A method for testing precision guidance assemblies in parallel

Abstract

The application discloses a kind of precise guidance assembly parallel test methods, comprising: based on physical calibration, the sensitivity factor of quantization test task to specific interference source, establish logical resource availability model, interference suppression demand is converted into calculable, assignable logical resource unit;Analysis of the timing distribution characteristics of test task, predict future idle time window, and based on logical resource availability for interference-sensitive test task is actively reserved;Real-time monitoring of the quality of service of test task, when detecting the quality of service degradation based on logical resource availability, trigger the bypass arbitration mechanism based on policy snapshot and lock-free queue to make decision intervention.The application realizes the engineering and resource management of interference suppression demand, fundamentally breaks through the limitation that traditional scheduling can only manage physical hardware, provides a new, engineering-implemented management dimension for solving the complex systematic problem of multi-physical field coupling interference.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing, and more particularly to a parallel testing method for precision guidance components. Background Technology

[0002] In the field of modern high-end equipment manufacturing, precision-guided weapon systems integrate a large number of high-precision sensing and control components. During mass production, to improve testing efficiency, multiple performance testing tasks are typically executed in parallel. However, this parallel approach introduces irreconcilable coupling interference problems at the physical level, posing a serious challenge to the validity and reliability of test data. Its particularity lies primarily in the fact that transient coupling interference from multiple physics fields severely contaminates the test data.

[0003] Specifically, during parallel testing, the physical processes of different test tasks interact, forming complex interference chains. For example, during testing of a servo system, its drive motor generates a drastic current transient of up to 5-10 A / ms. According to engineering measurement data, this current change can induce transient magnetic field distortion exceeding ±2 μT in the test environment through spatial radiation and conduction paths. For high-sensitivity geomagnetic sensors, such abrupt changes in the background magnetic field are sufficient to introduce an outlier rate exceeding 15%, causing the heading angle calculation results to completely fail. Simultaneously, this high-speed current change can couple through the common power supply impedance, generating significant noise and voltage drops on the system power rail, thereby affecting the signal reception and processing quality of the GNSS module, leading to signal loss or a sharp drop in positioning accuracy. These interferences are not steady-state but exist in the form of transient pulses on the microsecond to millisecond scale, making it difficult for traditional hardware filtering or software averaging algorithms to fundamentally eliminate their effects.

[0004] To address these challenges, existing technologies primarily focus on optimizing the utilization of physical resources (CPU time, memory, bus bandwidth). They concentrate on the efficiency of physical resource allocation but fail to recognize that interference suppression is itself a logical resource that can be abstracted, quantified, and negotiated. Therefore, they cannot solve the core problem of ensuring the confidence level of test data under multi-physics interference.

[0005] Therefore, proposing a framework that can break through the traditional physical resource scheduling and construct a dynamic, negotiable, and traceable scheduling mechanism from the logical level to address the special challenges in the mass production testing of high-precision components is an urgent problem to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a parallel testing method for precision guidance components.

[0007] The objective of this invention is achieved through the following technical solution: A first aspect of the present invention provides a parallel testing method for precision guidance components, comprising the following steps: Based on physical calibration, the sensitivity factor of the test task to the corresponding interference source is quantified, and a logical resource availability model is established to transform the interference suppression requirement into a calculable and allocable logical resource unit. Analyze the temporal distribution characteristics of test tasks, predict future idle time windows, and proactively schedule test tasks that are sensitive to interference based on the availability of logical resources. The service quality of test tasks is monitored in real time. When service quality degradation is detected based on logical resource availability, a bypass arbitration mechanism based on policy snapshots and lock-free queues is triggered to make a decision intervention. The bypass arbitration includes idle window prediction and scheduling and task resumption execution. Based on the contract data of all registered test tasks, the system automatically orchestrates intelligent test sequences and automatically determines the validity of test results based on policy snapshots, generating audit reports.

[0008] Furthermore, the calculation formula for the logical resource availability model is as follows: ; In the formula, This represents the availability of logical resources at time t, with a value range of (0,1]. A higher value indicates a cleaner test environment; N is the number of currently active interference sources. This represents the current of the i-th interference source. Let τ be the rate of change of the current of the i-th interference source, and τ be the instantaneous excitation of the interference; i Let be the attenuation time constant of the i-th interference source; is the sensitivity factor for the i-th interference source, which characterizes the sensitivity of the test task to the i-th interference source; It is a numerical stability correction term.

[0009] Furthermore, the sensitivity factor of the quantization test task to the corresponding interference source includes: In a controlled electromagnetic environment, a set of known rates of change is injected into a Helmholtz coil. The current signal; Synchronous acquisition of data from the sensor under test in each group The change in output ΔB under excitation; For multiple groups A linear regression analysis was performed on the ΔB sample data, and the slope obtained from the fitted data is the sensitivity factor. ; The calibration process must ensure that the relative error of the linear fit is less than the first threshold.

[0010] Furthermore, the analysis of the temporal distribution characteristics of test tasks, prediction of future idle time windows, and proactive reservation of interference-sensitive test tasks based on logical resource availability include: Analyze the execution logs of potentially interfering tasks, extract the timestamp sequence from the execution logs, calculate the phase sequence of the timestamp sequence relative to the baseline period, predict the execution phase and jitter range, and establish a time series distribution model; Based on the time-series distribution model, the probability of conflict between the interference-sensitive test task and all potential interference tasks within the target time window is calculated; wherein, the conflict probability is obtained by calculating the cumulative distribution function overlap integral of the target time window and the interference recovery period predicted by each interference task. Window reservations for interfering tasks are approved only when the availability of logical resources is higher than the second threshold and the calculated total conflict probability is lower than the preset third threshold.

[0011] Furthermore, the establishment of the time series distribution model includes: Calculate the phase sequence of the timestamp sequence relative to a set reference period; Calculate the concentration of the phase sequence, whereby the concentration is defined as: 1 - (standard deviation of the phase sequence / mean of the phase sequence); If the concentration is higher than the preset fourth threshold, the potential interference task is determined to be periodic, and a periodic time distribution model of the corresponding potential interference task is established based on the average phase and phase jitter range.

[0012] Furthermore, the service quality of test tasks is monitored in real time. When service quality degradation is detected based on logical resource availability, a bypass arbitration mechanism based on policy snapshots and lock-free queues is triggered to intervene in decision-making, including: Before each scheduling decision, an immutable policy snapshot of the contract status, priority, and environmental parameters of all tasks in the system is generated and stored. When service quality degradation is detected based on logical resource availability, an arbitration command containing the target task ID and the new policy ID is sent through a lock-free circular queue. The execution unit receives arbitration instructions and executes interventions within microsecond delays, while using the associated policy snapshot as evidence for decision retrospection.

[0013] Furthermore, the discrimination condition for detecting service quality degradation based on logical resource availability includes: If the confidence level of the test result is lower than the fifth threshold, the historical error record is greater than the sixth threshold, and the error rate is greater than the seventh threshold, then it is determined that the service quality has deteriorated, and the bypass arbitration is triggered. If the decay rate of real-time monitoring of logical resource availability exceeds the eighth threshold, it is predicted that the interference will worsen, and the idle window is activated in advance to predict the migration of scheduled tasks.

[0014] Furthermore, the criteria for automatically determining the validity of the test results are as follows: The measured values ​​of the test data themselves meet the preset specification limits; During the test execution, the scheduling metadata in the policy snapshot must meet the standards, including a confidence level higher than the eighth threshold and the absence of contract default events requiring bypass arbitration.

[0015] Furthermore, the step of automatically orchestrating intelligent test sequences based on contract data of all registered test tasks and automatically determining the validity of test results based on policy snapshots, generating an audit report, is replaced by: In response to user commands, it automatically invokes intelligent test sequence orchestration, dynamic task scheduling and execution, and automatic test result determination functions. Based on the determination results and stored policy snapshots and contract breach records, it automatically generates audit reports that comply with quality standards.

[0016] Furthermore, the method dynamically manages the execution of multiple tasks with zero intrusion, which is achieved through the following steps: Initialize a protocol-independent event bus as a unified entry point for communication with the device under test, and configure it to passive listening mode; Throughout the test in passive listening mode, the event bus only receives all data packets on the device under test's communication bus and adds a high-precision timestamp to each packet, without sending any instructions to the device under test or occupying communication bandwidth.

[0017] The beneficial effects of this invention are: (1) To achieve the engineering and resource-based management of interference suppression requirements, by quantifying sensitivity factors based on physical calibration and establishing a logical resource availability model, abstract and qualitative anti-interference requirements are transformed into concrete and computable logical resource units. This allows the requirements of different test tasks for a clean environment to be quantified, allocated, and negotiated, just like CPU time and memory bandwidth. This fundamentally breaks through the limitation of traditional scheduling that can only manage physical hardware, and provides a new and engineering-implementable management dimension for solving the complex systemic problem of multi-physics coupling interference.

[0018] (2) A paradigm shift from passive response to proactive avoidance is achieved. The system no longer waits for interference to occur before compensating or retrying. Instead, it proactively predicts idle windows by analyzing historical data and makes advance reservations for sensitive tasks based on the availability of logical resources. This paradigm of prediction before execution significantly advances the timing of interference avoidance, fundamentally reducing the probability of high-value test tasks being exposed to risky environments.

[0019] (3) Through decision intervention based on policy snapshots and lock-free arbitration, the system is provided with the ultimate guarantee for dealing with sudden and unpredictable interference. When the prediction mechanism fails or service quality degradation is detected in real time, the system can quickly intervene through microsecond-level bypass arbitration, and its decision basis (policy snapshot) is completely preserved. This not only ensures that critical tasks (such as geomagnetic calibration) can be completed through mechanisms such as task recovery execution, but more importantly, it forms an immutable and fully traceable chain of decision evidence, meeting the stringent requirements of the high reliability field for the credibility of the testing process.

[0020] (4) The system achieves an intelligent closed loop across the entire testing process. Based on unified contract data and policy snapshots, it automatically completes the entire process from sequence arrangement and result judgment to report generation. This is not a simple script concatenation, but an intelligent evaluation centered on data confidence and scheduling context. This closed loop achieves seamless integration of test execution and quality auditing, greatly improving testing efficiency and the credibility of results. Attached Figure Description

[0021] Figure 1 A flowchart of a parallel testing method for a precision guidance component is provided as an exemplary embodiment of the present invention; Figure 2 A block diagram of a batch automated testing system provided as an exemplary embodiment of the present invention; Figure 3 A schematic diagram of the interface connection of a batch automated testing system provided in an exemplary embodiment of the present invention; Figure 4 A main flowchart of contract negotiation and scheduling provided for an exemplary embodiment of the present invention; Figure 5 A fast response timing diagram for bypass arbitration provided as an exemplary embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0023] See Figure 1 , Figure 1 A flowchart illustrating a parallel testing method for a precision guidance component according to an exemplary embodiment of the present invention is shown, comprising the following steps: Based on physical calibration, the sensitivity factor of the test task to the corresponding interference source is quantified, and a logical resource availability model is established to transform the interference suppression requirement into a calculable and allocable logical resource unit. Analyze the temporal distribution characteristics of test tasks, predict future idle time windows, and proactively schedule test tasks that are sensitive to interference based on the availability of logical resources. The service quality of test tasks is monitored in real time. When service quality degradation is detected based on logical resource availability, a bypass arbitration mechanism based on policy snapshots and lock-free queues is triggered to make a decision intervention. The bypass arbitration includes idle window prediction and scheduling and task resumption execution. Based on the contract data of all registered test tasks, the system automatically orchestrates intelligent test sequences and automatically determines the validity of test results based on policy snapshots, generating audit reports.

[0024] Specifically, in this exemplary embodiment: The first step is to achieve the engineering and resource-based management of interference suppression requirements. This is achieved by quantifying sensitivity factors based on physical calibration and establishing a logical resource availability model, transforming abstract, qualitative anti-interference requirements into concrete, computable logical resource units. This allows the clean environment requirements of different testing tasks to be quantified, allocated, and negotiated, just like CPU time and memory bandwidth. This fundamentally breaks through the limitations of traditional scheduling, which can only manage physical hardware, and provides a completely new, engineering-implementable management dimension for solving the complex systemic problem of multi-physics coupling interference.

[0025] The second step achieves a paradigm shift from passive response to proactive avoidance. Predictive scheduling based on time-series characteristics is the core manifestation of this exemplary embodiment's proactiveness. Instead of waiting for interference to occur before compensating or retrying, the system proactively predicts idle windows by analyzing historical data and pre-books sensitive tasks based on logical resource availability. This predict-then-execute paradigm significantly advances the timing of interference avoidance, fundamentally reducing the probability of high-value test tasks being exposed to risky environments.

[0026] The third step, through decision intervention based on policy snapshots and lock-free arbitration, provides the system with the ultimate guarantee against sudden and unpredictable interference. When the prediction mechanism fails or service quality degradation is detected in real time, the system can quickly intervene through microsecond-level bypass arbitration, and its decision basis (policy snapshot) is completely preserved. This not only ensures that critical tasks (such as geomagnetic calibration) can be completed through mechanisms such as task recovery execution, but more importantly, it forms an immutable and fully traceable chain of decision evidence, meeting the stringent requirements for the credibility of the testing process in high-reliability fields.

[0027] The fourth step achieves an intelligent closed loop across the entire testing process. Based on unified contract data and policy snapshots, the system automatically completes the entire process from sequence orchestration and result judgment to report generation. This is not a simple script concatenation, but rather an intelligent evaluation centered on data confidence and scheduling context. This closed loop achieves seamless integration of test execution and quality auditing, greatly improving testing efficiency and the reliability of results.

[0028] The following content will describe in detail the specific implementation methods of each exemplary embodiment: More preferably, in an exemplary embodiment, the calculation formula for the logical resource availability model is: ; In the formula, This represents the availability of logical resources at time t, with a value range of (0,1]. A higher value indicates a cleaner test environment; N is the number of currently active interference sources. This represents the current of the i-th interference source. Let τ be the rate of change of the current of the i-th interference source, and τ be the instantaneous excitation of the interference; i Let be the attenuation time constant of the i-th interference source; is the sensitivity factor for the i-th interference source, which characterizes the sensitivity of the test task to the i-th interference source; It is a numerical stability correction term (to prevent division by zero errors and take the smallest positive number).

[0029] Specifically, in this exemplary embodiment, the calculation formula of the constructed logical resource availability model and the physical meaning of each parameter are clear and explicit, jointly realizing accurate modeling, quantitative evaluation, and dynamic perception of complex interference environments, used for predictive scheduling, decay identification, and subsequent traceability as scheduling metadata. Wherein: (1) Formula The output range is (0,1], providing a standardized, dimensionless purity index. The higher the value, the purer the test environment and the more usable the logic resources. This fundamentally transforms the fuzzy concept of strong anti-interference capability into a continuous value that can be calculated in real time and compared, providing a precise quantitative basis for subsequent scheduling decisions.

[0030] (2) It deeply integrates the key features of multi-physics interference. The model does not simply accumulate interference sources, but rather uses three key parameters ( τ i , It scientifically describes the nature of interference: (Current change rate): It captures the core excitation source of transient electromagnetic interference, rather than steady-state current, which is more in line with the pulse-type interference characteristics caused by servo motor actions, power switching, etc. in reality.

[0031] τ i (Decayation Time Constant): The time decay characteristics of the disturbance are introduced, enabling the model to reflect the aftereffects of the disturbance, i.e. the time required for the environment to recover after the disturbance disappears. This is crucial for scheduling a clean window.

[0032] (Sensitivity Factor): Establishes a quantitative mapping from the interference source to the object being interfered with. Obtained through physical calibration. This makes the model no longer general, but specific to specific sensors (such as magnetometers) and specific interference sources (such as servo motors), which greatly improves the accuracy and practicality of the model.

[0033] (3) The model calculates in real time. The value is the core input driving the entire method, providing the core decision input for predictive scheduling and intelligent arbitration, including: For predictive avoidance: the system can predict a certain point in the future. Estimate the value to determine if the window is clean enough to meet the needs of highly sensitive tasks.

[0034] For service quality monitoring: through real-time monitoring A drop in service quality can immediately detect unexpected environmental deterioration and trigger an alert for service quality decline.

[0035] Regarding "arbitration and retroactive application": By combining historical curves with strategy snapshots, the environmental conditions at the time of the interference event can be fully reproduced, providing irrefutable data evidence for why the arbitration decision was made.

[0036] More preferably, in an exemplary embodiment, the sensitivity factor of the quantization test task to the corresponding interference source includes: In a controlled electromagnetic environment, a set of known rates of change is injected into a Helmholtz coil. The current signal; Synchronous acquisition of data from the sensor under test in each group The change in output ΔB under excitation; For multiple groups A linear regression analysis was performed on the ΔB sample data, and the slope obtained from the fitted data is the sensitivity factor. ; The calibration process must ensure that the relative error of the linear fit is less than the first threshold (preferably 5%).

[0037] Specifically, to ensure To ensure physical authenticity rather than subjective design, a reproducible physical calibration process was designed in this exemplary embodiment.

[0038] First, this process utilizes a Helmholtz coil to generate a uniform and controllable background magnetic field. Then, using a high-precision current probe and data acquisition card, it accurately measures the changes in current at different rates. Under excitation, the change in magnetic field (ΔB) output by the sensor under test (such as a magnetometer) is measured. This eliminates interference from stray magnetic fields in the environment and ensures the excitation signal. The purity and accuracy of the causal relationship between the calibrated sensor and the measured sensor response ΔB make the calibration... It truly reflects the sensitivity of the sensor's inherent physical characteristics to specific types of current transients, rather than a spurious correlation mixed with environmental noise.

[0039] Secondly, through multiple groups ( A linear regression analysis is performed on the sample (ΔB), and the slope obtained from the fitted result is the sensitivity factor. By collecting multiple sets of samples for fitting, random errors in a single measurement can be effectively suppressed, resulting in a more statistically robust dataset. The estimated value. And the slope. It directly characterizes the change in the sensor's output magnetic field caused by a unit current change rate. Its dimensions are clear, making it easy to compare and normalize between different sensors and interference sources.

[0040] Finally, the calibration process requires the relative error of linear fitting to be less than 5%, and the performance indicators of the calibration device (such as magnetic field uniformity and synchronous acquisition error) are solidified as technical specifications to ensure... These are traceable and verifiable physical measurements. This first threshold verifies the applicability of the linear model within this calibration range; if the relative error exceeds the limit, it indicates that the linear assumption is invalid or that there is a systematic error in the experimental process, requiring investigation and recalibration. Strict error control ensures the accuracy of the calibrated values. It features high accuracy and low dispersion, improving the availability of subsequent logic resources. This laid a solid foundation for accurate calculations.

[0041] More preferably, in an exemplary embodiment, the step of analyzing the temporal distribution characteristics of test tasks, predicting future idle time windows, and proactively scheduling interference-sensitive test tasks based on logical resource availability includes: Analyze the execution logs of potentially interfering tasks, extract the timestamp sequence from the execution logs, calculate the phase sequence of the timestamp sequence relative to the baseline period, predict the execution phase and jitter range, and establish a time series distribution model; Based on the time-series distribution model, the probability of conflict between the interference-sensitive test task and all potential interference tasks within the target time window is calculated; wherein, the conflict probability is obtained by calculating the cumulative distribution function overlap integral of the target time window and the interference recovery period predicted by each interference task. Window reservations for interfering tasks are approved only when the availability of logical resources is higher than the second threshold and the calculated total conflict probability is lower than the preset third threshold.

[0042] Specifically, in this exemplary embodiment, the predictive reservation mechanism achieves intelligent prediction and proactive avoidance of interference windows in the test environment by deeply integrating task behavior analysis, probabilistic conflict modeling, and real-time resource assessment. Specifically, in each step: First, the core mechanism of idle window reservation based on temporal distribution characteristics lies in identifying and utilizing the periodic or quasi-periodic characteristics exhibited by test tasks (such as servo motor tests) on a macroscopic scale. By extracting timestamp sequences, calculating their phase sequences relative to a baseline period (such as system scheduling beats), and analyzing their phase concentration, the system can accurately identify tasks with stable temporal patterns from seemingly chaotic historical logs. This provides a reliable data foundation for predicting their future behavior, making prediction possible rather than blind guessing.

[0043] Meanwhile, this mechanism abandons the simple "yes / no" conflict judgment and instead adopts a conflict probability calculation model based on the cumulative distribution function (CDF) overlap integral. Through quantitative risk assessment, it can calculate the specific probability value (e.g., 8.7%) of sensitive tasks encountering interference within the target time window, providing precise quantitative basis for decision-making, rather than a vague "high / low risk" qualitative judgment. By using the time-series CDF, the model naturally incorporates the timing jitter (uncertainty) of task execution, and the calculated probability value already includes the risk of the interfering task being executed earlier or later, making the assessment more comprehensive and robust. Furthermore, it supports multiple interference sources; the model can be easily extended to multiple potential interference sources, and by calculating the total conflict probability, it achieves a comprehensive risk assessment of complex interference environments.

[0044] Finally, the goal of prediction is to proactively reserve time windows with low conflict probability for disruptive tasks (such as the subsequent MAG_01), achieving predictive avoidance rather than passive response. Reservation approval is not based solely on conflict probability, but introduces a dual-condition judgment: Condition 1 (logical resource availability) The second threshold (e.g., 0.9) is a real-time environmental status check. Even if the probability of conflict is low, if the overall logical resource availability of the current system (i.e., the baseline of environmental purity) is already poor, it is not suitable to schedule highly sensitive tasks. This prevents futile attempts at local optimization in an overall poor environment. Condition 2 (total conflict probability < third threshold, e.g., 0.1): This is a specific risk assessment for a future window. Approval is only granted if the window is predicted to be sufficiently safe. This dual criterion of real-time status and future prediction makes the scheduling decision both proactive (actively seeking opportunities) and conservative (strictly controlling risks), maximizing the success rate of highly sensitive tasks while improving resource utilization.

[0045] Overall, this exemplary embodiment upgrades the scheduling strategy from a fixed time sequence to a dynamic, adaptive optimization approach. It dynamically updates the time sequence distribution model based on continuously updated historical logs, enabling predictions to adapt to the slow changes in task behavior. Instead of inserting tasks into a predetermined time schedule, the system proactively seeks and reserves the optimal low-conflict time window for sensitive tasks.

[0046] More preferably, in an exemplary embodiment, establishing the time-series distribution model includes: Calculate the phase sequence of the timestamp sequence relative to a set reference period; Calculate the concentration of the phase sequence, whereby the concentration is defined as: 1 - (standard deviation of the phase sequence / mean of the phase sequence); If the concentration is higher than the preset fourth threshold, the potential interference task is determined to be periodic, and a periodic time distribution model of the corresponding potential interference task is established based on the average phase and phase jitter range.

[0047] Specifically, the periodic time-series distribution model established in this exemplary embodiment achieves automatic identification and quantitative modeling of task periodicity through an intuitive and robust statistical measure called concentration. This indicator directly measures the degree of clustering of the phase sequence within the interval [0, period T). The higher the concentration, the more stable the execution time of each task is relative to the start of the period, and the more significant the periodicity. Only the mean and standard deviation of the phase sequence need to be calculated, resulting in low computational complexity. This makes it suitable for operation in resource-constrained embedded testing systems or monitoring threads requiring real-time analysis, thus meeting the system's real-time requirements. By comparing the calculated concentration with a preset fourth threshold (e.g., 0.85), the system can automatically and unambiguously determine whether a task has sufficient periodicity for prediction, eliminating the subjectivity and inconsistency of human intervention.

[0048] Once a task is determined to be periodic (with a concentration exceeding a threshold), the system establishes a time-series distribution model (such as a uniform distribution or Gaussian distribution) based on its average phase and phase jitter range. The average phase determines the anchor point or expected execution position of the periodic task on the time axis, serving as the benchmark for predicting its next execution time. The phase jitter range quantifies the uncertainty of the task's execution time; a small jitter range indicates very timely task execution and high prediction confidence, while a large jitter range indicates significant fluctuations in task execution time, requiring a wider window or a more conservative probability estimate for prediction. This probabilistic model is the precise input for subsequent calculations of conflict probabilities (e.g., through CDF overlap integrals).

[0049] In a specific exemplary embodiment, the specific implementation of conflict probability calculation is demonstrated based on two core steps: time series distribution modeling and cumulative distribution function integration. The first step is to establish a temporal distribution model for the interfering tasks. The system extracts the timestamp sequence of specific interfering tasks (such as SERVO_01 mentioned later) from the historical execution logs and calculates their phase distribution relative to the baseline period. If the concentration is higher than the threshold of 0.85, it is fitted with a normal distribution N(μ,σ²), where μ is the predicted execution phase and σ is the jitter standard deviation.

[0050] The second step is to calculate the overlap probability between the sensitive task window and the interference recovery period. The test window for the sensitive task request is [t]. start , t end The recovery window for the interference task is [μ+τ-3σ, μ+τ+3σ] (τ is the physically calibrated decay time constant, and 3σ covers the 99.7% confidence interval). The collision probability is obtained by calculating the CDF value within the recovery window: ; In the formula, Φ is the cumulative distribution function of the standard normal distribution, and t start The start time of the test window requested for interference-sensitive test tasks (such as geomagnetic calibration), t end The end time of the test window requested for test tasks that are sensitive to interference. It is the central time point of the interference recovery period. , It is the predicted average execution phase (e.g., 12.3ms) for interference tasks (such as servo motor testing). The electromagnetic decay time constant of the interference source is obtained through physical calibration (e.g., 3.2 ms). It is the standard deviation of the phase jitter (uncertainty) that interferes with the execution time of the task.

[0051] In this formula, The calculation is for the sensitive window end time t. endThe cumulative probability (i.e., t) falling within the recovery period distribution end (Area on the left). The calculation is based on the sensitive window start time t. start The cumulative probability (i.e., t) falling within the recovery period distribution start (The area on the left). Subtract the two: the result is on the recovery period distribution curve, from t start to t end The area between these points, probabilistically speaking, represents the probability that the entire execution window of a sensitive task will be covered or fall within the interference recovery period. The larger the area (…), the higher the probability. The higher the value, the greater the risk of a time conflict between sensitive tasks and interference.

[0052] The third step is threshold determination and reservation decision-making. If... If the percentage is less than 10% (safety threshold), the system approves the appointment; otherwise, the application is rejected or delayed. Example: SERVO_01 predicts phase. =12.3ms, τ=3.2ms, recovery period center 15.5ms, sensitive window [22.5ms, 23.0ms] has no overlap with it, collision probability is obtained by Gaussian tail integration. The success rate was 8.7%, which is below the threshold, so the appointment was approved.

[0053] More preferably, in an exemplary embodiment, the service quality of the test task is monitored in real time. When service quality degradation is detected based on logical resource availability, a bypass arbitration mechanism based on policy snapshots and lock-free queues is triggered to intervene in the decision-making process, including: Before each scheduling decision, an immutable policy snapshot of the contract status, priority, and environmental parameters of all tasks in the system is generated and stored. When service quality degradation is detected based on logical resource availability, an arbitration command containing the target task ID and the new policy ID is sent through a lock-free circular queue. The execution unit receives arbitration instructions and executes interventions within microsecond delays, while using the associated policy snapshot as evidence for decision retrospection.

[0054] Specifically, in this exemplary embodiment, the bypass arbitration system is a comprehensive emergency support system integrating intelligent early warning, rapid decision-making, reliable execution, and trustworthy traceability, designed specifically to address sudden and unpredictable interference after the failure of the predictive model. More specifically: First, this approach forms a consistent and tamper-proof chain of decision-making, achieving a complete and trustworthy closed loop through full-process credible traceability: (1) Pre-decision snapshot solidification: An immutable policy snapshot is automatically generated before each scheduling decision, permanently recording the complete context of the decision (contract, priority, environmental parameters). (2) Strong event correlation: When arbitration is triggered, the system will associate and bind the triggering cause (such as specific decay indicators), the snapshot on which it is based, the issued arbitration instruction, and the final new strategy. This complete chain of evidence, including cause, state, decision, and consequence, is the ultimate basis for root cause analysis, process review, and quality auditing, and fully complies with the mandatory requirements of relevant standards for "traceable requirements, verifiable design, and auditable process" for complex safety-critical systems.

[0055] Secondly, a deterministic microsecond-level emergency command channel was constructed, ensuring the ultimate real-time response. Arbitration commands are simply compact binary packets containing the target task ID, new strategy ID, and checksum; the data structure is extremely simple, and the parsing overhead is negligible. Furthermore, a lock-free circular queue is employed, completely eliminating the random latency and throughput bottlenecks caused by lock contention in traditional message queues. The transmission path is highly efficient, ensuring deterministic command delivery latency under high concurrency pressure. Actual P99 latency is less than 600μs (i.e., command delivery time is less than 600 microseconds in 99% of cases). This robust performance indicator proves that the channel can meet the highest level of real-time requirements, ensuring an extremely short response time from problem detection to intervention, providing a millisecond-level time window for rescue missions and loss mitigation.

[0056] By enhancing proactivity through proactive early warnings, ensuring reliability through precise triggering, achieving deterministic latency through lock-free channels, and ultimately building an unbreakable foundation of trust through policy snapshots, this system ensures that even in the most demanding and unpredictable testing environments, the system's behavior remains knowable, controllable, verifiable, and reliable, thereby simultaneously elevating the success rate and reliability of high-precision testing to new engineering heights.

[0057] It should be noted that the specific implementation of policy snapshots includes the following key steps: Data Structure: Before each scheduling decision, the system packages the contract status, priority, environment parameters, and other information of all current tasks into an immutable snapshot. For example, the subsequent test data packet structure shows that the snapshot ID (such as SNAP_12345) is stored together with the scheduling metadata, including fields such as activecontracts, confidence, and calibrationversion.

[0058] Immutable storage: Snapshots are stored in a time-series database, and their "immutable" nature is guaranteed by a checksum (such as sha256_hash), providing a chain of evidence for subsequent auditing and tracing.

[0059] Triggering the association: When the decay identification module detects three consecutive errors exceeding 15% and a confidence level below 95%, it immediately triggers bypass arbitration. The arbitration command is transmitted through a lock-free circular queue, which is a "compact binary data structure containing the target task ID, new strategy ID, and checksum," achieving microsecond-level response.

[0060] Audit traceability: After arbitration, the system queries the time-series database for policy snapshots and default events by linking the snapshot_id, and generates an audit report that complies with the standards.

[0061] More preferably, in an exemplary embodiment, the discrimination condition for detecting service quality degradation based on logical resource availability includes: If the confidence level of the test result is lower than the fifth threshold (preferably 95%), the number of historical error records is greater than the sixth threshold (preferably 3 times), and the error rate is greater than the seventh threshold (preferably 15%), then it is determined that the service quality has deteriorated, and the bypass arbitration is triggered. If the decay rate of real-time monitoring of logical resource availability exceeds the eighth threshold, it is predicted that the interference will worsen, and the idle window is activated in advance to predict the migration of scheduled tasks.

[0062] Specifically, in this exemplary embodiment, the triggering of bypass arbitration is not based on a single condition, but rather on a multi-criteria joint decision: three consecutive tests with an error exceeding 15% and a confidence level below 95%. This design is resistant to noise interference, effectively filtering out sporadic, isolated measurement glitches or transient disturbances by requiring multiple consecutive exceedances, preventing false triggering and ensuring the seriousness and necessity of arbitration initiation. Furthermore, it simultaneously examines result quality (error) and process reliability (confidence level), triggering only when both deteriorate simultaneously, ensuring that arbitration targets systemic degradation that truly jeopardizes the fundamental effectiveness of the task, rather than tolerable performance fluctuations. This precise triggering mechanism is crucial for maintaining high system availability and stability.

[0063] Furthermore, the degradation identification module not only monitors absolute service quality (such as error and confidence level), but also innovatively monitors the availability of logical resources in real time. attenuation rate dR logic / dt. By setting a threshold for the decay rate (e.g., -0.1 / ms), the system can predict an impending escalation of interference before environmental purity begins to deteriorate rapidly but before task failure occurs. The value of this early warning mechanism lies in providing the system with a lead time. For example, it can initiate task migration or resource reservation in advance when the environmental trend is clearly downward, even before sensitive task errors exceed the limit, thus shifting intervention from after a failure to during or even before the failure. This proactive defense capability significantly reduces the risk of complete mission failure due to delayed response.

[0064] More preferably, in an exemplary embodiment, the criterion for automatically determining the validity of the test results is: The measured values ​​of the test data themselves meet the preset specification limits; During the test execution, the scheduling metadata in the policy snapshot must meet the standards, including a confidence level higher than the eighth threshold and the absence of contract default events requiring bypass arbitration.

[0065] Specifically, existing testing and evaluation methods only focus on whether the measured value falls within the specification limits. However, in complex electromagnetic environments, interference may cause the sensor to output a seemingly normal but actually distorted signal, generating false qualified data. In this exemplary embodiment, this problem is solved by introducing scheduling metadata compliance as a necessary criterion in parallel with the qualified measurement value: Condition 1 (Result Qualified): Ensuring the data numerically meets the technical requirements is a fundamental requirement. Condition 2 (Process Reliability): Simultaneously satisfying 1) a confidence level higher than a threshold (e.g., 95%) and 2) no contractual breach arbitration. This means that the system must not only ensure the data appears correct, but also that the data is obtained in a clean environment with high confidence, and that the execution process is not forced to be interrupted or switched due to sudden interference.

[0066] Simultaneously, the scheduling system's assurance capabilities are directly injected into the quality assessment stage, achieving a strong binding between process and result. The scheduling metadata (from policy snapshots) upon which the assessment relies—confidence level and arbitration event records—is precisely the product of the scheduling system's real-time calculation, monitoring, and assurance during task execution, as described in the exemplary embodiment above. The confidence level originates from the logical resource availability model's real-time assessment of the test environment, representing the system's self-diagnosis of whether measurement is suitable at this moment. The arbitration event records indicate whether the bypass arbitration system has been triggered. The significance of this binding lies in creating an inseparable closed loop between front-end dynamic scheduling and back-end quality assessment. This ensures that only data obtained under the system's full monitoring and assurance is recognized as valid, achieving deep penetration of quality management into the production process.

[0067] More preferably, in an exemplary embodiment, the step of automatically orchestrating intelligent test sequences based on contract data of all registered test tasks and automatically determining the validity of test results based on policy snapshots, and generating an audit report, is replaced by: In response to user commands, it automatically invokes intelligent test sequence orchestration, dynamic task scheduling and execution, and automatic test result determination functions. Based on the determination results and stored policy snapshots and contract breach records, it automatically generates audit reports that comply with quality standards.

[0068] Specifically, this exemplary embodiment implements a one-click rapid testing function by seamlessly connecting and fully automating four core technology modules (intelligent orchestration, dynamic scheduling, automatic judgment, and report generation). Users only need to issue a start command (such as start_one_key_test(batch_id)), and the system can automatically complete all stages from sequence planning, task scheduling, process monitoring, result evaluation to report generation, without any manual intervention.

[0069] Based on the QoS contracts of all tasks, the system automatically calculates the globally optimal or near-optimal test sequences, replacing the error-prone and inefficient manual orchestration that relies on engineer experience. The scheduler autonomously manages task start-up and shutdown, window scheduling, and conflict resolution through dynamic negotiation and arbitration mechanisms, requiring no manual monitoring or adjustment. Finally, it automatically provides a validity conclusion based on dual judgment criteria and instantly generates a structured audit report.

[0070] Each sub-function invoked (such as scheduling and decision-making) incorporates a trusted mechanism (such as a logical resource model and policy snapshot) as described in the exemplary embodiments above. Therefore, the automated process produces not only results, but also results with a complete and trusted chain of evidence. The data validity conclusions in the report are directly related to the scheduling metadata and policy snapshots during the execution process.

[0071] More preferably, in an exemplary embodiment, the method dynamically manages multi-task execution with zero intrusion, the zero-intrusion premise being achieved through the following steps: Initialize a protocol-independent event bus as a unified entry point for communication with the device under test, and configure it to passive listening mode; Throughout the test in passive listening mode, the event bus only receives all data packets on the device under test's communication bus and adds a high-precision timestamp to each packet, without sending any instructions to the device under test or occupying communication bandwidth.

[0072] Specifically, in this exemplary embodiment, by designing and initializing a protocol-independent event bus operating in passive listening mode, a reliable and trustworthy solution is provided for achieving true zero-intrusion test management. Specifically: In passive listening mode, the event bus only receives data and does not send any instructions or messages. This means that: (1) it does not occupy communication bandwidth: the bus load will not be aggravated by the data acquisition behavior of the test system, avoiding bus delay, message loss or communication timeout of the system under test that may be caused by bandwidth competition, and ensuring that the original timing and performance of the internal communication of the system under test are not affected in any way. (2) it does not change the system state: it will not send any instructions to the device under test (such as precision guidance components) that may trigger its state machine transition, parameter reset or abnormal handling. The device under test is completely unaware of the existence of the test system, and its operating logic, control flow and self-test behavior can be carried out in a completely natural and real environment. (3) In traditional testing, injecting instructions or querying the state may interfere with the system itself. This mechanism realizes pure observation that is read-only and does not write, so that all the timing data collected (such as sensor reporting and servo feedback) are the products of the system's autonomous and real behavior, rather than the response to the test probe.

[0073] Furthermore, the event bus adopts a protocol-independent design and supports various industrial fieldbuses or network protocols such as CAN, RS-422, and Ethernet through plug-in drivers. The same core logic of the test system can be adapted to devices under test with different communication interfaces without major modifications, greatly expanding the application scope of this exemplary embodiment.

[0074] The following content will demonstrate a batch automated testing system for a certain guidance component (such as an inertial navigation unit, geomagnetic sensor, satellite positioning module, servo system, etc.), the components of which are as follows: Figure 2 As shown, it includes a ground test computer, a test turntable, and a guidance component, wherein: The ground test computer operates on 220V AC power input and includes a ruggedized computer, a programmable power supply module, a GNSS signal relay module, a GNSS signal receiver module, and a communication module; the test turntable includes a servo motor, an RS485 communication interface, and a conductive slip ring; a guidance component includes a CAN communication interface and an RS422 communication interface; the interface connections between the three are as follows: Figure 3 As shown. The ground test computer is powered by a 220V AC input. Internally, a ruggedized computer runs a scheduling algorithm. The programmable power supply module powers the test turntable and guidance components and monitors the servo motor current change rate. The GNSS signal receiving module receives outdoor reference signals, and the GNSS signal relay module injects GNSS signals into the guidance components. The communication module passively listens to the guidance components' CAN / RS422 bus. The servo motors on the test turntable generate electromagnetic interference, and conductive slip rings ensure continuous rotation. Data flow: The programmable power supply module sends the current change rate to the ruggedized computer to calculate the availability of logical resources. The communication module sends timestamped bus data to the ruggedized computer for scheduling decisions. The GNSS signal receiving module sends reference positioning data to the ruggedized computer for comparison with the guidance component output.

[0075] Figure 2 and Figure 3 Correspondence: Figure 2 Ground test computer corresponding Figure 3 The ground test computer X1 to X6 consists of the following components: the 220V AC input is connected to the X1 interface via the L1 cable; the communication module corresponds to X2; the programmable power supply module corresponds to the power conversion function of X1; the GNSS signal forwarding module corresponds to X4 (output) and X5 (input); and the GNSS signal receiving module corresponds to X6. Figure 2 The test turntable corresponds to Figure 3 The central functional box has X1 as the interface for connecting to the ground test computer and X2 as the interface for connecting to the guidance components. Figure 2 A certain guidance component has a test interface and a telemetry interface. The corresponding connection relationships are as follows: Figure 2 Corresponding connection between ground test computer and test turntable Figure 3 The X2 unit connects to the test turntable X1 via cable L2. Figure 2 The connection between the test turntable and the guidance component corresponds Figure 3 The test turntable X2 is connected to the test interface of the guidance component via the L8 cable. The telemetry interface of the guidance component is another communication channel.

[0076] The entire testing process is described in detail, including deployment, configuration, and execution. Figure 4 As shown: I. System Deployment and Task Registration Phase Step 1: Event bus initialization (achieving zero-intrusion listening) The first step in system deployment is to initialize a protocol-independent event bus as a unified entry point for communication with the device under test (DUT). The core objective of this configuration is to achieve zero intrusion, that is, to passively listen to all test-related communication data streams without consuming any bus bandwidth or sending any commands to the DUT.

[0077] In this configuration, the event bus driver operates in mirror mode, receiving only all messages on the bus and stamping them with high-precision timestamps (microsecond resolution), providing basic data support for subsequent timing analysis and interference correlation.

[0078] Step 2: Contractual Registration of Core Test Tasks ( Figure 4 (Task contract registration in the middle) Before the system runs, all test tasks and their unique Quality of Service (QoS) requirements must be registered with the test task orchestration engine in the form of a "contract". This embodiment includes the following core test tasks: Geomagnetic sensor calibration task (MAG_01): This task is extremely sensitive to the magnetic field environment.

[0079] Servo system zero-position test task (SERVO_01): This task is the main source of interference during execution.

[0080] Satellite positioning module performance evaluation task (GNSS_01): This task has high requirements for power supply stability.

[0081] Component Comprehensive Self-Check Task (SELF_CHECK_01): Basic diagnostic task, low priority.

[0082] Step 3: Physical calibration of sensitivity factors (ensuring model traceability) To ensure the sensitivity factor in the logical resource quantification model The physical authenticity needs to be calibrated in a controlled environment. This process is the key difference between this invention and a purely software solution.

[0083] Calibration environment: Constant temperature and humidity laboratory (temperature 23±1℃, humidity 45%-55%RH), electromagnetic shielding.

[0084] Calibration setup: Helmholtz coil: 0.15 m radius, 100 turns, generating a uniform magnetic field with a deviation of <1% in the uniform region. High-precision current probe: 1 MHz bandwidth, 0.1 A resolution, used for accurate measurement of the transient rate dI / dt of the injected current. Data acquisition card (DAQ): 1 MSa / s sampling rate, 16-bit accuracy, synchronously acquiring the outputs of the current probe and the geomagnetic sensor under test.

[0085] Calibration procedure: Place the geomagnetic sensor under test in the center region of the Helmholtz coil. Inject a current ramp signal covering typical operating conditions through the coil. The value was increased logarithmically from 0.1 A / ms to 100 A / ms, with a total of 50 test points. At each test point, the steady-state magnetic field change ΔB was recorded, forming a set of data. The sample data is ΔB). Least squares linear regression is performed on all samples to obtain the slope of the fitted curve, which is the sensitivity factor. .

[0086] Verification: Calculate the standard deviation of the fitted residuals. If the ratio of the standard deviation to the slope is greater than 5%, the calibration process is deemed unqualified, and the environment or equipment needs to be checked.

[0087] Curing: The final calibration result =0.32 (example value) along with calibration environment parameters, equipment model and other information are fixed in the calibration.conf configuration file and deployed with the test system to ensure its reproducibility and auditability.

[0088] II. Dynamic Scheduling Execution Phase Step 1: Real-time identification of service quality degradation patterns ( Figure 4 (Real-time identification of decay patterns) After the system starts up, a highest-priority monitoring thread runs continuously, analyzing the data on the event bus in real time and identifying any patterns that may indicate a decline in service quality.

[0089] Its core discrimination logic is as follows: Event-driven: The monitoring thread polls the event bus with a timeout period of 10ms.

[0090] Pattern matching: When a test result event for a task (such as MAG_01) is received, check the confidence value in its metadata.

[0091] Mathematical discriminant: A service quality degradation is determined to have occurred if all three of the following conditions are met simultaneously: 1) evt.confidence < 0.95 2) `degradation_detector.error_history.size()>=3` (at least 3 historical error records) Figure 4 (not shown in the image) 3) all_of(degradation_detector.error_history,[](e){returne>0.15;}) (All error rates are greater than 15%) Trigger Arbitration: Once determined, immediately send an on_contract_breach event to the bypass arbitration module, containing the affected task ID and the reason for the decline ( Figure 4 Bypass arbitration triggering and processing in (the process).

[0092] Step 2: Booking based on predicted clean time windows ( Figure 4 (Idle window prediction reservation, taking geomagnetic testing as an example) To proactively avoid interference, the system predicts and schedules a "clean" time window for the highly sensitive MAG_01 task. The system also predicts and avoids interference from the SERVO_01 task for the MAG_01 task. The two tasks run asynchronously; the system uses historical data from the SERVO_01 task to predictively schedule the MAG_01 task, thus achieving proactive avoidance.

[0093] Historical data analysis: The system reads the execution logs (approximately 2.1GB of CSV data) of the SERVO_01 task from the time-series database for the past 30 days. Through time-series analysis, the phase distribution of its execution cycle relative to a baseline cycle (e.g., 20ms) is statistically analyzed, and its average phase predicted_phase = 12.3ms and phase jitter = 0.8ms are calculated.

[0094] Interference window calculation: Based on the attenuation time constant τ=3.2ms obtained from physical calibration, the recovery period of magnetic field interference after the execution of the SERVO_01 task is calculated to be [12.3ms+3.2ms, 12.3ms+3.2ms+5ms], i.e. [15.5ms, 20.5ms]. 5ms is a safety margin window set based on engineering experience.

[0095] Reservation Decision: The MAG_01 task requests a 500μs test window. The system calculates the probability of conflict with potential interference from SERVO_01 within this window [22.5ms, 23.0ms]. Based on its time-series distribution CDF model, the conflict probability is calculated to be 8.7%, which is lower than the preset safety threshold of 10%, therefore the reservation is approved.

[0096] Conflict Handling: If unforeseen interference (such as power fluctuations) occurs within the window reserved for MAG_01, the decay identification module will immediately trigger bypass arbitration. The arbitrator will make a decision within 50μs, suspending the MAG_01 task and rescheduling it with an EXCLUSIVE_TIMESLOT (exclusive time slot) to ensure that it can eventually complete the test in an interference-free environment. Figure 4 (Task execution in the process).

[0097] In conclusion, in such cases... Figure 4 In the main flowchart of contract negotiation and scheduling shown, after the process begins, task contract registration is performed, registering the test task and its QoS requirements in the form of a contract. Then, the real-time decay mode identification stage is entered, monitoring the confidence level and error rate of the test task in real time. Next, a bypass arbitration judgment is triggered, with the judgment formula being a confidence level < 95% and three consecutive errors > 15%. If the judgment is yes, bypass arbitration and processing are triggered, suspending the task and rescheduling the exclusive time slot, after which the task execution resumes. If the judgment is no, the idle window prediction and reservation stage is entered, calculating and approving the window reservation based on logical resource availability and conflict probability. After reservation is completed, the task is executed, and the process ends. Tasks after bypass arbitration can return to the idle window prediction and reservation stage via a re-reservation path.

[0098] In such Figure 5The bypass arbitration fast response timing diagram shown illustrates the timeline from event detection to arbitration execution. The horizontal axis represents time (0μs to 800μs), and the vertical axis represents the event bus, arbitrator processing, bypass message queue, CANFD bus, and the response of the device under test.

[0099] When an event is triggered (corresponding to) Figure 5 The event bus detects and transmits data to the arbitrator within 50µs (corresponding to 0µs). Figure 5 The first line of the event bus rises to a high level between 0µs and 50µs; the arbitrator processes the event within 100µs (corresponding to...). Figure 5 The second line, "Arbitrator Processing," rises to a high level between 50µs and 150µs and then falls to a low level. When the arbitrator processing results in triggering bypass arbitration and processing, the task is added to the bypass information queue within 100µs (corresponding to...). Figure 5 The third line, "Bypass Message Queue," rises to a high level between 150µs and 250µs; subsequently, arbitration commands or data are transmitted within 200µs on the CAN FD bus (the communication bus between the device under test and the test system), supporting communication interaction between the test system and the device under test (corresponding to...). Figure 5 The fourth line of the code shows the "CAN FD bus" exhibiting high and low level jitter between 250µs and 450µs; finally, the device under test responds within 50µs (corresponding to...). Figure 5 The fifth line, "DUT response," rises to a high level between 450µs and 500µs. The entire process, from event triggering to the DUT response reaching a high level, has a total P99 delay of less than 600µs.

[0100] The time delays for each stage are as follows: event detection and transmission delay <50μs, arbitrator processing delay <100μs, bypass message queue delay <100μs, CAN FD transmission delay ≈200μs, device under test response delay <50μs, and total P99 delay <600μs. This figure visually demonstrates the real-time nature of the bypass arbitration mechanism's intervention within microseconds, ensuring rapid response and recovery of test tasks under sudden interference.

[0101] Step 3: One-click quick test of the entire process In a production environment, operators can initiate automated testing of an entire batch of components with a single command.

[0102] 1. Intelligent orchestration: Generates the optimal test sequence based on contract data, taking approximately 5ms; 2. Execution test: The scheduler runs the sequence according to the dynamic negotiation mechanism, with an average execution time of approximately 15 minutes per batch; 3. Automatic evaluation: Validity is determined by combining test data and scheduling metadata, taking approximately 200ms; 4. Generate Report: Automatically generate a compliant audit report, taking approximately 300ms.

[0103] III. Data Traceability and Compliance Stage This exemplary embodiment ensures complete traceability of the testing process through structured data storage and automated report generation.

[0104] Test packet structure: After each test item is completed, the system generates a packet containing complete context information and stores it in the time-series database. The key is the scheduling_metadata field, which records the system state during test execution, the snapshot ID of the strategy used, the real-time calculated confidence level, and the calibration version used, providing irrefutable evidence for the validity of the test results.

[0105] Audit report generation: The system can automatically generate audit reports in batches, as follows: Retrieve all contract_breaches (contract breach events) from the time-series database for a specified batch. For each breach event, retrieve the corresponding policy snapshot based on its snapshot_id. Compile the breach details, snapshot content, and corrective actions taken by the system into a structured audit log. Use the report generation engine to export the audit log as a PDF report that meets the quality system requirements for review by the relevant quality department.

[0106] To objectively and quantitatively evaluate the technical effects of this invention, comparative experiments were conducted on the same hardware platform as existing products, targeting core test items of the precision guidance component. Experimental results show that this invention achieves significant improvements in test data validity, test efficiency, and automation level. The following is a comparative data table of key indicators:

[0107] From the table, we can see that: (1) Significantly improved data validity: The effective sample rate of geomagnetic calibration increased from 84.7% to 98.8%, and the signal loss events of the GNSS module decreased from 15 times / day to 2 times / day. This directly proves that the logical resource quantification model and predictive scheduling mechanism of this invention have excellent effects in suppressing multi-physics coupling interference. By transforming intangible interference into computable logical resources and actively avoiding predicted interference windows, the system ensures high fidelity and high reliability of test data.

[0108] (2) Dual optimization of test quality and efficiency: The out-of-tolerance batch rate of servo motor zero-position test was reduced by 75%, indicating that this solution not only improved the data quality of a single test, but also improved the consistency and accuracy of the entire batch of product testing, effectively reducing the risk of misjudgment caused by interference. This fundamentally improves the yield rate and quality control level of the production line.

[0109] (3) Automation and reliability reach new heights: The accuracy of automatic evaluation of "one-click testing" jumped from 62% to 98.5%, which fully demonstrates the core value of this invention in terms of intelligence and automation. The fundamental reason is that the evaluation criteria of this invention are two-dimensional: not only checking whether the measured value is qualified, but more importantly, verifying the scheduling metadata (such as confidence level, whether arbitration is triggered) during its execution process. This context-based intelligent evaluation completely solves the problem of false qualification caused by the inability to trace interference in traditional solutions. The report generation time has been shortened from 0.5 hours of manual compilation to 5 minutes of automatic generation, realizing a qualitative leap in the closed loop of the testing process, greatly liberating manpower and improving the timeliness of data delivery.

[0110] (4) The system's real-time performance and reliability have been verified through engineering: the P99 delay of the scheduling decision is stable at 496μs (standard deviation of only 23μs), and the trigger success rate of bypass arbitration reaches 100%. These robust performance indicators strongly demonstrate the efficiency and robustness of the lockless bypass arbitration system in this invention. It ensures that even when faced with sudden and unpredictable interference, the system can respond within microseconds, guaranteeing the final success of key test tasks.

[0111] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A parallel testing method for precision guidance components, characterized in that: Includes the following steps: Based on physical calibration, the sensitivity factor of the test task to the corresponding interference source is quantified, and a logical resource availability model is established to transform the interference suppression requirement into a calculable and allocable logical resource unit. Analyze the temporal distribution characteristics of test tasks, predict future idle time windows, and proactively schedule test tasks that are sensitive to interference based on the availability of logical resources. The service quality of test tasks is monitored in real time. When service quality degradation is detected based on logical resource availability, a bypass arbitration mechanism based on policy snapshots and lock-free queues is triggered to make a decision intervention. The bypass arbitration includes idle window prediction and scheduling and task resumption execution. Based on the contract data of all registered test tasks, the system automatically orchestrates intelligent test sequences and automatically determines the validity of test results based on policy snapshots, generating audit reports. The calculation formula for the logical resource availability model is as follows: ； In the formula, This represents the availability of logical resources at time t, with a value range of (0,1]. A higher value indicates a cleaner test environment; N is the number of currently active interference sources. This represents the current of the i-th interference source. Let τ be the rate of change of the current of the i-th interference source, and τ be the instantaneous excitation of the interference; i Let be the attenuation time constant of the i-th interference source; is the sensitivity factor for the i-th interference source, which characterizes the sensitivity of the test task to the i-th interference source; It is a numerical stability correction term; The sensitivity factor of the quantization test task to the corresponding interference source includes: In a controlled electromagnetic environment, a set of known rates of change is injected into a Helmholtz coil. The current signal; Synchronous acquisition of data from the sensor under test in each group The change in output ΔB under excitation; For multiple groups A linear regression analysis was performed on the ΔB sample data, and the slope obtained from the fitted data is the sensitivity factor. ; The calibration process must ensure that the relative error of the linear fit is less than the first threshold.

2. The parallel testing method for a precision guidance component according to claim 1, characterized in that: The analysis of the temporal distribution characteristics of test tasks, prediction of future idle time windows, and proactive reservation of interference-sensitive test tasks based on logical resource availability include: Analyze the execution logs of potentially interfering tasks, extract the timestamp sequence from the execution logs, calculate the phase sequence of the timestamp sequence relative to the baseline period, predict the execution phase and jitter range, and establish a time series distribution model; Based on the time-series distribution model, the probability of conflict between the interference-sensitive test task and all potential interference tasks within the target time window is calculated; wherein, the conflict probability is obtained by calculating the cumulative distribution function overlap integral of the target time window and the interference recovery period predicted by each interference task. Window reservations for interfering tasks are approved only when the availability of logical resources is higher than the second threshold and the calculated total conflict probability is lower than the preset third threshold.

3. The parallel testing method for a precision guidance component according to claim 2, characterized in that: The establishment of the time series distribution model includes: Calculate the phase sequence of the timestamp sequence relative to a set reference period; Calculate the concentration of the phase sequence, whereby the concentration is defined as: 1 - (standard deviation of the phase sequence / mean of the phase sequence); If the concentration is higher than the preset fourth threshold, the potential interference task is determined to be periodic, and a periodic time distribution model of the corresponding potential interference task is established based on the average phase and phase jitter range.

4. The parallel testing method for a precision guidance component according to claim 1, characterized in that: Real-time monitoring of the service quality of test tasks. When service quality degradation is detected based on logical resource availability, a bypass arbitration mechanism based on policy snapshots and lock-free queues is triggered for decision intervention, including: Before each scheduling decision, an immutable policy snapshot of the contract status, priority, and environmental parameters of all tasks in the system is generated and stored. When service quality degradation is detected based on logical resource availability, an arbitration command containing the target task ID and the new policy ID is sent through a lock-free circular queue. The execution unit receives arbitration instructions and executes interventions within microsecond delays, while using the associated policy snapshot as evidence for decision retrospection.

5. The parallel testing method for a precision guidance component according to claim 4, characterized in that: The criteria for detecting service quality degradation based on logical resource availability include: If the confidence level of the test result is lower than the fifth threshold, the historical error record is greater than the sixth threshold, and the error rate is greater than the seventh threshold, then it is determined that the service quality has deteriorated, and the bypass arbitration is triggered. If the decay rate of real-time monitoring of logical resource availability exceeds the eighth threshold, it is predicted that the interference will worsen, and the idle window is activated in advance to predict the migration of scheduled tasks.

6. The parallel testing method for a precision guidance component according to claim 1, characterized in that: The standard for automatically determining the validity of test results is as follows: The measured values ​​of the test data themselves meet the preset specification limits; During the test execution, the scheduling metadata in the policy snapshot must meet the standards, including a confidence level higher than the eighth threshold and the absence of contract default events requiring bypass arbitration.

7. A parallel testing method for a precision guidance component according to claim 1 or 6, characterized in that: The previous step of automatically orchestrating intelligent test sequences based on contract data of all registered test tasks and automatically determining the validity of test results based on policy snapshots, generating an audit report, is replaced with: In response to user commands, it automatically invokes intelligent test sequence orchestration, dynamic task scheduling and execution, and automatic test result determination functions. Based on the determination results and stored policy snapshots and contract breach records, it automatically generates audit reports that comply with quality standards.

8. The parallel testing method for a precision guidance component according to claim 1, characterized in that: The method dynamically manages the execution of multiple tasks without intrusion, and the zero-intrusion premise is achieved through the following steps: Initialize a protocol-independent event bus as a unified entry point for communication with the device under test, and configure it to passive listening mode; Throughout the test in passive listening mode, the event bus only receives all data packets on the device under test's communication bus and adds a high-precision timestamp to each packet, without sending any instructions to the device under test or occupying communication bandwidth.

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