operating cycle and set the cloud platform's operating cycle as a time threshold. Set each edge node connected to the cloud platform as a source node and obtain the image request information of each source node within the time threshold. The image request information includes feature reconstruction request code.

[0051] Detect whether the source node generates image request information: If the source node generates image request information, generate a reconstruction request instruction and set the image data of the source node corresponding to the reconstruction request instruction as the image data to be reconstructed; if the source node does not generate image request information, generate a direct save instruction and set the image data of the source node corresponding to the direct save instruction as regular image data.

[0052] This embodiment provides a detailed description of the single-point semantic fidelity monitoring and analysis process in Embodiment 1; the system executes a time window setting, collects the cloud platform's operating cycle, and sets the cloud platform's operating cycle as a time threshold. This threshold defines the time span for the system to perform one complete state polling.

[0053] The system performs source-end locking, designating each edge node connected to the cloud platform as a source node and obtaining time thresholds. The system receives image request information from each source node. During this process, the image request information not only includes basic metadata but also a feature reconstruction request code, which is used to identify whether the edge node requests to use the AI ​​generative reconstruction service. The system performs request type routing and detects whether the source node generates image request information. In response to the generation of image request information from the source node, it means that the edge node has an active, high-bandwidth-efficiency transmission need. The system generates a reconstruction request instruction and sets the image data of the source node corresponding to the instruction as the image data to be reconstructed.

[0054] In response to an image request message that has not generated a source node, which means that the data only needs to be archived or does not require real-time AI processing, the system generates a direct storage instruction and sets the image data of the source node corresponding to the instruction as regular image data.

[0055] In this embodiment, by recognizing feature reconstruction request codes, the system can distinguish between real-time diagnostic data that urgently needs AI enhancement and archived data that only requires cold storage. This diversion mechanism avoids performing high-computational-consumption semantic analysis on all data in a one-size-fits-all manner, thereby reducing the computing load on the cloud platform and ensuring that core computing resources are concentrated on serving high-priority images to be reconstructed.

[0056] Example 3:

[0057] The system acquires the preset feature complexity value of the image data to be reconstructed within a time threshold, and simultaneously acquires the confidence score value of the current generative model reconstruction of the image data to be reconstructed within the time threshold. The confidence interval value of the current generative model reconstruction is set as the fidelity maintenance value. The difference between the preset feature complexity value and the fidelity maintenance value is set as the semantic entropy evaluation value. The semantic entropy evaluation value is compared and analyzed with the preset semantic entropy threshold: if the semantic entropy evaluation value is greater than the preset semantic entropy threshold, a drift risk signal is generated; if the semantic entropy evaluation value is less than or equal to the preset semantic entropy threshold, a steady-state semantic signal is generated.

[0058] This embodiment provides a detailed explanation of the calculation and discrimination process of the semantic entropy evaluation value in Embodiment 1 or 2; in order to quantify the potential risk of the AI ​​model generating hallucinations when reconstructing images, the system introduces a semantic entropy evaluation value. The calculation formula is as follows:

[0059]

[0060] When the value is less than 0, the calculation result may be negative, in which case it will be forced to zero; this indicates that under the high confidence coverage of the current model, the complexity of image features is completely controllable, and there is no semantic uncertainty; among which, The source is the calculation result of the above formula, and its physical meaning is the uncertainty or disorder of the current image features for the generative model; the source is the comprehensive calculation of texture density, edge gradient, and noise distribution of the image data to be reconstructed within the acquired time threshold; the specific calculation uses the following weighted normalization formula:

[0061]

[0062] in, For the preset weighting coefficients, satisfy In this embodiment, considering that texture details have the greatest impact on reconstruction quality, the settings were determined experimentally. ;

[0063] This is the texture entropy calculated based on the gray-level co-occurrence matrix; the specific extraction algorithm is as follows: convert the image into a single-channel grayscale image, and quantize and compress the grayscale levels to... Grayscale range, specifically 64 levels of grayscale, to reduce computational load; the sliding window size is set to... Pixels, generation step size Calculate separately , , , The gray-level co-occurrence matrices in four directions are used to extract texture entropy, and their arithmetic mean is taken as the final value. ;

[0064] in, This represents the average edge gradient magnitude extracted based on the Sobel operator.

[0065] and These are the statistical extreme values ​​of the corresponding features in the cloud benchmark dataset. The cloud benchmark dataset consists of the most recent 10,000 frames of fault-free image data accumulated by the system in history, and is dynamically updated monthly to adapt to changes in image benchmarks in different seasons or environments.

[0066] in, This is a noise estimation based on the Gaussian difference principle. The specific calculation steps are as follows: The image data to be reconstructed is subjected to kernel Gaussian blur processing to obtain a smooth image; the difference map between the original image and the smooth image is calculated; the standard deviation of all pixel values ​​in the difference map is calculated; and this standard deviation is set as... This value reflects the intensity of high-frequency random noise in the image.

[0067] Take the preset minimum value This is used to prevent the calculation error of the denominator being zero when the historical maximum value in the denominator is equal to the minimum value; A higher value indicates more complex image details; The source is the confidence score output by the discriminator of the generative model, and its value range is also [missing information]. The physical meaning of is to maintain the true value, that is, the degree of confidence that the model is that the current generated result conforms to the true distribution;

[0068] The system executes discriminant analysis logic and calculates the semantic entropy evaluation value. Compared with the preset semantic entropy threshold Perform comparison; respond to Greater than This indicates that the image features are extremely complex and the model confidence is low, meaning the difference is large, posing a very high risk of false generation, and the system generates a drift risk signal; in response to Less than or equal to This indicates that the model's understanding of the current image is within a controllable range, and the system generates steady-state semantic signals;

[0069] This embodiment innovatively performs adversarial computation between feature complexity and model confidence, revealing the essence of high-risk scenarios: the model attempts to process complex features it cannot understand. By monitoring semantic entropy, the system can issue timely alerts before the AI ​​model starts fabricating details, ensuring the rigor of medical or industrial diagnostics.

[0070] Example 4:

[0071] The non-typical feature verification feedback analysis process is as follows: Obtain the vector version information of the feature extraction model in the image data to be reconstructed within the time threshold. The vector version information includes the vector feature code and the drift risk value; extract the characters of the vector feature code, and set the string composed of the extracted characters of the vector feature code as the feature information string;

[0072] Simultaneously, the latest benchmark feature code from the cloud benchmark library is obtained, and the string composed of the extracted characters of the latest benchmark feature code is set as the benchmark feature string. The feature information string is compared and analyzed with the benchmark feature string: if the feature information string is consistent with the benchmark feature string, a benchmark consistency signal is generated; if the feature information string is inconsistent with the benchmark feature string, a verification instruction is generated.

[0073] This embodiment details the atypical feature verification feedback analysis process in Embodiment 1. The system performs version information extraction by accessing model metadata to obtain the vector version information of the feature extraction model in the image data to be reconstructed within the time threshold. This information is a digital fingerprint of the model state, including the vector feature code representing the current model's focus feature set and the drift risk value. The system performs feature string extraction by extracting the characters from the vector feature code. Specifically, the floating-point values ​​of each dimension in the vector feature code are concatenated into a raw byte stream after retaining a preset number of valid bits. The raw byte stream is then subjected to MD5 hash operation or Base64 encoding. The resulting hash value or encoded string is set as the feature information string. ;

[0074] Simultaneously, the system retrieves the latest benchmark feature code from the cloud-based benchmark library. This code represents a rigorously verified, unbiased standard model feature set, and the string composed of its extracted characters is set as the benchmark feature string. The system performs consistency verification, and will and Perform comparative analysis; respond to and Consistency indicates that the feature extraction logic of the edge node is consistent with the cloud standard, and the system generates a benchmark consistency signal; in response to inconsistency, it indicates that the edge node may have encountered unknown image features or that the model parameters have changed unexpectedly, and the system generates a verification instruction.

[0075] This embodiment uses string-level feature code comparison to quickly identify cognitive discrepancies between edge nodes and cloud benchmarks. This ensures that the consistency of model versions or basic feature extraction logic is checked before complex risk quantification, preventing misjudgments caused by version inconsistencies.

[0076] Example 5:

[0077] When a verification command is generated, the drift risk value is compared with a preset drift risk value threshold: if the drift risk value is greater than the preset drift risk value threshold, an intervention command is generated; if the drift risk value is less than or equal to the preset drift risk value threshold, an adaptive calibration command is generated.

[0078] The drift risk value represents the product of the total number of unseen features in the current feature vector of the image data to be reconstructed and the feature drift response delay time after data normalization. The feature drift response delay time represents the time between the moment the drift feature is generated and the moment the model recognizes it.

[0079] This embodiment provides a detailed explanation of the subsequent processing of the verification command and the calculation of the drift risk value in Embodiment 4; when a verification command is generated, the system needs to further quantify the degree of danger of this inconsistency and calculate the drift risk value. The formula is as follows:

[0080]

[0081] in, This represents the total number of unseen features actually detected; the specific calculation process is as follows: the current feature vector of the image data to be reconstructed... The input is fed into a pre-trained autoencoder model, which adopts a symmetric structure. The encoder part contains three fully connected layers with ReLU activation function, aiming to compress the input into a low-dimensional manifold space. The model is pre-trained in unsupervised mode using a set of historical drift-free standard image data features stored in a cloud benchmark library.

[0082] calculate With reconstructed vector Residual vector between dimensions It is defined as the absolute value of the element-wise difference between the two, that is:

[0083]

[0084] Statistical residual vector The median value is greater than the preset deviation threshold. The number of elements, and set that number as... This process involves calculating and reconstructing the residual vector. Does it exceed the deviation threshold? To quantify whether image features deviate from the known training distribution manifold of the model;

[0085] In this embodiment, a preset feature quantity saturation threshold is set. This value is approximately 20% of the total feature dimensions. Based on experience, when more than 20% of the feature dimensions deviate from the reconstruction, it can be considered a complete drift. In this case, the formula for calculating the normalized value is:

[0086]

[0087] in, The source is the normalized value of the time interval between the generation of drift features and the model recognition time, recorded by a timer; the specific calculation formula is set as follows:

[0088]

[0089] in, This represents the actual response delay. The maximum timeout threshold allowed by the system, for example, 500ms, is used by this formula to quantify how sluggish the system is in its response to drift.

[0090] Based on this, the system executes the decision-making logic, and Compared with the preset drift risk threshold Perform comparison; respond to Greater than This indicates the presence of numerous unknown characteristics and a delayed system response, posing a significant hidden danger. The system generates intervention commands, such as interrupting transmission or transferring the data to manual review. Less than or equal to This indicates that the drift is still within a controllable range and may be due to slight parameter fluctuations. The system generates an adaptive calibration command to trigger online fine-tuning of the model.

[0091] This embodiment constructs a dynamic risk assessment model by combining the number of unknown features and response delay. The model not only focuses on how many unseen features are encountered, but also on the system response time. This dual constraint mechanism effectively prevents system downtime caused by occasional noise, while decisively blocking system failures that may lead to major accidents.

[0092] Example 6:

[0093] The throughput demand coefficient acquisition and analysis process is as follows: Obtain the transmission environment information of the image data to be reconstructed within the time threshold, including bandwidth evaluation value and load evaluation value; compare and analyze the bandwidth evaluation value and load evaluation value with the preset bandwidth evaluation value threshold and preset load evaluation value threshold respectively, count the number of parameters in the bandwidth evaluation value and load evaluation value that are greater than or equal to the corresponding preset threshold, and set the number of parameters as the throughput demand coefficient;

[0094] The bandwidth assessment value is equal to the product of the normalized value of the link instability score and the normalized value of the packet loss rate of the image data to be reconstructed. The link instability score represents the proportion of time that the transmission link of the image data to be reconstructed is disconnected or unstable, and the packet loss rate represents the proportion of data packet loss to the total number of transmissions. The load assessment value is equal to the product of the normalized value of the number of queued requests for the image data to be reconstructed that exceeds the preset threshold and the normalized value of the node latency test value of the image data to be reconstructed. The node latency test value represents the portion of the round-trip latency of the image data to be reconstructed that exceeds the preset threshold.

[0095] This embodiment provides a detailed explanation of the throughput demand coefficient acquisition and analysis and related parameter calculation in Embodiment 1. To ensure the executability of the technical solution, the system clarifies the specific calculation logic of each normalized parameter. In particular, it unifies the polarity of each factor in the bandwidth evaluation value and load evaluation value, i.e., whether a larger value represents better or worse, to ensure the effectiveness of the product operation logic.

[0096] System bandwidth evaluation value While bandwidth evaluation typically employs product operations, to prevent an overly favorable single metric from masking the deterioration of another—for example, an extremely low packet loss rate masking extremely high link instability—this embodiment preferably uses a complementary product operation for evaluation. The formula is as follows:

[0097]

[0098] in, This is a bandwidth evaluation value; the higher the value, the worse the bandwidth quality.

[0099] The preset weighted balancing coefficients have a range of values. Considering that continuous link connectivity is more important than occasional packet loss in industrial / medical image transmission, the following settings are configured: Focusing on the impact on link stability;

[0100] The normalized value for link instability is calculated using the following formula:

[0101]

[0102] The specific statistical method is as follows: The system sends heartbeat detection packets at a frequency of 1Hz to the edge nodes, counts the total number of heartbeat response packets successfully received within the period, and multiplies this total number by the heartbeat interval. ;in, To be at the time threshold The cumulative duration of the internal link connection; this formula ensures that the more stable the connection, the closer the duration is to the period. The closer the value is to 0, the closer it is to 1; conversely, if the link is frequently disconnected, the value will approach 1. Number of packet losses Percentage of total transmissions The proportion, that is It should be noted that the product operation logic is used here to comprehensively consider the stability of the physical connection and the integrity of data transmission, by adjusting the weighting coefficients. The system can balance the combined impact of link jitter and substantial packet loss on bandwidth quality assessment, preventing the degradation of one metric from being completely masked by the advantage of another; the system calculates the load assessment value. The formula is as follows:

[0103]

[0104] in: This is a load assessment value; the larger the value, the more severe the node congestion. This is a normalized value representing the number of requests waiting in the queue that exceeds a preset threshold. The specific calculation formula is as follows:

[0105]

[0106] in, This is the current queue length. To preset alarm thresholds, This is the design limit for the queue buffer; the formula implements linear mapping and saturation truncation for the excess portion. The normalized value of the node latency test is calculated using the following formula:

[0107]

[0108] in, To measure the round-trip delay, The delay tolerance threshold, This is the maximum permissible jitter range; similarly, The product operation logic is used to confirm substantial congestion: that is, only when the request queue is backlogged, The latency is large, and the actual processing delay does indeed increase. Only when the load is large will it be considered a high load, to avoid being affected by instantaneous request pulses, i.e. Big but Small, or simply physical distance delay, i.e. Big but Small size, leading to misjudgment;

[0109] System throughput demand coefficient Acquisition: Compared with the preset bandwidth evaluation threshold Compare and Compared with the preset load assessment threshold The system performs a comparison; it counts the number of parameters that meet the condition (i.e., are greater than or equal to the corresponding threshold) and sets this number as the throughput requirement coefficient. The system performs a decision mapping: based on the number of conditions met (0, 1, 2), KTP is assigned values ​​of 1.0, 3.0, and 5.0 respectively; the higher the coefficient, the worse the network environment and the more urgent the need for compression throughput.

[0110] This embodiment abandons the traditional single speed measurement method and instead adopts a two-dimensional stress assessment of packet loss evaluation with stability weighting and congestion delay. By calculating the throughput demand coefficient, the system discretizes the complex network degradation state into a simple decision level, so that the subsequent model matching decision can accurately identify the severe operating conditions.

[0111] Example 7:

[0112] The process of quantitative matching analysis for reconstruction decision is as follows: First, obtain the model reliability information of the image data to be reconstructed within the time threshold, including the model risk level. Second, multiply the throughput requirement coefficient by the value corresponding to the model reliability level and set the result as the strategy evaluation index. Third, compare the strategy evaluation index with a preset strategy evaluation index threshold: if the strategy evaluation index is greater than the preset threshold, a high-compression reconstruction signal is generated; if the strategy evaluation index is less than or equal to the preset threshold, a high-fidelity reconstruction signal is generated. The analysis process for the model risk level is as follows: Obtain the total number of historical hallucinations generated, the total number of parameter distribution shifts, and the number of lagging model updates for the image data to be reconstructed within the time threshold. Then, perform reverse mapping safety scoring on the values ​​corresponding to these parameters and set them as the model reliability level.

[0113] This embodiment provides a detailed explanation of the reconstruction decision quantitative matching analysis process in Embodiment 1, focusing on the reverse definition logic of the model risk level and its game relationship with the transmission strategy.

[0114] The system assesses the potential risks of the currently used generative model; it should be noted that the definition in this embodiment... Essentially, it represents the model's safety confidence level. The numerical logic is: the larger the value, the safer / more reliable the model, meaning the lower the risk; when... When the value approaches 1, it indicates that the probability of the model producing semantic illusions or risks is extremely low; the calculation formula is as follows:

[0115]

[0116] in, These are the safety scores after inverse decay mapping, representing the total number of historical illusions generated, the total number of parameter distribution shifts, and the number of lagging model update versions; the specific mapping function used is:

[0117]

[0118] in, In this embodiment, the scaling factor is set as follows: The value of is related to the historical statistics of risk indicators, and is specifically set as follows: ,in, This refers to the average number of occurrences of this type of risk indicator within a historical baseline period, for example, when At that time, take If no historical data is available, the default setting can be used. This setting ensures that when the number of risk events reaches the average level, the function value... The risk sensitivity is reduced to half the baseline, thereby achieving non-linear risk sensitivity adjustment, for example:

[0119]

[0120] Through this reverse mapping, the lower the original risk count, such as the number of hallucinations, the corresponding numerical value... The closer the risk count is to 1, the higher it is; when the risk count is higher, The closer it is to 0; therefore, the final product... The higher the value, the more reliable the model.

[0121]

[0122] in The throughput demand coefficient obtained in the preceding steps takes values ​​of 1.0, 3.0, and 5.0. A larger value indicates a worse network or a more urgent demand.

[0123] The system executes decision-making logic, and Evaluation index threshold of preset strategy For example, set it to 1.5 for comparison; if The system generates a highly compressed reconstructed signal; this decision logic embodies a strict dual admission mechanism: when the network environment is extremely poor, i.e. The height is, for example, 3.0 or 5.0, and the model is very reliable, i.e. For example, when the product of the two is close to 1, it can exceed the threshold. ,For example This means the system will only activate AI reconstruction when compression is unavoidable (i.e., the network is poor) and the system is willing to compress (i.e., the model is good); if The system generates a high-fidelity reconstructed signal.

[0124] This logic includes a crucial security circuit breaker mechanism: if the network is good, i.e. Low bandwidth, no compression required, direct high-fidelity transmission; poor network bandwidth, i.e. High, but the model is unreliable, i.e. Low, for example, 0.2; in this case, the product... The system forcibly abandons high-risk AI compression, preferring to tolerate transmission delays and downgrade to high-fidelity or original data transmission, thereby avoiding misdiagnosis accidents caused by blind compression due to poor network when the model has a risk of hallucination.

[0125] This embodiment cleverly solves the decision-making game between network coercion and model credibility by performing reverse mapping on the original risk count; it ensures that high compression reconstruction is only enabled under the dual conditions of network need and model reliability, thus achieving a dynamic balance between efficiency and security in medical-grade image transmission.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A cloud platform-based image product customization data management system, characterized in that, It includes a cloud platform data management center, a semantic monitoring unit, a feature drift analysis unit, a transmission strategy unit, a model matching unit, and a data reconstruction unit; The cloud platform data management center is configured to retrieve image request information from each edge node and send the image request information to the semantic monitoring unit for single-point semantic fidelity monitoring and analysis. The single-point semantic fidelity monitoring and analysis is used to analyze whether each edge node generates image request information including feature reconstruction request code, thereby obtaining regular image data and image data to be reconstructed. The semantic entropy evaluation value of the image data to be reconstructed is discriminated to obtain steady-state semantic signal or drift risk signal. The process of judging the semantic entropy evaluation value is as follows: obtain the preset feature complexity value of the image data to be reconstructed within the time threshold, and at the same time obtain the confidence score value of the current generative model reconstruction of the image data to be reconstructed within the time threshold, and set the confidence score value of the current generative model reconstruction as the fidelity maintenance value. The difference between the preset feature complexity value and the fidelity maintenance value is set as the semantic entropy evaluation value. The semantic entropy evaluation value is compared and analyzed with the preset semantic entropy threshold: if the semantic entropy evaluation value is greater than the preset semantic entropy threshold, a drift risk signal is generated; if the semantic entropy evaluation value is less than or equal to the preset semantic entropy threshold, a steady-state semantic signal is generated. When a drift risk signal is generated, the feature drift analysis unit is configured to perform atypical feature verification feedback analysis on the feature vector distribution information of the acquired image data to be reconstructed, and obtain intervention instructions or adaptive calibration instructions. The atypical features refer to rare and atypical lesions encountered at the edge nodes; When generating a steady-state semantic signal, the transmission strategy unit is configured to perform throughput demand coefficient acquisition analysis on the transmission environment information of the acquired image data to be reconstructed, process the obtained bandwidth evaluation value and load evaluation value to obtain the throughput demand coefficient, and the model matching unit is configured to perform reconstruction decision quantitative matching analysis on the model reliability information and throughput demand coefficient of the acquired image data to be reconstructed, compare and analyze the obtained strategy evaluation index to obtain a high-compression reconstruction signal or a high-fidelity reconstruction signal.

2. The cloud platform-based image product customization data management system of claim 1, wherein, The single-point semantic fidelity monitoring and analysis process is as follows: The cloud platform's operating cycle is collected and set as a time threshold. Each edge node connected to the cloud platform is set as a source node. Image request information of each source node within the time threshold is obtained. The image request information includes feature reconstruction request code. Detect whether the source node generates image request information: If the source node generates image request information, generate a reconstruction request instruction and set the image data of the source node corresponding to the reconstruction request instruction as the image data to be reconstructed; if the source node does not generate image request information, generate a direct save instruction and set the image data of the source node corresponding to the direct save instruction as regular image data.

3. The cloud-based image product customized data management system according to claim 1, characterized in that, The non-typical feature verification feedback analysis process is as follows: Obtain the vector version information of the feature extraction model in the image data to be reconstructed within the time threshold. The vector version information includes the vector feature code and the drift risk value. Extract the characters of the vector feature code and set the string composed of the extracted characters of the vector feature code as the feature information string. At the same time, obtain the latest benchmark feature code from the cloud benchmark library, and set the string composed of the characters extracted from the latest benchmark feature code as the benchmark feature string; The feature information string is compared and analyzed with the reference feature string: if the feature information string is consistent with the reference feature string, a reference consistency signal is generated. If the feature information string is inconsistent with the baseline feature string, a verification instruction is generated.

4. The cloud-based image product customized data management system according to claim 3, characterized in that, When a verification command is generated, the drift risk value is compared with a preset drift risk value threshold: if the drift risk value is greater than the preset drift risk value threshold, an intervention command is generated; if the drift risk value is less than or equal to the preset drift risk value threshold, an adaptive calibration command is generated. The drift risk value represents the product of the total number of unseen features in the current feature vector of the image data to be reconstructed and the feature drift response delay time after data normalization. The feature drift response delay time represents the time between the moment the drift feature is generated and the moment the model recognizes it.

5. The cloud-based image product customized data management system according to claim 1, characterized in that, The process for obtaining and analyzing the throughput demand coefficient is as follows: Acquire transmission environment information of the image data to be reconstructed within a time threshold, the transmission environment information including bandwidth assessment value and load assessment value; The bandwidth assessment value and load assessment value are compared and analyzed with the preset bandwidth assessment value threshold and preset load assessment value threshold, respectively. The number of parameters in the bandwidth assessment value and load assessment value that are greater than or equal to the corresponding preset threshold is counted, and the number of parameters is set as the throughput demand coefficient.

6. The cloud-based image product customized data management system according to claim 5, characterized in that, The bandwidth assessment value is equal to the product of the normalized value of the link instability score of the image data to be reconstructed and the normalized value of the packet loss rate. The link instability score represents the proportion of time that the transmission link of the image data to be reconstructed is disconnected or unstable, and the packet loss rate represents the proportion of the number of lost data packets to the total number of transmissions. The load assessment value is equal to the product of the normalized value of the number of queue waiting requests for the image data to be reconstructed exceeding a preset threshold and the normalized value of the node latency test value of the image data to be reconstructed. The node latency test value represents the portion of the round-trip latency of the image data to be reconstructed that exceeds the preset threshold.

7. The cloud-based image product customized data management system according to claim 1, characterized in that, The quantitative matching analysis process for the reconstruction decision is as follows: The model reliability information of the image data to be reconstructed within the time threshold is obtained, and the model reliability information includes the model risk level; the value obtained by multiplying the throughput demand coefficient by the value corresponding to the model reliability level is set as the strategy evaluation index. The strategy evaluation index is compared with the preset strategy evaluation index threshold: if the strategy evaluation index is greater than the preset strategy evaluation index threshold, a high-compression reconstruction signal is generated; if the strategy evaluation index is less than or equal to the preset strategy evaluation index threshold, a high-fidelity reconstruction signal is generated. The analysis process for the model risk level is as follows: obtain the total number of historical hallucinations generated, the total number of parameter distribution shifts, and the number of lagging model update versions of the image data to be reconstructed within the time threshold, and then perform reverse mapping safety scoring on the values ​​corresponding to the total number of historical hallucinations generated, the total number of parameter distribution shifts, and the number of lagging model update versions, and set them as the model reliability level.

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