A method and system for monitoring electrical variables in a wide temperature range

Abstract

The present invention relates to the technical field of electric variable monitoring and analysis, and in particular to a method and system for monitoring electric variables in a wide temperature range. The method comprises the following steps: obtaining thick film integrated circuit design data; performing circuit distribution functional characteristic analysis based on the thick film integrated circuit design data to generate circuit distribution functional characteristic data; collecting temperature partition electric variable distribution signals; performing potential representation quantitative data analysis of abnormal multi-temperature zone electric variable distribution based on the temperature partition electric variable distribution signals to generate abnormal multi-temperature zone electric variable distribution potential quantitative data; analyzing circuit distribution passive chip signal compensation mapping data based on abnormal multi-temperature zone electric variable distribution potential quantitative data; performing iterative closed-loop monitoring of temperature partition electric variable distribution signals based on circuit distribution passive chip signal compensation mapping data to generate iterative temperature partition electric variable distribution signals. The present invention realizes precise monitoring of electric variable parameters with temperature changes in complex circuit systems.

Description

Technical Field

[0001] The present invention relates to the technical field of electric variable monitoring and analysis, and in particular to a method and system for monitoring electric variables in a wide temperature range. Background Art

[0002] With the rapid development of science and technology, the application of integrated circuits (ICs) in various fields continues to expand, especially in the electronics, communications, computers, automobiles and other industries, where the stability and performance of circuits are crucial to the reliability of the system. Integrated circuit design is usually limited to a standard temperature range (such as room temperature environment). As the impact of high or low temperature environments on the performance of electronic equipment has become increasingly important, many high-performance systems require operation within a wider temperature range. How to monitor and optimize the performance of these circuits within a wide temperature range has become an important technical challenge. However, the existing wide temperature range electrical variable monitoring methods rely on the collection and analysis of data related to the impact of temperature on electrical variables, ignoring the complex interactions between circuit components, resulting in the distortion and abnormal distribution of electrical variables being difficult to grasp in real time when the temperature changes, as well as a general lack of accurate analysis of the functional characteristics of active and passive chips in the circuit and their interactions, making it difficult to dynamically and effectively monitor complex circuit systems under different temperature conditions. Summary of the invention

[0003] Based on this, the present invention provides a method and system for monitoring electrical variables in a wide temperature range to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for monitoring electrical variables in a wide temperature range comprises the following steps:

[0005] Step S1: Acquire thick film integrated circuit design data; perform circuit network topology structure analysis based on the thick film integrated circuit design data to generate circuit network topology structure data; perform circuit distribution functional feature analysis based on the circuit network topology structure data to generate circuit distribution functional feature data, wherein the circuit distribution functional feature data includes circuit distribution active chip functional feature data and circuit distribution passive chip functional feature data;

[0006] Step S2: using the functional characteristic data of the circuit distributed active chip to collect the electrical variable signals of each circuit distribution in a wide temperature range of the thick film integrated circuit, and generate the electrical variable distribution signal in the wide temperature range; performing temperature partition processing on the electrical variable distribution signal in the wide temperature range, and generating the temperature partition electrical variable distribution signal;

[0007] Step S3: Based on the temperature partition electric variable distribution signal, the electric variable distribution signal index of each temperature partition is analyzed to generate the temperature partition electric variable distribution signal index; based on the temperature partition electric variable distribution signal index, the multi-temperature zone electric variable distribution coupling response characteristic analysis is performed to generate the multi-temperature zone electric variable distribution coupling response characteristic data;

[0008] Step S4: performing cluster outlier analysis processing according to the multi-temperature zone electric variable distribution coupled response characteristic data to generate outlier multi-temperature zone electric variable distribution coupled response characteristic data; performing potential representation quantitative data analysis of abnormal multi-temperature zone electric variable distribution according to the outlier multi-temperature zone electric variable distribution coupled response characteristic data to generate abnormal multi-temperature zone electric variable distribution potential quantitative data;

[0009] Step S5: Analyze the distortion signal of the electric variable distribution affected by the temperature zones according to the potential quantitative data of the abnormal multi-temperature zone electric variable distribution, and generate the distortion signal data of the electric variable distribution affected by the temperature zones; analyze the signal compensation mapping data of the circuit distribution passive chip based on the distortion signal data of the temperature zone electric variable distribution, and generate the signal compensation mapping data of the circuit distribution passive chip; perform iterative closed-loop monitoring processing of the electric variable distribution signal after the passive chip optimization adjustment on the temperature partition electric variable distribution signal based on the signal compensation mapping data of the circuit distribution passive chip and the functional characteristic data of the circuit distribution passive chip, and generate the iterative temperature partition electric variable distribution signal; perform the wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

[0010] Further, step S1 includes the following steps:

[0011] Step S11: obtaining thick film integrated circuit design data;

[0012] Step S12: performing packaging circuit multi-chip assembly analysis processing according to thick film integrated circuit design data to generate circuit multi-chip assembly data;

[0013] Step S13: performing multi-chip component feature analysis on the circuit multi-chip component data to generate circuit multi-chip component feature data, wherein the circuit multi-chip component feature data includes active chip component type, passive chip component type, chip component parameters and chip component distribution position;

[0014] Step S14: performing circuit network topology structure analysis according to the circuit multi-chip component characteristic data to generate circuit network topology structure data;

[0015] Step S15: Perform circuit distribution functional characteristic analysis based on the circuit network topology data to generate circuit distribution functional characteristic data.

[0016] Further, step S14 includes the following steps:

[0017] Step S141: performing communication circuit physical model modeling processing according to circuit multi-chip component characteristic data to generate a communication circuit physical model;

[0018] Step S142: Perform circuit network topology analysis based on the communication circuit physical model to generate circuit network topology data.

[0019] Further, step S2 includes the following steps:

[0020] Step S21: using preset wide temperature range test data to perform wide temperature range circuit operation on thick film integrated circuits, and using circuit distribution active chip functional characteristic data as circuit distribution electrical signal sampling decision to collect various circuit distribution electrical variable signals in the wide temperature range for the wide temperature range circuit operation, and generating wide temperature range electrical variable distribution signals;

[0021] Step S22: performing temperature partitioning processing on the wide temperature range electric variable distribution signal to generate a temperature partitioned electric variable distribution signal.

[0022] Further, step S3 includes the following steps:

[0023] Step S31: performing an electric variable distribution signal index analysis of each temperature partition based on the temperature partition electric variable distribution signal to generate a temperature partition electric variable distribution signal index;

[0024] Step S32: performing temperature partition electric variable linear coefficient analysis on the temperature partition electric variable distribution signal index to generate the temperature partition electric variable linear coefficient;

[0025] Step S33: performing nonlinear offset analysis of the temperature partition electrical variable according to the temperature partition electrical variable distribution signal index and the temperature partition electrical variable linear coefficient, and generating temperature partition electrical variable nonlinear offset data;

[0026] Step S34: performing a first-order fitting process of the temperature partition and the electric variable signal according to the temperature partition electric variable linear coefficient, and generating first-order fitting data of the temperature partition electric variable signal;

[0027] Step S35: Designing a temperature-electric variable correlation kernel function through the temperature partition electric variable nonlinear offset data to generate a temperature-electric variable correlation kernel function;

[0028] Step S36: performing secondary fitting data analysis of temperature partitions and electric variable signals according to the temperature electric variable correlation kernel function and the primary fitting data of the temperature partition electric variable signals, and generating secondary fitting data of the temperature partition electric variable signals;

[0029] Step S37: performing multi-temperature zone electrical variable distribution coupled response characteristic analysis based on the secondary fitting data of the temperature zone electrical variable signal to generate multi-temperature zone electrical variable distribution coupled response characteristic data.

[0030] Further, step S31 includes the following steps:

[0031] Step S311: performing time series characteristic analysis on the temperature partition electrical variable distribution signal to generate time series characteristic data of the temperature partition electrical variable distribution signal;

[0032] Step S312: performing a trend analysis of the temperature partition electrical variable distribution signal according to the time series characteristic data of the temperature partition electrical variable distribution signal to generate trend data of the temperature partition electrical variable distribution signal;

[0033] Step S313: performing an electric variable distribution signal index analysis of each temperature partition according to the temperature partition electric variable distribution signal trend data, and generating a temperature partition electric variable distribution signal index.

[0034] Further, step S4 includes the following steps:

[0035] Step S41: performing a correlation mapping relationship analysis between temperature zones and electric variable distribution signals based on the multi-temperature zone electric variable distribution coupling response characteristic data to construct a multi-temperature zone electric variable distribution correlation matrix;

[0036] Step S42: performing matrix potential representation quantitative data analysis on the multi-temperature zone electrical variable distribution association matrix to generate multi-temperature zone electrical variable distribution potential quantitative data;

[0037] Step S43: performing cluster analysis processing on the multi-temperature-zone electrical variable distribution coupled response characteristic data according to the multi-temperature-zone electrical variable distribution potential quantization data to generate clustered multi-temperature-zone electrical variable distribution coupled response characteristic data;

[0038] Step S44: performing cluster outlier identification processing on the clustered multi-temperature zone electrical variable distribution coupled response characteristic data to generate outlier multi-temperature zone electrical variable distribution coupled response characteristic data;

[0039] Step S45: performing a potential representation quantitative data analysis of the abnormal multi-temperature-zone electrical variable distribution according to the outlier multi-temperature-zone electrical variable distribution coupled response characteristic data to generate the abnormal multi-temperature-zone electrical variable distribution potential quantitative data.

[0040] Further, step S5 includes the following steps:

[0041] Step S51: analyzing the temperature zone effect on the electric variable distribution distortion signal according to the abnormal multi-temperature zone electric variable distribution potential quantified data, and generating the temperature zone effect on the electric variable distribution distortion signal data;

[0042] Step S52: analyzing the signal compensation mapping data of the circuit distributed passive chip based on the temperature zone-affected electrical variable distribution distortion signal data, and generating the circuit distributed passive chip signal compensation mapping data;

[0043] Step S53: performing circuit distributed passive chip adjustment strategy analysis according to the circuit distributed passive chip signal compensation mapping data and the circuit distributed passive chip functional characteristic data, and generating a circuit distributed passive chip adjustment strategy;

[0044] Step S54: executing the circuit distributed passive chip optimization adjustment operation based on the circuit distributed passive chip adjustment strategy, and performing iterative closed-loop monitoring processing on the temperature partition electrical variable distribution signal corresponding to the circuit distributed passive chip optimization adjustment operation, to generate an iterative temperature partition electrical variable distribution signal;

[0045] Step S55: performing a wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

[0046] Further, step S52 includes the following steps:

[0047] Step S521: analyzing the frequency data of abnormal electric variables in each temperature zone according to the temperature zone electric variable distribution distortion signal data, and generating abnormal frequency data of electric variables in each temperature zone;

[0048] Step S522: performing temperature zone compensation weight analysis based on the abnormal frequency data of the temperature zone electrical variables to generate a temperature zone compensation weight coefficient;

[0049] Step S523: performing temperature zone electric variable distribution error compensation demand analysis according to the temperature zone electric variable distribution distortion signal data and the temperature zone compensation weight coefficient, and generating temperature zone electric variable distribution error compensation demand data;

[0050] Step S524: analyzing the signal compensation mapping data of the circuit distributed passive chip according to the temperature zone electrical variable distribution error compensation demand data, and generating the circuit distributed passive chip signal compensation mapping data.

[0051] This specification provides a wide temperature range electric variable monitoring system for executing the wide temperature range electric variable monitoring method as described above, and the wide temperature range electric variable monitoring system includes:

[0052] A circuit design parsing and analysis module, used to obtain thick film integrated circuit design data; perform circuit network topology structure analysis based on the thick film integrated circuit design data to generate circuit network topology structure data; perform circuit distribution functional characteristic analysis based on the circuit network topology structure data to generate circuit distribution functional characteristic data, wherein the circuit distribution functional characteristic data includes circuit distribution active chip functional characteristic data and circuit distribution passive chip functional characteristic data;

[0053] The temperature partition electric variable distribution signal analysis module is used to collect the electric variable signals of each circuit distribution of the thick film integrated circuit in a wide temperature range by using the functional characteristic data of the circuit distribution active chip to generate the electric variable distribution signal in the wide temperature range; perform temperature partition processing on the electric variable distribution signal in the wide temperature range to generate the temperature partition electric variable distribution signal;

[0054] The multi-temperature zone electric variable distribution coupling response characteristic analysis module is used to analyze the electric variable distribution signal index of each temperature zone based on the temperature zone electric variable distribution signal, and generate the temperature zone electric variable distribution signal index; perform multi-temperature zone electric variable distribution coupling response characteristic analysis according to the temperature zone electric variable distribution signal index, and generate multi-temperature zone electric variable distribution coupling response characteristic data;

[0055] The module for potential quantitative analysis of abnormal multi-temperature-zone electric variable distribution is used to perform cluster outlier analysis based on the coupled response characteristic data of multi-temperature-zone electric variable distribution, and generate outlier multi-temperature-zone electric variable distribution coupled response characteristic data; perform potential representation quantitative data analysis of abnormal multi-temperature-zone electric variable distribution based on the coupled response characteristic data of multi-temperature-zone electric variable distribution of outliers, and generate potential quantitative data of abnormal multi-temperature-zone electric variable distribution;

[0056] The wide temperature range electric variable monitoring feedback module is used to analyze the distortion signal of the electric variable distribution affected by the temperature zone according to the potential quantitative data of the electric variable distribution in abnormal multiple temperature zones, and generate the distortion signal data of the electric variable distribution affected by the temperature zone; to analyze the signal compensation mapping data of the circuit distribution passive chip based on the distortion signal data of the electric variable distribution affected by the temperature zone, and generate the signal compensation mapping data of the circuit distribution passive chip; to perform iterative closed-loop monitoring processing of the electric variable distribution signal after the passive chip optimization adjustment on the temperature partition electric variable distribution signal based on the signal compensation mapping data of the circuit distribution passive chip and the functional characteristic data of the circuit distribution passive chip, and generate the iterative temperature partition electric variable distribution signal; and to perform the wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

[0057] The beneficial effect of the present application is that obtaining the design data of thick film integrated circuits can provide a detailed circuit design basis for subsequent analysis and monitoring work. And by parsing the thick film integrated circuit design data, the multi-chip component information in the circuit can be accurately extracted. The parsing process helps to reveal the relationship between different chip modules, identify all active and passive components involved in the circuit, as well as their packaging and connection methods, and effectively grasp the overall structure and working mode of the circuit. The multi-chip component data is feature analyzed and the network topology of the circuit is constructed to identify the function of each chip component and its role in the circuit. The response characteristics of the circuit components can be accurately identified, so that the functions of different chips can be optimized and adjusted. The analysis of the topological structure helps to understand the connection relationship and signal transmission path between the components in the circuit, identify the performance fluctuations of the circuit under different environments, and provide targeted optimization suggestions, thereby improving the overall performance and adaptability of the circuit and ensuring its stable operation over a wide temperature range. Using the preset wide temperature range test data, the thick film integrated circuit is actually operated, so as to comprehensively evaluate the performance of the circuit under different temperature conditions. The functional characteristic data of the active chip of the circuit distribution is used as the sampling decision of the electrical signal to ensure that the electrical variable distribution signal of the circuit in a wide temperature range is fully captured during the test process, and reliable data support is provided for subsequent analysis and optimization. The electrical variable distribution signal in a wide temperature range is processed by temperature partitioning, and the electrical variable signals in different temperature intervals are effectively analyzed independently, so as to find the impact of temperature change on circuit performance in a targeted manner. The index analysis of the electrical variable distribution signal in the temperature partition can reveal the key characteristics of circuit performance in different temperature intervals, identify the working state of different circuit distributions in each temperature interval, and be used for the specific impact of temperature change on circuit performance. The linear coefficient and nonlinear offset analysis of the electrical variable distribution signal index in the temperature partition can quantitatively describe the linear change trend of the electrical variable in different temperature intervals, identify the temperature sensitivity problem in the circuit, reveal the influence of temperature on the electrical variable distribution of the circuit, especially the nonlinear change, identify the nonlinear effect of the circuit at different temperatures, and reveal the irregular fluctuation or offset phenomenon in the circuit performance. The first-level fitting processing of the temperature partition and the electrical variable signal provides a simple linear relationship between temperature and electrical variable through a mathematical model. Using the temperature partitioned electrical variable nonlinear offset data, the correlation kernel function is designed to capture the complex nonlinear relationship between temperature and electrical variables, and enhance the understanding of the circuit's working state at different temperatures. Through this function, the changes in electrical variables at different temperatures can be predicted in advance during the circuit design process. Through secondary fitting data analysis, the relationship between temperature and electrical variables is accurately described, especially under the influence of nonlinear factors, the details of circuit performance changes are captured, and a more accurate temperature dependence model is provided.In addition, the analysis of the coupled response characteristics of the electrical variable distribution in multiple temperature zones reveals the overall performance of the circuit in multiple temperature intervals, comprehensively evaluates the comprehensive impact of temperature changes on the circuit, identifies the coupled response characteristics of the electrical variable between different temperature intervals, and identifies and solves the multi-dimensional problem of the electrical variable distribution signal caused by temperature changes. The correlation relationship mapping analysis of the coupled response characteristic data of the electrical variable distribution in multiple temperature zones is carried out to understand the mutual influence of the electrical variables of the circuit distribution signal in different temperature intervals, reveal the law of electrical variable changes between different temperature intervals, and establish a relationship matrix between each temperature zone. The potential representation quantitative data analysis of the correlation matrix is ​​carried out to identify the potential laws and trends of the distribution of electrical variables in different temperature intervals from a macro perspective, depict the response characteristics of the circuit in a multi-temperature environment, and ensure that the circuit design is more in line with the actual application requirements. Through cluster analysis, the coupled response characteristic data of the electrical variable distribution in multiple temperature zones are grouped according to similarity, and the similar response patterns of the circuit in different temperature intervals are found, further revealing the different working states of the circuit in a multi-temperature environment. By identifying outliers on the clustering results, the irregularity or abnormality of the electrical variable distribution in the temperature interval is found. The existence of outliers usually means that there are potential faults or unstable factors in the circuit in certain temperature ranges, revealing the law of change of the circuit's electrical variables in abnormal temperature ranges. The generated potential quantitative data of abnormal multi-temperature zone electrical variable distribution provides a specific basis for discovering the abnormal performance of the circuit under extreme temperature conditions, and provides a more accurate direction for circuit optimization and improvement, avoiding circuit failure or performance degradation caused by abnormal temperature fluctuations. Analyzing the potential quantitative data of abnormal multi-temperature zone electrical variable distribution can reveal the distortion effect of different temperature ranges on the electrical variable distribution, identify and quantify the change of electrical variables and their deviations in the circuit under different temperature environments. By analyzing the distortion signal data of the electrical variable distribution affected by the temperature zone, corresponding compensation strategies are provided for the electrical variable distortion occurring in different temperature ranges. Combined with the circuit distributed passive chip signal compensation mapping data and functional characteristic data, the circuit distributed passive chip adjustment strategy is analyzed, and used to perform the optimization and adjustment operation of the circuit distributed passive chip, so as to improve the performance and efficiency of the passive chip under different temperature conditions, which can not only compensate for the influence of temperature zone distortion and reduce the influence of temperature fluctuation on the electrical variable signal, but also enable the circuit to adapt to more complex environmental changes and enhance the overall stability and reliability of the circuit. Through iterative closed-loop monitoring and processing, the temperature partition electrical variable distribution signal is evaluated and adjusted in real time to ensure that the circuit is always in the best working state. Based on the iterative temperature partition electrical variable distribution signal, the electrical variable monitoring feedback operation in a wide temperature range is performed to ensure that the performance of the circuit under different temperature conditions continues to meet expectations.

[0058] Therefore, the wide temperature range electrical variable monitoring method of the present invention is aimed at the influence of the wide temperature range on the electrical variables under thick film integrated circuits. By introducing the functional characteristic analysis of the circuit multi-chip components and combining the interaction of active chips and passive chips, the influence of temperature changes on the overall performance of the circuit can be accurately identified. Through a detailed analysis of the topological structure and electrical variable signals of the circuit, the distortion and abnormal distribution of the electrical variables can be captured in real time during the temperature change process, and potential problems can be identified in advance. Based on the multi-level electrical variable analysis and iterative closed-loop optimization mechanism of the temperature range, through the cluster analysis of multi-temperature zone coupling response characteristics and abnormal data, the working state of the circuit can be dynamically adjusted from the system level to ensure the stability and reliability of the circuit under different temperature conditions. Especially in different temperature ranges, for the temperature adaptability of each component in the circuit, the signal compensation and optimization adjustment of the passive chip enable the circuit to operate more efficiently and stably, greatly improving the monitoring accuracy and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the steps of a method for monitoring electrical variables in a wide temperature range according to the present invention;

[0060] Figure 2 for Figure 1 Detailed implementation steps of step S4 in FIG. DETAILED DESCRIPTION

[0061] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities are implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. are used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit is referred to as the second unit, and similarly the second unit is referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figure 1 to Figure 2 The present invention provides a method for monitoring electrical variables in a wide temperature range, comprising the following steps:

[0065] Step S1: Acquire thick film integrated circuit design data; perform circuit network topology structure analysis based on the thick film integrated circuit design data to generate circuit network topology structure data; perform circuit distribution functional feature analysis based on the circuit network topology structure data to generate circuit distribution functional feature data, wherein the circuit distribution functional feature data includes circuit distribution active chip functional feature data and circuit distribution passive chip functional feature data;

[0066] In an embodiment of the present invention, the design data of the thick film integrated circuit stored in the database is obtained. The design data generally includes information such as circuit layout, chip connection relationship, and distribution of functional modules. For example, the physical layout and electrical connection data of the circuit are extracted from the integrated circuit design file (such as GDSII file). The design data includes the multi-chip component composition of the circuit, the type of each chip (such as active chip or passive chip), the functional requirements of each chip and its relative position in the circuit. These data are parsed by EDA tools (such as Cadence, Synopsys) to generate electronic design data files including parameters of each chip, wiring information, and interface protocols. After obtaining the design data of the thick film integrated circuit, the circuit network topology structure analysis is performed. The analysis process requires detailed analysis of the multi-chip components in the circuit to extract the connection relationship and data transmission path between each chip. The core purpose of generating the circuit network topology structure data is to fully describe the interconnection relationship between each chip module and the flow direction and mode of the electrical signal. The circuit network topology structure analysis first identifies the electrical connection and signal transmission path between each chip based on the circuit multi-chip component data. This information will be represented as a graph structure, where the nodes represent chips and the edges represent the connections or data transmission channels between chips. Evaluate the distribution of functions of each part of the circuit and the contribution of each functional module to the overall performance of the circuit, especially the functional analysis of active chips and passive chips. The generation of circuit distribution functional characteristic data involves analyzing the specific functions and performance characteristics of each chip in the circuit, and further understanding the role of each chip in the circuit and how it interacts with other chips. In specific implementation, first identify the active chips and passive chips in the circuit. Active chips have the ability to activate signals, and their functional characteristics need to be analyzed in detail, such as their input / output signals, current / voltage requirements, power consumption, etc. Passive chips (such as resistors, capacitors, inductors, etc.) do not generate active signals, but they play a role in stabilizing voltage, filtering, and adjusting frequency in the circuit. The results of circuit distribution functional characteristic data usually include two parts: one part is the active chip functional characteristic data, which describes the performance indicators of active chips, such as input and output current, voltage range, etc.; the other part is the passive chip functional characteristic data, which describes the working characteristics of passive chips, such as capacitance value, resistance value, etc.

[0067] Step S2: using the functional characteristic data of the circuit distributed active chip to collect the electrical variable signals of each circuit distribution in a wide temperature range of the thick film integrated circuit, and generate the electrical variable distribution signal in the wide temperature range; performing temperature partition processing on the electrical variable distribution signal in the wide temperature range, and generating the temperature partition electrical variable distribution signal;

[0068] In an embodiment of the present invention, according to the functional characteristic data of the active chip in the circuit, the circuit operation operation is performed, and the electrical variable signal of the circuit is collected under different temperature conditions. For different regions of the thick film integrated circuit, the temperature dependence analysis is performed in consideration of the difference in its electrical behavior in different temperature ranges. By testing at multiple temperature points (for example, -40°C, 25°C, 85°C, etc.), the electrical variable performance of the circuit at these temperatures, such as parameters such as voltage, current, and power, is obtained. At this time, the functional characteristic data of the active chip, such as the input signal range, output current capacity, and operating frequency, are used to determine the test conditions at each temperature point. When the circuit is working, the electrical signal data of the circuit, including parameters such as voltage, current, and signal frequency, are collected by collecting instruments (such as oscilloscopes, data acquisition cards, etc.) at different temperatures. These data form an electrical variable distribution signal in a wide temperature range. For example, assuming that an integrated circuit includes multiple sensor modules and a power control module, the input signal of the sensor module may show a higher resistance in a low temperature environment, while the power consumption of the power control module at high temperature may change significantly. By collecting signals such as current and voltage, a set of electrical variable distribution signals in a wide temperature range is obtained. These signals not only reflect the performance of each part of the circuit at different temperatures, but also provide data support for subsequent analysis. Subsequently, the electrical variable distribution signal in a wide temperature range is processed by temperature partitioning. Temperature partitioning is to divide the electrical variable signal data at different temperatures into different areas according to the temperature range. For example, -40°C to 25°C, 25°C to 85°C, and 85°C to 150°C are used as different temperature partitions. In each temperature partition, the electrical variable signal may show different trends or change rules. Through temperature partitioning, the electrical variable behavior of different temperature segments is analyzed separately, which is convenient for identifying the specific impact of temperature on circuit performance. In specific implementation, temperature partitioning will segment the electrical variable signal according to a predetermined temperature range. For example, the voltage signal from -40°C to 150°C is divided into multiple temperature intervals in groups of 10°C, and the electrical variable signal is processed independently in each interval. The generated temperature partition electrical variable distribution signal is based on signal data under different temperature ranges, which can reflect the performance characteristics of the circuit distribution in each temperature partition.

[0069] Step S3: Based on the temperature partition electric variable distribution signal, the electric variable distribution signal index of each temperature partition is analyzed to generate the temperature partition electric variable distribution signal index; based on the temperature partition electric variable distribution signal index, the multi-temperature zone electric variable distribution coupling response characteristic analysis is performed to generate the multi-temperature zone electric variable distribution coupling response characteristic data;

[0070] In an embodiment of the present invention, a temperature partition electric variable distribution signal index analysis is performed based on a temperature partition electric variable distribution signal. For the electric variable distribution signal index in each temperature interval, a linear relationship analysis between the electric variable and the temperature is performed, and the linear coefficient of the electric variable is calculated. During the analysis process, a linear regression model (such as the least squares method) between temperature and electric variables is selected, and signals such as voltage, current, and power are linearly fitted to obtain the relationship between the electric variable and the temperature in each temperature interval. Nonlinear offset analysis is performed based on the temperature partition electric variable distribution signal index and the electric variable linear coefficient. The purpose of nonlinear offset analysis is to identify the nonlinear relationship between the electric variable and the temperature and calculate the nonlinear offset of the electric variable at different temperatures. By comparing the actual change of the electric variable in each temperature interval with the linear prediction result, the nonlinear offset data in each interval is obtained. A first-level fitting process is performed between the temperature partition and the electric variable signal according to the temperature partition electric variable linear coefficient. The first-level fitting process uses a linear regression method to model the relationship between the electric variable and the temperature, and generates the first-level fitting data of the temperature partition electric variable signal. The first-level fitting data is a linear change trend of electric variables such as current, power, and voltage in each temperature interval, providing a simple temperature and electric variable relationship model. The temperature-electric variable correlation kernel function is designed by using the nonlinear offset data of the temperature-partitioned electric variable. The kernel function is used to describe the nonlinear influence between temperature and electric variable. According to the actual offset of the electric variable with temperature, the appropriate function form is selected to capture the complex nonlinear relationship between temperature and electric variable. For example, Gaussian kernel function or polynomial kernel function is used to construct the nonlinear relationship, and then accurately model the influence of temperature on electric variable. The kernel function will perform weighted calculation on the relationship between temperature and electric variable according to the nonlinear offset characteristics of electric variables such as current, voltage, and power in each temperature interval. By analyzing the changes of electric variables in each temperature interval, the optimal kernel function parameters in each temperature interval are determined, and a temperature-electric variable correlation kernel function with good adaptability is generated within the temperature range. The temperature-electric variable correlation kernel function is combined with the first-level fitting data of the temperature-partitioned electric variable signal to perform the second-level fitting processing of the temperature partition and the electric variable signal. The first-level fitting data usually describes the linear relationship between the electric variable and temperature, but in practical applications, due to the nonlinear characteristics of the influence of temperature on the electric variable, the second-level fitting analysis introduces the kernel function to correct the first-level fitting results, so as to more accurately fit the relationship between the electric variable and temperature. Specifically, the secondary fitting analysis combines the primary fitting data of the temperature partition electrical variable signal with the temperature electrical variable correlation kernel function, and eliminates the nonlinear part omitted in the primary fitting by weighted averaging the changes in electrical variables in different temperature intervals. This process takes into account the comprehensive influence of temperature on electrical variables, so that the fitting results can better reflect the electrical variable distribution characteristics of the circuit system in a wide temperature range. Based on the secondary fitting data of the temperature partition electrical variable signal, the coupled response characteristics of the multi-temperature zone electrical variable distribution are analyzed.Through this analysis, the mutual influence and coupling effects between electrical variables in multiple temperature intervals can be identified, thereby obtaining the overall response characteristics of the changes in electrical variables in the circuit over a wide temperature range. Specifically, the secondary fitting data of electrical variables in multiple temperature intervals are jointly analyzed to identify the coupling relationship between electrical variables such as voltage, current, and power. The generated multi-temperature zone electrical variable distribution coupling response characteristic data can provide an accurate relationship model between temperature and electrical variables for circuit design.

[0071] Step S4: performing cluster outlier analysis processing according to the multi-temperature zone electric variable distribution coupled response characteristic data to generate outlier multi-temperature zone electric variable distribution coupled response characteristic data; performing potential representation quantitative data analysis of abnormal multi-temperature zone electric variable distribution according to the outlier multi-temperature zone electric variable distribution coupled response characteristic data to generate abnormal multi-temperature zone electric variable distribution potential quantitative data;

[0072] In an embodiment of the present invention, analysis is performed based on the coupled response characteristic data of the electric variable signals collected from multiple temperature intervals. The purpose is to find out the mutual relationship between the changes in the electric variable signals between the temperature zones and to establish the corresponding association matrix. The core of this analysis is to map the correlation and mutual influence between them by quantifying the coupling relationship between the temperature zones and the electric variable signals. The potential representation quantitative analysis of the multi-temperature zone electric variable distribution association matrix is ​​performed using matrix decomposition methods, principal component analysis (PCA) or singular value decomposition (SVD) and other techniques to further extract and compress the key information in the matrix. The purpose of the potential representation quantitative data analysis is to convert high-dimensional matrix information into a more easily processable low-dimensional data representation while maintaining the key information it contains. Based on the potential quantitative data of the multi-temperature zone electric variable distribution, cluster analysis is performed on the multi-temperature zone electric variable distribution coupled response characteristic data. The purpose of cluster analysis is to group the electric variable signals in different temperature intervals according to their similarities in order to better understand the interaction pattern of the electric variables. Through clustering, the differences in the response characteristics of the electric variable signals in different temperature intervals can be clearly seen, and a basis for subsequent data analysis and model optimization can be provided. Each group of data has similarities in the fluctuation range and change trend of current and voltage signals, which effectively reveals the similarities and differences between temperature intervals. The purpose of outlier identification is to find those electrical variable signals that are abnormal in the electrical variable distribution and significantly different from most data points. These outliers usually represent some unusual or erroneous circuit behaviors caused by faults or other external factors. The data is processed using density-based outlier detection methods (such as DBSCAN) or distance-based outlier detection methods (such as K nearest neighbors). Identify those data points that show different characteristics from most electrical variable signals and mark them as outliers. For example, assume that the clustered current signal data contains signals within the normal range and some abnormal high-amplitude fluctuation signals. Through outlier identification, it is found that in some temperature intervals, the current signal has a large jump at a specific time, which exceeds the normal range. Based on the outlier multi-temperature zone electrical variable distribution coupled response characteristic data, the potential representation quantitative data analysis of abnormal electrical variable distribution is performed. The goal is to extract the key features of abnormal electrical variable distribution and transform the complex features of abnormal signals into a few key components. By analyzing these potential quantitative data, we can further understand the pattern of abnormal signals, such as the cause and frequency of current or voltage mutations, and generate potential quantitative data of abnormal multi-temperature zone electrical variable distribution.

[0073] Step S5: Analyze the distortion signal of the electric variable distribution affected by the temperature zones according to the potential quantitative data of the abnormal multi-temperature zone electric variable distribution, and generate the distortion signal data of the electric variable distribution affected by the temperature zones; analyze the signal compensation mapping data of the circuit distribution passive chip based on the distortion signal data of the temperature zone electric variable distribution, and generate the signal compensation mapping data of the circuit distribution passive chip; perform iterative closed-loop monitoring processing of the electric variable distribution signal after the passive chip optimization adjustment on the temperature partition electric variable distribution signal based on the signal compensation mapping data of the circuit distribution passive chip and the functional characteristic data of the circuit distribution passive chip, and generate the iterative temperature partition electric variable distribution signal; perform the wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

[0074] In an embodiment of the present invention, the potential quantitative data of the distribution of electric variables in multiple temperature zones are analyzed for the purpose of identifying and analyzing the influence of temperature zones on the distribution of electric variable signals, especially the signal distortion caused by temperature changes. This process uses a signal distortion analysis method to focus on analyzing the influence of temperature fluctuations on the timing and amplitude changes of electric variable signals. Determine the distortion mode and trend of electric variable signals in different temperature intervals caused by temperature zone changes. Through methods such as frequency analysis and amplitude analysis, extract the key features of these distorted signals, and generate temperature zone-affected electric variable distribution distortion signal data. Based on the temperature zone-affected electric variable distribution distortion signal data, analyze the signal compensation mapping data of passive chips in the circuit. The signal distortion exhibited by passive chips (such as resistors, capacitors, inductors, etc.) under different temperature conditions will affect the performance of the entire circuit, so it is necessary to design an appropriate compensation scheme based on the temperature zone distortion signal. Through the analysis of the signal compensation mapping data, specific compensation functions are designed for passive chips at different temperatures. These functions can quantify the influence of temperature changes on electric variable signals and perform timely circuit compensation. Finally, the generated circuit distribution passive chip signal compensation mapping data contains compensation relationship data for signal distortion in each temperature interval. Based on the signal compensation mapping data of the circuit distributed passive chip and the functional characteristic data of the circuit distributed passive chip (such as the electrical characteristics of resistance, capacitance, and inductance), the circuit adjustment strategy analysis is performed to generate the corresponding adjustment strategy. Through the signal compensation mapping data and the chip functional characteristic data, the passive chip characteristics in the circuit, such as the temperature coefficient of resistance and capacitance, are analyzed. Then, the adjustment strategy design of multiple temperature zones is carried out in combination with the actual working environment of the circuit. For example, a strategy is designed to adjust the resistance value according to the temperature change. When the temperature rises, the resistance value is automatically adjusted to offset the current distortion caused by temperature. Through these adjustment strategies, the normal operation of the circuit is guaranteed under various temperature conditions. According to the adjustment strategy of the circuit distributed passive chip, the passive chip in the circuit is optimized and adjusted. After the adjustment is completed, the electric variable distribution signal is monitored and feedback processed in real time through the closed-loop control system. For example, the current change caused by temperature is eliminated by adjusting the value of the capacitor, and then the current signal is collected in real time and compared with the target signal. If there is still an error in the current signal, the circuit parameters are adjusted again through the feedback mechanism until the signal meets the target requirements, and the iterative temperature partition electric variable distribution signal is generated. Based on the iterative temperature partition electric variable distribution signal, a wide temperature range electric variable monitoring feedback operation is performed. The goal of this step is to continuously monitor the optimized electrical variable signal and adjust the circuit working state according to the feedback data to ensure that the circuit always maintains stability and high performance over a wide temperature range. The collected signal is compared with the preset target signal to determine whether the circuit is in normal working condition through the feedback control system.

[0075] Further, step S1 includes the following steps:

[0076] Step S11: obtaining thick film integrated circuit design data;

[0077] In an embodiment of the present invention, the design data of the thick film integrated circuit is obtained from the integrated circuit design tool. The design data generally includes detailed information such as the layout of the circuit, the design description of the functional modules, the type of components, and the connection method. In the specific implementation, the designer generates the design file through the IC design software (such as Cadence, Allegro, etc.), and exports the data including the circuit hierarchy, wiring information, component parameters, and package size. For example, in a certain design project, the design file contains "thick film integrated circuit, using a 12-layer packaging structure, the interlayer interconnection uses a metal aluminum layer, and the main chip is an embedded processor". At this time, the thick film integrated circuit design data is all the design parameters, layers, material properties, etc. of the project.

[0078] Step S12: performing packaging circuit multi-chip assembly analysis processing according to thick film integrated circuit design data to generate circuit multi-chip assembly data;

[0079] In an embodiment of the present invention, the multi-chip component in the packaged circuit is parsed based on the acquired thick film integrated circuit design data. In specific implementation, the parsing process includes the analysis of the function, position and electrical connection relationship of each chip in the package. For example, for a package with 4 integrated chips, the function (for example: CPU, memory, I / O chip, etc.) and position (such as the upper left corner, lower right corner) of each chip must be clear. Through a dedicated parsing tool, the circuit design data is analyzed, the connection method between each chip is identified, and the functional characteristics of each chip (such as power consumption, signal transmission rate, etc.) are converted into a data format to form circuit multi-chip component data, including the chip location, connection method, function allocation, etc.

[0080] Step S13: performing multi-chip component feature analysis on the circuit multi-chip component data to generate circuit multi-chip component feature data, wherein the circuit multi-chip component feature data includes active chip component type, passive chip component type, chip component parameters and chip component distribution position;

[0081] In the embodiment of the present invention, the circuit multi-chip component data is analyzed to identify the characteristics of each chip component, including the type, parameters and distribution position of active chip (such as CPU, amplifier, etc.) and passive chip (such as resistor, capacitor, etc.) components. In the specific implementation, the chip model or the database-based method is used to analyze the electrical parameters, functions, packaging type and other information of each component. For example, for a specific CPU chip, its functional characteristics include clock frequency, power supply voltage, power consumption, etc.; the characteristics of the memory chip include storage capacity, access speed, input and output type, etc. Through analysis, the generated circuit multi-chip component characteristic data includes: active chip component: "CPU, function: calculation, frequency: 2.5GHz, power consumption: 3W; amplifier, function: signal amplification, input and output type: differential signal, gain: 30dB". Passive chip component: "resistor, resistance: 10kΩ, power: 0.1W; capacitor, capacity: 100nF, working voltage: 25V". The chip distribution position is the distribution position of each chip in the circuit.

[0082] Step S14: performing circuit network topology structure analysis according to the circuit multi-chip component characteristic data to generate circuit network topology structure data;

[0083] In an embodiment of the present invention, the circuit network topology structure analysis is performed using the circuit multi-chip component characteristic data to generate the circuit network topology structure data. The analysis will involve the connection relationship, signal flow direction and electrical interface between the chips in the circuit. In specific implementation, the electrical connection between different chips is identified by simulation or rule-based algorithm. For example, based on the analysis of the circuit diagram and the signal transmission path, a detailed description of the circuit topology structure is obtained, revealing how the power line, data line, signal transmission path, etc. are transmitted from one chip to another. The generated circuit network topology structure data includes each chip node, connection mode, transmission signal type, etc. For example, the topology data of a circuit includes: chip 1 (CPU) and chip 2 (memory) are connected through a high-frequency data bus; chip 3 (power management chip) provides power for chips 1 and 2; chip 4 (communication module) communicates with the memory module through I2C; the topology structure data is helpful for the subsequent optimization analysis of circuit performance, function, and power consumption.

[0084] Step S15: Perform circuit distribution functional characteristic analysis based on the circuit network topology data to generate circuit distribution functional characteristic data.

[0085] In an embodiment of the present invention, a circuit distribution functional characteristic analysis is performed based on the circuit network topology data to generate circuit distribution functional characteristic data. The analysis will focus on the performance, operating characteristics and mutual influence of the circuit function modules. For example, by analyzing the power distribution, signal processing, data transmission and other functions of the circuit, the stability and efficiency of the circuit under different working conditions (such as different temperatures and load conditions) are evaluated. During implementation, the functional characteristic analysis is performed in combination with the design parameters taking into account the characteristics of each component, each connection path and signal interaction in the circuit network topology. The functional characteristic data of the circuit distribution, such as the response time, load processing capacity and power consumption of each functional module, are obtained through circuit simulation or actual measurement. For example, the circuit distribution functional characteristic data includes: functional module 1 (CPU): response time: 5ns, power consumption: 3W, signal transmission rate: 1Gbps; functional module 2 (memory): access time: 20ns, power consumption: 1.5W, data bandwidth: 8GB / s; functional module 3 (power management chip): operating voltage: 3.3V, power consumption: 0.8W. Provide basic data support for the subsequent circuit optimization design, performance adjustment and electrical variable monitoring.

[0086] Further, step S14 includes the following steps:

[0087] Step S141: performing communication circuit physical model modeling processing according to circuit multi-chip component characteristic data to generate a communication circuit physical model;

[0088] In an embodiment of the present invention, a physical model of a communication circuit is modeled according to the characteristic data of a circuit multi-chip assembly. In a specific implementation, first, according to the design data of the circuit, including the function, position and connection mode of each chip, an electrical signal transmission path in the circuit is constructed. Then, a physical model of the communication circuit is established through electromagnetic field simulation, signal transmission rate and delay calculation. This model should take into account factors such as the electrical connection mode between chips, data transmission rate, transmission medium (such as power line, signal line, etc.), and signal delay and attenuation. In implementation, it is necessary to use electromagnetic simulation tools such as ANSYS HFSS and Keysight ADS to simulate the circuit signal and obtain the physical characteristics of the signal transmission between the chips. For example, assume that a thick film integrated circuit design includes a CPU, memory and communication module, and these chips are connected via power lines and high-speed data buses. In the physical model modeling process, the electrical characteristics of each chip must first be defined (such as the CPU clock frequency is 2.5 GHz and the memory access rate is 8GB / s). Secondly, considering the signal transmission characteristics of high-speed buses, such as signal attenuation and delay during transmission, these physical factors should be added to the circuit model. The electromagnetic field simulation tool is used to simulate the transmission of signals between different chips, and calculate the signal strength, signal delay, and reflection effects during the transmission process. Ultimately, the generated communication circuit physical model will include parameters such as the connection between chips, the electrical signal transmission path, voltage drop, delay, and signal attenuation.

[0089] Step S142: Perform circuit network topology analysis based on the communication circuit physical model to generate circuit network topology data.

[0090] In an embodiment of the present invention, the network topology of the circuit is analyzed based on the physical model of the communication circuit. In the specific implementation, it is first necessary to analyze the chips and their connection relationships in the physical model one by one to construct a circuit network topology diagram. This process involves detailed analysis of signal flow, data transmission path, and power distribution, especially in complex circuit systems, it is necessary to ensure the connectivity and effectiveness of each signal path. Topology analysis requires identifying information such as direct connection between chips, signal flow direction, and transmission rate, and generating network topology data based on this information. In the specific implementation process, it is assumed that the four components (CPU, memory, power management chip, and communication module) in the circuit design are connected together through different transmission paths. First, based on the analysis of the physical model of the communication circuit, the functional characteristics of each chip (such as data transmission rate, power consumption, etc.) are incorporated into the network topology. For example, the CPU and the memory are connected through a high-speed data bus with a data transmission rate of 10Gbps; the power management chip provides voltage to the CPU and the memory through the power line. According to these connection relationships, the circuit network topology data is generated through a topology analysis tool (such as MATLAB, Simulink, etc.). These data include each chip and its connection relationship, for example: chip 1 (CPU) is connected to chip 2 (memory) through a high-speed data bus, with a data transmission rate of 10Gbps; chip 3 (power management chip) is connected to the CPU and memory through a power line, with an output voltage of 3.3V; chip 4 (communication module) is connected to the memory through an I2C bus, with a data transmission rate of 400kbps. The generated circuit network topology data will describe the connection relationship, data transmission path, signal rate, power consumption and other information of each chip node, providing a basis for subsequent analysis of circuit performance, function and the impact of temperature changes on circuit behavior. This data is the basis for further analysis of the functional characteristics of circuit distribution.

[0091] Further, step S2 includes the following steps:

[0092] Step S21: using preset wide temperature range test data to perform wide temperature range circuit operation on thick film integrated circuits, and using circuit distribution active chip functional characteristic data as circuit distribution electrical signal sampling decision to collect various circuit distribution electrical variable signals in the wide temperature range for the wide temperature range circuit operation, and generating wide temperature range electrical variable distribution signals;

[0093] In an embodiment of the present invention, a wide temperature range circuit operation is performed on a thick film integrated circuit (for example, a temperature range of -40°C to +125°C) using preset wide temperature range test data. When the circuit operation is implemented, it is necessary to collect functional characteristic data of the active chips of the circuit (such as microprocessors, sensors, power amplifiers, etc.). These functional characteristic data include parameters such as the operating voltage, current, power consumption, and clock frequency of each active chip. During the operation of the circuit, the electrical signal output of the active chip will change at different temperatures, so these data must be collected and recorded in real time. In specific implementation, when the temperature range increases from -40°C to 125°C, electrical variable signals related to the circuit, such as voltage, current, power, etc., are collected. The process of collecting these signals is monitored in real time using temperature sensors and voltage and current sampling modules (such as oscilloscopes or data acquisition cards). For example, at a temperature of 25°C, the operating current of a microprocessor is 200mA and the power consumption is 2.5W; at a temperature of 85°C, the power consumption rises to 2.7W. Therefore, all electrical variable signals are sampled at different temperatures and the data is recorded to generate electrical variable distribution signals in a wide temperature range. These signals include but are not limited to: at 25°C, the voltage is 3.3V, the current is 100mA, and the power consumption is 330mW; at 85°C, the voltage is 3.3V, the current is 120mA, and the power consumption is 396mW; at 125°C, the voltage is 3.3V, the current is 125mA, and the power consumption is 412.5mW. Through the collection of these data, the electrical variable distribution signals of the entire circuit under different temperature conditions are obtained as the basic data for subsequent analysis.

[0094] Step S22: performing temperature partitioning processing on the wide temperature range electric variable distribution signal to generate a temperature partitioned electric variable distribution signal.

[0095] In an embodiment of the present invention, the wide temperature range electrical variable distribution signal collected in step S21 is subjected to temperature partition processing. The purpose of temperature partition processing is to classify the electrical variable signals in a wide temperature range according to different temperature intervals, so as to further analyze the electrical variable characteristics in each temperature interval. In specific implementation, the temperature range (such as -40°C to +125°C) is divided into multiple temperature intervals, for example: temperature interval 1: -40°C to 0°C; temperature interval 2: 0°C to 40°C; temperature interval 3: 40°C to 85°C; temperature interval 4: 85°C to 125°C. For each temperature interval, it is first necessary to extract the electrical variable signal data in the corresponding interval. For each temperature interval, all collected voltage, current, power consumption and other signals need to be calibrated according to the temperature, and the stability and change trend of the electrical signal are determined. For example, in temperature interval 3 (40°C to 85°C), when the voltage of a circuit is 3.3V, the current increases from 100mA to 120mA in this interval. Therefore, the electrical variable signal in this interval includes signal change data such as voltage, current and power consumption. After temperature partition processing, the obtained temperature partition electrical variable distribution signal data will contain electrical variable data in different intervals. These data can be further used to analyze the performance changes of the circuit under different temperature conditions. For example: Temperature interval 1 (-40°C to 0°C): the voltage is stable at 3.3V, the current is 95mA, and the power consumption is 315mW; Temperature interval 2 (0°C to 40°C): the voltage is stable at 3.3V, the current increases from 98mA to 110mA, and the power consumption increases from 323mW to 363mW; Temperature interval 3 (40°C to 85°C): the voltage is stable at 3.3V, the current increases from 100mA to 120mA, and the power consumption increases from 330mW to 396mW; Temperature interval 4 (85°C to 125°C): the voltage is stable at 3.3V, the current increases from 120mA to 130mA, and the power consumption increases from 396mW to 429mW. At this point, the temperature-zoned electrical variable distribution signal data will serve as the basis for subsequent analysis (such as electrical variable linear analysis, nonlinear offset analysis, etc.), helping to monitor the circuit's working performance over a wide temperature range and provide data support for circuit optimization.

[0096] Further, step S3 includes the following steps:

[0097] Step S31: performing an electric variable distribution signal index analysis of each temperature partition based on the temperature partition electric variable distribution signal to generate a temperature partition electric variable distribution signal index;

[0098] In an embodiment of the present invention, based on the temperature partition electrical variable distribution signal, the temperature partition electrical variable distribution signal index analysis is performed. Specifically, it is first necessary to associate the electrical variable signal (such as voltage, current, power consumption) in each temperature interval with the temperature, and calculate the relevant time series trend statistical indicators, such as trend mean, trend standard deviation, trend change rate, etc. For example, in temperature interval 1 (-40°C to 0°C), the voltage signal of a certain circuit distribution is always stable at 3.3V, the current signal is 95mA, and the power signal is 315mW. The current in this interval is analyzed, and its change rate and standard deviation are calculated. The output current of a certain circuit distribution in temperature interval 3 (40°C to 85°C) increases from 100mA to 120mA, with a change rate of 20%. The mean value of the current is 110mA, and the standard deviation is plus or minus 5mA. Similar voltage, current and power related data will be calculated through statistical analysis to provide basic data for subsequent analysis.

[0099] Step S32: performing temperature partition electric variable linear coefficient analysis on the temperature partition electric variable distribution signal index to generate the temperature partition electric variable linear coefficient;

[0100] In an embodiment of the present invention, for the electric variable distribution signal index in each temperature interval, the linear relationship between the electric variable and the temperature is analyzed, and the linear coefficient of the electric variable is calculated. During the analysis process, a linear regression model (such as the least squares method) between the temperature and the electric variable is selected, and the voltage, current, power and other signals are linearly fitted to obtain the relationship between the electric variable and the temperature in each temperature interval. For example, in temperature interval 1 (-40°C to 0°C), the voltage signal is always 3.3V, the current signal is 95mA, and the power signal is 315mW. After linear regression analysis, it is found that there is no significant linear relationship between the current and the temperature. Therefore, the linear coefficient of the current is set to 0. In temperature interval 2 (0°C to 40°C), the current increases from 98mA to 110mA, and the temperature increases from 0°C to 40°C. Through linear regression analysis, it is concluded that the linear coefficient between the current and the temperature is 0.25mA / °C. Similarly, the linear coefficient of the power is 3.5mW / °C. The purpose of this step is to accurately capture the linear relationship between the influence of temperature on the electrical variables, and to help with the fitting and compensation between temperature and electrical variables in subsequent steps.

[0101] Step S33: performing nonlinear offset analysis of the temperature partition electrical variable according to the temperature partition electrical variable distribution signal index and the temperature partition electrical variable linear coefficient, and generating temperature partition electrical variable nonlinear offset data;

[0102] In an embodiment of the present invention, nonlinear offset analysis is performed based on the temperature partition electrical variable distribution signal index and the electrical variable linear coefficient. The purpose of nonlinear offset analysis is to identify the nonlinear relationship between electrical variables and temperature and calculate the nonlinear offset of electrical variables at different temperatures. For example, in temperature interval 3 (40°C to 85°C), the current increases from 100mA to 120mA, but based on the linear coefficient, the current change is expected to be 15mA, and the actual measured current change is 20mA. This indicates that there is a nonlinear offset with an offset of 5mA. By comparing the actual change of the electrical variable in each temperature interval with the linear prediction result, the nonlinear offset data in each interval is obtained. The nonlinear offset data will be used in subsequent fitting and compensation strategies to help accurately adjust the performance of the circuit over a wide temperature range. For example, if the current increases from 120mA to 130mA in temperature interval 4 (85°C to 125°C), the calculated nonlinear offset is +2mA, and the actual increase in current exceeds the linear prediction.

[0103] Step S34: performing a first-order fitting process of the temperature partition and the electric variable signal according to the temperature partition electric variable linear coefficient, and generating first-order fitting data of the temperature partition electric variable signal;

[0104] In an embodiment of the present invention, a first-order fitting process is performed based on the linear coefficient of the electrical variable. The first-order fitting process uses a linear regression method to model the relationship between electrical variables (such as current, voltage, power, etc.) and temperature, and generates first-order fitting data of the temperature partition electrical variable signal. For example, in temperature interval 1 (-40°C to 0°C), since the changes in electrical variables such as voltage and current are relatively stable, a linear fitting is used to obtain the relationship between current change and temperature. The first-order fitting data is the linear change trend of electrical variables such as current, power, voltage, etc. in each temperature interval, providing a simple temperature and electrical variable relationship model.

[0105] Step S35: Designing a temperature-electric variable correlation kernel function through the temperature partition electric variable nonlinear offset data to generate a temperature-electric variable correlation kernel function;

[0106] In an embodiment of the present invention, a correlation kernel function that adapts to the relationship between electrical variables and temperature is designed based on the nonlinear offset data of the temperature partitioned electrical variables. The kernel function is used to describe the nonlinear influence between temperature and electrical variables. To this end, first, according to the actual offset of the electrical variables with temperature, a suitable function form is selected to capture the complex nonlinear relationship between temperature and electrical variables. For example, a Gaussian kernel function or a polynomial kernel function is used to construct the nonlinear relationship, thereby accurately modeling the influence of temperature on the electrical variables. The kernel function performs a weighted calculation on the relationship between temperature and electrical variables according to the nonlinear offset characteristics of electrical variables such as current, voltage, and power in each temperature interval. By analyzing the changes in electrical variables in each temperature interval, the optimal kernel function parameters under each temperature interval are determined, thereby generating a temperature electrical variable correlation kernel function with good adaptability in a wider temperature range. For example, if the voltage and current changes show a strong nonlinear relationship in a certain temperature interval, a suitable kernel function is designed according to the current and voltage nonlinear offset data in the temperature interval to help accurately describe the nonlinear dependence between temperature and electrical variables. The kernel function will help to perform a joint analysis of temperature and electrical variables in different temperature intervals.

[0107] Step S36: performing secondary fitting data analysis of temperature partitions and electric variable signals according to the temperature electric variable correlation kernel function and the primary fitting data of the temperature partition electric variable signals, and generating secondary fitting data of the temperature partition electric variable signals;

[0108] In an embodiment of the present invention, a temperature electric variable correlation kernel function is used in combination with the first-level fitting data of the temperature partition electric variable signal to perform a second-level fitting process of the temperature partition and the electric variable signal. The first-level fitting data usually describes the linear relationship between the electric variable and the temperature, but in practical applications, due to the nonlinear characteristics of the influence of temperature on the electric variable, the second-level fitting analysis corrects the first-level fitting result by introducing a kernel function, thereby more accurately fitting the relationship between the electric variable and the temperature. Specifically, the second-level fitting analysis combines the first-level fitting data of the temperature partition electric variable signal with the temperature electric variable correlation kernel function, and eliminates the nonlinear part omitted in the first-level fitting by weighted averaging the changes in the electric variables in different temperature intervals. This process takes into account the comprehensive influence of temperature on the electric variable, so that the fitting result can better reflect the distribution characteristics of the electric variable of the circuit system in a wide temperature range. For example, if the current change in a certain temperature interval cannot be fully captured by the first-level linear fitting, the predicted value of the current in the temperature interval is corrected according to the correlation kernel function through the second-level fitting process, thereby improving the fitting accuracy.

[0109] Step S37: performing multi-temperature zone electrical variable distribution coupled response characteristic analysis based on the secondary fitting data of the temperature zone electrical variable signal to generate multi-temperature zone electrical variable distribution coupled response characteristic data.

[0110] In an embodiment of the present invention, based on the secondary fitting data of the temperature partition electric variable signal, the coupled response characteristic analysis of the electric variable distribution in multiple temperature zones is performed. Through this analysis, the mutual influence and coupling effect between the electric variables in multiple temperature intervals can be identified, so as to obtain the overall response characteristics of the change of the electric variables in the circuit within a wide temperature range. Specifically, the secondary fitting data of the electric variables in multiple temperature intervals are jointly analyzed to identify the coupling relationship between the electric variables such as voltage, current, and power. Through statistical analysis, correlation analysis and other methods, the linkage effect of temperature change on the electric variables is revealed. For example, temperature change causes the current and power to change simultaneously in multiple temperature intervals, thereby forming a coupled response. In this process, considering the mutual dependence of the electric variables in different temperature intervals, by analyzing the change trend of variables such as current, voltage and power in each temperature interval, the coupled response characteristic data under multiple temperature intervals are obtained. For example, when the temperature rises from -40°C to 40°C, the current increases with the increase of temperature, and the power changes accordingly. Through the coupled response analysis, the positive correlation between current and power is found, and the response characteristics of the two in different temperature intervals are obtained. Ultimately, the generated multi-temperature zone electrical variable distribution coupled response characteristic data can provide an accurate temperature-electrical variable relationship model for circuit design, helping to optimize circuit performance and improve stability over a wide temperature range.

[0111] Further, step S31 includes the following steps:

[0112] Step S311: performing time series characteristic analysis on the temperature partition electrical variable distribution signal to generate time series characteristic data of the temperature partition electrical variable distribution signal;

[0113] In an embodiment of the present invention, a time series feature analysis is performed on the temperature partition electrical variable distribution signal, the purpose of which is to extract the dynamic characteristics of the signal changing over time, so as to help understand the time series behavior of the electrical variable in different temperature intervals. First, the temperature partition electrical variable signal is organized in time series, and the electrical variable signals in different temperature intervals are analyzed one by one. The analysis content includes the fluctuation amplitude, frequency, periodic change, mutation point and long-term trend of the signal. For example, assuming that in the temperature range of -40°C to 25°C, the voltage signal shows a strong periodic change, and the period of its amplitude change is 5 seconds, while in the temperature range of 25°C to 50°C, the period of voltage fluctuation gradually increases and the amplitude decreases. In this process, a time series feature extraction algorithm (such as Fourier transform, waveform analysis, etc.) is used to process the electrical variable signal to extract the periodic change, maximum fluctuation amplitude, mean and standard deviation of the signal in each temperature partition. For example, between -40°C and 25°C, the current signal shows a large fluctuation, with a period of 1 second and a fluctuation range of plus or minus 0.5A, while between 25°C and 50°C, the fluctuation range is reduced to plus or minus 0.2A, and the period becomes 2 seconds. Through this process, the time series characteristic data of the electrical variable signal can be obtained, such as period, fluctuation range, maximum value, minimum value, etc. These data can help in the subsequent steps to analyze the trend of the impact of temperature on the electrical variable.

[0114] Step S312: performing a trend analysis of the temperature partition electrical variable distribution signal according to the time series characteristic data of the temperature partition electrical variable distribution signal to generate trend data of the temperature partition electrical variable distribution signal;

[0115] In an embodiment of the present invention, the time series characteristic data is analyzed to further extract the change trend of the electric variable signal. The focus of trend analysis is to reveal the change trend of the electric variable signal with temperature, especially the gradual change law of the electric variable under each temperature partition. Trend analysis includes using statistical methods to smooth, regress or curve fit the time series characteristic data to capture the long-term change direction and pattern of the signal. For example, in the temperature range of -40°C to 25°C, after trend analysis, it is found that the voltage shows a linear upward trend, while in the temperature range of 25°C to 50°C, the current shows an exponential growth trend. In a specific implementation, the long-term trend of the signal is analyzed by using methods such as linear regression, weighted moving average or exponential smoothing. These analyses can help determine whether there is a sudden change or significant nonlinear phenomenon in the trend change of the electric variable signal during the temperature change process. For example, if the current signal fluctuates greatly between the temperature range of -40°C and 25°C, but the trend analysis results show that its average value gradually increases with the increase of temperature, it means that the current in this temperature range is on the rise. In the temperature range of 25°C to 50°C, the current changes relatively smoothly, and trend analysis shows that the current changes are small. Ultimately, these trend data will help predict the long-term change behavior of electrical variables under different temperature conditions.

[0116] Step S313: performing an electric variable distribution signal index analysis of each temperature partition according to the temperature partition electric variable distribution signal trend data, and generating a temperature partition electric variable distribution signal index.

[0117] In an embodiment of the present invention, according to the trend data of the electric variable distribution signal of the temperature partition, the electric variable distribution signal of each temperature partition is further analyzed by indicators. The purpose of this analysis is to extract the key performance indicators of the electric variable signal in each temperature partition, and to help understand the changing characteristics of the electric variable in different temperature intervals. In the specific operation, by quantifying the trend data, multiple statistical indicators of the electric variable signal of the temperature partition are generated, such as the maximum value, minimum value, mean value, standard deviation, fluctuation range, rate of change, etc. For example, in the temperature interval of -40°C to 25°C, the maximum value of the voltage signal is 15V, the minimum value is 5V, the fluctuation range is plus or minus 5V, the mean value is 10V, and the standard deviation is 2V; in the temperature interval of 25°C to 50°C, the maximum value of the current signal is 3A, the minimum value is 1.5A, the fluctuation range is plus or minus 0.5A, the mean value is 2A, and the standard deviation is 0.2A. Through these electric variable distribution signal indicators, the stability, variation range and sensitivity to temperature changes of the electric variable in different temperature intervals are revealed. For example, if the current fluctuation amplitude in a certain temperature range is large and the rate of change is high, it means that the current in this temperature range is more sensitive to temperature changes and the circuit needs to be optimized and adjusted. If the current signal has a small fluctuation amplitude and stable changes within the temperature range, it means that the current is more stable and can withstand large temperature changes. Ultimately, the generated temperature partition electrical variable distribution signal index will provide a key basis for subsequent steps, helping to achieve accurate monitoring and optimization adjustments in multiple temperature environments.

[0118] Further, step S4 includes the following steps:

[0119] Step S41: performing a correlation mapping relationship analysis between temperature zones and electric variable distribution signals based on the multi-temperature zone electric variable distribution coupling response characteristic data to construct a multi-temperature zone electric variable distribution correlation matrix;

[0120] In an embodiment of the present invention, analysis is performed based on the coupled response characteristic data of the electrical variable signal collected from multiple temperature intervals. The purpose is to find out the mutual relationship between the changes in the electrical variable signals between the temperature zones and establish the corresponding correlation matrix. The core of this analysis is to map the correlation and mutual influence between them by quantifying the coupling relationship between the temperature zone and the electrical variable signal. In specific operations, mathematical methods such as the Pearson correlation coefficient and the mutual information are used to calculate the correlation between each temperature interval and the electrical variable signal in other intervals. For example, for the current signal between the temperature intervals -40°C to 0°C and 0°C to 25°C, the correlation between the two is analyzed. If it is found that the current signal change trends in these two temperature intervals are similar, the calculated correlation coefficient is a positive value, otherwise it is a negative value. Finally, the correlation between multiple temperature intervals will be quantified and constitute an association matrix. For example, assuming that the voltage signal in the temperature interval -40°C to 0°C has a low correlation with the current signal in the temperature interval 25°C to 50°C, the correlation value of the two in the generated association matrix is ​​close to zero. The current signals between the temperature ranges of 0°C to 25°C and 25°C to 50°C show a high positive correlation, and the correlation values ​​in the correlation matrix are large. Quantifying and displaying the coupling relationship between electrical variables in different temperature ranges provides a basis for further analysis.

[0121] Step S42: performing matrix potential representation quantitative data analysis on the multi-temperature zone electrical variable distribution association matrix to generate multi-temperature zone electrical variable distribution potential quantitative data;

[0122] In an embodiment of the present invention, a potential representation quantitative analysis is performed on the multi-temperature zone electrical variable distribution association matrix to further extract and compress the key information in the matrix. The purpose of the potential representation quantitative data analysis is to convert high-dimensional matrix information into a more easily processable low-dimensional data representation while maintaining the key information contained therein. Techniques such as matrix decomposition methods, principal component analysis (PCA) or singular value decomposition (SVD) are used. For example, it is assumed that the association matrix contains correlation data of electrical variable signals between multiple temperature intervals. After decomposition using SVD, several main component vectors are obtained, which represent the core patterns of the correlation of electrical variable signals within the temperature interval. Through these main components, the multi-dimensional association matrix data is compressed into potential quantitative data of several dimensions, retaining key correlation features. For example, after matrix decomposition, a 3-dimensional potential representation data is obtained, which contains the main correlation patterns of current and voltage within the temperature interval. It is assumed that the first component in the potential quantitative data represents the current fluctuation pattern in the temperature interval of 0°C to 50°C, the second component represents the change pattern of the voltage signal, and the third component represents the coupling effect between current and voltage. In this way, more valuable electrical variable signal characteristics can be captured through fewer dimensions and data representations.

[0123] Step S43: performing cluster analysis processing on the multi-temperature-zone electrical variable distribution coupled response characteristic data according to the multi-temperature-zone electrical variable distribution potential quantization data to generate clustered multi-temperature-zone electrical variable distribution coupled response characteristic data;

[0124] In an embodiment of the present invention, based on the potential quantitative data of the electrical variable distribution in multiple temperature zones, a cluster analysis is performed on the coupled response characteristic data of the electrical variable distribution in multiple temperature zones. The purpose of cluster analysis is to group the electrical variable signals in different temperature intervals according to their similarities in order to better understand the interaction patterns of the electrical variables. For example, it is assumed that the potential quantitative data obtained represents the key patterns of the current and voltage signals between the temperature interval of -40°C and 50°C. In the cluster analysis, the K-means clustering algorithm is used to divide these signal data into three categories. The results show that the electrical variable signals between the temperature interval of -40°C and 0°C exhibit one characteristic, the temperature interval of 0°C to 25°C exhibits another characteristic, and the temperature interval of 25°C to 50°C exhibits a third characteristic. Through clustering, the differences in the response characteristics of the electrical variable signals in different temperature intervals can be clearly seen, and a basis for subsequent data analysis and model optimization can be provided. After clustering, three groups of signal feature data are obtained. The first group contains data from -40°C to 0°C, the second group contains data from 0°C to 25°C, and the third group contains data from 25°C to 50°C. Each group of data has similarities in the fluctuation range and change trend of current and voltage signals, which effectively reveals the similarities and differences between temperature ranges.

[0125] Step S44: performing cluster outlier identification processing on the clustered multi-temperature zone electrical variable distribution coupled response characteristic data to generate outlier multi-temperature zone electrical variable distribution coupled response characteristic data;

[0126] In an embodiment of the present invention, outlier identification is performed on the coupled response characteristic data of the multi-temperature zone electrical variable distribution after clustering, with the purpose of finding electrical variable signals that are abnormal in the electrical variable distribution and significantly different from most data points. These outliers usually represent some unusual or erroneous circuit behaviors caused by faults or other external factors. In specific operations, a density-based outlier detection method (such as DBSCAN) or a distance-based outlier detection method (such as K nearest neighbor) is used to process the data. Identify those data points that show different characteristics from most electrical variable signals and mark them as outliers. For example, assume that the clustered current signal data contains signals within the normal range and some abnormal high-amplitude fluctuation signals. Through outlier identification, it is found that in certain temperature ranges, the current signal has a large jump at a specific time, which exceeds the normal range. These outliers are caused by external interference, device failure or temperature mutation. Distinguishing these outlier data from normal data can help to perform more accurate fault diagnosis and performance optimization later.

[0127] Step S45: performing a potential representation quantitative data analysis of the abnormal multi-temperature-zone electrical variable distribution according to the outlier multi-temperature-zone electrical variable distribution coupled response characteristic data to generate the abnormal multi-temperature-zone electrical variable distribution potential quantitative data.

[0128] In an embodiment of the present invention, a potential representation quantitative data analysis of abnormal electrical variable distribution is performed based on the coupled response characteristic data of the outlier multi-temperature zone electrical variable distribution. The goal is to extract the key features of the abnormal electrical variable distribution and compress it into low-dimensional data through the potential representation quantization method for further abnormal analysis and fault location. For example, for outlier data, the principal component analysis (PCA) method is used for dimensionality reduction to convert the complex features of the abnormal signal into a few key components. By analyzing these potential quantitative data, the pattern of the abnormal signal, such as the cause and frequency of the sudden change of current or voltage, is further understood. For example, after quantitative analysis, a potential representation data is obtained, which shows that there is abnormal fluctuation in the current signal in the temperature range of -40°C to 0°C, and the fluctuation frequency is high. Through further analysis, it is found that the abnormal behavior is related to power supply noise interference. In this way, the abnormal electrical variable distribution can be quickly identified and located, providing a basis for subsequent correction and optimization.

[0129] Further, step S5 includes the following steps:

[0130] Step S51: analyzing the temperature zone effect on the electric variable distribution distortion signal according to the abnormal multi-temperature zone electric variable distribution potential quantified data, and generating the temperature zone effect on the electric variable distribution distortion signal data;

[0131] In an embodiment of the present invention, the potential quantitative data of the distribution of electric variables in multiple temperature zones is analyzed for the purpose of identifying and analyzing the influence of temperature zones on the distribution of electric variable signals, especially the signal distortion caused by temperature changes. This process uses a signal distortion analysis method to focus on analyzing the influence of temperature fluctuations on the timing and amplitude changes of electric variable signals. Determine the distortion mode and trend of electric variable signals in different temperature intervals caused by temperature zone changes. In specific operations, it is first necessary to analyze the signal changes in the potential quantitative data by statistical methods, such as the voltage and current signal fluctuation characteristics between the temperature intervals of -40°C to 0°C, 0°C to 25°C, and 25°C to 50°C. Through comparative analysis, it is found that the amplitude of the electric variable signal in certain temperature intervals has changed significantly. For example, the current signal between 0°C and 25°C will be distorted, causing the output current to exceed the expected range. Through frequency analysis, amplitude analysis and other methods, the key features of these distorted signals are extracted to generate distorted signal data of the electric variable distribution affected by the temperature zone. For example, the current signal in the temperature range of -40°C to 0°C has a larger amplitude fluctuation compared to other temperature ranges, indicating that the current distribution of the circuit is greatly distorted in a low temperature environment. Through distortion analysis, this distortion feature can be accurately extracted and recorded to provide a reference for subsequent compensation and adjustment.

[0132] Step S52: analyzing the signal compensation mapping data of the circuit distributed passive chip based on the temperature zone-affected electrical variable distribution distortion signal data, and generating the circuit distributed passive chip signal compensation mapping data;

[0133] In an embodiment of the present invention, based on the temperature zone effect electric variable distribution distortion signal data, the signal compensation mapping data of the passive chip in the circuit is analyzed. The signal distortion exhibited by the passive chip (such as resistors, capacitors, inductors, etc.) under different temperature conditions will affect the performance of the entire circuit, so it is necessary to design an appropriate compensation scheme according to the temperature zone distortion signal. The core of this step is to construct a signal compensation mapping relationship and compensate for the distorted signal to restore the normal function of the circuit. Specifically, first identify the abnormal waveform in the temperature zone effect electric variable distribution distortion signal, and pay special attention to the relationship between the distortion signal and the passive chip parameters (such as impedance, capacitive reactance, etc.). For example, in the temperature range of 0°C to 25°C, the change in capacitance value caused by the temperature coefficient of the capacitor will cause distortion to the current signal. By calculating the compensation coefficient of the capacitance changing with temperature, the corresponding circuit adjustment measures are designed. Through the signal compensation mapping data analysis, specific compensation functions are designed for passive chips at different temperatures. These functions can quantify the impact of temperature changes on electric variable signals and perform timely circuit compensation. Finally, the generated circuit distribution passive chip signal compensation mapping data contains compensation relationship data for signal distortion in each temperature range. For example, for distortion in the temperature range of 0°C to 25°C, a compensation formula based on the change in resistance and capacitance is designed, and the circuit is compensated through compensation mapping data to eliminate the influence of the capacitance temperature coefficient on the current signal.

[0134] Step S53: performing circuit distributed passive chip adjustment strategy analysis according to the circuit distributed passive chip signal compensation mapping data and the circuit distributed passive chip functional characteristic data, and generating a circuit distributed passive chip adjustment strategy;

[0135] In an embodiment of the present invention, the circuit adjustment strategy analysis is performed based on the signal compensation mapping data of the passive chip distributed in the circuit and the functional characteristic data of the passive chip distributed in the circuit (such as the electrical characteristics of resistance, capacitance, and inductance), and the corresponding adjustment strategy is generated. The core of this step is to formulate a specific adjustment strategy based on the signal distortion characteristics and compensation relationship of the passive chip in different temperature ranges to ensure the stability and accuracy of the circuit in a wide temperature range. In the specific implementation, the passive chip characteristics in the circuit, such as the temperature coefficient of resistance and capacitance, are first analyzed through the signal compensation mapping data and the chip functional characteristic data. Then, in combination with the actual working environment of the circuit, the adjustment strategy design for multiple temperature zones is performed. For example, a strategy for adjusting the resistance value according to temperature changes is designed. When the temperature rises, the resistance value is automatically adjusted to offset the current distortion caused by temperature. Through these adjustment strategies, the normal operation of the circuit is guaranteed under various temperature conditions. For example, in the temperature range of 0°C to 25°C, a resistance regulator is designed for current distortion, and the resistance value is automatically adjusted when necessary to compensate for the current change caused by temperature, so that the current signal tends to the normal range.

[0136] Step S54: executing the circuit distributed passive chip optimization adjustment operation based on the circuit distributed passive chip adjustment strategy, and performing iterative closed-loop monitoring processing on the temperature partition electrical variable distribution signal corresponding to the circuit distributed passive chip optimization adjustment operation, to generate an iterative temperature partition electrical variable distribution signal;

[0137] In an embodiment of the present invention, the passive chip in the circuit is optimized and adjusted according to the circuit distribution passive chip adjustment strategy. After the adjustment is completed, the electric variable distribution signal is monitored and feedback processed in real time through a closed-loop control system to ensure that the optimized circuit can work stably in a wide temperature range, and further adjustments are made as necessary according to the feedback signal. In the specific operation, the optimization adjustment operation is first performed, such as using the software module to dock and adjust the parameters of components such as resistors and capacitors to compensate for the distortion caused by temperature changes. After the adjustment is completed, the electric variable distribution signal is monitored in real time using a temperature sensor and a current / voltage sensor. For example, the current change caused by temperature is eliminated by adjusting the value of the capacitor, and then the current signal is collected in real time and compared with the target signal. If there is still an error in the current signal, the circuit parameters are adjusted again through the feedback mechanism until the signal meets the target requirements. For example, in the temperature range of 25°C to 50°C, the current distortion caused by temperature changes is reduced by adjusting the passive chip components in the circuit. Through closed-loop control, the optimized circuit can ensure the stability of the current signal, and dynamically compensate and adjust according to the temperature change, and finally generate the iterative temperature partition electric variable distribution signal.

[0138] Step S55: performing a wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

[0139] In an embodiment of the present invention, a wide temperature range electric variable monitoring feedback operation is performed based on the aforementioned iterative temperature partition electric variable distribution signal. The goal of this step is to continuously monitor the optimized electric variable signal, adjust the circuit working state according to the feedback data, and ensure that the circuit always maintains stability and high performance within a wide temperature range. In specific implementation, high-precision temperature sensors and voltage / current sensors are used to collect electric variable signals in each temperature interval in real time. The collected signal is compared with the preset target signal, and the feedback control system is used to determine whether the circuit is in a normal working state. For example, assuming that the fluctuation amplitude of the current signal is too large in the temperature range of -40°C to 0°C, the circuit parameters are immediately adjusted according to the feedback signal to reduce distortion until the current signal returns to the expected range. Finally, through the feedback mechanism, the circuit parameters are adjusted and optimized in real time to ensure that the circuit can maintain a stable working state throughout the wide temperature range, providing reliable data support for circuit design and application.

[0140] Further, step S52 includes the following steps:

[0141] Step S521: analyzing the frequency data of abnormal electric variables in each temperature zone according to the temperature zone electric variable distribution distortion signal data, and generating abnormal frequency data of electric variables in each temperature zone;

[0142] In the embodiment of the present invention, the distorted signal data of the temperature zone affecting the distribution of electric variables is statistically analyzed for the abnormal frequency of electric variables in each temperature zone to identify the abnormal conditions of electric variables in each temperature zone. The key to this process is to determine the frequency of signal abnormality in each temperature interval by analyzing the time sequence and amplitude changes of the distorted signal data. For example, in the temperature interval of -40°C to 0°C, due to the low temperature, some electric variables such as current or voltage fluctuate greatly, resulting in certain signal abnormalities. By performing frequency statistics on the distorted signal in each temperature interval, the abnormal frequency data of electric variables in the temperature zone is generated, and the frequency of occurrence and related characteristics of the abnormal signal in each temperature interval are recorded. In the specific operation, the collected signal data is divided into time windows, and the electric variable signal of each temperature zone is statistically analyzed according to a specific time window, and the frequency of abnormal signals in each temperature zone is calculated. For example, in the temperature interval of 0°C to 25°C, the frequency of voltage abnormality is 10 times / hour, indicating that the frequency of abnormal voltage signals in this temperature zone is high. This data will be used for subsequent compensation weight analysis and error compensation demand analysis.

[0143] Step S522: performing temperature zone compensation weight analysis based on the abnormal frequency data of the temperature zone electrical variables to generate a temperature zone compensation weight coefficient;

[0144] In an embodiment of the present invention, based on the abnormal frequency data of the electric variable in the temperature zone, the temperature zone compensation weight analysis is performed to generate the temperature zone compensation weight coefficient. The abnormal frequency data of the electric variable in the temperature zone is the frequency of occurrence of abnormal distortion of the electric variable in different temperature intervals. According to these frequency data, the weight distribution of each temperature zone in the compensation of the electric variable distortion is analyzed. The goal is to assign a corresponding compensation weight coefficient to each temperature zone according to the frequency of abnormal occurrence of the electric variable in different temperature zones, which is used for appropriate adjustment in the subsequent compensation mapping. For example, if the abnormal frequency of the current signal in the temperature zone of 0°C to 25°C is 10 times / hour, and in the temperature zone of 25°C to 50°C, it is only 2 times / hour, then the abnormal frequency of the electric variable in the temperature zone of 0°C to 25°C is higher, so a higher compensation weight coefficient is assigned. Assume that the compensation weight coefficient of the temperature zone of 0°C to 25°C is set to 1.5, and the compensation weight coefficient of the temperature zone of 25°C to 50°C is set to 0.8. In this way, when processing signal distortion, the compensation system will give priority to those temperature zones with higher abnormal frequencies and provide more compensation resources. In specific implementation, a weighted average analysis is performed based on the abnormal frequency data of electrical variables to determine the compensation weight of each temperature zone. For example, the abnormal frequency in the temperature range of -40°C to 0°C is low, and the compensation weight coefficient is 0.5; the abnormal frequency in the temperature range of 0°C to 25°C is high, and the compensation weight coefficient is 1.5. Through this analysis, reasonable compensation weights are assigned to different temperature zones to ensure the accuracy and effectiveness of the compensation strategy.

[0145] Step S523: performing temperature zone electric variable distribution error compensation demand analysis according to the temperature zone electric variable distribution distortion signal data and the temperature zone compensation weight coefficient, and generating temperature zone electric variable distribution error compensation demand data;

[0146] In the embodiment of the present invention, the temperature zone electric variable distribution error compensation demand analysis is performed in combination with the temperature zone electric variable distribution distortion signal data and the temperature zone compensation weight coefficient. The goal of the analysis is to determine the specific requirements of each temperature zone in the electric variable compensation based on the distortion characteristics and compensation weights of the electric variable signals in different temperature zones, so as to provide a basis for the subsequent circuit optimization and adjustment. In the specific operation, first, the compensation requirements of the electric variable in each temperature zone are calculated based on the distortion signal data and the compensation weight coefficient. For example, in the temperature zone of 0°C to 25°C, the abnormal frequency of the current signal is high, and the compensation weight coefficient is 1.5, and the error compensation requirement of the electric variable is large. Therefore, it is necessary to design a compensation mechanism to perform a stronger signal correction for this temperature zone. Assuming that in this temperature zone, the error of the current signal is plus or minus 5%, compensation is required to ensure that the current signal returns to the normal range. According to these demand data, the temperature zone electric variable distribution error compensation demand data is generated, which contains the compensation demand values ​​of each temperature zone. For example, the error compensation requirement for the temperature range of 0°C to 25°C is 5%, while the error compensation requirement for the temperature range of 25°C to 50°C is only 2%. This data will provide a basis for subsequent signal compensation mapping and circuit adjustment.

[0147] Step S524: analyzing the signal compensation mapping data of the circuit distributed passive chip according to the temperature zone electrical variable distribution error compensation demand data, and generating the circuit distributed passive chip signal compensation mapping data.

[0148] In an embodiment of the present invention, based on the temperature zone electrical variable distribution error compensation demand data, the signal compensation mapping data of the circuit distribution passive chip is analyzed to generate the circuit distribution passive chip signal compensation mapping data. This step converts the error compensation demand of the temperature zone into a specific circuit adjustment scheme to provide signal compensation parameters for passive chips (such as resistors, capacitors, inductors, etc.) to reduce electrical variable distortion and improve circuit performance. According to the temperature zone electrical variable distribution error compensation demand data, analyze which passive chip parameters need to be adjusted. For example, within the temperature zone of 0°C to 25°C, the current signal error demand is 5%. The circuit designer needs to adjust the resistance value according to the temperature coefficient of the resistor, or add appropriate capacitors to the circuit for compensation. By matching the compensation demand data of each temperature zone with the characteristics of the passive chip, appropriate signal compensation mapping data is generated. For example, if the current distortion demand is 5% within the temperature zone of 0°C to 25°C, and the error can be effectively compensated by optimizing the capacitor, the generated circuit distribution passive chip signal compensation mapping data will include a specific solution for capacitor adjustment.

[0149] This specification provides a wide temperature range electric variable monitoring system for executing the wide temperature range electric variable monitoring method as described above, and the wide temperature range electric variable monitoring system includes:

[0150] A circuit design parsing and analysis module, used to obtain thick film integrated circuit design data; perform circuit network topology structure analysis based on the thick film integrated circuit design data to generate circuit network topology structure data; perform circuit distribution functional characteristic analysis based on the circuit network topology structure data to generate circuit distribution functional characteristic data, wherein the circuit distribution functional characteristic data includes circuit distribution active chip functional characteristic data and circuit distribution passive chip functional characteristic data;

[0151] The temperature partition electric variable distribution signal analysis module is used to collect the electric variable signals of each circuit distribution of the thick film integrated circuit in a wide temperature range by using the functional characteristic data of the circuit distribution active chip to generate the electric variable distribution signal in the wide temperature range; perform temperature partition processing on the electric variable distribution signal in the wide temperature range to generate the temperature partition electric variable distribution signal;

[0152] The multi-temperature zone electric variable distribution coupling response characteristic analysis module is used to analyze the electric variable distribution signal index of each temperature zone based on the temperature zone electric variable distribution signal, and generate the temperature zone electric variable distribution signal index; perform multi-temperature zone electric variable distribution coupling response characteristic analysis according to the temperature zone electric variable distribution signal index, and generate multi-temperature zone electric variable distribution coupling response characteristic data;

[0153] The module for potential quantitative analysis of abnormal multi-temperature-zone electric variable distribution is used to perform cluster outlier analysis based on the coupled response characteristic data of multi-temperature-zone electric variable distribution, and generate outlier multi-temperature-zone electric variable distribution coupled response characteristic data; perform potential representation quantitative data analysis of abnormal multi-temperature-zone electric variable distribution based on the coupled response characteristic data of multi-temperature-zone electric variable distribution of outliers, and generate potential quantitative data of abnormal multi-temperature-zone electric variable distribution;

[0154] The wide temperature range electric variable monitoring feedback module is used to analyze the distortion signal of the electric variable distribution affected by the temperature zone according to the potential quantitative data of the electric variable distribution in abnormal multiple temperature zones, and generate the distortion signal data of the electric variable distribution affected by the temperature zone; to analyze the signal compensation mapping data of the circuit distribution passive chip based on the distortion signal data of the electric variable distribution affected by the temperature zone, and generate the signal compensation mapping data of the circuit distribution passive chip; to perform iterative closed-loop monitoring processing of the electric variable distribution signal after the passive chip optimization adjustment on the temperature partition electric variable distribution signal based on the signal compensation mapping data of the circuit distribution passive chip and the functional characteristic data of the circuit distribution passive chip, and generate the iterative temperature partition electric variable distribution signal; and to perform the wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

[0155] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0156] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for monitoring electrical variables in a wide temperature range, characterized in that: The following steps are involved: Step S1: Acquire thick film integrated circuit design data; perform circuit network topology structure analysis based on the thick film integrated circuit design data to generate circuit network topology structure data; perform circuit distribution functional feature analysis based on the circuit network topology structure data to generate circuit distribution functional feature data, wherein the circuit distribution functional feature data includes circuit distribution active chip functional feature data and circuit distribution passive chip functional feature data; Step S2: using the functional characteristic data of the circuit distributed active chip to collect the electrical variable signals of each circuit distribution in a wide temperature range of the thick film integrated circuit, and generate the electrical variable distribution signal in the wide temperature range; performing temperature partition processing on the electrical variable distribution signal in the wide temperature range, and generating the temperature partition electrical variable distribution signal; Step S3: Based on the temperature partition electric variable distribution signal, the electric variable distribution signal index of each temperature partition is analyzed to generate the temperature partition electric variable distribution signal index; based on the temperature partition electric variable distribution signal index, the multi-temperature zone electric variable distribution coupling response characteristic analysis is performed to generate the multi-temperature zone electric variable distribution coupling response characteristic data; Step S4: performing cluster outlier analysis processing according to the multi-temperature zone electrical variable distribution coupled response characteristic data to generate outlier multi-temperature zone electrical variable distribution coupled response characteristic data; According to the coupled response characteristic data of the multi-temperature-zone electrical variable distribution of outliers, the potential representation quantitative data of the abnormal multi-temperature-zone electrical variable distribution is analyzed to generate the potential quantitative data of the abnormal multi-temperature-zone electrical variable distribution; Step S5: Analyze the distortion signal of the electric variable distribution affected by the temperature zones according to the potential quantitative data of the abnormal multi-temperature zone electric variable distribution, and generate the distortion signal data of the electric variable distribution affected by the temperature zones; analyze the signal compensation mapping data of the circuit distribution passive chip based on the distortion signal data of the temperature zone electric variable distribution, and generate the signal compensation mapping data of the circuit distribution passive chip; perform iterative closed-loop monitoring processing of the electric variable distribution signal after the passive chip optimization adjustment on the temperature partition electric variable distribution signal based on the signal compensation mapping data of the circuit distribution passive chip and the functional characteristic data of the circuit distribution passive chip, and generate the iterative temperature partition electric variable distribution signal; perform the wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

2. The method for monitoring electrical variables in a wide temperature range according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining thick film integrated circuit design data; Step S12: performing packaging circuit multi-chip assembly analysis processing according to thick film integrated circuit design data to generate circuit multi-chip assembly data; Step S13: performing multi-chip component feature analysis on the circuit multi-chip component data to generate circuit multi-chip component feature data, wherein the circuit multi-chip component feature data includes active chip component type, passive chip component type, chip component parameters and chip component distribution position; Step S14: performing circuit network topology structure analysis according to the circuit multi-chip component characteristic data to generate circuit network topology structure data; Step S15: Perform circuit distribution functional characteristic analysis based on the circuit network topology data to generate circuit distribution functional characteristic data.

3. The method for monitoring electrical variables in a wide temperature range according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing communication circuit physical model modeling processing according to circuit multi-chip component characteristic data to generate a communication circuit physical model; Step S142: Perform circuit network topology analysis based on the communication circuit physical model to generate circuit network topology data.

4. The method for monitoring electrical variables in a wide temperature range according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: using preset wide temperature range test data to perform wide temperature range circuit operation on thick film integrated circuits, and using circuit distribution active chip functional characteristic data as circuit distribution electrical signal sampling decision to collect various circuit distribution electrical variable signals in the wide temperature range for the wide temperature range circuit operation, and generating wide temperature range electrical variable distribution signals; Step S22: performing temperature partitioning processing on the wide temperature range electric variable distribution signal to generate a temperature partitioned electric variable distribution signal.

5. The method for monitoring electrical variables in a wide temperature range according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing an electric variable distribution signal index analysis of each temperature partition based on the temperature partition electric variable distribution signal to generate a temperature partition electric variable distribution signal index; Step S32: performing temperature partition electric variable linear coefficient analysis on the temperature partition electric variable distribution signal index to generate the temperature partition electric variable linear coefficient; Step S33: performing nonlinear offset analysis of the temperature partition electrical variable according to the temperature partition electrical variable distribution signal index and the temperature partition electrical variable linear coefficient, and generating temperature partition electrical variable nonlinear offset data; Step S34: performing a first-order fitting process of the temperature partition and the electric variable signal according to the temperature partition electric variable linear coefficient, and generating first-order fitting data of the temperature partition electric variable signal; Step S35: Designing a temperature-electric variable correlation kernel function through the temperature partition electric variable nonlinear offset data to generate a temperature-electric variable correlation kernel function; Step S36: performing secondary fitting data analysis of temperature partitions and electric variable signals according to the temperature electric variable correlation kernel function and the primary fitting data of the temperature partition electric variable signals, and generating secondary fitting data of the temperature partition electric variable signals; Step S37: performing multi-temperature zone electrical variable distribution coupled response characteristic analysis based on the secondary fitting data of the temperature zone electrical variable signal to generate multi-temperature zone electrical variable distribution coupled response characteristic data.

6. The method for monitoring electrical variables in a wide temperature range according to claim 5, characterized in that: Step S31 The following steps are involved: Step S311: performing time series characteristic analysis on the temperature partition electrical variable distribution signal to generate time series characteristic data of the temperature partition electrical variable distribution signal; Step S312: performing a trend analysis of the temperature partition electrical variable distribution signal according to the time series characteristic data of the temperature partition electrical variable distribution signal to generate trend data of the temperature partition electrical variable distribution signal; Step S313: performing an electric variable distribution signal index analysis of each temperature partition according to the temperature partition electric variable distribution signal trend data, and generating a temperature partition electric variable distribution signal index.

7. The method for monitoring electrical variables in a wide temperature range according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a correlation mapping relationship analysis between temperature zones and electric variable distribution signals based on the multi-temperature zone electric variable distribution coupling response characteristic data to construct a multi-temperature zone electric variable distribution correlation matrix; Step S42: performing matrix potential representation quantitative data analysis on the multi-temperature zone electrical variable distribution association matrix to generate multi-temperature zone electrical variable distribution potential quantitative data; Step S43: performing cluster analysis processing on the multi-temperature-zone electrical variable distribution coupled response characteristic data according to the multi-temperature-zone electrical variable distribution potential quantization data to generate clustered multi-temperature-zone electrical variable distribution coupled response characteristic data; Step S44: performing cluster outlier identification processing on the clustered multi-temperature zone electrical variable distribution coupled response characteristic data to generate outlier multi-temperature zone electrical variable distribution coupled response characteristic data; Step S45: performing a potential representation quantitative data analysis of the abnormal multi-temperature-zone electrical variable distribution according to the outlier multi-temperature-zone electrical variable distribution coupled response characteristic data to generate the abnormal multi-temperature-zone electrical variable distribution potential quantitative data.

8. The method for monitoring electrical variables in a wide temperature range according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: analyzing the temperature zone effect on the electric variable distribution distortion signal according to the abnormal multi-temperature zone electric variable distribution potential quantified data, and generating the temperature zone effect on the electric variable distribution distortion signal data; Step S52: analyzing the signal compensation mapping data of the circuit distributed passive chip based on the temperature zone-affected electrical variable distribution distortion signal data, and generating the circuit distributed passive chip signal compensation mapping data; Step S53: performing circuit distributed passive chip adjustment strategy analysis according to the circuit distributed passive chip signal compensation mapping data and the circuit distributed passive chip functional characteristic data, and generating a circuit distributed passive chip adjustment strategy; Step S54: executing the circuit distributed passive chip optimization adjustment operation based on the circuit distributed passive chip adjustment strategy, and performing iterative closed-loop monitoring processing on the temperature partition electrical variable distribution signal corresponding to the circuit distributed passive chip optimization adjustment operation, to generate an iterative temperature partition electrical variable distribution signal; Step S55: performing a wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

9. The method for monitoring electrical variables in a wide temperature range according to claim 8, characterized in that: Step S52 includes the following steps: Step S521: analyzing the frequency data of abnormal electric variables in each temperature zone according to the temperature zone electric variable distribution distortion signal data, and generating abnormal frequency data of electric variables in each temperature zone; Step S522: performing temperature zone compensation weight analysis based on the abnormal frequency data of the temperature zone electrical variables to generate a temperature zone compensation weight coefficient; Step S523: performing temperature zone electric variable distribution error compensation demand analysis according to the temperature zone electric variable distribution distortion signal data and the temperature zone compensation weight coefficient, and generating temperature zone electric variable distribution error compensation demand data; Step S524: analyzing the signal compensation mapping data of the circuit distributed passive chip according to the temperature zone electrical variable distribution error compensation demand data, and generating the circuit distributed passive chip signal compensation mapping data.

10. A wide temperature range electrical variable monitoring system, characterized in that: For executing the wide temperature range electric variable monitoring method as claimed in claim 1, the wide temperature range electric variable monitoring system comprises: A circuit design parsing and analysis module, used to obtain thick film integrated circuit design data; perform circuit network topology structure analysis based on the thick film integrated circuit design data to generate circuit network topology structure data; perform circuit distribution functional characteristic analysis based on the circuit network topology structure data to generate circuit distribution functional characteristic data, wherein the circuit distribution functional characteristic data includes circuit distribution active chip functional characteristic data and circuit distribution passive chip functional characteristic data; The temperature partition electric variable distribution signal analysis module is used to collect the electric variable signals of each circuit distribution of the thick film integrated circuit in a wide temperature range by using the functional characteristic data of the circuit distribution active chip to generate the electric variable distribution signal in the wide temperature range; perform temperature partition processing on the electric variable distribution signal in the wide temperature range to generate the temperature partition electric variable distribution signal; The multi-temperature zone electric variable distribution coupling response characteristic analysis module is used to analyze the electric variable distribution signal index of each temperature zone based on the temperature zone electric variable distribution signal, and generate the temperature zone electric variable distribution signal index; perform multi-temperature zone electric variable distribution coupling response characteristic analysis according to the temperature zone electric variable distribution signal index, and generate multi-temperature zone electric variable distribution coupling response characteristic data; The module for potential quantitative analysis of abnormal multi-temperature-zone electric variable distribution is used to perform cluster outlier analysis based on the coupled response characteristic data of multi-temperature-zone electric variable distribution, and generate outlier multi-temperature-zone electric variable distribution coupled response characteristic data; perform potential representation quantitative data analysis of abnormal multi-temperature-zone electric variable distribution based on the coupled response characteristic data of multi-temperature-zone electric variable distribution of outliers, and generate potential quantitative data of abnormal multi-temperature-zone electric variable distribution; The wide temperature range electric variable monitoring feedback module is used to analyze the distortion signal of the electric variable distribution affected by the temperature zone according to the potential quantitative data of the electric variable distribution in abnormal multiple temperature zones, and generate the distortion signal data of the electric variable distribution affected by the temperature zone; to analyze the signal compensation mapping data of the circuit distribution passive chip based on the distortion signal data of the electric variable distribution affected by the temperature zone, and generate the signal compensation mapping data of the circuit distribution passive chip; to perform iterative closed-loop monitoring processing of the electric variable distribution signal after the passive chip optimization adjustment on the temperature partition electric variable distribution signal based on the signal compensation mapping data of the circuit distribution passive chip and the functional characteristic data of the circuit distribution passive chip, and generate the iterative temperature partition electric variable distribution signal; and to perform the wide temperature range electric variable monitoring feedback operation based on the iterative temperature partition electric variable distribution signal.

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