Cross-modal large model fine-tuning training system based on cue word embedding

Abstract

This invention relates to the field of cross-modal training technology and discloses a cross-modal large-scale model fine-tuning training system based on cue word embedding. The system includes a cue word embedding generation module, a cross-modal alignment module, a fine-tuning execution module, a training monitoring module, and an optimization feedback terminal. The cue word embedding generation module parses text instructions to generate dynamic semantic vectors. The cross-modal alignment module receives this vector and the target modality baseline feature vector, calculates modality adaptation parameters through semantic space projection, and transmits them to the fine-tuning execution module. The latter adjusts the model weight distribution and performs cross-modal feature fusion accordingly. The training monitoring module captures the weight update trajectory, generates training signals through convergence analysis, and triggers warnings at the optimization feedback terminal for abnormal signals; stable signals activate the model evaluation protocol and generate performance optimization instructions. The optimization feedback terminal also performs visual mapping of the dynamic semantic vectors. This system can improve the accuracy and adaptability of cross-modal large-scale model fine-tuning.

Description

Technical Field

[0001] This invention relates to the field of cross-modal training technology, specifically to a cross-modal large model fine-tuning training system based on cue word embedding. Background Technology

[0002] With the rapid development of artificial intelligence technology, cross-modal large models have demonstrated powerful capabilities in processing various types of data such as text, images, and audio, and their application scenarios have covered multiple fields such as intelligent retrieval, content generation, and human-computer interaction. However, existing cross-modal large models often need to be fine-tuned to adapt to actual needs when facing specific tasks or scenarios, and there are many technical challenges in the fine-tuning process.

[0003] Currently, fine-tuning training of large cross-modal models largely relies on fixed modal feature mapping relationships, lacking the ability to dynamically respond to user text commands. During fine-tuning, the model struggles to accurately capture the semantic information contained in text commands, leading to deviations between the generated modality adaptation parameters and actual task requirements, thus affecting the effectiveness of cross-modal feature fusion. Furthermore, the feature spaces of different modal data differ significantly. Existing fine-tuning systems often suffer from reduced correlation between cross-modal data due to unreasonable semantic space projection calculations during modality alignment, thereby impacting the overall model performance.

[0004] The existing training monitoring mechanism is inadequate, failing to track model weight updates in real time and perform effective convergence analysis. When anomalies occur during training, the system cannot issue timely warnings or adjust the training strategy, easily leading to training getting stuck in local optima or divergent states, wasting significant computational resources and time. Furthermore, after training, the lack of an effective model evaluation and optimization feedback mechanism makes it difficult to make targeted adjustments to the model based on the training results, thus limiting the model's adaptability in practical applications.

[0005] Traditional fine-tuning training systems handle cue words in a simplistic way, treating them merely as fixed input parameters. They fail to deeply analyze the semantic meaning of the cue words and transform them into effective embedding vectors, resulting in weak correlations between the cue words and modal features. This fails to fully leverage the guiding role of the cue words in the fine-tuning process. These problems severely limit the efficiency and effectiveness of fine-tuning large cross-modal models, making it difficult to meet the increasingly complex demands of real-world applications. Summary of the Invention

[0006] The purpose of this invention is to provide a cross-modal large model fine-tuning training system based on cue word embedding, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a cross-modal large model fine-tuning training system based on cue word embedding, the system comprising:

[0008] The system includes a prompt word embedding and generation module, a cross-modal alignment module, a fine-tuning execution module, a training monitoring module, and an optimization feedback module.

[0009] The prompt word embedding generation module parses the text instructions input by the user through the natural language processing unit, generates dynamic semantic vectors, and sends them to the cross-modal alignment module and the optimization feedback end. The optimization feedback end performs a visual mapping of the dynamic semantic vectors.

[0010] The cross-modal alignment module receives the dynamic semantic vector and the reference feature vector of the target modality, calculates the modality adaptation parameters through semantic space projection, and transmits the modality adaptation parameters to the fine-tuning execution module.

[0011] The fine-tuning execution module adjusts the weight distribution of the cross-modal large model according to the modal adaptation parameters and performs cross-modal feature fusion operation;

[0012] The training monitoring module captures the weight update trajectory of the fine-tuning execution module and generates a training stability signal or a training anomaly signal through convergence analysis. The training anomaly signal triggers the early warning protocol of the optimization feedback end.

[0013] When the optimization feedback terminal receives a stable training signal, it activates the model evaluation protocol and generates performance optimization instructions through analysis.

[0014] Preferably, the analysis process of the training monitoring module includes:

[0015] By analyzing the iterative trajectory, each training cycle is marked as an effective iteration or an inefficient iteration. A monitoring period is set, and the proportion of inefficient iterations in the total number of iterations is calculated as the inefficient training coefficient.

[0016] When the inefficient training coefficients exceed the preset failure threshold, a training anomaly signal is generated.

[0017] When the inefficient training coefficient does not exceed the preset failure threshold, the product of the gradient change magnitude and the learning rate adjustment magnitude of each iteration is extracted as the iteration efficiency value, and the standard deviation of the iteration efficiency value is calculated as the training fluctuation coefficient.

[0018] The system aggregates inefficient training coefficients and training volatility coefficients to generate training risk indicators. When the training risk indicators exceed a preset risk threshold, a training anomaly signal is generated; otherwise, a training stability signal is generated and sent to the optimization feedback end.

[0019] Preferably, the specific process of iterative trajectory analysis is as follows:

[0020] Record the start time of receiving modal adaptation parameters and the end time of completing weight updates in the fine-tuning execution module, and calculate the time interval as the iteration duration;

[0021] The ratio of the norm of the modal adaptation parameter to the iteration duration is marked as the iteration intensity value, and the convergence fluctuation coefficient is generated through gradient stability analysis.

[0022] When the iteration intensity value exceeds the preset intensity range or the convergence fluctuation coefficient exceeds the fluctuation threshold, the current training cycle is marked as an inefficient iteration.

[0023] When the iteration intensity value is within the preset intensity range and the convergence fluctuation coefficient does not exceed the fluctuation threshold, the current training cycle is marked as a valid iteration.

[0024] Preferably, the specific procedure for gradient stability analysis is as follows:

[0025] Establish a rectangular coordinate system with the number of iterations as the horizontal axis and the weight gradient as the vertical axis, and plot the gradient change curve for a single training cycle;

[0026] Sampling points are uniformly selected on the gradient change curve, and the gradient change between adjacent sampling points is calculated as the step offset value.

[0027] The variance of the step offset values ​​is statistically analyzed to generate gradient dispersion, and the proportion of step offset values ​​that exceed the preset offset range is calculated as the abnormal offset coefficient.

[0028] The gradient dispersion and the abnormal offset coefficient are combined to generate a convergence fluctuation coefficient and sent to the iterative trajectory analysis unit.

[0029] Preferably, the communication connection of the optimized feedback end is embedded in the optimization module, which triggers semantic evaluation when a training stabilization signal is received;

[0030] The embedding optimization module calculates the projection residual of the dynamic semantic vector in the cross-modal feature space, and generates an embedding optimization instruction when the projection residual exceeds the semantic fault tolerance threshold.

[0031] The embedding optimization instruction triggers the prompt word embedding generation module to update the semantic mapping matrix, and at the same time sends the optimization verification result to the optimization feedback terminal.

[0032] Preferably, the specific process of semantic evaluation is as follows:

[0033] Collect the feature projection distances of dynamic semantic vectors in the visual and auditory modalities, and calculate the difference values ​​with the corresponding modal standard projection templates respectively;

[0034] When the visual modal difference value or the auditory modal difference value exceeds the modal difference threshold, it is determined that there is a cross-modal mismatch in the current semantic vector;

[0035] The proportion of the duration of cross-modal mismatch to the total training time during the statistical monitoring period is used as the mismatch risk coefficient, and the maximum duration of a single cross-modal mismatch is recorded as the peak mismatch intensity.

[0036] The semantic reconstruction index is generated by fusing the mismatch risk coefficient and the peak mismatch intensity. When the semantic reconstruction index exceeds the reconstruction threshold, the embedding optimization instruction is generated.

[0037] Preferably, the hardware degradation diagnosis module for optimizing the feedback end communication connection is activated when a training abnormal signal is generated or an optimization instruction is embedded;

[0038] The hardware degradation diagnosis module collects the core temperature of the graphics processor and the memory usage rate. When the core temperature exceeds the temperature safety line or the memory usage rate exceeds the memory warning line, the hardware overload status is marked.

[0039] The total duration of accumulated hardware overload states during the device's operating cycle is used as the degradation duration, which is combined with the device's operating logs to generate hardware degradation warnings or device health signals.

[0040] Preferably, the specific process of the degradation diagnosis protocol is as follows:

[0041] The time difference between the cumulative runtime of the graphics processor and the manufacturing date is used as the device's service life.

[0042] The number of times the video memory usage rate exceeded the critical value during historical operation was extracted as the peak load frequency.

[0043] The dust concentration and humidity deviation of the equipment's operating environment are obtained by environmental sensors and then fused to generate the equipment's environmental erosion coefficient.

[0044] The system aggregates the service life of the equipment, the frequency of peak loads, the duration of degradation, and the environmental erosion coefficient of the equipment to generate hardware loss indicators. When the hardware loss indicators exceed the loss threshold, a hardware degradation warning is generated.

[0045] Preferably, the method for generating the equipment environmental erosion coefficient is as follows:

[0046] The temperature rise coefficient is calculated by comparing the intake air temperature of the graphics processor's cooling duct with the ambient reference temperature.

[0047] The concentration of airborne particles in the area where the equipment is installed is used as the dust exposure value.

[0048] The environmental erosion coefficient of the equipment is generated by weighted fusion of the temperature rise coefficient and the dust exposure value, and the coefficient is transmitted to the hardware degradation diagnosis module.

[0049] Preferably, the optimized feedback terminal communication connection interruption recovery module activates the interruption recovery protocol when the training monitoring module generates a training abnormal signal;

[0050] The interrupt recovery module captures the abnormal termination time of the fine-tuning execution process and scans the gradient temporary storage area and weight cache area data in the current memory.

[0051] A recovery feasibility index is generated by calculating the effective data ratio in the gradient buffer and the checksum difference value in the weight buffer.

[0052] When the recovery feasibility index reaches the preset recovery threshold, the training context snapshot is automatically reconstructed and the fine-tuning execution module is restarted.

[0053] Key data conflict points identified during the reconstruction process are sent to the training and monitoring module for stability marking.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] By setting up a prompt word embedding generation module, the system can perform deep analysis of user-input text commands to generate dynamic semantic vectors. These dynamic semantic vectors reflect the semantic changes of the text commands in real time, providing precise semantic guidance for cross-modal alignment and making the generation of modality adaptation parameters more closely aligned with the user's actual needs. Simultaneously, optimizing the visualization mapping of the dynamic semantic vectors at the feedback end allows users to intuitively understand the characteristics of the semantic vectors, facilitating timely adjustments to text commands during training.

[0056] After receiving the dynamic semantic vector and the baseline feature vector of the target modality, the cross-modal alignment module calculates modality adaptation parameters through semantic space projection. This method effectively reduces the differences in feature spaces between different modalities, enhances the semantic correlation between cross-modal data, and makes cross-modal feature fusion smoother and more natural. After the modality adaptation parameters are transmitted to the fine-tuning execution module, they guide the module to make targeted adjustments to the weight distribution of the large model, promoting better preservation of key information in cross-modal features during the fusion process and improving the representational ability of the fused features.

[0057] The training monitoring module captures the weight update trajectory of the fine-tuning execution module and generates corresponding signals through convergence analysis, enabling real-time monitoring of the training process. When an abnormal training signal occurs, an early warning protocol is triggered at the optimization feedback end, which can promptly interrupt the abnormal training process and avoid unnecessary consumption of computational resources. The model evaluation protocol activated by the training stabilization signal allows for a comprehensive analysis of the trained model, generating performance optimization instructions to make the model more adaptable to different scenarios in subsequent applications.

[0058] The optimization feedback mechanism, which generates model evaluation and optimization instructions after receiving stable training signals, forms a closed-loop training optimization mechanism. This mechanism allows for adjustments to the fine-tuning strategy based on the model's actual performance, enabling continuous improvement through multiple training iterations. Simultaneously, the collaborative work of the prompt word embedding generation module and the cross-modal alignment module strengthens the connection between prompt word semantics and modal features, allowing prompt words to play a more effective guiding role in the fine-tuning process. This enables the model to better understand and execute the tasks corresponding to the user's text commands.

[0059] The coordinated operation of all modules within the system forms a complete workflow from prompt word parsing, modal alignment, fine-tuning execution to training monitoring and optimization feedback, ensuring the continuity and relevance of the fine-tuning training process. This workflow design reduces the randomness in the training process, making fine-tuning training more directional, enabling the model to adapt to specific tasks more quickly in complex cross-modal scenarios, and improving the model's performance in real-world applications. Attached Figure Description

[0060] Figure 1 This is a schematic diagram illustrating the working principle of the cross-modal large model fine-tuning training system based on cue word embedding described in this invention.

[0061] Figure 2 This is a flowchart of the iterative trajectory analysis;

[0062] Figure 3 This is a flowchart of gradient stability analysis;

[0063] Figure 4 A flowchart for hardware degradation diagnosis;

[0064] Figure 5 A detailed flowchart for the degradation diagnostic protocol. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Please see Figure 1 This invention provides a cross-modal large model fine-tuning training system based on cue word embedding. The system includes: a cue word embedding generation module, a cross-modal alignment module, a fine-tuning execution module, a training monitoring module, and an optimization feedback terminal. The specific implementation steps are as follows:

[0067] The prompt word embedding generation module parses the user's input text commands through the natural language processing unit, generates dynamic semantic vectors, and sends them to the cross-modal alignment module and the optimization feedback end. The optimization feedback end performs a visual mapping of the dynamic semantic vectors.

[0068] The cross-modal alignment module receives the dynamic semantic vector and the baseline feature vector of the target modality, calculates the modality adaptation parameters through semantic space projection, and transmits the modality adaptation parameters to the fine-tuning execution module.

[0069] The fine-tuning execution module adjusts the weight distribution of the cross-modal large model based on the modal adaptation parameters and performs cross-modal feature fusion operations.

[0070] The training monitoring module captures the weight update trajectory of the fine-tuning execution module, and generates a training stability signal or a training anomaly signal through convergence analysis. The training anomaly signal triggers the early warning protocol of the optimization feedback end.

[0071] When the feedback end receives a stable training signal, it activates the model evaluation protocol and generates performance optimization instructions through analysis.

[0072] Example 1: See Figure 2 The training monitoring module analyzes the state changes during the fine-tuning process through quantitative evaluation. This process begins with iterative trajectory analysis, recording the system clock timestamp as the starting point for each received modal adaptation parameter and recording the timestamp again as the ending point after the weight update. The millisecond-level difference between the two timestamps is defined as the iteration duration, and this data is transmitted to the trajectory analysis unit in real time. The modal adaptation parameters are input to the analysis unit as a tensor data structure. The L2 norm value is extracted using a parameter norm calculator and divided by the iteration duration to generate the iteration intensity value, a key indicator. This intensity value reflects the intensity of parameter adjustment per unit time.

[0073] Gradient stability analysis runs synchronously within an independent analysis unit. When establishing a two-dimensional coordinate system, the horizontal axis iteration step count uses the step counter of the fine-tuning execution module as the data source, and records the magnitude of the current weight gradient as the vertical axis value for each step. During a complete training cycle, coordinate points are automatically connected to form a gradient change curve. Data sampling adopts a uniform distribution mode: sampling points are intercepted along the horizontal axis with a fixed step size. The gradient magnitude difference is calculated between every two adjacent sampling points to generate a step offset value; this operation is continuously performed through a gradient differencing unit. When analyzing the distribution characteristics of all offset values, a variance calculator is executed to obtain the gradient dispersion. A preset offset range threshold is simultaneously detected and interval judgment is performed. The ratio of the number of offset values ​​exceeding the threshold to the total number of offset values ​​generates an abnormal offset coefficient. The dispersion and coefficient are input into a weighted fusion unit, which outputs a convergence fluctuation coefficient based on the configured parameters.

[0074] The iteration intensity value and convergence fluctuation coefficient are synchronously input into the trajectory analysis unit's judgment logic. The preset intensity range includes an upper and lower limit forming a closed interval, and the convergence fluctuation coefficient is compared with a preset fluctuation threshold. If the iteration intensity value is higher than the upper limit or lower than the lower limit, or if the convergence fluctuation coefficient exceeds the fluctuation threshold, the current training cycle is determined to be an inefficient iteration. Conversely, if the iteration intensity is within the preset range and the convergence fluctuation coefficient is within the safe range, it is marked as a valid iteration. This marking result is written to the cycle status record database.

[0075] Within a set monitoring period (e.g., a continuous 2-hour training window), the statistics module automatically calculates the percentage of inefficient iterations out of the total number of iterations, generating an inefficient training coefficient. This coefficient is transmitted to a threshold comparator: when the value exceeds a preset failure threshold (e.g., 35%), the monitoring module immediately generates a training anomaly signal; if the coefficient is safe, a secondary analysis mode is initiated. At this point, three data items are extracted from the training log: the gradient change magnitude of each iteration cycle (difference in gradient magnitude between adjacent cycles), the learning rate adjustment magnitude (value recorded by the optimizer), and the product of these two values ​​to obtain the iteration performance value. The standard deviation of all iteration performance values ​​within the monitoring period is calculated to generate a training volatility coefficient.

[0076] The final stage involves a comprehensive risk assessment: an aggregation processor weights and fuses inefficient training coefficients with training volatility coefficients (weight ratios depend on system configuration), outputting a training risk index value. This index is input to a second-level threshold comparator; if the risk exceeds a preset threshold, a training anomaly signal is generated; otherwise, a training stability signal is output. The entire analysis process constructs a closed-loop data processing chain: a timestamp collector monitors the event triggers of the fine-tuning execution module in real time; the gradient recorder is directly connected to the optimizer log interface; the coefficient calculation unit has a built-in floating-point arithmetic core; and the threshold judge uses a hardware comparison circuit to achieve microsecond-level response. Status marking results are transmitted via a data bus to the signal parser at the optimization feedback end, simultaneously updating the visual status indicator on the training monitoring dashboard. All intermediate calculation results are stored in a monitoring cache, and analysis archives are built according to time series for traceability. The monitoring period length can be dynamically adjusted in the configuration file according to the training scale, and threshold parameters are updated in real time through a remote management interface.

[0077] Example 2: See Figure 3 The implementation of gradient stability analysis begins with the data acquisition phase. The weight update process of the fine-tuning execution module is monitored in real time, and the gradient recorder captures weight gradient change data at a fixed frequency. The operation of establishing a two-dimensional Cartesian coordinate system is performed by the graphics processing unit: the horizontal axis iteration step count data comes from the built-in counter of the fine-tuning execution module, which automatically increments with each weight adjustment step; the vertical axis weight gradient value is taken from the floating-point tensor data in the gradient buffer, which is converted into scalar values ​​by the modulus calculator. Each iteration step corresponds to a coordinate point, and the drawing engine automatically connects them according to the step sequence to generate a gradient change curve. This curve is stored in a dedicated video memory area for subsequent analysis.

[0078] The process of uniform sampling on the gradient change curve is managed by a sampling controller. The sampling interval is determined by dividing the total step length of the curve by the preset number of sampling points, and the position coordinates of each sampling point are automatically located by an interval segmentation algorithm. The offset value calculation unit reads the ordinate values ​​of two adjacent sampling points and performs a subtraction operation to generate a step offset value. All offset values ​​are stored sequentially in a temporary array, and the statistics module processes the array: the variance calculator calculates the dispersion of array elements and outputs the gradient dispersion value; the preset offset range threshold is stored in the configuration register, the comparator traverses the array elements and counts the number of offset values ​​exceeding the threshold, divides it by the total length of the array, and outputs the abnormal offset coefficient. The gradient dispersion and abnormal offset coefficient are input to the coefficient synthesizer, which performs a weighted summation operation according to the preset fusion weights to generate a convergence fluctuation coefficient and write it into the analysis report.

[0079] The optimization feedback end receives the training stabilization signal output by the training monitoring module via the data bus, triggering the startup of the embedding optimization module. During module initialization, the latest generated dynamic semantic vector is retrieved from the prompt word embedding generation module; this vector serves as the floating-point high-dimensional tensor input feature projection unit. Cross-modal feature space data mapping is achieved through a matrix multiplier: the dynamic semantic vector is left-multiplied by the cross-modal feature projection matrix, outputting the projection vector. The projection residual calculator performs two steps: it reads the target modality baseline feature vector (loaded from the pre-trained model parameter library) and calculates its Euclidean distance to the projection vector; it then compares this distance value with the semantic fault tolerance threshold stored in the system configuration unit. When the distance value exceeds the threshold, the residual calculator sends an over-limit signal to the instruction generator, triggering the generation of embedding optimization instructions.

[0080] The deep execution of semantic evaluation involves multimodal feature analysis. The feature projection unit includes parallel processing channels: the visual modality channel calls an image feature extraction model to map dynamic semantic vectors to the visual feature space, outputting visual projection vectors; the auditory modality channel calls an audio feature extraction model to generate auditory projection vectors. A standard projection template library pre-stores ideal feature vectors for each modality. A difference detector calculates the inverse cosine similarity between the visual projection vector and the visual standard template as the visual modality difference value, and calculates the spectral distance between the auditory projection vector and the auditory standard template as the auditory modality difference value. Modality difference thresholds are stored in system registers, and a dual-channel comparator synchronously monitors the two difference values; if either value exceeds the threshold, the current semantic vector is flagged as having a cross-modal mismatch.

[0081] Statistical analysis over time runs continuously during the monitoring period. A mismatch status recorder marks the start and end times of each mismatch, while a timer calculates the duration of each mismatch and stores the maximum value as the peak mismatch intensity. The total training time is obtained from the system timer. The accumulator sums the durations of all mismatch events and divides the sum by the total training time to generate a mismatch risk coefficient. The semantic reconstruction index synthesizer receives the mismatch risk coefficient and the peak mismatch intensity, performs normalization, and then performs a linear combination according to the weight parameters. The reconstruction threshold judgment unit compares the combination result with the configured threshold; if the threshold is exceeded, the embedding optimization instruction generation process is triggered.

[0082] Embedding optimization instructions are transmitted to the prompt word embedding generation module via the system bus. The semantic mapping matrix update operation is performed by the matrix optimizer: based on the error direction fed back by the projection residual calculator, the matrix parameters are adjusted using a stochastic gradient descent algorithm. The optimization verification process starts immediately after the update: dynamic semantic vectors are regenerated, cross-modal projection is performed, and it is verified whether the residuals have decreased to within the fault tolerance threshold. The verification results are encapsulated as data packets and sent to the optimization feedback end, where the result parser unpacks them and updates the system status log. All computations are supported by a floating-point accelerator hardware, the data buffer uses a dual-channel design to avoid processing blockage, and the control signals use a priority arbitration mechanism to manage the instruction execution order. Configuration parameters can be dynamically updated via a remote interface to adjust analysis accuracy and response speed in real time.

[0083] Example 3: See Figure 4 The semantic evaluation module operates based on a collaborative analysis mechanism across multimodal feature spaces. Upon input of dynamic semantic vectors, the system initiates parallel processing pipelines to construct visual and auditory modality analysis paths, respectively. The visual modality processing unit integrates a pre-trained deep convolutional network containing five residual blocks and three attention layers, capable of mapping text semantic vectors to image feature space. The auditory modality processing unit employs a time-frequency transform architecture, including short-time Fourier transform layers and a Mel-scale filter bank, to convert text semantics into acoustic features. The two processing paths share computational resources and are executed in a round-robin fashion through a hardware scheduler allocating time slices.

[0084] The feature projection process employs an adaptive mapping algorithm. The visual modality projection matrix has a dimension of 768×1024, and memory usage is optimized through sparse storage; the auditory modality projection matrix has a dimension of 256×512, and block decomposition is used to accelerate matrix operations. A hierarchical caching strategy is used for storing standard projection templates. The visual template library contains 100 cluster center vectors, and the auditory template library stores 50 typical acoustic patterns. A modal characteristic compensation factor is introduced for difference value calculation, and an improved similarity metric is used for visual modality difference calculation.

[0085] in Indicates visual modality difference value, Represents the visual projection vector. Represents a visual standard template. The calculation uses the L2 norm. Auditory modality difference is calculated using spectral energy-weighted distance, adjusted in conjunction with the importance coefficient of the Mel scale. The difference threshold is set using a dynamic adjustment mechanism, with initial values ​​of 0.3 for the visual modality and 0.2 for the auditory modality, automatically optimized based on historical evaluation results.

[0086] Cross-modal mismatch detection employs a multi-level decision-making logic. The primary decision-maker monitors real-time difference signals; when the difference between the visual or auditory channels exceeds a threshold, a secondary verification process is triggered. This verification process calls upon a historical data buffer to examine trend changes over the last three evaluation periods, eliminating transient fluctuations. The mismatch state recorder uses a first-in-first-out queue structure, storing 100 events, each containing a start timestamp, end timestamp, and modality type marker. Peak mismatch intensity analysis introduces a sliding time window, with a window size of 10 consecutive evaluation periods, to track the maximum duration in real time.

[0087] The hardware degradation diagnostic module constructs a multi-dimensional monitoring system. Graphics processor status acquisition is divided into three levels: core temperature sampling period is 500 milliseconds, directly reading sensor data via the PCIe interface; memory usage monitoring interval is 1 second, calling driver-level APIs to obtain detailed allocation information; and compute unit utilization statistics use a hardware performance counter to record the proportion of active cycles of the stream processor. Overload status determination implements an intelligent debouncing mechanism, confirming overload only when temperature or memory indicators exceed limits three consecutive times, avoiding false triggers. The degradation duration accumulator uses a 32-bit high-precision timer with a minimum resolution of 1 millisecond, supporting reset to zero for hot-swappable devices.

[0088] A closed-loop feedback system is established for environmental parameter monitoring. Dust sensors are installed at the equipment's air inlet, sampling at a flow rate of 2.8 liters / minute, with a particle size range of 0.3-10 micrometers. Humidity sensors are arranged around the circuit board, employing a capacitive measurement principle with an accuracy of ±2%RH. Environmental temperature compensation is incorporated into the temperature rise coefficient calculation, and second-order filtering eliminates the influence of diurnal temperature differences. The equipment environmental erosion coefficient synthesis algorithm includes a temperature-dust coupling term; when both exceed limits, the coefficient weight is automatically increased.

[0089] The hardware degradation assessment model implements dynamic parameter adjustments. Equipment service life is converted into standard aging units, considering the manufacturer's recommended service life curve. Peak load frequency analysis incorporates a time decay factor, with recent events weighted higher than historical events. Degradation duration assessment distinguishes between continuous and intermittent overload modes, using different damage coefficients for calculation. The environmental erosion coefficient is nonlinearly mapped, and an exponential growth model is activated when the dust concentration exceeds 100 μg / m³. Hardware degradation indicators are normalized before output, converted into a 0-100 percentage score.

[0090] The early warning and decision-making system implements a tiered response mechanism. When the loss indicator is between 60 and 80, a primary early warning is triggered, reducing the system's computing load by 10%; when the indicator is between 80 and 90, a secondary early warning is activated, and the backup cooling system is started; when it exceeds 90, an emergency plan is executed, gradually migrating computing tasks to backup nodes. All early warning events are recorded in the security audit log, including complete environmental parameter snapshots and device status images. The health status database adopts a distributed storage architecture, supporting cross-device comparative analysis and historical trend prediction.

[0091] Example 4: See Figure 5 The data acquisition system during hardware degradation diagnostic protocol execution comprises a three-layer architecture. The bottom-layer sensor network is deployed at key locations on the graphics processing unit (GPU): a temperature sensor is embedded in the GPU core package substrate, outputting digital signals via the I²C bus; a memory usage monitor connects to the performance counters of the memory controller; and an environmental sensor array is installed within the cabinet, including a laser dust detection module at the air intake grille and a capacitive humidity probe near the circuit board. The middle-layer data processing unit preprocesses the raw signals: temperature data is filtered using a moving average to eliminate instantaneous fluctuations; memory usage values ​​are sampled ten times per second to generate a time series; dust concentration values ​​are converted to standard mass density (μg / m³); and humidity measurements are adjusted by subtracting baseline offset to generate a deviation percentage. The high-layer diagnostic engine synchronously analyzes multi-source data streams, achieving millisecond-level time synchronization accuracy.

[0092] The degradation duration is accumulated using an event-driven timing mechanism. When the temperature comparator detects that the core temperature exceeds the 85°C safety threshold, or the memory comparator detects that the memory usage exceeds the 90% warning threshold, the overload state trigger outputs a high-level signal. The rising edge of this signal activates the high-precision timing chip (1ms resolution), and simultaneously writes the event log to record the occurrence time and overload type. When the state is cleared, the falling edge of the signal stops timing, and the duration of this event is accumulated in the degradation duration register. The device operation log parser periodically scans the system log file to extract key historical records, including: the device's first power-on timestamp (converted to cumulative operating hours), the manufacturing date (calculated as the number of months of service), and historical peak memory usage (the number of times the critical value was exceeded).

[0093] The environmental erosion coefficient is generated using a two-parameter fusion model. Temperature rise coefficient measurement requires simultaneous acquisition of two sets of temperatures: the intake air temperature of the cooling duct uses a PT100 platinum resistance sensor, and the ambient reference temperature is taken from the air conditioning system's temperature control probe. The difference between the two is output as a digital signal via an AD converter. Dust exposure value measurement employs particle size classification counting technology, converting the number of suspended particles in the 0.3-5μm range into mass concentration values. The data fusion unit receives the temperature rise coefficient and dust exposure value and performs a fusion calculation: the temperature rise coefficient is assigned a 70% weight (0.7 points for every 1℃ difference), and the dust exposure value is assigned a 30% weight (0.3 points for every 10μg / m³). The weighted total score is normalized to an environmental erosion coefficient in the 0-1 range. This coefficient is updated in real time and written to the hardware status register.

[0094] The following table shows a data record snippet illustrating the execution of the diagnostic protocol:

[0095] Diagnostic time window Core temperature (°C) Video memory usage (%) Dust concentration (μg / m³) Humidity deviation (%) status flags 09:00-09:30 82±1.2 87±3 35±5 5±1 normal 09:30-10:00 87±0.8 92±2 48±7 8±2 Video memory overload 10:00-10:15 89±0.5 91±1 120±15 12±3 Temperature overload 10:15-10:45 84±1.5 88±4 85±10 7±1 Dust warning 10:45-11:30 86±0.7 93±2 65±8 6±1 Double overload

[0096] The hardware depreciation index synthesis process within the aforementioned time window is as follows: equipment service life is 28 months (corresponding coefficient 0.35), peak load frequency is 58 times (coefficient 0.42), cumulative degradation time is 92 minutes (coefficient 0.28), and the environmental erosion coefficient is calculated as 0.82 based on the peak dust concentration of 120 μg / m³ (scored as 3.6 points) and peak temperature rise of 8℃ (scored as 5.6 points) during the period. The weighted calculation process is as follows: service life weight 0.3 × 0.35 = 0.105, load frequency weight 0.25 × 0.42 = 0.105, degradation time weight 0.2 × 0.28 = 0.056, environmental erosion weight 0.25 × 0.82 = 0.205, totaling a depreciation index of 0.471 (equivalent to 82 points after conversion to a percentage system).

[0097] When the loss index exceeds the 80-point threshold, the diagnostic engine executes a three-level response strategy: Level 1 activates the early warning protocol, sending a hardware degradation early warning code to the optimization feedback end via the system bus; Level 2 initiates a resource control strategy, limiting the number of computation threads in the fine-tuning execution module; Level 3 writes to the equipment maintenance log, generating a maintenance request form containing detailed diagnostic data. All raw data is stored in a circular buffer in time series. Data sampling intervals, weight configuration parameters, and early warning thresholds are dynamically adjustable through the system configuration interface. Maintenance personnel can remotely set the geographical calibration parameters for dust concentration and the filtering coefficient for temperature monitoring. The fusion algorithm for the environmental erosion coefficient retains a custom interface, supporting adjustments to the weighting ratio based on different data center environmental characteristics. Diagnostic records are stored in an encrypted format in a separate partition. Data access requires authentication through a hardware security module to ensure the integrity and traceability of historical records.

[0098] Example 5: The operation of the interrupt recovery module begins with the signal response mechanism. Training anomaly signals generated by the training monitoring module are transmitted to the signal parsing unit at the optimization feedback end via a dedicated data channel. This unit includes a multiplexer structure, capable of distinguishing different types of anomaly signals and routing them to the corresponding processing units. When the interrupt recovery protocol activation command arrives, the state controller immediately freezes the computation process of the fine-tuning execution module and synchronously sends a clock pause request to the system time manager. The anomaly termination time is recorded by a high-precision timer, accurate to the millisecond level timestamp, and this time data is written to the first entry of the recovery log.

[0099] The memory scanning process employs a hierarchical retrieval strategy. The first priority is accessing the gradient buffer, which stores the gradient data from the last valid iteration; its physical address range is located through a memory mapping table. The data integrity verification unit performs a block scan: reading data blocks in 128-byte units, calculating the cyclic redundancy check (CRC) code, comparing it with a preset checksum, marking valid data blocks, and calculating their total percentage. The second priority is scanning the weight buffer, which contains copies of the current model's weight parameters, organized in a multidimensional tensor format. Checksum and difference calculations use a hierarchical hashing method: calculating SHA-256 hash values ​​grouped by parameter level, comparing them layer by layer with the baseline hash sequence saved before the anomaly occurred, and outputting a difference value to quantify the degree of difference.

[0100] The feasibility index generation module integrates dual-channel scan results. The percentage of valid data is converted to a percentage format and used to generate an analog signal in the 0-1 range via an analog-to-digital converter. Checksum and difference values ​​are compressed to the same numerical range by a normalization processor. The index synthesis circuit receives the two signals, performs multiplication, and outputs the original index value, which is then adjusted to a preset range by a proportional amplifier. The threshold comparison unit compares the index value with the recovery threshold: if the index value is greater than or equal to the threshold, a continue recovery instruction is generated; otherwise, an abandon recovery signal is generated. This decision is written to the system status register, simultaneously triggering a color change in the recovery status indicator light.

[0101] The context snapshot reconstruction operation is initiated upon receiving a continue instruction. The model state reconstructor first reads the valid data blocks from the gradient buffer and repairs missing data units using an interpolation compensation algorithm. The weight parameter recovery process invokes a two-layer verification mechanism: the first layer uses the repaired gradient data to inversely calculate the weight change path; the second layer loads the most recent safe backup point data for cross-validation. The data consistency arbitration unit monitors the output results of the two paths and initiates a voting arbitration when the deviation exceeds the tolerance limit. A successfully reconstructed training context contains seven core elements: model weight parameter snapshot, optimizer momentum state, learning rate plan progress, data loader position pointer, loss function statistics, gradient buffer state, and current iteration cycle counter.

[0102] The module restart sequence executes an automated process. After receiving the initialization command, the fine-tuning execution module loads seven context elements in a predetermined order: first, weight parameters are written to the computation engine's register array; then, the optimizer state is restored to a dedicated memory area; the data loader repositions the dataset offset based on the position pointer; the loss statistician is reset to the snapshot saved values; and the gradient buffer is pre-filled to repair the data. The restart controller activates the subsystem in three phases: the first phase enables the computation core clock signal; the second phase unfreezes the data bus; and the third phase sends a recovery-ready signal to the training monitoring module. Hardware-level delay circuits are used to ensure signal stability at the intervals between each phase.

[0103] The conflict point marking system synchronously records the recovery process. During the weight parameter reconstruction phase, the divergence points identified by the data consistency arbitration unit are recorded as a set of critical conflict points. Each conflict point contains three types of information: parameter index address, computation path difference value, and arbitration result code. The marker generator creates a unique identifier for each conflict point, associating it with a timestamp and processor core number. The stability marking executor encapsulates this information into a data structure package and transmits it to the event memory of the training monitoring module via a high-speed data bus. On the monitoring dashboard interface, conflict points are marked in three-dimensional coordinates on the model architecture diagram, with color depth indicating the severity of the difference. All recovery process data is encrypted and stored in non-volatile memory, and a log chain structure ensures operational traceability. The recovery threshold and scan parameters can be adjusted in real time via a remote configuration interface to adapt to the fault tolerance requirements of models of different sizes.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0105] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cross-modal large model fine-tuning training system based on cue word embedding, characterized in that, It includes a prompt word embedding and generation module, a cross-modal alignment module, a fine-tuning execution module, a training monitoring module, and an optimization feedback terminal; The prompt word embedding generation module parses the text instructions input by the user through the natural language processing unit, generates dynamic semantic vectors, and sends them to the cross-modal alignment module and the optimization feedback end. The optimization feedback end performs a visual mapping of the dynamic semantic vectors. The cross-modal alignment module receives the dynamic semantic vector and the reference feature vector of the target modality, calculates the modality adaptation parameters through semantic space projection, and transmits the modality adaptation parameters to the fine-tuning execution module. The fine-tuning execution module adjusts the weight distribution of the cross-modal large model according to the modal adaptation parameters and performs cross-modal feature fusion operation; The training monitoring module captures the weight update trajectory of the fine-tuning execution module and generates a training stability signal or a training anomaly signal through convergence analysis. The training anomaly signal triggers the early warning protocol of the optimization feedback end. When the optimization feedback terminal receives the training stability signal, it activates the model evaluation protocol and generates performance optimization instructions through analysis. The analysis process of the training monitoring module includes: By analyzing the iterative trajectory, each training cycle is marked as an effective iteration or an inefficient iteration. A monitoring period is set, and the proportion of inefficient iterations in the total number of iterations is calculated as the inefficient training coefficient. When the inefficient training coefficients exceed a preset failure threshold, a training anomaly signal is generated. When the inefficient training coefficient does not exceed the preset failure threshold, the product of the gradient change magnitude and the learning rate adjustment magnitude of each iteration is extracted as the iteration efficiency value, and the standard deviation of the iteration efficiency value is calculated as the training fluctuation coefficient. The inefficient training coefficient and the training fluctuation coefficient are aggregated to generate a training risk index. When the training risk index exceeds the preset risk threshold, a training anomaly signal is generated; otherwise, a training stability signal is generated and sent to the optimization feedback end. The specific process of iterative trajectory analysis is as follows: Record the start time of receiving modal adaptation parameters and the end time of completing weight updates in the fine-tuning execution module, and calculate the time interval as the iteration duration; The ratio of the norm of the modal adaptation parameter to the iteration duration is marked as the iteration intensity value, and the convergence fluctuation coefficient is generated through gradient stability analysis. When the iteration intensity value exceeds the preset intensity range or the convergence fluctuation coefficient exceeds the fluctuation threshold, the current training cycle is marked as an inefficient iteration. When the iteration intensity value is within the preset intensity range and the convergence fluctuation coefficient does not exceed the fluctuation threshold, the current training cycle is marked as a valid iteration.

2. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 1, characterized in that, The specific process of gradient stability analysis is as follows: Establish a rectangular coordinate system with the number of iterations as the horizontal axis and the weight gradient as the vertical axis, and plot the gradient change curve for a single training cycle; Sampling points are uniformly selected on the gradient change curve, and the gradient change between adjacent sampling points is calculated as the step offset value. The variance of the step offset values ​​is statistically analyzed to generate gradient dispersion, and the proportion of step offset values ​​that exceed the preset offset range is calculated as the abnormal offset coefficient. The gradient dispersion and the abnormal offset coefficient are combined to generate a convergence fluctuation coefficient and sent to the iterative trajectory analysis unit.

3. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 1, characterized in that, The communication connection between the feedback end and the embedded optimization module is optimized. When a training stability signal is received, the embedded optimization module is triggered to perform semantic evaluation. The embedding optimization module calculates the projection residual of the dynamic semantic vector in the cross-modal feature space, and generates an embedding optimization instruction when the projection residual exceeds the semantic fault tolerance threshold. The embedding optimization instruction triggers the prompt word embedding generation module to update the semantic mapping matrix, and at the same time sends the optimization verification result to the optimization feedback terminal.

4. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 3, characterized in that, The specific process of semantic evaluation is as follows: Collect the feature projection distances of dynamic semantic vectors in the visual and auditory modalities, and calculate the difference values ​​with the corresponding modal standard projection templates respectively; When the visual modal difference value or the auditory modal difference value exceeds the modal difference threshold, it is determined that there is a cross-modal mismatch in the current semantic vector; The proportion of the duration of cross-modal mismatch to the total training time during the statistical monitoring period is used as the mismatch risk coefficient, and the maximum duration of a single cross-modal mismatch is recorded as the peak mismatch intensity. The semantic reconstruction index is generated by fusing the mismatch risk coefficient and the peak mismatch intensity. When the semantic reconstruction index exceeds the reconstruction threshold, the embedding optimization instruction is generated.

5. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 1, characterized in that, Optimize the feedback end communication connection hardware degradation diagnosis module, and activate the degradation diagnosis protocol when training abnormal signals are generated or optimization instructions are embedded; The hardware degradation diagnosis module collects the core temperature of the graphics processor and the memory usage rate. When the core temperature exceeds the temperature safety line or the memory usage rate exceeds the memory warning line, the hardware overload status is marked. The total duration of accumulated hardware overload states during the device's operating cycle is used as the degradation duration, which is combined with the device's operating logs to generate hardware degradation warnings or device health signals.

6. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 5, characterized in that, The specific process of the degradation diagnosis protocol is as follows: The time difference between the cumulative runtime of the graphics processor and the manufacturing date is used as the device's service life. The number of times the video memory usage rate exceeded the critical value during historical operation was extracted as the peak load frequency. The dust concentration and humidity deviation of the equipment's operating environment are obtained by environmental sensors and then fused to generate the equipment's environmental erosion coefficient. The system aggregates the service life of the equipment, the frequency of peak loads, the duration of degradation, and the environmental erosion coefficient of the equipment to generate hardware loss indicators. When the hardware loss indicators exceed the loss threshold, a hardware degradation warning is generated.

7. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 6, characterized in that, The method for generating the environmental erosion coefficient of equipment is as follows: The temperature rise coefficient is calculated by comparing the intake air temperature of the graphics processor's cooling duct with the ambient reference temperature. The concentration of airborne particles in the area where the equipment is installed is used as the dust exposure value. The environmental erosion coefficient of the equipment is generated by weighted fusion of the temperature rise coefficient and the dust exposure value, and the coefficient is transmitted to the hardware degradation diagnosis module.

8. The cross-modal large model fine-tuning training system based on cue word embedding according to claim 1, characterized in that, The optimized feedback terminal communication connection interruption recovery module activates the interruption recovery protocol when the training monitoring module generates a training abnormal signal. The interrupt recovery module captures the abnormal termination time of the fine-tuning execution process and scans the gradient temporary storage area and weight cache area data in the current memory. A recovery feasibility index is generated by calculating the effective data ratio in the gradient buffer and the checksum difference value in the weight buffer. When the recovery feasibility index reaches the preset recovery threshold, the training context snapshot is automatically reconstructed and the fine-tuning execution module is restarted. Key data conflict points identified during the reconstruction process are sent to the training and monitoring module for stability marking.

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