High-Temperature Fuel Cell Cogeneration System Fuel Gas Flow Measurement System and Method

Abstract

This invention discloses a fuel gas flow measurement system and method for a high-temperature fuel cell combined heat and power system, comprising the following steps: acquiring temperature, pressure, and potential difference signals generated during fuel gas flow; performing preprocessing to construct a standardized signal matrix; performing multi-field collaborative spectrum analysis to generate preliminary fuel gas flow estimation results; establishing a dynamic energy balance relationship and performing reverse correction on the preliminary fuel gas flow estimation results; periodically applying plasma pulse excitation signals to perturb the fuel gas flow field and correcting the fuel gas energy-constrained flow estimation results; performing fusion matching, performing spatiotemporal interpolation and consistency correction of fuel gas flow, and reconstructing the distribution state of fuel gas flow; and performing real-time measurement and intelligent calibration of fuel gas flow. This invention integrates multi-physics spectrum analysis and energy-constrained self-calibration technology to achieve high-precision, real-time, and intelligent measurement of fuel gas flow.

Description

Technical Field

[0001] This invention relates to the field of gas flow measurement, and more particularly to a fuel gas flow measurement system and method for a high-temperature fuel cell combined heat and power system. Background Technology

[0002] Currently, high-temperature fuel cell combined heat and power systems require precise measurement of fuel gas flow rate during operation to ensure the stability of the stack reaction and the system's energy efficiency. However, existing fuel gas flow rate measurement technologies mostly rely on mechanical or thermal flow sensors. These sensors are prone to signal drift, thermal response hysteresis, and measurement error accumulation under high temperature, high pressure, and multi-field coupling environments. In addition, some studies have attempted to calculate the flow rate inversely through energy balance or stack power feedback, but these methods cannot reflect the dynamic flow characteristics of fuel gas in the channel in real time, and the measurement accuracy is still limited by model assumptions and boundary condition stability.

[0003] Current technologies lack comprehensive utilization of multi-physics information and dynamic calibration mechanisms, failing to effectively eliminate the combined errors caused by electrochemical coupling, thermal disturbances, and fuel flow fluctuations. Furthermore, traditional methods fail to reconstruct the spatiotemporal distribution of fuel gas flow, resulting in flow measurement remaining at a single point or single-dimensional level, unable to reflect the true transmission state within the system. Therefore, there is an urgent need for a fuel gas flow measurement method that can integrate multi-physics information and introduce dynamic energy constraints and plasma perturbation self-calibration mechanisms to achieve high-precision, real-time, and intelligent measurement of fuel gas flow. Summary of the Invention

[0004] One objective of this invention is to propose a fuel gas flow measurement system and method for a high-temperature fuel cell combined heat and power system. This invention integrates multiphysics spectrum analysis and energy constraint self-calibration technology to achieve high-precision, real-time, and intelligent measurement of fuel gas flow.

[0005] The method for measuring fuel gas flow rate in a high-temperature fuel cell combined heat and power system according to an embodiment of the present invention includes the following steps:

[0006] In a fuel cell combined heat and power system, temperature, pressure and potential difference signals generated during the flow of fuel gas are collected to form a set of original signals from multiple physics fields.

[0007] Preprocessing is performed on the original multiphysics signal set to construct a standardized signal matrix;

[0008] Multi-field collaborative spectral analysis is performed based on a standardized signal matrix to extract the resonant spectral features of fuel gas caused by fuel gas flow, and a mapping relationship with fuel gas flow rate is established to generate preliminary fuel gas flow rate inference results.

[0009] A dynamic energy balance relationship is established, and the preliminary fuel gas flow inference results are reversed to generate fuel gas energy-constrained flow estimation results.

[0010] A micro-plasma disturbance cavity is embedded in the fuel inlet channel. Plasma pulse excitation signals are periodically applied to disturb the fuel gas flow field and the fuel gas energy-constrained flow estimation results are corrected to form a fuel gas self-calibrated flow result.

[0011] The resonant spectrum characteristics of fuel gas, energy-constrained flow estimation results, and self-calibrated flow results are fused and matched. Based on energy conservation constraints and flow continuity constraints, spatiotemporal interpolation and consistency correction of fuel gas flow are performed to reconstruct the distribution state of fuel gas flow and generate a spatiotemporal distribution map of fuel gas flow.

[0012] Real-time measurement and intelligent calibration of fuel gas flow rate are performed based on the spatiotemporal distribution map of fuel gas flow rate.

[0013] Optionally, the preprocessing includes time synchronization, noise filtering, and normalization.

[0014] Optionally, the generation of the preliminary fuel gas flow inference result specifically includes:

[0015] Based on the standardized signal matrix, multi-field collaborative spectrum analysis is performed on the fuel gas temperature signal, fuel gas pressure signal and fuel gas potential difference signal. The frequency domain transformation of each signal sequence in the standardized signal matrix is ​​performed under a unified sampling period to obtain the fuel gas temperature spectrum, fuel gas pressure spectrum and fuel gas potential difference spectrum.

[0016] The fuel gas temperature spectrum, fuel gas pressure spectrum, and fuel gas potential difference spectrum are weighted and superimposed according to the frequency axis to generate the fuel gas cooperative spectrum.

[0017] Identify the resonant frequency band of fuel gas in the fuel gas co-spectrum, extract the center frequency, amplitude variation and energy distribution of the fuel gas resonant frequency band, and generate fuel gas resonant spectrum characteristics;

[0018] Establish a mapping relationship between fuel gas flow rate and fuel gas resonance spectrum characteristics, map the center frequency offset of the fuel gas resonance band to the fuel gas velocity variation parameters, and determine the instantaneous fuel gas velocity.

[0019] The instantaneous flow rate of fuel gas is calculated based on the instantaneous velocity of the fuel gas, forming a sequence of instantaneous fuel gas flow rates;

[0020] The instantaneous flow rate sequence of fuel gas is integrated according to the sampling time order to generate preliminary flow rate inference results for fuel gas.

[0021] Optionally, the generation of the instantaneous fuel gas flow rate sequence specifically includes:

[0022] The instantaneous flow velocity of fuel gas and the effective cross-sectional area of ​​the fuel gas channel are obtained and paired according to the same time sampling interval to form a fuel gas flow velocity-cross-sectional area paired dataset.

[0023] In the fuel gas velocity-cross-sectional area paired dataset, the instantaneous fuel gas velocity value at each time sampling point is multiplied with the corresponding fuel gas channel cross-sectional area to obtain the instantaneous fuel gas flow rate at that time point.

[0024] The instantaneous flow rates of fuel gas at all sampling time points are summarized in chronological order and smoothed to remove outliers caused by sampling fluctuations, thus forming a fuel gas instantaneous flow rate sequence.

[0025] Optionally, the generation of the fuel gas energy-constrained flow estimation result specifically includes:

[0026] Establish a dynamic energy balance relationship among fuel gas flow rate, fuel gas thermal energy output, and fuel gas electrical energy output;

[0027] During the operation of the fuel cell cogeneration system, the electrical output power signal and reaction exothermic signal of the fuel cell stack are collected. The electrical output power signal is recorded as a fuel gas electrical energy output data sequence according to the time sampling order, and the reaction exothermic signal is recorded as a fuel gas thermal energy output data sequence according to the same time sampling order. The sampling time index is simultaneously marked in the two types of data sequences to form a fuel gas energy output sampling dataset.

[0028] The fuel gas energy output sampling dataset is subjected to time accumulation calculation. The fuel gas electrical energy output data sequence and the fuel gas thermal energy output data sequence are summed within the same sampling time range to obtain the total fuel gas energy output value.

[0029] The fuel gas input energy is calculated based on the preliminary fuel gas flow rate inference results, and the fuel gas input energy is compared with the total fuel gas energy output value to obtain the fuel gas energy deviation.

[0030] A reverse correction operation is performed on the initial fuel gas flow inference result based on the fuel gas energy deviation to obtain the fuel gas energy-constrained flow estimation result.

[0031] The fuel gas energy deviation calculation and reverse correction operation are performed cyclically within the continuous sampling period to dynamically update the fuel gas energy constraint flow estimation results, so that the fuel gas input energy and the total fuel gas energy output value gradually meet the dynamic energy balance relationship, and output the fuel gas energy constraint flow estimation results.

[0032] Optionally, the generation of the fuel gas self-calibration flow rate result specifically includes:

[0033] A micro-plasma disturbance cavity is embedded in the fuel gas inlet channel of a fuel cell cogeneration system;

[0034] A plasma pulse excitation signal is input into the micro-plasma disturbance cavity, and the frequency, pulse width and duty cycle of the plasma pulse excitation signal are controlled to form a periodic disturbance flow field in the fuel gas flow channel.

[0035] Under the action of plasma pulse excitation signal, the current response signal of the anode reaction zone of the fuel cell stack is collected, and the current response signal is stored as a fuel gas current response data sequence according to the sampling time sequence.

[0036] The fuel gas current response data sequence is decoupled, and the phase information of the fuel gas current response signal is extracted from the decoupled data to obtain the fuel gas response phase value.

[0037] Extract the phase information of the plasma pulse excitation signal to obtain the plasma excitation phase value;

[0038] The fuel gas response phase value and the plasma excitation phase value are compared one-to-one according to the time sampling index, and the phase difference value in each sampling period is calculated to form a fuel gas phase difference sequence.

[0039] Call the preset fuel gas phase difference reference value, subtract the fuel gas phase difference reference value from the fuel gas phase difference sequence to obtain the fuel gas phase offset sequence;

[0040] By matching the fuel gas phase shift sequence with the fuel gas energy-constrained flow estimation result at the same sampling time, the ratio between the two is calculated to obtain the fuel gas instantaneous flow response ratio.

[0041] The instantaneous flow response ratio of fuel gas for all sampling periods is processed by time-weighted averaging, and the weighted average calculation result is used as the fuel gas flow deviation correction coefficient.

[0042] The fuel gas energy constraint flow estimation result is multiplied by the fuel gas flow deviation correction coefficient to obtain the fuel gas corrected flow value. The fuel gas corrected flow value is used as the initial self-calibrated flow result of the fuel gas. The initial self-calibrated flow result of the fuel gas is smoothed in the time dimension to filter out short-term fluctuations and generate the smoothed self-calibrated flow result of the fuel gas.

[0043] Optionally, the self-calibrated flow rate result of the fuel gas is used to compensate for the deviation in the fuel gas energy-constrained flow rate estimation result caused by disturbance response lag, gas property fluctuations, and electrochemical coupling changes.

[0044] Optionally, the generation of the spatiotemporal distribution map of the fuel gas flow rate specifically includes:

[0045] The fuel gas resonance spectrum characteristics, fuel gas energy-constrained flow estimation results, and fuel gas self-calibrated flow results are fused and aligned according to the sampling time index.

[0046] In the time dimension, the fuel gas energy-constrained flow estimation results and the fuel gas self-calibrated flow results are interpolated over time to obtain fuel gas instantaneous flow interpolation data.

[0047] In the spatial dimension, the spatial distribution weights are determined based on the resonant spectrum characteristics of fuel gas, and weighted interpolation is performed on each spatial location according to the fuel gas flow direction and the distance to adjacent measuring points to generate fuel gas spatial flow interpolation data.

[0048] The instantaneous flow rate interpolation data of fuel gas is fused with the spatial flow rate interpolation data of fuel gas. The fuel gas flow rate correction coefficient is calculated based on the energy conservation constraint and the flow continuity constraint. Physical consistency correction is performed on the interpolation data to obtain the spatiotemporal flow rate correction data of fuel gas.

[0049] The spatiotemporal flow correction data of fuel gas are rearranged according to the sampling time order and arranged in sequence according to the location of fuel gas channels in the spatial dimension to generate a spatiotemporal distribution map of fuel gas flow.

[0050] Optionally, the real-time measurement and intelligent calibration of fuel gas flow specifically includes:

[0051] Real-time fuel gas flow data of fuel cell cogeneration system is collected, and the real-time measured fuel gas flow data is matched with the flow value at the corresponding position in the spatiotemporal distribution map of fuel gas flow. The difference between the real-time measured flow and the predicted flow in the distribution map is calculated to obtain the real-time deviation sequence of fuel gas.

[0052] A sliding window smoothing process is performed on the real-time deviation sequence of fuel gas, the average deviation value within a continuous time period is calculated, and the direction of fuel gas flow offset is determined based on the trend of the average deviation value.

[0053] Based on the spatial gradient information in the spatiotemporal distribution map of fuel gas flow rate, the offset direction of fuel gas flow rate is compared with the spatial gradient direction of the channel to calculate the fuel gas flow rate calibration weight coefficient.

[0054] Multiply the real-time deviation sequence of fuel gas by the fuel gas flow rate calibration weighting coefficient to obtain the real-time calibration deviation of fuel gas.

[0055] The real-time calibration deviation of fuel gas is added to the corresponding node flow value in the spatiotemporal distribution map of fuel gas, the spatiotemporal distribution map of fuel gas flow is updated, and a dynamic calibration distribution map of fuel gas is generated, so as to realize the real-time measurement and intelligent calibration of fuel gas flow.

[0056] A fuel gas flow measurement system for a high-temperature fuel cell combined heat and power system according to an embodiment of the present invention includes:

[0057] The multiphysics signal acquisition module is used to acquire temperature, pressure and potential difference signals generated during the flow of fuel gas, forming a set of original multiphysics signals;

[0058] The signal preprocessing module is used to perform preprocessing on the original multiphysics signal set;

[0059] The multi-field collaborative spectrum analysis module is used to perform collaborative spectrum analysis, extract the resonant spectrum features of fuel gas, establish a fuel gas flow mapping relationship, and generate preliminary fuel gas flow inference results.

[0060] The energy constraint correction module is used to establish a dynamic energy balance relationship and perform reverse correction on the preliminary fuel gas flow inference results based on energy conservation constraints to obtain the fuel gas energy constraint flow estimation results.

[0061] The plasma perturbation calibration module is used to apply periodic plasma pulse excitation signals through the micro-plasma perturbation cavity to perturb the fuel gas flow field, and thereby correct the fuel gas energy constraint flow estimation results and generate fuel gas self-calibrated flow results.

[0062] The spatiotemporal distribution reconstruction module is used to fuse the resonant spectrum characteristics of fuel gas, energy-constrained flow estimation results, and self-calibrated flow results. Based on energy conservation constraints and flow continuity constraints, it performs spatiotemporal interpolation and consistency correction of fuel gas flow to generate a spatiotemporal distribution map of fuel gas flow.

[0063] The real-time measurement and intelligent calibration module is used to analyze the changes in fuel gas flow rate in real time based on the spatiotemporal distribution map of fuel gas flow rate and perform deviation calibration.

[0064] The beneficial effects of this invention are:

[0065] This invention proposes a method and system for measuring fuel gas flow in a high-temperature fuel cell cogeneration system. It achieves a full-link measurement technology innovation, including multi-physics field fusion, energy constraint back-calculation, plasma perturbation self-calibration, and spatiotemporal consistency correction. Compared with traditional fuel gas flow measurement methods that rely on mechanical or thermal sensors, this invention eliminates the need for direct contact flowmeters in high-temperature and high-pressure environments. Instead, it constructs a multi-physics field signal matrix by collecting temperature, pressure, and potential difference signals, and extracts the resonant spectrum characteristics of the fuel gas using multi-field collaborative spectral analysis. This enables non-contact identification of the fuel gas flow state and preliminary flow inference at the frequency domain. This method not only avoids the problems of sensor drift and measurement lag but also significantly improves the long-term stability and anti-interference capability of flow measurement.

[0066] Furthermore, this invention establishes a dynamic energy balance relationship between fuel gas flow rate, thermal energy output, and electrical energy output, introduces an energy conservation constraint mechanism, performs reverse correction on the preliminary flow rate inference results, and generates fuel gas energy-constrained flow rate estimation results. This ensures that the flow rate measurement and system energy output remain quantitatively consistent, fundamentally reducing the energy closure error caused by system state fluctuations. On this basis, by embedding a micro-plasma disturbance cavity in the fuel inlet channel and applying a periodic plasma pulse excitation signal, a flow deviation correction model is established using the phase response characteristics of plasma and fuel gas. This achieves adaptive closed-loop calibration of fuel gas flow rate. This self-calibration mechanism can automatically identify and compensate for measurement deviations caused by changes in gas properties, nonlinearity of electrochemical reactions, and local flow field disturbances during operation, improving the system's self-stability and intelligence.

[0067] Furthermore, this invention introduces a spatiotemporal interpolation and consistency correction mechanism at the data level, which integrates the fuel gas energy-constrained flow estimation results with the self-calibrated flow results in multiple dimensions. Combining energy conservation constraints and flow continuity constraints, the spatiotemporal flow distribution of fuel gas is reconstructed, achieving a breakthrough from single-point measurement to dynamic reconstruction across the entire domain. This spatiotemporal distribution map can reflect the flow gradient and distribution state of fuel gas within the system in real time, further supporting fine-tuning and intelligent optimization of fuel supply. Attached Figure Description

[0068] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0069] Figure 1 This is a flowchart of the fuel gas flow measurement method for a high-temperature fuel cell cogeneration system proposed in this invention;

[0070] Figure 2This is a schematic diagram of the fuel gas phase difference detection and self-calibration correction process in the fuel gas flow measurement method for a high-temperature fuel cell cogeneration system proposed in this invention. Detailed Implementation

[0071] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0072] refer to Figure 1 and Figure 2 A method for measuring fuel gas flow rate in a high-temperature fuel cell combined heat and power system includes the following steps:

[0073] In a fuel cell combined heat and power system, temperature, pressure and potential difference signals generated during the flow of fuel gas are collected to form a set of original signals from multiple physics fields.

[0074] Preprocessing is performed on the original multiphysics signal set to construct a standardized signal matrix;

[0075] Multi-field collaborative spectral analysis is performed based on a standardized signal matrix to extract the resonant spectral features of fuel gas caused by fuel gas flow, and a mapping relationship with fuel gas flow rate is established to generate preliminary fuel gas flow rate inference results.

[0076] A dynamic energy balance relationship is established, and the preliminary fuel gas flow inference results are reversed to generate fuel gas energy-constrained flow estimation results.

[0077] A micro-plasma disturbance cavity is embedded in the fuel inlet channel. Plasma pulse excitation signals are periodically applied to disturb the fuel gas flow field and the fuel gas energy-constrained flow estimation results are corrected to form a fuel gas self-calibrated flow result.

[0078] The resonant spectrum characteristics of fuel gas, energy-constrained flow estimation results, and self-calibrated flow results are fused and matched. Based on energy conservation constraints and flow continuity constraints, spatiotemporal interpolation and consistency correction of fuel gas flow are performed to reconstruct the distribution state of fuel gas flow and generate a spatiotemporal distribution map of fuel gas flow.

[0079] Real-time measurement and intelligent calibration of fuel gas flow rate are performed based on the spatiotemporal distribution map of fuel gas flow rate.

[0080] In this embodiment, the preprocessing includes time synchronization, noise filtering, and normalization.

[0081] In this embodiment, the generation of the preliminary fuel gas flow rate estimation result specifically includes:

[0082] Based on the standardized signal matrix, multi-field collaborative spectrum analysis is performed on the fuel gas temperature signal, fuel gas pressure signal and fuel gas potential difference signal. The frequency domain transformation of each signal sequence in the standardized signal matrix is ​​performed under a unified sampling period to obtain the fuel gas temperature spectrum, fuel gas pressure spectrum and fuel gas potential difference spectrum.

[0083] The fuel gas temperature spectrum, fuel gas pressure spectrum, and fuel gas potential difference spectrum are weighted and superimposed according to the frequency axis to generate a fuel gas co-current spectrum. The fuel gas co-current spectrum is used to characterize the coupling response characteristics of the temperature field, pressure field, and potential field of the fuel gas during the flow process.

[0084] In the fuel gas co-spectrum, the resonant frequency band of the fuel gas is identified, and the center frequency, amplitude variation and energy distribution of the resonant frequency band of the fuel gas are extracted to generate the resonant spectrum features of the fuel gas. The resonant spectrum features of the fuel gas are used to reflect the multi-field resonance state of the fuel gas under high temperature electrochemical reaction conditions.

[0085] The generation of the fuel gas resonance spectrum features specifically includes: performing peak scanning on the fuel gas co-spectrum according to the amplitude distribution, identifying continuous frequency intervals with amplitudes higher than the average amplitude threshold across the entire frequency range, and defining these continuous intervals as fuel gas resonance bands; performing quadratic fitting on the amplitude curves within the fuel gas resonance bands to determine the center frequency of the fuel gas resonance bands, where the center frequency corresponds to the main oscillation point of the fuel gas under multi-physics coupling; calculating the amplitude variation within the fuel gas resonance bands, where the amplitude variation characterizes the energy response intensity of the fuel gas during high-temperature flow; then integrating the amplitude within the fuel gas resonance bands to obtain the energy distribution value of the fuel gas resonance bands, which represents the degree of energy concentration of the fuel gas in different frequency intervals; and combining the center frequency, amplitude variation, and energy distribution value of the fuel gas resonance bands in sequence to generate fuel gas resonance spectrum features.

[0086] Establish a mapping relationship between fuel gas flow rate and fuel gas resonance spectrum characteristics, map the center frequency offset of the fuel gas resonance band to the fuel gas velocity variation parameters, and determine the instantaneous fuel gas velocity.

[0087] The generation of the instantaneous fuel gas velocity specifically includes: extracting the resonant spectrum features of the fuel gas based on the fuel gas cooperative spectrum; obtaining the center frequency offset of the fuel gas resonant band by calculating the difference between the center frequency and the reference frequency; collecting actual flow state data of the fuel gas at the same sampling point; matching the center frequency offset of the fuel gas resonant band with the actual flow state data to generate a fuel gas frequency-velocity paired dataset; performing linear fitting calculation on the fuel gas frequency-velocity paired dataset to obtain fuel gas velocity change parameters, which are used to characterize the quantitative mapping relationship between the center frequency offset of the fuel gas resonant band and the fuel gas velocity change; and performing data mapping calculation between the fuel gas velocity change parameters and the real-time collected center frequency offset of the fuel gas resonant band to obtain the instantaneous fuel gas velocity at the current sampling time.

[0088] The linear fitting process specifically includes: reading the center frequency offset of the fuel gas resonant band and the corresponding actual fuel gas velocity value of all sampling points in the fuel gas frequency-velocity pairing dataset to form a fuel gas frequency sequence and a fuel gas velocity sequence; performing data registration on the fuel gas frequency sequence and the fuel gas velocity sequence to ensure that the two sequences are completely consistent at the time sampling points, generating a fuel gas frequency-velocity synchronization dataset; then performing minimum error fitting calculation on each data point in the fuel gas frequency-velocity synchronization dataset to obtain the linear change trend between the center frequency offset of the fuel gas resonant band and the fuel gas velocity; extracting the slope and intercept values ​​of the fitted curve, and defining them together as fuel gas velocity change parameters, where the slope value represents the degree of response of the fuel gas velocity to the change of the center frequency offset of the fuel gas resonant band, and the intercept value is used to describe the static flow correction of the fuel gas under the reference state;

[0089] The instantaneous flow rate of fuel gas is calculated based on the instantaneous velocity of the fuel gas, forming a sequence of instantaneous fuel gas flow rates;

[0090] The instantaneous flow rate sequence of fuel gas is integrated according to the sampling time order to generate a preliminary flow rate inference result of fuel gas. The preliminary flow rate inference result of fuel gas is used to characterize the dynamic flow distribution state of fuel gas in the fuel cell cogeneration system.

[0091] In this embodiment, the generation of the instantaneous flow rate sequence of the fuel gas specifically includes:

[0092] The instantaneous flow velocity of fuel gas and the effective cross-sectional area of ​​the fuel gas channel are obtained and paired according to the same time sampling interval to form a fuel gas flow velocity-cross-sectional area paired dataset.

[0093] In the fuel gas velocity-cross-sectional area paired dataset, the instantaneous fuel gas velocity value at each time sampling point is multiplied with the corresponding fuel gas channel cross-sectional area to obtain the instantaneous fuel gas flow rate at that time point.

[0094] The instantaneous flow rates of fuel gas at all sampling time points are summarized in chronological order and smoothed to remove outliers caused by sampling fluctuations, thus forming a fuel gas instantaneous flow rate sequence.

[0095] In this embodiment, the generation of the fuel gas energy-constrained flow estimation result specifically includes:

[0096] A dynamic energy balance relationship is established between fuel gas flow rate, fuel gas thermal energy output and fuel gas electrical energy output. The dynamic energy balance relationship of fuel gas is based on the energy conservation constraint condition, which is used to constrain the fuel gas input energy, fuel gas thermal energy output and fuel gas electrical energy output to maintain energy balance during the operating cycle.

[0097] The establishment of the dynamic energy balance relationship specifically includes: obtaining the instantaneous fuel gas flow sequence corresponding to the preliminary fuel gas flow inference result, and simultaneously acquiring the fuel gas thermal energy output signal and the fuel gas electrical energy output signal within the same time sampling period; aligning the fuel gas instantaneous flow sequence, fuel gas thermal energy output signal, and fuel gas electrical energy output signal according to the sampling time order to form a fuel gas energy balance input dataset; then performing time correspondence calculation on the fuel gas energy balance input dataset to obtain the fuel gas input energy value, fuel gas thermal energy output value, and fuel gas electrical energy output value in each sampling period; comparing the fuel gas input energy value with the fuel gas thermal energy output value and the fuel gas electrical energy output value to calculate the fuel gas energy balance difference, which is used to represent the degree of dynamic balance between fuel gas flow and system energy output; and establishing a quantitative correspondence between fuel gas input energy, fuel gas thermal energy output, and fuel gas electrical energy output with the fuel gas energy balance difference as the output result.

[0098] During the operation of the fuel cell cogeneration system, the electrical output power signal and reaction exothermic signal of the fuel cell stack are collected. The electrical output power signal is recorded as a fuel gas electrical energy output data sequence according to the time sampling order, and the reaction exothermic signal is recorded as a fuel gas thermal energy output data sequence according to the same time sampling order. The sampling time index is simultaneously marked in the two types of data sequences to form a fuel gas energy output sampling dataset.

[0099] The fuel gas energy output sampling dataset is subjected to time accumulation calculation. The fuel gas electrical energy output data sequence and the fuel gas thermal energy output data sequence are summed within the same sampling time range to obtain the total fuel gas energy output value. The total fuel gas energy output value is used to characterize the overall energy release level of the system in the current sampling period.

[0100] The fuel gas input energy is calculated based on the preliminary fuel gas flow rate estimation result, and the fuel gas input energy is compared with the total fuel gas energy output value to obtain the fuel gas energy deviation. The fuel gas energy deviation is used to represent the difference between the preliminary fuel gas flow rate estimation result and the fuel gas energy balance state.

[0101] Based on the fuel gas energy deviation, a reverse correction operation is performed on the preliminary fuel gas flow inference result. The reverse correction operation takes the fuel gas energy deviation as input, calculates the fuel gas flow correction amount through the proportional correction coefficient, and superimposes the preliminary fuel gas flow inference result with the fuel gas flow correction amount to obtain the fuel gas energy-constrained flow estimation result. The fuel gas energy-constrained flow estimation result is used to compensate for the flow deviation under the energy conservation constraint.

[0102] The fuel gas energy deviation calculation and reverse correction operation are performed cyclically within the continuous sampling period to dynamically update the fuel gas energy constraint flow estimation results, so that the fuel gas input energy and the total fuel gas energy output value gradually meet the dynamic energy balance relationship, and output the fuel gas energy constraint flow estimation results.

[0103] In this embodiment, the generation of the fuel gas self-calibration flow rate result specifically includes:

[0104] A micro-plasma disturbance cavity is embedded in the fuel gas inlet channel of a fuel cell cogeneration system. The micro-plasma disturbance cavity is located in the fuel gas inlet channel and is a discharge cavity for generating plasma pulses. It is arranged along the fuel gas flow direction and connected to the fuel gas passage to apply a controllable disturbance signal to the interior of the fuel gas.

[0105] A plasma pulse excitation signal is input into the micro-plasma disturbance cavity, and the frequency, pulse width and duty cycle of the plasma pulse excitation signal are controlled to keep it in a coupled state with the dynamic characteristics of the fuel gas flow, so as to form a periodic disturbance flow field in the fuel gas flow channel.

[0106] The formation of periodic perturbation gas clouds specifically includes: applying a pulsed voltage signal to the electrode of the micro-plasma perturbation cavity, setting the initial frequency, pulse width, and duty cycle to generate a basic plasma pulse excitation signal; real-time detection of the flow pulse frequency of fuel gas in the inlet channel, comparing the monitored fuel gas flow pulse frequency with the output frequency of the basic plasma pulse excitation signal, and calculating the frequency difference; automatically adjusting the pulse power supply output according to the frequency difference to keep the frequency of the plasma pulse excitation signal and the flow pulse frequency of fuel gas in a coupled state; controlling the pulse discharge duration and pulse interval to keep the discharge energy constant in each pulse cycle, ensuring that the plasma is stably generated and maintained in an ionized state inside the micro-plasma perturbation cavity; ionized fuel gas molecules form high-energy gas clouds in the channel, and multiple gas clouds are continuously formed in the direction of fuel gas flow, forming a periodic perturbation flow field with a fixed spacing;

[0107] The flow pulse frequency refers to the characteristic frequency of gas flow formed by the periodic fluctuations of flow velocity, pressure and temperature in the fuel gas inlet channel of the fuel gas cogeneration system, and is used to characterize the dynamic change characteristics of the fuel gas flow state.

[0108] Under the action of plasma pulse excitation signal, the current response signal of the anode reaction zone of the fuel cell stack is collected, and the current response signal is stored as a fuel gas current response data sequence according to the sampling time sequence. The fuel gas current response data sequence is used to characterize the electrochemical dynamic response characteristics of fuel gas under disturbance conditions.

[0109] The fuel gas current response data sequence is decoupled, and the phase information of the fuel gas current response signal is extracted from the decoupled data to obtain the fuel gas response phase value.

[0110] Extract the phase information of the plasma pulse excitation signal to obtain the plasma excitation phase value;

[0111] The fuel gas response phase value and the plasma excitation phase value are compared one-to-one according to the time sampling index, and the phase difference value in each sampling period is calculated to form a fuel gas phase difference sequence.

[0112] Call the preset fuel gas phase difference reference value, subtract the fuel gas phase difference reference value from the fuel gas phase difference sequence to obtain the fuel gas phase offset sequence. The positive or negative sign of the fuel gas phase offset sequence is used to reflect the advance or lag characteristics of fuel gas flow field transmission.

[0113] The fuel gas phase shift sequence and the fuel gas energy-constrained flow estimation result are correlated with the same sampling time, and the ratio between the two is calculated to obtain the fuel gas instantaneous flow response ratio. The fuel gas instantaneous flow response ratio is used to quantify the influence of phase change on flow estimation.

[0114] The instantaneous flow response ratio of fuel gas for all sampling periods is processed by time-weighted averaging, and the weighted average calculation result is used as the fuel gas flow deviation correction coefficient.

[0115] The fuel gas energy constraint flow estimation result is multiplied by the fuel gas flow deviation correction coefficient to obtain the fuel gas corrected flow value. The fuel gas corrected flow value is used as the initial self-calibrated flow result of the fuel gas. The initial self-calibrated flow result of the fuel gas is smoothed in the time dimension to filter out short-term fluctuations and generate the smoothed self-calibrated flow result of the fuel gas.

[0116] In this embodiment, the self-calibrated flow rate result of the fuel gas is used to compensate for the deviation in the fuel gas energy-constrained flow rate estimation result caused by disturbance response lag, gas property fluctuations and electrochemical coupling changes, so as to realize adaptive calibration and closed-loop correction of fuel gas flow rate.

[0117] In this embodiment, the generation of the spatiotemporal distribution map of the fuel gas flow rate specifically includes:

[0118] The resonant spectrum characteristics of fuel gas, the energy-constrained flow estimation results of fuel gas, and the self-calibrated flow results of fuel gas are fused and aligned according to the sampling time index to ensure that the signals at each sampling time are synchronously corresponding.

[0119] In the time dimension, the fuel gas energy-constrained flow estimation results and the fuel gas self-calibrated flow results are interpolated over time to obtain fuel gas instantaneous flow interpolation data.

[0120] The generation of the instantaneous fuel gas flow interpolation data specifically includes: in the time dimension, reading the fuel gas energy-constrained flow estimation results and the fuel gas self-calibration flow results in the order of sampling time, averaging the flow values ​​of the two sets of sequences at the same sampling time to obtain the fuel gas reference flow value corresponding to each sampling time; determining the interpolation weight coefficient between adjacent sampling times based on the sampling time interval, wherein the weight coefficient is determined by the time ratio of the current time to the previous sampling time; then multiplying the fuel gas reference flow values ​​of two adjacent sampling times by the corresponding interpolation weight coefficients and summing them to obtain the fuel gas instantaneous flow interpolation data at the current time;

[0121] In the spatial dimension, the spatial distribution weights are determined based on the resonant spectrum characteristics of fuel gas, and weighted interpolation is performed on each spatial location according to the fuel gas flow direction and the distance to adjacent measuring points to generate fuel gas spatial flow interpolation data.

[0122] The generation of the fuel gas spatial flow interpolation data specifically includes: performing main frequency band analysis on the fuel gas resonance spectrum characteristics in the spatial dimension, extracting the peak amplitude of the main resonance frequency band at each measuring point, taking the absolute value of the difference between the peak amplitudes of the main resonance frequency bands of adjacent measuring points, and calculating the change in fuel gas amplitude at each measuring point; dividing the change in fuel gas amplitude at each measuring point by the sum of the changes in amplitude at all measuring points to obtain the spatial distribution weight of each measuring point; then determining the order of upstream and downstream measuring points according to the fuel gas flow direction, calculating the spatial distance difference between two adjacent measuring points, and dividing the spatial distance difference with the spatial distribution weight of the two measuring points. Divide the weighted differences to obtain the fuel gas spatial interpolation ratio coefficient; then multiply the fuel gas flow rate value at the upstream measuring point by the fuel gas spatial interpolation ratio coefficient, multiply the fuel gas flow rate value at the downstream measuring point by (1 minus the fuel gas spatial interpolation ratio coefficient), and add the two together to obtain the fuel gas interval interpolated flow rate value for that spatial interval; calculate the interval weight for each spatial interval, where the interval weight is the average of the spatial distribution weights of the measuring points at both ends of the interval; and perform a weighted average of the fuel gas interval interpolated flow rate values ​​for all spatial intervals according to the corresponding interval weights to obtain the fuel gas spatial flow rate interpolation data;

[0123] The instantaneous flow rate interpolation data of fuel gas is fused with the spatial flow rate interpolation data of fuel gas. The fuel gas flow rate correction coefficient is calculated based on the energy conservation constraint and the flow continuity constraint. Physical consistency correction is performed on the interpolation data to obtain the spatiotemporal flow rate correction data of fuel gas.

[0124] The generation of the fuel gas spatiotemporal flow correction data specifically includes: pairing and matching the fuel gas instantaneous flow interpolation data and the fuel gas spatial flow interpolation data according to the same time index and spatial coordinates; calculating the difference between the two for each matching node to obtain a fuel gas spatiotemporal flow difference sequence; averaging the absolute values ​​of all nodes in the fuel gas spatiotemporal flow difference sequence to obtain the average fuel gas flow difference, which is used to measure the consistency deviation between the time interpolation and spatial interpolation results; calculating the rate of change of the sum of fuel gas thermal energy output and fuel gas electrical energy output according to energy conservation constraints, and comparing it with the rate of change of the fuel gas energy constraint flow estimation result to obtain the energy conservation deviation; and comparing the average fuel gas flow difference with the energy conservation deviation. The results are summed and then divided by the average value of the fuel gas energy-constrained flow estimation results to calculate the fuel gas flow correction coefficient. This coefficient characterizes the degree of deviation between the flow interpolation results and the energy conservation conditions. The instantaneous flow interpolation data and the spatial flow interpolation data are multiplied by the fuel gas flow correction coefficient to obtain the corrected instantaneous flow correction data and spatial flow correction data. The instantaneous flow correction data and spatial flow correction data are then weighted and averaged according to the node order to calculate the spatiotemporal flow correction data. This spatiotemporal flow correction data characterizes the physical consistency distribution of fuel gas under energy conservation constraints and flow continuity constraints.

[0125] The spatiotemporal flow correction data of fuel gas is rearranged according to the sampling time order and arranged sequentially according to the location of fuel gas channels in the spatial dimension to generate a spatiotemporal distribution map of fuel gas flow. The spatiotemporal distribution map of fuel gas flow is used to reflect the dynamic transmission state and distribution characteristics of fuel gas within the system, and realizes spatiotemporal interpolation and consistency correction of fuel gas flow.

[0126] In this embodiment, the real-time measurement and intelligent calibration of fuel gas flow specifically includes:

[0127] Real-time fuel gas flow data of fuel cell cogeneration system is collected, and the real-time measured fuel gas flow data is matched with the flow value at the corresponding position in the spatiotemporal distribution map of fuel gas flow. The difference between the real-time measured flow and the predicted flow in the distribution map is calculated to obtain the real-time deviation sequence of fuel gas.

[0128] A sliding window smoothing process is performed on the real-time deviation sequence of fuel gas, the average deviation value within a continuous time period is calculated, and the direction of fuel gas flow offset is determined based on the trend of the average deviation value.

[0129] Based on the spatial gradient information in the spatiotemporal distribution map of fuel gas flow rate, the offset direction of fuel gas flow rate is compared with the spatial gradient direction of the channel to calculate the calibration weight coefficient of fuel gas flow rate. The calibration weight coefficient is used to reflect the degree of influence of spatial distribution on flow rate change.

[0130] The generation of the fuel gas flow calibration weight coefficient specifically includes: subtracting the fuel gas flow values ​​of adjacent measuring points in the spatiotemporal distribution map of fuel gas flow one by one to calculate the change in fuel gas flow between each measuring point, and dividing the change in fuel gas flow at each measuring point by the spatial distance difference between the corresponding measuring points to obtain the fuel gas spatial gradient value of each measuring point interval; normalizing all fuel gas spatial gradient values ​​according to the channel direction to form a channel spatial gradient direction vector; using the fuel gas flow offset direction obtained in the previous step, multiplying the fuel gas flow offset direction vector with the channel spatial gradient direction vector element by element to obtain a direction consistency product sequence, and averaging all product values ​​in the direction consistency product sequence to calculate the fuel gas flow direction consistency coefficient; taking the absolute value of the fuel gas flow direction consistency coefficient and performing a ratio operation with the maximum value of the fuel gas spatial gradient value to obtain the fuel gas spatial gradient correction ratio; multiplying the fuel gas spatial gradient correction ratio with the fuel gas flow direction consistency coefficient to obtain the fuel gas flow calibration weight coefficient, which is used to characterize the degree of matching between the fuel gas flow offset direction and the channel spatial gradient direction.

[0131] The fuel gas flow rate offset direction vector is obtained by symbol extraction and normalization of the fuel gas average deviation value sequence after sliding window smoothing, and is used to characterize the dominant change direction of fuel gas flow rate on a continuous time scale.

[0132] Multiply the real-time deviation sequence of fuel gas by the fuel gas flow rate calibration weighting coefficient to obtain the real-time calibration deviation of fuel gas.

[0133] The real-time calibration deviation of fuel gas is added to the corresponding node flow value in the spatiotemporal distribution map of fuel gas, the spatiotemporal distribution map of fuel gas flow is updated, and a dynamic calibration distribution map of fuel gas is generated, so as to realize the real-time measurement and intelligent calibration of fuel gas flow.

[0134] A fuel gas flow measurement system for a high-temperature fuel cell combined heat and power system includes:

[0135] The multiphysics signal acquisition module is used to acquire temperature, pressure and potential difference signals generated during the flow of fuel gas, forming a set of original multiphysics signals;

[0136] The signal preprocessing module is used to perform preprocessing on the original multiphysics signal set;

[0137] The multi-field collaborative spectrum analysis module is used to perform collaborative spectrum analysis, extract the resonant spectrum features of fuel gas, establish a fuel gas flow mapping relationship, and generate preliminary fuel gas flow inference results.

[0138] The energy constraint correction module is used to establish a dynamic energy balance relationship and perform reverse correction on the preliminary fuel gas flow inference results based on energy conservation constraints to obtain the fuel gas energy constraint flow estimation results.

[0139] The plasma perturbation calibration module is used to apply periodic plasma pulse excitation signals through the micro-plasma perturbation cavity to perturb the fuel gas flow field, and thereby correct the fuel gas energy constraint flow estimation results and generate fuel gas self-calibrated flow results.

[0140] The spatiotemporal distribution reconstruction module is used to fuse the resonant spectrum characteristics of fuel gas, energy-constrained flow estimation results, and self-calibrated flow results. Based on energy conservation constraints and flow continuity constraints, it performs spatiotemporal interpolation and consistency correction of fuel gas flow to generate a spatiotemporal distribution map of fuel gas flow.

[0141] The real-time measurement and intelligent calibration module is used to analyze the changes in fuel gas flow rate in real time based on the spatiotemporal distribution map of fuel gas flow rate and perform deviation calibration.

[0142] Example 1:

[0143] To verify the feasibility of this invention in practice, it was applied to an operational test platform for a fuel cell cogeneration system. This system uses hydrogen as fuel gas and undergoes an electrochemical reaction under high-temperature operating conditions to achieve synchronous output of electrical and thermal energy. The fuel cell stack of the test platform has a high rated output power, and the control accuracy of the fuel gas flow rate is extremely critical. Traditional flow detection methods based on a single sensor suffer from severe drift, response delay, and difficulty in dynamic correction under high-temperature and high-pressure environments. This leads to asynchrony between fuel gas flow fluctuations and energy output, resulting in a series of problems such as decreased fuel utilization and deviation of thermoelectric efficiency from the design value. This invention, by deploying a multi-physics field signal acquisition and collaborative analysis mechanism in the fuel cell cogeneration system, achieves high-precision measurement and adaptive calibration of fuel gas flow rate under complex operating conditions, effectively solving the technical problems of lag in real-time fuel gas flow measurement and difficulty in correcting energy imbalance.

[0144] In practical implementation, temperature sensors, pressure sensors, and potential difference acquisition electrodes are arranged in the intake branch of the fuel cell cogeneration system to acquire the temperature, pressure, and potential difference signals of the fuel gas during the flow process in real time, forming a set of original signals of the fuel gas multi-physics field. The output signals of all sensors are clock-synchronized and input to the signal preprocessing unit. Through filtering, normalization, and time-domain alignment, a standardized signal matrix of fuel gas is constructed. Subsequently, this invention uses a multi-field collaborative spectrum analysis algorithm to perform joint frequency domain transformation on the fuel gas temperature, pressure, and potential difference signals to extract the resonant spectrum characteristics of the fuel gas. These characteristics reflect the multi-field coupled oscillation law of the fuel gas under high-temperature flow conditions. By combining with a preset flow-spectrum mapping model, the preliminary flow inference result of the fuel gas is calculated, realizing the real-time capture of the flow change trend.

[0145] To further improve the accuracy of fuel gas flow rate estimation, this invention introduces an energy conservation constraint mechanism in the analysis phase. By establishing a dynamic energy balance relationship between fuel gas flow rate, fuel gas thermal energy output, and electrical energy output, the input energy of fuel gas is dynamically compared with the system energy output. Energy deviations are identified and reverse corrections are performed. The fuel gas energy-constrained flow rate estimation result obtained through this process can effectively correct flow deviations caused by temperature drift or sensor errors, ensuring that the fuel gas flow rate estimation result is consistent with the system energy output state, and ensuring that the fuel gas utilization rate and system thermoelectric efficiency remain within a stable range.

[0146] In the fuel gas inlet channel, this invention further embeds a micro-plasma disturbance cavity. This structure applies a periodic plasma pulse excitation signal to the fuel gas flow field without changing the overall flow direction, forming a controllable disturbance flow field. Under the disturbance, the current response signal of the electrochemical reaction zone of the fuel gas will exhibit periodic phase changes. By extracting the phase difference between the plasma excitation signal and the fuel gas response signal, the flow field transmission characteristics of the fuel gas can be obtained. Based on this phase difference, this invention calculates the fuel gas flow deviation correction coefficient, corrects the energy-constrained flow estimation result, and forms a self-calibrated flow result for the fuel gas. This process enables dynamic adaptive correction of the fuel gas flow, thereby eliminating measurement errors caused by disturbance response lag, gas property fluctuations, and electrochemical coupling instability.

[0147] During the stable operation phase of the system, this invention further integrates the resonant spectrum characteristics of fuel gas, the energy-constrained flow estimation results, and the self-calibrated flow results according to the sampling time. The spatiotemporal distribution map of fuel gas flow is reconstructed through time interpolation and spatial interpolation methods. This spatiotemporal distribution map can clearly reflect the instantaneous flow state and distribution pattern of fuel gas in the channel, so that the fuel gas supply state at different locations can be visualized during the high-temperature electrochemical reaction process. Subsequently, the system performs consistency correction on the interpolation results in the spatiotemporal distribution map based on the energy conservation constraint and the flow continuity constraint, forming fuel gas spatiotemporal flow correction data, thereby ensuring the physical consistency of the flow calculation results across the entire channel.

[0148] During long-term operation testing, this invention achieves real-time flow measurement and intelligent calibration by continuously monitoring the spatiotemporal distribution of fuel gas flow. When the system experiences load changes or temperature fluctuations, the control module can automatically identify the direction of fuel gas flow deviation based on the spatiotemporal distribution map of the fuel gas flow and calculate the calibration weighting coefficient. By dynamically updating the flow distribution map, it ensures the continuity of fuel gas supply and the balance of energy transfer. The entire process requires no manual intervention and can maintain the stability and high accuracy of fuel gas flow under complex operating environments.

[0149] To verify the performance of the present invention, it was compared with the traditional fuel gas flow measurement method. The comparison results are shown in Table 1.

[0150] Table 1. Performance Comparison of the Method of the Present Invention and Traditional Fuel Gas Flow Measurement Methods

[0151]

[0152] As can be seen from the comparison results in Table 1, the multi-physics field collaborative measurement and self-calibration method for fuel gas proposed in this invention has advantages in terms of flow measurement accuracy, response speed, thermal stability and system energy balance.

[0153] Traditional differential pressure flow measurement methods suffer from significant delays and noise amplification in high-temperature environments due to the susceptibility of sensing elements to thermal drift. This results in flow measurement errors exceeding 2%. While traditional hot-film methods offer improved response speeds, the thermal conductivity of the film layer remains unstable under high-temperature and high-humidity conditions, necessitating frequent calibration. This invention utilizes multi-physics field signal collaborative analysis of temperature, pressure, and potential difference to extract the resonant spectrum characteristics of fuel gas in the frequency domain. This fundamentally solves the problems of signal drift and nonlinear response, improving flow measurement accuracy to ±0.6%FS.

[0154] Regarding dynamic response characteristics, this invention introduces an energy conservation constraint model and a plasma perturbation self-calibration mechanism. By periodically applying an excitation signal in the micro-plasma perturbation cavity, the hysteresis effect of the fuel gas flow field is corrected in real time, enabling the system to complete a flow change response in less than 100ms, which is more than 70% higher than the traditional method. In terms of high temperature stability, due to the adoption of dynamic energy balance relationship and flow adaptive closed-loop correction algorithm, the drift rate of this invention at 850℃ is only 0.4%, which is far lower than the traditional flow detection method.

[0155] The significant reduction in energy balance error and mean square error of flow fluctuation indicates that the present invention has higher synchronicity and physical consistency in flow regulation in the combined heat and power system. In particular, during the long-term operation of fuel cells, the present invention can automatically perform self-calibration and time interpolation compensation, shortening the flow calibration cycle from 48 hours to 12 hours, significantly reducing the maintenance frequency, and reducing maintenance costs by more than 50%.

[0156] Furthermore, through the construction of the spatiotemporal distribution map of the flow rate and the intelligent calibration mechanism, the overall energy utilization rate of the system is significantly improved, the output power stability rate of the fuel cell stack increases from 91.6% to 98.9%, and the supply fluctuation of fuel gas is effectively suppressed. This indicates that the present invention not only achieves a breakthrough in high precision and high response at the measurement level, but also demonstrates significant technical advantages in system energy coordination and operational economy. The fundamental reason for the performance improvement lies in the fact that the present invention establishes a two-way coupling mechanism between multi-dimensional physical information and energy constraints, realizing a unified closed loop of flow measurement, energy balance and disturbance calibration. This makes the detection and correction of fuel gas flow no longer dependent on the characteristics of a single sensor, thereby realizing a truly adaptive, highly robust and engineering-grade reliable measurement system.

[0157] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method of measuring the flow rate of fuel gas for a high-temperature fuel cell cogeneration system, characterized by, The method comprises the following steps: In a fuel cell combined heat and power system, temperature, pressure and potential difference signals generated during fuel gas flow are collected to form a multi-physical field original signal set; The multi-physical field original signal set is preprocessed to construct a standardized signal matrix; Based on the standardized signal matrix, multi-field collaborative spectrum analysis is performed to extract fuel gas resonance spectrum characteristics triggered by fuel gas flow, and a mapping relationship with fuel gas flow is established to generate a preliminary fuel gas flow inference result; A dynamic energy balance relationship is established to perform reverse correction on the preliminary fuel gas flow inference result to generate a fuel gas energy-constrained flow estimation result; A micro-plasma disturbance cavity is embedded in the fuel inlet channel, and a plasma pulse excitation signal is periodically applied to disturb the fuel gas flow field, and the fuel gas energy-constrained flow estimation result is corrected to form a fuel gas self-calibration flow result; The fuel gas resonance spectrum characteristics, energy-constrained flow estimation result and self-calibration flow result are fused and matched, and the fuel gas flow is time and space interpolated and consistently corrected based on the energy conservation constraint and flow continuity constraint to reconstruct the distribution state of the fuel gas flow and generate a fuel gas flow time and space distribution map; According to the fuel gas flow time and space distribution map, real-time measurement and intelligent calibration of the fuel gas flow are performed.

2. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to claim 1, characterized by, The preprocessing includes time synchronization, noise filtering and normalization.

3. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to Claim 1, wherein The generation of the preliminary fuel gas flow inference result specifically includes: Based on the standardized signal matrix, multi-field collaborative spectrum analysis is performed on the fuel gas temperature signal, fuel gas pressure signal and fuel gas potential difference signal, and each signal sequence in the standardized signal matrix is frequency domain converted under a unified sampling period to obtain fuel gas temperature spectrum, fuel gas pressure spectrum and fuel gas potential difference spectrum; The fuel gas temperature spectrum, fuel gas pressure spectrum and fuel gas potential difference spectrum are weighted and superimposed along the frequency axis to generate a fuel gas collaborative spectrum; In the fuel gas collaborative spectrum, the fuel gas resonance frequency band is identified, the center frequency, amplitude change and energy distribution of the fuel gas resonance frequency band are extracted, and the fuel gas resonance spectrum characteristics are generated; A mapping relationship between the fuel gas flow and the fuel gas resonance spectrum characteristics is established, the center frequency offset of the fuel gas resonance frequency band is corresponded to the fuel gas flow rate change parameter, and the fuel gas instantaneous flow rate is determined; The fuel gas instantaneous flow rate is calculated according to the fuel gas instantaneous flow rate to form a fuel gas instantaneous flow rate sequence; The fuel gas instantaneous flow rate sequence is integrated in sequence according to the sampling time to generate the preliminary fuel gas flow inference result.

4. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to claim 3, characterized by, The generation of the fuel gas instantaneous flow rate sequence specifically includes: The fuel gas instantaneous flow rate and the effective cross-sectional area of the fuel gas channel are obtained, and they are paired according to the same time sampling interval to form a fuel gas flow rate-cross-sectional area paired data set; In the fuel gas flow rate-cross-sectional area paired data set, the fuel gas instantaneous flow rate value at each time sampling point is multiplied by the corresponding fuel gas channel cross-sectional area to obtain the fuel gas instantaneous flow rate of the fuel gas at that time point. The fuel gas instantaneous flow values of all sampling time points are sequentially collected and smoothed to remove abnormal points caused by sampling fluctuations, and a fuel gas instantaneous flow sequence is formed.

5. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to Claim 1, wherein The generation of the fuel gas energy-constrained flow estimation result specifically includes: a dynamic energy balance relationship between the fuel gas flow, the fuel gas heat energy output and the fuel gas electric energy output is established; During the operation of the fuel cell combined heat and power system, the electric output power signal and the reaction heat release signal of the fuel cell stack are collected, the electric output power signal is recorded as a fuel gas electric energy output data sequence according to the time sampling sequence, the reaction heat release signal is recorded as a fuel gas heat energy output data sequence according to the same time sampling sequence, and the sampling time indexes are marked synchronously in the two types of data sequences to form a fuel gas energy output sampling data set; The fuel gas energy output sampling data set is calculated by time accumulation, and the fuel gas electric energy output data sequence and the fuel gas heat energy output data sequence are added within the same sampling time range to obtain a fuel gas total energy output value; The fuel gas input energy is calculated according to the fuel gas preliminary flow inference result, and the fuel gas input energy is compared with the fuel gas total energy output value to obtain a fuel gas energy deviation amount; Based on the fuel gas energy deviation amount, a reverse correction operation is performed on the fuel gas preliminary flow inference result to obtain a fuel gas energy-constrained flow estimation result; The fuel gas energy-constrained flow estimation result is dynamically updated by cyclically performing the fuel gas energy deviation amount calculation and the reverse correction operation within a continuous sampling period, so that the fuel gas input energy and the fuel gas total energy output value gradually satisfy the dynamic energy balance relationship, and the fuel gas energy-constrained flow estimation result is output.

6. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to Claim 1, wherein The generation of the fuel gas self-calibration flow result specifically includes: a micro-plasma disturbance cavity is embedded in the fuel gas inlet channel of the fuel cell combined heat and power system; an ion pulse excitation signal is input to the micro-plasma disturbance cavity, and the frequency, pulse width and duty cycle of the ion pulse excitation signal are controlled to form a periodic disturbance flow field in the fuel gas flow channel; Under the action of the ion pulse excitation signal, the current response signal of the anode reaction area of the fuel cell stack is collected, and the current response signal is stored as a fuel gas current response data sequence according to the sampling time sequence; The fuel gas current response data sequence is decoupled, the phase information of the fuel gas current response signal is extracted from the decoupled data, and a fuel gas response phase value is obtained; The phase information of the ion pulse excitation signal is extracted to obtain an ion excitation phase value; The fuel gas response phase value and the ion excitation phase value are one-to-one corresponding compared according to the time sampling index, the phase difference value in each sampling period is calculated, and a fuel gas phase difference sequence is formed; A preset fuel gas phase difference reference value is called, the fuel gas phase difference sequence is subtracted by the fuel gas phase difference reference value to obtain a fuel gas phase shift sequence; The fuel gas phase shift sequence and the fuel gas energy-constrained flow estimation result are corresponding according to the same sampling time, and the ratio between them is calculated to obtain a fuel gas instantaneous flow response ratio; The fuel gas instantaneous flow response ratio of all sampling periods is time-weighted average processed, and the weighted average calculation result is taken as a fuel gas flow deviation correction coefficient; The fuel gas energy constraint flow estimation result is multiplied by the fuel gas flow deviation correction coefficient to obtain a fuel gas correction flow value, the fuel gas correction flow value is taken as a fuel gas preliminary self-calibration flow result, and the fuel gas preliminary self-calibration flow result is smoothed in the time dimension to filter out short-time fluctuations to generate a fuel gas smoothed self-calibration flow result.

7. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to claim 6, characterized by, The fuel gas self-calibration flow result is used to compensate for the deviation in the fuel gas energy constraint flow estimation result caused by disturbance response lag, gas property fluctuation and electrochemical coupling change.

8. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to Claim 1, wherein The generation of the fuel gas flow spatio-temporal distribution map specifically includes: The fuel gas resonance spectrum characteristics, the fuel gas energy constraint flow estimation result and the fuel gas self-calibration flow result are fused and aligned according to the sampling time index; In the time dimension, the fuel gas energy constraint flow estimation result and the fuel gas self-calibration flow result are time-interpolated to obtain fuel gas instantaneous flow interpolation data; In the spatial dimension, the spatial distribution weight is determined based on the fuel gas resonance spectrum characteristics, and the weighted interpolation operation is performed on each spatial position according to the fuel gas flow direction and the distance between adjacent measuring points to generate fuel gas spatial flow interpolation data; The fuel gas instantaneous flow interpolation data and the fuel gas spatial flow interpolation data are fused, the fuel gas flow correction coefficient is calculated according to the energy conservation constraint and the flow continuity constraint, the interpolation data is physically consistent corrected to obtain fuel gas spatio-temporal flow correction data; The fuel gas spatio-temporal flow correction data is rearranged in the order of sampling time, and arranged in the fuel gas channel position in the spatial dimension to generate a fuel gas flow spatio-temporal distribution map.

9. The fuel gas flow rate measuring method for a high-temperature fuel cell cogeneration system according to Claim 1, wherein The real-time measurement and intelligent calibration of the fuel gas flow specifically include: Real-time fuel gas flow data of the fuel cell combined heat and power system is collected, the real-time measured fuel gas flow data is time-synchronously matched with the corresponding position flow value in the fuel gas flow spatio-temporal distribution map, the difference between the real-time measured flow and the distribution map predicted flow is calculated to obtain a fuel gas real-time deviation sequence; The fuel gas real-time deviation sequence is executed with a sliding window smoothing process, the average deviation value in a continuous time period is calculated, and the fuel gas flow shift direction is determined according to the average deviation value trend; According to the spatial gradient information in the fuel gas flow spatio-temporal distribution map, the fuel gas flow shift direction is compared with the channel spatial gradient direction, and the fuel gas flow calibration weight coefficient is calculated; The fuel gas real-time deviation sequence is multiplied by the fuel gas flow calibration weight coefficient to obtain a fuel gas real-time calibration deviation amount; The fuel gas real-time calibration deviation amount is added to the corresponding node flow value of the fuel gas spatio-temporal distribution map to update the fuel gas flow spatio-temporal distribution map, generate a fuel gas dynamic calibration distribution map, and realize real-time measurement and intelligent calibration of the fuel gas flow.

10. A fuel gas flow rate measuring system for a high-temperature fuel cell cogeneration system, which executes the high-temperature fuel cell cogeneration system fuel gas flow rate measuring method according to any one of claims 1 to 9, characterized by It includes: A multi-physical field signal acquisition module is configured to acquire temperature, pressure, and electric potential difference signals generated during the flow of fuel gas, thereby forming a multi-physical field original signal set; A signal preprocessing module is configured to perform preprocessing on the multi-physical field original signal set; A multi-field collaborative spectrum analysis module is configured to perform collaborative spectrum analysis, extract fuel gas resonance spectrum features, establish a fuel gas flow mapping relationship, and generate a preliminary fuel gas flow inference result; An energy constraint correction module is configured to establish a dynamic energy balance relationship, perform reverse correction on the preliminary fuel gas flow inference result based on the energy conservation constraint, and obtain a fuel gas energy constraint flow estimation result; A plasma disturbance calibration module is configured to apply a periodic plasma pulse excitation signal to a micro-plasma disturbance cavity to disturb the fuel gas flow field, and correct the fuel gas energy constraint flow estimation result based on the disturbance, thereby generating a fuel gas self-calibration flow result; A space-time distribution reconstruction module is configured to fuse the fuel gas resonance spectrum features, the energy constraint flow estimation result, and the self-calibration flow result, perform space-time interpolation and consistency correction of the fuel gas flow based on the energy conservation constraint and the flow continuity constraint, and generate a fuel gas flow space-time distribution map; A real-time measurement and intelligent calibration module is configured to analyze the fuel gas flow variation state in real time based on the fuel gas flow space-time distribution map, and perform deviation calibration.

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