Testing method and system of thermoelectric power generation system

Abstract

The invention discloses a test method and system for a thermoelectric power generation system, and relates to the field of thermoelectric power generation, and the system comprises a generation module which is used for generating an analog thermoelectric signal with adjustable amplitude, frequency and fluctuation characteristics according to preset thermoelectric conversion core parameters, so as to simulate thermoelectric input scenes under different working conditions; the adaptation module is used for receiving external wide power supply, carrying out voltage stabilization and transformation processing and outputting electric energy matched with the energy consumption of each electric device; according to the invention, analog signals with adjustable amplitude, frequency and fluctuation characteristics can be generated according to thermoelectric conversion core parameters, thermoelectric input scenes under different working conditions can be accurately reproduced, wide external power supply is adapted, electric energy matched with the energy consumption of electric equipment is output, dynamic parameter scheduling and synchronous adjustment are realized by analyzing a test instruction, and the test efficiency is improved. Output voltage, current and temperature difference distribution data are collected synchronously, quantized data associated with thermoelectric input and electric energy output are generated through noise reduction, error correction and feature extraction, and a reliable test report is output efficiently.

Description

Technical Field

[0001] This invention relates to the field of thermoelectric power generation technology, specifically to a testing method and system for a thermoelectric power generation system. Background Technology

[0002] Thermoelectric power generation system is based on the principle of thermoelectric energy conversion. It consists of a temperature sensing module, an energy conversion unit, a power regulation component and an output interface. By capturing the temperature gradient in the environment or industrial scenario, it directly converts thermal energy into stable electrical energy. It is suitable for low-power electronic devices and distributed power supply scenarios.

[0003] The invention patent application with application number 201410256942.6 discloses a testing system for a thermoelectric module. The application aims to solve the problem that "the parallelism of the upper and lower contact surfaces of the thermoelectric module has a great influence on the quality of thermal contact, which will determine the accuracy of the temperature test results. Moreover, existing testing devices generally achieve self-adjustment of parallelism by applying pressure with multiple springs, but this method is relatively passive and the accuracy is not controllable."

[0004] However, existing tests for thermal power generation systems suffer from limitations such as single, fixed test signals, poor coordination among test components, low accuracy in test data processing, and unintuitive result output. These limitations result in limited test coverage and an inability to accurately reflect the actual performance of the system.

[0005] Therefore, we propose a testing method and system for thermoelectric power generation systems. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a testing method and system for a thermoelectric power generation system, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a testing system for a thermoelectric power generation system, comprising: The generation module generates a simulated thermoelectric signal with adjustable amplitude, frequency, and fluctuation characteristics based on preset thermoelectric conversion core parameters to simulate thermoelectric input scenarios under different operating conditions. The adaptation module receives external wide-range power supply and performs voltage stabilization and transformation processing to output electrical energy that matches the energy consumption of each electrical device. The control module receives test commands input by testers through the human-machine interface unit, parses the parameters in the commands, schedules the output state of the adaptation module based on preset logic, and synchronously controls the dynamic adjustment of the parameters of the generation module. The acquisition module collects the output voltage, current, and temperature difference distribution data of the thermoelectric power generation system based on time synchronization. The processing module performs noise reduction, error correction, and feature extraction on the acquired data to generate quantitative data that correlates thermoelectric input and electrical energy output. The output module receives the processed quantitative data and uses the quantitative data to generate a test report. Among them, the core parameters of thermoelectric conversion include temperature gradient, heat flux density and change period, and the human-machine interaction unit is a built-in functional unit of the system used to output and transfer test instructions input by testers; The generating module is interconnected with an adapter module and a control module via a wireless network. The generating module, adapter module, and control module are interconnected with a data acquisition module via a wireless network. The data acquisition module is interconnected with a processing module via a wireless network. The processing module is interconnected with an output module via a wireless network.

[0008] Furthermore, the adjustable simulated hotspot signal generated in the generation module conforms to: Amplitude of the analog thermoelectric signal: ; In the formula: This represents the Seebeck coefficient of the thermoelectric material at standard temperature. The Seebeck coefficient is the temperature dependence coefficient. For dynamic temperature gradient; This is the heat flow-temperature gradient coupling coefficient; For dynamic heat flux density; The frequency of the analog thermoelectric signal: ; In the formula: The period of change in the core parameters of thermoelectric conversion; This is the frequency dynamic compensation factor; This represents the fluctuation coefficient at the previous moment; The preset reference heat flux density; The wave characteristics of analog thermoelectric signals are characterized as follows: ; in, The degree of fluctuation of the simulated thermoelectric signal at time t; These represent the maximum and minimum instantaneous amplitude values ​​of the simulated thermoelectric signal within the observation time window corresponding to time t, respectively. The moving average amplitude before time t; For wave modulation coefficients; Angular frequency; This represents the phase offset of the fluctuation. It is the basic fluctuation attenuation coefficient.

[0009] Furthermore, the aforementioned , The value follows: ; In the formula: Preset reference temperature gradient; ; The preset reference heat flux density; This is the heat flux attenuation factor.

[0010] Furthermore, the adaptation module integrates a load dynamic identification unit, a wide-range rectification unit, and an intelligent DC-DC conversion unit; The load dynamic identification unit constructs an equivalent impedance model (1) for the electrical equipment and calculates the equivalent impedance model parameters (2) in real time. ; In the formula: Let be the dynamic equivalent impedance of the electrical equipment at time t; Equivalent resistance; The imaginary unit; It is the angular frequency, that is ; , Equivalent inductance and capacitance; To adapt to the real-time output power of the module; , For output voltage and current; The duty cycle dynamic adjustment in the intelligent DC-DC converter unit follows the following rules: ; In the formula: This refers to the real-time duty cycle of the DC-DC converter unit. Preset value to match the target output voltage of the module; The real-time conversion efficiency of the DC-DC converter unit; This is the impedance deviation compensation coefficient; Preset the optimal load impedance for electrical equipment; Real-time input voltage for external wide-range power supply; The topology transformer ratio coefficient of the DC-DC converter unit; This is the temperature decay coefficient; This is to accommodate the difference between the module's real-time operating temperature and the standard ambient temperature.

[0011] Furthermore, the preset logic in the control module is as follows: Step 1: Analyze the set of core parameters for the target thermoelectric conversion in the test command. , These represent the target dynamic temperature gradient, target dynamic heat flux density, and target change period, respectively, through a pre-defined thermoelectric-electric signal mapping model. Target control parameters for generation module , These represent the amplitude, frequency, and fluctuation coefficient of the target simulated thermoelectric signal, respectively. Step 2: The data acquisition module obtains the real-time output data of the thermal power generation system. , These represent the real-time output voltage, real-time output current, and real-time temperature difference of the thermoelectric power generation system, respectively. Combined with the preliminary processing results from the processing module, and through a pre-defined inverse mapping model... Inversely calculate the actual values ​​of the core parameters of thermoelectric conversion , These represent the actual dynamic temperature gradient, the actual dynamic heat flux density, and the actual change period, respectively. Step 3: Calculate the coupling deviation between the target value and the actual value. Adjustment amount is generated based on multivariable PID algorithm. ,in , , For multivariable PID matrix parameters, This refers to the dynamic damping coefficient; Step 4: Synchronize the phase of the parameter adjustment of the generator module and the output status adjustment of the adapter module through a preset time synchronization protocol, and the phase difference... ≤ ,in To simulate the maximum frequency of thermoelectric signals, This is a preset synchronization time threshold.

[0012] Furthermore, during the operation of the acquisition module, the acquisition frequency follows the following rules: ; In the formula: for; To simulate the real-time frequency of thermoelectric signals; The reference sampling multiple; , These are the fluctuation response coefficient and the power change rate response coefficient. To simulate the real-time dynamic fluctuation coefficient of thermoelectric signals; This represents the real-time output power of the thermoelectric power generation system.

[0013] Furthermore, the acquisition module includes a voltage and current synchronous sampling channel and a distributed fiber optic temperature measurement node. When correcting for sampling errors, the following rules apply: ; In the formula: The target sample value after correction at time t; These are the original sampled values; This is the gain correction factor; Standard calibration voltage; This is the original sampled value of the calibration voltage; This is the time drift coefficient; The temperature measurement data from the distributed fiber optic temperature measurement node is corrected according to the following rules: ; In the formula: The temperature gradient at any target location on the thermoelectric core component of a thermoelectric power generation system after interpolation correction at time t; The total number of distributed fiber optic temperature measurement nodes deployed for the data acquisition module; These are weighting coefficients; This represents the raw temperature gradient data collected by the i-th distributed fiber optic temperature measurement node at time t. This is the position correction factor; The coordinates are arbitrary positions of the core component; The coordinates of the temperature measurement node; This represents the Euclidean distance between two coordinates.

[0014] Furthermore, the noise reduction objective function used by the processing module when performing noise reduction processing on the collected data is: ; In the formula: The signal-to-noise ratio of the acquired signals before and after noise reduction; This is the balance coefficient; The signal entropy before and after noise reduction; When the processing module performs error correction on the collected data, it follows the following principle: ; In the formula: This is the data after error correction; The data is after noise reduction; , , , These are preset multi-field coupling correction coefficients; , This represents the difference between ambient temperature and humidity and the standard environment. This refers to the real-time output power of the thermoelectric power generation system. This refers to the rated output power of the thermoelectric power generation system. Feature extraction includes dynamic transformation efficiency and output dynamic stability; ; In the formula: For dynamic conversion efficiency; This refers to the heat-receiving area of ​​the thermoelectric core component; The heat flux density is a space-time coupled parameter. The heat loss coefficient; Real-time temperature gradient at different locations of the core component; To output dynamic stability; Standard deviation; This is the average value; The real-time output voltage within the statistical window; For statistical time windows; The average temperature gradient within the window; This represents the real-time temperature gradient within the statistical window. Quantitative data generation: Establishing a nonlinear mapping model of thermoelectric input-output , where X(t) is the thermoelectric input parameter matrix, Y(t) is the electrical output parameter matrix, A(t) is the dynamic gain matrix, B(t) is the dynamic coefficient matrix, and C(t) is the dynamic offset vector; ,in For the prediction error vector, This is the actual output matrix. The model predicts the output matrix. To identify the step size, The derivative of the hyperbolic tangent function. This means converting a vector into a diagonal matrix. Let X(t) be the transpose of the thermoelectric input parameter matrix X(t).

[0015] Secondly, a testing method for a thermoelectric power generation system includes: Based on preset temperature gradients, heat flux density, and change periods, an adjustable analog thermoelectric signal with adjustable amplitude, frequency, and fluctuation characteristics is generated. It receives external wide-range power supply, which is then rectified, regulated, and intelligently converted to DC-DC for adaptation, dynamically matching the equivalent impedance of the electrical equipment and outputting stable electrical energy adapted to the equipment's energy consumption. The system analyzes the target parameters in the test commands, generates adjustment values ​​through a multivariate PID algorithm, and synchronously controls the power supply output state and the analog thermoelectric signal parameters to ensure phase synchronization. Based on a time synchronization mechanism, real-time data on the output voltage, current, and temperature difference distribution of the thermoelectric power generation system are collected through voltage and current synchronous sampling channels and distributed fiber optic temperature measurement nodes. The collected data undergoes noise reduction, error correction, and feature extraction; dynamic conversion efficiency and output stability are calculated; a nonlinear mapping model between thermoelectric input and electrical output is established to generate quantized data; the processed quantized data is received, and a test report on the performance of the thermoelectric power generation system is generated.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides a testing method and system for a thermoelectric power generation system. During execution, this method and system can generate analog signals with adjustable amplitude, frequency, and fluctuation characteristics based on core thermoelectric conversion parameters. This accurately reproduces thermoelectric input scenarios under different operating conditions, adapts to wide-range external power supply, and outputs electrical energy matching the energy consumption of the power-consuming equipment. By parsing test commands, it achieves dynamic scheduling and synchronous adjustment of parameters, synchronously collects output voltage, current, and temperature difference distribution data, and then generates quantitative data relating thermoelectric input and electrical output through noise reduction, error correction, and feature extraction. This efficiently outputs a reliable test report, significantly improving the comprehensiveness and accuracy of the test, flexibly adapting to complex and changing test requirements, reducing operating condition simulation deviations and data errors, enhancing the scientific rigor of thermoelectric conversion performance evaluation, and providing precise support for the optimization and improvement of thermoelectric power generation systems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a test system for a thermoelectric power generation system. Figure 2 This is a flowchart illustrating a testing method for a thermoelectric power generation system. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example 1: This embodiment provides a test system for a thermoelectric power generation system, such as... Figure 1 As shown, it includes: The generation module is used to generate a simulated thermoelectric signal with adjustable amplitude, frequency and fluctuation characteristics based on the preset thermoelectric conversion core parameters, so as to simulate thermoelectric input scenarios under different operating conditions. The adjustable analog hotspot signal generated in the generation module follows the following rules: Amplitude of the analog thermoelectric signal: ; In the formula: This represents the Seebeck coefficient of the thermoelectric material at standard temperature. The Seebeck coefficient is the temperature dependence coefficient. For dynamic temperature gradient; This is the heat flow-temperature gradient coupling coefficient; For dynamic heat flux density; It should be noted that the formula includes unit conversion processing to ensure dimensional matching before performing the calculation. The above formula fully considers the Seebeck coefficient of thermoelectric materials at standard temperatures. Combining the temperature dependence characteristics of the Seebeck coefficient, it also incorporates the nonlinear coupling effect of dynamic temperature gradient and dynamic heat flux density. Through the temperature dependence coefficient that can be adjusted according to the degree of temperature change and the coupling coefficient set according to the interaction strength of heat flux and temperature gradient, it accurately describes the dynamic changes of the simulated thermoelectric signal amplitude under different operating conditions. It achieves a comprehensive adaptation to the thermoelectric material characteristics and operating condition coupling effect, making the amplitude simulation more consistent with the real thermoelectric input scenario. The frequency of the analog thermoelectric signal: ; In the formula: The period of change in the core parameters of thermoelectric conversion; This is the frequency dynamic compensation factor; This represents the fluctuation coefficient at the previous moment; The preset reference heat flux density is used; after frequency calculation, it is constrained within the operating frequency range of the generating module by a limiting circuit. Based on the change period in the core parameters of thermoelectric conversion, combined with the frequency dynamic compensation factor, the signal fluctuation coefficient of the previous moment and the preset reference heat flux density, and the compensation factor can be flexibly adjusted according to the signal fluctuation amplitude and frequency stability requirements, the fluctuation coefficient of the previous moment can reflect the stability of the historical signal, thereby dynamically generating a simulated thermoelectric signal frequency that matches the operating conditions, which not only ensures the responsiveness of the frequency to changes in operating conditions, but also ensures the differentiated requirements for frequency stability in different scenarios. The wave characteristics of analog thermoelectric signals are characterized as follows: ; in, The degree of fluctuation of the simulated thermoelectric signal at time t; These represent the maximum and minimum instantaneous amplitude values ​​of the simulated thermoelectric signal within the observation time window corresponding to time t, respectively. The moving average amplitude before time t; For wave modulation coefficients; Angular frequency; This represents the phase offset of the fluctuation. The basic fluctuation attenuation coefficient; The above formula is based on the instantaneous maximum amplitude, minimum amplitude and previous moving average amplitude of the signal within the observation time window at a specific moment. It introduces the fluctuation modulation coefficient, angular frequency, fluctuation phase offset and basic fluctuation attenuation coefficient. The modulation coefficient can be adapted to the complexity and amplitude requirements of the operating condition fluctuation, and the attenuation coefficient can be adjusted according to the heat loss and fluctuation duration requirements. At the same time, combined with the signal fluctuation state at the previous moment, it comprehensively describes the dynamic change law of the signal fluctuation, thereby realizing the accurate simulation of fluctuation conditions with different complexity and different attenuation characteristics. in, The range of values ​​is The Seebeck coefficient of a thermoelectric material is larger when the temperature changes more significantly within the test temperature range, and smaller when the temperature changes more gradually. ∈[0.01,0.5], the value is larger when the nonlinear interaction between heat flow and temperature gradient is stronger in the simulation condition, and smaller when the interaction between the two is weaker; ∈[0.1,2], the larger the fluctuation amplitude of the simulated thermoelectric signal and the higher the requirement for frequency stability, the larger the value; the smaller the signal fluctuation and the lower the requirement for frequency stability, the smaller the value. ∈[0.001, 0.3], the larger the value is when the instantaneous amplitude fluctuation of the simulated thermoelectric signal is more violent at the previous moment, and the smaller the value is when the signal amplitude is more stable at the previous moment; ∈[0.05, 0.8], the larger the value is when the fluctuation characteristics of the operating condition to be simulated are more complex and the fluctuation amplitude requirement is larger, and the smaller the value is when the fluctuation of the simulated operating condition is simpler and the amplitude requirement is smaller; ∈[10, 1000]s, the value is larger when the heat loss of the simulated working condition is smaller and the fluctuation duration is required to be longer, and the value is smaller when the heat loss of the working condition is larger and the fluctuation needs to decay quickly. , The value follows: ; In the formula: Preset reference temperature gradient; ; The preset reference heat flux density; It is the heat flux attenuation factor; The above formula is based on a preset reference temperature gradient and reference heat flux density. Combined with the angular frequency derived from the change period, a heat flux attenuation factor is introduced. This factor can be flexibly set according to the heat loss rate and heat retention capacity under the operating conditions. It takes a larger value when the heat loss is fast and the heat retention capacity is weak, and a smaller value when the heat loss is fast and the heat retention capacity is weak. This dynamically simulates the change process of temperature gradient and heat flux density, so that the parameter change fits the heat loss law in actual thermoelectric conversion and improves the realism of the operating condition simulation. in, ∈[0.001,0.1] The value is larger when the heat loss rate is faster and the heat retention capability is weaker under the simulated operating conditions, and smaller when the heat loss rate is slower and the heat retention capability is stronger. The generation module generates a simulated thermoelectric signal according to the above formula through a programmable signal generator. This signal is equivalent to the output voltage / current characteristics of the thermoelectric power generation system under the corresponding temperature difference and heat flow conditions. The adapter module is used to receive external wide-range power supply and perform voltage regulation and transformation, and output electrical energy that matches the energy consumption of each electrical device. The adapter module integrates a load dynamic identification unit, a wide-range rectification unit, and an intelligent DC-DC conversion unit; The load dynamic identification unit constructs an equivalent impedance model of the electrical equipment (1) and calculates the equivalent impedance model parameters in real time (2): ; In the formula: Let be the dynamic equivalent impedance of the electrical equipment at time t; Equivalent resistance; The imaginary unit; It is the angular frequency, that is ; , Equivalent inductance and capacitance; To adapt to the real-time output power of the module; , For output voltage and current; The above formula constructs a dynamic equivalent impedance model that includes equivalent resistance, inductance, and capacitance. It uses the power, voltage, and current data output in real time by the adapter module to calculate the model parameters. This can dynamically capture the impedance changes of the electrical equipment under different operating conditions, providing a precise load status basis for the adapter module to adjust the voltage and current in the future, and ensuring dynamic matching between the output power and the energy consumption of the electrical equipment. The wide-range rectifier unit adopts a multi-topology adaptive rectifier architecture and is equipped with a switchable single-phase / three-phase bridge rectifier circuit to adapt to the AC / DC input type and wide voltage range of the external wide-range power supply. The wide-range rectifier unit has a built-in input voltage detection module to identify the voltage amplitude, frequency and waveform type of the external power supply in real time, and automatically switches the corresponding rectifier topology through the built-in logic control unit. At the same time, an LC low-pass filter circuit is configured to suppress the ripple of the rectified output signal, and the ripple coefficient of the filtered DC bus voltage does not exceed the preset ripple threshold. The duty cycle dynamic adjustment in the intelligent DC-DC converter unit follows the following rules: ; In the formula: This refers to the real-time duty cycle of the DC-DC converter unit. Preset value to match the target output voltage of the module; The real-time conversion efficiency of the DC-DC converter unit; This is the impedance deviation compensation coefficient; Preset the optimal load impedance for electrical equipment; Real-time input voltage for external wide-range power supply; The topology transformer ratio coefficient of the DC-DC converter unit; This is the temperature decay coefficient; To accommodate the difference between the module's real-time operating temperature and the standard ambient temperature, the duty cycle D(t) is calculated and then constrained within a reasonable range using a saturation limiting mechanism. The above formula comprehensively considers the real-time input voltage of the external wide-range power supply, the target output voltage of the adapter module, and also incorporates the real-time conversion efficiency of the DC-DC converter unit, the deviation compensation between the load impedance and the preset optimal load impedance, as well as the temperature decay effect caused by the difference between the operating temperature and the standard ambient temperature. Through the dynamic adaptation of various relevant coefficients, the duty cycle is adjusted in real time. Under different input voltage, load conditions and temperature conditions, the output power can be accurately matched with the energy consumption of the electrical equipment, thereby improving the voltage regulation accuracy and energy conversion efficiency. The voltage regulation control dynamically optimizes the core control parameters by providing real-time feedback on the operating conditions. The proportional coefficient in the core control parameters is dynamically adjusted in real time according to the following formula: ,in This is the preset baseline ratio coefficient. For real-time voltage regulation error, This represents the rate of change of the voltage regulation error. The above formula is based on a preset benchmark proportional coefficient and dynamically adjusted in combination with the real-time voltage regulation error and its rate of change. When the real-time voltage regulation error is large or the error rate of change is high, the proportional coefficient is optimized by the formula to quickly respond to the deviation. When the error is small or tends to be stable, the coefficient is adjusted to avoid system overshoot. This realizes the adaptive optimization of the proportional coefficient under different error scenarios, taking into account both the voltage regulation response speed and system stability. in, ∈[0.01, 0.1], the value is larger when the fluctuation range of the load impedance and the optimal load impedance is large and the impact on the output efficiency of the thermoelectric power generation system is more significant, and the value is smaller when the load impedance is stable and close to the optimal load impedance and the fluctuation has little impact on the output efficiency. ∈[1,5], the value is larger when the difference between the input voltage of the external wide-range power supply and the target output voltage of the adapter module is large and a larger transformation amplitude is required to match the energy consumption of the electrical equipment; the value is smaller when the input voltage is close to the target output voltage and the required transformation amplitude is small. ∈[0.005, 0.02] The value is larger when the heat dissipation performance of the DC-DC converter unit is poor and the effect of rising operating temperature on the attenuation of transformer efficiency is more obvious; the value is smaller when the heat dissipation performance of the unit is excellent and the effect of temperature change on transformer efficiency is small. The control module receives test commands input by testers through the human-computer interaction unit and parses the parameters in the commands. Based on the preset logic, it schedules the output state of the adaptation module and synchronously controls the dynamic adjustment of the parameters of the generation module. The preset logic in the control module is as follows: Step 1: Analyze the set of core parameters for the target thermoelectric conversion in the test command. , These represent the target dynamic temperature gradient, target dynamic heat flux density, and target change period, respectively, through a pre-defined thermoelectric-electric signal mapping model. Target control parameters for generation module , That is, the positive mapping function of the multi-parameter coupled dynamic correlation model. These represent the amplitude, frequency, and fluctuation coefficient of the target simulated thermoelectric signal, respectively. Step 2: The data acquisition module obtains the real-time output data of the thermal power generation system. , These represent the real-time output voltage, real-time output current, and real-time temperature difference of the thermoelectric power generation system, respectively. Combined with the preliminary processing results from the processing module, and through a pre-defined inverse mapping model... Inversely calculate the actual values ​​of the core parameters of thermoelectric conversion ,in, For mapping functions The inverse operation function, These represent the actual dynamic temperature gradient, the actual dynamic heat flux density, and the actual change period, respectively. Step 3: Calculate the coupling deviation between the target value and the actual value. Adjustment amount is generated based on multivariable PID algorithm. ,in , , For multivariable PID matrix parameters, This refers to the dynamic damping coefficient; The above formula calculates the coupling deviation between the target value and the actual value of the core parameters of thermoelectric conversion, adopts a multivariable PID control algorithm, integrates the actions of proportional, integral and derivative terms, and introduces a dynamic damping coefficient. The damping coefficient can be adjusted according to the signal fluctuation and system oscillation risk to suppress high-frequency oscillation, fully adapt to the coupling relationship between core parameters, and accurately generate the parameter adjustment amount of the generation module. This ensures both the speed of parameter adjustment and improves the accuracy of adjustment and the stability of the system. Step 4: Synchronize the phase of the parameter adjustment of the generator module and the output status adjustment of the adapter module through a preset time synchronization protocol, and the phase difference... ≤ ,in To simulate the maximum frequency of thermoelectric signals, The preset synchronization time threshold; in, ∈[0.1, 5], the larger the amplitude of the coupling deviation of the core parameter of thermoelectric conversion, the larger the value is when the system needs to respond quickly to the deviation, and the smaller the value is when the amplitude of the coupling deviation is small or the system has the risk of overshoot; The value of ∈[0.01,1] is larger when there is a continuous steady-state deviation or significant cumulative effect of deviation in the core parameters of thermoelectric conversion, and smaller when the system response is slow, prone to oscillation, or has a high proportion of transient deviation. ∈[0.05, 3.0], the larger the rate of change of the coupling deviation of the core parameter of thermoelectric conversion and the higher the risk of system overshoot, the larger the value; the smaller the value when the deviation changes slowly, the system response is lagging or the adjustment sensitivity needs to be improved. ∈[0.2, 2], the larger the value is when the simulated thermoelectric signal fluctuates violently or when high-frequency oscillation occurs during module parameter adjustment, the smaller the value is when the simulated thermoelectric signal is stable, the system needs to quickly approach the target parameter, or the adjustment response needs to be more sensitive; The data acquisition module is used to acquire output voltage, current and temperature difference distribution data of the thermoelectric power generation system based on time synchronization. During the operation of the data acquisition module, the acquisition frequency follows the following rules: ; In the formula: for; To simulate the real-time frequency of thermoelectric signals; The reference sampling multiple; , These are the fluctuation response coefficient and the power change rate response coefficient. To simulate the real-time dynamic fluctuation coefficient of thermoelectric signals; This refers to the real-time output power of the thermoelectric power generation system. The above formula is based on a reference sampling multiple of no less than 8. It combines the real-time frequency of the simulated thermoelectric signal, the real-time dynamic fluctuation coefficient, and the real-time output power change rate of the thermoelectric power generation system. The sampling frequency is dynamically adjusted by the fluctuation response coefficient and the power change rate response coefficient. The more violent the signal fluctuation and the greater the power change rate, the higher the sampling frequency, and vice versa. This satisfies the high sampling requirements under complex fluctuation and rapid power change conditions, while avoiding redundant sampling under stable conditions, thus balancing the accuracy and efficiency of data acquisition. in, It is the preset value, and ≥8, ∈[0.5, 3], the larger the value is for the more violent the fluctuation of the simulated thermoelectric signal, and the smaller the value is for the more gentle the signal fluctuation; ∈[0.1, 2], the larger the real-time change rate of the output power of the thermoelectric power generation system, the larger the value; the smaller the power change rate, the smaller the value; the core is to match the acquisition requirements of dynamic power response. The acquisition module includes a synchronous voltage and current sampling channel and a distributed fiber optic temperature measurement node. The voltage and current sampling channel adopts a differential sampling architecture, and the sampling error is corrected according to the following rules: ; In the formula: The target sample value after correction at time t; These are the original sampled values; This is the gain correction factor; Standard calibration voltage; This is the original sampled value of the calibration voltage; This is the time drift coefficient; The above formula is for the raw data of voltage and current sampling. The gain deviation in the sampling process is calibrated by the gain correction coefficient. This coefficient is adjusted according to the degree of deviation between the raw sampled value and the standard calibration voltage. At the same time, a time drift coefficient is introduced to compensate for the drift error caused by the long-term operation of the sampling module or the fluctuation of ambient temperature. The dual correction mechanism comprehensively improves the accuracy of voltage and current sampling data and provides a reliable foundation for subsequent data processing. The temperature data from distributed fiber optic temperature measurement nodes, when corrected, follows the following rules: ; In the formula: The temperature gradient at any target location on the thermoelectric core component of a thermoelectric power generation system after interpolation correction at time t; The total number of distributed fiber optic temperature measurement nodes deployed for the data acquisition module; These are weighting coefficients; This represents the raw temperature gradient data collected by the i-th distributed fiber optic temperature measurement node at time t. This is the position correction factor; The coordinates are arbitrary positions of the core component; The coordinates of the temperature measurement node; Indicates the Euclidean distance between two coordinates; The above formula is based on the original temperature gradient data collected by each distributed optical fiber temperature measurement node. It introduces weighting coefficients and position correction coefficients. The weighting coefficients are assigned weights according to the spatial distance between the target temperature measurement position and each node and the temperature measurement accuracy of the node. The position correction coefficients compensate for the errors caused by spatial distance and temperature distribution gradient. By interpolating and correcting the temperature gradient at any position of the thermoelectric core component, the acquisition accuracy of temperature difference distribution data and the comprehensiveness of spatial coverage are improved. Among them, the core thermoelectric components of a thermoelectric power generation system include a thermoelectric stack and a thermoelectric generator chip. ∈[0.8,1.2], the larger the deviation between the original sampled value and the standard calibration voltage, the larger the value; the smaller the deviation, the smaller the value. The value is within a preset small positive range. The longer the cumulative working time of the sampling module and the more drastic the fluctuation of the ambient temperature, the larger the value; the shorter the working time and the more stable the ambient temperature, the smaller the value. ∈(0,1], and the sum of the weighting coefficients corresponding to all temperature measurement nodes in the interval is 1. The value is larger when the spatial distance between the target temperature measurement location and the corresponding temperature measurement node is closer and the temperature measurement accuracy of the temperature measurement node is higher, and the value is smaller when the spatial distance is farther and the temperature measurement accuracy is lower. >0 indicates that the value is larger when the spatial distance between the target temperature measurement location and the temperature measurement node is greater and the temperature distribution gradient of the thermoelectric core component is more significant, and smaller when the spatial distance is closer and the temperature distribution is more uniform. The processing module is used to perform noise reduction, error correction and feature extraction on the collected data, and generate quantitative data that correlates thermoelectric input and electrical output. The noise reduction objective function used by the processing module when performing noise reduction on the collected data is: ; In the formula: The signal-to-noise ratio of the acquired signals before and after noise reduction; This is the balance coefficient; The signal entropy before and after noise reduction; The above formula coordinates the signal-to-noise ratio and signal entropy of the acquired signals before and after noise reduction, and dynamically adjusts the weight of the two through a balance coefficient. When the noise interference in the test scene is strong, the coefficient is increased to prioritize improving the data signal-to-noise ratio. When the signal itself fluctuates complexly and the original features need to be preserved, the coefficient is decreased to avoid feature loss caused by excessive smoothing. This achieves a dynamic balance between the noise reduction effect and the preservation of the original signal features, ensuring the quality of subsequent data processing. When the processing module performs error correction on the collected data, it follows the following rules: ; In the formula: This is the data after error correction; The data is after noise reduction; , , , These are preset multi-field coupling correction coefficients; , This represents the difference between ambient temperature and humidity and the standard environment. This refers to the real-time output power of the thermoelectric power generation system. This refers to the rated output power of the thermoelectric power generation system. Based on noise reduction processing, the above formula comprehensively considers the difference between the test environment temperature and the standard environment, the humidity difference, the temperature and humidity synergistic coupling effect, and the ratio between the actual output power and the rated power of the thermoelectric power generation system. It compensates for the errors caused by each factor through preset multi-field coupling correction coefficients. The coefficient values ​​are adjusted according to the intensity of each influencing factor, comprehensively correcting the systematic error of the collected data and improving the accuracy and reliability of the data. Feature extraction includes dynamic transformation efficiency and output dynamic stability; ; In the formula: For dynamic conversion efficiency; This refers to the heat-receiving area of ​​the thermoelectric core component; The heat flux density is a space-time coupled parameter. The heat loss coefficient; Real-time temperature gradient at different locations of the core component; To output dynamic stability; Standard deviation; This is the average value; The real-time output voltage within the statistical window; For statistical time windows; The average temperature gradient within the window; This represents the real-time temperature gradient within the statistical window. The calculation formula combines the heated area of ​​the thermoelectric core component, the space-time coupled heat flux density, the heat loss coefficient, and the real-time temperature gradient at different locations of the core component. The heat loss coefficient is adjusted according to the heat dissipation conditions of the core component, and takes a larger value when the heat dissipation is poor and the heat loss ratio is high. It accurately calculates the thermoelectric conversion efficiency at different spatial locations and time points, and provides accurate indicators for quantifying the correlation between thermoelectric input and output. The calculation formula is based on the real-time output voltage of the thermoelectric power generation system within a statistical time window. By calculating the ratio of the voltage standard deviation to the average value, and combining the average temperature gradient and real-time temperature gradient within the window, the dynamic stability of the output voltage is quantified. Voltage fluctuations are correlated with temperature gradient changes, and the impact of thermoelectric input changes on output stability is comprehensively reflected. Quantitative data generation: Establishing a nonlinear mapping model of thermoelectric input-output , where X(t) is the thermoelectric input parameter matrix with dimension m×1, where m is the number of input parameters, Y(t) is the electrical energy output parameter matrix with dimension n×1, where n is the number of output parameters, A(t) is the dynamic gain matrix with dimension n×m, B(t) is the dynamic coefficient matrix with dimension m×m, and C(t) is the dynamic offset vector with dimension m×1; ,in The prediction error vector has dimension n×1. This is the actual output matrix. The model predicts the output matrix. To identify the step size, The derivative of the hyperbolic tangent function. This means converting a vector into a diagonal matrix of dimension n×n. Let X(t) be the transpose of the thermoelectric input parameter matrix. The above formula constructs a nonlinear mapping model that includes a dynamic gain matrix, a dynamic coefficient matrix, and a dynamic offset vector, and associates the thermoelectric input parameter matrix with the electrical energy output parameter matrix. At the same time, the gain matrix is ​​updated in real time by using the error between the actual output and the model's predicted output, the derivative of the hyperbolic tangent function, and the identification step size, thereby realizing dynamic adaptive optimization of the model parameters, continuously improving the accuracy of the correlation quantization between thermoelectric input and output, and ensuring adaptation to the parameter change patterns under different test conditions. in, ∈[0.3, 1.8], the larger the value is when the noise interference in the test scenario is strong and the data signal-to-noise ratio needs to be improved first, the smaller the value is when the signal itself fluctuates complexly and the original signal characteristics need to be preserved first to avoid excessive smoothing; ∈[0.002, 0.015], the larger the value is when the temperature fluctuation range of the test environment is large and the sensitivity of the linear influence of temperature on the collected data is high, and the smaller the value is when the temperature fluctuation is small and the linear interference of temperature on the data can be ignored; ∈[0.001, 0.012], the larger the value is when the humidity of the test environment fluctuates drastically and the linear effect of humidity on the collected data is significant, and the smaller the value is when the humidity is stable and the linear interference of humidity on the data is weak; ∈[0.0005, 0.008], the larger the value is when the temperature and humidity fluctuations in the test environment are obvious and the coupling effect between the two has a strong impact on the data error, and the smaller the value is when the temperature and humidity fluctuations are independent and the coupling effect can be ignored; ∈[0.02, 0.15], the larger the value is when the actual output power of the thermal power generation system is close to the rated power and the influence of power change on data error is high, and the smaller the value is when the output power is far below the rated power and the influence of power change on error is small; ∈[0.003, 0.02], the value is larger when the heat dissipation conditions of the thermoelectric core component are poor and the proportion of heat loss to input heat power is high, and the value is smaller when the heat dissipation conditions are good and the proportion of heat loss is low; The output module is used to receive the processed quantized data and use the quantized data to generate a test report. Among them, the core parameters of thermoelectric conversion include temperature gradient, heat flux density and change period, and the human-machine interaction unit is a built-in functional unit of the system used to output and transfer test instructions input by testers; The generating module is interconnected with the adapter module and the control module via a wireless network. The generating module, the adapter module, and the control module are interconnected with the acquisition module via a wireless network. The acquisition module is interconnected with the processing module via a wireless network. The processing module is interconnected with the output module via a wireless network.

[0022] In this embodiment, the generation module operates based on preset thermoelectric conversion core parameters to generate a simulated thermoelectric signal with adjustable amplitude, frequency, and fluctuation characteristics to simulate thermoelectric input scenarios under different operating conditions. The adaptation module operates in the background to receive external wide-range power supply and perform voltage stabilization and transformation processing, outputting electrical energy that matches the energy consumption of each electrical device. The control module further receives test instructions input by the test personnel through the human-machine interaction unit and parses the parameters in the instructions. Based on preset logic, it schedules the output state of the adaptation module and synchronously controls the dynamic adjustment of the parameters of the generation module. Then, the acquisition module collects the output voltage, current, and temperature difference distribution data of the thermoelectric power generation system based on time synchronization. The processing module performs noise reduction, error correction, and feature extraction on the collected data to generate quantitative data that correlates thermoelectric input and electrical energy output. Finally, the output module receives the processed quantitative data and uses the quantitative data to generate a test report.

[0023] In the above embodiments, the system accurately simulates thermoelectric input scenarios under different operating conditions, adapts to wide power supply and ensures power matching, accurately collects and processes relevant data, effectively reduces noise and corrects errors, extracts key features, and generates quantitative test reports. It can comprehensively reflect thermoelectric conversion efficiency and output stability, provide accurate basis for system optimization, improve test reliability and efficiency, and efficiently meet the test needs of various thermoelectric power generation systems.

[0024] Example 2: At the implementation level, based on Example 1, this example refers to... Figure 2 The test system for a thermoelectric power generation system in Example 1 will be further described in detail below: A test method for a thermoelectric power generation system includes: Based on the preset temperature gradient, heat flux density and change period, an analog thermoelectric signal with adjustable amplitude, frequency and fluctuation characteristics is generated. It receives external wide-range power supply, and after rectification, voltage regulation and intelligent DC-DC conversion adaptation processing, it dynamically matches the equivalent impedance of the power-consuming equipment and outputs stable power that is adapted to the energy consumption of the equipment. The target parameters in the test command are analyzed, and the adjustment amount is generated through a multivariate PID algorithm to synchronously control the power supply output state and the simulated thermoelectric signal parameters to make the two phase synchronized. Based on the time synchronization mechanism, real-time data of the output voltage, current and temperature difference distribution of the thermoelectric power generation system are collected through voltage and current synchronous sampling channels and distributed optical fiber temperature measurement nodes. The system performs noise reduction, error correction, and feature extraction on the collected data, calculates dynamic conversion efficiency and output stability, establishes a nonlinear mapping model between thermoelectric input and electrical output, and generates quantified data. It receives processed quantitative data and generates a test report on the performance of the thermal power generation system.

[0025] In summary, the systems and methods described in the above embodiments can generate analog signals with adjustable amplitude, frequency, and fluctuation characteristics based on the core parameters of thermoelectric conversion during execution. This accurately reproduces thermoelectric input scenarios under different operating conditions, adapts to wide-range external power supply, and outputs electrical energy matching the energy consumption of the electrical equipment. By parsing test commands, parameters are dynamically scheduled and synchronously adjusted. Output voltage, current, and temperature difference distribution data are collected synchronously, and then quantified data relating thermoelectric input and electrical energy output are generated through noise reduction, error correction, and feature extraction. This efficiently outputs reliable test reports, significantly improving the comprehensiveness and accuracy of the tests, flexibly adapting to complex and ever-changing test requirements, reducing operating condition simulation deviations and data errors, enhancing the scientific nature of thermoelectric conversion performance evaluation, and providing precise support for the optimization and improvement of thermoelectric power generation systems.

[0026] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A testing system for a thermoelectric power generation system, characterized in that, Comprise: Occur module, for generating adjustable amplitude, frequency and fluctuation characteristics of analog thermoelectric signal according to preset thermoelectric conversion core parameters, to simulate different working conditions of thermoelectric input scene; Adaptation module, for receiving external wide power supply and carrying out voltage stabilization and voltage conversion, outputting power matching the energy consumption of each electrical equipment; Control module, for receiving test instructions input by test personnel through human-computer interaction unit and analyzing parameters in the instructions, scheduling the output state of the adaptation module based on preset logic, and synchronously controlling the dynamic adjustment of the parameter of the generation module; Acquisition module, for synchronously collecting output voltage, current and temperature difference distribution data of thermoelectric power generation system based on time; Processing module, for denoising, error correction and feature extraction of collected data, generating quantitative data related to thermoelectric input and power output; Output module, for receiving processed quantitative data and generating test report by applying quantitative data; Wherein, the thermoelectric conversion core parameters include temperature gradient, heat flux density and change period, and the human-computer interaction unit is a functional unit built in the system for outputting and transferring test instructions input by test personnel.

2. A test system for a thermoelectric power generation system according to claim 1, wherein The adjustable analog thermoelectric signal generated in the generation module obeys: The amplitude of the analog thermoelectric signal: ； wherein: S is the Seebeck coefficient of the thermoelectric material at standard temperature; is the temperature-dependent coefficient of the Seebeck coefficient; is the dynamic temperature gradient; is the heat flow-temperature gradient coupling coefficient; is the dynamic heat flow density; The frequency of the analog thermoelectric signal: ； In the formula: is a change period in a thermoelectric conversion core parameter; is a frequency dynamic compensation factor; is a fluctuation coefficient at a previous time; is a preset reference heat flux density; The fluctuation characteristics of the analog thermoelectric signal are represented as: ； wherein, is the fluctuation degree of the analog thermal power signal at time t; are the maximum and minimum of the instantaneous amplitude of the analog thermal power signal in the observation time window corresponding to time t, respectively; is the moving average amplitude before time t; is the fluctuation modulation coefficient; is the angular frequency; is the fluctuation phase offset; is the basic fluctuation attenuation coefficient.

3. A test system for a thermoelectric power generation system as claimed in claim 2, wherein The , values subject to: ； In the formulae: is a preset reference temperature gradient; ; is a preset reference heat flux density; is a heat flux decay factor.

4. The test system of a thermoelectric power generation system according to claim 1, wherein, The adaptation module is integrated by load dynamic identification unit, wide rectification unit and intelligent DC-DC conversion unit; The load dynamic identification unit runs to build the equivalent impedance model (1) of the electrical equipment, and calculates the equivalent impedance model parameters (2) in real time: ； In the formula: is the dynamic equivalent impedance of the electrical equipment at time t; is the equivalent resistance; is the imaginary unit; is the angular frequency, i.e. ; , is the equivalent inductance, capacitance; is the real-time output power of the adaptive module; , is the output voltage, current; The duty cycle dynamic adjustment in the intelligent DC-DC conversion unit obeys: ； In the formula: is the real-time duty cycle of the DC-DC conversion unit; is the target output voltage of the adaptation module, preset value; is the real-time conversion efficiency of the DC-DC conversion unit; is the impedance deviation compensation coefficient; is the preset optimal load impedance of the electrical equipment; is the real-time input voltage of the external wide-range power supply; is the topology transformation ratio coefficient of the DC-DC conversion unit; is the temperature attenuation coefficient; is the difference between the real-time working temperature of the adaptation module and the standard ambient temperature.

5. The test system of a thermoelectric power generation system according to claim 1, wherein, The preset logic in the control module is: Step 1: analyze the target thermoelectric conversion core parameter set in the test instruction , respectively represent the target dynamic temperature gradient, the target dynamic heat flux density, and the target change period, and the target control parameters of the generation module are generated through a preset thermoelectric-electric signal mapping model , , respectively represent the target simulated thermoelectric signal amplitude, the target simulated thermoelectric signal frequency, and the target simulated thermoelectric signal fluctuation coefficient; Step 2: The acquisition module acquires real-time output data of the thermoelectric power generation system , respectively represent the real-time output voltage of the thermoelectric power generation system, the real-time output current of the thermoelectric power generation system, and the real-time temperature difference of the thermoelectric power generation system, combined with the preliminary processing result of the processing module, through a pre-set inverse mapping model to back-propagate the actual value of the thermoelectric conversion core parameter , respectively represent the actual dynamic temperature gradient, the actual dynamic heat flow density, and the actual change period Step 3: Calculate the coupling deviation of the target value and the actual value , generate an adjustment amount based on a multivariable PID algorithm , wherein , , is a multivariable PID matrix parameter, is a dynamic damping coefficient; Step4: The phase synchronization of the parameter adjustment of the occurrence module and the output state adjustment of the adaptation module is performed through a preset time synchronization protocol, and the phase difference ≤ wherein is the maximum frequency of the analog thermal electric signal, is a preset synchronization time threshold.

6. The test system of a thermoelectric power generation system according to claim 1, wherein, The acquisition module runs in the stage, and the acquisition frequency obeys: ； In the formula: is; is the real-time frequency of the analog thermoelectric signal; is the reference sampling multiple; , is the fluctuation response coefficient, the power change rate response coefficient; is the real-time dynamic fluctuation coefficient of the analog thermoelectric signal; is the real-time output power of the thermoelectric power generation system.

7. The test system of a thermoelectric power generation system according to claim 1, wherein, The acquisition module includes voltage and current synchronous sampling channel and distributed optical fiber temperature measurement node. When the sampling error is corrected, it obeys: ； In the formula: is the corrected target sampling value at time t; is the original sampling value; is the gain correction coefficient; is the standard calibration voltage; is the original sampling value of the calibration voltage; is the time drift coefficient; When the temperature measurement data of the distributed optical fiber temperature measurement node is corrected, it obeys: ； In the formula: is the interpolated and corrected temperature gradient of any target position on the thermoelectric core component of the thermoelectric power generation system at time t; is the total number of distributed optical fiber temperature measurement nodes deployed by the acquisition module; is the weighting coefficient; is the original temperature gradient data collected by the i th distributed optical fiber temperature measurement node at time t; is the position correction coefficient; is the coordinate of any position of the core component; is the coordinate of the temperature measurement node; represents the Euclidean distance between the two coordinates.

8. The test system of a thermoelectric power generation system according to claim 1, wherein, The denoising objective function used by the processing module for denoising the collected data is: ； In the formula: is the signal-to-noise ratio of the collected signals before and after noise reduction; is the balance coefficient; is the signal entropy before and after noise reduction; When the processing module corrects the error of the collected data, it obeys: ； In the formula: is the error-corrected data; is the noise-reduced data; , , , is a preset multi-field coupling correction coefficient; , is the difference between the ambient temperature, humidity, and the standard environment; is the real-time output power of the thermoelectric power generation system; is the rated output power of the thermoelectric power generation system; Feature extraction includes dynamic conversion efficiency and output dynamic stability; ； wherein: is the dynamic conversion efficiency; is the heated area of the thermoelectric core component; is the space-time coupled heat flux density; is the thermal loss coefficient; is the real-time temperature gradient at different locations of the core component; is the output dynamic stability; is the standard deviation; is the average value; is the real-time output voltage within the statistical window; is the statistical time window; is the average temperature gradient within the window; is the real-time temperature gradient within the statistical window; Quantized data generation: establishing a nonlinear mapping model of thermoelectric input-output where X(t) is a thermoelectric input parameter matrix, Y(t) is an electrical energy output parameter matrix, A(t) is a dynamic gain matrix, B(t) is a dynamic coefficient matrix, and C(t) is a dynamic offset vector; where is a prediction error vector, is an actual output matrix, is a model predicted output matrix, is an identification step, is a derivative of a hyperbolic tangent function, denotes a conversion of a vector into a diagonal matrix, is a transpose matrix of the thermoelectric input parameter matrix X(t).

9. The test system of a thermoelectric power generation system according to claim 1, wherein, The generation module is connected with the adaptation module and the control module through wireless network interaction. The generation module, the adaptation module and the control module are connected with the acquisition module through wireless network interaction. The acquisition module is connected with the processing module through wireless network interaction. The processing module is connected with the output module through wireless network interaction.

10. A method of testing a thermoelectric power generation system, the method being a method of operating a testing system for a thermoelectric power generation system as claimed in any one of claims 1 to 9, characterised by, Comprise: According to preset temperature gradient, heat flux density and change period, generate amplitude, frequency and fluctuation characteristics adjustable analog thermoelectric signal; Receive external wide power supply, rectification, voltage stabilization and intelligent DC-DC conversion adaptation processing, dynamically match the equivalent impedance of electrical equipment, output stable power matching the energy consumption of the equipment; Analyze the target parameters in the test instructions, generate adjustment amount through multivariable PID algorithm, synchronously control the power output state and the parameter of the analog thermoelectric signal, so that the two are in phase synchronization; Based on time synchronization mechanism, through voltage and current synchronous sampling channel and distributed optical fiber temperature measurement node, the output voltage, current and temperature difference distribution real-time data of the thermoelectric power generation system are collected; The collected data are subjected to noise reduction, error correction and feature extraction, the dynamic conversion efficiency and output stability are calculated, the nonlinear mapping model of thermoelectric input and electric energy output is established, and the quantitative data are generated; The processed quantitative data are received, and the test report of the performance of the thermoelectric power generation system is generated.

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