Impact machine temperature control method based on multi-standard dynamic mapping and scene perception

Abstract

The invention relates to an impact machine temperature control method based on multi-standard dynamic mapping and scene perception. The method comprises the following steps: acquiring original temperature data of a tested device through a preset thermal, electric and magnetic three-in-one isolated temperature acquisition device; and constructing a standard adaptation rule base based on dynamic mapping of the original temperature data, and obtaining a result of matching of multi-source data conversion and a target format through a standard factor algorithm. Performing standardized verification on the data by adopting multi-scale anomaly detection and cross-standard verification; analyzing the characteristics of the test interface through a scene recognition module, generating a scene recognition result, driving a mode switching rule, and outputting scene-adaptive temperature control adjustment data; generating a dynamic temperature control instruction based on an adaptive strategy, and driving a heat flow module through a multi-mode actuator to carry out standard state temperature control on a tested device; multi-standard diagnosis results are generated through an abnormity diagnosis engine, temperature control strategy parameters are dynamically adjusted through an online updating mechanism, and closed-loop control over a temperature control device is achieved.

Description

Technical Field

[0001] This invention belongs to the field of industrial automation and intelligent control, specifically relating to a method for temperature control of an impact machine based on multi-standard dynamic mapping and scene perception. Background Technology

[0002] Currently, the contact-type high and low temperature impact tester still has the following areas for improvement: Currently, contact-type high and low temperature impact testers have significant limitations in multi-standard adaptation. The measurement dimensions and statistical calibers of temperature data under different test standards vary greatly, and there is a lack of unified conversion rules and adaptation mechanisms, which makes it difficult to achieve effective comparison of multi-source heterogeneous data, affecting the universality and accuracy of test results.

[0003] Temperature acquisition is susceptible to external interference. Existing equipment lacks a comprehensive thermal, electrical, and magnetic isolation design. Temperature coupling and electromagnetic interference from external circuits directly affect the accuracy of the acquired data. Furthermore, it lacks compatibility with different types of devices under test, such as bare dies and packaged chips, and cannot flexibly meet the acquisition needs of diverse testing scenarios.

[0004] The temperature control mode has poor scenario adaptability. Most existing devices adopt a fixed temperature control strategy, which makes it difficult to dynamically identify test scenarios based on test connection interface characteristics, instruction logic, etc. This results in the temperature control mode type and parameter adjustment dimensions failing to accurately match actual test requirements, leading to problems such as delayed temperature control response and unreasonable parameter adjustment.

[0005] The temperature control system lacks closed-loop optimization capabilities and a real-time intelligent monitoring and failure attribution analysis mechanism for temperature control performance. When temperature control deviations or device failures occur, it is impossible to quickly locate the root cause and generate targeted parameter compensation instructions. Furthermore, the temperature control strategy parameters are difficult to dynamically adjust through online updates, affecting the long-term stability and reliability of the equipment. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a multi-standard dynamic mapping and scene perception method for temperature control of impact machines.

[0007] The objective of this invention can be achieved through the following technical solutions: S1: Obtain the raw temperature data of the device under test by using a pre-set temperature acquisition device that integrates thermal, electrical, and magnetic isolation. S2: Based on the original temperature data, a standard temperature data adaptation rule base is obtained through dynamic mapping. Based on the rule base, a standard factor conversion algorithm is used to convert the temperature data of the corresponding standard to obtain the conversion result of the original temperature data. The conversion result of the original temperature data is matched with the target format to obtain the matching result of the original data to the target format. A multi-scale anomaly detection component is used to perform multi-dimensional anomaly screening on the matching result, and a cross-validation engine is used to call a preset anomaly judgment threshold to perform cross-standard consistency verification on the screened anomaly data to obtain the verified temperature data. S3: Based on the temperature data, the scene recognition module's test connection interface is used to obtain scene recognition results through a corresponding matching algorithm; based on the scene recognition results, the temperature control mode type and parameter adjustment dimension under the corresponding scene are converted using mode switching rules to obtain scene-adaptive temperature control adjustment data; dynamic temperature control commands are generated using the temperature control strategy adaptive generation module through the temperature control adjustment data; the multimodal actuator drive module is used to control the impact machine heat flow module to execute the dynamic temperature control commands, and standard-state temperature control is performed on the tested device to obtain the impact machine temperature control device; S4: Intelligent monitoring of the temperature control device is performed through the standard temperature control of the impact machine to obtain the monitoring data of the temperature control device; multi-standard diagnostic results of failure attribution analysis are generated using the anomaly diagnosis and root cause analysis engine; based on the diagnostic results, parameter compensation instructions are generated through the adaptive correction strategy library; based on the compensation instructions, the temperature control strategy parameters of the target device of the impact machine are dynamically adjusted through the online update mechanism to obtain the temperature control device under the corresponding temperature control strategy.

[0008] Specifically, the process of obtaining the original temperature is as follows: by isolating the environment through multi-layer circuitry, the composite contact head of the graphene thermal conductive film is bonded to the device under test, and the high thermal conductivity of graphene is used to construct a low thermal resistance interface to obtain the true temperature of the device under test; based on the true temperature, the original temperature data is obtained by adapting the die to the chip package device.

[0009] Specifically, the process of obtaining the adaptation rule base for standard temperature data includes: based on rule templates and semantic parsers, performing structured parsing of the target standard text, extracting parameter definitions, units of measurement, test conditions and tolerance requirements from the standard, and obtaining a set of standard metadata; Based on the aforementioned standard metadata set, a similarity matching algorithm combining keyword weight and contextual relevance is used to establish mapping pairs between corresponding standard terms; and a difference calculation model is used to quantify the systematic deviations between measurement dimensions and statistical standards. Based on the mapping relationship and systematic deviation, a set of conversion rules between standards is generated through a deterministic rule engine and an interpolation function library, and a standard temperature data adaptation rule library is constructed.

[0010] Specifically, the conversion process of the original temperature data is as follows: based on the standard mapping relationship and systematic deviation stored in the adaptation rule base, the measurement unit, sampling frequency, and accuracy level of the multi-source heterogeneous temperature data are converted to the benchmark framework of the target standard through the dimension normalization engine and statistical caliber alignment algorithm; based on the benchmark framework, the dimensions, scale, and distribution characteristics of the original temperature data are standardized and reconstructed using the standard factor matrix and linear transformation model, and the converted dataset of the original temperature is obtained through data consistency verification.

[0011] Specifically, the process of matching the raw data to the target standard format includes: based on the structured format specification of the target standard, extracting the data field definitions, data type constraints, data hierarchy relationships and validation rules of the target standard through a format template parsing engine, and generating a standard format template; Based on the standard format template and the original temperature conversion data, the data elements are intelligently matched and mapped according to field name, semantic similarity and data type through field mapping algorithm and data structure adapter, and the correspondence between the original data fields and the target standard fields is established. Based on the aforementioned correspondence, the data format converter and verification rule engine perform data type conversion, unit unification, precision adjustment, and logical verification to generate standardized temperature data that meets the target standard format requirements and passes validity verification, thereby achieving automatic matching of the original data to the target standard format.

[0012] Specifically, the multi-dimensional anomaly screening process is as follows: through an adaptive sliding window mechanism, the converted temperature signal is decomposed on a time scale to extract multi-scale statistical feature vectors; Based on a predefined expert knowledge rule base and statistical process control model, abnormal pattern matching and deviation calculation are performed on feature vectors at corresponding scales to identify point anomalies, context anomalies and trend anomalies. An anomaly screening report is generated based on the anomaly determination results of the multi-source evidence fusion strategy and the corresponding scale.

[0013] Specifically, the verified temperature data includes: based on a multi-standard protocol compatibility rule base, multiple rule verifications are performed on the standard compliance of abnormal data through a protocol consistency verification engine; a dynamic tolerance matching algorithm is used to arbitrate compliance of fuzzy abnormal data that is under standard boundary conditions; and based on the arbitration result, verified temperature data is generated through a data repair and marking mechanism.

[0014] Specifically, obtaining scene recognition results using the corresponding matching algorithm includes: based on the physical characteristics and electrical parameters of the test connection interface, extracting connector type, pin definition, and communication protocol stack features through an interface feature parsing engine to generate an interface feature vector; based on the temporal logic and semantic structure of the test instructions, extracting instruction sequence patterns, parameter configuration templates, and operation flow features through an instruction feature analysis engine to generate an instruction feature vector; and based on the interface feature vector and instruction feature vector, calculating the matching degree with a predefined scene template through a multi-feature weighted fusion algorithm and rule reasoning mechanism to obtain the scene recognition result.

[0015] Specifically, the process of converting the temperature control mode type and parameter adjustment dimension in the corresponding scenario includes: based on the scene type identifier and feature weight in the scene recognition result, matching a predefined temperature control mode template library through a rule engine and fuzzy inference mechanism to obtain the basic temperature control mode type; based on the scene feature vector and historical optimization records, dynamically adjusting the temperature control parameter dimension through a multi-objective optimization algorithm to generate a parameter optimization set; and based on real-time system status feedback, performing online correction of the parameter set through an adaptive compensation mechanism to obtain temperature control adjustment data matching the test scenario.

[0016] Specifically, the generation of the dynamic temperature control command is carried out as follows: based on the mode configuration and parameter set in the temperature control adjustment data, the control quantity is obtained through multi-objective optimization; based on real-time system state feedback and environmental disturbance observation, the control quantity is dynamically compensated through a feedforward and feedback composite control algorithm, and the dynamic temperature control command is generated by utilizing the boundary protection and smoothing filtering mechanism of the command safety constraint library.

[0017] Specifically, the process of generating parameter compensation instructions is as follows: based on the fault type, location information and confidence data in the multi-standard diagnostic results, a predefined correction strategy template is matched through a fault strategy mapping and rule reasoning mechanism; based on the historical correction effect database and real-time system status, a parameter adjustment scheme is dynamically generated using a compensation amount calculation model, and the feasibility of the adjustment scheme is verified to obtain a parameter compensation instruction set.

[0018] Specifically, the dynamic adjustment of the temperature control strategy parameters of the target device of the impact machine includes: updating the code based on the parameter compensation instruction set using hot patch loading technology and dual memory area switching mechanism; dynamically calculating the parameter correction amount based on real-time system status monitoring data through incremental parameter adjustment algorithm and rolling optimization window; and using formal verification method to verify the feasibility of the adjusted parameter set and generate a temperature control strategy parameter update scheme.

[0019] The beneficial effects of this invention are as follows: By pre-setting temperature acquisition hardware with thermal, electrical, and magnetic three-in-one isolation, integrating graphene thermal conductive film composite contact head to enhance heat conduction efficiency, and combining vacuum insulation layer and electromagnetic shielding design to suppress external interference, it can not only ensure the accuracy of temperature acquisition data, but also be compatible with various devices under test such as bare dies, packaged chips and system-level onboard chips. This solves the problems of poor compatibility and susceptibility to interference of traditional acquisition hardware, and provides a high-quality data foundation for subsequent temperature control analysis.

[0020] By building an adaptation rule base based on a multi-standard dynamic mapping engine and combining it with a standard factor conversion algorithm, a unified conversion of measurement dimensions and statistical calibers of multi-source heterogeneous temperature data is achieved. Then, through a multi-scale anomaly detection component and a cross-validation engine, anomaly screening and cross-standard consistency verification are completed. This effectively solves the problems of data format incompatibility and poor comparability under different standards, ensures that temperature data meets the requirements of the target standard, and improves data availability and test result credibility.

[0021] Based on the scene recognition module, the test connection interface features and instruction logic are analyzed. The test scene is accurately identified through the matching algorithm. Then, the temperature control mode type and parameter adjustment dimension are converted according to the mode switching rules. With the temperature control strategy adaptive generation module and the multi-modal actuator drive module, the dynamic temperature control command is generated and executed quickly. This allows the temperature control strategy to accurately match the requirements of different test scenarios, avoiding the problems of lag response and poor parameter adaptability of traditional fixed temperature control mode, and improving the efficiency and accuracy of standard temperature control of the impact machine.

[0022] By acquiring the performance indicators of temperature control devices through intelligent monitoring of the impact machine's standard temperature control, generating multi-standard diagnostic results with the help of an anomaly diagnosis and root cause analysis engine, and then generating parameter compensation instructions based on the adaptive correction strategy library and dynamically adjusting the temperature control parameters through an online update mechanism, a closed-loop optimization process of monitoring, diagnosis, compensation and adjustment is formed. This effectively solves the problems of traditional systems that are difficult to locate the root cause of failure and parameter adjustment lag, ensuring the long-term stability and temperature control accuracy of the impact machine, extending the service life of the equipment and reducing maintenance costs. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating a multi-standard dynamic mapping and scene perception-based temperature control method for an impact machine according to the present invention. Figure 2 This is a block diagram of the device temperature control structure in this invention; Detailed Implementation

[0025] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0026] Please see Figure 1-2 A multi-standard dynamic mapping and scene-aware impact machine temperature control method, comprising: S1: Obtain the raw temperature data of the device under test by using a pre-set temperature acquisition device that integrates thermal, electrical, and magnetic isolation. S2: Based on the original temperature data, a standard temperature data adaptation rule base is obtained through dynamic mapping. Based on the rule base, a standard factor conversion algorithm is used to convert the temperature data of the corresponding standard to obtain the conversion result of the original temperature data. The conversion result of the original temperature data is matched with the target format to obtain the matching result of the original data to the target format. A multi-scale anomaly detection component is used to perform multi-dimensional anomaly screening on the matching result, and a cross-validation engine is used to call a preset anomaly judgment threshold to perform cross-standard consistency verification on the screened anomaly data to obtain the verified temperature data. S3: Based on the temperature data, the scene recognition module's test connection interface is used to obtain scene recognition results through a corresponding matching algorithm; based on the scene recognition results, the temperature control mode type and parameter adjustment dimension under the corresponding scene are converted using mode switching rules to obtain scene-adaptive temperature control adjustment data; dynamic temperature control commands are generated using the temperature control strategy adaptive generation module through the temperature control adjustment data; the multimodal actuator drive module is used to control the impact machine heat flow module to execute the dynamic temperature control commands, and standard-state temperature control is performed on the tested device to obtain the impact machine temperature control device; S4: Intelligent monitoring of the temperature control device is performed through the standard temperature control of the impact machine to obtain the monitoring data of the temperature control device; multi-standard diagnostic results of failure attribution analysis are generated using the anomaly diagnosis and root cause analysis engine; based on the diagnostic results, parameter compensation instructions are generated through the adaptive correction strategy library; based on the compensation instructions, the temperature control strategy parameters of the target device of the impact machine are dynamically adjusted through the online update mechanism to obtain the temperature control device under the corresponding temperature control strategy.

[0027] Specifically, the process of obtaining the original temperature is as follows: the environment is isolated by a multi-layer circuit, the composite contact head of the graphene thermal conductive film is attached to the device under test, and the high thermal conductivity of graphene is used to construct a low thermal resistance interface to obtain the true temperature of the device under test; based on the true temperature, the original temperature data is obtained by adapting the bare die to the chip package device.

[0028] Specifically, the process of obtaining the adaptation rule base for standard temperature data includes: based on rule templates and semantic parsers, performing structured parsing of the target standard text, extracting parameter definitions, units of measurement, test conditions and tolerance requirements from the standard, and obtaining a set of standard metadata; Based on the aforementioned standard metadata set, a similarity matching algorithm combining keyword weight and contextual relevance is used to establish mapping pairs between corresponding standard terms; and a difference calculation model is used to quantify the systematic deviations between measurement dimensions and statistical standards. Based on the mapping relationship and systematic deviation, a set of conversion rules between standards is generated through a deterministic rule engine and an interpolation function library, and a standard temperature data adaptation rule library is constructed.

[0029] In this embodiment, rule templates and semantic parsers are used to analyze the target standard (Std). T The text content of the standard and other standards to be adapted is structured and parsed to accurately extract the core information of each standard, including temperature parameter definitions, units of measurement, test conditions and tolerance requirements, and integrate them to form a standardized set of standard metadata.

[0030] By employing a similarity matching method combining keyword weighting analysis and contextual relevance assessment, a correspondence between terms in different standards is established. For example, temperature change rate and temperature variation rate, which are consistently expressed in different standards, are established as matching term pairs. Simultaneously, a difference calculation model is used to quantitatively analyze the systematic differences in measurement dimensions and statistical calibers among the standards, clarifying the degree of deviation between different standards in aspects such as unit conversion, sampling frequency, and accuracy level.

[0031] Keyword weight calculation (TF-IDF formula) is as follows: , Where TF-IDF(t, d) represents the weight of keyword t in standard text d; the larger the value, the more core the keyword. TF(t, d) represents the term frequency of keyword t in text d. , IDF(t): The inverse document frequency of keyword t, i.e.: , Based on the established terminology mapping relationship and the systematic deviation of quantification, the deterministic rule engine calls the interpolation function library to formulate a set of data conversion rules between standards, covering various conversion logics such as unit of measurement unification, sampling frequency alignment, and accuracy level adaptation, and constructs a temperature data adaptation rule library that is compatible with multiple standards.

[0032] Specifically, the conversion process of the original temperature data is as follows: based on the standard mapping relationship and systematic deviation stored in the adaptation rule base, the measurement unit, sampling frequency, and accuracy level of the multi-source heterogeneous temperature data are converted to the benchmark framework of the target standard through the dimension normalization engine and statistical caliber alignment algorithm; based on the benchmark framework, the dimensions, scale, and distribution characteristics of the original temperature data are standardized and reconstructed using the standard factor matrix and linear transformation model, and the converted dataset of the original temperature is obtained through data consistency verification.

[0033] In this embodiment, the mapping relationships and deviation data in the adaptation rule base are called. Through the dimension normalization engine and the statistical caliber alignment algorithm, the multi-source heterogeneous temperature data collected under different standards are uniformly standardized. The measurement units, sampling frequencies and accuracy levels of various types of data are all converted to the benchmark framework of the target standard (StdT), eliminating the measurement deviation caused by the difference in data sources.

[0034] Within the target standard benchmark framework, the normalized raw temperature data is reconstructed using a standard factor matrix and a linear transformation model, standardizing its dimensions, scale, and distribution characteristics to ensure that the data representation fully conforms to the statistical requirements of the target standard. During the reconstruction process, data consistency verification is performed simultaneously, eliminating logical inconsistencies and invalid data exceeding reasonable limits, resulting in the original temperature conversion dataset.

[0035] The formula for reconstructing data distribution characteristics is as follows: , Among them, T std-norm Temperature data that conforms to the target standard statistical distribution after standardization and reconstruction; T norm Normalized temperature data after unit conversion and sampling frequency alignment; The average temperature data specified by the target standard (StdT) (which can be obtained from Std...) T (Search within standard texts or obtain data through historical compliance statistics); Target Standard (Std) T The standard deviation of the specified temperature data is the same as... , is a value calculated from standard publicly available statistical parameters or historical compliant data.

[0036] Specifically, the process of matching the raw data to the target standard format includes: based on the structured format specification of the target standard, extracting the data field definitions, data type constraints, data hierarchy relationships and validation rules of the target standard through a format template parsing engine, and generating a standard format template; Based on the standard format template and the original temperature conversion data, the data elements are intelligently matched and mapped according to field name, semantic similarity and data type through field mapping algorithm and data structure adapter, and the correspondence between the original data fields and the target standard fields is established. Based on the aforementioned correspondence, the data format converter and verification rule engine perform data type conversion, unit unification, precision adjustment, and logical verification to generate standardized temperature data that meets the target standard format requirements and passes validity verification, thereby achieving automatic matching of the original data to the target standard format.

[0037] In this embodiment, based on the target standard (Std) T The structured format specification extracts standard data field definitions, data type constraints, data hierarchy relationships, and validation rules through a format template parsing engine, generating a standard format template. The original temperature conversion data is compared with this template, and a field mapping algorithm and data structure adapter are used to establish the correspondence between the original data fields and the target standard fields according to field name, semantic similarity, and data type. The data format converter performs data type conversion, unit unification, and precision adjustment, and the validation rule engine performs logical validation to generate standardized temperature data that fully meets the target standard format requirements and passes validity validation.

[0038] Specifically, the multi-dimensional anomaly screening process is as follows: through an adaptive sliding window mechanism, the converted temperature signal is decomposed on a time scale to extract multi-scale statistical feature vectors; Based on a predefined expert knowledge rule base and statistical process control model, abnormal pattern matching and deviation calculation are performed on feature vectors at corresponding scales to identify point anomalies, context anomalies and trend anomalies. An anomaly screening report is generated based on the anomaly determination results of the multi-source evidence fusion strategy and the corresponding scale.

[0039] Specifically, the verified temperature data includes: based on a multi-standard protocol compatibility rule base, multiple rule verifications are performed on the standard compliance of abnormal data through a protocol consistency verification engine; a dynamic tolerance matching algorithm is used to arbitrate compliance of fuzzy abnormal data that is under standard boundary conditions; and based on the arbitration result, verified temperature data is generated through a data repair and marking mechanism.

[0040] In this embodiment, an adaptive sliding window mechanism is employed to perform hierarchical decomposition of the converted temperature signal across different time scales, extracting statistical feature vectors at different time scales. Combining a predefined expert knowledge rule base and a statistical process control model, anomaly pattern matching and deviation analysis are performed on the feature vectors at each scale to accurately identify point anomalies, contextual anomalies, and trend anomalies, generating a detailed anomaly screening report.

[0041] Based on a multi-standard protocol compatibility rule base, a protocol consistency verification engine performs multiple rule checks on the screened abnormal data to determine whether the abnormal data meets the requirements of the target standard and related compatibility standards. For ambiguous abnormal data at the standard boundary conditions, a dynamic tolerance matching algorithm is used for compliance arbitration, and the validity of the data is comprehensively determined by combining the test scenario and environmental factors. Finally, a data repair mechanism processes the correctable abnormal data, and marks and removes the uncorrectable abnormal data to generate high-quality temperature data after verification.

[0042] Specifically, obtaining scene recognition results using the corresponding matching algorithm includes: based on the physical characteristics and electrical parameters of the test connection interface, extracting connector type, pin definition, and communication protocol stack features through an interface feature parsing engine to generate an interface feature vector; based on the temporal logic and semantic structure of the test instructions, extracting instruction sequence patterns, parameter configuration templates, and operation flow features through an instruction feature analysis engine to generate an instruction feature vector; and based on the interface feature vector and instruction feature vector, calculating the matching degree with a predefined scene template through a multi-feature weighted fusion algorithm and rule reasoning mechanism to obtain the scene recognition result.

[0043] In this embodiment, the scene recognition part is carried out through the interface feature parsing engine: the physical characteristics and electrical parameters of the test connection interface are collected, the connector type, pin function distribution and communication protocol stack characteristics are confirmed, and a standardized interface feature vector is generated; at the same time, the timing logic of the test command is parsed with the help of the instruction feature analysis engine, such as: start, heating, constant temperature, cooling, loop and stop sequence, parameter configuration template and operation process specification, and instruction feature vector is generated.

[0044] Then, matching is completed through a multi-feature weighted fusion algorithm and a rule-based reasoning mechanism: by Std T The standard assigns corresponding weights to interface feature vectors and instruction feature vectors, calculates the similarity with a predefined scenario template library, and finds that the highest matching degree is with the multi-stage high and low temperature shock scenario template for electronic devices; then, based on Std... T Scene determination rule validation confirms that the current scene is based on Std. T Standard electronic devices are subjected to multi-stage high and low temperature shock scenarios, and the output identification results are used to support the formulation of subsequent temperature control strategies.

[0045] The formula for weight normalization is as follows: , Among them, w k The final weight of the k-th feature vector (such as interface feature vector or instruction feature vector), with a value range of [0,1]. The sum of the weights of all feature vectors is 1; s kSubjective importance score of the k-th eigenvector (based on Std). T Standard or expert experience-based assignment, such as 6 points for interface features and 4 points for instruction features); n: the total number of feature vectors participating in weight allocation (here n=2, i.e., interface and instruction feature vectors).

[0046] Specifically, the process of converting the temperature control mode type and parameter adjustment dimension in the corresponding scenario includes: based on the scene type identifier and feature weight in the scene recognition result, matching a predefined temperature control mode template library through a rule engine and fuzzy inference mechanism to obtain the basic temperature control mode type; based on the scene feature vector and historical optimization records, dynamically adjusting the temperature control parameter dimension through a multi-objective optimization algorithm to generate a parameter optimization set; and based on real-time system status feedback, performing online correction of the parameter set through an adaptive compensation mechanism to obtain temperature control adjustment data matching the test scenario.

[0047] Specifically, the generation of the dynamic temperature control command is carried out as follows: based on the mode configuration and parameter set in the temperature control adjustment data, a control quantity is obtained through a multi-objective optimization engine; based on real-time system state feedback and environmental disturbance observation, the control quantity is dynamically compensated through a feedforward and feedback composite control algorithm; and based on the command safety constraint library, a dynamic temperature control command is generated using boundary protection and smoothing filtering mechanisms.

[0048] In this embodiment, for Std-based T For standard electronic devices experiencing multi-stage high and low temperature shock scenarios, the system first uses type identifiers and feature weights from the scenario recognition results. Then, a rule engine calls a predefined temperature control mode template library and combines it with a fuzzy inference mechanism. For example, based on the core requirements of multi-stage temperature changes and high-precision constant temperature, the system matches the basic temperature control mode type for staged dynamic power adjustment and constant temperature micro-compensation. The system then extracts scenario feature vectors and combines them with historical optimization records. A multi-objective optimization algorithm is used to dynamically adjust the dimensions of temperature control parameters to generate a parameter optimization set. Finally, based on real-time system status feedback, the system fine-tunes the parameters through an adaptive compensation mechanism to obtain temperature control adjustment data suitable for the scenario.

[0049] Based on the aforementioned temperature control data, the multi-objective optimization engine first transforms the phased dynamic power adjustment mode configuration and parameter set into basic control quantities, such as the heating power duty cycle during the heating phase and the cooling capacity allocation during the cooling phase. Combining real-time system status feedback and environmental disturbance observation, the control quantities are corrected through a feedforward and feedback composite control algorithm. The instruction safety constraint library is then invoked, such as ensuring that the power of the heat flow module does not exceed the rated value and the temperature change rate does not exceed the safety threshold. Violations of control quantities are eliminated through boundary protection, and a smoothing filtering mechanism is used to avoid power abrupt changes, generating dynamic temperature control instructions that can be directly issued to the execution module.

[0050] Specifically, the process of generating parameter compensation instructions is as follows: based on the fault type, location information and confidence data in the multi-standard diagnostic results, a predefined correction strategy template is matched through a fault strategy mapping and rule reasoning mechanism; based on the historical correction effect database and real-time system status, a parameter adjustment scheme is dynamically generated using a compensation amount calculation model, and the feasibility of the adjustment scheme is verified to obtain a parameter compensation instruction set.

[0051] Specifically, the dynamic adjustment of the temperature control strategy parameters of the target device of the impact machine includes: updating the code based on the parameter compensation instruction set using hot patch loading technology and dual memory area switching mechanism; dynamically calculating the parameter correction amount based on real-time system status monitoring data through incremental parameter adjustment algorithm and rolling optimization window; and using formal verification method to verify the feasibility of the adjusted parameter set and generate a temperature control strategy parameter update scheme.

[0052] In this embodiment, when generating parameter compensation instructions, the multi-standard diagnostic results output by the anomaly diagnosis and root cause analysis engine are first used. For example, if the fault type is temperature drift during the constant temperature stage and the location information points to a decrease in the heat dissipation efficiency of the heat flow module, the corresponding scheme is matched in the predefined correction strategy template library through the fault strategy mapping mechanism. The historical correction effect database is called to extract the optimal compensation record for the same type of fault. Combined with the real-time system status, the parameter adjustment scheme is dynamically generated using the compensation amount calculation model. Then, the feasibility of the scheme is verified through simulation to form a parameter compensation instruction set.

[0053] In the stage of dynamically adjusting the temperature control strategy parameters, based on the aforementioned parameter compensation instruction set, the parameter adjustment scheme is first written into the impact machine control program using hot patch loading technology. At the same time, a dual storage area switching mechanism is used to ensure that the update process does not interrupt the test process. Based on real-time system status monitoring data, the parameter correction amount is dynamically calculated using an incremental parameter adjustment algorithm and a rolling optimization window. Formal verification methods are used to confirm that the adjusted parameter set meets the equipment stability requirements and has no risk of overshoot or oscillation. The final temperature control strategy parameter update scheme is then generated, completing the dynamic parameter adjustment.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for temperature control of an impact machine based on multi-standard dynamic mapping and scene perception, characterized in that, include: S1: Obtain the raw temperature data of the device under test by using a pre-set temperature acquisition device that integrates thermal, electrical, and magnetic isolation. S2: Based on the original temperature data, a standard temperature data adaptation rule base is obtained through dynamic mapping. Based on the rule base, the corresponding standard temperature data is converted using a standard factor conversion algorithm to obtain the conversion result of the original temperature data. The conversion result of the original temperature data is matched with the target format to obtain the matching result of the original data to the target format. The matching results are screened for anomalies in multiple dimensions using a multi-scale anomaly detection component, and the cross-validation engine is used to call a preset anomaly judgment threshold to perform cross-standard consistency verification on the screened anomaly data to obtain the verified temperature data. S3: Based on the temperature data, the scene recognition module's test connection interface is used to obtain the scene recognition result through a corresponding matching algorithm; Based on the scene recognition results, the temperature control mode type and parameter adjustment dimension under the corresponding scene are converted using mode switching rules to obtain scene-adapted temperature control adjustment data. The temperature control adjustment data is used to generate dynamic temperature control commands through the temperature control strategy adaptive generation module; the multi-modal actuator drive module is used to control the impact machine heat flow module to execute the dynamic temperature control commands, and the tested device is subjected to standard temperature control to obtain the impact machine temperature control device. S4: Intelligent monitoring of the temperature control device is performed through the standard temperature control of the impact machine to obtain the monitoring data of the temperature control device; multi-standard diagnostic results of failure attribution analysis are generated using the anomaly diagnosis and root cause analysis engine; based on the diagnostic results, parameter compensation instructions are generated through the adaptive correction strategy library; based on the compensation instructions, the temperature control strategy parameters of the target device of the impact machine are dynamically adjusted through the online update mechanism to obtain the temperature control device under the corresponding temperature control strategy.

2. The method according to claim 1, characterized in that, The process of obtaining the original temperature is as follows: the environment is isolated by a multi-layer circuit, the composite contact head of the graphene thermal conductive film is attached to the device under test, and the high thermal conductivity of graphene is used to construct a low thermal resistance interface to obtain the true temperature of the device under test. Based on the actual temperature, the raw temperature data is obtained by adapting the die to the chip package device.

3. The method according to claim 1, characterized in that, The specific process of obtaining the adaptation rule base for standard temperature data includes: based on rule templates and semantic parsers, performing structured parsing of the target standard text, extracting parameter definitions, units of measurement, test conditions and tolerance requirements from the standard, and obtaining a set of standard metadata; Based on the aforementioned standard metadata set, a similarity matching algorithm combining keyword weight and contextual relevance is used to establish mapping pairs between corresponding standard terms; and a difference calculation model is used to quantify the systematic deviations between measurement dimensions and statistical standards. Based on the mapping relationship and systematic deviation, a set of conversion rules between standards is generated through a deterministic rule engine and an interpolation function library, and a standard temperature data adaptation rule library is constructed.

4. The method according to claim 1, characterized in that, The specific process for converting the original temperature data is as follows: Based on the standard mapping relationship and systematic deviation stored in the adaptation rule base, the measurement unit, sampling frequency, and accuracy level of the multi-source heterogeneous temperature data are converted to the benchmark framework of the target standard through the dimension normalization engine and statistical caliber alignment algorithm; based on the benchmark framework, the dimensions, scale, and distribution characteristics of the original temperature data are standardized and reconstructed using the standard factor matrix and linear transformation model, and the converted dataset of the original temperature is obtained through data consistency verification.

5. The method according to claim 1, characterized in that, The process of matching the raw data to the target standard format includes: based on the structured format specification of the target standard, extracting the data field definitions, data type constraints, data hierarchy relationships and validation rules of the target standard through a format template parsing engine, and generating a standard format template; Based on the standard format template and the original temperature conversion data, the data elements are intelligently matched and mapped according to field name, semantic similarity and data type through field mapping algorithm and data structure adapter, and the correspondence between the original data fields and the target standard fields is established. Based on the aforementioned correspondence, the data format converter and verification rule engine perform data type conversion, unit unification, precision adjustment, and logical verification to generate standardized temperature data that meets the target standard format requirements and passes validity verification, thereby achieving automatic matching of the original data to the target standard format.

6. The method according to claim 1, characterized in that, The multi-dimensional anomaly screening process is as follows: the transformed temperature signal is decomposed on a time scale using an adaptive sliding window mechanism to extract multi-scale statistical feature vectors. Based on a predefined expert knowledge rule base and statistical process control model, abnormal pattern matching and deviation calculation are performed on feature vectors at corresponding scales to identify point anomalies, context anomalies and trend anomalies. An anomaly screening report is generated based on the anomaly determination results of the multi-source evidence fusion strategy and the corresponding scale.

7. The method according to claim 1, characterized in that, The verified temperature data includes: multiple rule verifications of the standard compliance of abnormal data based on a multi-standard protocol compatibility rule base and a protocol consistency verification engine; compliance arbitration of fuzzy abnormal data under standard boundary conditions using a dynamic tolerance matching algorithm; and generation of verified temperature data based on the arbitration results through a data repair and marking mechanism.

8. The method according to claim 1, characterized in that, The process of obtaining scene recognition results using the corresponding matching algorithm includes: based on the physical characteristics and electrical parameters of the test connection interface, extracting connector type, pin definition, and communication protocol stack features through an interface feature parsing engine to generate an interface feature vector; based on the temporal logic and semantic structure of the test instructions, extracting instruction sequence patterns, parameter configuration templates, and operation flow features through an instruction feature analysis engine to generate an instruction feature vector; and based on the interface feature vector and instruction feature vector, calculating the matching degree with a predefined scene template through a multi-feature weighted fusion algorithm and a rule-based reasoning mechanism to obtain the scene recognition result.

9. The method according to claim 1, characterized in that, The specific process of converting the temperature control mode type and parameter adjustment dimension in the corresponding scenario includes: based on the scene type identifier and feature weight in the scene recognition result, matching a predefined temperature control mode template library through a rule engine and fuzzy inference mechanism to obtain the basic temperature control mode type; based on the scene feature vector and historical optimization records, dynamically adjusting the temperature control parameter dimension through a multi-objective optimization algorithm to generate a parameter optimization set; based on real-time system status feedback, performing online correction of the parameter set through an adaptive compensation mechanism to obtain temperature control adjustment data matching the test scenario.

10. The method according to claim 1, characterized in that, The generation of the dynamic temperature control command is specifically as follows: based on the mode configuration and parameter set in the temperature control adjustment data, the control quantity is obtained through multi-objective optimization; based on real-time system state feedback and environmental disturbance observation, the control quantity is dynamically compensated through a feedforward and feedback composite control algorithm, and the dynamic temperature control command is generated by utilizing the boundary protection and smoothing filtering mechanism of the command safety constraint library.

11. The method according to claim 1, characterized in that, The specific process of generating parameter compensation instructions is as follows: based on the fault type, location information and confidence data in the multi-standard diagnostic results, a predefined correction strategy template is matched through a fault strategy mapping and rule reasoning mechanism; based on the historical correction effect database and real-time system status, a parameter adjustment scheme is dynamically generated using a compensation amount calculation model, and the feasibility of the adjustment scheme is verified to obtain a parameter compensation instruction set.

12. The method according to claim 1, characterized in that, The dynamic adjustment of the temperature control strategy parameters of the target device in the impact machine includes: updating the code based on the parameter compensation instruction set using hot patch loading technology and dual memory area switching mechanism; dynamically calculating the parameter correction amount based on real-time system status monitoring data through incremental parameter adjustment algorithm and rolling optimization window; and using formal verification method to verify the feasibility of the adjusted parameter set and generate a temperature control strategy parameter update scheme.

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