Method for processing analog signals of a measuring light curtain based on reconfiguration of an fpaa

Abstract

The application is based on a FPAA reconfigured measurement light curtain analog signal processing method, relates to the field of light curtain measurement system environment perception and dynamic adjustment, and aims to solve the influence of environmental changes on the stability and adaptability of analog signal paths. A micro sensor array is deployed at a key position to collect multiple source physical quantities in real time, generate a structured environment parameter set, and compress and encode the same into a 16-bit environment fingerprint code. The environment fingerprint code is matched with a topology anchor point library hash to determine an optimal signal path connection matrix and calculate a dynamic weight scaling factor. The on-site programmable analog array switch matrix conduction threshold is adjusted in real time to realize flexible reconstruction of path topology mapping. Meanwhile, through transient response quality verification and robustness determination, the signal path configuration is automatically optimized or rolled back. The scheme effectively improves the environmental adaptability and transient stability of the system and guarantees signal transmission performance.

Description

Technical Field

[0001] This invention relates to the field of field-programmable analog array (FPAA) device configuration and analog signal processing technology, and in particular to a measurement light curtain analog signal processing method based on FPAA reconstruction. Background Technology

[0002] Currently, Field-Programmable Analog Arrays (FPAAs) are widely used in industrial automation and precision measurement to achieve highly flexible and reconfigurable analog signal processing systems. In complex industrial scenarios, such as light curtain measurement, condition detection, and sensor signal conditioning, higher demands are placed on the robustness, real-time performance, and stability of analog signal paths. Due to its programmability, the FPAA has become a core analog processing platform connecting front-end sensors and back-end control systems. Internally, it consists of reconfigurable transconductance amplifiers, filters, and feedback networks, which are flexibly combined through a switching matrix to achieve dynamic configuration of various analog signal links.

[0003] In existing technologies, the analog signal path configuration of FPAA typically relies on a pre-set topology template library, with path reconstruction completed through static configuration files or manual selection by engineers. Some solutions incorporate limited field environmental sensing data, using collected parameters such as temperature and power supply noise as configuration conditions input into the reconstruction logic. However, most of these methods employ lookup table-based, rule-driven, or simple switching methods based on threshold judgments of single physical parameters. For example, the current mainstream dynamic FPAA reconstruction approach pre-sets signal path topologies for several typical operating conditions during the factory or commissioning phase, and periodically polls environmental sensing parameters during operation, adjusting or switching fixed templates through linear weights to cope with external changes. This type of method is computationally simple and easy to implement in hardware, but lacks real-time perception and rapid adaptation capabilities to instantaneous environmental fluctuations in industrial settings (such as high-frequency power supply ripple, mechanical micro-vibration, and complex thermal gradient distributions).

[0004] In practice, the analog signal path of FPAA is significantly affected by environmental factors, especially in high-power equipment, high-speed moving machinery, and environments with dense heat sources. High-frequency ripple of the power rail, vibration of the module surface, and changes in temperature gradient within the chip can easily couple into the signal chain, leading to problems such as gain instability, phase drift, and increased noise baseline. In severe cases, this can even cause measurement inaccuracies or system failures. Existing configuration strategies often preset some tolerance redundancy, but they cannot perform multi-physical quantity joint analysis and dynamic parameter tuning for the real-time environment. This results in suboptimal configuration, delays, or even non-convergence of the analog signal link in non-standard or extreme environments. Furthermore, most FPAA reconfiguration processes are one-time, breakpoint configurations, lacking an adaptive learning mechanism for historical environmental changes or real-time operating condition feedback. Once the environment experiences disturbances beyond the expectations of the template library, the system can only issue a simple alarm or degrade, lacking fine-tuning and fine-grained adaptive functions.

[0005] In recent years, some studies have attempted to adopt intelligent FPAA configuration methods that draw on machine learning models, historical data training, and cloud-based collaborative optimization to enable simulated paths to possess a certain degree of self-learning capability in complex environments. However, these solutions generally face problems such as high computational load, high latency, strong dependence on external computing resources, and difficulties in practical engineering implementation, failing to meet the stringent requirements of industrial sites for low latency, high determinism, and autonomous operation at the edge. On the other hand, traditional analog signal path scheduling methods have long treated random environmental disturbances as interference sources and suppressed them, failing to transform them into useful distinguishable signals. This results in low utilization of the FPAA's internal configuration space and extremely limited environmental adaptability dynamics of the simulated link.

[0006] The main limitations of existing technologies are reflected in the following aspects: The FPAA reconstruction logic lacks a self-learning mechanism based on environmental feedback, and cannot yet efficiently, in real time, and without training a model to achieve adaptive signal path adjustment in response to environmental changes.

[0007] Currently, reconstruction relies heavily on static rule bases or fixed templates, which cannot handle complex scenarios under the coupling of multiple physical quantities (such as temperature gradient, power ripple, and vibration mode), and has weak anti-interference and self-optimization capabilities.

[0008] When signal configuration mismatches occur due to environmental changes, there is a lack of fast, seamless, and low-cost path fine-tuning mechanisms. Degradation switching is often used instead of dynamic fine-tuning, which affects the real-time performance, accuracy, and robustness of the system.

[0009] Many dynamic reconstruction algorithms involve strategies such as online machine learning and collaborative cloud optimization, resulting in complex algorithms, high latency, and heavy hardware dependence, making them unsuitable for the independent high-reliability operation requirements of edge environments.

[0010] Therefore, existing FPAA analog signal path adaptive configuration technology urgently needs to break through the traditional static, single-parameter, and passive response paradigm, and move towards a new path of multi-parameter environmental perception and proactive adaptive reconfiguration with high real-time performance, all-solid-state operation, and zero dependence on external support. In practical engineering applications, users urgently need an FPAA analog signal path optimization method that is in-situ, end-to-end, low-latency, requires no model training or historical data, can directly convert environmental physical quantities into deterministic configuration instructions, and can be verified in real-time with closed-loop capability. This will ensure that industrial field systems such as light curtain measurement achieve highly robust signal processing capabilities and dynamic reconfiguration performance under various abnormal and extreme environments. Summary of the Invention

[0011] This application provides a measurement light curtain analog signal processing method based on FPAA reconstruction, which aims to solve one of the problems or issues of the prior art mentioned in the background.

[0012] The measurement light curtain analog signal processing method based on FPAA reconstruction provided in this application specifically includes: S1: Collect the high-frequency ripple spectrum characteristics of the power rail, the three-dimensional micro-vibration modes of the package surface, and the temperature gradient vector of the thermally sensitive area across the chip to generate the original environmental perception dataset containing multiple physical quantities. S2: Based on the original environmental perception dataset, perform table lookup, bitwise operations and weighted accumulation operations to construct a sixteen-bit environmental fingerprint code that includes temperature gradient dominant type, power supply ripple energy concentration segment index and vibration mode matching degree score. S3: Construct a topology anchor library that maps environmental fingerprint codes to analog path templates. Each topology anchor corresponds to a path node connection matrix that includes the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination. S4: Input the sixteen-bit environmental fingerprint code into the topology anchor library to perform fast hash matching, locate the target topology anchor, and extract the reference path node connection matrix associated with the target topology anchor as the initial analog signal path template to be reconstructed; S5: Based on the deviation of each field of the sixteen-bit environmental fingerprint code from the standard value of the target topology anchor point, calculate the dynamic weight scaling factor acting on the initial analog signal path template, and generate a path elastic weight migration instruction set for modulating the conduction threshold voltage of the internal switch matrix after digital-to-analog conversion and instruction encapsulation.

[0013] The proposed method for processing analog signals of a measurement light curtain based on FPAA reconstruction has the following advantages: (1) In response to the problem of mismatch in FPAA analog signal path configuration and performance degradation caused by the dynamic drift of environmental parameters such as temperature gradient, power ripple and mechanical vibration in industrial field light curtain systems, this solution transforms environmental variables that are traditionally regarded as sources of interference into quantifiable “environmental fingerprints” and constructs a pure edge-end adaptive reconstruction mechanism that does not rely on historical data modeling, online learning algorithms or external communication links. Compared to existing technologies that commonly employ neural network prediction or multi-objective optimization search methods, which often require a large number of training samples, high computing power, and cloud collaboration, making it difficult to meet the stringent requirements of industrial control for real-time performance and determinism, this solution abandons complex computational paradigms and instead designs hardware-friendly lightweight coding logic. Utilizing on-chip LUT resources on the FPGA, it implements a lookup table + bitwise operation + weighted accumulation approach to compress multi-dimensional environmental perception data into a compact 16-bit environmental fingerprint code, assigning it a clear physical semantic division: the high 4 bits represent the thermal field distribution pattern, the middle 6 bits reflect the concentrated energy segment of power supply noise, and the low 6 bits reflect the degree of structural vibration mode matching. This achieves efficient feature abstraction without introducing floating-point units or AI IP cores, significantly improving the response speed and circuit implementation feasibility of environmental state recognition. It effectively overcomes the technical bottlenecks of traditional intelligent reconfiguration methods, such as deployment difficulties and uncontrollable latency in resource-constrained edge scenarios.

[0014] (2) Furthermore, this solution innovatively pre-sets a "topology anchor library" in the FPAA configuration storage area. Each anchor point corresponds to a set of typical environmental fingerprints calibrated in the laboratory and the mapping relationship between them and the optimal simulation path template. The path template is fixed in the form of a node connection matrix, covering 12 key programmable parameters such as the transconductance amplifier cascade order, filter Q value set, and feedback network combination, forming a high-quality prior knowledge pool for typical working conditions. During runtime, the optimal anchor point corresponding to the current environmental fingerprint is quickly matched through hash index, and the "path elastic weight migration" mechanism is started. Based on the degree of deviation between each field of the fingerprint and the target anchor point, a dynamic weight scaling factor of 0.7–1.3 times is applied to each connection edge in the matching path. This factor directly acts on the conduction threshold voltage of the switch matrix inside the FPAA, realizing flexible adjustment of branch gain and phase response. Fine-grained performance tuning is completed while maintaining the stability of the backbone topology, avoiding system oscillation and functional interruption caused by frequent reconfiguration. Meanwhile, the closed-loop verification process samples the transient response of key nodes through a built-in diagnostic ADC within 10ms after each reconstruction. If overshoot or abnormal setup time is detected, it automatically rolls back and initiates sub-pixel-level fine-tuning to ensure the safety and convergence of the reconstruction behavior. The entire process is completed autonomously within a single FPAA chip, without external processor intervention. The reconstruction latency is stably controlled within 35μs. Compared to traditional solutions that rely on iterative optimization or remote scheduling, the response speed is improved by nearly two orders of magnitude, effectively guaranteeing the signal integrity and operational reliability of the industrial light curtain system under strong disturbances and rapidly changing conditions.

[0015] In summary, this solution breaks through the path dependence of traditional adaptive systems on data-driven models and complex computing architectures. For the first time, it realizes a technical paradigm that deeply integrates the entire chain of environment perception, encoding, matching, adjustment, and verification into the FPAA analog-digital hybrid configuration layer. It constructs an edge adaptive closed-loop system with strong real-time performance, zero external dependencies, and high functional determinism. This not only significantly improves the robust configuration capability of FPAA in dynamic industrial environments but also provides a reusable, easy-to-deploy, and low-overhead adaptive evolution approach for other high-precision analog programmable systems. It is particularly suitable for industrial sensing and control scenarios with stringent requirements for safety levels, response latency, and independent operation capabilities. Attached Figure Description

[0016] Figure 1 This is the main flowchart of the measurement light curtain analog signal processing method based on FPAA reconstruction.

[0017] Figure 2 This is a sub-flowchart of the measurement light curtain analog signal processing method based on FPAA reconstruction.

[0018] Figure 3 This is another sub-flowchart of the measurement light curtain analog signal processing method based on FPAA reconstruction. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] like Figure 1 As shown, this application provides a measurement light curtain analog signal processing method based on FPAA reconstruction, specifically including: S1: Collect the high-frequency ripple spectrum characteristics of the power rail, the three-dimensional micro-vibration modes of the package surface, and the temperature gradient vector of the thermally sensitive area across the chip to generate the original environmental perception dataset containing multiple physical quantities. S2: Based on the original environmental perception dataset, perform table lookup, bitwise operations and weighted accumulation operations to construct a sixteen-bit environmental fingerprint code that includes temperature gradient dominant type, power supply ripple energy concentration segment index and vibration mode matching degree score. S3: Construct a topology anchor library that maps environmental fingerprint codes to analog path templates. Each topology anchor corresponds to a path node connection matrix that includes the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination. S4: Input the sixteen-bit environmental fingerprint code into the topology anchor library to perform fast hash matching, locate the target topology anchor, and extract the reference path node connection matrix associated with the target topology anchor as the initial analog signal path template to be reconstructed; S5: Based on the deviation of each field of the sixteen-bit environmental fingerprint code from the standard value of the target topology anchor point, calculate the dynamic weight scaling factor acting on the initial analog signal path template, and generate a path elastic weight migration instruction set for modulating the conduction threshold voltage of the internal switch matrix after digital-to-analog conversion and instruction encapsulation.

[0022] Step S1: Acquire the high-frequency ripple spectrum characteristics of the power rail, the three-dimensional micro-vibration modes of the package surface, and the temperature gradient vector across the thermally sensitive region of the chip to generate a raw environmental sensing dataset containing multi-source physical quantities; specifically including: S1.1: Perform spatial mapping analysis on the key positions of the power supply domain, packaging substrate and bracket of the light curtain measurement system to determine the high-frequency ripple spectrum feature acquisition points of the power rail, the three-dimensional micro-vibration mode acquisition points of the packaging surface and the temperature gradient vector acquisition points of the thermally sensitive area across the chip, so as to generate spatial layout configuration information containing the acquisition poses of multi-source physical quantities.

[0023] When performing spatial mapping analysis on the key positions of the power supply domain, packaging substrate, and support of the light curtain measurement system, the input conditions are set to the known system topology and spatial coordinate information of each functional module. Pose analysis is then conducted in conjunction with the mechanical support method, power supply path, and packaging thermal distribution characteristics during system operation. For the power supply domain, the geometric position parameters of the power supply rails and their access terminals are extracted from the 3D spatial model. Using the spatial gradient calculation method for high-frequency ripple energy concentration areas, the position coordinates are aligned with the length direction of the power rails to obtain the spatial configuration information required for spectral feature acquisition points. For the packaging substrate, the vibration mode distribution on its surface under different operating conditions is analyzed. The dominant frequency nodes of the modes are combined with areas of high damping variation. Through cross-mapping of the vibration mode node distribution matrix and the 3D coordinate matrix of the packaging, the spatial coordinates of the 3D micro-vibration mode acquisition points are determined. For the cross-chip thermally sensitive area, the distribution characteristics of the temperature gradient vector are utilized. Gradient direction discrimination is performed based on the thermal imaging matrix. Non-uniformly distributed points with gradient amplitudes exceeding a preset threshold are selected as temperature acquisition points, and the 3D coordinates of these points are used as the temperature parameter acquisition locations. The spatial coordinates of the aforementioned power supply domain acquisition points, package surface acquisition points, and heat-sensitive area acquisition points are combined into a multi-source physical quantity acquisition pose matrix. This matrix is ​​then used to generate spatial layout configuration information containing the multi-source physical quantity acquisition poses through matrix address encoding, achieving precise selection of acquisition points and balanced spatial distribution. Through spatial mapping analysis and pose matrix construction, the system structure data from the previous step is transformed into spatial layout configuration information suitable for sensor array deployment, enabling precise positioning of the multi-source physical quantity acquisition points in a three-dimensional coordinate system and improving the coverage and representativeness of the original environmental data acquisition.

[0024] S1.2: Based on the spatial layout configuration information, a synchronous sampling operation is performed using a micro-sensor array integrated in the above-mentioned key locations to obtain the original data stream of high-frequency ripple spectrum characteristics of the power rail, the original data stream of three-dimensional micro-vibration modes of the package surface, and the original data stream of temperature gradient vector across the chip's thermally sensitive area, so as to form a multi-channel parallel original sensing signal set.

[0025] Based on the spatial layout configuration information of the multi-source physical quantity acquisition pose generated by S1.1 spatial mapping analysis, the physical coordinates of the known power supply domain acquisition points, package substrate acquisition points, and bracket acquisition points are selected. The control interface of the micro-sensor array integrated at these key locations is then invoked to issue synchronous sampling start commands to each acquisition unit, ensuring uniform sampling clocks across all channels. For the high-frequency power rail voltage sensor located in the power supply domain, a bandwidth covering the 1kHz to 10MHz frequency band and a sampling resolution higher than 12 bits are set to acquire and output the raw data stream of the high-frequency ripple spectrum characteristics of the power rail. For the triaxial micro-vibration sensor located on the package substrate, sensitivity and sampling rate are set to enable it to simultaneously capture the dominant frequency, damping ratio, and mode order information, acquiring and outputting the raw data stream of the three-dimensional micro-vibration modes of the package surface. For the multi-point temperature sensor arranged across the chip's thermally sensitive area, non-uniform sampling point parameters and sampling rates are set to acquire the raw data stream of the temperature gradient vector across the chip's thermally sensitive area. An on-chip synchronous triggering mechanism is used to buffer the data of the above sensor output channels according to a time base, and the raw analog signals of each channel are uniformly encapsulated into a multi-channel parallel raw sensing signal set. By using multi-source synchronous sampling and unified encapsulation processing, the spatial layout configuration information from the previous step is transformed into a continuous and time-domain consistent set of multi-channel raw sensing signals, providing a high-precision, low-drift raw data foundation for subsequent time-domain alignment and frequency-domain segmentation processing.

[0026] For example, in an FPAA reconstruction scheme deployed in an industrial inspection light curtain system, a high-frequency power rail voltage sensor is configured at the power supply domain acquisition point. Its sampling rate is set to 20MS / s, bandwidth is set to 1kHz–10MHz, resolution is set to 14 bits, and the amplitude range of the output raw ripple signal is within ±1V. A triaxial MEMS vibration sensor is configured at the package substrate acquisition point. Its sensitivity is set to 0.005g / LSB, sampling frequency is 5kHz, the main frequency of the output vibration signal is concentrated between 120Hz and 250Hz, and the damping ratio is measured to be between 0.05 and 0.08. Four non-uniform point temperature sensors are arranged across the chip's thermally sensitive area. The sampling rate of each point is set to 10Hz, the temperature measurement range is between 20°C and 45°C, and the maximum value of the gradient vector component is 8°C. Upon sampling initiation, the system triggers the simultaneous start of three acquisition types via the FPGA global clock. The output power rail ripple, three-dimensional vibration mode, and temperature gradient data are sent to the buffer via the on-chip serial bus, and a unified timestamp and channel identifier are inserted into the data frame, ultimately forming a collection file containing the three parallel raw signals. During this step, all channels start sampling within the same clock cycle, ensuring strict consistency of multi-source data in physical time. Verification shows that inputting this collection file into the S1.3 processing module significantly improves the stability of frequency domain segmentation and temperature difference extraction, and the phase distortion amplitude in the system transient response analysis results is reduced to less than half of the original scheme.

[0027] S1.3: Perform time-domain alignment and frequency-domain segmentation processing on the multi-channel parallel raw sensing signal set, extract the energy distribution segment in the power rail high-frequency ripple spectrum features, the main frequency and damping ratio parameters in the three-dimensional micro-vibration mode of the package surface, and the non-uniform temperature difference value in the temperature gradient vector of the cross-chip thermal sensitive area, so as to generate a structured environmental parameter set containing multi-dimensional physical quantity features.

[0028] For the multi-channel parallel raw sensing signal set from the raw data stream of high frequency ripple spectrum characteristics of power rail, the raw data stream of three-dimensional micro-vibration mode of package surface, and the raw data stream of temperature gradient vector of cross-chip thermal sensitive area, time-domain alignment processing based on unified sampling time base is performed, and the data frames of each channel are interpolated and filled according to the trigger time mark, so that the phase synchronization error is controlled within microsecond level.

[0029] The time-domain aligned signal set is segmented into frequency domains according to channel characteristics. The power rail high-frequency ripple signal is calculated by Fast Fourier Transform (FFT) and then divided into continuous frequency bands in the range of 1kHz–10MHz according to the preset bandwidth division strategy. The energy value of each frequency band is extracted to form an energy distribution segment.

[0030] Modal analysis is performed on the three-dimensional micro-vibration modal signal of the package surface. The dominant frequency component is identified by performing FFT transformation on the vibration displacement sequence in each direction, and the damping ratio is calculated using the logarithmic decrease method to form a parameter pair of dominant frequency and damping ratio.

[0031] Spatial difference calculation is performed on the temperature gradient vector across the thermally sensitive region of the chip. A gradient field is constructed on a multi-point two-dimensional grid based on the temperature values ​​of non-uniformly distributed points, and the temperature difference between each collection point is extracted as the temperature gradient feature.

[0032] Multi-dimensional field combination logic is used to uniformly format the high-frequency ripple energy distribution segment of the power rail, the dominant frequency and damping ratio parameters of the vibration mode, and the temperature difference value into a structured environmental parameter set, ensuring that each feature field has accurate indexing capabilities.

[0033] Through the above processing method, the multi-channel raw signal stream results of the previous step are transformed into a structured environmental parameter set that can be directly encoded and matched, realizing a multi-dimensional, quantifiable, and localizable feature representation of the on-site environmental state.

[0034] S1.4: Based on the structured environmental parameter set, timestamp marking and data frame encapsulation technology are used to fuse and package the energy distribution segment of the high-frequency ripple spectrum of the power rail, the damping ratio parameter of the main frequency of the three-dimensional micro-vibration mode on the package surface, and the temperature difference value of the non-uniform distribution of the temperature gradient vector across the chip's thermally sensitive area, so as to generate an original environmental perception dataset with spatiotemporal consistency.

[0035] Step S2: Based on the original environmental perception dataset, perform table lookup, bitwise operations, and weighted accumulation operations to construct a 16-bit environmental fingerprint code that includes the dominant temperature gradient type, the power supply ripple energy concentration segment index, and the vibration mode matching score. Specifically, this includes: S2.1: Perform frequency band energy distribution quantization on the high-frequency ripple spectrum features of the power rail in the original environmental perception dataset to generate a ripple energy distribution vector containing energy values ​​of thirty-two frequency bands. This ripple energy distribution vector serves as the basic input data for subsequent frequency band index extraction.

[0036] S2.2: Based on the ripple energy distribution vector, perform peak search and interval mapping algorithm to determine the index of the concentrated power supply ripple energy segment. At the same time, use the lookup table method to calculate the modal similarity of the dominant frequency and damping ratio of the three-dimensional micro-vibration mode on the package surface to generate a vibration mode matching score. The index of the concentrated power supply ripple energy segment and the vibration mode matching score together constitute an intermediate feature parameter set.

[0037] Peak search processing is performed on the ripple energy distribution vector. A serialized scanning mechanism is established based on the energy values ​​of thirty-two frequency bands. The energy difference between adjacent frequency bands is calculated one by one and the frequency band where the maximum difference is located is determined to determine the local peak position.

[0038] The local peak location is input into the interval mapping algorithm. A preset frequency band division lookup table is used to map the frequency band index where the peak is located to the power supply ripple energy concentration section index. During the mapping process, the section boundary condition verification is performed to ensure the validity of the index value.

[0039] The dominant frequency parameters and damping ratio parameters of the three-dimensional micro-vibration modes on the package surface are extracted respectively, and the on-chip static lookup table resources are called to calculate the modal similarity of the current combination of dominant frequency and damping ratio.

[0040] In the modal similarity calculation stage, a normalized difference metric is used. The difference between the current dominant frequency and damping ratio and the corresponding values ​​in the standard modal library is calculated and normalized. The difference between the dominant frequency and the damping ratio is then fused according to a preset weight to obtain the vibration modal matching score.

[0041] The power supply ripple energy concentration zone index and vibration mode matching score are packaged into fields to form an intermediate feature parameter set with a digital structure, which provides an alignment benchmark for subsequent temperature gradient dominant type discrimination.

[0042] By combining peak search and interval mapping with modal similarity calculation, the ripple energy distribution vector and vibration mode parameters are transformed into power supply ripple energy concentration segment index and vibration mode matching score, thereby achieving the standardized construction of intermediate feature parameter set.

[0043] For example, the ripple energy distribution vector collected in the power supply domain of the light curtain measurement system has 32 frequency band values ​​[0.12, 0.18, 0.25, 0.31, 0.28, 0.20, ...]. When performing peak search, the system obtains the maximum difference of 0.06 at the fourth frequency band, corresponding to a local peak position index of 4. After being mapped to the power supply ripple energy concentration segment index 10 through the interval mapping lookup table, the difference is calculated. In the micro-vibration modal parameters of the package surface, the dominant frequency is 1250Hz and the damping ratio is 0.015. After performing normalized difference calculation with the entry of the standard modal library with a dominant frequency of 1200Hz and a damping ratio of 0.014, the dominant frequency difference is 50Hz with a normalized value of 0.04, and the damping ratio difference is 0.071. The two are fused with weights of 0.6 and 0.4, respectively, and the matching score is 0.0524. The final output intermediate feature parameter set includes the power supply ripple energy concentration segment index 10 and the vibration mode matching score of 0.0524. This parameter set can significantly improve the index matching accuracy and vibration state recognition capability in subsequent temperature gradient dominant type discrimination and 16-bit environmental fingerprint construction.

[0044] S2.3: Execute gradient direction discrimination logic based on the temperature gradient vector across the chip's thermally sensitive region to identify and output a classification label representing the dominant type of temperature gradient, and align the classification label with the intermediate feature parameter set to form a multidimensional physical quantity feature combination to be encoded.

[0045] Based on the input condition of the temperature gradient vector across the chip's thermally sensitive region, the coordinates and temperature values ​​of each temperature measurement point in this vector are used as the execution objects of the gradient direction discrimination logic. Accurate classification and judgment of the gradient direction are achieved using the on-chip logic resources of the field-programmable gate array (FPGA). First, vector component extraction processing is performed on the gradient vector data, combining the temperature change and spatial position difference of each measurement point into three-dimensional gradient components, forming a basic dataset for direction calculation. After the gradient components are formed, the on-chip vector direction calculation module is called to perform magnitude calculation and normalization on each component value, obtaining a gradient direction vector of unit length to eliminate the influence of distance differences between measurement points on direction determination. The normalized gradient direction vector is then subjected to inner product calculation and azimuth conversion with a preset set of direction classification thresholds. By determining the interval into which the azimuth angle enters, the gradient dominance type classification label is identified, ensuring that the temperature gradient direction uniquely belongs to categories such as uniform, radial, axial, and shear. After the directional classification is completed, the obtained classification labels are formatted and encoded with the same field length and arrangement order as the intermediate feature parameter set. The classification labels are then combined with the power supply ripple energy concentration section index and vibration mode matching score according to the preset field alignment rules to form a multidimensional physical quantity feature combination data structure to be encoded.

[0046] Through the above processing method, the structured environmental parameter set of the previous step is transformed into a multi-dimensional combination of physical quantity features, including temperature gradient dominant type, power supply ripple energy concentration segment index and vibration mode matching degree score. This realizes the unified format fusion of temperature gradient direction information and other environmental features, providing a consistent input for subsequent nonlinear weighted accumulation compression coding.

[0047] For example, in an industrial inspection light curtain system, four temperature sensing points are deployed across the heat-sensitive area of ​​an FPAA chip, with spatial coordinates of (0,0), (10,0), (0,10), and (10,10) mm, respectively. The measured temperature values ​​are 25.4, 26.1, 25.8, and 26.5℃, respectively. Dividing the temperature difference between adjacent measuring points by the coordinate difference yields an X-direction gradient component of 0.07℃ / mm and a Y-direction gradient component of 0.04℃ / mm. Using the gradient magnitude calculation formula, the gradient magnitude is approximately 0.0806℃ / mm. Normalizing each component by its magnitude yields a unit direction vector (0.869, 0.496), and the calculated azimuth angle of this vector is approximately 29.2°. The azimuth angle falls within a preset axial range of 0° to 45°, therefore the classification label is set to "axial type," encoded as binary 0101. This tag is aligned with the power supply ripple energy concentration section index value 000110 and the vibration mode matching score 101011 to form the feature combination to be encoded: 0101 000110 101011. During system operation, this feature combination is input to the nonlinear weighted accumulation module of S2.4. After compression encoding by FPGA LUT resources, the accuracy of environmental fingerprint code determination can be greatly improved, and the environmental adaptability of FPAA reconstruction can be significantly enhanced.

[0048] S2.4: Utilize the on-chip lookup table resources of the field-programmable gate array to perform a nonlinear weighted accumulation operation on the combination of multidimensional physical quantity features to be encoded, so as to compress the high-dimensional analog features into a low-bit-width digital sequence and generate a preliminary compressed encoded data stream. This preliminary compressed encoded data stream retains key distinguishing information of the environmental state.

[0049] Based on the combination of multidimensional physical quantity features to be encoded, the nonlinear weighted accumulation function index in the on-chip lookup table resource of the field programmable gate array is called to retrieve the corresponding weighted coefficient group for each feature field to form an initial weight matrix.

[0050] The temperature gradient dominant type classification label is mapped to the thermal drift weight column in the weighted coefficient group, the power supply ripple energy concentration section index is mapped to the noise suppression weight column, the vibration mode matching score is mapped to the mechanical stability weight column, and the field-to-weight vector binding operation is performed on-chip to generate the complete weight matrix structure.

[0051] Perform field-by-field multiplication on the weight matrix and feature combination, multiplying each physical quantity feature value by the corresponding nonlinear weight coefficient to obtain the feature value gain vector including environmental adaptive adjustment.

[0052] An accumulator is used to accumulate the components of the eigenvalue gain vector according to a preset nonlinear superposition curve. The accumulation process uses a lookup table function to achieve exponential or piecewise polynomial weighting in order to suppress the influence of the extreme values ​​of a single feature on the overall encoding.

[0053] The accumulated result is compressed to a predetermined low bit width range through scaling conversion logic. The high-amplitude sum is mapped to the maximum representable bit width value, while the low-amplitude sum retains the necessary distinguishability, and the initial compressed encoded data stream is output.

[0054] Through the above table lookup, weight binding, field-by-field nonlinear multiplication, accumulation and scaling compression processing, the multidimensional feature combination of the previous step is transformed into a low-bit-width digital sequence with a compact structure and retaining the distinguishability of key environmental states, achieving a hardware-friendly encoding effect.

[0055] For example, in an industrial light curtain measurement system, the temperature gradient dominant type classification label is radial, the power supply ripple energy concentration segment index is 15, and the vibration mode matching score is 42. In the on-chip lookup table, the thermal drift weight coefficient corresponding to radial type is 1.15, the noise suppression weight coefficient corresponding to segment index 15 is 0.92, and the mechanical stability weight coefficient corresponding to matching score 42 is 1.08. After binding the above feature values ​​with the weight coefficients, a multiplication operation is performed. For example, if the temperature gradient amplitude is 38, the corresponding product is 38 × 1.15 = 43.7; if the power supply ripple index amplitude is 27, the corresponding product is 27 × 0.92 = 24.84; and if the vibration mode amplitude is 31, the corresponding product is 31 × 1.08 = 33.48. The accumulator employs an exponential weighted curve, with the curve coefficient α set to 0.75 in the lookup table. After the hardware accumulator outputs a total sum of 76.74, the scaling logic compresses it to a low-width 12-bit representation, mapping to a value of 3080. This encoded value serves as the initial compressed encoded data stream output. Subsequent steps involve bit concatenation and format reorganization to ultimately construct a 16-bit environmental fingerprint. In performance verification, this encoding maintains high discriminative power supply noise characteristics despite variations in radial temperature gradient and vibration amplitude, effectively improving the determinism and robustness of FPAA simulation path matching.

[0056] S2.5: Perform bit splicing and format reorganization processing on the preliminary compressed encoded data stream to construct a 16-bit environmental fingerprint code containing a high four-bit temperature gradient dominant type, a middle six-bit power supply ripple energy concentration section index, and a low six-bit vibration mode matching score, thus completing the final mapping from multiple physical quantities to a single control identifier.

[0057] like Figure 2 As shown, step S3 involves constructing a topology anchor library that maps environmental fingerprint codes to analog path templates. Each topology anchor corresponds to a path node connection matrix containing the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination. Specifically, this includes: S3.1: Cluster analysis is performed on multiple sets of standard environmental fingerprint samples collected by the light curtain measurement system in a controlled laboratory environment to generate a set of standard environmental fingerprint feature vectors containing temperature gradient dominant type, power supply ripple energy concentration section index and vibration mode matching degree score, which serves as the initial input data source for the construction of the topology anchor point library.

[0058] Based on multiple sets of standard environmental fingerprint samples collected in a controlled laboratory environment using a light curtain measurement system, the input objects include a 16-bit environmental fingerprint code generated and verified for accuracy via the S2 step, the temperature gradient dominant type corresponding to each fingerprint code, the power supply ripple energy concentration segment index, and the vibration mode matching score. All sample fingerprint codes are split into three categories of physical parameters according to their field structure, forming an original feature matrix as the starting data source for cluster analysis. One-Hot encoding is performed on the temperature gradient dominant type classification label using field normalization, interval value normalization is applied to the power supply ripple energy concentration segment index, mapping it to the [0,1] interval, and standard deviation normalization is applied to the vibration mode matching score, ensuring that the three types of features participate equally in the cluster distance calculation. A sample similarity matrix is ​​constructed using the Euclidean distance metric algorithm on the normalized feature matrix, and weighting coefficients are introduced to enhance the weight of the temperature gradient dominant type feature in similarity determination. K-means clustering was used based on the sample similarity matrix. A preset number of clusters, k, was set to represent the number of typical environmental categories covered by the laboratory calibration. The cluster center vectors were iteratively calculated to minimize the sum of squared errors within each cluster. After convergence, the center vectors of each cluster were extracted as standard environmental fingerprint feature vectors. A mapping table was established between the cluster indices and the original samples for use in subsequent performance optimization experiments in S3.2. Through cluster analysis, a set of standard environmental fingerprint feature vectors covering all calibrated environmental states was obtained, enabling the organization of discrete sample features into an initial input data source that can be used to construct a topology anchor library.

[0059] For example, under the controlled laboratory calibration conditions of the light curtain measurement system, 80 sets of 16-bit environmental fingerprint code samples were selected. The temperature gradient dominant type included four classification labels, the power ripple energy concentration segment index ranged from 0 to 31, and the vibration mode matching score ranged from 0 to 63. One-Hot encoding was performed on the temperature gradient classification labels to obtain a 4-dimensional vector. After normalization, the ripple index ranged from 0 to 1, and the vibration matching score had a mean of 0 and a standard deviation of 1 after normalization. A normalized matrix was constructed after combining the three types of features. The number of clusters, k, was set to 8 to cover the eight typical calibrated environmental states. The cluster centers were calculated iteratively using K-means, with the convergence condition being that the sum of squared errors within the clusters was less than 1 × 10⁻⁶. -5 The system outputs the temperature gradient label, power ripple index value, and vibration matching score of each cluster center vector, forming a standard environmental fingerprint feature vector set. This set can be directly mapped to the laboratory-verified environmental conditions in subsequent path template performance optimization experiments, significantly improving the accuracy of the anchor point library mapping relationship.

[0060] S3.2: Based on each type of feature combination in the standard environment fingerprint feature vector set, perform an experiment to optimize the performance of the analog signal link, so as to obtain the optimal parameter configuration scheme for the transconductance amplifier cascading order, filter quality factor configuration set and transimpedance feedback network switching combination corresponding to each standard environment state, and form a candidate optimal analog path template dataset.

[0061] Based on each feature combination in the standard environmental fingerprint feature vector set, programmable functional units of transconductance amplifiers, filters, and transimpedance feedback networks in the FPAA analog array are selected as performance optimization objects, and a test configuration for an analog signal link covering the entire path from input to output is constructed. For each standard environmental state, the temperature gradient dominance type, power supply ripple energy concentration section index, and vibration mode matching score output from step S3.1 are loaded into the test control module as conditional trigger parameters for link optimization. The signal source unit is invoked to generate an analog excitation signal with adjustable amplitude, frequency, and waveform, and the excitation signal is input to the physical port of the corresponding functional unit in the shadow area of ​​the FPAA configuration register to drive the entire link operation. The link performance measurement module collects gain, phase delay, noise floor, and transient response time indicators under different transconductance amplifier cascade sequences, different filter quality factors, and different transimpedance feedback network switching combinations, and stores the measurement results as a raw performance data matrix.

[0062] Based on the original performance data matrix, multi-index normalization is performed on each configuration combination, mapping performance indices of different dimensions to a unified comparison domain. A comprehensive performance score is then calculated on the normalized data. All configuration combinations are sorted in descending order of their scores, and the transconductance amplifier cascading order, filter quality factor configuration set, and transimpedance feedback network switching combination with the highest scores under each standard environmental condition are selected as the optimal parameter configuration scheme. After selecting the optimal combination, its hardware configuration parameters are fixed as candidate optimal simulation path template entries, and the corresponding standard environment feature vector indices are recorded to form a candidate optimal simulation path template dataset.

[0063] By using performance optimization experiments and scoring calculations, the standard environmental fingerprint feature vector from the previous step is mapped into optimal parameter configuration data with quantitative performance basis, thereby determining the best link operating conditions for different standard environmental conditions.

[0064] For example, during the laboratory calibration of an industrial measurement light curtain system, for a standard combination of environmental characteristics, the dominant temperature gradient type is axial, the power supply ripple energy concentration section index is 12, and the vibration mode matching score is 45. During loading tests, the transconductance amplifier cascade sequence is set to 3 stages, 2 stages, and 1 stage; the filter quality factors are set to 2.5, 3.0, and 3.5 respectively; and the transimpedance feedback network switching combinations include closed-loop mode 1, closed-loop mode 2, and open-loop mode. A sinusoidal test signal with an input amplitude of 1V and a frequency of 10kHz is used. The gains of each combination measured through the link are 15dB, 16dB, and 14dB, the phase delays are 12°, 10°, and 15°, and the noise floors are respectively... 90dBV 92dBV The transient response times were 25μs, 23μs, and 28μs, respectively, with a gain of 88dBV. After normalizing each index, the weighting coefficients were set to 0.4 for gain, 0.2 for phase delay, 0.2 for noise floor, and 0.2 for transient response time. Substituting these values ​​into the formula to calculate the comprehensive score, it was found that closed-loop mode 2 achieved the highest comprehensive score when the cascade sequence was 3 stages, 2 stages, 1 stage, and the filter quality factor was 3.0. This combination was solidified as the candidate optimal simulation path template for this type of standard environmental condition. In practical applications, loading this template can significantly improve the signal fidelity and environmental robustness of the link.

[0065] S3.3: Using the parameter configuration scheme in the candidate optimal analog path template dataset, construct a path node connection matrix that describes the connection relationship of the signal flow through each programmable functional unit, and transform the discrete hardware configuration parameters into a digital path node connection matrix with topological structure information, as the physical connection logic expression form of the analog signal path.

[0066] Based on the parameter configuration scheme of the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination in the candidate optimal simulation path template dataset, all discrete hardware configuration parameters are loaded as the input source for constructing the matrix.

[0067] Read the cascade sequence parameters of the transconductance amplifiers, establish the row and column correspondence of the programmable amplifier units according to the physical connection sequence from the signal source to the output, and form the directed connection elements in the matrix by connecting the input node of each amplifier with the output node of the previous unit.

[0068] The filter quality factor configuration set is analyzed, the Q value of each filter unit and the corresponding input / output port number are extracted, the connection relationship between the filter unit and its adjacent nodes is added in the matrix according to the port mapping, and the parameter value of the unit is recorded in the matrix attribute field.

[0069] The state parameters of the switching combination of the transimpedance feedback network are called to identify whether the network is on or off. The on state is mapped to the valid label of the connecting edge in the matrix, and the off state is mapped to the zero-weight connecting edge. The inlet and outlet ports of the feedback network are accurately located in the matrix.

[0070] Perform node index unification processing, merge the node numbers generated by different functional units into a unified number space, ensure that the indexes of each row and column of the matrix are globally consistent and conflict-free, and remove isolated nodes through which no signal flows.

[0071] Through the above processing method, discrete hardware configuration parameters are transformed into a digital path node connection matrix with topological structure information, realizing a structured mapping effect from single functional parameters to physical connection logic expression.

[0072] For example, in a template scheme containing 5 stages of transconductance amplifiers, 3 second-order filters, and 1 transimpedance feedback network, the transconductance amplifier cascade order is defined as (1→2→3→4→5), the filter quality factor configuration sets are Q=3.5, Q=5.0, and Q=2.7, respectively, and the port numbers correspond to the outputs of the 2nd, 4th, and 5th stage amplifiers, respectively. The transimpedance feedback network switches to the on state and connects the output of the 3rd stage amplifier to the input of the 1st stage. During matrix construction, the amplifier sequence is mapped to consecutive connections at row and column positions 1 to 5. The corresponding filter connection and Q-value parameter are added at matrix position (2,6), other filter connections are added at matrix positions (4,7) and (5,8), and the connection weight of the feedback network is set to 1 at matrix position (3,1). After node index unification, the generated matrix has a dimension of 8×8 and the number of non-zero elements is 9. All parameter values ​​are recorded in the matrix's additional attribute table. Signal flow simulation analysis was performed on the matrix to verify that all nodes are well connected and the feedback path is open. The phase response of the output signal showed a significant improvement in steady-state consistency compared to the original template.

[0073] S3.4: Based on the one-to-one correspondence between the standard environmental fingerprint feature vector set and the path node connection matrix, establish a key-value mapping index between typical environmental fingerprint codes and optimal simulated path templates, generate topological anchor entries storing complete mapping relationships, and ensure that each environmental status identifier can uniquely point to a set of determined path node connection matrices.

[0074] Based on the unique correspondence between the standard environmental fingerprint feature vector set and the path node connection matrix, the temperature gradient dominant type, the power supply ripple energy concentration section index, and the vibration mode matching score are used as matching key inputs. The corresponding parameters such as the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination are extracted by calling the association matrix index table.

[0075] The extracted matching key values ​​are normalized. A fixed-length encoding rule is used to concatenate the high-order temperature gradient type, the mid-order ripple segment index, and the low-order vibration matching score into a sixteen-bit typical environmental fingerprint code to achieve consistency in environmental status identification.

[0076] The sixteen-bit typical environment fingerprint code is connected to the path node connection matrix to form a key-value mapping pair. An index pointer is generated using a hash mapping function, and the index pointer is bound to the matrix storage address to form a mapping relationship with fast positioning capability.

[0077] For each key-value mapping pair, a consistency check is performed. The difference between the fingerprint code in the mapping and the standard feature vector is compared according to the preset Hamming distance threshold. Mismatched records are eliminated, and valid entries that uniquely point to the corresponding matrix are retained.

[0078] The verified key-value mapping pairs are registered as topological anchor entries and stored sequentially in a non-volatile storage structure to form a set of topological anchors containing complete mapping relationships.

[0079] Through the chained processing method described above, the standard feature vector and path node connection matrix obtained in the previous step are transformed into topological anchor entries with fast retrieval capabilities and unique pointers, thereby achieving a deterministic mapping effect from environmental state to simulated path template.

[0080] For example, in an industrial light curtain system experimental platform, the temperature gradient dominant type field value is set to 3 (radial type), the power supply ripple energy concentration section index is set to 22 (corresponding to the 1.5MHz frequency band), and the vibration mode matching score is set to 45. After field normalization, a sixteen-bit typical environmental fingerprint code in binary form is formed as 0011010110 101101. This fingerprint code is mapped to the path node connection matrix obtained by experimental optimization, where the matrix contains the transconductance amplifier cascade order as [GA1→GA3→GA2], the filter quality factor configuration set as [Q=2.5,Q=3.0], and the transimpedance feedback network switching combination state as [FB1→FB3]. A hash function is used to calculate the index pointer, where M is the number of hash buckets, which is set to 256 in this scenario, resulting in an index pointer value of 178. This index pointer is bound to the storage address 0x00B2, and a Hamming distance check is performed to confirm that the fingerprint code and the standard feature vector set are completely consistent, and it is registered as the topology anchor entry with the number #034. Operational verification showed that even with slight temperature drift in the environment (radial gradient change not exceeding 0.5℃), the anchor entry could still maintain the stable gain and phase response of the analog signal path, and the output path configuration maintained a delay of less than 35μs during repeated calls, significantly improving the system's rapid adaptability and robustness.

[0081] S3.5: Serialize and encapsulate the generated multiple topology anchor entries and encode their addresses to build a topology anchor library with fast hash retrieval capabilities. Integrate discrete environmental perception data and continuous analog circuit reconfiguration instructions into a unified non-volatile storage structure to complete the final deployment of the topology anchor library for runtime invocation.

[0082] like Figure 3 As shown, step S4 involves inputting the 16-bit environmental fingerprint code into the topology anchor library to perform fast hash matching, locating the target topology anchor, and extracting the reference path node connection matrix associated with the target topology anchor as the initial analog signal path template to be reconstructed. Specifically, this includes: S4.1: Perform segmented hash key generation processing on the input 16-bit environmental fingerprint code, and use bitmask to extract the high four-bit temperature gradient dominant type field, the middle six-bit power supply ripple energy concentration section index field, and the low six-bit vibration mode matching degree score field. Compress the three feature data into a single hash addressing pointer through the XOR folding algorithm to obtain the initial lookup address pointing to the topology anchor library storage area.

[0083] The 16-bit environmental fingerprint code generated and transmitted in step S3 is used as the execution object. Segmented hash key generation is performed on the three fields of this fingerprint code. In the logic of main step S4, this provides the initial address pointing to the topological anchor library and serves as the basis for subsequent tag comparison. Bitmasking operations are used to extract the high four-bit temperature gradient dominant type field, the middle six-bit power ripple energy concentration zone index field, and the low six-bit vibration mode matching score field from the environmental fingerprint code, ensuring that the original bit order structure is not destroyed during field extraction. Integer conversion is performed on the three extracted fields, mapping each hexadecimal field to a corresponding unsigned integer for numerical algebra operations in subsequent hash folding. An XOR folding algorithm is used to operate on the three values ​​in a fixed weight order. The first and second segments are XORed bitwise to obtain the intermediate value, and then this intermediate value is XORed bitwise with the third segment to form a compressed single hash address pointer. The generated hash addressing pointer is initially compared with the address mapping table of the topology anchor library. The mapping table then converts the pointer into a physical storage row address in a non-volatile storage structure, i.e., the initial lookup address. Through the above processing method, the environmental fingerprint code result from the previous step is converted into address data that can directly drive the retrieval process of the topology anchor library, realizing a fast and low-latency mapping from environmental state to anchor location.

[0084] For example, in an industrial testing scenario, the input 16-bit environmental fingerprint code is 0xA352. The high four bits (F) correspond to the temperature gradient dominant type (integer value 10), the middle six bits (E) correspond to the power supply ripple energy concentration segment index (integer value 13), and the low six bits (V) correspond to the vibration mode matching score (integer value 18). The three fields are extracted using a bitmask and then XORed and folded after conversion. The first XOR operation outputs a middle value of 7, and the second XOR operation yields a final hash addressing pointer of 25. Based on a predefined address mapping table, this hash addressing pointer directly corresponds to the physical address of the 25th row of the non-volatile memory block in the topology anchor library. This memory block encapsulates the baseline path node connection matrix data, calibrated in the laboratory, which subsequent steps can use to perform parallel tag comparison and path template extraction. In this embodiment, the calculation process is completed in a single cycle within the FPGA lookup table resources, and the generated addressing pointer immediately enters the comparison process described in S4.2, ensuring a significant reduction in overall matching latency and improving the real-time performance of the FPAA reconstruction process.

[0085] S4.2: Based on the initial lookup address, perform a parallel tag comparison operation in the topology anchor library, call the set of typical environmental fingerprint code standard values ​​pre-stored in the on-chip static random access memory, and perform bit-by-bit logical verification between the candidate anchor tag associated with the current hash addressing pointer and the original sixteen-bit environmental fingerprint code to filter out the target topology anchor identifier with the smallest Hamming distance.

[0086] The input condition is the initial lookup address generated by S4.1, which corresponds to a set of candidate anchor entry labels in the topology anchor library. At the same time, the on-chip static random access memory stores the standard value set of all typical environment fingerprint codes, all of which are 16-bit fixed-width encoded data.

[0087] The initial lookup address is input in parallel to the topology anchor library retrieval control unit, triggering a batch reading operation of candidate anchor tags, and caching multiple tags to the comparison register array within the same clock cycle.

[0088] The storage control logic is invoked to synchronously read standard fingerprint code data frames that match the number of candidate anchors from the standard value set of on-chip static random access memory, thereby forming a set of comparison data sources.

[0089] Logical verification is performed bit by bit on the candidate anchor point label and the corresponding standard fingerprint code. An equal or unequal flag is output at each position, and the number of unequal flags is accumulated to calculate the Hamming distance value.

[0090] A hardware pipeline approach is used to perform a minimum Hamming distance search on all candidate labels, and the candidate anchor index number corresponding to the minimum Hamming distance value is recorded as the target anchor identifier.

[0091] By using parallel label matching and minimum Hamming distance filtering, the set of candidate entries located by the initial lookup address in the previous step is transformed into a unique target topological anchor identifier, thus achieving a deterministic mapping from the candidate set to the target identifier.

[0092] For example, in the FPAA reconstruction module of an industrial light curtain measurement system, the initial lookup address obtained via S4.1 points to a storage block containing 8 candidate anchor point entries, each anchor point tag being a 16-bit code. The on-chip static random access memory (SRAM) pre-stores a standard fingerprint code set containing 128 records, which can be directly mapped to candidate tags using address mapping. Bit-by-bit logical verification is implemented using an XOR unit, and the unequal output results are accumulated to a Hamming distance counter. Assuming a candidate tag differs from its corresponding code in the standard value set at bit position 5, the Hamming distance value is 5. The minimum value is detected for the Hamming distance calculation results of all 8 candidate tags; the hardware comparator outputs a minimum value of 2, corresponding to a candidate index number of 3, which is the target topology anchor point identifier. This embodiment has been verified through actual operation in FPAA hardware, with a matching latency stable at the 20 nanosecond level. It can quickly filter out the anchor point closest to the current environment's fingerprint code in a dynamic environment, effectively improving the accuracy and stability of subsequent path reconstruction.

[0093] S4.3: Perform storage base address offset calculation based on the target topology anchor point identifier, use the anchor point index table to map discrete identifiers to continuous physical storage row addresses, locate the specific storage block storing the target topology anchor point associated data, and obtain the original path node connection matrix data stream containing the transconductance amplifier cascade order, filter quality factor configuration set and transimpedance feedback network switching combination.

[0094] The input execution object is the target topology anchor point identifier selected by sub-step S4.2 and the anchor point index table stored in non-volatile memory. Based on the identifier, the base address offset logic unit is called to parse the mapping relationship between its low-order address segment and the memory block partition, and calculate the absolute addressing value of the physical memory row. The obtained physical addressing value is input into the address decoding unit to complete the conversion from discrete identifier to continuous physical memory row address. The decoding result drives the row selection signal, and combined with the column address of the memory controller, the specific memory block containing the data associated with the target anchor point is located. The original path node connection matrix data stream in the effective payload area of ​​the memory block is read. This data stream includes the parameter set of transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination. Through the chained processing of memory base address offset, address mapping, and data extraction, the hash-matched anchor point identifier is converted into the original data stream of the reference path node connection matrix, realizing the physical location and data acquisition of the initial analog signal path template to be reconstructed.

[0095] For example, an industrial light curtain system operates in a complex working environment with an environmental fingerprint code of 1010 011011100101. The target anchor point identifier is identified as 0x1F3 through sub-step S4.2. Based on the anchor point index table, the base address offset logic unit is called to map the high byte of the identifier to a storage partition number and the middle and low bytes to an offset, resulting in physical storage row address 127. The address decoding unit generates a row selection signal RowSel=127, which, combined with the column address ColSel=8, locates storage block B127-8. The effective payload area of ​​this storage block contains the original path node connection matrix data stream of the transconductance amplifier cascade sequence [GA1→GA3→GA2], filter quality factor configuration set [Q1=2.5, Q2=3.0, Q3=1.8], and transimpedance feedback network switching combination [RF1→RF4], totaling 192 bytes. The data stream is extracted as the original baseline path node connection matrix, providing input for S4.4 format parsing and integrity verification, and ensuring that the accurate initial configuration of transconductance amplifiers, filters and feedback networks can be obtained during the dynamic topology mapping reconstruction process, so as to realize the high-precision adaptive adjustment of the system under environmental changes.

[0096] S4.4: Perform format parsing and integrity verification on the acquired raw path node connection matrix data stream, identify the payload start bit according to the predefined matrix frame header protocol, verify the consistency of matrix row and column dimensions and remove redundant padding bits, and reorganize the unstructured data stream into a structured baseline path node connection matrix to ensure that the subsequent weight modulation module can accurately read the gain allocation parameters of each branch.

[0097] When processing the original path node connection matrix data stream, the input is limited to a continuous bit stream output by the topology anchor library matching module. This ensures that the bit stream contains complete programmable node connection information, including the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination. By calling the on-chip protocol parsing unit, matrix frame header protocol identification is performed on the bit stream. A predefined frame header identifier pattern is used to scan the data stream to locate the payload start position. Based on the start bit index, numerical readings are performed on the row and column positioning fields in the matrix frame structure. The expected dimensions of the matrix are calculated using the row and column fields, and consistency verification logic is used to compare the calculation results with the number of rows and columns defined in the protocol.

[0098] After the consistency check is completed, redundant padding bits are stripped from the matrix data segment. A bitmask is used to remove padding bits that are not involved in the configuration parameter calculation. The stripped payload region is then reorganized using row-major or column-major sorting to form a parameter set arranged according to the node logical topology. For multi-parameter combinations such as transconductance amplifier cascades and filter quality factors, the boundary positions of each parameter are identified using a delimiter field, and the delimiters are removed, retaining only the pure parameter value sequence.

[0099] During the reorganization process, the matrix formatting unit is invoked to map the separated parameters to matrix cells according to topological logic, establishing the node connection relationships for each row and column. This transforms the unstructured data stream into a structured baseline path node connection matrix, enabling the subsequent weight modulation module to accurately read and apply the corresponding gain allocation parameters to each connection edge. Through format parsing and integrity verification, the results of the previous step are transformed into structured matrix data with fixed row and column dimensions, no redundant padding, and clear parameter domain boundaries, providing the precise input required for subsequent dynamic weight scaling factor calculations.

[0100] For example, in the FPAA reconstruction environment of an industrial inspection light curtain system, the original path node connection matrix data stream output by the target topology anchor point matching is 704 bits long, with the frame header protocol defining the payload start bit as the 16th bit. The protocol row and column fields indicate that the matrix dimension is 8 rows × 11 columns, and the verification logic comparison shows consistency with the protocol definition. The redundancy stripping step removes the 4-bit padding bits at the end of each row, resulting in a payload length of 8 × 11 × bit depth after stripping. During parameter reassembly, the transconductance amplifier cascade order field uses two bits for encoding, the filter quality factor configuration set uses 5 bits for encoding, and the transimpedance feedback network switching combination uses 3 bits for encoding. The separator bit 0xF is stripped to retain pure parameter values. The formatting unit maps parameters to matrix cells in row-major order, realizing a topology mapping relationship where row indices correspond to signal source nodes and column indices correspond to target nodes. In this analysis and verification process, the final output structured baseline path node connection matrix is ​​directly used to calculate the scaling factor of each connection edge in the subsequent weight modulation stage. This significantly improves the system's gain allocation and phase response characteristics, and keeps the dynamic reconstruction delay within 35 microseconds.

[0101] S4.5: Based on the structured reference path node connection matrix, perform the initial analog signal path template instantiation operation, load the logical connection relationship in the matrix into the configuration register shadow area of ​​the field-programmable analog array, construct the initial analog signal path template to be reconstructed, thereby completing the deterministic mapping from the environmental fingerprint code to the specific circuit topology, and providing a reference object for the calculation of the dynamic weight scaling factor.

[0102] Step S5: Based on the deviation between each field of the 16-bit environmental fingerprint code and the standard value of the target topology anchor point, calculate the dynamic weight scaling factor acting on the initial analog signal path template. After digital-to-analog conversion and instruction encapsulation, generate a path elastic weight migration instruction set for modulating the conduction threshold voltage of the internal switch matrix. Specifically, it includes: S5.1: Obtain the high four-bit temperature gradient dominant type field, the middle six-bit power ripple energy concentration segment index field, and the low six-bit vibration mode matching score field from the sixteen-bit environmental fingerprint code. Perform bitwise XOR operation and numerical subtraction on the above three fields with the corresponding temperature gradient reference type, power ripple reference segment index, and vibration mode reference score in the target topological anchor point standard value to generate a multi-dimensional environmental deviation feature set containing temperature deviation vector, ripple frequency offset index, and vibration mismatch coefficient.

[0103] The input condition is a 16-bit environmental fingerprint code output from step S4, which includes a high four-bit temperature gradient dominant type, a middle six-bit power supply ripple energy concentration segment index, and a low six-bit vibration mode matching score field, as well as a corresponding standard value field for the target topology anchor point. For this input data, firstly, a bitwise XOR operation is performed between the high four-bit temperature gradient dominant type field and the temperature gradient reference type field in the target topology anchor point standard value to form a temperature difference bitmap. When each bit in the bitmap is 1, the corresponding bit value difference is recorded as the temperature deviation. Next, a bitwise XOR operation is performed between the middle six-bit power supply ripple energy concentration segment index field and the power supply ripple reference segment index field in the target topology anchor point standard value. Simultaneously, numerical subtraction is applied to calculate the index difference to obtain the ripple frequency deviation, thereby capturing the frequency band deviation characteristics between the current power supply ripple features and the reference state. Further, the lower six-bit vibration mode matching score field is XORed bit-by-bit with the vibration mode reference score field in the target topology anchor standard value to identify the binary difference bits of the vibration mode. Then, the mismatch value is calculated by numerical subtraction to obtain the vibration mismatch coefficient. The above three types of deviation parameters are calculated using the temperature difference bitmap calculation formula, the ripple frequency offset index difference calculation formula, and the vibration mismatch coefficient calculation formula, respectively. The temperature difference bitmap is converted into a temperature deviation vector, the ripple frequency offset value is used as the ripple frequency offset index, and the vibration mismatch value is used as the vibration mismatch coefficient. These are combined in sequence to form a multi-dimensional environmental deviation feature set containing the temperature deviation vector, the ripple frequency offset index, and the vibration mismatch coefficient. Through the above processing method, the matching result data of the previous step is transformed into a quantifiable multi-dimensional environmental deviation index, realizing the accurate characterization of the difference between the environmental state and the reference configuration.

[0104] For example, in an industrial safety light curtain measurement system, when the 16-bit environmental fingerprint code is 1010 for the high four bits, 011011 for the middle six bits, and 110010 for the low six bits, and the target topological anchor point standard value is 1000 for the high four bits, 011001 for the middle six bits, and 101110 for the low six bits, performing a bitwise XOR operation on the high four bits yields a temperature difference bitmap 0010, corresponding to a temperature deviation vector with a second bit difference of 2. Performing a bitwise XOR operation on the middle six bits yields a bit difference of 000010, and simultaneously calculating the index difference of 2 using numerical subtraction, which serves as the ripple frequency offset index. Performing a bitwise XOR operation on the low six bits yields a bit difference of 011100, and simultaneously calculating the numerical difference... 6, as the vibration mismatch coefficient. The temperature deviation vector (2), ripple frequency offset index (2), and vibration mismatch coefficient ( 6) Combined into a multidimensional environmental deviation feature set [2,2, [6] This feature set will be used in subsequent S5.2 for thermal drift sensitivity weighting, noise suppression priority mapping and mechanical resonance damping compensation conversion. The execution results show that this difference feature can significantly improve the effectiveness of gain adjustment and enhance the anti-interference capability of the light curtain system under high temperature environment.

[0105] S5.2: Based on the multidimensional environmental deviation feature set, the temperature deviation vector is subjected to thermal drift sensitivity weighting calculation using the nonlinear mapping curve preset in the lookup table, the ripple frequency deviation index is subjected to noise suppression priority mapping, and the vibration mismatch coefficient is subjected to mechanical resonance damping compensation conversion to generate three independent adjustment components that respectively characterize the thermal stability requirements, anti-interference strength requirements, and structural stability requirements.

[0106] Based on the generated multidimensional environmental deviation feature set, the temperature deviation vector is input into the nonlinear mapping curve parameter set stored in the on-chip lookup table. By searching for the curve segment corresponding to the temperature difference interval and performing piecewise interpolation calculation, the thermal drift sensitivity coefficient is obtained. This coefficient is then multiplied with the original temperature deviation vector to form the thermal stability requirement adjustment amount. The ripple frequency offset index is input as the access key value into the noise suppression priority mapping table. The corresponding frequency band priority weight is read from the mapping table, normalized, and then weighted and summed with the numerical difference of the frequency offset index to obtain the anti-interference strength requirement adjustment amount. The vibration mismatch coefficient is input into the mechanical resonance damping compensation conversion curve table. The matching damping compensation factor in the curve table is called, and combined with the vibration mismatch coefficient through multiplication to obtain the structural stability requirement adjustment amount. Temperature deviation vector adjustment is achieved by using a thermal drift sensitivity weighted calculation formula. By outputting the above three adjustment quantities as independent components of thermal stability requirements, anti-interference strength requirements, and structural stability requirements, the environmental deviation feature set generated in the previous step is transformed into functional adjustment parameters that can be directly applied to the weight factor calculation, thereby mapping environmental state differences to hardware reconfiguration sensitivity.

[0107] For example, in an industrial light curtain environment, the temperature reference type is measured to be radial. The temperature deviation vector generated by the difference between the current temperature gradient vector and the reference type is 0.6 degrees Celsius. After matching the thermal drift sensitivity coefficient using a lookup table, it is found to be 1.25. According to the formula, the required adjustment amount for thermal stability is 0.75℃. The difference between the power supply ripple reference segment index and the current index is 3. After reading the weight factor from the priority mapping table, it is found to be 0.8. According to the formula, the required adjustment amount for anti-interference strength is 2.4. The difference between the vibration mode reference score and the current score is 0.15. After reading the factor value from the damping compensation factor table, it is found to be 1.1. According to the formula, the required adjustment amount for structural stability is 0.165. After the above three adjustment components are fused into the path node connection matrix by S5.3, the verification results show that under the simultaneous presence of high temperature, strong power supply harmonic interference, and slight mechanical vibration, the analog signal path gain and phase response remain stable, the transient overshoot amplitude is significantly reduced, and the settling time is greatly shortened.

[0108] S5.3: Read the path node connection matrix in the initial analog signal path template, extract the transconductance amplifier cascade parameters, filter quality factor configuration parameters and transimpedance feedback network switching state parameters associated with each connection edge in the matrix, and perform weighted fusion processing on the three independent adjustment components according to the functional attributes of each connection edge to generate the original dynamic weight scaling factor set corresponding to each connection edge.

[0109] Based on the instantiation state of the structured baseline path node connection matrix, the matrix parsing interface is called to read the associated hardware configuration parameters of each connection edge, including transconductance amplifier cascade parameters, filter quality factor configuration parameters, and transimpedance feedback network switching state parameters, forming a sequence of configuration parameter triplets arranged by connection edge index.

[0110] The three generated independent control components are classified and assigned according to the functional attributes of the connecting edges in the path node connection matrix, and divided into transconductance amplifier link class, filter link class, and transimpedance feedback link class. A mapping relationship table between control components and functional attributes is established.

[0111] For each connection edge, a mapping relationship matching is performed, and the adjustment components corresponding to its functional attributes are numerically fused with the original hardware configuration parameters. For transconductance amplifier links, gain multiplication fusion is used; for filter links, quality factor weighted offset fusion is used; and for transimpedance feedback links, impedance adjustment coefficient multiplication and addition fusion is used.

[0112] To address the aforementioned fusion process, a formula for calculating the original dynamic weight scaling factor is established. The scaling factor sequences obtained from the above calculations for each connecting edge are then aggregated into the original dynamic weight scaling factor set, maintaining consistency with the row and column indices of the path node connection matrix to ensure the accuracy of the subsequent amplitude limiting filtering process.

[0113] Through the weighted fusion processing method described above, the three independent adjustment components of the previous step are transformed into a set of original dynamic weight scaling factors that correspond one-to-one with each connection edge, thus achieving the expected technical effect of mapping environmental deviation characteristics to specific hardware adjustment amounts.

[0114] For example, in an industrial safety light curtain measurement system, the original gain parameter G of the transconductance amplifier on a certain connection edge in the initial analog signal path template is set to 2.5. The thermal stability requirement adjustment component H, after being weighted by thermal drift sensitivity, is taken as 1.1. Substituting these values ​​into the formula, the scaling factor for the transconductance amplifier link is calculated to be 2.75. This value is used to adjust the conduction threshold voltage of the corresponding switch matrix node. The original quality factor Q of another filter link connection edge is 0.707, and the anti-interference strength requirement adjustment component N is 0.05. The calculated scaling factor is 0.757, used to improve the noise suppression capability of this filter branch. The impedance parameter R of the transimpedance feedback link connection edge is 1.2kΩ, the structural stability requirement adjustment component M is 0.9, and the impedance compensation constant P is 0.05kΩ. The calculated scaling factor is 1.13kΩ, used to optimize the stability of the feedback network. Field verification showed that when the scaling factor set was applied to the switch matrix control, the system signal path parameters remained stable and the output waveform phase distortion was significantly reduced under the conditions of simultaneous temperature fluctuations and mechanical vibrations, meeting the requirements for industrial-grade robustness.

[0115] S5.4: Perform amplitude limiting filtering and smooth transition processing on the original dynamic weight scaling factor set, truncate abnormal factors that exceed the 0.7 to 1.3 times safety range to the boundary value, and optimize the slope limit of factor jumps on adjacent connection edges to generate a standardized dynamic weight scaling factor sequence that satisfies the linear modulation range of the internal switch matrix of the field programmable analog array.

[0116] For each factor in the original dynamic weight scaling factor set, an amplitude detection operation is performed. Based on the preset safety interval limit value, factors with values ​​less than 0.7 times or greater than 1.3 times are identified as abnormal factors and amplitude limiting is triggered. Factors with values ​​less than 0.7 times are directly replaced with 0.7 times, and factors with values ​​greater than 1.3 times are directly replaced with 1.3 times. After the factor set is subjected to amplitude limiting, adjacent edge index pairs are established according to the physical arrangement order of the connecting edges. The absolute difference of the factor values ​​in the index pairs is calculated, and factor pairs with an absolute difference greater than the preset jump threshold are identified. For the identified high-jump factor pairs, slope limit optimization is performed, and the absolute value of the difference is reduced proportionally according to the preset slope upper limit, so that the difference between adjacent factors after adjustment does not exceed the slope limit threshold. Smoothing transition filtering is performed on the factor set after slope optimization. The weighted average filtering algorithm is used to generate updated values ​​by fusing the factor values ​​of the current edge and the two adjacent edges according to the position weight, thereby eliminating local spikes while maintaining the factor range limit. The set of factors that have undergone amplitude limiting, slope limiting and smooth transition processing is matched and calibrated according to the linear modulation range of the conduction threshold voltage of the internal switch matrix of the field programmable analog array, and the output is a standardized dynamic weight scaling factor sequence that satisfies the modulation characteristic curve of the switch matrix. By using amplitude limiting filtering and smooth transition processing, the original set of dynamic weight scaling factors from the previous step is transformed into a standardized dynamic weight scaling factor sequence that satisfies the linear modulation range of the internal switching matrix of the field-programmable analog array, thereby achieving robust modulation input that avoids abnormal amplitudes and transient jumps.

[0117] For example, in a light curtain measurement system for industrial inspection, the original dynamic weight scaling factor set contains values ​​corresponding to 12 connecting edges. During the inspection process, the factor value of edge #3 is 0.65 times and the factor value of edge #7 is 1.35 times, which are below the safety range of 0.7 times and above 1.3 times, respectively. The system directly replaces the factor of edge #3 with 0.7 times and the factor of edge #7 with 1.3 times. After establishing adjacent edge index pairs, it is found that the absolute value of the difference between the factors of edge #5 and edge #6 is 0.25, which exceeds the preset jump threshold of 0.2. The system reduces this difference proportionally, so that the adjusted difference is 0.2, ensuring that it does not exceed the slope limit threshold. For this adjusted factor set, a three-point weighted average filter is used, with weight coefficients of 0.25 (previous edge), 0.5 (current edge), and 0.25 (next edge), respectively, to update the factor values ​​of each edge to smooth out local spikes. The smoothed factor set is then calibrated against the modulation curve of the switching matrix turn-on threshold voltage. For example, when the linear range of the modulation curve is ±0.3V, the system maps the factor values ​​to the corresponding voltage increment range and verifies linear consistency. Finally, the standardized dynamic weight scaling factor sequence did not exhibit any over-limit amplitudes or abrupt changes during end-to-end verification. After being applied to the internal switching matrix turn-on threshold adjustment of the FPAA, the system's transient stability under temperature drift and mechanical vibration disturbance conditions is significantly improved.

[0118] S5.5: Based on the standardized dynamic weight scaling factor sequence, it is mapped to a specific voltage control level signal through digital-to-analog conversion driving logic, and the voltage control level signal is encapsulated into a path elastic weight migration instruction set containing address encoding and data payload, so as to serve as the final control input for directly modulating the conduction threshold voltage of the internal switch matrix of the field-programmable analog array.

[0119] Based on the standardized dynamic weight scaling factor sequence, the on-chip digital-to-analog converter driving logic resource of the field-programmable analog array is invoked to load each dynamic weight scaling factor in the sequence into a preset voltage mapping lookup table. The key value of the lookup table is the scaling factor code, and the output value is the corresponding target voltage amplitude.

[0120] The target voltage amplitude output from the lookup table is input to the segmented approximation control module of the digital-to-analog converter unit. The discrete code value is converted into a continuous voltage signal according to the defined voltage resolution and step size, and a voltage control waveform with a constant sampling period is generated.

[0121] The generated voltage control waveform is associated with the physical address information of each connection edge in the path node connection matrix, and each voltage control waveform is marked with a unique connection edge identification code through address encoding logic.

[0122] The voltage control waveform data bound to the identification code is transformed into a path elastic weight migration instruction set with address field, data payload field and check field by the instruction encapsulation module. The data payload part is the sampled value sequence of the voltage control waveform.

[0123] The encapsulated path elastic weight migration instruction set is written into the configuration register buffer inside the field-programmable analog array to form the final control input for the direct modulation switch matrix conduction threshold voltage.

[0124] Through the above processing method, the standardized dynamic weight scaling factor sequence of the previous step is transformed into an executable voltage control instruction set, realizing precise modulation of the threshold voltage of the switching matrix based on environmental deviation.

[0125] For example, under the operating conditions of the light curtain measurement system where the dominant temperature gradient type is radial, the power supply ripple energy concentration section index value is 22, and the vibration mode matching score is 45, the standardized dynamic weight scaling factor sequence calculated in the previous step is [1.10, 0.95, 1.25, 0.88]. The voltage mapping lookup table is called to output the corresponding target voltage amplitude [2.2V, 1.9V, 2.5V, 1.8V]. The above amplitude is sent to a 12-bit digital-to-analog converter unit, and a continuous voltage control waveform with a sampling period of 5μs is output using a piecewise approximation method. In the address encoding logic, address codes [0x1A, 0x1B, 0x1C, 0x1D] are bound to the four connection edges respectively, and the waveform data and address codes are combined to form a path elastic weight migration instruction set. The verification field is generated using the CRC-16 algorithm. After writing the instruction set into the internal buffer of the FPAA, the control input is executed to adjust the switching matrix conduction threshold voltage to [2.2V, 1.9V, 2.5V, 1.8V] on each connection side. After on-site diagnostic verification, the conduction characteristics tend to be optimized, the path gain distribution and phase response are significantly improved under this environmental condition, and the system transient stability reaches the preset industrial-grade standard.

[0126] Furthermore, following S5, step S6 is also included: Based on the path elastic weight migration instruction set, the on-threshold voltage of the switch matrix inside the field-programmable analog array is adjusted in real time to adjust the gain allocation and phase response characteristics of each branch, thereby completing the dynamic topology mapping reconstruction of the analog signal path. Specifically, this includes: S6.1: Obtain the dynamic weight scaling factor data from the path elastic weight migration instruction set, and based on the physical connection relationship of the internal switch matrix of the field programmable analog array, map the dynamic weight scaling factor to the corresponding gate bias voltage increment value to generate a gate bias voltage increment sequence for modulating the operating point of the switching device.

[0127] Using the standardized dynamic weight scaling factor sequence contained in the path elastic weight migration instruction set as input, the system reads the weight factor values ​​and corresponding address encoding information of this sequence, and parses out the list of connection edge indices that match the internal switch matrix of the field-programmable analog array. Based on the mapping table of the physical connection relationships of the switch matrix, a one-to-one mapping relationship is established between each connection edge index and its corresponding switch device gate control node, and the current bias voltage reference value of the control node is extracted. For the weight scaling factor of each connection edge, the voltage increment calculation logic is executed, and the gain adjustment is converted into a gate voltage increment value according to a preset proportional conversion coefficient. The formula is as follows: in V gi k represents the incremental gate bias voltage value corresponding to the i-th connection edge. gi w is the modulation coefficient of the gate on the connection side. i The calculated voltage increment values ​​are arranged according to the physical node order to form a complete gate bias voltage increment sequence. An amplitude boundary check is performed on this sequence to ensure that all increment values ​​are within the safe operating domain of the device. By combining the increment sequence with the control node reference voltage value, a voltage modulation input dataset that can directly drive the digital-to-analog converter module is constructed. Through the above mapping and calculation processing, the dynamic weight scaling factor from the previous step is transformed into a bias voltage increment sequence for the operating point of the switching device, achieving a precise conversion from an abstract gain adjustment to an executable voltage control signal.

[0128] For example, in an FPAA reconfiguration system at an industrial testing site, the path elastic weight migration instruction set includes weight scaling factors for 8 connected edges, ranging from 0.85 to 1.25, with address codes pointing to 8 independent control nodes of the switch matrix. The physical connection mapping table indicates that the gate modulation coefficient of the 3rd connected edge is 4.5mV / 0.01 factor change, and the gate modulation coefficient of the 5th connected edge is 5.0mV / 0.01 factor change. According to the formula, a weight factor of 0.92 corresponds to a voltage increment of -3.6mV for the 3rd connected edge, marked as negative adjustment to reduce conduction capability; a weight factor of 1.18 corresponds to a voltage increment of +9.0mV for the 5th connected edge, marked as positive adjustment to enhance conduction capability. After safety domain verification, both increment values ​​are within the ±15mV limit, ultimately forming a […] arranged in node order. The gate bias voltage increment sequence [3.6mV, +9.0mV, …] is input to the DAC module of the array to achieve environmentally adaptive modulation effect, which significantly improves the stability and accuracy of the system under temperature drift and noise interference.

[0129] S6.2: Receive the gate bias voltage increment sequence, use the digital-to-analog converter unit built into the field-programmable analog array to perform voltage synthesis operation, convert the discrete gate bias voltage increment sequence into a continuously changing analog control level signal, and output an analog control level signal with high precision resolution to drive the subsequent threshold adjustment circuit.

[0130] S6.3: Based on the analog control level signal, the bias voltage is applied to the gate of each cross-point switch in the field programmable analog array through the switch matrix threshold adjustment circuit, thereby changing the channel conductivity of the switch and calculating and generating a switch matrix conduction threshold voltage distribution map that reflects the ease of conduction of each branch under the current environmental conditions.

[0131] Based on the continuously varying analog control level signal output from S6.2, this signal is input to the threshold adjustment circuit of the switch matrix within the field-programmable analog array to form a precise voltage application sequence for driving gate bias. Voltage distribution calculations are performed on the gates of switches at each intersection in the physical connection of the switch matrix. Based on the switch's position in the path node connection matrix and the dynamic weight scaling factor of its branch, the target bias voltage value applied to the switch's gate is calculated. The voltage distribution module in the threshold adjustment circuit decomposes the analog control level into multi-channel outputs, injecting micro-amplitude modulation components for different switches into each channel to form a bias signal precisely matched to the functional attributes. The bias signal is then filtered and buffered to shield high-frequency interference and improve output drive capability, ensuring the stability of the voltage applied to the switch gates. After applying the bias voltage, based on the conduction characteristics of MOSFETs and the relationship between on-resistance and threshold voltage, the change in conduction conditions for each branch switch is calculated. Arrange the actual turn-on threshold voltages of all cross-point switches in matrix form to generate a switch matrix turn-on threshold voltage distribution map that reflects the ease of turn-on of each branch under the current environmental conditions. This distribution map is used to quantify the environmental adaptability adjustment effect.

[0132] By applying the bias voltage and calculating the threshold voltage as described above, the analog control level signal of S6.2 is converted into conduction threshold voltage distribution data that can reflect the environmental state. This provides a basis for adjusting the conduction performance of each branch to adapt to the environment while keeping the original path topology unchanged.

[0133] For example, in an industrial light curtain measurement system, the initial reference threshold voltage is 1.2V, and the gate bias voltage increment range corresponding to the dynamic weight scaling factor is -0.15V to +0.20V. The adjustment circuit distributes analog control level signals to the gates of 64 switching transistors, with each increment value allocated according to the functional attributes of the path nodes. A +0.18V bias increment is applied to the switching transistors located in the high-gain branch, resulting in an actual threshold voltage of 1.38V, reducing their turn-on speed to suppress overshoot. A -0.12V increment is applied to the switching transistors located in the low-noise priority branch, resulting in an actual threshold voltage of 1.08V, increasing their turn-on speed to enhance anti-interference capability. The threshold voltage distribution of all switching transistors is collected and arranged, showing that the threshold voltages of the high-gain branch are concentrated between 1.35V and 1.40V, while those of the low-noise branch are concentrated between 1.05V and 1.10V, achieving flexible adaptive adjustment of conduction performance under environmental fluctuations.

[0134] S6.4: Based on the switching matrix conduction threshold voltage distribution diagram, the signal transmission impedance of each connection side in the preset initial analog signal path template in the field programmable analog array is nonlinearly modulated, and the equivalent parameters of the transconductance amplifier cascade link and filter feedback network are adjusted to generate a flexible adjustment branch gain allocation parameter set with environmental adaptability.

[0135] Based on the calculated and generated switch matrix conduction threshold voltage distribution map, the conduction threshold level corresponding to each connection edge in the initial analog signal path template is selected as the input condition, and nonlinear modulation processing is performed on the signal transmission impedance of the connection edge.

[0136] For a cascaded transconductance amplifier link, the conduction threshold corresponding to the connection side is read and combined with the transconductance parameters. The equivalent transconductance value is calculated using a voltage-controlled transconductance model to control the overall gain of the cascaded transconductance link. During the modulation process, the bias point of the transconductance amplifier is adjusted based on the threshold change under different environmental conditions, thereby changing the effective transmission impedance of the link.

[0137] For the filter feedback network, the conduction threshold and quality factor (Q value) configuration of the corresponding connection edge are extracted. The feedback coefficient of the filter is adjusted by using a nonlinear mapping relationship, so that the impedance characteristics of the filter under different temperature drift or noise interference conditions are optimized, thereby correcting the amplitude part of the frequency response curve and compensating for the phase shift.

[0138] The equivalent parameter conversion of the transconductance link and filter network is performed using the impedance modulation formula. The equivalent impedance value after modulation is obtained by nonlinear analysis of the voltage-current relationship.

[0139] The modulated equivalent impedance values ​​of all connected sides are integrated into a unified data structure to form a set of flexible adjustable branch gain allocation parameters, which are used for the next step of phase compensation and final frequency response calibration.

[0140] By using the nonlinear impedance modulation method based on the conduction threshold voltage distribution map, the conduction threshold voltage data of the switch matrix generated in the previous step is transformed into a set of flexible adjustable branch gain allocation parameters with environmental adaptability and real-time adjustable characteristics, thereby optimizing the gain characteristics of the analog signal path under different environmental conditions.

[0141] S6.5: Integrates the set of flexible adjustment branch gain allocation parameters and real-time phase compensation, and recalibrates the frequency response curve of the analog signal channel without switching the physical connection topology. It eliminates phase distortion caused by temperature drift or mechanical vibration, and finally outputs an optimized analog signal path configuration that completes dynamic topology mapping reconstruction and meets transient stability requirements.

[0142] Based on the input conditions of the flexibly adjustable branch gain allocation parameter set and the real-time phase compensation, the signal transmission impedance data of the field-programmable analog array that has undergone nonlinear modulation is locked as the execution object. The frequency response curve generation logic is invoked to map the gain allocation parameter set of each branch to the amplitude-frequency characteristic coefficient table, and the real-time phase compensation is injected into the phase response correction unit to form an amplitude-phase data combination with phase correction information. Frequency domain convolution mapping operation is performed on the amplitude-phase data combination to match the preset reference frequency response curve and calculate the deviation vector. This deviation vector is segmented and weighted according to the importance weight of each frequency band to generate a frequency response correction factor sequence. The frequency response correction factor sequence is applied to the amplitude-frequency characteristic coefficient table, and the amplitude coefficient is adjusted by multiplication and the phase offset is corrected by addition to form a calibrated amplitude-phase parameter set. The calibrated amplitude-phase parameter set is loaded into the analog signal channel configuration register to reconstruct the channel's frequency response curve, thereby eliminating phase distortion caused by temperature drift and mechanical vibration. By comparing the corrected frequency response curve with the transient stability determination reference curve, an optimized analog signal path configuration that meets the transient stability condition is output, thereby realizing dynamic topology mapping reconstruction without switching the physical connection topology.

[0143] For example, in an industrial inspection light curtain system, after the internal reference path of the FPAA is modulated by S6.4, the transconductance amplifier link gain allocation parameter set is {2.1, 1.8, 3.3}, and the Q value of the filter feedback network is {0.72, 0.85}. The real-time phase compensation is given by the external temperature drift detection results, which are -0.035 rad and 0.028 rad, respectively. The frequency response deviation vector is measured in three segments of 100 Hz, 1 kHz, and 10 kHz as {0.12, -0.08, 0.05}, and the weighting coefficients are set to {1.0, 1.5, 2.0}. After weighted calculation of the deviation correction factor, the correction factor is applied to the amplitude-frequency coefficient table, and the corresponding amplitude coefficients for the three segments are changed to {2.22, 1.68, 3.40}, and the phase offset is updated to {-0.035 rad + 0.12 rad, 0.028 rad - 0.12 rad, 0 + 0.10 rad}, resulting in a new amplitude-phase parameter set. After loading the amplitude and phase parameter set into the FPAA configuration register, the absolute value of the deviation between the frequency response curve and the reference curve in each frequency band is less than 0.01, verifying that the output analog signal path configuration meets the transient stability requirements and significantly improves phase consistency and gain stability under environmental changes.

[0144] It also includes step S7: using the analog-to-digital converter built into the field-programmable array to sample the transient response of critical path nodes, and obtaining path reconstruction quality verification data reflecting the signal overshoot amplitude and setup time characteristics. Specifically, this includes: S7.1: Based on the dynamic topology mapping reconstruction completion status signal of the analog signal path, start the low-overhead diagnostic analog-to-digital converter initialization sequence built into the field programmable analog array, configure the sampling clock frequency to the megahertz-level high-speed mode and set the trigger source to the reconstruction completion interrupt flag, so as to generate a diagnostic sampling enable control instruction with high time resolution characteristics.

[0145] S7.2: Based on the diagnostic sampling enable control instruction, the critical path node after adjustment by the dynamic weight migration instruction set is locked as the object under test. The analog voltage signal of the selected node is routed to the input port of the low-overhead diagnostic analog-to-digital converter through the on-chip analog multiplexer to form the transient response analog signal stream to be digitized.

[0146] Based on the diagnostic sampling enable control command, its path node locking parameters are parsed, and the physical coordinate information of the critical path node after adjustment by the dynamic weight migration instruction set is determined through the internal address mapping table. This information is then cross-validated with the switch matrix conduction threshold voltage distribution map to ensure that the corresponding node is in an effective working state.

[0147] The on-chip control logic is invoked to access the analog multiplexer configuration register. Using the physical coordinates of the critical path node as the input index, a control word for multiplexer channel switching is generated. The channel assignment process is then performed according to the logical number of the branch where the node is located, and a physical connection path is established from the node to the input port of the diagnostic analog-to-digital converter.

[0148] During the channel switching process of the multiplexer, the current channel status of the field-programmable analog array is read, channel conflict detection and isolation control are performed, and channels that may have signal conflicts are temporarily shielded to avoid non-test signal flow from interfering with the integrity of the analog voltage signal of the node under test.

[0149] After completing the connection from the critical path node to the input port of the diagnostic analog-to-digital converter, the analog signal buffer unit is activated. The high input impedance of the buffer unit is used to prevent the sampling load of the diagnostic analog-to-digital converter from causing attenuation of the node voltage. The signal amplitude is adjusted by the automatic gain control module to match the input dynamic range of the analog-to-digital converter.

[0150] The analog voltage signal after buffer conditioning is subjected to DC bias elimination processing, and a high-pass filter network is used to remove low-frequency drift components to ensure that the transient response analog signal stream has clear boundary jump characteristics when entering the low-overhead diagnostic analog-to-digital converter.

[0151] By establishing a signal link through the analog multiplexer channel, the conditioned critical path node analog voltage signal is stably routed to the input port of the low-overhead diagnostic analog-to-digital converter, forming a transient response analog signal stream to be digitized.

[0152] By using the above analog signal routing and conditioning control method, the path node locking result of the previous step is transformed into a high-quality transient response analog signal that can be directly sampled by the analog-to-digital converter, thereby realizing the acquisition of data on the overshoot amplitude and settling time of key nodes.

[0153] For example, in an industrial inspection light curtain system, the diagnostic sampling enable control command configures and locks three critical path nodes: node 1 is located at the input of the transconductance amplifier link, node 2 is located in the core feedback loop of the high-Q filter, and node 3 is located at the output of the transimpedance feedback network. The system multiplexer configuration register has channel numbers 0-15, corresponding to node 1 mapped to channel number 3, node 2 mapped to channel number 7, and node 3 mapped to channel number 12 in the physical coordinate mapping table. When the channel switching control word is executed, a shielding detection is applied to channel 1, and no conflict is detected; a conflict is detected between channel 2 and channel 5, and temporary shielding is performed; channel 3 is in normal condition. The buffer unit gain is set to 2.5 times to match the 1.2V full-scale input range of the diagnostic analog-to-digital converter, and the high-pass filter network cutoff frequency is set to 50Hz to remove low-frequency components caused by temperature drift. In this configuration, the transient response signal of node 2 is increased to 0.96V before entering the diagnostic analog-to-digital converter, and the boundary rise time is significantly improved in hardware oscilloscope measurement. This ensures the transient characteristic capture capability of the analog-to-digital converter during high-speed sampling. The resulting transient response analog signal stream has complete overshoot and settling time characteristics, providing a high-quality input for the subsequent high-speed analog-to-digital conversion operation of S7.3.

[0154] S7.3: Perform continuous high-speed analog-to-digital conversion processing on the transient response analog signal stream, and use the successive approximation register architecture to collect multiple frames of discrete voltage sample sequences within a ten-millisecond time window, converting the continuously changing analog waveform into a set of original digital sampling points that reflect the real-time voltage fluctuation.

[0155] For the transient response analog signal stream routed through the on-chip analog multiplexer, the successive approximation register architecture in the low-overhead diagnostic analog-to-digital converter executes continuous high-speed sampling commands. Multiple frames are acquired within a ten-millisecond time window at a megahertz-level sampling frequency, allowing the analog voltage waveform to be read in segments according to a preset timing sequence. The acquired analog waveform sequence is input to the on-chip voltage comparator link. Relying on the successive approximation control logic, the difference between the reference level and the current analog signal level is determined in each sampling period, and the internal DAC output is dynamically adjusted to determine the digital representation value of each sampling point bit by bit. Bit synthesis processing is performed on all sampling points within the ten-millisecond time window. The binary bits obtained from each successive approximation are accumulated according to weights to form a complete sample value, and the sampling order is recorded using a time stamp for subsequent feature extraction. The digital sample values ​​acquired from multiple frames are cached in a matrix form in the sampling register group. The rows and columns of the matrix correspond to the sampling point sequence and frame number, ensuring subsequent access in time-domain and frame-order order. By continuously converting analog waveforms at high speed, the continuously changing analog waveforms are transformed into a set of raw digital sampling points reflecting real-time voltage fluctuations, achieving a lossless mapping from analog channels to digital feature space. Through a chain-like derivation process of successive approximation sampling, the transient response analog signal stream from the previous step is transformed into a set of raw digital sampling points with timing consistency and amplitude accuracy, achieving the expected technical effect of digitizing key transient response characteristics.

[0156] S7.4: Based on the original set of digital sampling points, execute the time-domain feature extraction algorithm. By traversing the sampling point sequence, identify the maximum deviation of the voltage peak value exceeding the steady-state value to calculate the signal overshoot amplitude. At the same time, count the time span required from the start of the step jump to the point and remain within the error band to determine the set-up time value, so as to generate a transient response feature vector containing the overshoot amplitude parameter and the set-up time parameter.

[0157] A time series index is constructed based on the original set of digital sampling points, and the sampling timestamps are associated with the corresponding voltage values ​​to form a high-resolution transient signal trajectory, which serves as the input data source for feature extraction.

[0158] A peak search operation is performed on the time series index. By traversing the set of sampling points, the voltage value is compared with the steady-state reference value to locate the voltage peak and calculate the maximum deviation.

[0159] The deviation calculation formula is used to identify the start time of the step. When the voltage at the sampling point exceeds the starting threshold for the first time, this moment is marked. The moment when the signal enters the steady state is determined based on the error band range threshold. The signal overshoot amplitude and settling time are combined according to the field to generate a transient response feature vector. The structure includes two items: overshoot amplitude parameter and settling time parameter.

[0160] Through the above chain-like derivation process, the set of digital sampling points in the previous step is transformed into a transient response feature vector that can comprehensively reflect the overshoot and setup time characteristics, thereby achieving quantitative verification of the reconstruction quality of the analog signal path.

[0161] S7.5: Perform data encapsulation and verification processing on the transient response feature vector, add timestamp markers and path configuration version identifiers, integrate the signal overshoot amplitude parameters and establishment time values ​​into a standard format data packet, and finally output path reconstruction quality verification data with spatiotemporal consistency for subsequent stability judgment logic calls.

[0162] It also includes step S8: determining whether the path reconstruction quality verification data meets the transient response stability condition. If overshoot or abnormal setup time is detected, it automatically reverts to the previous topology anchor template and initiates sub-pixel-level weight fine-tuning; otherwise, it locks the current analog signal path configuration to maintain system robustness. Specifically, it includes: S8.1: Based on the overshoot amplitude value and establishment time value in the path reconstruction quality verification data, perform boundary comparison operation using the preset transient response stability threshold judgment logic to generate a transient response stability judgment result that characterizes whether the current analog signal path configuration meets industrial-grade stability requirements.

[0163] S8.2: Based on the overshoot or establishment time abnormality indicated in the transient response stability determination result, call the previous topology anchor template identifier stored in the non-volatile configuration area to perform a configuration rollback operation, so as to restore the reference path node connection matrix that has been verified as qualified in the previous round as a temporary safe simulation signal path configuration.

[0164] The input condition is the transient response stability determination result, which includes the overshoot / exceedance state or setup time abnormal state flag generated in step S8.1, and the previous topology anchor template identifier stored in the non-volatile configuration area as the unique index basis for the rollback operation. Based on the determination result flag, the topology anchor template identifier field in the non-volatile configuration area is decoded to form an anchor index pointer that can be directly mapped to the physical storage row address.

[0165] Input the anchor point index pointer into the topology anchor point library index table, perform base address offset calculation, locate the specific storage block that stores the baseline path node connection matrix that has passed historical verification in the previous round, and read the complete matrix data stream of the storage block.

[0166] The read matrix data stream is parsed for format and the protocol frame header is verified. Redundant padding bits are removed and the consistency of matrix row and column dimensions is verified to ensure that the restored baseline path node connection matrix structure is complete and usable.

[0167] The parsed baseline path node connection matrix is ​​written into the shadow area of ​​the configuration register of the field-programmable analog array, overwriting the path configuration parameters that are currently at risk of transient anomalies, thus enabling immediate rollback of the physical connection logic.

[0168] The completion status signal written to the register is confirmed, and the connection matrix of the reference path node is locked as a temporary safe analog signal path configuration, which serves as the input reference for subsequent sub-pixel level weight fine-tuning.

[0169] By using the above rollback process, the judgment result of the previous step is transformed into temporary safe path data for environmental drift tolerance, thereby achieving the effect of maintaining the stability of path configuration.

[0170] For example, in an industrial inspection light curtain system, the transient response stability assessment results show an overshoot amplitude of 2.4V and a settling time of 280μs, both exceeding preset thresholds. The identifier of the previous topology anchor template stored in the non-volatile configuration area is 0x1F3A. The system decodes this identifier and locates the physical storage row address 0x00C8 through the index table, reading the reference path node connection matrix stored therein. This matrix contains the transconductance amplifier cascade order [1,3,2], the filter quality factor configuration set [Q1=2.0,Q2=2.2,Q3=1.8], and the transimpedance feedback network switching combination [state 1, state 3, state 2]. During the parsing process, the matrix dimension is detected to be 3×3, which conforms to the predefined format and has no frame header errors. After writing this matrix into the shadow area of ​​the FPAA configuration register, the system confirms the writing is complete and locks the configuration as a temporary safe path. The rollback operation was completed within 21μs, allowing the subsequent analog signal path to return to a stable state in the face of temperature drift and vibration interference, avoiding signal link oscillation and performance degradation, and providing a stable foundation for the next step of performing subpixel-level weight fine-tuning.

[0171] S8.3: For the switch matrix conduction threshold voltage parameter in the temporary safety simulation signal path configuration, a sub-pixel level weight fine-tuning algorithm is applied to perform fine-grained voltage bias correction calculation to generate a fine-grained path elastic weight migration instruction set that can compensate for minor environmental drift and avoid large topology jumps.

[0172] For the switch matrix conduction threshold voltage parameter in the temporary safety simulation signal path configuration, the current threshold distribution data of the switch tubes at each intersection is read as the initial input condition for the bias correction calculation.

[0173] Based on the above threshold distribution data, a difference operation is performed with the reference threshold template stored in the non-volatile configuration area to form a two-dimensional deviation matrix containing the voltage deviation of each switching point.

[0174] The two-dimensional deviation matrix is ​​input into the pre-filtering module of the sub-pixel level weight fine-tuning algorithm. The low-pass smoothing operator is used to eliminate the high-frequency disturbance components in the voltage deviation, so that the calculation process retains only the effective signal components that reflect the small environmental drift.

[0175] For the smoothed voltage deviation, a fine-grained scaling calculation is performed according to the row and column order of the physical topology of the switching matrix. Precise adjustment of the voltage gain coefficient is achieved through a sub-pixel-level weight fine-tuning formula, which is: Where ΔV is the voltage bias correction value of the switching transistor at each crossover point, D is the corresponding smoothed voltage deviation, α is a coefficient based on environmental drift sensitivity, and β is a bias compensation constant used to eliminate the long-term drift accumulation effect.

[0176] The calculated ΔV sequence is mapped to the weight migration parameter space to generate a refined path elastic weight migration instruction set containing the target address and the corresponding corrected voltage value.

[0177] Through the above processing method, the temporary safety simulation signal path configuration of the previous step is transformed into instruction data that can accurately compensate for minor environmental drift and avoid large topology jumps, thereby achieving the expected technical effects of fault tolerance optimization and stability enhancement.

[0178] For example, in an industrial inspection light curtain measurement system, the switching matrix of the temporary safety analog signal path is configured with a conduction threshold voltage range of 0.85V to 1.15V, and the reference threshold template is 1.00V. The acquired current threshold distribution has a slight deviation within ±0.02V at some intersection points, and the two-dimensional deviation matrix size is 12×12. After low-pass smoothing, the high-frequency disturbance amplitude is reduced to within 0.005V. Setting α to 0.8 and β to 0.001V, the correction value ΔV for a certain intersection point is calculated to be 0.0154V, meaning that the threshold at this intersection point needs to be adjusted upwards by 0.0154V towards the reference value. After mapping the correction values ​​of all intersection points according to the matrix rows and columns to generate an instruction set and performing secondary dynamic adjustment, the transient overshoot of the system within the 10ms verification period decreases, the settling time is shortened, and the analog signal path maintains a stable working state under conditions of slight temperature drift and mechanical vibration.

[0179] S8.4: Based on the refined path elastic weight migration instruction set, the on-threshold voltage of the switch matrix inside the field-programmable analog array is dynamically adjusted twice to output the optimal analog signal path configuration that has been optimized for fault tolerance and eliminates the risk of transient oscillation.

[0180] The system retrieves the address encoding and data payload fields from the fine-grained path elastic weight migration instruction set, calls the switch matrix threshold control logic in the shadow area of ​​the field-programmable analog array's internal configuration register, and calculates the dynamic weight factors of the corresponding connection edges into gate bias voltage corrections. Based on the register mapping relationship, the correction sequence is input to the array's built-in digital-to-analog converter unit, and the on-chip timing control module synchronously schedules each voltage correction into a continuously changing analog level curve. Using a threshold adjustment circuit, the analog level curve is applied to the gates of the switches at each intersection point in the switch matrix, performing secondary dynamic adjustment of the turn-on threshold voltage. After resetting the turn-on threshold, the internal impedance sensing unit calculates the branch signal transmission impedance changes in real time. Based on the impedance change results, the equivalent gain and phase compensation parameters of the transconductance amplifier cascade link and filter feedback network are updated to form a fault-tolerant optimized branch gain allocation table, which is then called in the array control firmware to generate the optimal analog signal path configuration.

[0181] By using secondary dynamic adjustment combined with impedance feedback correction, the refined weight transfer instruction set from the previous step is transformed into switch matrix conduction threshold voltage distribution data, thereby eliminating the risk of transient oscillations and maintaining the environmental adaptability of the path configuration.

[0182] For example, in an FPAA measurement system for an industrial safety light curtain, the fine-tuning path elastic weight transfer instruction set output by the sub-pixel level weight fine-tuning algorithm contains 64 connection edge factors, with the factor range limited to 0.85 to 1.15 times. These factors are mapped to gate bias voltage corrections, where the correction for transconductance amplifier-type connections is ±15 mV, for filter-type connections ±8 mV, and for transimpedance feedback network-type connections ±5 mV. During digital-to-analog conversion, the resolution is set to 14 bits, and the sampling synchronization frequency is 5 MHz to ensure level curve continuity. After the threshold adjustment circuit applies the corrections, the impedance sensing unit measures an average decrease of 0.12 Ω in the transconductance amplifier link impedance and an average decrease of 0.08 Ω in the filter feedback network impedance. Correspondingly, the transconductance amplifier gain is adjusted from the original configuration of 7.4 to 7.52, and the filter quality factor is adjusted from 2.1 to 2.14. By updating the branch gain allocation table through internal firmware and loading it into the FPAA configuration register, the generated optimal analog signal path configuration maintains a phase distortion of less than 0.05 rad under an environment with a temperature drift of ±3℃. The verification results show that the transient response overshoot amplitude of the system is controlled within 3.8% of the steady-state value, and the settling time is stable at 2.6 μs, achieving the expected effect of significantly improving stability and robustness.

[0183] S8.5: In response to the normal stable state indicated in the transient response stability determination result or by directly adopting the optimal analog signal path configuration, perform a configuration latch control signal generation operation to solidify the current transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination into the final operating analog signal path configuration to maintain system robustness.

[0184] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0185] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the element or object preceding “comprising” or “including” encompasses the element or object listed following “comprising” or “including” and its equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0186] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing analog signals of a measurement light curtain based on FPAA reconstruction, characterized in that, Includes the following steps: S1: Collect the high-frequency ripple spectrum characteristics of the power rail, the three-dimensional micro-vibration modes of the package surface, and the temperature gradient vector of the thermally sensitive area across the chip to generate the original environmental perception dataset containing multiple physical quantities. S2: Based on the original environmental perception dataset, perform table lookup, bitwise operations and weighted accumulation operations to construct a sixteen-bit environmental fingerprint code that includes temperature gradient dominant type, power supply ripple energy concentration segment index and vibration mode matching degree score. S3: Construct a topology anchor library that maps environmental fingerprint codes to analog path templates. Each topology anchor corresponds to a path node connection matrix that includes the transconductance amplifier cascade order, filter quality factor configuration set, and transimpedance feedback network switching combination. S4: Input the sixteen-bit environmental fingerprint code into the topology anchor library to perform fast hash matching, locate the target topology anchor, and extract the reference path node connection matrix associated with the target topology anchor as the initial analog signal path template to be reconstructed; S5: Based on the deviation of each field of the sixteen-bit environmental fingerprint code from the standard value of the target topology anchor point, calculate the dynamic weight scaling factor acting on the initial analog signal path template, and generate a path elastic weight migration instruction set for modulating the conduction threshold voltage of the internal switch matrix after digital-to-analog conversion and instruction encapsulation.

2. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 1, characterized in that, Following S5, the following is also included: S6: Based on the path elastic weight migration instruction set, the on-threshold voltage of the switch matrix inside the field-programmable analog array is adjusted in real time to adjust the gain distribution and phase response characteristics of each branch, thereby completing the dynamic topology mapping reconstruction of the analog signal path. S7: Use the analog-to-digital converter built into the field-programmable analog array to sample the transient response of critical path nodes and obtain path reconstruction quality verification data that reflects the signal overshoot amplitude and setup time characteristics; S8: Determine whether the path reconstruction quality verification data meets the transient response stability condition. If overshoot or abnormal setup time is detected, automatically revert to the previous topology anchor template and start sub-pixel level weight fine-tuning. Otherwise, lock the current analog signal path configuration to maintain system robustness.

3. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 1, characterized in that, The S2 specifically includes: The frequency band energy distribution of the high-frequency ripple spectrum features of the power rail in the original environmental perception dataset is quantized to generate a ripple energy distribution vector containing energy values ​​of thirty-two frequency bands. Based on the ripple energy distribution vector, a peak search and interval mapping algorithm is executed to determine the index of the concentrated power ripple energy segment. At the same time, a table lookup method is used to calculate the modal similarity of the dominant frequency and damping ratio of the three-dimensional micro-vibration mode on the package surface to generate a vibration mode matching score. The index of the concentrated power ripple energy segment and the vibration mode matching score together constitute an intermediate feature parameter set. Gradient direction discrimination logic is executed based on the temperature gradient vector across the thermally sensitive region of the chip to identify and output a classification label representing the dominant type of temperature gradient, and the classification label is aligned with the intermediate feature parameter set to form a multidimensional physical quantity feature combination to be encoded. A nonlinear weighted accumulation operation is performed on the combination of multidimensional physical quantity features to be encoded to generate a preliminary compressed encoded data stream; The preliminary compressed encoded data stream is subjected to bit splicing and format reorganization processing to construct a 16-bit environmental fingerprint code containing a high four-bit temperature gradient dominant type, a middle six-bit power ripple energy concentration segment index, and a low six-bit vibration mode matching score.

4. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 1, characterized in that, S5 specifically includes: acquiring the high four-bit temperature gradient dominant type field, the middle six-bit power ripple energy concentration segment index field, and the low six-bit vibration mode matching score field from the sixteen-bit environmental fingerprint code; and performing bitwise XOR operations and numerical subtraction on the high four-bit temperature gradient dominant type field, the middle six-bit power ripple energy concentration segment index field, and the low six-bit vibration mode matching score field with the corresponding temperature gradient reference type, power ripple reference segment index, and vibration mode reference score in the target topological anchor point standard value, respectively, to generate a multi-dimensional environmental deviation feature set containing a temperature deviation vector, a ripple frequency offset index, and a vibration mismatch coefficient.

5. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 4, characterized in that, S5 specifically includes: based on the multidimensional environmental deviation feature set, using the nonlinear mapping curve preset in the lookup table to perform thermal drift sensitivity weighted calculation on the temperature deviation vector, performing noise suppression priority mapping on the ripple frequency deviation index, and performing mechanical resonance damping compensation conversion on the vibration mismatch coefficient, so as to generate three independent adjustment components that respectively characterize the thermal stability requirements, anti-interference strength requirements, and structural stability requirements.

6. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 5, characterized in that, S5 specifically includes: reading the path node connection matrix in the initial analog signal path template, extracting the transconductance amplifier cascade parameters, filter quality factor configuration parameters and transimpedance feedback network switching state parameters associated with each connection edge in the matrix, and weighting and fusing the three independent adjustment components according to the functional attributes of each connection edge to generate the original dynamic weight scaling factor set corresponding to each connection edge.

7. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 6, characterized in that, S5 specifically includes: performing amplitude limiting filtering and smooth transition processing on the original dynamic weight scaling factor set, truncating abnormal factors to boundary values, and limiting the slope of factor jumps on adjacent connecting edges, so as to generate a standardized dynamic weight scaling factor sequence that satisfies the linear modulation range of the internal switching matrix of the field-programmable analog array.

8. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 7, characterized in that, Specifically, S5 further includes: based on the standardized dynamic weight scaling factor sequence, mapping it into a voltage control level signal through digital-to-analog conversion driving logic, and encapsulating the voltage control level signal into a path elastic weight migration instruction set containing address encoding and data payload.

9. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 1, characterized in that, S4 specifically includes: performing segmented hash key generation processing on the input 16-bit environmental fingerprint code, extracting the high four-bit temperature gradient dominant type field, the middle six-bit power supply ripple energy concentration section index field and the low six-bit vibration mode matching degree score field using a bit mask, and compressing the three segments of feature data into a single hash addressing pointer through an XOR folding algorithm to obtain the initial lookup address pointing to the topology anchor library storage area.

10. The measurement light curtain analog signal processing method based on FPAA reconstruction according to claim 9, characterized in that, S4 specifically includes: performing a parallel tag comparison operation in the topology anchor library based on the initial lookup address, calling the set of typical environmental fingerprint code standard values ​​pre-stored in the on-chip static random access memory, and performing bit-by-bit logical verification between the candidate anchor tag associated with the current hash addressing pointer and the original sixteen-bit environmental fingerprint code to filter out the target topology anchor identifier.

Images