Product testing center environment simulation and safety protection integrated system

Abstract

The present application relates to the technical field of product testing, and discloses a product testing center environment simulation and safety protection integrated system, which comprises: an immune prediction module that obtains product weighted prediction results and prediction coding free energy through the evolution of a heterogeneous predictor population based on multi-source sensing data; a community monitoring module that constructs a sensor interaction matrix, calculates spectral radius and signal deviation, and synthesizes a comprehensive abnormality index; a risk envelope module that sets four levels of performance, solves constraint optimization to obtain a graded environmental tolerance spectrum, and dynamically switches working conditions and outputs control trajectories based on the aforementioned parameters; and an abnormality reconstruction module that analyzes root cause probabilities with the help of a neural network and issues differentiated instructions, which are executed by an environment execution module and a safety protection module to complete environmental regulation and physical protection, respectively; thus solving the technical problem that existing testing systems cannot predict the hidden degradation of the tested product and the risk of multi-parameter coupling instability in the cabin under a closed black box state.

Description

Technical Field

[0001] This invention relates to the field of product testing technology, and specifically to an integrated system for simulating and protecting the environment of a product testing center. Background Technology

[0002] Existing product testing centers typically use closed environment simulation chambers as the core testing platform. Through equipment such as temperature and humidity control devices, vibration tables, and dust applicators, single or combined environmental conditions such as high temperature, high humidity, vibration, and dust are reproduced in the chamber to verify the performance and durability of the tested products under extreme working conditions.

[0003] The aforementioned existing technologies have the following shortcomings: The environmental simulation system continuously applies environmental parameters according to a preset program. Even if the product under test shows signs of hidden degradation such as abnormal internal temperature rise or insulation performance decline, the environmental application continues as planned until the safety protection system detects an explicit hazard and then responds passively. This makes it impossible to dynamically adjust the environmental application trajectory based on the real-time tolerance status of the product under test during the test, which can easily lead to test interruption or product damage.

[0004] During the closed application phase, the test chamber is completely sealed, preventing personnel from entering for visual observation. Furthermore, the harsh environment, characterized by high temperatures and dust levels, renders conventional visual monitoring equipment inoperable. Existing technologies largely rely on fixed threshold alarms from single sensors; however, such methods suffer from high false alarm rates for normal, procedural environmental changes and fail to capture latent signs of instability arising from the coupling of multiple environmental parameters.

[0005] Current testing centers typically initiate emergency shutdowns or full-cabin fire suppression upon detecting anomalies, failing to differentiate between excessive environmental stress, product failure, and cabin equipment malfunction. For anomalies caused by excessive environmental stress, direct shutdown results in incomplete test data; for product failure, full-cabin fire suppression may cause secondary damage to undamaged products; and for cabin equipment malfunction, it delays the appropriate emergency response. Summary of the Invention

[0006] The present invention aims to provide an integrated system for environmental simulation and safety protection in product testing centers, in order to solve the technical problem that existing testing systems cannot predict the latent degradation of the tested product and the risk of instability of multi-parameter coupling in the chamber under closed black box conditions.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an integrated system for environmental simulation and safety protection in a product testing center, comprising: The immune prediction module is used to receive data from multiple sources of sensors in the cabin, maintain a model population composed of multiple heterogeneous predictors, perform affinity evolution and clonal mutation based on prediction coding error, generate a weighted prediction of the future state of the tested product, and calculate the prediction coding free energy. The risk envelope module is used to set four performance target levels and corresponding loss functions, solve probabilistic multi-level constrained optimization problems based on conditional risk value, generate four-level probabilistic environmental parameter tolerance spectrum, and automatically switch the current performance level according to the predicted coding free energy and comprehensive anomaly index to generate the control input trajectory of the environmental execution module. The community monitoring module is used to construct a dynamic interaction matrix from multi-source sensor data in the cabin, calculate its spectral radius and the intermittent deviation of each sensor signal, and fuse them to generate the comprehensive anomaly index in order to detect distribution deviation and coupling instability within the closed application section. The anomaly reconstruction module is used to concatenate the system state vector, comprehensive anomaly index and predictive coding free energy and input them into the neural network, output the root cause probability vector, and perform differentiated self-healing reconstruction based on it. The environment execution module is used to receive the trajectory correction command and drive the environment simulation device; The security protection module is used to receive the differentiated reconstruction instructions and perform physical security protection actions; The immune prediction module outputs the weighted prediction to the risk envelope module to dynamically update the safety boundary, and outputs the predicted encoded free energy to the risk envelope module and the anomaly reconstruction module; the community monitoring module outputs the comprehensive anomaly index to the risk envelope module and the anomaly reconstruction module; the anomaly reconstruction module sends differentiated reconstruction instructions to the risk envelope module, the environment execution module and the security protection module according to the root cause type.

[0008] The principle and advantages of this solution are as follows: In practical applications, the system integrates safety protection into the environmental control loop through the collaboration of the immune prediction module, risk envelope module, community monitoring module and anomaly reconstruction module. Within the closed application section of the sealed environment simulation chamber, the integrated linkage of environmental simulation and safety protection is achieved based on data from multiple sources of sensors within the chamber.

[0009] To address the challenges of visually observable degradation within a closed testing environment and the difficulty of predicting latent degradation in tested products using traditional monitoring methods, the immune prediction module receives data from multiple sensors within the testing chamber. It maintains a model population composed of several heterogeneous predictors, performs affinity evolution and clonal mutation based on prediction coding errors, generates a weighted prediction of the future state of the tested product, and calculates the prediction coding free energy. Thus, before physical sensors trigger threshold alarms, the system can predict whether the tested product has entered a latent degradation region not covered by training data by observing changes in the prediction coding free energy, providing an early warning window for closed black-box testing. Simultaneously, the general-purpose predictor in the heterogeneous predictor population initially takes over prediction tasks in new product testing where historical data is lacking, gradually switching to a dedicated predictor as data accumulates. This alleviates the problem of insufficient cold-start prediction accuracy when a single model is used with new tested products.

[0010] To address the disconnect between environmental simulation and safety protection, and the inability of environmental application strategies to dynamically adjust based on real-time risks, the risk envelope module sets four performance target levels and corresponding loss functions. Based on conditional value at risk (VAT), it solves a probabilistic multi-level constrained optimization problem, generating a four-level probabilistic environmental parameter tolerance spectrum. The module automatically switches the current performance level based on the predicted coding free energy and comprehensive anomaly index to generate the control input trajectory for the environmental execution module. Thus, the generation process of the environmental application trajectory is directly governed by safety constraints. The environmental simulation strategy and the safety protection strategy share the same set of probabilistic safety boundaries, achieving threshold homogeneity and response synchronization between environmental application and safety protection. Simultaneously, the automatic switching of the four performance targets allows the same system to adapt to individual differences in different batches of products, avoiding overly conservative testing or insufficient safety margins caused by uniform fixed thresholds.

[0011] To address the issues of high false alarm rates and inability to detect early signs of coupling failures in normal programmed environmental changes using traditional fixed-threshold anomaly detection methods, the community monitoring module constructs a dynamic interaction matrix from multi-source sensor data within the cabin, calculates its spectral radius, and measures the intermittent deviation of each sensor signal, fusing these data to generate a comprehensive anomaly index. Thus, the system detects system instability critical states under the coupling of multiple environmental parameters through spectral radius detection, identifies non-Gaussian sudden deviations through intermittent deviation, and the comprehensive anomaly index generated by fusing these two methods is immune to normal programmed environmental changes, reducing the false alarm rate and enabling early detection of coupling failure precursors.

[0012] To address the issue of existing testing centers indiscriminately shutting down systems or implementing full-cabin fire suppression upon detecting anomalies without considering the root cause, the anomaly reconstruction module concatenates the system state vector, comprehensive anomaly indicators, and predictive encoded free energy, inputting the result into a neural network. It then outputs a root cause probability vector and performs differentiated self-healing reconstruction accordingly. This allows the system to distinguish between environmental over-application, product failure, and cabin equipment malfunction: In cases of environmental over-application, it sends a boundary contraction command to the risk envelope module and a conservative mode command to the environmental execution module; in cases of product failure, it sends a product isolation command to the safety protection module and a single-factor maintenance mode command to the environmental execution module; and in cases of cabin equipment malfunction, it initiates full-cabin nitrogen inerting and staged depressurization. This avoids the loss of test data caused by indiscriminate shutdowns without considering the root cause, as well as secondary damage to non-failed products caused by full-cabin fire suppression.

[0013] In summary, under closed black-box testing conditions, this system achieves dynamic safety constraints imposed by the environment, early warning of latent degradation, accurate detection of coupling instability, and differentiated response to the root causes of anomalies, thereby improving the continuity of testing while reducing the risk of product damage and equipment loss.

[0014] Preferably, as an improvement, the immune prediction module includes a population prediction unit, an affinity evolution unit, and a free energy early warning unit; The population prediction unit calculates the prediction coding error of the k-th predictor at step n based on historical sensor data from inside the cabin:

[0015] In the formula, Let be the system state vector. This is the k-th predictor, and N is the length of the input time window; The affinity evolution unit calculates affinity:

[0016] In the formula, The affinity attenuation coefficient; The population prediction unit outputs the weighted prediction based on affinity-normalized weights:

[0017] In the formula, Let M be the prediction of the k-th predictor for the next M steps, where M is the prediction time domain. For normalized weights; The free energy early warning unit calculates and predicts the coded free energy:

[0018] In the formula, This is the complexity penalty coefficient. Let u be the current weight distribution, where u is a uniform distribution. Let KL divergence be denoted as KL divergence.

[0019] The beneficial effects of this improvement are as follows: through affinity evolution and clonal mutation of heterogeneous predictor populations, in new product testing where historical data is lacking, the general predictor takes over the prediction task first, and gradually switches to the specialized predictor as data accumulates, alleviating the problem of insufficient cold-start prediction accuracy of a single model when facing a new product under test; at the same time, the predictive encoding free energy reflects the degree of collective confidence decay of the model population in the current state, and can predict whether the product under test has entered a hidden degradation area not covered by training data before the physical sensor has triggered the threshold, providing an early warning window for closed black-box testing.

[0020] Preferably, as an improvement, the risk envelope module includes a loss modeling unit, a CVaR optimization unit, and a spectrum switching unit; The loss modeling unit sets four performance target levels, corresponding to normal service, limited performance degradation, structural function preservation, and prevention of catastrophic failure, respectively, and defines the corresponding loss function and confidence level for each level. The CVaR optimization unit solves for the conditional risk value:

[0021] In the formula, For loss function, Value at risk threshold, For confidence level, For conditional expectations; And satisfy multi-level hard constraints:

[0022] In the formula, The maximum conditional risk value allowed for performance level 1. This represents the first level of confidence. The spectrum switching unit automatically switches the current activation level in the four-level probabilistic environmental parameter tolerance spectrum based on the predicted coding free energy and the comprehensive anomaly index, so as to generate the control input trajectory of the environmental execution module.

[0023] The beneficial effects of this improvement are: by transforming deterministic safety constraints into probabilistic multi-level constraints through conditional value at risk, the safety boundary can adapt to individual differences in different batches of products, avoiding overly conservative testing or insufficient safety margins caused by uniform fixed thresholds; the four-level performance targets, in conjunction with spectrum switching, enable the system to approach higher-level performance targets when the tested state is stable to fully verify the product limits, and to retreat to lower-level performance targets when risk indicators rise, achieving a dynamic balance between testing rigor and safety protection.

[0024] Preferably, as an improvement, the community monitoring module includes an interaction unit, a spectral radius unit, an intermittency unit, and a comprehensive fusion unit; The interaction unit constructs the interaction matrix in step n based on data from multiple in-cabin sensors, and its elements are:

[0025] In the formula, For the output of the i-th sensor, For covariance, For variance, For regularization terms; The spectral radius unit calculates the spectral radius:

[0026] In the formula, It is the k-th eigenvalue of the interaction matrix; The intermittent unit calculates the intermittent deviation:

[0027] In the formula, Let be the 3rd order structure function of the i-th sensor. This represents the number of time lag steps. The integrated fusion unit generates a comprehensive anomaly index:

[0028] In the formula, and These are the weighting coefficients.

[0029] The beneficial effects of this improvement are: by quantifying the system instability critical state under the coupling of multiple environmental parameters through the interaction matrix and spectral radius, and by identifying non-Gaussian sudden deviations through intermittent deviation, the comprehensive anomaly index generated by the fusion of the two is immune to normal programmed environmental jumps, reducing the false alarm rate of the fixed threshold method for normal changes such as temperature jumps, and at the same time capturing the precursors of multi-parameter coupling failure in advance.

[0030] Preferably, as an improvement, the anomaly reconstruction module includes a root cause classification unit and a policy execution unit; The root cause classification unit concatenates the system state vector, comprehensive anomaly index, and predictive coding free energy into an input vector, and outputs a root cause probability vector through a neural network.

[0031] In the formula, For the probability of environmental over-application, The probability of product failure. Probability of cabin equipment failure; When the environment is overloaded, the strategy execution unit sends an emergency contraction command to the risk envelope module and a conservative mode command to the environment execution module. When the product itself fails, it sends a product isolation command to the safety protection module and a single-factor maintenance mode command to the environment execution module. When the cabin equipment fails, it sends a full-cabin nitrogen inerting and graded depressurization command to the safety protection module.

[0032] The beneficial effects of this improvement are: by fusing the system state vector, comprehensive anomaly index and predictive coding free energy for root cause classification, it distinguishes between environmental over-treatment, product failure and cabin equipment failure, and triggers differentiated self-healing reconstruction accordingly, avoiding the problems of incomplete test data and secondary damage caused by the indiscriminate shutdown or full cabin fire suppression in the existing technology without distinguishing the root cause.

[0033] Preferably, as an improvement, the system also includes a sensor acquisition module for synchronously acquiring system state vectors and caching historical sequences; the system state vectors include temperature, relative humidity, vibration acceleration, dust concentration, internal pressure, bulkhead strain, oxygen concentration, combustible gas concentration, insulation resistance, and product internal temperature; The combustible gas concentration sensor, used to collect oxygen and combustible gas concentrations, and the pressure sensor, used to collect cabin pressure, are also directly connected to the safety protection module via a hard-wired safety circuit.

[0034] The beneficial effects of this improvement are: by comprehensively covering the cabin structure, environmental parameters, product status, and gas safety status through a ten-dimensional system state vector, it provides a complete decision-making basis for the algorithm protection layer; at the same time, gas composition and pressure data are independently transmitted to the safety protection module through a hard-wired safety loop, which can still ensure the reliable acquisition of safety-related data when the algorithm protection layer fails, thus meeting the functional safety standard requirements for independent safety channels.

[0035] Preferably, as an improvement, the environmental execution module includes a temperature control unit, a humidity control unit, a vibration control unit, and a dust control unit. Each unit transmits its actual output value back to the sensing acquisition module for closed-loop correction of the risk envelope module and construction of the interaction matrix of the community monitoring module.

[0036] The beneficial effect of this improvement is that the environmental execution module sends the actual output value back to the sensing acquisition module, enabling the algorithm protection layer to perform closed-loop correction and interaction matrix construction based on the actual applied value of the environmental parameters rather than the set value, thus avoiding the distortion of the safety boundary calculation caused by the deviation between the set value and the actual value.

[0037] Preferably, as an improvement, the safety protection module adopts a hard-wired safety relay circuit, independent of the control circuit of the environmental execution module; the safety protection module includes an explosion suppression unit, an inerting unit, a pressure relief unit, and an interlocking unit; The pressure relief unit satisfies the following rate of pressure rise within the chamber:

[0038] The staged pressure relief is initiated at the specified time, where... The threshold for the rate of pressure rise; The interlocking unit locks the hatch during the closed application phase and releases the lock after confirming that the oxygen concentration is higher than 19.5%, the temperature is lower than 40 degrees Celsius, and the combustible gas concentration is lower than 10% during the safe steady-state phase.

[0039] The beneficial effects of this improvement are as follows: the safety protection module adopts a hard-wired safety relay circuit, which is independent of the control circuit of the environmental execution module, ensuring that the minimum safety action can still be performed when the algorithm protection layer fails; the pressure relief unit adopts a staged pressure relief strategy, opening the pre-pressure relief valve first and then linking it to open the explosion relief port, avoiding the impact of sudden pressure changes in the cabin on the cabin structure; the interlocking unit releases the lock after confirming the triple conditions of oxygen concentration, temperature and combustible gas concentration, preventing accidental opening of the cabin from causing personnel injury.

[0040] Preferably, as an improvement, the system also includes a post-evaluation module for automatically judging the degree of impact on the product after the test is completed; the post-evaluation module includes a visual inspection unit, a laser scanning unit, and an electrical measurement unit, which are used to identify appearance defects, calculate structural deformation, and measure electrical performance parameters, respectively, and output a graded result of the degree of impact from level one to level four; The test database uses the grading results, probabilistic environmental tolerance spectrum, comprehensive anomaly index, and evolution records of predicted coding free energy as incremental training samples, which are fed back to the immune prediction module to update the predictor population and to the risk envelope module to update the loss function parameters.

[0041] The beneficial effects of this improvement are: by automatically judging and classifying the degree of impact after testing, the test results, along with the evolution records of the probabilistic environmental tolerance spectrum, comprehensive anomaly index, and predictive coding free energy, are used as incremental training samples and fed back to the immune prediction module to update the predictor population, and to the risk envelope module to update the loss function parameters, so as to realize the continuous optimization of the algorithm protection layer by the test database and form a closed loop of "test-evaluation-evolution". Attached Figure Description

[0042] Figure 1 This is a system structure block diagram according to an embodiment of the present invention. Detailed Implementation

[0043] The following detailed description illustrates the specific implementation method: Example The integrated system for environmental simulation and safety protection in the product testing center adopts a three-layer closed-loop architecture of "perception layer - protection layer - control layer".

[0044] The protective layer consists of an immune prediction module, a risk envelope module, a community monitoring module, and an anomaly reconstruction module coupled together. These four modules share multi-source data provided by the sensor acquisition module and use a unified environmental parameter vector as the control object, achieving threshold homogeneity, response synchronization, and data isomorphism between the environmental simulation strategy and the safety protection strategy. The system workflow is divided into four stages: environmental pre-setting, closed application, safe steady-state, and post-evaluation.

[0045] The basics are as follows: Figure 1 As shown, the integrated system for environmental simulation and safety protection in the product testing center specifically includes: The sensing and acquisition module, as the perception layer, is deployed inside the closed environment simulation chamber and at key nodes of the chamber wall structure. It is used to synchronously collect data on the state of the chamber, the environment, and the product under test throughout the entire testing cycle, and to provide the synchronous multivariate time series required for the algorithm protection layer to construct the community interaction matrix.

[0046] The sensor acquisition module includes a temperature and humidity sensor unit, a vibration sensor unit, a dust sensor unit, a pressure sensor unit, a cabin strain unit, a gas composition unit, a product temperature measurement unit, and an insulation monitoring unit.

[0047] The temperature and humidity sensing unit consists of three sets of sensors evenly distributed throughout the cabin space, used to collect the cabin temperature at the nth time step. (Unit: °C) and relative humidity (Unit: %RH).

[0048] The vibration sensing unit is located at the connection between the vibration table base and the cabin, and is used to collect the vibration acceleration amplitude at step n. (Unit: g)

[0049] The dust sensing unit is located at the outlet of the dust circulation pipeline and is used to collect the dust mass concentration inside the chamber at step n. (unit: ).

[0050] Pressure sensing units are located on the top and side walls of the chamber to collect the internal gas pressure at step n. (Unit: kPa).

[0051] The hull strain elements are arranged at four equal points on the hull to collect the dimensionless strain values ​​of the hull structure in step n. .

[0052] The gas composition unit is located at the hatch seal and is used to collect the oxygen concentration in step n. With combustible gas concentration (Lower explosive limit percentage).

[0053] The product temperature measurement unit consists of fiber optic temperature sensors attached to key heat-generating parts of the product under test. The output of these sensors is then estimated using a temperature field inversion algorithm to obtain the core internal temperature of the product at step n. (Unit: °C)

[0054] The insulation monitoring unit is connected to the power supply circuit of the product under test via a high-voltage isolation interface to collect the insulation resistance at step n. (unit: ).

[0055] The outputs of all the units mentioned above at step n together constitute the system state vector:

[0056] This vector is based on the sampling period. Synchronous data acquisition is performed, and the data is transmitted through a fiber optic network to the data buffer queue of the protective layer; at the same time, the most recent data is cached in a sliding time window manner. The historical sequence of each time step is used to construct a dynamic interaction matrix for the community monitoring module.

[0057] In addition, the data from the gas composition unit and the pressure sensing unit are directly connected to the safety protection module through a hard-wired safety loop, meeting the requirement for independent transmission of safety-related data.

[0058] The immune prediction module, as the boundary detection layer of the protective layer, is used to maintain a heterogeneous predictor population, generate state evolution predictions for the tested product and cabin within a future time window, and output the predicted encoded free energy as a meta-level early warning signal for system instability.

[0059] The immune prediction module includes a population prediction unit, an affinity evolution unit, and a free energy early warning unit.

[0060] The population prediction unit maintains a model population consisting of K lightweight heterogeneous predictors. ,in, For the k-th individual predictor in set M, each Simplified time series models with different structures are employed, including exponential smoothing, difference integrated moving average models, or locally weighted regression.

[0061] The population prediction unit receives historical sequences from the data buffer queue. Where N=10 is the length of the input time window. The prediction coding error of the k-th predictor at step n is defined as:

[0062] In the formula, This indicates that the k-th predictor makes its prediction of the current state based on historical data from the past N steps. The predicted output.

[0063] The population prediction unit will predict the coding error. Output the affinity evolutionary unit, and simultaneously output a weighted prediction for the next M steps based on the weights of each predictor:

[0064] In the formula, The k-th model predicts the next M steps, where M=30 represents the prediction time domain; weights Allocation based on affinity normalization:

[0065] In the formula, The affinity of the k-th predictor at step n is calculated and provided by the affinity evolution unit. The affinity evolution unit receives the prediction encoding error from the population prediction unit. Calculate affinity:

[0066] In the formula, This is the affinity attenuation coefficient. Each time... At each time step, the affinity evolution unit selects the top 50% of models with the highest affinity for cloning. During cloning, a Gaussian mutation N(0, 1) is introduced. The perturbation model parameters form a new generation of population and are fed back to the population prediction unit; models with affinity below the elimination threshold are eliminated.

[0067] The free energy early warning unit receives the prediction coding error from the population prediction unit. With weight Calculate the predictive coding free energy in step n:

[0068] In the formula, This is the complexity penalty coefficient. Let u be the current weight distribution, where u is a uniform distribution. This is the KL divergence, used to prevent the population from degenerating into a single model.

[0069] Free energy early warning unit for recent Perform linear regression on the free energy sequence at each time step to obtain the regression slope. with goodness of fit .when and When the free energy warning unit determines that the tested product or environment mode has undergone a fundamental change not covered by the training data, it sends a high-sensitivity mode activation signal to the anomaly reconstruction module and writes this signal as a system instability warning to the control bus; when and When this state is reached, it is determined to be stable, at which point the spectrum switching unit is allowed to approach a higher-level performance target. For the most recent The relative rate of change of the step.

[0070] At the same time, the free energy early warning unit will The phylogenetic switching unit, output to the risk envelope module, assists in performance level switching decisions. The population prediction unit continuously outputs prediction results. Output to the risk envelope module for dynamic updating of security boundaries.

[0071] The risk envelope module, as the trajectory planning layer of the protection layer, is used to generate the control input trajectory of the environmental execution module within the probabilistic safety constraint space.

[0072] The risk envelope module includes a loss modeling unit, a CVaR optimization unit, and a spectrum switching unit.

[0073] The loss modeling unit defines an environmental parameter vector and a four-level performance objective. The environmental parameter vector consists of the environmental parameters actually applied in step n.

[0074] In the formula, These are temperature, humidity, vibration, and dust concentration, respectively. The loss modeling unit sets four performance target levels l∈{1,2,3,4}, corresponding to "normal service", "limited performance degradation", "structural function preservation", and "prevention of catastrophic failure", respectively.

[0075] Each level l defines a corresponding loss function. With confidence level The loss function comprehensively considers both the performance deviation of the tested product and the structural risk of the cabin. The CVaR optimization unit receives the loss function from the loss modeling unit. With confidence level and prediction results from the immune prediction module. Solve the probabilistic multilevel constraint optimization problem.

[0076] Define conditional value at risk as:

[0077] In the formula, Value at Risk (VaR) threshold represents the confidence level. The loss shall not exceed this value; Let $\frac{ ...

[0078] Simultaneously satisfying multiple levels of hard constraints:

[0079] In the formula, The maximum conditional risk value allowed for performance level 1. For example, the confidence level corresponding to level l. =0.99 corresponds to normal service. =0.95 corresponds to preventing catastrophic failure.

[0080] The CVaR optimization unit is solved using a sample-based linear programming method, in each control cycle. Inside, with the current Starting from a given point, the optimal environmental parameter settings for the next time step are calculated iteratively. The CVaR optimization unit outputs the solution results to the spectrum switching unit.

[0081] The spectrum switching unit receives the probabilistic environmental parameter tolerance spectrum under the fourth-level performance objective from the CVaR optimization unit:

[0082] In the formula, The optimal environmental parameter settings are defined for the performance objective at level l in step n. The lineage switching unit simultaneously receives free energy from the immune prediction module. Combined anomaly indicators from the community monitoring module Automatically switches between current performance levels: when Stable and When the performance falls below a threshold, the system approaches a higher-level performance objective; when... Rise or In case of an anomaly, the system rolls back to a lower-level performance objective.

[0083] The spectrum switching unit will send the trajectory correction command for the current activation level. The output is sent to the environment execution module. If, at any point, the multi-level constraints become infeasible, the CVaR optimization unit triggers an emergency load reduction command, which is then handled by the spectrum switching unit. Move towards the center of the safe area according to the preset gradient. Backtrack and output to the environment execution module.

[0084] The community monitoring module serves as an anomaly detection layer within the protective layer, used to detect distribution deviations and coupling instabilities in multi-source sensor data during the "black box" operation of the closed application section.

[0085] The community monitoring module includes interaction units, spectral radius units, and intermittent units.

[0086] The interaction unit receives a sliding time window history sequence from the data buffer queue, constructs a dynamic community from the 10-dimensional sensor data, and generates the interaction matrix at step n. , of which elements Estimated via local linear regression:

[0087] In the formula, The system state vector The i-th component, Describing covariance, Represents variance. To prevent division by zero for regularization terms. The spectral radius unit receives the interaction matrix from the interacting unit. Calculate its spectral radius:

[0088] In the formula, For matrix The k-th eigenvalue. If This indicates that the coupling between sensor variables has caused the system to enter a critical instability state. The intermittent unit receives time series data from each sensor from the data buffer queue, and for each sensor signal... Calculate the turbulence intermittency index.

[0089] Define the q-order structure function as:

[0090] In the formula, The number of time lag steps. This represents the time average within the sliding time window, where q is the order. In an ideal Gaussian process, and Define the intermittent deviation of the i-th sensor in step n as:

[0091] In the formula, is the actual scaling exponent of the third-order structure function. The scaling exponent is the scaling factor for an ideal Gaussian process.

[0092] when A value significantly greater than 0 indicates a non-Gaussian sudden deviation in the sensor signal. The intermittent unit represents the intermittent deviation of each sensor. Output to the synthesis and fusion unit. The synthesis and fusion unit receives data from the spectral radius unit. With from intermittent units Calculate the comprehensive anomaly index in step n:

[0093] In the formula, and These are weighting coefficients. The integrated fusion unit will... Output to the exception refactoring module, when An exception confirmation signal is triggered at any time, where Through historical events of instability 95th percentile calibration of the distribution.

[0094] The anomaly reconstruction module serves as the response execution layer of the protection layer. After the community monitoring module or the immune prediction module triggers an early warning, it automatically distinguishes the root causes of anomalies based on multi-source sensor data and anomaly indicators and triggers differentiated self-healing reconstruction strategies.

[0095] The anomaly reconstruction module includes a root cause classification unit and a policy execution unit. The root cause classification unit receives the system state vector from the sensor acquisition module. Comprehensive anomaly indicators from the community monitoring module and the predictive coding free energy from the immune prediction module The three are concatenated into an input vector, which is then passed through a three-layer fully connected neural network to output the root cause probability vector at step n.

[0096] In the formula, For the probability of environmental over-application, The probability of product failure. For the probability of cabin equipment failure, the three conditions must be met. The classifier's training labels are derived from manually labeled root cause types in historical anomalous events.

[0097] The policy execution unit receives the root cause probability vector from the root cause classification unit. ,according to The index performs differentiated self-healing reconstruction: like If the maximum load is detected, indicating an overloaded environment, the strategy execution unit sends an emergency boundary contraction command to the risk envelope module and a conservative mode switch command to the environment execution module, causing all environmental parameters to decrease at the maximum permissible rate. convergence; like If the maximum value is reached, the product is determined to be faulty. The strategy execution unit sends a product isolation instruction to the safety protection module, which includes cutting off the power supply circuit of the tested product, starting the local fine water mist cooling nozzle, maintaining the concentration of inert gas in the chamber, and sending a single-factor maintenance mode instruction to the environmental execution module to avoid secondary damage to the failed product by other environmental parameters. like If the maximum value is reached, it is determined to be a cabin equipment failure. The strategy execution unit sends an emergency steady-state load reduction command to the environmental execution module, a full-cabin nitrogen inerting and staged depressurization command to the safety protection module, and a hold-lock command to the cabin door interlock unit until the cabin pressure and temperature drop below the safety threshold.

[0098] The strategy execution unit records the execution timing and parameter changes of all self-healing reconfiguration actions to non-volatile memory for impact attribution analysis in the post-evaluation phase.

[0099] The environment execution module, acting as the execution layer, receives the current activation level trajectory correction instruction output by the risk envelope module. It also achieves closed-loop tracking by driving various environmental simulation devices through a distributed controller.

[0100] The environmental control module includes a temperature control unit, a humidity control unit, a vibration control unit, and a dust control unit. The temperature and humidity control units employ PID cascade control with outer loop tracking. The temperature and humidity setpoints are configured, and the inner loop regulates the heater power and steam valve opening. The vibration control unit generates a random vibration power spectral density signal based on the acceleration setpoint and achieves closed-loop waveform reproduction through acceleration feedback. The dust control unit maintains the dust concentration in the chamber by tracking the setpoint through a variable frequency fan and a cyclone separator. The environmental execution module displays the actual output values ​​of each unit.

[0101] The data is transmitted back to the control bus, where it is collected by the sensor acquisition module and then fed into the data buffer queue of the algorithm protection layer. This data is used for closed-loop correction of the risk envelope module and construction of the interaction matrix of the community monitoring module.

[0102] The safety protection module is the execution layer, which uses a hard-wired safety relay circuit as the final execution guarantee. It is independent of the control circuit of the environmental execution module, ensuring that the minimum safety action can still be performed when the algorithm protection layer fails.

[0103] The safety protection module includes an explosion suppression unit, an inerting unit, a pressure relief unit, and an interlocking unit.

[0104] The explosion suppression unit consists of multiple atomizing nozzles arranged on the top and sides of the compartment, with a response time of less than 100ms, and receives the trigger signal from the abnormal reconstruction module under the product failure judgment.

[0105] The inerting unit creates a displacement airflow through the bottom air inlet and the top exhaust outlet, which can reduce the oxygen concentration inside the cabin to below 12% within 60 seconds; its operating status is collected by the gas composition unit. Closed-loop feedback control. The pressure relief unit includes a small-diameter pre-pressure relief valve and a large-diameter explosion relief port. When the rate of pressure rise inside the chamber meets the following conditions:

[0106] When the pressure continues to rise, first open the pre-pressure relief valve. If the pressure continues to rise, then the explosion relief port will be opened in conjunction with it. Pressure rise rate threshold; pressure data Hardwired safety loop from the sensor acquisition module.

[0107] The interlocking unit controls the electromagnetic lock on the hatch, maintaining a continuous lock during the closure application phase, and is only confirmed by the gas composition unit during the safe steady-state phase. , , Then unlock.

[0108] The post-evaluation module is deployed at the evaluation station outside the environmental simulation chamber. It is used to automatically determine the degree of impact on the tested product after the safe steady-state phase is completed, and write the results into the test database to provide feedback for optimizing the algorithm protection layer.

[0109] The post-evaluation module includes a visual inspection unit, a laser scanning unit, and an electrical measurement unit.

[0110] The vision inspection unit acquires appearance images through industrial cameras and uses image comparison algorithms to identify macroscopic defects such as cracks, deformation, and coating blistering.

[0111] The laser scanning unit acquires point cloud data of the product surface, and calculates the structural deformation by registering it with the baseline point cloud before testing. .

[0112] The electrical testing unit connects to the product's power supply and signal ports via an automatic measuring fixture to measure the product's functional performance. For example, it measures the insulation resistance of electrical components. Luminous flux maintenance rate for lighting products Regarding the sound pressure level variation of audio products .

[0113] The post-evaluation module ultimately outputs the impact level classification results: if the deviation of all indicators is less than 5%, it is judged as Level 1, with no significant impact on performance; if there is a 5%–20% performance degradation but no structural damage, it is judged as Level 2, with performance degradation; if there is observable structural deformation but the function is not completely lost, it is judged as Level 3, with structural damage; if the function is completely lost or there is a through fracture, it is judged as Level 4, with functional failure.

[0114] The classification results and structural deformation Electrical performance parameters along with the probabilistic environmental tolerance spectrum throughout the entire testing cycle Safe distance trajectory, comprehensive anomaly indicators and predictive coding free energy The evolutionary record is also written into the test database.

[0115] The data in the test database is used as incremental training samples and is periodically fed back to the immune prediction module to update the predictor population, and to the risk envelope module to update the historical performance map and loss function parameters, forming a closed loop of "testing-evaluation-optimization".

[0116] The usage method of the integrated system for environmental simulation and safety protection in the product testing center is as follows: In the environment pre-setting section, the operator inputs the target environment parameter vector of the test task through the intelligent control platform. and test duration The system first retrieves historical related records from the test database to search for... The most similar historical test record is used to extract the probabilistic performance map from the record as the initial safety envelope parameter. Then, the product under test is placed in the chamber, and the post-evaluation module completes the baseline data acquisition, including appearance images, 3D point clouds and initial electrical performance parameters. The immune prediction module reads the product model and material properties, and loads or initializes the corresponding predictor population. The risk envelope module constructs a four-level safety constraint based on the historical performance map and the initial prediction results, and solves the initial probabilistic tolerance spectrum.

[0117] During the closed application phase, the interlocking unit closes the hatch, and the sensor acquisition module... Synchronous acquisition The data is then fed into the data cache queue; the immune prediction module updates the prediction coding error every 10 seconds based on historical sequences. Affinity Population weight With free energy The prediction results Send to the risk envelope module, Sending data to the spectral switching unit and the anomaly reconstruction module; the risk envelope module sends data every 1 second based on... With the present Solving the probabilistic tolerance spectrum is done by the system switching unit based on... and Select the activation level, The signal is sent to the environmental execution module; the environmental execution module drives the temperature control, humidity control, vibration control, and dust control units to execute, and transmits the actual output. The data is transmitted back to the sensor acquisition module; the community monitoring module calculates the interaction matrix in real time based on a sliding time window. spectral radius Intermittent deviation Generate comprehensive anomaly indicators Send to the exception refactoring module. When the immune prediction module continuously and monotonically increases, it sends a high-sensitivity mode activation signal to the anomaly reconstruction module, which then sets the anomaly detection threshold of the community monitoring module. Temporarily lower and then increase the sampling frequency of the sensor acquisition module to 0.2s; when At that time, the community monitoring module directly triggers the root cause classification and strategy execution of the anomaly reconstruction module.

[0118] During the safe steady-state phase, the environmental execution module reduces load according to a preset gradient, the safety protection module initiates cabin purification circulation and gas replacement, and the interlocking unit monitors the process. , and Until the hatch opening safety threshold is met.

[0119] In the post-evaluation phase, the post-evaluation module performs automated detection and impact level classification, and writes the results into the test database. The test database periodically feeds incremental samples back to the immune prediction module and the risk envelope module to complete closed-loop optimization.

[0120] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An integrated system for environmental simulation and safety protection in a product testing center, characterized in that: include: The immune prediction module is used to receive data from multiple sources of sensors in the cabin, maintain a model population composed of multiple heterogeneous predictors, perform affinity evolution and clonal mutation based on prediction coding error, generate a weighted prediction of the future state of the tested product, and calculate the prediction coding free energy. The risk envelope module is used to set four performance target levels and corresponding loss functions, solve probabilistic multi-level constrained optimization problems based on conditional risk value, generate four-level probabilistic environmental parameter tolerance spectrum, and automatically switch the current performance level according to the predicted coding free energy and comprehensive anomaly index to generate the control input trajectory of the environmental execution module. The community monitoring module is used to construct a dynamic interaction matrix from multi-source sensor data in the cabin, calculate its spectral radius and the intermittent deviation of each sensor signal, and fuse them to generate the comprehensive anomaly index in order to detect distribution deviation and coupling instability within the closed application section. The anomaly reconstruction module is used to concatenate the system state vector, comprehensive anomaly index and predictive coding free energy and input them into the neural network, output the root cause probability vector, and perform differentiated self-healing reconstruction based on it. The environment execution module is used to receive the trajectory correction command and drive the environment simulation device; The security protection module is used to receive the differentiated reconstruction instructions and perform physical security protection actions; The immune prediction module outputs the weighted prediction to the risk envelope module to dynamically update the safety boundary, and outputs the predicted encoded free energy to the risk envelope module and the anomaly reconstruction module; the community monitoring module outputs the comprehensive anomaly index to the risk envelope module and the anomaly reconstruction module; the anomaly reconstruction module sends differentiated reconstruction instructions to the risk envelope module, the environment execution module and the security protection module according to the root cause type.

2. The integrated system for product testing center environment simulation and safety protection according to claim 1, characterized in that: The immune prediction module includes a population prediction unit, an affinity evolution unit, and a free energy early warning unit. The population prediction unit calculates the prediction coding error of the k-th predictor at step n based on historical sensor data from inside the cabin: In the formula, Let be the system state vector. This is the k-th predictor, and N is the length of the input time window; The affinity evolution unit calculates affinity: In the formula, The affinity attenuation coefficient; The population prediction unit outputs the weighted prediction based on affinity-normalized weights: In the formula, Let M be the prediction of the k-th predictor for the next M steps, where M is the prediction time domain. For normalized weights; The free energy early warning unit calculates and predicts the coded free energy: In the formula, This is the complexity penalty coefficient. Let u be the current weight distribution, where u is a uniform distribution. Let KL divergence be denoted as KL divergence.

3. The integrated system for product testing center environment simulation and safety protection according to claim 2, characterized in that: The risk envelope module includes a loss modeling unit, a CVaR optimization unit, and a spectrum switching unit; The loss modeling unit sets four performance target levels, corresponding to normal service, limited performance degradation, structural function preservation, and prevention of catastrophic failure, respectively, and defines the corresponding loss function and confidence level for each level. The CVaR optimization unit solves for the conditional risk value: In the formula, For loss function, Value at risk threshold, For confidence level, For conditional expectations; And satisfy multi-level hard constraints: In the formula, The maximum conditional risk value allowed for performance level 1. This represents the first level of confidence. The spectrum switching unit automatically switches the current activation level in the four-level probabilistic environmental parameter tolerance spectrum based on the predicted coding free energy and the comprehensive anomaly index, so as to generate the control input trajectory of the environmental execution module.

4. The integrated system for product testing center environment simulation and safety protection according to claim 3, characterized in that: The community monitoring module includes interaction units, spectral radius units, intermittent units, and integrated fusion units; The interaction unit constructs the interaction matrix in step n based on data from multiple in-cabin sensors, and its elements are: In the formula, For the output of the i-th sensor, For covariance, For variance, For regularization terms; The spectral radius unit calculates the spectral radius: In the formula, It is the k-th eigenvalue of the interaction matrix; The intermittent unit calculates the intermittent deviation: In the formula, Let be the 3rd order structure function of the i-th sensor. This represents the number of time lag steps. The integrated fusion unit generates a comprehensive anomaly index: In the formula, and These are the weighting coefficients.

5. The integrated system for environmental simulation and safety protection of a product testing center according to claim 4, characterized in that: The anomaly reconstruction module includes a root cause classification unit and a policy execution unit; The root cause classification unit concatenates the system state vector, comprehensive anomaly index, and predictive coding free energy into an input vector, and outputs a root cause probability vector through a neural network. In the formula, For the probability of environmental over-application, The probability of product failure. Probability of cabin equipment failure; When the environment is overloaded, the strategy execution unit sends an emergency contraction command to the risk envelope module and a conservative mode command to the environment execution module. When the product itself fails, it sends a product isolation command to the safety protection module and a single-factor maintenance mode command to the environment execution module. When the cabin equipment fails, it sends a full-cabin nitrogen inerting and graded depressurization command to the safety protection module.

6. The integrated system for product testing center environment simulation and safety protection according to claim 5, characterized in that: The system also includes a sensor acquisition module for synchronously acquiring system state vectors and caching historical sequences; the system state vectors include temperature, relative humidity, vibration acceleration, dust concentration, internal pressure, bulkhead strain, oxygen concentration, combustible gas concentration, insulation resistance, and product internal temperature; The combustible gas concentration sensor, used to collect oxygen and combustible gas concentrations, and the pressure sensor, used to collect cabin pressure, are also directly connected to the safety protection module via a hard-wired safety circuit.

7. The integrated system for product testing center environment simulation and safety protection according to claim 6, characterized in that: The environmental execution module includes a temperature control unit, a humidity control unit, a vibration control unit, and a dust control unit. Each unit transmits its actual output value back to the sensor acquisition module for closed-loop correction of the risk envelope module and construction of the interaction matrix of the community monitoring module.

8. The integrated system for environmental simulation and safety protection of a product testing center according to claim 7, characterized in that: The safety protection module adopts a hard-wired safety relay circuit, which is independent of the control circuit of the environmental execution module; the safety protection module includes an explosion suppression unit, an inerting unit, a pressure relief unit, and an interlocking unit. The pressure relief unit satisfies the following rate of pressure rise within the chamber: The staged pressure relief is initiated at the specified time, where... The threshold for the rate of pressure rise; The interlocking unit locks the hatch during the closed application phase and releases the lock after confirming that the oxygen concentration is higher than 19.5%, the temperature is lower than 40 degrees Celsius, and the combustible gas concentration is lower than 10% during the safe steady-state phase.

9. The integrated system for environmental simulation and safety protection of a product testing center according to claim 8, characterized in that: The system also includes a post-evaluation module, which is used to automatically determine the degree of impact on the product after the test is completed. The post-evaluation module includes a visual inspection unit, a laser scanning unit, and an electrical measurement unit, which are used to identify appearance defects, calculate structural deformation, and measure electrical performance parameters, respectively, and output the degree of impact classification results from level one to level four. The test database uses the grading results, probabilistic environmental tolerance spectrum, comprehensive anomaly index, and evolution records of predicted coding free energy as incremental training samples, which are fed back to the immune prediction module to update the predictor population and to the risk envelope module to update the loss function parameters.

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