Dynamic scheduling and power consumption balancing method for multi-site parallel testing of IC chips

Abstract

This invention provides a dynamic scheduling and power balancing method for multi-station parallel testing of IC chips, belonging to the field of industrial control task scheduling technology. Addressing the issues of excessive transient power consumption and timing jitter in concurrent testing, this method extracts the instruction progress and power load boundaries of each station, inputs them into a pre-trained neural network, and obtains a polynomial coefficient prediction envelope that eliminates timestamp constraints. This envelope is then mapped to a high-dimensional matrix space to perform orthogonal rank reduction operations, solving for the anti-collision translation vector set. This is then combined with a dynamic environmental derating mechanism to generate a spatiotemporal offset control vector. Based on the trend information entropy momentum of the derivative, topological isolation is performed on nodes exceeding limits. Through spatiotemporal decoupling and algebraic rank reduction mechanisms, the method solves the risks of computational disasters and hidden thermal breakdowns under concurrency, achieving synergistic maximization of test throughput and resource utilization while ensuring absolute power supply safety for the testing equipment.

Description

Technical Field

[0001] This invention relates to the field of industrial control task scheduling technology, specifically a dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips. Background Technology

[0002] In the field of mass production of intelligent integrated circuits, the clustering and high-density parallelization of automated testing equipment is the core evolution direction for improving the overall throughput of the production line. As chip architectures become more complex, industrial control software in multi-station testing environments needs to handle massive concurrent testing tasks. This not only requires the control mechanism to have extreme instruction distribution efficiency, but also imposes stringent industrial-grade standards on its energy consumption management and physical resource scheduling capabilities at the global level.

[0003] Existing technologies face significant challenges in handling high-load concurrent testing of intelligent chips. Current scheduling mechanisms (based on static time-slice round-robin (TDM) or theoretical peak reservation) rely excessively on absolute physical time coordinates, failing to mitigate clock drift and transmission jitter in distributed networks. This causes theoretically "safe peak shifting" to easily evolve into nonlinear superposition of transient power consumption across multiple workstations and fatal peak overlap during actual physical execution. Global power consumption avoidance for massive concurrent tasks is essentially a complex combinatorial optimization problem, and the limited computing power of edge industrial control computers cannot support real-time optimization within microsecond-level scheduling windows using existing heuristic search algorithms. Existing frameworks commonly suffer from blind scheduling due to hardware-software decoupling, mistaking power distortion caused by wafer micro-deterioration (abnormal leakage current) for normal fluctuations, resulting in a continuous waste of ineffective testing computing power. Patent document CN119271418B proposes a load balancing method for low-power AI processors, which partitions core resources by constructing a directed acyclic graph and a multi-layered ring computing network. However, this solution mainly focuses on optimizing the local static load within a single chip, and still has significant limitations when dealing with the superposition of transient power consumption across workstations and high concurrency, as well as the global physical power supply safety constraints of the test equipment. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic scheduling and power balancing method for multi-station parallel testing of IC chips. It deeply integrates a resource scheduling framework with a deep learning prediction model. By extracting a polynomial prediction envelope that eliminates absolute physical timestamp constraints, the high-dimensional concurrent time-domain power consumption collision prevention problem is reduced in dimensionality and projected into an orthogonal rank reduction operation of algebraic substitution in a virtual matrix space. This establishes a deterministic algebraic optimization mechanism unaffected by physical timing jitter, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A dynamic scheduling and power balancing method for multi-station parallel testing of IC chips, the specific steps of which include: S0: Obtain a neural network model trained based on historical industrial test sequences from multiple workstations, and a manifold space topology mapping rule with the number of instruction logic execution steps as an independent variable; S1: Obtain first instruction logic progress data representing the distribution of task microstate transitions within each independent test station via a preset logic interaction interface, and obtain first power load boundary data representing the dynamic physical boundary limitations of the power supply efficiency of the global test environment. S2: Input the first instruction logic progress data into the neural network model and extract the second prediction envelope data representing the topological equation of the future energy allocation requirements of each independent test station; wherein, the data structure of the second prediction envelope data is a set of polynomial coefficients after removing the absolute physical timestamp constraint; S3: Based on the manifold space topology mapping rule, normalization reconstruction processing is performed on the second predicted envelope data, and the reconstructed second predicted envelope data is independently mapped to the virtual high-dimensional matrix space. The substitution number orthogonal rank reduction operation is performed to calculate the target translation vector set used to characterize the collision-free distribution state of concurrent power consumption of each independent test station. S4: Based on the target translation vector set and the first power load boundary data, generate a cross-domain spatiotemporal offset control vector to instruct the underlying time domain resource allocation node to perform time slice offset rearrangement; S5: Output the cross-domain spatiotemporal offset control vector and continuously calculate the time evolution derivative corresponding to each translation vector in the target translation vector set; perform divergence trend calculation based on the time evolution derivative; when the calculated momentum feature representing the divergence trend of the workstation translation vector exceeds the preset convergence threshold, generate a topology node isolation configuration vector to represent the logic blocking state of the corresponding target test workstation.

[0006] Compared with the prior art, the beneficial effects of the present invention are: This invention (steps S0 and S2) uses a pre-trained neural network model to truncate and reconstruct the predicted transient power consumption manifold curve into a set of orthogonal discrete polynomial coefficients that eliminates physical timestamp constraints. This core approach completely cuts off the pollution path of underlying network jitter and communication delays on the scheduling algorithm, transforming the fragile temporal collision avoidance detection into envelope cross-validation in an abstract orthogonal space to eliminate the risk of "fatal peak overlap" caused by physical clock drift.

[0007] To address the scheduling latency caused by the concurrency of massive testing tasks, this invention (step S3) constructs a topological envelope intersection matrix within a virtual high-dimensional matrix space and introduces Gaussian principal component elimination logic to perform orthogonal rank reduction operations using algebraic substitution. This mechanism reduces the factorial-explosive nonlinear combinatorial optimization problem to a solution with precisely quantized directional linear algebraic constants. This allows resource-constrained microprocessors on the underlying testing equipment to output absolutely collision-free target translation vector sets within a nanosecond-level decision window without relying on high computing power.

[0008] This invention (steps S4 and S5) extracts voltage fluctuations and temperature gradients to generate a dynamic safety margin penalty factor, and uses the time-compensated integral of the derivative to extract a trend information entropy scalar. This method overcomes the blind scheduling defects caused by existing static thresholds, enabling the global power supply boundary to adaptively derating with environmental degradation to avoid critical fuse failure under hidden operating conditions; it also defines high-frequency noise and actual leakage current based on evolutionary momentum information entropy, and uses dual-loop control to trigger hardware topology isolation to reduce the ineffective waste of test resources. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the overall method flow of the present invention.

[0010] Figure 2 This is a schematic diagram of the technical route for steps S0 to S3 of the present invention.

[0011] Figure 3 This is a schematic diagram of the technical route for steps S4 to S5 of the present invention.

[0012] Figure 4 The graph shows the execution response and boundary determination verification of this method under multidimensional physical disturbances. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0014] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various elements, but unless otherwise stated, these elements are not limited by these terms. These terms are used only to distinguish one element from another.

[0015] Example 1: Please see Figures 1 to 4 The present invention provides a technical solution: A dynamic scheduling and power balancing method for multi-station parallel testing of IC chips includes: S0: Obtain a neural network model trained based on historical industrial test sequences from multiple workstations, and a manifold space topology mapping rule with the number of instruction logic execution steps as an independent variable; S1: Obtain first instruction logic progress data representing the distribution of task microstate transitions within each independent test station via a preset logic interaction interface, and obtain first power load boundary data representing the dynamic physical boundary limitations of the power supply efficiency of the global test environment. S2: Input the first instruction logic progress data into the neural network model and extract the second prediction envelope data representing the topological equation of the future energy allocation requirements of each independent test station; wherein, the data structure of the second prediction envelope data is a set of polynomial coefficients after removing the absolute physical timestamp constraint; S3: Based on the manifold space topology mapping rule, normalization reconstruction processing is performed on the second predicted envelope data, and the reconstructed second predicted envelope data is independently mapped to the virtual high-dimensional matrix space. The substitution number orthogonal rank reduction operation is performed to calculate the target translation vector set used to characterize the collision-free distribution state of concurrent power consumption of each independent test station. S4: Based on the target translation vector set and the first power load boundary data, generate a cross-domain spatiotemporal offset control vector to instruct the underlying time domain resource allocation node to perform time slice offset rearrangement; S5: Output the cross-domain spatiotemporal offset control vector and continuously calculate the time evolution derivative corresponding to each translation vector in the target translation vector set; perform divergence trend calculation based on the time evolution derivative; when the calculated momentum feature representing the divergence trend of the workstation translation vector exceeds the preset convergence threshold, generate a topology node isolation configuration vector to represent the logic blocking state of the corresponding target test workstation.

[0016] Further definition of step S0: In step S0, the neural network model is configured with an input data structure representing the microstate transition characteristics and an output data structure representing the polynomial coefficients. The neural network model is a nonlinear mapping model with temporal memory characteristics trained by the convergence of a historical concurrent power consumption feature vector set after outlier removal and dimensionless normalization. Chebyshev polynomial approximation verification logic is pre-constrained in the output layer. The manifold space topology mapping rule is configured to force the parsing and discard of asynchronous time-series label sequences contained in the input data.

[0017] Regarding the further definition of step S1, in step S1: the first instruction logic progress data represents the cumulative count characteristics of atomic instruction flow within the preset logic boundary test sequence; The first power load boundary data characterizes the transient current redundancy upper limit of the multi-station power supply bus.

[0018] Further defining step S2, the specific steps in step S2, which involves extracting the second predicted envelope data representing the topological equations of future energy allocation requirements for each independent test station, include: Process the first instruction logic progress data to predict the nonlinear transient power consumption manifold mapping curve with continuous fluctuation characteristics; Based on the Chebyshev polynomial approximation verification logic, the nonlinear transient power consumption manifold mapping curve is truncated and reconstructed into an orthogonal discrete coefficient value sequence with the characteristic that the polynomial approximation margin converges to the preset truncation error band, and then encapsulated as the polynomial coefficient set.

[0019] Further specifying step S3, step S3, which involves performing the crossover number orthogonal rank reduction operation, includes: Based on the set of polynomial coefficients of each independent test station, a topological envelope intersection matrix is ​​constructed in the virtual high-dimensional matrix space; Extract the non-zero off-diagonal elements from the topological envelope intersection matrix. These non-zero off-diagonal elements characterize the transient concurrent power consumption overlap region features of the target integrated circuit chip at the physical execution level. Based on Gaussian-like principal component elimination logic, the principal component elimination multipliers required to make all non-zero, non-diagonal elements in the intersection matrix of the topological envelope zero are solved sequentially. For all principal component elimination multipliers belonging to the same independent test station matrix row, row-by-row accumulation and aggregation operation is performed. The output aggregated scalar is established as the corresponding minimum affine transformation parameter. Then, all the minimum affine transformation parameters are aggregated and encapsulated into a one-dimensional target translation vector set.

[0020] Further specifying, the step of constructing the topological envelope intersection matrix in the virtual high-dimensional matrix space based on the polynomial coefficient set of each independent test station specifically includes: Extract the set of polynomial coefficients corresponding to each independent test station; The preset historical instruction execution cycle distribution characteristics and historical static power consumption distribution characteristics are extracted as benchmark parameters; a dimensionless spatiotemporal mapping transformation coefficient is introduced based on the range ratio of the benchmark parameters, and the spatiotemporal mapping transformation coefficient is used to perform normalization scaling on the polynomial coefficient set to eliminate the dimension inconsistency caused by the difference in hardware physical baseline between different test stations. Based on the set of polynomial coefficients after normalization and scaling, a step-by-step product and continuous accumulation operation of coefficients of the same order between two independent test stations is performed in the virtual high-dimensional matrix space to form the envelope inner product cross-validation logic. Extract the pure algebraic scalars from the output of the envelope inner product cross-validation logic and use them as off-diagonal matrix elements to characterize the degree of intersection interference. Simultaneously extract the pure algebraic scalars from the autocorrelation inner product of the polynomial coefficient set itself and use them as diagonal principal component matrix elements. Then, based on the preset workstation topology index, combine the off-diagonal matrix elements with the diagonal principal component matrix elements to generate a full-rank topological envelope intersection matrix.

[0021] The workstation topology index represents the deterministic mapping relationship between the hardware communication addressing sequence number of each independent test workstation and the row and column dimension coordinates in the virtual high-dimensional matrix space.

[0022] The following is a detailed explanation of the implementation of steps S0 to S3 above: Define the atomic instruction flow cumulative count feature (denoted as ). This characterizes the monotonically increasing property of the micro-logic execution state of the target integrated circuit chip within a preset logic boundary test sequence. Based on a preset logic interaction interface and via a preset global hardware collaborative triggering network with equal-length impedance matching characteristics, at the same absolute clock edge upon receiving a global physical synchronization gating pulse with extremely low phase offset, the state pulses output by each independent test station are synchronously latched and captured in each sampling period, and an accumulation operation is performed to generate this counting characteristic.

[0023] Define the upper limit characteristic of transient current redundancy (denoted as ). This characterizes the dynamic physical boundary constraints of the power supply performance of the global test environment. The difference is obtained by subtracting the rated fuse threshold of the global test environment power supply bus from the current transient load value. A set of polynomial coefficients is defined (denoted as...). This characterizes the topological equation representing the future energy allocation requirements of independent test stations after removing absolute physical timestamp constraints. It outputs a dimension-reduced mapping of continuous waveforms using a pre-defined neural network model. The spatiotemporal mapping transformation coefficient is defined (denoted as...). This is used to eliminate dimensional inconsistencies between different test stations caused by physical baselines (including differences in line resistance and chip batch leakage current), forcibly mapping heterogeneous physical quantities to uniform dimensionless values. The envelope inner product verification value is defined (denoted as...). This characterizes the overlapping region of transient concurrent power consumption between two independent test stations in a virtual high-dimensional matrix space. It is obtained by performing a purely algebraic vector inner product operation on the normalized set of polynomial coefficients.

[0024] When handling massive parallel testing tasks of large batches of intelligent integrated circuits (IC chips), high-concurrency test stations are prone to triggering power consumption peaks within the same microsecond-level time slice, causing the machine bus current to instantly break through the fuse threshold. Existing heuristic search scheduling algorithms require calculating concurrent power consumption accumulation every millisecond, facing the "curse of dimensionality" that explodes exponentially with the number of test stations. This method utilizes manifold space mapping and rank reduction elimination logic to replace probabilistic search with deterministic linear algebra solutions. Based on the above principles, the specific steps of the dynamic scheduling and power balancing method are as follows: For step S0, the pre-processing basic object acquisition step is performed as follows: In this embodiment, before executing the scheduling logic, based on the industrial control processor embedded in the test machine, a block of address-isolated physical memory space is allocated in the read-only static random access memory as the operation carrier of the virtual high-dimensional matrix space, and a hardware-level hard interrupt listening mapping is established between this operation carrier and the analog-to-digital converter output pin of the multi-station power supply bus. Based on the above-mentioned physical underlying carrier, a neural network model trained based on the historical industrial test sequence of the multi-station is obtained, as well as a manifold space topology mapping rule with the instruction logic execution step number as an independent variable. In this step, the neural network model is configured to have an input data structure representing the microstate transition characteristics and an output data structure representing the polynomial coefficients, forming an end-to-end data pipeline. Before the output layer of the model, the Chebyshev polynomial approximation verification logic is forcibly constrained.

[0025] The manifold space topology mapping rules are configured and decoupled into three deterministic sub-logic steps executed sequentially: timing stripping verification, normalization scaling calibration, and high-dimensional matrix mapping. The timing stripping verification logic forcibly parses and strips all asynchronous timing tag sequences contained in the input data, converting the independent variables of the original data from absolute physical timestamps into discrete instruction logic execution steps. By stripping timestamps, the path of network jitter and communication delays affecting the underlying hardware timing is cut off, establishing the spatiotemporal basis for subsequent pure logic deduction.

[0026] Further explanation of obtaining the neural network model in step S0: A historical industrial test sequence dataset is obtained to train the neural network model. All raw input data representing multi-station concurrent power consumption calibration in this dataset undergoes mandatory zero-mean normalization and dimensionless processing. Continuous time-series data are formatted as fixed-width feature vectors, and glitch anomalies caused by poor probe contact in the industrial field are removed using pre-defined unsupervised anomaly detection logic. This unsupervised anomaly detection logic (specifically, the Isolation-Forest algorithm) constructs a population of 100 randomly branched isolation trees. Based on historical environmental noise statistics, the global outlier contamination rate threshold hyperparameter is set to 2%. An anomaly assessment deviation score is generated by calculating the average traversal path length of each feature vector within the isolation tree population. The anomaly assessment deviation scores of all samples output by the isolation tree population are extracted to construct a one-dimensional score vector representing the global deviation trend. The one-dimensional score vector is then sorted in descending algebraic order to generate a discrete distribution sequence. The total number of global samples in this sequence is extracted, multiplied by the 2% contamination rate threshold hyperparameter, and the product is rounded down to obtain the target truncation index scalar. Based on memory addressing logic, the specific score value corresponding to the target truncation index scalar in the discrete distribution sequence is extracted and fixed as the rejection threshold. If and only if the anomaly assessment deviation score within the sample slice is algebraically greater than the rejection threshold, the corresponding feature vector is determined to be a spurious outlier and is removed.

[0027] The internal architecture of the neural network model is set as a deep long short-term memory network layer with temporal memory characteristics, which aims to capture the nonlinear temporal dependency between the number of instruction flow steps and transient power consumption. The processed feature vector is extracted as input, and the corresponding real continuous transient power consumption waveform is extracted as the target output waveform to converge the training of the nonlinear mapping model. During the model inference phase, the intermediate output data structure representing the continuous transient power consumption manifold mapping curve of the nonlinear mapping model is unidirectionally input into the subsequent cascaded Chebyshev polynomial approximation verification logic to perform a truncation and reconstruction operation, thereby completing the cascaded transformation to the final output data structure representing the polynomial coefficients. A preset root mean square error index is used as the evaluation benchmark for the loss function. An adaptive momentum estimation optimization algorithm is loaded to perform iterative optimization operations. In this process, a dynamically decaying learning rate parameter is introduced (meaning it controls the iteration step size variable of each weight coefficient update span; dynamic decay). The learning rate parameter is a constant that monotonically decreases with each training epoch; the preferred initial value is 0.001; the empirical range constraint is 0.0005 to 0.01. In each epoch of the iterative optimization operation, the first and second moment estimates of the target gradient are calculated using the adaptive momentum estimation optimization algorithm. Based on the first and second moment estimates, bias correction and dynamic scaling operations are performed on the dynamically decaying learning rate parameter, and the weight coefficients and bias term parameters between the hidden layer nodes of the network are continuously updated according to the scaled learning rate. When the decrease in the loss function within a consecutive preset number of epochs (in this embodiment, the preferred initial value is 10, and the range constraint is a closed interval of 5 to 20) fails to exceed the minimum convergence tolerance (a constant representing the error tolerance limit, the preferred initial value is 0.0005 to 0.01), the learning rate parameter is determined by the algorithm. , range anchored to to When the learning rate parameter is within the specified range, it is halved. The rate of decline of the loss function is continuously monitored until the absolute fluctuation of the rate of decline continuously drops and remains at the preset training minimum threshold (preferred value is set to 0). Engineering theory boundaries are forcibly limited to to Within the convergence band defined between (between), all parameter weights are frozen, and the final trained neural network model is output.

[0028] For step S1, the heterogeneous input data acquisition step is performed as follows: The atomic instruction flow cumulative count features within each independent test station are extracted via a preset logic interaction interface. Further details regarding the extraction of atomic instruction flow cumulative count features in step S1 are provided. The underlying state machine logs of each independent test station are extracted via the preset logic interaction interface. The logic interaction interface is configured to have a polling sampling frequency of no less than 100 MHz and a signal transmission bit error rate of no more than 0.1% to ensure the physical integrity of the microstate transition edge data. The original state pulse sequence containing glitches is acquired. For the original state pulse sequence, a dynamic sliding window step size parameter is forcibly introduced, and median filtering based on this parameter is performed to eliminate high-frequency noise pulse interference caused by the high-frequency switching power supply of the industrial machine. The specific determination logic of the dynamic sliding window step size parameter (meaning to define the number of discrete sampling data points contained in a single filtering smoothing interval, with a preferred value of 5; the range constraint is an odd number from 3 to 15) is as follows: obtain the preset logic interaction interface polling sampling frequency and the rated instruction transition maximum frequency of the target integrated circuit chip; divide the polling sampling frequency by the rated instruction transition maximum frequency to obtain the Nyquist sampling density base representing a single instruction cycle; extract 20% of the Nyquist sampling density base as an intermediate floating-point value, and perform an algebraic mapping operation of rounding down to an odd integer on the intermediate floating-point value, and establish its final output as the dynamic sliding window step size parameter; extract the effective state transition edge signal after filtering; for each effective state transition edge signal in the preset logic boundary test sequence, extract the preset auto-incrementing step size parameter, and perform a monotonically accumulating numerical update algorithm operation on the current cumulative scalar; extract the final scalar value of the accumulation count operation, output it and establish it as the atomic instruction flow cumulative count feature corresponding to the independent test station, and encapsulate it as the first instruction logic progress data; The transient current redundancy upper limit feature of the multi-station power supply bus is extracted synchronously. The specific explanation for extracting the transient current redundancy upper limit feature in step S1 is as follows: The rated fusing current threshold of the global power supply bus is obtained through the hardware management control layer of the test equipment, and this threshold is used as a static reference parameter. Based on the global physical synchronization gating pulse synchronously driving the underlying analog-to-digital sample-and-hold logic gate, within a nanosecond-level tolerance band that eliminates bus arbitration delay, the effective value of the transient load current of the bus current transformer in the current sampling slice is synchronously read. A subtraction algebra operation is performed between the rated fusing current threshold and the effective value of the transient load current. The absolute difference result of the subtraction algebra operation is extracted, and after verifying that the difference result is greater than zero, it is established and output as the transient current redundancy upper limit feature. The transient current redundancy upper limit feature is encapsulated as the first power load boundary data.

[0029] For step S2, the feature dimensionality reduction extraction step is performed: the extracted first instruction logic progress data is input into the hidden layer of the neural network model to deduce and predict the nonlinear transient power consumption manifold mapping curve with continuous fluctuation characteristics. The Chebyshev polynomial approximation verification logic is triggered: a preset truncation error band parameter is set to limit the accuracy of the Chebyshev approximation. The preset truncation error band parameter constrains the convergence boundary of the approximation margin of the polynomial reconstruction. In this embodiment, the preset truncation error band is determined by obtaining the residual test dataset of the Chebyshev polynomial approximation reconstruction under historical multi-station operating conditions; calculating the cumulative distribution function of the absolute values ​​of the deviations of all verification samples in the residual test dataset; extracting the quantile values ​​of the cumulative distribution function corresponding to the 95% confidence interval; and establishing these quantile values ​​as the benchmark point of the preset truncation error band. In this embodiment, the value of this parameter is set to 5%, which serves as an external static configuration item controlling the polynomial truncation order and is instantiated and loaded by accessing the locally stored parameter spreadsheet file in read-only mode. A technical balance can be achieved between "ensuring that the envelope characteristics of the high-frequency power consumption waveform are fully preserved" and "avoiding the introduction of high-order computational redundancy". Based on the preset truncation error band, the continuous nonlinear transient power consumption manifold mapping curve is truncated and reconstructed into an orthogonal discrete coefficient value sequence; the nonlinear transient power consumption manifold mapping curve output by the neural network is obtained; based on the Chebyshev extreme value interleaving rule, all curvature inversion extreme value distribution nodes of the mapping curve within the domain of effective execution steps are extracted; the global scalar extreme value boundary of the original physical execution step independent variable is extracted, and the maximum and minimum physical execution steps of the boundary are determined; the reciprocal of the difference between the maximum and minimum values ​​is multiplied by two to establish the scaling factor; and the sum of the maximum and minimum values ​​is divided by the difference between the maximum and minimum values, and its negative value is established as the offset constant; a linear algebraic mapping operation based on the scaling factor and offset constant obtained by the dynamic calculation is performed to forcibly compress and map the global scalar extreme value boundary to a normalized convergence domain from negative one to positive one; under the constraint of the normalized convergence domain, for all curvature inversion extreme value distribution nodes, the underlying arithmetic unit is called to sequentially parse the following first Chebyshev extreme value node distribution equation and second orthogonal coefficient discrete summation equation: ; The orthogonal discrete amplitude coefficients corresponding to each order are calculated. Here, k is defined as the index number of the discrete sampling node; the range is... N is defined as the total number of valid transition nodes in the instruction flow captured within a single sampling window; the preferred value is 64; the empirical range is 32 to 256. Defined as the normalized physical execution steps after extreme value interleaving mapping; the optimal point value changes dynamically with the input; the empirical range is limited to the interval [-1, 1]. Defined as the transient power consumption envelope function mapping value under the normalized execution steps; the preferred point value varies with the input waveform; the empirical range is 0 to the machine's rated maximum current. m is defined as the order dimension index of the orthogonal polynomial; the range is... ( The preset truncation order is 5 (preferred value). Defined as the m-th order orthogonal discrete amplitude coefficient output by DCT transformation, it represents the fundamental orthogonal coefficient value; the preferred point value varies with the input; the range is -100 to 100. This embodiment introduces the Chebyshev extreme node mapping formula and DCT expansion operator to achieve lossless projection of infinite-dimensional continuous physical waveforms to extremely low-dimensional orthogonal algebraic space, thereby compressing the data exchange bandwidth required for high-concurrency test station scheduling; thereby extracting a set of fundamental orthogonal coefficient values ​​with the smallest approximation distance to the manifold mapping curve space; then obtaining the preset truncation error band parameter; performing a reverse traversal comparison and filtering operation from high frequency to low frequency on the fundamental orthogonal coefficient values ​​based on the preset truncation error band parameter; further executing the coefficient truncation comparison logic based on the energy envelope ratio metric, specifically calculating the global algebraic sum of the absolute values ​​of all coefficients in a set of fundamental orthogonal coefficient values, using it as the benchmark energy denominator; extracting the current high-order values ​​one by one in reverse order from high order to low order. The absolute value of the order coefficient is used as the energy numerator. For each higher-order coefficient, the corresponding energy numerator is divided by the baseline energy denominator to calculate the local normalized amplitude parameter (which measures the influence weight of a single coefficient on the overall manifold topology; a constant with a value between 0 and 1; the preferred value depends on the signal itself but does not exceed 0.05; the empirical range is between 0.001 and 0.1). The local normalized amplitude parameter is monotonically compared with the preset truncation error band. If the local normalized amplitude parameter is less than the preset truncation error band, the current higher-order coefficient is determined to be a useless coefficient term and a forced zeroing and removal action is performed. The remaining lower-order coefficient terms after filtering are extracted and integrated into a polynomial coefficient set.

[0030] For step S3, an algebraic rank reduction step and a normalized inner product mechanism are performed to solve the problem of envelope interference misjudgment caused by differences in physical baselines at different test stations. Specifically, an envelope inner product cross-validation mechanism based on spatiotemporal mapping transformation is implemented.

[0031] In this embodiment, the system's central control node sends polling commands to each independent test station via an industrial Ethernet communication interface, and simultaneously receives and extracts the polynomial coefficient sets returned by each independent test station based on the command response. The characteristic data flow of this extraction action can be directly parsed from the intercepted data frame payload using a non-intrusive protocol analysis tool mounted on the communication network link, thus providing a clear external determination entity for algorithm deployment. The determination of dimensionless spatiotemporal mapping transformation coefficients is explained, and the baseline normalization parameter configuration steps are as follows: Obtain the historical static power consumption distribution characteristics of each model's test station under no-load calibration conditions (specifically characterized by the historical sample dataset of basic leakage current), and the preset historical instruction execution cycle distribution characteristics (specifically characterized by the historical dataset of rated instruction execution clock cycles); calculate the global numerical range of the historical sample dataset of basic leakage current and the global numerical range of the historical dataset of rated instruction execution clock cycles respectively; obtain the preset static dimensional alignment constant, the physical unit of which is amperes per clock cycle; perform a series of algebraic operations on the global numerical range of the historical sample dataset of basic leakage current, the reciprocal of the static dimensional alignment constant, and the reciprocal of the global numerical range of the historical dataset of rated instruction execution clock cycles, following the following dimensional cancellation multiplication equation in the underlying logic controller: This cancels out the dimensional units of heterogeneous physical quantities, yielding a dimensionless reference constant in pure algebraic scalar form. Defined as a dimensionless benchmark constant characterizing the physical volatility benchmark; the preferred value is 0.85; the empirical range is 0.1 to 5.0. Defined as the global numerical range of the historical sample dataset of basic leakage current; the preferred value is 15.0; the empirical range is 5.0 to 50.0. Defined as the global numerical range of the historical dataset of the rated instruction execution clock cycle; unit: nanosecond; preferred point value is 2.5; empirical range is 0.5 to 10.0. Defined as a static dimensional alignment constant; a fixed constant characterizing the conversion rate of current and time physical properties, its dimensional unit is configured to match the units of other range variables in the calculation formula in amperes per clock cycle; the preferred value is 6.0; the empirical range is 1 to 20. This embodiment, by introducing a multiplicative equation with a reciprocal penalty factor and a static alignment constant, achieves forced assimilation of microampere-level current range and nanosecond-level clock range within the same mathematical space, thereby eliminating collision avoidance judgment errors caused by heterogeneous circuit resistance at different test stations from a physical source.

[0032] Extract the dimensionless reference constant obtained from the calculation. The algebraic reciprocal operation is then performed directly on the polynomials to generate a reciprocal scaling factor. The physical meaning of this reciprocal operation is to force the originally positively divergent maximum expected physical parameter to converge and map to a unified canonical range of 0 to 1. Subsequently, this reciprocal scaling factor is established as the final dimensionless spatiotemporal mapping transformation coefficient. This spatiotemporal mapping transformation coefficient is written as a static dictionary key-value pair into a spreadsheet file in local read-only storage. Before each parallel test scheduling execution, the configuration read logic is forcibly executed to parse and load the spatiotemporal mapping transformation coefficient from the spreadsheet file into the control memory space. This preemptive step eliminates the logical paradox of subsequent cross-dimensional polynomial operations in terms of physical dimensions. A multiplicative scaling operation is performed on the polynomial coefficient set and the spatiotemporal mapping transformation coefficient to generate a normalized and scaled polynomial coefficient set. At this point, all parameters are uniformly mapped to a dimensionless pure algebraic dimension.

[0033] Verification of the inner envelope product value The specific calculation process involves the following steps for quantizing the inner product space interferometry: The input parameter is identified as the set of polynomial coefficients from the first and second test stations after normalization and scaling. For the two sets, they are aligned according to the absolute order of the polynomials. The first coefficient pair corresponding to the zeroth-order constant term is obtained, and multiplication is performed to obtain the first intermediate product. The second coefficient pair corresponding to the first-order term is obtained progressively, and multiplication is performed to obtain the second intermediate product. This process is iterated until the highest-order coefficient pair in the input set is reached, generating an intermediate product sequence containing complete orders. All values ​​in the intermediate product sequence are sequentially accumulated. The sum of the pure algebraic scalars that converges from the continuous accumulation is extracted, established, and output as the envelope inner product verification value. This value maps the nonlinear overlap area of ​​the two test stations on the future energy distribution topology, non-destructively transforming the complex physical collision avoidance determination into a pure geometric vector inner product scalar measurement.

[0034] For obtaining the first coefficient pair corresponding to the zeroth-order constant term, within the physical computing unit, the discrete mapping logic based on corresponding-order multiplication and continuous accumulation operations specifically follows the following orthogonal space discrete dot product equation: After the system completes all loop traversal cycles, the sum of the final convergent pure algebraic scalars of the discrete dot product equation is extracted. Here, u and v are defined as unique logical identifier indices of the first and second test stations participating in the interferometric trial calculation in the virtual high-dimensional matrix space; the empirical range is... ( (Maximum number of workstations configured for the system). Defined as the order of the maximum retained characteristic after truncation of the approximating polynomial; the preferred value is 5; the empirical range is 3 to 10. Defined as the fundamental orthogonal coefficient value characterized by the m-th order orthogonal discrete amplitude coefficients of the subordinate workstations u and v respectively; the empirical range is -100.0 to 100.0. Defined as a dimensionless reference constant The dimensionless spatiotemporal mapping transformation coefficients obtained by calculating the reciprocal are used to perform a forced scaling that maps the original polynomial coefficients to a pure algebraic dimension within the dot product operation. Defined as the transient concurrent power consumption envelope interference area metric for each pair of target test stations within a preset clock cycle; the empirical range is 0 to 1000.0. By introducing the discrete dot product equation of orthogonal space, a dimensionality reduction attack is achieved by transforming complex nonlinear time-domain interference judgment into extremely low-dimensional geometric vector multiplication and addition operations, thereby ensuring the accuracy of collision avoidance scheduling while avoiding the computational disaster caused by heuristic search.

[0035] The calculated envelope inner product verification value is extracted as an off-diagonal matrix element representing the physical layer transient concurrent power consumption overlap interference characteristics. For each normalized and scaled polynomial coefficient set, a continuous accumulation algebraic operation of the sum of squares of coefficients of the same order is forced, and the pure algebraic scalar sum output by the algebraic operation is extracted as the diagonal principal component matrix element representing the autocorrelation energy topology characteristics of the corresponding independent test station. The high-dimensional matrix mapping logic in the manifold space topology mapping rule is triggered. Based on the preset station topology index and the hardware communication addressing sequence number of each independent test station, the diagonal principal component matrix element representing the autocorrelation characteristics is filled into the diagonal coordinate position of the corresponding sequence number, and the off-diagonal matrix element representing the interference characteristics between two stations is filled into the off-diagonal coordinate position of the intersection of the corresponding two sequences, thereby jointly mapping to the virtual high-dimensional matrix space to generate a full-rank topological envelope intersection matrix.

[0036] This embodiment details the calculation process for extracting the target translation vector set (representing the minimum affine transformation parameters). Specifically, it extracts the generated topological envelope intersection matrix as the global feature to be processed; performs a reduced-order traversal along the main diagonal of this matrix; in each round of local traversal, it locks the element at the current diagonal position as the pivot element; before performing the division operation, it introduces a preset algebraic singularity overflow prevention threshold parameter (meaning it is a minimum positive real number safety boundary to prevent register overflow caused by division by zero; the preferred value is set to 10). -6 The empirical range constraint is 10. -8 Up to 10 -4(between); extract the absolute value of the current pivot element and compare it with the algebraic singularity overflow prevention threshold parameter; if the absolute value of the current pivot element is less than the algebraic singularity overflow prevention threshold parameter, then search for the target row element with the largest absolute value among the remaining non-zero non-diagonal elements in the column where the current pivot element is located, and force a data permutation operation between the row where the current pivot element is located and the row where the target row element is located to update the pivot element; after confirming that the absolute value of the updated pivot element exceeds the algebraic singularity overflow prevention threshold parameter, for all non-zero non-diagonal elements in the column where the current pivot element is located, obtain the division quotient value between the non-zero non-diagonal element and the pivot element, and establish the calculated division quotient value as the pivot element elimination multiplier; extract the feature sequence of the entire row matrix where the pivot element is located, and perform a term-by-term multiplication operation with the pivot element elimination multiplier to obtain the offset reference row data sequence; and include the target non-zero non-diagonal elements. The data sequence to be eliminated and the data sequence of the elimination reference row are subjected to corresponding term subtraction operations to force the non-zero off-diagonal elements of the target to be rewritten to zero; the above loop logic of generating pivot elimination multipliers and corresponding row subtraction elimination is repeated until all non-zero off-diagonal elements in the topological envelope intersection matrix are cleared to zero, thereby driving the global matrix to reach a fully diagonalized orthogonal convergence state; for each matrix feature row corresponding to an independent test station, the absolute values ​​of all pivot elimination multipliers applied to that row in the above rank reduction elimination loop are extracted, and row-wise continuous accumulation algebra operation is performed; the scalar of the accumulated absolute value output of each feature row is established as the minimum affine transformation parameters required by the corresponding test station on the underlying time domain resource allocation node; according to the preset station topology index, all the above one-dimensional minimum affine transformation parameters are aggregated and uniformly encapsulated into a one-dimensional target translation vector set. This operation logic avoids concurrent tentative interference that explodes in high-dimensional spacetime.

[0037] Specifically, the aforementioned principal component elimination multiplier calculation and real-time update of matrix data are performed using the following elementary transformation iterative equation group: ; The first sub-equation analyzes the logic for generating the division quotient to establish the elimination of multipliers by the pivot. The second sub-equation, by introducing a pivot to eliminate multipliers, algebraically completes the generation of the cancellation reference row sequence and the subtraction operation of the corresponding terms of the row to be cancelled, thereby outputting the updated matrix elements. k1 is defined as the column index of the selected pivot element within the current iteration cycle; the empirical range is limited to... N2 is the maximum number of column indices containing the pivot element; i,j are defined as the row and column indices of the non-zero, non-diagonal elements whose interference needs to be forcibly blocked; the empirical range is... and The step is defined as the current evolution step identifier of a high-dimensional algebraic orthogonal reduced-rank logical operation; the empirical range is... . Defined as the baseline principal component physical energy value at the step-th collision avoidance iteration; the empirical range is limited to an absolute value greater than [value missing]. . Defined as the value of the physical intersecting interference element that urgently needs to be forced to zero under the current time series; the empirical range is -1000.0 to 1000.0. Defined as the principal component elimination multiplier that characterizes the energy cancellation coefficient. Defined as the physical space update matrix element after undergoing one anti-interference decoupling cancellation operation; this embodiment realizes the conversion of concurrent energy consumption collision probability to deterministic algebraic equation recursively solved by introducing a group of elementary transformation iterative equations of matrix rows. In the specific physical implementation environment, since the concurrent power consumption peak of the batch intelligent integrated circuit test task has natural discreteness in the time domain, the topological envelope intersection matrix is ​​essentially an extremely sparse block diagonal matrix; in order to avoid the dimensionality curse of high-dimensional matrix elimination, the system forcibly calls the preset sparse matrix non-zero interference node index compression addressing logic before performing the principal component elimination multiplier calculation. This logic directly skips the multiplication and addition iteration of all zero-value interference terms, strictly constraining the scale of floating-point operations that actually need to be performed from the full order of the cube to within the computing power threshold boundary that is linear with the number of active interference workstations, so that the test machine microprocessor can converge to the best translation vector that makes all workstations avoid peak shifting within milliseconds without high computing power.

[0038] Further defining step S4, step S4 of generating the cross-domain spatiotemporal offset control vector includes: Extract the amplitude features of each translation vector in the target translation vector set and convert them into logical idle time offset parameters configured at the corresponding test station; Based on the first power load boundary data, an overflow check is performed on the set of logical idling time offset parameters, and the cross-domain spatiotemporal offset control vector is generated after the check passes.

[0039] Further specifying, the step of performing overflow verification on the set of logic idling time offset parameters based on the first power load boundary data specifically includes: Extract the historical bus voltage fluctuation characteristics and ambient temperature gradient parameters within a preset time sliding window; Extract the pre-configured dimensionless boundary derating scaling constant, and perform a nonlinear mapping on the boundary derating scaling constant based on the historical bus voltage fluctuation characteristics and the ambient temperature gradient parameter to generate a dynamic safety margin penalty factor. The dynamic power load boundary data is obtained by performing a multiplicative scaling operation on the first power load boundary data using the dynamic safety margin penalty factor; The nominal bus voltage constant, which characterizes the bus potential, is extracted. The predicted transient concurrent power consumption peak value, derived from the set of logic idle time offset parameters, is divided by the nominal bus voltage constant to perform a dimension reduction operation, thereby obtaining the predicted transient concurrent current peak value. The predicted transient concurrent current peak value is then compared with the dynamic power load boundary data using algebraic comparison with the same dimensions to complete the overflow verification.

[0040] Further limiting step S5, in step S5 of continuously calculating the time evolution derivatives corresponding to each translation vector in the target translation vector set and generating the topology node isolation configuration vector, the parallel closed-loop control actions of the first logic branch and the second logic branch are triggered: In the first logic branch, the logic idle time offset parameter of the cross-domain spatiotemporal offset control vector for the target test station is overwritten with a preset blocking extreme value upper limit to block the instruction execution flow of the corresponding target integrated circuit chip. In the second logic branch, for the matrix position of the target test station in the topological envelope intersection matrix of the subsequent period, the corresponding diagonal principal element matrix element is extracted, and the diagonal principal element matrix element is forcibly reset to a preset penalty limit constant, so as to force the principal elimination multiplier of the target test station to converge to zero, thereby excluding the data feature input of the corresponding target test station at the pure algebraic operation level.

[0041] Further specifying the divergence trend calculation based on the time evolution derivative, the determination step when the calculated momentum characteristic exceeds a preset convergence threshold specifically includes: Construct a first-in-first-out logical buffer queue with a preset depth, extract the time evolution derivative within the continuous scheduling period associated with the preset window step size, and push it into the logical buffer queue in absolute time order. For the derivative sequence in the logical buffer queue, differential algebra operations are performed sequentially based on the derivative values ​​of adjacent timestamps to extract a set of second-order evolution gradient features that characterize the divergence acceleration of the time evolution derivative. Extract the physical timestamps synchronously latched by the global hardware clock unit for adjacent scheduling cycles, perform algebraic subtraction on adjacent physical timestamps to obtain the dynamic compensation time step parameter representing the real physical time span; perform algebraic multiplication on each feature item in the second-order evolution gradient feature set with the dynamic compensation time step parameter of the corresponding cycle, and then perform continuous integral accumulation operation; and establish the output of the continuous integral accumulation operation as the trend information entropy scalar. The trend information entropy scalar is established as the momentum feature, and the irreversible divergent momentum threshold is established as the preset convergence threshold; the trend information entropy scalar is compared with the preset irreversible divergent momentum threshold; if and only if the trend information entropy scalar is greater than the irreversible divergent momentum threshold, the determination condition that the momentum feature exceeds the preset convergence threshold is established and triggered.

[0042] The following are specific implementation instructions for steps S4 to S5 above: The detailed implementation of step S4, which generates the cross-domain spatiotemporal offset control vector and performs dynamic overflow verification, is described below: After obtaining the ideal concurrent power consumption collision-free distribution state (target translation vector set) through rank reduction and elimination, it is transformed into low-level time-domain control parameters. If the rated static power load boundary is directly used for overflow prevention verification, the continuous operation of high-concurrency test stations during the processing of massive parallel testing tasks of large batches of intelligent integrated circuits (IC chips) will cause a sharp rise in the internal ambient temperature of the test equipment, while the accumulation of power grid noise will cause nonlinear attenuation of the bus power supply capacity. This method sets the following "environmentally coupled dynamic safety margin derating mechanism" in the original spatiotemporal offset reconstruction logic.

[0043] Specifically, this is achieved by obtaining the target translation vector set established in the preceding steps; and then extracting the magnitude features of each translation vector in the target translation vector set sequentially. A detailed explanation of step S4, which converts this into a logic idle time offset parameter configured at the corresponding test station, is provided: the input parameter is anchored to the dimensionless algebraic magnitude features of the output from rank reduction and elimination. A preset clock cycle mapping base is obtained. This base serves as a conversion coefficient and is configured to map dimensionless algebraic scalars to specific values ​​representing physical time spans, thereby eliminating the dimensional barrier between the virtual matrix space and the underlying physical execution state. In the logic operation unit, the amplitude features corresponding to each independent test station are extracted and subjected to term-by-term algebraic multiplication with the clock cycle mapping base. The product results output by the algebraic multiplication operation are forcibly truncated by rounding down to filter out microscopic time fragments that cannot be divided by the underlying clock crystal oscillator. The final pure value after rounding is established as the logic idle time offset parameter configured for the corresponding test station and written into the scheduling instruction header for that station. The preset clock cycle mapping base means that it represents the equivalent conversion ratio between the absolute time scale corresponding to a single physical tick of the underlying industrial control bus and the algebraic space vector amplitude unit; the preferred value is determined according to the main frequency of different test equipment, for example, it is set to 10.0 in a test equipment with a main frequency of 100 MHz; the empirical range is limited to 1.0 to 50.0.

[0044] The logical idle time offset parameter represents the total number of absolute sleep clock cycles that the micro-test thread must undergo before being awakened and executed; it is a non-negative positive integer scalar; the preferred value is not less than 50; the empirical range is limited to 0 to 10000.

[0045] The original first power supply load boundary data is retrieved. Based on the conflict optimization judgment logic, given that the original first power supply load boundary data only characterizes the upper limit of static transient current redundancy under ideal operating conditions, its rigid threshold becomes an algorithmic bottleneck limiting scheduling robustness under complex operating conditions. This embodiment performs environmentally adaptive reconstruction of this parameter to generate derating dynamic data. A preset time sliding window is set. This time sliding window is used to limit the historical backtracking depth of environmental parameter sampling; in this embodiment, the value of the time sliding window is set to 200 global physical synchronization gating pulse cycles prior to the current scheduling time; it represents a technical balance between "ensuring sufficient capture of environmental heat accumulation / grid low-frequency jitter trends" and "avoiding the introduction of outdated data leading to feedback lag." Based on this time sliding window, environmental parameter extraction and preprocessing pipeline steps are executed to obtain historical bus voltage fluctuation characteristics and environmental temperature gradient parameters.

[0046] Extraction of historical bus voltage fluctuation characteristics: The data source module for acquiring this signal meets the following attributes: a polling sampling frequency of not less than 50 kHz and a full-range absolute measurement error of not more than 0.2%, in order to capture high-frequency transient ripples on the power supply bus. The original bus voltage time-series sampling point sequence continuously output by this data source module within a time sliding window is extracted; the original bus voltage time-series sampling point sequence is unidirectionally pushed into a low-pass digital filter with a preset boundary frequency of 100 Hz, and smoothing filtering algebraic operations are performed to remove high-frequency switching noise, outputting a clean voltage baseband sequence; the voltage baseband sequence is traversed, and the highest and lowest extreme points of the global voltage within the window are retrieved and extracted, and algebraic subtraction is performed on the two to generate a peak-to-peak difference scalar; the pre-fixed rated standard voltage constant of the power supply bus is obtained, and the peak-to-peak difference scalar is divided by the rated standard voltage constant, and a division algebraic operation is performed to obtain its quotient; this dimensionless quotient result is finally established as the historical bus voltage fluctuation characteristic. The historical bus voltage fluctuation characteristic represents the relative volatility of the power grid's power supply efficiency over a specified period in the past; it is a dimensionless floating-point proportional constant with a value between 0 and 1; the preferred value fluctuates between 0.01 and 0.05; the empirical range is limited to 0 to 0.15.

[0047] The process further involves a nonlinear derating step for the dynamic safety margin penalty factor: extracting a pre-configured dimensionless boundary derating scaling constant. Its core function is to eliminate the dimensional differences between heterogeneous environmental parameters such as temperature gradient and voltage fluctuation rate, mapping them to a unified penalty weight base. This boundary derating scaling constant is defined as an externally controllable configuration dictionary item. Before each scheduling task initialization, the underlying logic controller performs a read-only retrieval operation on a locally stored non-volatile parameter spreadsheet file via a preset interface protocol, reading the corresponding key-value values ​​and loading them into the control memory space. The boundary derating scaling constant represents the basic scaling ratio characterizing the transformation of environmental degradation parameters into safety margin penalty weights; the preferred value is preset to 0.025; the empirical range is limited to 0.01 to 0.10. Based on the dimensionless boundary derating scaling constant, historical bus voltage fluctuation characteristics, and environmental temperature gradient parameters, a nonlinear mapping is performed to generate the dynamic safety margin penalty factor. Specifically, the nonlinear mapping employs a least-squares quadratic polynomial fitting logic derived from experimental calibration.

[0048] By executing the calibration benchmark construction steps, a controlled test environment chamber is constructed, and the test equipment is placed in this environment. Historical full-load stress test sample instruction sets are continuously input, forcing the underlying hardware to execute concurrently at full speed. An independent environmental monitoring interface is configured to synchronously extract the environmental temperature gradient parameter and historical bus voltage fluctuation characteristics at a sampling rate of one second. The process continues until the bus power supply module triggers the hard fuse protection mechanism, recording the temperature gradient value and voltage fluctuation rate at the critical breakdown point, as well as the ratio of the equipment's rated load capacity to its actual drop load capacity. This ratio is established as the ideal penalty dependent variable. Further, at least two thousand sets of scatter data pairs containing temperature gradient, voltage fluctuation rate, and ideal penalty dependent variable are collected to construct a three-dimensional dataset. Based on the least squares statistical analysis logic, with temperature gradient and voltage fluctuation rate as independent variables and the ideal penalty dependent variable as the target output variable, the coefficient scalars of each order of independent variables under the condition of minimizing the mean square error are solved, thereby fitting and generating a second-order polynomial relationship. In actual scheduling and operation, the real-time acquired heterogeneous parameters are input into the pre-fitted second-order polynomial, and algebraic operations are performed: Dimensional alignment logic is executed beforehand. By extracting the preset temperature gradient alignment transformation constant, the ambient temperature gradient parameter is divided by this temperature gradient alignment transformation constant to eliminate the physical dimensions of temperature and time, outputting the first dimensionless mapped scalar; a preset voltage fluctuation relative normalization constant is extracted, and the historical bus voltage fluctuation rate characteristics are multiplied and scaled by this voltage fluctuation relative normalization constant, outputting the second dimensionless mapped scalar; the boundary derating scaling constant is extracted and used as the basis constant of the fitted equation, executing the following algebraic operation flow to concretize the implementation of the second-order polynomial: in the arithmetic logic unit of the physical controller, respectively... Calculate the algebraic squares of the first and second dimensionless mapping scalars; extract the pre-set empirical fitting coefficients for temperature degradation and voltage degradation based on the stress test sample dataset, and perform pure numerical cross-multiplication operations with the two algebraic squares respectively to extract the output temperature penalty component and voltage penalty component; perform addition and accumulation on the temperature penalty component and voltage penalty component to obtain the comprehensive degradation weight; perform algebraic subtraction on the algebraic constant 1 and the comprehensive degradation weight to obtain the intermediate margin retention coefficient; finally, perform algebraic multiplication on the boundary derating scaling constant and the intermediate margin retention coefficient. Output the final pure numerical value of the product, which is established as the dynamic safety margin penalty factor.

[0049] The empirical fitting coefficient for temperature deterioration is a statistical constant representing the weight of the impact of ambient temperature step change on power supply dips; the preferred value is 0.015; the empirical range is limited to 0.001 to 0.1. The empirical fitting coefficient for voltage deterioration is a statistical constant representing the weight of the impact of bus fluctuation on power supply dips; the preferred value is 0.035; the empirical range is limited to 0.001 to 0.1.

[0050] The temperature gradient alignment conversion constant characterizes the maximum expected temperature rise rate that the environmental cooling system can tolerate, serving as the denominator benchmark for eliminating the dimension of temperature; it is a constant with the dimension of "degrees per second"; the preferred value is 0.5; the empirical range is limited to 0.1 to 2.0. The voltage fluctuation relative normalization constant characterizes the linear mapping gain of grid fluctuation amplitude to the algebraic penalty weight; the preferred value is 10.0; the empirical range is limited to 1.0 to 20.0. The dynamic safety margin penalty factor (denoted as...) ), which represents the nonlinear attenuation ratio caused by environmental degradation to the bearing capacity of the power supply bus; the preferred value is 0.92; the empirical range is limited to the interval [0.85, 1.0].

[0051] The process involves performing dynamic overflow verification and vector generation steps. A pure algebraic multiplication scaling operation is applied to the first power load boundary data using a dynamic safety margin penalty factor, forcibly stripping away some available margin. After obtaining the derated dynamic power load boundary data, a pre-configured nominal bus voltage constant characterizing the bus potential characteristics is extracted. The predicted transient concurrent power consumption peak value, derived from a calculation based on the logical idle time offset parameter set scheduling arrangement, is also extracted. Dimensional alignment is triggered before comparison. Specifically, this involves extracting the nominal bus voltage constant stored in the machine's local read-only memory; extracting the predicted transient concurrent power consumption peak value derived from the logical idle time offset parameter set scheduling arrangement calculation; performing a dimensionality reduction division operation by dividing the predicted transient concurrent power consumption peak value by the nominal bus voltage constant; extracting the quotient output of this division operation, mapping it, and establishing it as the predicted transient concurrent current peak value; and performing an algebraic comparison of the predicted transient concurrent current peak value with the derated dynamic power load boundary data using the same dimensions. When the peak value of the predicted transient concurrent current is less than the dynamic power supply load boundary data, the verification is considered successful.

[0052] The nominal bus voltage constant represents the rated potential difference of the power supply network of the machine under ideal no-load conditions; it is a static constant with the dimension of volts; the preferred value is 48.0 volts or 12.0 volts as specified on the local machine hardware nameplate; the empirical range is strictly locked within the physical specification value of the machine hardware, without any flexible adjustment space, and generates a cross-domain spatiotemporal offset control vector to instruct the underlying time domain resource allocation nodes to perform time slice offset rearrangement.

[0053] For step S5, the detailed implementation of generating the topology node isolation configuration vector and the dual-path parallel closed-loop control action is described below: During stable operation, it is necessary to monitor the time evolution derivative of the scheduling translation vector representing each independent workstation. If leakage, short circuit, or micro-logic deadlock occurs in the underlying hardware, the energy topology projected into the virtual space causes the equations to diverge, and the derivative spikes. In industrial settings, due to millisecond-level high-frequency spike interference from the power grid or algorithm jitter in Chebyshev polynomial fitting at a few singular points, the derivative is prone to brief transient overshoots. If an absolute single-point instantaneous decision mechanism is adopted, it will cause large-scale "false positives," frequently blocking healthy test workstations. This invention introduces an "evolutionary momentum information entropy verification mechanism based on a buffer queue."

[0054] The execution steps include outputting the control vector and initiating the underlying monitoring. The cross-domain spatiotemporal offset control vector generated in step S4 is output to the underlying control hardware layer. The system continuously extracts each translation vector from the target translation vector set. Based on the translation vector amplitudes obtained from adjacent clock cycles, it performs algebraic subtraction between the target translation vector amplitude of the current clock cycle and the historical translation vector amplitude of the previous adjacent clock cycle to generate an amplitude difference component. This amplitude difference component is then divided by the absolute time span of the adjacent clock cycle. Through discrete difference operations, the corresponding time evolution derivative is continuously calculated, which represents the differential gradient of the vector rate of change.

[0055] The momentum accumulation and transformation step of the divergent characteristics is executed to construct a first-in-first-out logical buffer queue with a preset depth. The function of this queue is to provide a low-pass smoothing tolerance band in the time dimension for high-frequency jitter data. In this embodiment, the preset depth is set to a storage addressing slot with a length of 8. It can perfectly cover a complete low-level polling retransmission cycle of the industrial control bus, filtering out numerical glitches caused by occasional communication errors and ensuring that continuous divergence caused by real physical leakage is not missed.

[0056] Based on the obtained preset window step size parameter, a corresponding number of continuous scheduling cycles are extracted; the time evolution derivatives within the extracted continuous scheduling cycles are extracted and pushed into the logical buffer queue in the order of absolute timestamps. The preset window step size parameter represents the total number of discrete sampling slices that the logical buffer queue can backtrack and accommodate in the temporal evolution; it is a positive integer scalar greater than zero; the preferred value is set to 8; the empirical range is limited to integers between 4 and 32; for the derivative sequence in the logical buffer queue, a pair of derivative values ​​based on adjacent timestamps are extracted sequentially; an algebraic subtraction operation is performed on the pair of derivative values ​​and their difference is obtained; the entire queue is traversed in this way and a set of pure algebraic sequences representing the divergence acceleration of the derivative is extracted and encapsulated as a second-order evolutionary gradient feature set.

[0057] The process involves calculating the trend information entropy scalar and determining anomalies. Mapping logic is then executed to calculate the trend information entropy scalar, with the input parameter anchored to the second-order evolutionary gradient feature set generated in the preceding steps. In the physical logic controller, a scalar register is initialized, its internal data is forcibly cleared to zero, and used as the initial base value for accumulation. The first differential feature value in the second-order evolutionary gradient feature set is extracted, and the process proceeds to the dynamic time step compensation step. Based on the global hardware clock unit at the test equipment's underlying layer, the current physical timestamp when the first differential feature value was generated, and the historical physical timestamp of the corresponding feature value from the previous scheduling cycle are extracted and recorded. An algebraic subtraction operation is performed between the current and historical physical timestamps, and their absolute difference is extracted and established as the dynamic compensation time step parameter representing the true physical time span. In the logic operation unit, an algebraic multiplication operation is performed between the first differential feature value and the dynamic compensation time step parameter calculated in real time for this cycle, obtaining an intermediate algebraic product with jitter immunity characteristics, representing the increment of the single-cycle step change. The dynamic compensation time step parameter represents the actual absolute time span between two adjacent evaluation actions after being affected by the jitter of the underlying bus transmission; it is a dynamic calculation scalar with nanosecond or microsecond dimensions; the preferred point value fluctuates in real time with the network state of each polling cycle, and in this embodiment, it fluctuates around 2.0 milliseconds. The intermediate algebraic product representing the increment of the single-cycle step momentum is obtained, and this intermediate algebraic product is added to the current value in the scalar register. The result is then overwritten back to the scalar register. Following the monotonically increasing order of the absolute timestamps, the next difference feature value in the second-order evolutionary gradient feature set is extracted cyclically, and the algebraic multiplication operation is repeated to obtain a new intermediate algebraic product. Addition algebraic operations and data overwriting operations are continuously performed on the scalar register. After verifying that all numerical items in the second-order evolutionary gradient feature set have been fully traversed without omission, the final pure algebraic scalar sum residing in the scalar register is extracted, established, and output as the trend information entropy scalar representing the system's divergent momentum accumulation state. By cascading discrete differentiation with continuous accumulation, the underlying breakdown momentum quantization is achieved without physical sensing. The trend information entropy scalar (denoted as...) The meaning of is the sum of momentum accumulation that characterizes the algebraic equation system falling into an unsolvable state due to leakage of micro-hardware; the preferred value is the dynamic cumulative output; the empirical range is limited to the interval between 0 and 100.

[0058] A preset irreversible divergent momentum threshold is obtained. This threshold is determined using a statistical boundary determination strategy derived from the distribution characteristics of historical fault sample data. Specifically, historical fault log files of machines that crashed or melted due to actual physical leakage or thermal breakdown during a past major overhaul cycle are extracted. For each log record confirmed as an actual physical failure, the preceding momentum accumulation algorithm is invoked to backtrack the trend information entropy scalar of the ten scheduling cycles prior to the fault. This batch of scalars is used to construct a reference divergent dataset, and discrete statistical analysis is performed to calculate the mean and standard deviation of all sample scalars. Based on the cumulative probability rule of normal distribution, quantile values ​​corresponding to the lower limit of the 99.7% confidence interval of this dataset are extracted. This quantile value is established as the preset irreversible divergent momentum threshold and burned as a static constant into a read-only configuration file for real-time system retrieval. This parameter represents the physical tolerance limit that allows the system to perform algorithm self-correction. The preset irreversible divergent momentum threshold is the critical criterion for determining whether the system is in an algebraic divergent self-healing state or an irreversible physical breakdown state; it is a non-negative positive real static constant; the preferred value is 4.5; the empirical range is limited to between 3.0 and 8.0. The trend information entropy scalar is compared with the irreversible divergent momentum threshold. If and only if the trend information entropy scalar is strictly greater than the irreversible divergent momentum threshold, the judgment condition that the time evolution derivative exceeds the preset convergence threshold is established and triggered, that is, the system is identified as having a real physical failure.

[0059] The dual-path closed-loop fallback degradation step is executed. Specifically, after establishing the abnormal limit condition, the abnormal blocking vector generation control flow is triggered: the underlying microprocessor extracts the physical topology index network address of the corresponding target test station where the energy overflow occurs; extracts the preset blocking operation code representing the highest interrupt priority and suspension action from the system read-only security layer; sends the physical topology index network address and blocking operation code into the register array, performs bit-by-bit splicing operation according to the preset industrial bus protocol specification, and encapsulates and generates a topology node isolation configuration vector to represent the logical blocking state of the corresponding target test station, and immediately triggers the parallel closed-loop control action of the first logic branch and the second logic branch. The physical topology index network address means that it is a medium addressing constant that uniquely identifies the physical access slot of the workstation on the industrial bus; the preferred point value is dynamically allocated and resolved; the blocking operation code is configured as a forced test thread shutdown machine code specified by the underlying microcode hardwired; its preferred machine word value is hexadecimal 0xFF, which aims to use the extreme value decoding characteristics of digital hardware circuits to trigger the underlying fast interrupt and thread blocking.

[0060] In the time-domain blocking branch represented by the first logic branch, a forced overwrite operation is performed on the logic idle time offset parameter of the corresponding fault target test station in the cross-domain spatiotemporal offset control vector. A preset blocking extreme value upper limit is obtained, and this preset blocking extreme value upper limit replaces the original logic idle time offset parameter. The numerical width of this preset blocking extreme value upper limit is defined as the theoretical maximum scalar consistent with the bus width of the spatiotemporal addressing register of the hardware platform where the current time-domain resource allocation node resides. During the execution of the action, by injecting this theoretical maximum scalar into the corresponding task timing control stack, the timing evolution steps of the target test station are locked indefinitely outside the blocking limit. Thus, against the backdrop of the continuous advancement of the system's global clock cycle, the subsequent physical power extraction handshake request initiated by the station to the common bus is directly cut off from the micro-logic control plane. The preset blocking extreme value upper limit represents the unreachable time distance boundary that the spatiotemporal addressing control plane can accommodate; the preferred value is the full-load extreme value of the corresponding system architecture, and in this embodiment, it represents the pre-overflow critical value of a non-specific 32-bit logic stack.

[0061] In the algebraic domain isolation branch represented by the second logical branch, operations are performed on the virtual high-dimensional matrix space to be executed by the system in the next scheduling cycle. The topological envelope intersection matrix to be generated in subsequent calculation cycles is located. Normal inner product calculation for the faulty target test station is abandoned. Instead, the diagonal principal component matrix elements representing the topological characteristics of the autocorrelation energy of the target test station are directly extracted, and the data address space of these diagonal principal component matrix elements is forcibly overwritten and reset to a preset penalty bound constant. Based on the division quotient principle of Gaussian principal component elimination logic, the baseline principal component is set to tend towards infinity, causing all principal component elimination multipliers generated for the faulty target station in that column to be hard-compressed and converge to zero. Through this reset and division annihilation action, in the next rank reduction operation, the matrix characteristic iteration interference of the faulty station on other healthy stations is completely cleared and isolated in a purely algebraic manner.

[0062] The penalty limit constant is defined as the extreme value of the barrier representing the absolute logical isolation state in the virtual algebra space; it is the maximum legal constant that the system's floating-point arithmetic unit can represent; and the preferred point value is the arithmetic overflow critical safety value of the corresponding underlying processor.

[0063] Furthermore, the method in this embodiment mainly outputs two core evaluation parameters for over-limit control and fault confirmation in the later stage of concurrent test scheduling. The mapping between their value range and technical meaning is as follows: The first output parameter is the "dynamic safety margin penalty factor," whose value converges to a dimensionless floating-point range of 0.85 to 1. This value objectively characterizes the nonlinear attenuation ratio caused by the degradation of the internal environment of the global test equipment (such as heat accumulation and transient power grid fluctuations) on the physical current carrying capacity of the global power supply bus. When this dynamic safety margin penalty factor approaches the lower limit of 0.85, it objectively indicates that the power supply bus has degraded to an extremely dangerous critical derating state.

[0064] The second output parameter is the "trend information entropy scalar," which ranges from 0 to 100 as a positive real number. This parameter quantitatively reflects the cumulative momentum of the algebraic equations caused by microscopic leakage or logic deadlock in the underlying hardware, leading to an unsolvable state. The higher this value, the greater the objective probability of the target integrated circuit's physical circuit experiencing permanent thermal breakdown.

[0065] When the values ​​of the aforementioned output parameters undergo continuous monotonic evolution or abrupt step changes, the underlying physical power supply boundary and logic deadlock state are experiencing irreversible evolution. The "environment-coupled dynamic safety margin derating mechanism" of this method extracts historical bus voltage fluctuation characteristics and environmental temperature gradient parameters, and performs nonlinear algebraic mapping operations using pre-configured dimensionless boundary derating scaling constants; simultaneously, it is combined with the "buffer queue-based evolutionary momentum information entropy verification mechanism" to perform differential algebraic operations and integral accumulation operations on the derivative sequence within the logic buffer queue.

[0066] As the total heat dissipation in the test environment increases, the dynamic safety margin penalty factor calculated by this method exhibits a deterministic monotonically decreasing evolution. When a real leakage occurs at the bottom-level workstation, the high-frequency jitter of the time evolution derivative is stripped away by the time-compensated integral action, driving the trend information entropy scalar to produce a monotonically increasing cumulative step. This overcomes the technical pain point of "hidden severe operating condition critical breakdown" caused by relying on the rated static early warning threshold in existing technologies, while eliminating the probability of false alarms caused by millisecond-level spikes in the power grid.

[0067] The logical source analysis for the generation of the dynamic safety margin penalty factor and the trend information entropy scalar is as follows: The final output value of the ambient temperature gradient parameter and the dynamic safety margin penalty factor are negatively correlated. The current control logic extracts the ambient temperature gradient parameter and performs an algebraic square multiplication and addition operation by combining it with the empirical fitting coefficients for temperature deterioration. An increase in the original value directly leads to a decrease in the final product derating scalar. The surge in the ambient temperature ramp-up rate objectively indicates that the test equipment's heat dissipation system is approaching its physical dissipation bottleneck, causing the internal leakage current of the chip to amplify exponentially with the thermal base, reducing the effective load-bearing capacity of the global bus. This negative correlation design, from a thermodynamic perspective, supports the beneficial effect of this method in reducing the concurrent task density threshold in advance before thermal runaway.

[0068] Historical bus voltage fluctuation characteristics exhibit a non-linear negative correlation with the dynamic safety margin penalty factor. After extracting this feature and performing relative normalization scaling and square-fit algebraic operations, the increase in its absolute value also strips away the available power supply boundary margin. The AC ripple caused by low-frequency grid jitter can trigger instantaneous drops in the bus absolute voltage. If the original static power consumption is maintained, the bus is easily damaged by the low-voltage, high-current effect. This design transforms the physical noise of the macroscopic power grid into a defined algebraic reserve protection line width, directly serving the invention's purpose of avoiding concealed circuit breakers.

[0069] The dynamic compensation time step parameter is positively correlated with the trend information entropy scalar. The compensation step is obtained by extracting adjacent physical timestamps and performing algebraic subtraction, then multiplying it with the derivative difference term using a term-by-term matching algebraic multiplication operation. When the underlying bus encounters severe communication congestion, the physical time interval of the evaluation action is passively lengthened; a larger absolute weight is given to the derivative divergence occurring within a longer delay period, consistent with the objective law of physical faults evolving into deeper states. This positive correlation design compensates for the blind spot in state evaluation caused by network jitter, supporting the method's ability to effectively distinguish between "transient noise interference" and "permanent physical breakdown."

[0070] Furthermore, the method of this embodiment is deployed in an abstract service execution environment for parallel scheduling testing of a high-density integrated circuit chip array. In this environment, a global hardware clock synchronization pulse continuously drives the flow of underlying atomic instructions, while environmental stress and external power grid noise apply dynamic physical disturbances to the underlying power supply bus.

[0071] The computational logic of this method follows a data flow pipeline: when the ambient temperature gradient and voltage fluctuation characteristics captured by the underlying hardware interface change, the current control logic extracts preset parameters and uses empirical attenuation and dimensionality reduction operations based on historical data to refresh the derating coefficient variable in the internal logic register in real time. When the derivative fluctuation caused by concurrent tentative scheduling calculation is input to the first-in-first-out logic buffer queue, the accumulated state of the internal trend information entropy register is continuously overwritten and updated through the adjacent timestamp differential accumulation logic.

[0072] Based on the above operating state model, when the input parameters fall within different physical extreme value distribution ranges, the execution response of the method in this embodiment is shown in the table below. The boundary derating scaling constant is set to 0.025, and the irreversible divergent momentum threshold is set to a constant of 4.5.

[0073] Table 1: Examples of response calculations performed by this method under different abstract physical disturbance conditions

[0074] Table 1 shows the comprehensive environmental degradation weighting factor, which characterizes the current control logic by performing algebraic division and multiplication dimensional alignment operations based on static constants on the environmental temperature gradient parameter and historical bus voltage fluctuation characteristics, respectively, to extract a dimensionless mapped scalar. Then, algebraic square operations are performed on the dimensionless mapped scalars, and cross-product operations are performed with the empirical fitting coefficients for temperature degradation and voltage degradation, respectively. Finally, the two sets of multiplicative quantities are summed algebraically, and their pure numerical sum is established as the comprehensive environmental degradation weighting factor. This parameter quantitatively calibrates the resource mismatch degree of the overall macroscopic processing logic controlled by this method under the stress coupling of multidimensional physical environment. Its nonlinear amplification directly maps the rate of fall of the global test power supply bus from a safe steady state to a critical fusing state.

[0075] Based on the execution logic response data presented in Table 1, this method establishes the following two advantages for direct verification: The first advantage is the verification of an adaptive derating interception mechanism for environmental coupling. Comparing the data evolution between scenario one and scenario four, when the input parameters jump from low disturbance to high-heat, high-disturbance coupled conditions, the dynamic safety margin penalty factor calculated by this method actively converges and decays from 0.999 to 0.902. An algebraic subtraction operation is performed between the constant value 1 and the derating factor value 0.902, yielding a difference of 0.098. This difference is used as the dividend, and the constant value 1 is used as the divisor for division. The resulting quotient is converted into a percentage, demonstrating that the method of this embodiment forcibly strips 9.8% of the theoretically available total power supply capacity under this adverse condition. This theoretical deduction not only proves that this method has a keen nonlinear suppression capability against environmental degradation trends but also quantitatively reveals its advancement over existing purely static boundary technologies in avoiding hidden thermal breakdown.

[0076] The second advantage is the verification of the momentum buffer mechanism's anti-false blocking performance. Comparing the feature evolution of scenarios five and six, in scenario five, facing the single-point time evolution derivative step caused by high-frequency noise, this method utilizes the difference and time compensation integral effect of the logic buffer queue, so that the final output trend information entropy scalar only accumulates to a value of 3.65, successfully residing within the lower limit of the irreversible divergent momentum threshold of 4.5. The value of 1, the number of false blocking triggered by the existing absolute single-point instantaneous decision logic, is extracted and algebraically subtracted from the number of blocking triggers of this method after momentum smoothing, to obtain the absolute difference value of 1. This absolute difference is used as the dividend, and the existing logic trigger count value of 1 is used as the divisor to perform a division operation and convert it into a percentage, proving that the method of this embodiment has a 100% relative improvement in the anti-false killing rate for high-frequency transient spikes. In contrast, in scenario six, the entropy scalar soars to a value of 6.88 due to the continuous second-order divergent acceleration caused by the physical short circuit. This method accurately identifies and fully triggers the topology node isolation configuration action. The above evolution quantitatively reveals that this method achieves a technical balance between ensuring peak shaving agility and maintaining test throughput stability, two conflicting metrics.

[0077] This embodiment defines the practical application range by constructing an objective quantitative model of continuous variables. For high-volume integrated circuit concurrent testing, the transient derivative divergence of a single workstation and the thermal accumulation of the global bus both manifest as continuous accumulation of physical energy. This quantitative model abandons single-dimensional transient extremum judgment and directly extracts continuous algebraic evaluation values ​​characterizing the degree of approximation to the physical limit boundary, thus establishing a solid objective mapping basis for the response actions of the executing entity. To accurately define the judgment boundary, the relevant high-order probability distribution statistics and limit convergence calculations are all deduced based on historical full-load stress test sample data during the offline evaluation phase, and the critical mutation points locked by the deduction are solidified as preset algebraic threshold constants for online execution of this method.

[0078] The critical breakdown risk approximation rate is defined as follows: it represents the "trend information entropy scalar" extracted by the current control logic from the real-time integral accumulation output of the target test station, and simultaneously extracts the "irreversible divergent momentum threshold" (a preset constant of 4.5 in this embodiment) pre-burned into read-only memory. Using the trend information entropy scalar as the dividend and the irreversible divergent momentum threshold as the divisor, an algebraic division operation is performed. The dimensionless quotient value of the final output of this division operation is extracted and established as the critical breakdown risk approximation rate. This parameter quantitatively calibrates the risk deterioration depth of the execution logic defined by this method in the dimension of single-node physical leakage interference. Based on the above quantitative evaluation system, this invention establishes the following three practical application intervals with clear boundaries and corresponding operations: Interval 1, normal throughput lossless release interval: The dynamic safety margin penalty factor calculated in real time by the current control logic falls within the preset safe steady-state interval, and the critical breakdown risk approximation rate calculated for each independent test station is less than the preset ultimate breakdown limit constant; the value range of the dynamic safety margin penalty factor is limited to between 0.98 and 1, and in this embodiment, it is preferably configured as 1; the value of the ultimate breakdown limit constant is fixed as an absolute algebraic constant 1. When the feature evaluation parameters fall into this interval, the method of this embodiment extracts the original first power load boundary data without derating, and directly generates and continuously sends cross-domain spatiotemporal offset control vectors based on the target translation vector set calculated by the current orthogonal rank reduction to instruct the underlying time domain resource allocation nodes to perform time slice offset rearrangement, driving the test machine to perform full test throughput at the highest physically permissible concurrency density.

[0079] Interval Two, Environmental Coupling Dynamic Derating Compensation Interval: The dynamic safety margin penalty factor, calculated in real-time by the current control logic, falls to the preset degradation suppression lower limit interval. Simultaneously, the critical breakdown risk approach rate of all single-point test stations is still forcibly constrained within a safe boundary region less than the ultimate breakdown limit constant. The value range of this dynamic safety margin penalty factor is defined between 0.85 and 0.985 (excluding the degradation suppression lower limit interval boundary of 0.985). The ultimate breakdown limit constant remains fixed as an absolute algebraic constant of 1. The objectivity of its boundary is established by the underlying electrical objective law that the actual physical safety bearing capacity of the power supply bus material undergoes nonlinear rapid decay under the dual pressure of high-temperature thermal accumulation and large low-frequency voltage fluctuations. When the characteristic value falls into this logic range, the method of this embodiment forcibly extracts the dynamic safety margin penalty factor that deviates from the benchmark, performs an algebraic multiplication scaling operation on it and the first power load boundary data, and forcibly shrinks the upper limit of the absolute current allowable threshold calculated by concurrent testing; based on the dynamic power load boundary data generated by this derating, the same dimension algebraic overflow verification logic is re-executed, and by passively lengthening the logic idle time offset parameter of the corresponding test station, the local instruction flow time efficiency is actively sacrificed in order to ensure the system-level defense safety of the global bus from thermal fuse failure.

[0080] Interval 3, Irreversible Physical Breakdown Isolation Interval: This interval is derived from the continuous integration of the differential and dynamic compensation time step based on the current control logic using the logic buffer queue. It represents the critical breakdown risk approach rate for the target test station, and its value is ultimately greater than or equal to the limit breakdown boundary constant. This evaluation constant has an absolute value of 1, which is mathematically equivalent to the calculated trend information entropy scalar exceeding the preset irreversible divergent momentum threshold of 4.5.

[0081] When the parameter determination falls within this boundary range, the current control logic immediately triggers a dual-path parallel closed-loop degradation action with the highest interrupt priority. The logic idle time offset parameter of the faulty target station is extracted and forcibly overwritten in the time-domain blocking branch, replaced by a preset blocking upper limit equal to the theoretical maximum scalar of the hardware register bit width, permanently severing its underlying instruction execution handshake flow. Simultaneously, the diagonal principal element matrix element representing the autocorrelation energy topology of the faulty target station is forcibly extracted in the algebraic domain isolation branch, and its data address space is forcibly overwritten and reset to a penalty limit constant equal to the arithmetic overflow critical safety value of the underlying floating-point unit. From a purely algebraic perspective, this completely excludes and clears the malicious interference input of this short-circuit node to the subsequent rank reduction feature iteration of all healthy stations.

[0082] Figure 1 This is a schematic diagram of the core technical route of the dynamic scheduling and power balancing method for multi-station parallel testing of IC chips provided in this embodiment of the invention. The diagram shows the physical entity of the multi-station test machine with an abstract hardware topology, and intuitively reveals the full-cycle deterministic control logic of the underlying industrial control software when handling massive concurrent test tasks, from state acquisition, algebraic dimensionality reduction, time-domain rearrangement to physical anomaly isolation in the form of flowcharts; Figure 1 The first flowchart, "Multidimensional State Acquisition and Predictive Envelope Extraction," corresponds to steps S0 to S2 of this invention. The current control logic obtains the atomic instruction flow progress and the upper limit of bus current redundancy through the hardware interface, and uses a pre-trained nonlinear neural network with time-series memory characteristics to transform it into polynomial predictive envelope data that eliminates absolute physical timestamp constraints, thus cutting off the pollution of scheduling by physical delay. The second flowchart, "Matrix Rank Reduction and Translation Vector Calculation," corresponds to step S3. By constructing a full-rank topological envelope intersection matrix in a virtual high-dimensional space and performing an orthogonal rank reduction operation based on the algebraic number, the target translation vector set representing the collision-free power consumption distribution is directly solved. The third flowchart, "Overflow Verification and Cross-Domain Spatiotemporal Rearrangement," corresponds to step S4. The translation vector is transformed into logical idle time, and a dynamic safety penalty factor generated by ambient temperature and voltage fluctuations is fused to perform a same-dimensional overflow verification. After the verification passes, a cross-domain spatiotemporal offset control vector is issued to achieve environmentally adaptive derating. The fourth flowchart, “Derivative Evolution Monitoring and Topology Isolation”, corresponds to step S5. By calculating the time evolution derivative of the translation vector and accumulating the trend information entropy scalar, once it is determined that the momentum exceeds the irreversible divergence threshold, the physical leakage failure state is immediately established, and the topology node isolation configuration vector is output to execute the highest priority dual-path hardware blocking.

[0083] Figure 4This diagram illustrates the numerical verification results of the dynamic safety margin derating mechanism for environmental coupling and the evolutionary momentum information entropy verification mechanism based on a buffer queue, as described in this invention embodiment. The numerical verification aims to test the theoretically calculated response characteristics and closed-loop control logic of this method under preset standardized boundary conditions (covering discrete physical disturbances such as normal stability, ambient temperature rise, high-frequency noise injection, and physical leakage, etc.). The horizontal axis represents the evolution of the continuously deduced verification scenario; the left vertical axis represents the dynamic safety margin penalty factor calculated by the current control logic; and the right vertical axis represents the extracted trend information entropy scalar. Two different curves quantify the extracted safety margin derating response value and the accumulated trend information entropy state, respectively. The horizontal dashed lines in the diagram mark the boundary of the degradation suppression lower limit interval with a value of 0.985 and the boundary of the irreversible divergent momentum threshold with a value of 4.5, respectively. The vertical background color layering of the graph maps the normal throughput lossless release interval, the environmental coupling dynamic derating compensation interval, and the physical breakdown irreversible blocking isolation interval, divided by the aforementioned algebraic boundaries.

[0084] like Figure 4 As shown, the numerical calculation results reveal the algebraic determinism of the method's accurate projection of multidimensional feature quantities onto the three practical application intervals when dealing with complex multidimensional physical interference. The heterogeneous parameter nonlinear mapping and time step differential accumulation method proposed in this embodiment can achieve highly adaptive and precise control. When the operating condition evolves towards high heat and high disturbance, the calculated safety margin derating curve exhibits a definite nonlinear convergence characteristic. Its calculated value smoothly drops from 0.999 below the absolute threshold reference line of 0.985, falling back to 0.974 and even lower at 0.902. This spans various mild to severe heat accumulation scenarios, quantitatively establishing the sensitivity of this method in performing defensive derating switching from interval one to interval two. In contrast, the trend information entropy accumulation curve shows a different trend when facing high-frequency grid noise spikes. Due to the single-cycle momentum absorption effect of this method, its calculated value (corresponding to a dynamic safety margin penalty factor of 0.988, still residing in interval one) converges to 3.65, safely residing below the threshold reference line calibrated to 4.5, thus avoiding a false fall into interval three. Only when the input is a continuous second-order divergent characteristic representing a real physical short circuit, the information entropy scalar curve undergoes a significant step, with its calculated output value being 6.88, crossing the irreversible divergent momentum threshold boundary and directly triggering isolation locking towards interval three. This directly confirms that this method can accurately distinguish between "transient noise interference" and "permanent physical breakdown," achieving global protection while avoiding false blocking.

[0085] The computational logic involved in this application can be constructed using algorithms such as regression analysis in machine learning, establishing a mathematical model by analyzing the inherent trends and interrelationships of the collected parameters. This process can be implemented using specialized computational tools (such as Python's Scikit-learn library or the R language environment). Throughout all calculations, to eliminate the influence of different physical dimensions and ensure that data is compared and analyzed on the same scale, the input parameters in each formula are dimensionless. The dimensionless techniques used include, but are not limited to, max-min normalization or Z-score standardization.

[0086] The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0087] It should be emphasized that the foregoing embodiments are merely illustrative of preferred implementations of the present invention and are not intended to limit the scope of protection of the present invention. This application also provides a computer-readable storage medium having computer program instructions stored thereon.

Claims

1. A dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips, characterized in that, The specific steps include: S0: Obtain a neural network model trained based on historical industrial test sequences from multiple workstations, and a manifold space topology mapping rule with the number of instruction logic execution steps as an independent variable; S1: Obtain first instruction logic progress data representing the distribution of task microstate transitions within each independent test station via a preset logic interaction interface, and obtain first power load boundary data representing the dynamic physical boundary limitations of the power supply efficiency of the global test environment. S2: Input the first instruction logic progress data into the neural network model and extract the second prediction envelope data representing the topological equation of the future energy allocation requirements of each independent test station; wherein, the data structure of the second prediction envelope data is a set of polynomial coefficients after removing the absolute physical timestamp constraint; S3: Based on the manifold space topology mapping rule, normalization reconstruction processing is performed on the second predicted envelope data, and the reconstructed second predicted envelope data is independently mapped to the virtual high-dimensional matrix space. The substitution number orthogonal rank reduction operation is performed to calculate the target translation vector set used to characterize the collision-free distribution state of concurrent power consumption of each independent test station. S4: Based on the target translation vector set and the first power load boundary data, generate a cross-domain spatiotemporal offset control vector to instruct the underlying time domain resource allocation node to perform time slice offset rearrangement; S5: Output the cross-domain spatiotemporal offset control vector and continuously calculate the time evolution derivative corresponding to each translation vector in the target translation vector set; perform divergence trend calculation based on the time evolution derivative; when the calculated momentum feature representing the divergence trend of the workstation translation vector exceeds the preset convergence threshold, generate a topology node isolation configuration vector to represent the logic blocking state of the corresponding target test workstation.

2. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 1, characterized in that: In step S0, the neural network model is configured with an input data structure representing the microstate transition characteristics and an output data structure representing the polynomial coefficients. The neural network model is a nonlinear mapping model with temporal memory characteristics trained by the convergence of a historical concurrent power consumption feature vector set after outlier removal and dimensionless normalization. Chebyshev polynomial approximation verification logic is pre-constrained in the output layer. The manifold space topology mapping rule is configured to force the parsing and discard of asynchronous time-series label sequences contained in the input data.

3. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 2, characterized in that: In step S1: the first instruction logic progress data represents the cumulative count characteristics of atomic instruction flow within the preset logic boundary test sequence; The first power load boundary data characterizes the transient current redundancy upper limit of the multi-station power supply bus.

4. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 3, characterized in that: In step S2, which involves extracting the second predicted envelope data representing the topological equations of future energy allocation requirements for each independent test station, the specific steps include: The first instruction logic progress data is processed to predict the nonlinear transient power consumption manifold mapping curve with continuous fluctuation characteristics; based on the Chebyshev polynomial approximation verification logic, the nonlinear transient power consumption manifold mapping curve is truncated and reconstructed into an orthogonal discrete coefficient value sequence with the characteristic that the polynomial approximation margin converges to the preset truncation error band, and it is encapsulated as the polynomial coefficient set.

5. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 4, characterized in that: In step S3 of performing the orthogonal rank reduction operation of the substitution number, the following is included: constructing a topological envelope intersection matrix in the virtual high-dimensional matrix space based on the set of polynomial coefficients of each independent test station; Extract the non-zero off-diagonal elements from the topological envelope intersection matrix. These non-zero off-diagonal elements characterize the transient concurrent power consumption overlap region features of the target integrated circuit chip at the physical execution level. Based on Gaussian-like principal component elimination logic, the principal component elimination multipliers required to make all non-zero, non-diagonal elements in the intersection matrix of the topological envelope zero are solved sequentially. For all principal component elimination multipliers belonging to the same independent test station matrix row, row-by-row accumulation and aggregation operation is performed. The output aggregated scalar is established as the corresponding minimum affine transformation parameter. Then, all the minimum affine transformation parameters are aggregated and encapsulated into a one-dimensional target translation vector set.

6. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 5, characterized in that: The step of constructing a topological envelope intersection matrix in the virtual high-dimensional matrix space based on the polynomial coefficient set of each independent test station specifically includes: Extract the set of polynomial coefficients corresponding to each independent test station; and extract the preset historical instruction execution cycle distribution characteristics and historical static power consumption distribution characteristics as benchmark parameters; introduce the dimensionless spatiotemporal mapping transformation coefficients established based on the range ratio of the benchmark parameters, and use the spatiotemporal mapping transformation coefficients to perform normalization scaling processing on the set of polynomial coefficients; Based on the set of polynomial coefficients after normalization and scaling, a step-by-step product and continuous accumulation operation of coefficients of the same order between two independent test stations is performed in the virtual high-dimensional matrix space to form the envelope inner product cross-validation logic. Extract the pure algebraic scalars from the output of the envelope inner product cross-validation logic and use them as off-diagonal matrix elements to characterize the degree of intersection interference. Simultaneously extract the pure algebraic scalars from the autocorrelation inner product of the polynomial coefficient set itself and use them as diagonal principal component matrix elements. Then, based on the preset workstation topology index, combine the off-diagonal matrix elements with the diagonal principal component matrix elements to generate a full-rank topological envelope intersection matrix.

7. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 6, characterized in that: Step S4, which generates the cross-domain spatiotemporal offset control vector, includes: Extract the amplitude features of each translation vector in the target translation vector set and convert them into logical idle time offset parameters configured at the corresponding test station; Based on the first power load boundary data, an overflow check is performed on the set of logical idling time offset parameters, and the cross-domain spatiotemporal offset control vector is generated after the check passes.

8. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 7, characterized in that: The step of performing overflow verification on the set of logic idling time offset parameters based on the first power load boundary data specifically includes: extracting the historical bus voltage fluctuation characteristics and ambient temperature gradient parameters within a preset time sliding window; Extract the pre-configured dimensionless boundary derating scaling constant, and perform a nonlinear mapping on the boundary derating scaling constant based on the historical bus voltage fluctuation characteristics and the ambient temperature gradient parameter to generate a dynamic safety margin penalty factor. The dynamic power load boundary data is obtained by performing a multiplicative scaling operation on the first power load boundary data using the dynamic safety margin penalty factor; The nominal bus voltage constant, which characterizes the bus potential, is extracted. The predicted transient concurrent power consumption peak value, derived from the set of logic idle time offset parameters, is divided by the nominal bus voltage constant to perform a dimension reduction operation, thereby obtaining the predicted transient concurrent current peak value. The predicted transient concurrent current peak value is then compared with the dynamic power load boundary data using algebraic comparison with the same dimensions to complete the overflow verification.

9. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 8, characterized in that: In step S5, which involves continuously calculating the time evolution derivative of each translation vector in the target translation vector set and generating the topology node isolation configuration vector, parallel closed-loop control actions are triggered for the first logic branch and the second logic branch: In the first logic branch, the logic idle time offset parameter of the cross-domain spatiotemporal offset control vector for the target test station is overwritten with a preset blocking extreme value upper limit to block the instruction execution flow of the corresponding target integrated circuit chip. In the second logic branch, for the matrix position of the target test station in the topological envelope intersection matrix of the subsequent cycle, the corresponding diagonal principal element matrix element is extracted, and the diagonal principal element matrix element is forcibly reset to a preset penalty limit constant.

10. The dynamic scheduling and power consumption balancing method for multi-station parallel testing of IC chips according to claim 9, characterized in that: The divergence trend calculation based on the time evolution derivative, and the determination step when the calculated momentum feature exceeds the preset convergence threshold, specifically include: Construct a first-in-first-out logical buffer queue with a preset depth, extract the time evolution derivative within the continuous scheduling period associated with the preset window step size, and push it into the logical buffer queue in absolute time order. For the derivative sequence in the logical buffer queue, differential algebra operations are performed sequentially based on the derivative values ​​of adjacent timestamps to extract a set of second-order evolution gradient features that characterize the divergence acceleration of the time evolution derivative. Extract the physical timestamps synchronously latched by the global hardware clock unit for adjacent scheduling cycles, perform algebraic subtraction on adjacent physical timestamps to obtain the dynamic compensation time step parameter representing the real physical time span; perform algebraic multiplication on each feature item in the second-order evolution gradient feature set with the dynamic compensation time step parameter of the corresponding cycle, and then perform continuous integral accumulation operation; and establish the output of the continuous integral accumulation operation as the trend information entropy scalar. The trend information entropy scalar is established as the momentum feature, and the preset irreversible divergent momentum threshold is established as the preset convergence threshold; the trend information entropy scalar and the irreversible divergent momentum threshold are compared in magnitude; if and only if the trend information entropy scalar is greater than the irreversible divergent momentum threshold, the determination condition that the momentum feature exceeds the preset convergence threshold is established and triggered.

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