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

Abstract

The invention relates to the technical field of cross-modal training, and discloses a cross-modal large model fine tuning training system based on cue word embedding. The system comprises a cue word embedding generation module, a cross-modal alignment module, a fine tuning execution module, a training monitoring module and an optimization feedback end. The cue word embedding generation module analyzes a text instruction to generate a dynamic semantic vector, the cross-modal alignment module receives the vector and a target modal reference feature vector, generates modal adaptation parameters through semantic space projection calculation and transmits the modal adaptation parameters to the fine adjustment execution module, and the fine adjustment execution module adjusts model weight distribution and executes cross-modal feature fusion according to the modal adaptation parameters. A training monitoring module captures a weight updating track, a training signal is generated through convergence analysis, and an abnormal signal triggers an optimization feedback end for early warning; and stabilizing a signal activation model evaluation protocol, and generating a performance optimization instruction. And the optimization feedback end also performs visual mapping on the dynamic semantic vector. According to the system, the accuracy and adaptability of fine tuning of the cross-modal large model can be improved.

Description

Technical Field

[0001] The present invention relates to the field of cross-modal training technology, and in particular to a cross-modal large model fine-tuning training system based on prompt word embedding. Background Art

[0002] With the rapid development of artificial intelligence (AI), large cross-modal models have demonstrated powerful capabilities in processing diverse data types, including text, images, and audio. Their application scenarios now encompass intelligent search, content generation, and human-computer interaction. However, existing large cross-modal models often require fine-tuning to adapt to specific tasks or scenarios, a process that presents numerous technical challenges. Currently, fine-tuning training of large cross-modal models relies heavily on fixed modal feature mapping relationships and lacks 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, resulting in deviations between the generated modal adaptation parameters and the actual task requirements, impacting the effectiveness of cross-modal feature fusion. Furthermore, the feature spaces of data from different modalities differ significantly. Existing fine-tuning systems often perform irrational semantic space projection calculations during modal alignment, reducing the correlation between cross-modal data and thus affecting the overall performance of the model. Existing training monitoring mechanisms are inadequate, unable to track model weight updates in real time and conduct effective convergence analysis. When training anomalies occur, the system fails to issue timely warnings and adjust training strategies, easily leading to training stuck in local optimality or divergence, wasting significant computing resources and time. Furthermore, after training is complete, the lack of an effective model evaluation and optimization feedback mechanism makes it difficult to make targeted adjustments to the model based on training results, limiting the model's adaptability in practical applications. Traditional fine-tuning training systems handle prompt words in a simplistic manner, treating them simply as fixed input parameters. They fail to deeply analyze the semantic connotations of prompt words and convert them into effective embedding vectors. This results in a weak correlation between prompt words and modal features, and prevents them from fully guiding the fine-tuning process. These issues severely restrict 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

[0003] The purpose of the present invention is to provide a cross-modal large model fine-tuning training system based on prompt word embedding to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides a cross-modal large model fine-tuning training system based on prompt word embedding, the system comprising: Prompt word embedding generation module, cross-modal alignment module, fine-tuning execution module, training monitoring module and optimization feedback end; The prompt word embedding generation module parses the text instructions input by the user through the natural language processing unit, generates a dynamic semantic vector and sends it to the cross-modal alignment module and the optimization feedback end, and the optimization feedback end performs visual mapping on the dynamic semantic vector; The cross-modal alignment module receives the dynamic semantic vector and the reference feature vector of the target modality, generates modal adaptation parameters through semantic space projection calculation, and transmits the modal 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 a 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 abnormality signal through convergence analysis. The training abnormality signal triggers the early warning protocol of the optimization feedback end; The optimization feedback end activates the model evaluation protocol when receiving the training stabilization signal, and generates performance optimization instructions through analysis.

[0005] Preferably, the analysis process of the training monitoring module includes: Through iterative trajectory analysis, a single training cycle is marked as an effective iteration or an inefficient iteration. The 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 coefficient exceeds the preset failure threshold, a training abnormality signal is generated; When the inefficient training coefficient does not exceed the preset failure threshold, the product of the gradient change amplitude of each iteration and the learning rate adjustment amplitude 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 abnormality signal is generated; otherwise, a training stable signal is generated and sent to the optimization feedback end.

[0006] Preferably, the specific process of iterative trajectory analysis is: Record the start time when the fine-tuning execution module receives the modal adaptation parameters and the end time when the weight update is completed, and calculate the time interval as the iteration duration; The ratio of the norm of the modal adaptation parameter to the iteration time 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.

[0007] Preferably, the specific process of gradient stability analysis is: Establish a rectangular coordinate system with the number of iterations as the horizontal axis and the weight gradient as the vertical axis, and draw the gradient change curve of a single training cycle; Sampling points are evenly 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 value is statistically calculated to generate the gradient dispersion, and the proportion of step offset values ​​exceeding the preset offset range is calculated as the abnormal offset coefficient; The convergence fluctuation coefficient is generated by fusing the gradient discreteness and the abnormal offset coefficient and sent to the iterative trajectory analysis unit.

[0008] Preferably, the optimization feedback terminal is communicatively connected to the embedding optimization module, and triggers the embedding optimization module to perform semantic evaluation when receiving the training stability signal; 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 a semantic error 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 end.

[0009] Preferably, the specific process of semantic evaluation is: Collect the feature projection distance of the dynamic semantic vector in the visual mode and the auditory mode, and calculate the difference between it and the standard projection template of the corresponding mode; When the visual modality difference value or the auditory modality difference value exceeds the modality difference threshold, it is determined that the current semantic vector has a cross-modal mismatch; The ratio of the duration of cross-modal mismatch during the monitoring period to the total training time was calculated as the mismatch risk coefficient, and the maximum duration of a single cross-modal mismatch was recorded as the peak mismatch intensity. The mismatch risk coefficient and the peak mismatch intensity are integrated to generate a semantic reconstruction index. When the semantic reconstruction index exceeds the reconstruction threshold, an embedding optimization instruction is generated.

[0010] Preferably, the optimization feedback terminal is communicatively connected to the hardware degradation diagnosis module, and the degradation diagnosis protocol is activated when a training abnormality signal is generated or an optimization instruction is embedded; The hardware degradation diagnosis module collects the graphics processor core temperature and video memory occupancy rate, and marks the hardware overload status when the core temperature exceeds the temperature safety line or the video memory occupancy rate exceeds the video memory warning line; The total duration of the accumulated hardware overload state during the device operation cycle is used as the degradation duration. Combined with the device operation log, a hardware degradation warning or device health signal is generated.

[0011] Preferably, the specific process of the degradation diagnosis protocol is: Obtain the time difference between the cumulative operating time of the graphics processor and the factory date as the device service time; The number of times the video memory usage exceeds the critical value in historical operations is extracted as the peak load frequency; The dust concentration and humidity deviation of the equipment operating environment are obtained through environmental sensors, and the equipment environmental erosion coefficient is generated by fusion. Aggregate the equipment's service life, peak load frequency, degradation duration, and equipment environmental erosion coefficient to generate a hardware loss index. When the hardware loss index exceeds the loss threshold, a hardware degradation warning is generated.

[0012] Preferably, the method for generating the equipment environmental erosion coefficient is: The difference between the air intake temperature of the GPU cooling duct and the ambient reference temperature is collected as the temperature rise coefficient; Measure the concentration of airborne particles in the equipment installation area as the dust exposure value; The equipment environmental erosion coefficient 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.

[0013] Preferably, the optimization feedback end communication connection interruption recovery module activates the interruption recovery protocol when the training monitoring module generates a training abnormality signal; The interrupt recovery module captures the abnormal termination moment of the fine-tuning execution process and scans the gradient temporary storage area and weight buffer area data in the current memory; The recovery feasibility index is generated by calculating the difference between the valid data ratio in the gradient buffer area and the checksum in the weight buffer area; When the recovery feasibility indicator reaches the preset recovery threshold, the training context snapshot is automatically rebuilt and the fine-tuning execution module is restarted; The key data conflict points identified during the reconstruction process are sent to the training monitoring module for stability marking.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By setting up a prompt word embedding generation module, it is possible to deeply analyze the text instructions entered by the user and generate a dynamic semantic vector. This dynamic semantic vector can reflect the semantic changes of the text instructions in real time, providing precise semantic guidance for cross-modal alignment, and making the generation of modal adaptation parameters more in line with the user's actual needs. At the same time, optimizing the visual mapping of the dynamic semantic vector on the feedback end allows users to intuitively understand the characteristics of the semantic vector, facilitating timely adjustments to the text instructions during the training process. After receiving the dynamic semantic vector and the baseline feature vector of the target modality, the cross-modal alignment module generates modal adaptation parameters through semantic space projection. This approach effectively reduces the differences between the feature spaces of different modalities, enhances the semantic relevance between cross-modal data, and makes cross-modal feature fusion smoother and more natural. After transmitting the modal adaptation parameters to the fine-tuning execution module, it guides the module to make targeted adjustments to the weight distribution of the large model, promoting the cross-modal features to better retain their respective key information during the fusion process and improving the representational capabilities of the fused features. The training monitoring module captures the weight update trajectory of the fine-tuning execution module and generates corresponding signals through convergence analysis, providing real-time monitoring of the training process status. When training anomaly signals occur, the early warning protocol on the optimization feedback side is triggered, promptly interrupting the abnormal training process and avoiding ineffective computing resource consumption. The model evaluation protocol, activated by the training stability signal, conducts a comprehensive analysis of the trained model and generates performance optimization instructions, making the model more adaptable to different scenarios in subsequent applications. After receiving the training stability signal, the optimization feedback end evaluates the model and generates optimization instructions, forming a closed-loop training optimization mechanism. This mechanism allows fine-tuning strategies to be adjusted based on the model's actual performance, allowing the model to continuously improve over multiple training iterations. Furthermore, the collaborative work of the prompt word embedding generation module and the cross-modal alignment module strengthens the connection between the prompt word semantics and modal features, allowing the prompt word to play a more effective guiding role in the fine-tuning process, enabling the model to better understand and execute the tasks corresponding to the user's text instructions. The coordinated collaboration between the various modules of the entire system forms a complete process from prompt word parsing, modality alignment, fine-tuning execution, training monitoring, and optimization feedback, ensuring the consistency and targeted nature of the fine-tuning training process. This process design reduces blind spots in the training process and makes fine-tuning more targeted. It enables the model to adapt to specific tasks more quickly in complex cross-modal scenarios, improving its performance in real-world applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a diagram showing the working principle of the cross-modal large model fine-tuning training system based on prompt word embedding according to the present invention; Figure 2 Flowchart for iterative trajectory analysis; Figure 3 Flowchart for gradient stability analysis; Figure 4 Flowchart for hardware degradation diagnosis; Figure 5 Detailed flow chart for the degeneration diagnostic protocol. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a cross-modal large model fine-tuning training system based on prompt word embedding. The system includes: a prompt 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: The prompt word embedding generation module parses the text instructions input by the user through the natural language processing unit, generates a dynamic semantic vector and sends it to the cross-modal alignment module and the optimization feedback end, which performs visual mapping of the dynamic semantic vector.

[0018] The cross-modal alignment module receives the dynamic semantic vector and the baseline feature vector of the target modality, generates modal adaptation parameters through semantic space projection calculation, and transmits the modal adaptation parameters to the fine-tuning execution module.

[0019] 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 operations.

[0020] 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.

[0021] When the optimization feedback end receives the training stability signal, it activates the model evaluation protocol and generates performance optimization instructions through analysis.

[0022] Example 1: See Figure 2 The training monitoring module implements analysis by quantitatively evaluating the state changes of the fine-tuning execution process. The process first performs iterative trajectory analysis, recording the system clock timestamp as the starting time each time the modal adaptation parameters are received, and recording the timestamp again as the ending time after the weight update is completed. The millisecond difference between the two timestamps is accurately calculated and defined as the iteration duration, and this data is transmitted to the trajectory analysis unit in real time. The modal adaptation parameters are input into the analysis unit as a tensor data structure, and the L2 norm value is extracted through the parameter norm calculator. It forms a division operation relationship with the iteration duration to generate the key indicator of the iteration intensity value. This intensity value reflects the parameter adjustment intensity per unit time.

[0023] Gradient stability analysis runs simultaneously in a separate analysis unit. When establishing a two-dimensional coordinate system, the number of iterations along the horizontal axis is derived from the step counter of the fine-tuning execution module. The modulus of the current weight gradient is recorded as the vertical axis value after each step. Throughout the training cycle, coordinate points are automatically connected to form a gradient curve. Data sampling uses a uniform distribution: sampling points are intercepted along the horizontal axis with a fixed step size. The gradient modulus difference between each two adjacent sampling points is calculated to generate a step offset value. This operation is performed continuously by a gradient differentiator. When calculating the distribution characteristics of all offset values, a variance operator is executed to obtain the gradient dispersion. Preset offset range thresholds are simultaneously detected and interval judgments are performed. The ratio of the number of offset values ​​exceeding the threshold to the total number is calculated to generate an abnormal offset coefficient. The dispersion and coefficient are input into a weighted fusion unit, which outputs a convergence fluctuation coefficient based on configuration parameters.

[0024] The iteration intensity value and the convergence fluctuation coefficient are simultaneously input into the trajectory analysis unit's judgment logic. The preset intensity range consists of an upper and lower limit, forming a closed interval. The convergence fluctuation coefficient is compared with a preset fluctuation threshold. If the iteration intensity value exceeds the upper or lower limit, or if the convergence fluctuation coefficient exceeds the fluctuation threshold, the current training cycle is judged as an inefficient iteration. Conversely, if the iteration intensity is within the preset range and the convergence fluctuation coefficient is within the safe value range, the iteration is marked as valid. This marking result is written to the cycle status record database.

[0025] During a set monitoring period (e.g., a continuous two-hour training window), the statistics module automatically calculates the percentage of inefficient iterations as a percentage of the total number of iterations to generate an inefficient training coefficient. This coefficient is transmitted to the threshold comparator: when the value exceeds the preset failure threshold (e.g., 35%), the monitoring module immediately generates a training anomaly signal; if the coefficient is safe, the secondary analysis mode is activated. At this point, three data items are extracted from the training log: the gradient change amplitude for each iteration (the difference in gradient modulus between adjacent cycles) and the learning rate adjustment amplitude (the value recorded by the optimizer). The two are multiplied together to obtain the iteration performance value. The standard deviation of all iteration performance values ​​within the monitoring period is calculated to generate the training fluctuation coefficient.

[0026] The final stage performs a comprehensive risk assessment: an aggregation operator performs a weighted fusion of the inefficient training coefficient and the training fluctuation coefficient (the weight ratio depends on the system configuration) to output a training risk index. This index is fed into a second-level threshold comparator, which generates a training anomaly signal if the pre-set risk threshold is exceeded; otherwise, it outputs a training stability signal. The entire analysis process forms a closed-loop data processing chain: a timestamp collector monitors event triggers from the fine-tuning execution module in real time; a gradient recorder is directly connected to the optimizer's log interface; the coefficient calculation unit has a built-in floating-point arithmetic core; and the threshold judgment uses a hardware comparison circuit to achieve microsecond response. Status marking results are transmitted to the signal analyzer at the optimization feedback end via a data bus, simultaneously updating the visual status indicator on the training monitoring dashboard. All intermediate calculation results are stored in a monitoring buffer, and analysis archives are created in a time series format for traceability. The monitoring period length can be dynamically adjusted in the configuration file based on the training scale, and threshold parameters are updated in real time via the remote management interface.

[0027] Example 2: See Figure 3 The implementation of gradient stability analysis begins in the data acquisition phase. The weight update process of the fine-tuning execution module is monitored in real time, and the gradient recorder captures the weight gradient change data at a fixed frequency. The operation of establishing a two-dimensional rectangular coordinate system is performed by the graphics processing unit: the horizontal axis iteration step data comes from the built-in counter of the fine-tuning execution module, which automatically accumulates with each weight adjustment step; the vertical axis weight gradient value is taken from the floating-point tensor data in the gradient buffer area and converted into a scalar value by the modulus calculator. Each iteration step corresponds to a coordinate point, which is automatically connected by the drawing engine according to the step sequence to generate a gradient change curve. This curve is stored in the dedicated video memory area for subsequent analysis.

[0028] The process of performing uniform sampling on the gradient change curve is managed by the 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 the interval segmentation algorithm. The offset value calculation unit reads the vertical coordinate values ​​of two adjacent sampling points and performs a subtraction operation to generate the step offset value. All offset values ​​are stored in a temporary array in order, and the statistical module processes the array data: the variance operator is executed to calculate the discreteness of the array elements and outputs the gradient discreteness value; the preset offset range threshold is stored in the configuration register, and the comparator traverses the array elements and counts the number of offset values ​​that exceed the threshold, and then outputs the abnormal offset coefficient after dividing it by the total length of the array. The gradient discreteness and abnormal offset coefficient are input into the coefficient synthesizer, which performs a weighted sum operation according to the preset fusion weight to generate the convergence fluctuation coefficient and write it into the analysis report.

[0029] The optimization feedback end receives the training stability signal output by the training monitoring module via the data bus, triggering the start of the embedding optimization module. During the module initialization phase, the newly generated dynamic semantic vector is retrieved from the prompt word embedding generation module and input into the feature projection unit as a floating-point high-dimensional tensor. Data mapping across the cross-modal feature space is implemented via a matrix multiplier: the dynamic semantic vector is multiplied by the cross-modal feature projection matrix to output the projection vector. The projection residual calculator performs two operations: 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; and it numerically 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 the embedding optimization instruction.

[0030] The in-depth execution of semantic evaluation involves multimodal feature analysis. The feature projection unit contains parallel processing channels: the visual modality channel calls the image feature extraction model, maps the dynamic semantic vector to the visual feature space, and outputs the visual projection vector; the auditory modality channel calls the audio feature extraction model to map and generate the auditory projection vector. The standard projection template library pre-stores the ideal feature vectors of each modality. The difference detector calculates the inverse of the 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. The modality difference threshold is stored in the system register, and the dual-channel comparator synchronously monitors the two difference values. If any value exceeds the limit, it will mark the current semantic vector as having a cross-modal mismatch.

[0031] The statistical analysis of the time dimension runs continuously during the monitoring period. The mismatch status recorder marks the start time and release time of each mismatch. The timer calculates the duration of a single 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 duration of all mismatch events and divides it by the total training time to generate the mismatch risk coefficient. The semantic reconstruction indicator synthesizer receives the mismatch risk coefficient and the peak mismatch intensity, performs normalization processing, and then linearly combines them according to the weight parameters. The reconstruction threshold judgment unit compares the combination result with the configuration threshold. When the limit is exceeded, the embedded optimization instruction generation process is triggered.

[0032] 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 stochastic gradient descent algorithm is applied to adjust the matrix parameters. The optimization verification process is initiated immediately after the update: the dynamic semantic vector is regenerated to perform cross-modal projection and verify whether the residual is reduced to within the fault tolerance threshold. The verification results are encapsulated as data packets and sent to the optimization feedback end, where they are unpacked by the result parser and the system status log is updated. All calculation processes are supported by floating-point arithmetic accelerator hardware. The data buffer adopts a dual-channel design to avoid processing blockages, and the control signal uses a priority arbitration mechanism to manage the order of instruction execution. Configuration parameters can be dynamically updated through the remote interface to adjust the analysis accuracy and response speed in real time.

[0033] Example 3: See Figure 4 The operation of the semantic evaluation module is based on the collaborative analysis mechanism of the multimodal feature space. After the dynamic semantic vector is input, the system starts the parallel processing pipeline to construct the visual modality analysis path and the auditory modality analysis path respectively. The visual modality processing unit integrates a pre-trained deep convolutional network, which contains five residual blocks and three attention layers, and can map the text semantic vector to the image feature space. The auditory modality processing unit adopts a time-frequency transformation architecture, which includes a short-time Fourier transform layer and a Mel-scale filter group to realize the conversion of text semantics to acoustic features. The two processing paths share computing resources and are allocated time slices for round-robin execution through the hardware scheduler.

[0034] The feature projection process uses an adaptive mapping algorithm. The visual modality projection matrix has a dimension of 768×1024, and sparse storage is used to optimize memory usage. The auditory modality projection matrix has a dimension of 256×512, and block decomposition is used to accelerate matrix operations. Standard projection templates are stored using a hierarchical caching strategy. The visual template library contains 100 cluster center vectors, and the auditory template library stores 50 typical acoustic patterns. The difference value calculation introduces a modal characteristic compensation factor, and the visual modality difference calculation uses an improved similarity metric: in represents the visual modality difference value, represents the visual projection vector, Represents a visual standard template, The L2 norm is used for computation. Auditory modality differences are calculated using spectral energy-weighted distance, adjusted in conjunction with the Mel-scale importance coefficient. 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, and is automatically optimized based on historical evaluation results.

[0035] Cross-modal mismatch detection implements multi-level decision logic. The primary decider monitors the real-time difference value signal and triggers the secondary verification process when the difference between the visual or auditory channels exceeds the threshold. The verification process calls the historical data buffer to check the trend changes of the last three evaluation cycles and eliminate the interference of transient fluctuations. The mismatch status recorder adopts a first-in-first-out queue structure with a storage depth of 100 events. Each event contains a start timestamp, end timestamp, and modality type tag. Peak mismatch intensity analysis introduces a sliding time window with a window size of 10 consecutive evaluation cycles, tracking the maximum duration in real time.

[0036] The hardware degradation diagnosis module establishes a multi-dimensional monitoring system. Graphics processor status collection is divided into three levels: core temperature sampling period is 500 milliseconds, directly reading sensor data through the PCIe interface; video memory occupancy monitoring interval is 1 second, calling the driver-level API to obtain detailed allocation information; computing unit utilization statistics use hardware performance counters to record the proportion of active cycles of the stream processor. Overload status determination implements an intelligent debouncing mechanism. Overload is only confirmed when the temperature or video memory indicators exceed the limit for three consecutive samplings to avoid false triggering. The degradation duration accumulator uses a 32-bit high-precision timer with a minimum resolution of 1 millisecond and supports resetting to zero for hot-swappable devices.

[0037] A closed-loop feedback system is established for environmental parameter monitoring. A dust sensor is installed at the equipment's air inlet, sampling at a rate of 2.8 liters / minute and detecting particles in the size range of 0.3-10 microns. Humidity sensors are located around the circuit board, utilizing a capacitive measurement principle and achieving an accuracy of ±2%RH. Temperature rise coefficient calculation incorporates ambient temperature compensation, using a second-order filter to eliminate the impact of daytime temperature fluctuations. The equipment's environmental erosion coefficient synthesis algorithm incorporates a temperature-dust coupling term, automatically increasing the coefficient weight when both exceed the specified limits.

[0038] The hardware loss assessment model implements dynamic parameter adjustment. Equipment service life is converted to standard aging units, taking into account the manufacturer's recommended service life curve. A time decay factor is incorporated into peak load frequency analysis, with recent events given higher weight than historical events. Degradation duration assessment distinguishes between continuous and intermittent overload modes, using different damage coefficients. Nonlinear mapping is implemented for the environmental erosion coefficient, with an exponential growth model initiated when dust concentration exceeds 100 μg / m³. Hardware loss indicators are normalized before output and converted to a 0-100 percentage score.

[0039] The early warning decision-making system implements a tiered response mechanism. When the loss index is between 60 and 80, a primary warning is triggered, reducing the system computing load by 10%. When the index is between 80 and 90, an intermediate warning is activated, activating the backup cooling system. When the index exceeds 90, the emergency plan is implemented, gradually migrating computing tasks to backup nodes. All warning events are recorded in the security audit log, including a complete snapshot of environmental parameters and device status images. The health status database uses a distributed storage architecture, supporting cross-device comparative analysis and historical trend prediction.

[0040] Example 4: See Figure 5 The data collection system for executing the hardware degradation diagnosis protocol comprises a three-layer architecture. The bottom-level sensor network is deployed at key locations on the graphics processing unit (GPU). Temperature sensors are embedded in the GPU core package substrate, outputting digital signals via the I²C bus. A memory utilization monitor connects to the performance counters of the memory controller. An environmental sensor suite is installed within the cabinet and includes a laser dust detection module located on the air intake grille and a capacitive humidity probe near the circuit board. The middle-level data processing unit preprocesses the raw signals: temperature data is filtered using a moving average filter to eliminate transient fluctuations; memory utilization 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 deducted from baseline offset to generate a deviation percentage. The upper-level diagnostic engine simultaneously analyzes multiple data streams, achieving millisecond-level time synchronization accuracy.

[0041] The degradation duration is accumulated using an event-driven timing mechanism. When the temperature comparator detects that the core temperature has exceeded the 85°C safety line, or when the memory comparator detects that the occupancy rate has exceeded the 90% warning line, the overload state trigger outputs a high-level signal. The rising edge of this signal activates the high-precision timing chip (resolution 1ms), and simultaneously writes the event log to record the time of occurrence and the type of overrun. When the state is released, the falling edge of the signal stops timing, and the duration of this time is accumulated in the degradation duration register. The device operation log parser regularly scans the system log file and extracts key historical records, including: the timestamp of the device's first power-on (converted into cumulative operating hours), the factory date (calculated in months of service), and the historical peak value of memory usage (the number of times the critical value is exceeded).

[0042] The environmental erosion coefficient is generated using a dual-parameter fusion model. Temperature rise coefficient measurement requires simultaneous acquisition of two temperature sets: the cooling duct inlet temperature is measured using a PT100 platinum resistance sensor, and the ambient reference temperature is obtained from the air conditioning system's temperature control probe. The difference between the two is converted into a digital signal via an analog-to-digital converter. Dust exposure is measured using particle size classification counting technology, converting the number of suspended particles between 0.3 and 5 μm into a mass concentration. The data fusion unit receives the temperature rise coefficient and dust exposure values ​​and performs a fusion operation: the temperature rise coefficient is weighted 70% (0.7 points for every 1°C difference) and the dust exposure value is weighted 30% (0.3 points for every 10 μg / m³). The weighted total score is normalized to an environmental erosion coefficient ranging from 0 to 1. This coefficient is updated in real time and written to the hardware status register.

[0043] The following table shows an example of a diagnostic protocol execution data record fragment: Diagnostic time window Core temperature (℃) Video memory usage (%) Dust value (μg / m³) Humidity deviation (%) Status Marker 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 The hardware loss index synthesis process for the above time window is as follows: the equipment service life is 28 months (corresponding to a coefficient of 0.35), the peak load frequency is 58 times (coefficient of 0.42), the cumulative degradation time is 92 minutes (coefficient of 0.28), and the environmental erosion coefficient is calculated as 0.82 based on the dust peak of 120 μg / m³ (3.6 points) and the peak temperature rise of 8°C (5.6 points) during the period. The weighted calculation process is: 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, and environmental erosion weight 0.25 × 0.82 = 0.205, resulting in a total loss index of 0.471 (82 points after conversion to a percentage system).

[0044] When the loss index exceeds the threshold of 80 points, the diagnosis engine executes a three-level response strategy: the first level activates the early warning protocol and sends the hardware degradation early warning code to the optimization feedback end through the system bus; the second level starts the resource control strategy to limit the number of computing threads of the fine-tuning execution module; the third level writes the equipment maintenance log and generates a maintenance application form containing detailed diagnostic data. All raw data are stored in a circular buffer in time series. The data sampling interval, weight configuration parameters, and early warning thresholds are dynamically adjustable through the system configuration interface. Maintenance personnel can remotely set the geographic calibration parameters of dust concentration and the filter coefficient of temperature monitoring. The fusion algorithm of the environmental erosion coefficient retains a custom interface and supports adjusting the weighting ratio according to the environmental characteristics of different computer rooms. Diagnostic records are stored in an encrypted format in a separate partition. Data access must be certified by the hardware security module to ensure the integrity and traceability of historical records.

[0045] Example 5: The operation of the interrupt recovery module begins with the signal response mechanism. The training abnormality signal generated by the training monitoring module is transmitted to the signal analysis unit of the optimization feedback end through a dedicated data channel. This unit includes a multiplexer structure that can distinguish different types of abnormal signals and route them to the corresponding processing units. When the interrupt recovery protocol activation instruction arrives, the state controller immediately freezes the calculation process of the fine-tuning execution module and synchronously issues a clock pause request to the system time manager. The abnormal termination moment is recorded by a high-precision timer with a millisecond-level timestamp, and the data at this moment is written into the first record of the recovery log.

[0046] The memory scanning process adopts a hierarchical retrieval strategy. The first priority is to access the gradient temporary storage area, which stores the gradient data of the last valid iteration, and the physical address range is located through the memory mapping table. The data integrity verification unit performs a block scanning operation: reads the data block in units of 128 bytes, calculates the cyclic redundancy check code and compares it with the preset check value, marks the valid data block and counts the total proportion. The second priority is to scan the weight cache area, which contains a copy of the current model weight parameters and is organized in a multi-dimensional tensor format. The checksum difference calculation adopts a hierarchical hashing method: the SHA-256 hash value is calculated by parameter level grouping, and compared layer by layer with the baseline hash sequence saved before the exception occurs, and the difference value is output to quantify the degree of difference.

[0047] The recovery feasibility indicator generation module integrates the results of the dual-channel scans. The valid data percentage is converted to a percentage, and an analog-to-digital converter generates an analog signal in the range of 0-1. The checksum difference is compressed to the same numerical range by a normalization processor. The indicator synthesis circuit receives the two signals, performs a multiplication operation, and outputs the original indicator value. The proportional amplifier adjusts the value to a preset range. The threshold comparison unit compares the indicator value with the recovery threshold. If the indicator value is greater than or equal to the threshold, a continue recovery instruction is generated; otherwise, a recovery abort signal is generated. This decision result is written to the system status register, simultaneously triggering the recovery status indicator to change color.

[0048] The context snapshot reconstruction operation is initiated after receiving the continue instruction. The model state reconstructor first reads the valid data block in the gradient temporary area and repairs the missing data unit through the interpolation compensation algorithm. The weight parameter recovery process calls a two-layer verification mechanism: the first layer uses the repaired gradient data to reversely 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 decision when the deviation exceeds the tolerance limit. The successfully reconstructed training context contains seven core elements: model weight parameter snapshot, optimizer momentum state, learning rate schedule progress, data loader location pointer, loss function statistics, gradient buffer state, and current iteration cycle counter.

[0049] The module restart sequence executes an automated process. After receiving initialization instructions, the fine-tuning execution module loads seven context elements in a predetermined order: first, the weight parameters are written to the compute engine's register array; then, the optimizer state is restored to a dedicated memory area; the data loader repositions the dataset offsets based on the location pointer; the loss counter is reset to the snapshot value; and the gradient buffer is pre-filled with repair data. The restart controller activates the subsystem in three stages: the first stage enables the compute core clock signal; the second stage unfreezes the data bus; and the third stage sends a resume-ready signal to the training monitoring module. Hardware-level delay circuits are set between each stage to ensure signal stability.

[0050] The conflict point marking system simultaneously 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 key conflict points. Each conflict point contains three types of information: parameter index address, calculation path difference value, and arbitration result code. The marking generator creates a unique identifier for each conflict point, associating 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 the model architecture diagram in three-dimensional coordinate form, and the color depth indicates the severity of the difference. All recovery process data is encrypted and stored in non-volatile memory, and a log chain structure is used to ensure traceability of operations. Recovery thresholds and scanning parameters can be adjusted in real time through the remote configuration interface to meet the fault tolerance requirements of models of different sizes.

[0051] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0052] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cross-modal large model fine-tuning training system based on prompt word embedding, characterized by: It includes a prompt word embedding generation module, a cross-modal alignment module, a fine-tuning execution module, a training monitoring module, and an optimization feedback end; The prompt word embedding generation module parses the text instructions input by the user through the natural language processing unit, generates a dynamic semantic vector and sends it to the cross-modal alignment module and the optimization feedback end, and the optimization feedback end performs visual mapping on the dynamic semantic vector; The cross-modal alignment module receives the dynamic semantic vector and the reference feature vector of the target modality, generates modal adaptation parameters through semantic space projection calculation, and transmits the modal 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 a 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 abnormality signal through convergence analysis. The training abnormality signal triggers the early warning protocol of the optimization feedback end; The optimization feedback end activates the model evaluation protocol when receiving the training stabilization signal, and generates performance optimization instructions through analysis.

2. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 1 is characterized in that The analysis process of the training monitoring module includes: Through iterative trajectory analysis, a single training cycle is marked as an effective iteration or an inefficient iteration. The 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 coefficient exceeds the preset failure threshold, a training abnormality signal is generated; When the inefficient training coefficient does not exceed the preset failure threshold, the product of the gradient change amplitude of each iteration and the learning rate adjustment amplitude 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 abnormality signal is generated; otherwise, a training stable signal is generated and sent to the optimization feedback end.

3. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 2 is characterized in that The specific process of iterative trajectory analysis is as follows: Record the start time when the fine-tuning execution module receives the modal adaptation parameters and the end time when the weight update is completed, and calculate the time interval as the iteration duration; The ratio of the norm of the modal adaptation parameter to the iteration time 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.

4. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 3 is 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 draw the gradient change curve of a single training cycle; Sampling points are evenly 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 value is statistically calculated to generate the gradient dispersion, and the proportion of step offset values ​​exceeding the preset offset range is calculated as the abnormal offset coefficient; The convergence fluctuation coefficient is generated by fusing the gradient discreteness and the abnormal offset coefficient and sent to the iterative trajectory analysis unit.

5. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 1 is characterized in that The optimization feedback end is connected to the embedding optimization module, and when it receives the training stability signal, it triggers the embedding optimization module 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 a semantic error 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 end.

6. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 5 is characterized in that The specific process of semantic evaluation is as follows: Collect the feature projection distance of the dynamic semantic vector in the visual mode and the auditory mode, and calculate the difference between it and the standard projection template of the corresponding mode; When the visual modality difference value or the auditory modality difference value exceeds the modality difference threshold, it is determined that the current semantic vector has a cross-modal mismatch; The ratio of the duration of cross-modal mismatch during the monitoring period to the total training time was calculated as the mismatch risk coefficient, and the maximum duration of a single cross-modal mismatch was recorded as the peak mismatch intensity. The mismatch risk coefficient and the peak mismatch intensity are integrated to generate a semantic reconstruction index. When the semantic reconstruction index exceeds the reconstruction threshold, an embedding optimization instruction is generated.

7. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 1 is characterized in that The optimization feedback end is communicated with the hardware degradation diagnosis module, and the degradation diagnosis protocol is activated when a training abnormality signal is generated or an optimization instruction is embedded; The hardware degradation diagnosis module collects the graphics processor core temperature and video memory occupancy rate, and marks the hardware overload status when the core temperature exceeds the temperature safety line or the video memory occupancy rate exceeds the video memory warning line; The total duration of the accumulated hardware overload state during the device operation cycle is used as the degradation duration. Combined with the device operation log, a hardware degradation warning or device health signal is generated.

8. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 7 is characterized in that: The specific process of the degradation diagnosis protocol is as follows: Obtain the time difference between the cumulative operating time of the graphics processor and the factory date as the device service time; The number of times the video memory usage exceeds the critical value in historical operations is extracted as the peak load frequency; The dust concentration and humidity deviation of the equipment operating environment are obtained through environmental sensors, and the equipment environmental erosion coefficient is generated by fusion. Aggregate the equipment's service life, peak load frequency, degradation duration, and equipment environmental erosion coefficient to generate a hardware loss index. When the hardware loss index exceeds the loss threshold, a hardware degradation warning is generated.

9. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 8 is characterized in that The method for generating the equipment environmental erosion coefficient is: The difference between the air intake temperature of the GPU cooling duct and the ambient reference temperature is collected as the temperature rise coefficient; Measure the concentration of airborne particles in the equipment installation area as the dust exposure value; The equipment environmental erosion coefficient 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.

10. The cross-modal large model fine-tuning training system based on prompt word embedding according to claim 1 is characterized in that The optimization feedback end communication connection interruption recovery module activates the interruption recovery protocol when the training monitoring module generates a training abnormality signal; The interrupt recovery module captures the abnormal termination moment of the fine-tuning execution process and scans the gradient temporary storage area and weight buffer area data in the current memory; The recovery feasibility index is generated by calculating the difference between the valid data ratio in the gradient buffer area and the checksum in the weight buffer area; When the recovery feasibility indicator reaches the preset recovery threshold, the training context snapshot is automatically rebuilt and the fine-tuning execution module is restarted; The key data conflict points identified during the reconstruction process are sent to the training monitoring module for stability marking.

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