Method and system for optimizing performance of core component of chip acquisition device

Abstract

The invention relates to the technical field of industrial data acquisition, and discloses a core component performance optimization method and system for a chip acquisition device, and the method comprises the steps: collecting key electrical and time sequence parameters of a main control chip, a front-end signal conditioning module and an analog-to-digital converter, and constructing a core component performance parameter set under a dynamic working condition; based on the set, analyzing a performance coupling relationship between core components, adopting a multivariable coupling analysis method to quantify a parameter matching degree, a collaboration degree and a correlation degree, identifying a performance bottleneck, generating an adaptive parameter optimization strategy, calling a main control chip firmware optimization algorithm library, and dynamically calculating collaborative optimization parameters; synchronously issuing the parameters to corresponding parts, verifying the signal integrity and the effective data rate through a standard signal acquisition test, and performing iterative optimization if the signal integrity and the effective data rate do not reach the standard; and the monitoring optimization process is periodically repeated, and performance optimization is completed in combination with an environment temperature and load trend predictive fine tuning strategy. The technical problem that fixed parameter configuration or off-line calibration is difficult to adapt to dynamic working conditions is solved.

Description

Technical Field

[0001] This invention relates to the field of industrial data acquisition technology, specifically to a method and system for optimizing the performance of core components of a chip acquisition device. Background Technology

[0002] With the accelerated development of China's domestic chip industry, industrial data acquisition devices built on domestically developed and controllable chips are widely used in high-end manufacturing, energy monitoring, and defense equipment. These devices typically consist of core components such as a main control chip, signal conditioning module, analog-to-digital converter, and communication interface. Their overall performance depends not only on the specifications of individual components but also on the coordinated matching of timing response, power consumption constraints, and signal integrity among the components. However, given that the domestic chip ecosystem is still in its early stages of development, core components from different manufacturers exhibit significant differences in electrical characteristics, driving capabilities, and protocol compatibility, making it difficult to achieve optimal performance coupling during system integration.

[0003] The core performance bottleneck of the acquisition device lies in the dynamic matching problem between the main control chip and the front-end analog signal chain. Specifically, if there is a lack of fine coordination between the sampling trigger timing and interrupt response delay of the main control chip and the setup time of the front-end amplifier and the filter group delay, it will directly cause the effective sampling window to shift or the signal to be distorted. Especially in high-frequency, high-precision acquisition scenarios, even microsecond-level timing mismatches can cause a significant drop in the effective data rate. Existing technologies typically use fixed parameter configurations or offline calibration methods for component selection and matching, which cannot adapt to dynamic operating conditions such as chip batch differences, temperature drift, and load fluctuations, resulting in insufficient performance stability of the device in actual deployment.

[0004] In existing technologies, some solutions attempt to compensate for hardware mismatches by dynamically adjusting the sampling frequency or gain parameters at the software layer. However, such methods only apply to the data post-processing stage and cannot fundamentally solve the physical layer coordination defects between the signal chain front-end and the main control logic. Other designs introduce FPGAs as intermediate coordination units, which improves flexibility but significantly increases system complexity and cost, violating the rigid requirements of domestically produced acquisition devices for high integration and low power consumption. More importantly, there is currently a lack of a performance matching optimization mechanism that can be embedded in the device firmware to address the heterogeneous characteristics of domestically produced chips, enabling adaptive alignment of dynamic parameters between core components without relying on external debugging tools. Therefore, there is an urgent need for a performance matching optimization method for the core components of domestically produced chip acquisition devices that can deeply integrate hardware characteristics and operating conditions and precisely control key timing and electrical parameters. Summary of the Invention

[0005] In order to overcome the defects of the prior art, the purpose of this invention is to provide a method and system for optimizing the performance of the core components of a chip acquisition device, so as to solve the technical problems in the prior art that are difficult to adapt to dynamic working conditions when using fixed parameter configuration or offline calibration, and that the introduction of FPGA increases complexity and cost.

[0006] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for optimizing the performance of core components of a chip acquisition device, comprising: Step 1: Collect key electrical and timing parameters of the main control chip, front-end signal conditioning module and analog-to-digital converter to construct a set of core component performance parameters under dynamic operating conditions; Step 2: Analyze the performance coupling relationship between core components based on the set of performance parameters, and use multivariate coupling analysis to quantify the matching degree, synergy and correlation between key parameters of each component to identify performance bottlenecks; Step 3: Generate an adaptive parameter optimization strategy based on the performance bottleneck, call the optimization algorithm library in the main control chip firmware, and dynamically calculate the collaborative optimization parameters of the core components for the performance mismatch point; Step 4: The collaborative optimization parameters are synchronously sent to the corresponding core components. The signal integrity index and effective data rate are verified through standard signal acquisition test. If they are not met, return to step 2 for iterative optimization. Step 5: Periodically repeat the monitoring and optimization process from Step 1 to Step 4, and combine the predictive fine-tuning optimization strategy based on ambient temperature fluctuations and load change trends to complete the performance optimization of the core components of the chip acquisition device.

[0007] Preferably, in step 1, the key electrical and timing parameters of the main control chip, the front-end signal conditioning module, and the analog-to-digital converter are collected by the performance monitoring unit deployed in the main control chip firmware to construct a set of core component performance parameters under dynamic operating conditions. The performance monitoring unit is integrated inside the main control chip and includes a time-to-digital conversion circuit and analog feature extraction logic. The resolution of the time-to-digital conversion circuit is used to capture the interrupt response delay and clock edge jitter of the main control chip. The analog feature extraction logic indirectly calculates the setup time and group delay parameters of the front-end signal conditioning module by monitoring the voltage slew rate and overshoot characteristics of the output node of the front-end signal conditioning module.

[0008] Furthermore, in step 1, the sampling and holding time and aperture jitter parameters of the analog-to-digital converter are obtained through the built-in self-test mode of the analog-to-digital converter; the built-in self-test mode of the analog-to-digital converter outputs a digital bitstream characterizing the stability of the sampling and holding time and the aperture jitter characteristics, and the performance monitoring unit parses the digital bitstream and compares it with the ideal response to extract the dynamic performance parameters of the analog-to-digital converter.

[0009] Furthermore, the multivariate coupling analysis method in step 2 adopts a correlation calculation model based on mutual information entropy. The correlation calculation model calculates the mutual information entropy between the sampling trigger timing of the main control chip and the establishment time of the front-end signal conditioning module, the interrupt response delay of the main control chip and the group delay of the front-end signal conditioning module, and the sampling and holding time of the analog-to-digital converter and the overall stability of the signal chain to form a coupling strength matrix, so as to locate the source of performance mismatch.

[0010] Furthermore, the optimization algorithm library in step 3 includes gradient descent optimization routines and a heuristic rule engine; wherein, the gradient descent optimization routines perform local optimization search in the continuous parameter space with signal integrity index as the objective function; the heuristic rule engine determines the initial optimization direction and step size based on pre-stored empirical rules for chip component combinations.

[0011] Furthermore, the process of dynamically calculating the collaborative optimization parameters in step 3 introduces a component aging factor; the component aging factor is estimated based on the cumulative operating time and operating temperature history of the core component, and is used to compensate for the lifetime of the sampling clock phase adjustment of the main control chip, the gain compensation coefficient of the front-end signal conditioning module, or the sampling window offset of the analog-to-digital converter.

[0012] Preferably, the standard signal acquisition test in step 4 uses a reference signal source integrated inside the device; the reference signal source outputs a signal with known amplitude and frequency, and by comparing the acquisition results with the ideal waveform, the signal-to-noise ratio, total harmonic distortion and effective number of bits are calculated as the basis for verifying signal integrity.

[0013] Preferably, in step 4, the collaborative optimization parameters are synchronously distributed to the corresponding core components using serial peripheral interface variants that support atomic configuration operations, ensuring that the optimization parameters of the main control chip, front-end signal conditioning module and analog-to-digital converter are updated synchronously, and preventing timing disorder caused by asynchronous configuration.

[0014] Preferably, the triggering conditions for monitoring and optimization in step 5 include fixed time intervals, data acquisition task switching events, or abnormal events reported by temperature sensors and load current monitoring circuits, so as to achieve on-demand optimization to balance performance and power consumption. Among them, the fine-tuning optimization strategy adopts a time series prediction algorithm. Based on the historical data of performance parameters in the most recent optimization cycle, the algorithm predicts the future preset internal ambient temperature and load change trends, and accordingly pre-adjusts the sampling clock phase, gain compensation coefficient or sampling window offset. All optimization parameters, performance history and coupling analysis results are stored in independent partitions of the device's internal non-volatile memory, forming the device's performance fingerprint.

[0015] Secondly, the present invention also provides a performance optimization system for the core components of a chip acquisition device, comprising: The parameter acquisition module is used to collect key electrical and timing parameters of the main control chip, front-end signal conditioning module and analog-to-digital converter, and to construct a set of performance parameters of core components under dynamic operating conditions. The coupling analysis module is used to analyze the performance coupling relationship between core components based on the set of performance parameters. It uses a multivariate coupling analysis method to quantify the matching degree, synergy and correlation between key parameters of each component and to identify performance bottlenecks. The strategy generation module is used to generate an adaptive parameter optimization strategy based on the performance bottleneck, call the optimization algorithm library in the main control chip firmware, and dynamically calculate the collaborative optimization parameters of the core components for the performance mismatch point. The closed-loop verification module is used to synchronously send the collaborative optimization parameters to the corresponding core components and verify whether the signal integrity index and effective data rate meet the standards through standard signal acquisition and testing. If they do not meet the standards, it returns to iterative optimization. The continuous adaptation module is used to periodically repeat the monitoring and optimization process described above. It combines the predictive fine-tuning optimization strategy based on ambient temperature fluctuations and load change trends to complete the performance optimization of the core components of the chip acquisition device.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a method for optimizing the performance of core components in a chip acquisition device. By acquiring key electrical and timing parameters of the main control chip, front-end signal conditioning module, and analog-to-digital converter in real time, a set of performance parameters under dynamic operating conditions is constructed. A multivariate coupling analysis method is then used to quantify the matching degree, synergy, and correlation between components, accurately identifying performance bottlenecks. This effectively solves the technical pain points of existing technologies where fixed parameter configurations or offline calibration are difficult to adapt to dynamic operating conditions such as chip batch differences, temperature drift, and load fluctuations, significantly improving the operating condition adaptability of the chip acquisition device. Relying on the optimization algorithm library embedded in the main control chip firmware, collaborative optimization parameters are dynamically calculated. There is no need to introduce an additional FPGA as a dedicated coordination unit; adaptive matching of core component performance is achieved solely through firmware-level algorithms. Compared to existing FPGA-dependent solutions, this significantly simplifies the system architecture, reduces hardware costs and integration complexity, and avoids compatibility issues and increased energy consumption that may result from introducing additional hardware units. Through a closed-loop parameter configuration and standard signal acquisition test verification mechanism, it is ensured that the signal integrity index and effective data rate can reach the preset standards after each optimization. The periodically repeated monitoring and optimization process and the predictive fine-tuning strategy based on the trend of ambient temperature fluctuation and load change can respond to the dynamic changes of the working conditions in real time, so that the performance matching state between core components is maintained at the optimal level in the long term, which significantly improves the performance stability and long-term operational reliability of the chip acquisition device.

[0017] Furthermore, the performance fingerprint data stored during the optimization process provides accurate historical data support for subsequent equipment fault diagnosis. At the same time, it can quickly achieve performance adaptation between the new component and the existing system when replacing components, further improving the convenience and efficiency of equipment maintenance, and comprehensively enhancing the practicality and competitiveness of the chip acquisition device in real-world application scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart of the performance optimization method for the core components of the chip acquisition device of the present invention; Figure 2 This is a schematic diagram of dynamic parameter acquisition for the core component of the present invention; Figure 3 This is a flowchart of the multivariate coupling analysis of the present invention; Figure 4 This is a flowchart of the optimization parameter generation process of the present invention; Figure 5 This is a flowchart illustrating the closed-loop configuration verification process of the present invention. Figure 6 This is a flowchart illustrating the continuous adaptation process of the present invention; Figure 7 This is a diagram of the overall optimized system architecture of the present invention; Figure 8 This is a schematic diagram of the performance optimization system for the core component of the chip acquisition device of the present invention; In the diagram: 1. Parameter acquisition module; 2. Coupling analysis module; 3. Strategy generation module; 4. Closed-loop verification module; 5. Continuous adaptation module. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] The purpose of this invention is to provide a method and system for optimizing the performance of core components of a chip acquisition device, so as to solve the technical problems in the prior art where fixed parameter configuration or offline calibration is difficult to adapt to dynamic working conditions and the introduction of FPGA increases complexity and cost.

[0022] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 See Figures 1-7 In one embodiment of the present invention, a method for optimizing the performance of a core component of a chip acquisition device is provided, comprising the following specific steps: S1. Establish a dynamic performance parameter model for core components. Through a performance monitoring unit deployed in the firmware of the main control chip, collect in real time the interrupt response delay, clock jitter parameters, setup time and group delay parameters of the main control chip, and the sampling and holding time and aperture jitter parameters of the analog-to-digital converter. Construct a set of parameters that reflect the electrical and timing characteristics of each core component under dynamic operating conditions. The core components specifically include the main control chip, the front-end signal conditioning module, and the analog-to-digital converter. S2. Analyze the performance coupling relationship between core components. Based on the parameter set, use a multivariate coupling analysis method to quantify the matching degree between the sampling trigger timing of the main control chip and the setup time of the front-end amplifier, the synergy between the interrupt response delay of the main control chip and the delay of the filter group, and the correlation between the sampling and holding time of the analog-to-digital converter and the overall stability of the signal chain, and identify performance bottlenecks. S3. Generate an adaptive parameter optimization strategy. Based on the analysis results of the performance bottleneck, call the optimization algorithm library embedded in the main control chip firmware. For the identified performance mismatch points, dynamically calculate the sampling clock phase adjustment of the main control chip, the gain compensation coefficient of the front-end signal conditioning module, and the sampling window offset of the analog-to-digital converter to form a set of collaborative optimization parameters. S4. Perform closed-loop parameter configuration and verification. The collaborative optimization parameters are synchronously sent to the corresponding core components through the device's internal configuration bus. Immediately after the parameters are configured, a round of standard signal acquisition test is started to verify whether the signal integrity index and effective data rate have reached the preset threshold. If they have not reached the threshold, return to step S2 for iterative optimization. S5. Achieve continuous performance adaptation during operation. During the normal data acquisition process of the device, the monitoring and optimization process of steps S1 to S4 is repeated periodically. Based on the fluctuation of ambient temperature and the trend of load change, the optimization strategy is predictively fine-tuned to ensure that the performance matching status of the three core components, namely the main control chip, the front-end signal conditioning module and the analog-to-digital converter, is maintained in the optimal performance matching range that meets the preset signal integrity index and effective data rate requirements for a long time.

[0023] Specifically, when the domestic chip acquisition device is running, the performance monitoring unit is activated first. This unit is deployed in the firmware of the main control chip and can capture the interrupt response delay and clock jitter of the main control chip in real time. At the same time, it monitors the setup time and group delay of the front-end signal conditioning module, as well as the sampling and holding time and aperture jitter of the analog-to-digital converter. These parameters reflecting electrical and timing characteristics are integrated into a dynamic parameter set to provide basic data support for subsequent analysis.

[0024] Based on this parameter set, a multivariate coupling analysis method is adopted. The core of this method is to use the mutual information entropy correlation calculation model in the existing technology. This model quantifies the matching degree between the sampling trigger timing of the main control chip and the setup time of the front-end amplifier, and determines whether there is a risk of timing mismatch between the two. It analyzes the synergy between the interrupt response delay of the main control chip and the delay of the filter group to avoid data loss due to asynchronous response. It evaluates the impact of the analog-to-digital converter sampling and holding time on the overall stability of the signal chain and accurately locates the bottleneck link that leads to performance degradation.

[0025] For the identified performance mismatch points, the firmware's built-in optimization algorithm library is invoked. The algorithm library includes gradient descent optimization routines and heuristic rule engines from existing technologies. The gradient descent optimization routines use the signal integrity index as the objective function to perform local optimization in the continuous parameter space. The heuristic rule engine determines the initial optimization direction and step size based on pre-stored empirical rules for the combination of domestic chip components. The two work together to dynamically calculate the phase adjustment of the main control chip's sampling clock, the gain compensation coefficient of the front-end signal conditioning module, and the sampling window offset of the analog-to-digital converter, forming a set of optimization parameters that can work together.

[0026] These collaborative optimization parameters are synchronously sent to the corresponding core components through the internal configuration bus of the device. After the parameters are configured, the standard signal acquisition test is started immediately to verify whether the signal integrity index and effective data rate meet the expectations. If the preset requirements are not met, the device returns to the coupling analysis stage for re-optimization until the index meets the requirements.

[0027] During the normal execution of data acquisition tasks, the above monitoring and optimization process is repeated periodically. At the same time, combined with the fluctuation of ambient temperature and the trend of load change, the optimization strategy is fine-tuned in advance with the help of existing time series prediction algorithms to ensure that the performance matching state of the core components is always in the optimal range. This effectively solves the problem that traditional fixed parameter configuration or offline calibration cannot adapt to dynamic working conditions. There is no need to introduce an additional FPGA coordination unit. While reducing system complexity and cost, it improves the coordination defects between components from the physical layer and enhances the performance stability of the device.

[0028] In this embodiment, the performance monitoring unit is integrated inside the main control chip and includes a time-to-digital converter circuit and an analog feature extraction logic. The time-to-digital converter circuit has a resolution better than 100 picoseconds and is used to capture interrupt response delay and clock edge jitter. The analog feature extraction logic indirectly calculates setup time and group delay parameters by monitoring the voltage slew rate and overshoot characteristics of the output node of the signal conditioning module.

[0029] Specifically, the performance monitoring unit is directly integrated into the main control chip, requiring no additional external hardware modules. This unit comprises two parts: a time-to-digital converter circuit and analog feature extraction logic. The time-to-digital converter circuit features high resolution, accurately capturing the interrupt response delay and clock edge jitter of the main control chip, ensuring accurate analysis of timing parameters. The analog feature extraction logic continuously monitors the voltage slew rate and overshoot characteristics of the signal conditioning module's output node. Combined with a pre-defined calculation model—built through the mapping relationship between voltage slew rate and setup time, and overshoot characteristics and group delay—it indirectly obtains the setup time and group delay parameters of the signal conditioning module. This allows for real-time acquisition of key parameters without requiring hardware modifications to the signal conditioning module, simplifying the device's hardware design while ensuring the real-time performance and accuracy of parameter acquisition, providing reliable analog data support for subsequent performance matching analysis.

[0030] In this embodiment, the sample-and-hold time and aperture jitter parameters of the analog-to-digital converter are obtained through its built-in self-test mode. The built-in self-test mode of the analog-to-digital converter outputs a digital bitstream that characterizes the stability of the sample-and-hold time and the aperture jitter characteristics. The performance monitoring unit parses the digital bitstream and compares it with the ideal response to extract dynamic parameters.

[0031] Specifically, when acquiring the sample-and-hold time and aperture jitter parameters of the analog-to-digital converter (ADC), the ADC's built-in self-test mode is activated. In this mode, the ADC outputs a specific digital stream that characterizes the stability of the sample-and-hold time and the aperture jitter characteristics. The performance monitoring unit receives and parses this digital stream, compares the parsing results with the ADC's ideal response model, and extracts the dynamic parameters of the sample-and-hold time and aperture jitter through difference analysis. This allows for the acquisition of key ADC parameters without the need for external testing instruments, simplifying the parameter acquisition process and ensuring that the parameters reflect the actual operating state of the ADC, thus improving the targeting of subsequent optimization strategies.

[0032] In this embodiment, the multivariate coupling analysis method adopts a correlation calculation model based on mutual information entropy. The correlation calculation model based on mutual information entropy calculates the mutual information entropy between the main control chip sampling trigger timing and the establishment time of the front-end signal conditioning module, the interrupt response delay and group delay, the sampling and holding time and the overall stability of the signal chain, forming a coupling strength matrix to locate the performance mismatch source.

[0033] Specifically, in the multivariate coupling analysis, the correlation degree calculation model based on mutual information entropy is used for the analysis, and its calculation formula is shown in equation (1): (1) In the formula, The mutual information entropy between variables X and Y is used to quantify the coupling strength between them; the larger the value, the tighter the coupling relationship. The information entropy of variable X, This represents the information entropy of variable Y, which is used to describe the uncertainty of a variable. This represents the joint information entropy of variables X and Y.

[0034] During the calculation, the sampling trigger timing of the main control chip is first used as variable X, and the setup time of the front-end signal conditioning module is used as variable Y. The mutual information entropy of the two is calculated by substituting them into equation (1) to quantify the degree of matching. Then, the interrupt response delay of the main control chip is used as variable X, and the filter group delay is used as variable Y. The mutual information entropy is calculated again by substituting them into equation (1) to evaluate the synergy. Finally, the sampling and holding time of the analog-to-digital converter is used as variable X, and the overall stability index of the signal chain is used as variable Y. The mutual information entropy is calculated by substituting them into equation (1) to analyze the degree of correlation. The results of these mutual information entropy calculations are integrated to form a coupling strength matrix. By analyzing the distribution of values ​​in the matrix, the source of performance mismatch can be clearly located, avoiding the blindness of mismatch source judgment in traditional analysis methods, and providing a precise direction for the formulation of subsequent optimization strategies.

[0035] In this embodiment, the optimization algorithm library includes a gradient descent optimization routine and a heuristic rule engine. The gradient descent optimization routine performs local optimization search in the continuous parameter space with the signal integrity index as the objective function. The heuristic rule engine determines the initial optimization direction and step size based on pre-stored empirical rules for the combination of domestic chip components.

[0036] Specifically, the optimization algorithm library pre-stores gradient descent optimization routines and heuristic rule engines, where the iterative formula of the gradient descent optimization routine is shown in equation (2): (2) In the formula, This represents the optimized parameter value after the (n+1)th iteration. The optimized parameters include the sampling clock phase adjustment of the main control chip, the gain compensation coefficient of the front-end signal conditioning module, and the sampling window offset of the analog-to-digital converter. This represents the optimized parameter value after the nth iteration; This represents the learning step size, used to control the parameter adjustment range in each iteration; This indicates that the objective function is the signal integrity index S. The gradient at point A points in the direction that the objective function value increases. Therefore, the objective function value is minimized by subtracting the gradient term (which is the optimal signal integrity index).

[0037] During parameter optimization calculations, the heuristic rule engine first calls pre-stored empirical rules for combining domestically produced chip components. These rules are derived from a large amount of integrated test data of domestically produced chips to determine the initial values ​​of the optimization parameters. With learning step size Subsequently, the gradient descent optimization routine uses the signal integrity index S as the objective function to... Substitute the values ​​into equation (2) to begin iterative calculation until the objective function value converges to the preset range, thus obtaining the optimal parameters. The two work together to significantly improve the optimization efficiency while ensuring the accuracy of parameter optimization, ensuring that the optimal parameter combination that fits each core component is found quickly.

[0038] In this embodiment, the dynamic calculation process introduces a component aging factor. The component aging factor is estimated based on the cumulative operating time and operating temperature history of the core component, and is used to compensate for the lifetime of the sampling clock phase adjustment, gain compensation coefficient, or sampling window offset.

[0039] Specifically, in the process of dynamically calculating and optimizing parameters, a component aging factor is introduced into the calculation. The cumulative operating time of the core components is statistically analyzed by the monitoring module inside the device, and the historical temperature data of each component during operation is recorded. Based on these data, an aging assessment model is established. This model is constructed by the correlation formula between cumulative operating time and aging degree, and between operating temperature and aging rate, to estimate the current aging factor of each component. The aging factor is integrated into the calculation process of the sampling clock phase adjustment of the main control chip, the gain compensation coefficient of the front-end signal conditioning module, and the sampling window offset of the analog-to-digital converter. Adaptive compensation is performed on these optimization parameters. During compensation, the iterative formula of gradient descent optimization (i.e., equation (2)) is referenced, and the aging factor is incorporated as a correction term into the calculation of the objective function S, so that the optimization parameters can adapt to the performance changes after component aging. This compensation method avoids performance deviation caused by aging of components during long-term operation, ensuring that even in the later stages of component use, a good performance matching state can still be maintained, extending the effective working cycle of the device.

[0040] In this embodiment, the standard signal acquisition test uses a reference signal source integrated inside the device. The reference signal source outputs a signal with known amplitude and frequency. By comparing the acquisition results with the ideal waveform, the signal-to-noise ratio, total harmonic distortion, and effective number of bits are calculated as the basis for signal integrity verification.

[0041] Specifically, during the standard signal acquisition test, the integrated reference signal source within the device is activated. This reference signal source can stably output a standard signal with known amplitude and frequency. This standard signal is input into the acquisition link. After acquisition, the acquired result is compared with a preset ideal waveform. Signal processing algorithms are used to calculate indicators such as the signal-to-noise ratio, total harmonic distortion, and effective bit depth of the acquired signal. These indicators are used as the basis for judging whether the signal integrity meets the standards. This method eliminates the need for an external reference signal source, allowing for rapid verification of the optimized acquisition effect, simplifying the testing process, improving the convenience and timeliness of verifying optimization results, and ensuring that the device performance is effectively confirmed after each optimization.

[0042] In this embodiment, the internal configuration bus adopts a variant of the serial peripheral interface that supports atomic configuration operations, ensuring that the optimized parameters of the main control chip, signal conditioning module and analog-to-digital converter are updated synchronously, and preventing timing disorders caused by asynchronous configuration.

[0043] Specifically, the internal configuration bus adopts a variant of the serial peripheral interface, which supports atomic configuration operations. When distributing collaborative optimization parameters, the bus first synchronously locks the configuration ports of each core component to ensure that all components are in a ready-to-configure state. Then, the optimization parameters are transmitted synchronously to the configuration registers of the main control chip, signal conditioning module, and analog-to-digital converter all at once. After the transmission is complete, the configuration ports of each component are unlocked simultaneously, achieving synchronous updates of the optimization parameters for all core components. This effectively avoids timing disorders caused by asynchronous parameter configuration, ensures the stability of the acquisition link during optimization, and guarantees reliable parameter optimization implementation.

[0044] In this embodiment, the triggering conditions for the periodic monitoring and optimization include fixed time intervals, data acquisition task switching events, or abnormal events reported by temperature sensors and load current monitoring circuits, so as to achieve on-demand optimization to balance performance and power consumption.

[0045] Specifically, the periodic monitoring and optimization process is triggered through multi-condition judgment. A fixed time interval can be preset; when the device operates continuously for the specified interval, monitoring and optimization automatically begin. When switching data acquisition tasks, the device detects a change in task type and triggers the process to adapt to the new task's performance requirements for components. Simultaneously, a temperature sensor monitors the ambient temperature in real time, and a load current monitoring circuit tracks the component's operating current. When the temperature or current exceeds the normal fluctuation range, it is identified as an abnormal event and the process is triggered. This multi-condition triggering achieves on-demand optimization, avoiding unnecessary power consumption during optimization operations, thus ensuring device performance while also meeting low-power requirements.

[0046] In this embodiment, the predictive fine-tuning optimization strategy adopts a time series prediction algorithm. Based on the historical performance parameter data of the most recent 50 optimization cycles, the algorithm predicts the trend of ambient temperature and load changes in the next hour, and accordingly pre-adjusts the sampling clock phase, gain compensation coefficient, or sampling window offset. All optimization parameters, performance history, and coupling analysis results are stored in an independent partition of the non-volatile memory inside the device, forming the performance fingerprint of the device.

[0047] Specifically, in the predictive fine-tuning optimization strategy, a time series prediction algorithm is used to process the performance parameter data. The algorithm uses the moving average algorithm, and the calculation formula is shown in equation (3): (3) In the formula, This indicates the predicted temperature value for the next hour; This indicates the number of samples; here, temperature samples from the most recent 50 optimization cycles are used. ; Indicates the current cycle number; This represents the ambient temperature value monitored during the i-th optimization cycle.

[0048] During the calculation, historical ambient temperature data from the most recent 50 optimization cycles are first extracted. Substituting into equation (3), the predicted temperature for the next hour is calculated. The same method is used to predict the load change trend. The predicted load current can be obtained by replacing the temperature parameter in equation (3) with the load current parameter. Based on the predicted temperature and load results, the sampling clock phase of the main control chip, the gain compensation coefficient of the front-end signal conditioning module, and the sampling window offset of the analog-to-digital converter are pre-adjusted. At the same time, all optimization parameters, historical performance data, and coupling analysis results are stored in an independent partition of the non-volatile memory inside the device. These data together constitute the performance fingerprint of the device, which is convenient for tracing the performance change trajectory and provides data support for fault diagnosis and rapid adaptation after component replacement.

[0049] In summary, this invention constructs a parameter set by real-time acquisition of dynamic parameters from the analog-to-digital converter of the main control chip's front-end signal conditioning module. It employs multivariate coupling analysis to quantify the matching degree between components and pinpoint performance bottlenecks. The firmware's built-in optimization algorithm library dynamically calculates collaborative optimization parameters. Closed-loop configuration and verification ensure that performance targets are met, solving the problem that traditional fixed parameter configurations or offline calibrations cannot adapt to dynamic operating conditions such as chip batch differences, temperature drift, and load fluctuations. It eliminates the need for an FPGA as a coordination unit, relying on the firmware's embedded optimization algorithms to achieve adaptive matching of component performance, significantly reducing system complexity and cost. Furthermore, through periodic monitoring and optimization, and a fine-tuning strategy based on time series prediction, the performance matching of core components remains optimal over the long term. The stored performance fingerprint also provides data support for subsequent fault diagnosis and component replacement, effectively improving the performance stability and adaptability flexibility of domestic chip acquisition devices.

[0050] Example 2 according to Figure 8 As shown, this embodiment also provides a core component performance optimization system for a chip acquisition device, including: Parameter acquisition module 1 is used to acquire key electrical and timing parameters of the main control chip, front-end signal conditioning module and analog-to-digital converter, and to construct a set of performance parameters of core components under dynamic operating conditions. The coupling analysis module 2 is used to analyze the performance coupling relationship between core components based on the set of performance parameters. It uses a multivariate coupling analysis method to quantify the matching degree, synergy and correlation between key parameters of each component and to identify performance bottlenecks. Strategy generation module 3 is used to generate an adaptive parameter optimization strategy based on the performance bottleneck, call the optimization algorithm library in the main control chip firmware, and dynamically calculate the collaborative optimization parameters of the core components for the performance mismatch point. Closed-loop verification module 4 is used to synchronously send the collaborative optimization parameters to the corresponding core components and verify whether the signal integrity index and effective data rate meet the standards through standard signal acquisition test. If they do not meet the standards, it returns to iterative optimization. The continuous adaptation module 5 is used to periodically repeat the monitoring and optimization process described above. It combines the predictive fine-tuning optimization strategy based on ambient temperature fluctuations and load change trends to complete the performance optimization of the core components of the chip acquisition device.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the performance of core components in a chip acquisition device, characterized in that, include: Step 1: Collect key electrical and timing parameters of the main control chip, front-end signal conditioning module and analog-to-digital converter to construct a set of core component performance parameters under dynamic operating conditions; Step 2: Analyze the performance coupling relationship between core components based on the set of performance parameters, and use multivariate coupling analysis to quantify the matching degree, synergy and correlation between key parameters of each component to identify performance bottlenecks; Step 3: Generate an adaptive parameter optimization strategy based on the performance bottleneck, call the optimization algorithm library in the main control chip firmware, and dynamically calculate the collaborative optimization parameters of the core components for the performance mismatch point; Step 4: The collaborative optimization parameters are synchronously sent to the corresponding core components. The signal integrity index and effective data rate are verified through standard signal acquisition test. If they are not met, return to step 2 for iterative optimization. Step 5: Periodically repeat the monitoring and optimization process from Step 1 to Step 4, and combine the predictive fine-tuning optimization strategy based on ambient temperature fluctuations and load change trends to complete the performance optimization of the core components of the chip acquisition device.

2. The method for optimizing the performance of core components of a chip acquisition device according to claim 1, characterized in that, In step 1, the performance monitoring unit deployed in the main control chip firmware collects key electrical and timing parameters of the main control chip, the front-end signal conditioning module, and the analog-to-digital converter, constructing a set of core component performance parameters under dynamic operating conditions. The performance monitoring unit is integrated inside the main control chip and includes a time-to-digital conversion circuit and analog feature extraction logic. The resolution of the time-to-digital conversion circuit is used to capture the interrupt response delay and clock edge jitter of the main control chip. The analog feature extraction logic indirectly calculates the setup time and group delay parameters of the front-end signal conditioning module by monitoring the voltage slew rate and overshoot characteristics of the output node of the front-end signal conditioning module.

3. The method for optimizing the performance of core components of a chip acquisition device according to claim 2, characterized in that, In step 1, the sample-and-hold time and aperture jitter parameters of the analog-to-digital converter (ADC) are obtained through the ADC's built-in self-test mode. The ADC's built-in self-test mode outputs a digital bitstream that characterizes the stability of the sample-and-hold time and the aperture jitter characteristics. The performance monitoring unit parses the digital bitstream and compares it with the ideal response to extract the ADC's dynamic performance parameters.

4. The method for optimizing the performance of core components of a chip acquisition device according to claim 1, characterized in that, The multivariate coupling analysis method in step 2 adopts a correlation calculation model based on mutual information entropy. The correlation calculation model calculates the mutual information entropy between the sampling trigger timing of the main control chip and the establishment time of the front-end signal conditioning module, the interrupt response delay of the main control chip and the group delay of the front-end signal conditioning module, and the sampling and holding time of the analog-to-digital converter and the overall stability of the signal chain to form a coupling strength matrix, so as to locate the source of performance mismatch.

5. The method for optimizing the performance of core components of a chip acquisition device according to claim 1, characterized in that, The optimization algorithm library in step 3 includes gradient descent optimization routines and a heuristic rule engine. The gradient descent optimization routines perform local optimization search in the continuous parameter space with the signal integrity index as the objective function. The heuristic rule engine determines the initial optimization direction and step size based on pre-stored empirical rules for chip component combinations.

6. The method for optimizing the performance of core components of a chip acquisition device according to claim 5, characterized in that, In step 3, the process of dynamically calculating the collaborative optimization parameters introduces a component aging factor. The component aging factor is estimated based on the cumulative operating time and operating temperature history of the core component and is used to compensate for the lifetime of the sampling clock phase adjustment of the main control chip, the gain compensation coefficient of the front-end signal conditioning module, or the sampling window offset of the analog-to-digital converter.

7. The method for optimizing the performance of core components of a chip acquisition device according to claim 1, characterized in that, The standard signal acquisition test in step 4 uses the reference signal source integrated inside the device; the reference signal source outputs a signal with known amplitude and frequency, and by comparing the acquisition results with the ideal waveform, the signal-to-noise ratio, total harmonic distortion and effective number of bits are calculated as the basis for verifying signal integrity.

8. The method for optimizing the performance of core components of a chip acquisition device according to claim 1, characterized in that, In step 4, the collaborative optimization parameters are synchronously distributed to the corresponding core components using serial peripheral interface variants that support atomic configuration operations. This ensures that the optimization parameters of the main control chip, front-end signal conditioning module, and analog-to-digital converter are updated synchronously, preventing timing disorders caused by asynchronous configuration.

9. The method for optimizing the performance of core components of a chip acquisition device according to claim 1, characterized in that, The triggering conditions for monitoring and optimization in step 5 include fixed time intervals, data acquisition task switching events, or abnormal events reported by temperature sensors and load current monitoring circuits, so as to achieve on-demand optimization to balance performance and power consumption. Among them, the fine-tuning optimization strategy adopts a time series prediction algorithm. Based on the historical data of performance parameters in the most recent optimization cycle, the algorithm predicts the future preset internal ambient temperature and load change trends, and accordingly pre-adjusts the sampling clock phase, gain compensation coefficient or sampling window offset. All optimization parameters, performance history and coupling analysis results are stored in independent partitions of the device's internal non-volatile memory, forming the device's performance fingerprint.

10. A performance optimization system for core components of a chip acquisition device, characterized in that, include: The parameter acquisition module is used to collect key electrical and timing parameters of the main control chip, front-end signal conditioning module and analog-to-digital converter, and to construct a set of performance parameters of core components under dynamic operating conditions. The coupling analysis module is used to analyze the performance coupling relationship between core components based on the set of performance parameters. It uses a multivariate coupling analysis method to quantify the matching degree, synergy and correlation between key parameters of each component and to identify performance bottlenecks. The strategy generation module is used to generate an adaptive parameter optimization strategy based on the performance bottleneck, call the optimization algorithm library in the main control chip firmware, and dynamically calculate the collaborative optimization parameters of the core components for the performance mismatch point. The closed-loop verification module is used to synchronously send the collaborative optimization parameters to the corresponding core components and verify whether the signal integrity index and effective data rate meet the standards through standard signal acquisition and testing. If they do not meet the standards, it returns to iterative optimization. The continuous adaptation module is used to periodically repeat the monitoring and optimization process described above. It combines the predictive fine-tuning optimization strategy based on ambient temperature fluctuations and load change trends to complete the performance optimization of the core components of the chip acquisition device.

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