Intelligent flow sensor test analysis method and system

Abstract

The invention discloses an intelligent flow sensor test analysis method and system, and relates to the technical field of sensor testing, and the method comprises the steps: applying a stepped flow excitation signal to a tested sensor through a standard flow generation device, and synchronously collecting the dynamic response data of the sensor; extracting a feature parameter set from the response data; performing similarity matching calculation to obtain a matching degree evaluation value of each error type; quantifying the induction relation strength among different error types; generating a composite weight coefficient in combination with the matching degree evaluation value and the evoked relationship strength; and making a multi-stage compensation strategy based on the composite weight coefficient. The method has the advantages that by combining error correlation network modeling driven by historical data, induction mechanisms and interaction relations of different error types can be analyzed, the pertinence of a compensation strategy is greatly improved, a multi-stage error suppression mechanism is formed, stable control over comprehensive errors is achieved under complex working conditions, and meanwhile the system maintenance requirement is lowered.

Description

Technical Field

[0001] The present invention relates to the field of sensor testing technology, and in particular to an intelligent flow sensor testing and analysis method and system. Background Art

[0002] In the existing technology, the testing and calibration of flow sensors mostly rely on static calibration methods under a single working condition, which makes it difficult to effectively identify complex errors in the dynamic response process. Traditional methods usually use fixed flow point testing or manual experience judgment, which cannot accurately separate multi-factor coupling errors such as sensor steady-state deviation, transient overshoot and environmental interference. Especially under complex working conditions such as temperature fluctuations and mechanical vibrations, error misjudgment and compensation lag problems are prone to occur, resulting in low calibration efficiency and increased maintenance costs. In addition, the existing error diagnosis system lacks the correlation mining of historical fault data, making it difficult to quantify the induced relationship between different error types, so that the compensation strategy is limited to local parameter adjustment, and it is difficult to achieve systematic optimization.

[0003] In response to the above-mentioned shortcomings, there is an urgent need for an intelligent testing and analysis method that can integrate dynamic excitation testing, multi-dimensional feature analysis, and error correlation modeling. Existing technologies have not yet solved the following core problems: First, there is a lack of a step-by-step flow excitation and multi-environment parameter synchronous testing mechanism, resulting in insufficient integrity of the feature parameter set; second, an error correlation network model based on historical data has not been constructed, making it difficult to assess the weighted impact of compound errors; third, the lack of synergy between hardware compensation and software algorithm correction makes it impossible to achieve accurate suppression of multi-level errors. Therefore, there is an urgent need to break through the bottleneck of existing technologies through innovations in dynamic response feature extraction, error correlation strength quantification, and collaborative calibration strategies. Summary of the Invention

[0004] In order to solve the above technical problems, an intelligent flow sensor testing and analysis method and system are provided. This technical solution solves the problem that the above-mentioned existing error diagnosis system lacks correlation mining of historical fault data, makes it difficult to quantify the induced relationship between different error types, and makes the compensation strategy limited to local parameter adjustment, making it difficult to achieve systematic optimization.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A method for testing and analyzing an intelligent flow sensor, comprising:

[0007] Apply a stepped flow excitation signal to the sensor under test through a standard flow generating device, and synchronously collect the dynamic response data of the sensor;

[0008] Extracting a characteristic parameter set including steady-state deviation, transient overshoot and noise interference level from the response data;

[0009] Perform similarity matching calculation on the characteristic parameter set and typical fault modes in a pre-stored error knowledge base to obtain a matching evaluation value for each error type;

[0010] An error correlation network model was constructed based on historical maintenance records to quantify the strength of the induced relationships between different error types.

[0011] Combine the matching evaluation value and the induced relationship strength to generate a composite weight coefficient to determine the dominant error type and associated influencing factors;

[0012] A multi-level compensation strategy is formulated based on the composite weight coefficient, and coordinated calibration of hardware parameter adjustment and software algorithm correction is implemented.

[0013] Preferably, the extracting of a characteristic parameter set including steady-state deviation, transient overshoot and noise interference level from the response data specifically includes:

[0014] Record the full-scale response curve under constant voltage test conditions and calculate the percentage deviation of the rise time from the theoretical value;

[0015] Measure the zero-point output drift in a variable temperature environment and establish a linear relationship model between temperature change and zero-point offset;

[0016] The high-frequency noise components are extracted through wavelet decomposition, and the energy proportion of a specific frequency band is counted as an interference level indicator.

[0017] Preferably, performing similarity matching calculation on the characteristic parameter set and typical fault modes in a pre-stored error knowledge base to obtain a matching evaluation value for each error type specifically includes:

[0018] Calculate the number of common features between the current feature parameter and each fault mode standard feature and compare it with the number of elements in the union of the current feature parameter and each fault mode standard feature to obtain a basic matching degree;

[0019] According to the degree of deviation of the test environment temperature from the standard working condition, the matching degree of the temperature-sensitive error is enhanced according to the preset proportional coefficient to obtain the temperature correction value;

[0020] The basic matching degree and the temperature correction value are added together to obtain the final matching degree evaluation value.

[0021] Preferably, the step of constructing an error association network model based on historical maintenance records and quantifying the strength of the induced relationships between different error types specifically includes:

[0022] Establish a directed relationship graph of error types, where nodes represent error types, the directions of directed edges represent the inducing relationships between error types, and the weights of directed edges represent the inducing probabilities between error types.

[0023] Specifically, if error type i induces error type j, a directed edge from error type i to error type j is constructed.

[0024] Preferably, the method for determining the directed edge weight is specifically as follows:

[0025] Determine the number of records in the historical maintenance records that contain error type i;

[0026] Determine the number of records in the historical maintenance records that contain both error type i and error type j.

[0027] Compare the number of records in which error type i and error type j occur simultaneously with the number of records in which error type i occurs to obtain the probability that error type i induces error type j.

[0028] Preferably, the generation of a composite weight coefficient by combining the matching evaluation value and the induced relationship strength to determine the dominant error type and associated influencing factors specifically includes:

[0029] Get all error types pointing to the error type, and form a set of induced error types that constitute the error type;

[0030] Get all error types pointed to by the error type to form the induced error type set of the error type;

[0031] Based on the matching evaluation values of all error types and the induced relationship strength between each error type, the composite weight coefficient of each error type is calculated through the correlation fitting formula;

[0032] Specifically, the correlation fitting formula is:

[0033]

[0034] Among them, R i is the composite weight coefficient of the i-th error type, β is the direct matching weight, S i is the matching evaluation value of the i-th error type, N i is the set of induced error types of the i-th error type, j is N i The jth element in S j N i The matching evaluation value of the jth element in w ij is the i-th error type pair N i The probability of inducing the jth element in U j N i The set of induced error types for the jth element in , K is N i The kth element of the set of induced error types for the jth element in w kj N iThe kth element of the set of induced error types of the jth element in N i The induction probability of the jth element in is , and γ is the associated influence factor.

[0035] Preferably, the multi-level compensation strategy is formulated based on the composite weight coefficient, and the collaborative calibration of hardware parameter adjustment and software algorithm correction is implemented specifically including:

[0036] Screen out the error types whose composite weight coefficients exceed the compensation threshold as compensation errors;

[0037] When the compensation error is dominated by temperature drift, dual-channel compensation is activated: the hardware side adjusts the bridge excitation voltage, and the software side corrects the flow conversion factor;

[0038] When the compensation error is the accumulated error of mechanical wear, the self-learning compensation algorithm is started to predict the wear trend curve based on historical calibration data;

[0039] When the compensation error is a strong vibration interference, it switches to high-frequency sampling mode and enables digital filtering, and triggers the installation stability alarm.

[0040] Furthermore, this solution proposes an intelligent flow sensor testing and analysis method system for implementing the above-mentioned intelligent flow sensor testing and analysis method, including:

[0041] A signal excitation module is used to apply a stepped flow excitation signal to the sensor under test through a standard flow generating device;

[0042] The dynamic data acquisition module is triggered synchronously with the signal excitation module to collect the dynamic response data of the sensor in real time and generate a time domain waveform sequence;

[0043] The characteristic parameter extraction module includes a steady-state analysis unit, a transient analysis unit, and a noise analysis unit. The steady-state analysis unit calculates the rise time percentage deviation based on the full-scale response curve. The transient analysis unit establishes a zero-point drift-temperature linear model based on variable temperature environment testing. The noise analysis unit uses a wavelet decomposition algorithm to extract the high-frequency noise energy ratio index.

[0044] An error matching engine, connected to the feature parameter extraction module, has a built-in pre-stored error knowledge base and a similarity calculator. The similarity calculator performs a feature intersection ratio calculation and superimposes a temperature deviation compensation amount to output a matching evaluation value for each fault mode.

[0045] The association network modeling module integrates the historical maintenance database and constructs a weighted directed graph model. The weight is calculated by the statistical calculation of the error co-occurrence probability, reflecting the induction intensity between error types.

[0046] The composite weight decision maker receives the evaluation value of the error matching engine and the induced probability data of the association network model, fuses the direct matching degree and the multi-level association influencing factors through the association fitting formula, and generates a composite weight coefficient of the error type;

[0047] The multi-level collaborative calibration device triggers the compensation strategy selection logic according to the composite weight coefficient, and includes a dual-channel temperature drift compensation unit, a mechanical wear self-learning unit, and a vibration interference suppression unit. The dual-channel compensation unit synchronously adjusts the hardware bridge voltage and software conversion coefficient. The self-learning unit predicts the wear curve based on historical data and dynamically corrects the gain parameters. The vibration suppression unit switches to high-frequency sampling mode and activates the digital filter and installation stability alarm function.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention significantly improves the systematicness and reliability of flow sensor error diagnosis through step-by-step dynamic excitation testing and multi-source data fusion analysis. Based on the synchronous monitoring of multimodal flow excitation patterns and environmental parameters, dynamic separation and precise tracing of complex errors are achieved, effectively enhancing the ability to identify steady-state deviations and transient overshoots; combined with historical data-driven error correlation network modeling, the inducing mechanisms and interactive relationships of different error types can be analyzed, greatly improving the pertinence of compensation strategies; through the coordinated optimization of hardware compensation modules and software adaptive algorithms, a multi-level error suppression mechanism is formed, which achieves stable control of comprehensive errors under complex working conditions while reducing system maintenance requirements. This technical solution breaks through the limitations of traditional calibration methods and provides a more adaptive error management path for industrial measurement and control scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Flowchart of the intelligent flow sensor test and analysis method proposed for this solution;

[0051] Figure 2 Flowchart of the method for extracting feature parameter sets proposed in this scheme;

[0052] Figure 3 Flowchart of the method proposed in this scheme for obtaining the matching evaluation value of each error type;

[0053] Figure 4 Flowchart of the method for determining directed edge weights proposed in this scheme;

[0054] Figure 5 Flowchart of the method proposed for this scheme to determine the dominant error types and associated influencing factors;

[0055] Figure 6 Flowchart of the collaborative calibration method for hardware parameter adjustment and software algorithm correction proposed in this solution;

[0056] Figure 7 This is a diagram of the architecture of the electronic equipment in this solution;

[0057] Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution.

[0058] The numbers in the figure are:

[0059] 500 - electronic device; 501 - bus; 502 - CPU; 503 - ROM; 504 - RAM; 505 - communication port; 506 - input / output component; 507 - hard disk; 508 - user interface; 600 - computer-readable storage medium. DETAILED DESCRIPTION

[0060] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0061] Reference Figure 1 As shown, a test and analysis method for an intelligent flow sensor includes:

[0062] Apply a stepped flow excitation signal to the sensor under test through a standard flow generating device, and synchronously collect the dynamic response data of the sensor;

[0063] The stepped flow excitation signal simulates the sudden or step-like flow changes that the sensor may encounter in actual operating conditions by gradually changing the flow value (such as from low to high or alternating between high and low). The synchronous acquisition system must use a high-precision clock source to ensure the timing alignment of the excitation signal and the response data to avoid distortion in dynamic characteristic analysis due to sampling delay. The standard flow generator must have programmable control capabilities to accurately reproduce step waveforms of different amplitudes and durations.

[0064] Extracting a characteristic parameter set including steady-state deviation, transient overshoot and noise interference level from the response data;

[0065] The steady-state deviation is calculated by calculating the difference between the mean value of the response curve during the step plateau period and the theoretical value, reflecting the sensitivity attenuation or zero-point drift of the sensor after long-term operation. The transient overshoot is determined by capturing the ratio of the maximum deviation amplitude at the moment of step switching to the steady-state value, and is used to evaluate the damping characteristics and dynamic response speed of the sensor. The noise interference level is determined by analyzing the standard deviation of the signal fluctuation or the proportion of high-frequency spectrum energy in the steady-state stage to quantify the sensor's anti-electromagnetic interference capability and the stability of the signal processing circuit.

[0066] Perform similarity matching calculation on the characteristic parameter set and the typical fault modes in the pre-stored error knowledge base to obtain the matching evaluation value of each error type;

[0067] The error knowledge base is built based on historical failure cases and experimental data, and includes threshold ranges for characteristic parameters of typical failure modes, such as "zero drift," "sensitivity degradation," and "circuit noise anomalies." Similarity matching uses a weighted Euclidean distance or cosine similarity algorithm to calculate the overall matching degree based on parameter importance, such as whether the weight of steady-state deviation exceeds the noise level. The resulting probabilistic evaluation value for each failure mode is output, ranging from 0 to 1.

[0068] An error correlation network model was constructed based on historical maintenance records to quantify the strength of the induced relationships between different error types.

[0069] The error association network is represented using a graph structure, with nodes representing error types and edge weights representing the strength of the induced relationship. The association strength is calculated by statistically analyzing the co-occurrence frequency of errors in historical data, combined with fault tree analysis or Bayesian network methods. The model requires dynamic updating, optimizing network weight parameters through incremental learning as new maintenance data is added.

[0070] Combine the matching evaluation value and the induced relationship strength to generate a composite weight coefficient to determine the dominant error type and associated influencing factors;

[0071] A multi-level compensation strategy is formulated based on the composite weight coefficient, and coordinated calibration of hardware parameter adjustment and software algorithm correction is implemented.

[0072] Multi-level compensation strategies are executed in a prioritized order, prioritizing hardware adjustments, such as adjusting the sensor bridge resistance to compensate for zero-point drift, followed by software filtering to suppress noise. Hardware parameter adjustments utilize PID closed-loop control with automatic potentiometer adjustment or digital fine-tuning circuits. Software corrections utilize adaptive Kalman filtering or compensation functions based on error models. Collaborative calibration verifies that the compensated characteristic parameters fall within acceptable thresholds; otherwise, an iterative optimization process is triggered.

[0073] Specifically, refer to Figure 2 As shown in Figure 2, the characteristic parameter set including steady-state deviation, transient overshoot and noise interference level extracted from the response data specifically includes:

[0074] Record the full-scale response curve under constant voltage test conditions and calculate the percentage deviation of the rise time from the theoretical value;

[0075] The constant pressure test applies a stable pressure step signal (such as a 0-100% FS step change) to ensure that the sensor records the complete dynamic response process without external interference. Rise time percentage deviation = (measured rise time - theoretical rise time) / theoretical value × 100%, where the theoretical value is derived from the sensor design specifications or factory calibration data. This indicator can effectively represent changes in the damping characteristics of the sensor's mechanical structure or the bandwidth attenuation of the signal conditioning circuit. During testing, it is necessary to ensure that the steepness of the excitation signal's rising edge (e.g., <1ms) exceeds the sensor's response capability, and capture detailed waveforms at a sampling rate of at least 1kHz;

[0076] Measure the zero-point output drift in a variable temperature environment and establish a linear relationship model between temperature change and zero-point offset;

[0077] The variable temperature environment test is usually carried out in a temperature control box, cycling in the range of -20℃ to 80℃ at a rate of 0.5-5℃ / min, and synchronously recording the temperature value and zero-point output. The linear model is fitted using the least squares method as ΔV=k·ΔT+b, where the slope k reflects the temperature drift coefficient (μV / ℃) and the intercept b corresponds to the zero-point deviation at normal temperature. When modeling, it is necessary to eliminate the lagging data in the temperature mutation stage (such as the data in the first 30 seconds after the temperature change), and verify whether the residual distribution conforms to the Gaussian characteristic to confirm the rationality of the linear assumption. This model provides key parameters for the temperature compensation algorithm, such as reverse correction of the zero-point output through real-time temperature sampling values in embedded systems;

[0078] The high-frequency noise components are extracted through wavelet decomposition, and the energy proportion of a specific frequency band is counted as an interference level indicator.

[0079] Wavelet decomposition uses the Daubechies series of basis functions to perform 5-8 multi-resolution analysis on the signal, isolating high-frequency detail components above 1kHz. The energy percentage of a specific frequency band = the sum of the squares of the wavelet coefficients in the target frequency band / the energy of the entire frequency band × 100%. The frequency band range is set based on the characteristics of the interference source. When the energy percentage exceeds a threshold, it indicates an electromagnetic compatibility issue or the risk of mechanical resonance. Compared to Fourier transforms, this method is more capable of capturing transient interference, and by selecting a wavelet basis that matches the noise characteristics, analysis sensitivity can be improved.

[0080] Reference Figure 3 As shown, the characteristic parameter set is matched with the typical fault modes in the pre-stored error knowledge base to perform similarity matching calculations, and the matching evaluation values of each error type are obtained, specifically including:

[0081] Calculate the number of common features between the current feature parameter and each fault mode standard feature and compare it with the number of elements in the union of the current feature parameter and each fault mode standard feature to obtain a basic matching degree;

[0082] According to the degree of deviation of the test environment temperature from the standard working condition, the matching degree of the temperature-sensitive error is enhanced according to the preset proportional coefficient to obtain the temperature correction value;

[0083] The basic matching degree and the temperature correction value are added together to obtain the final matching degree evaluation value.

[0084] The specific calculation formula is:

[0085]

[0086] Among them, S i is the final matching evaluation value between the current characteristic parameters and the i-th fault mode, F is the current characteristic parameter set, G i is the standard feature set of the i-th fault mode, ΔT is the deviation between the current temperature and the standard operating condition, and α is the temperature sensitivity factor, which ranges from 0.05 to 0.2;

[0087] A matching degree calculation mechanism combining multi-dimensional feature comparison with dynamic environmental compensation significantly improves fault pattern recognition accuracy and diagnostic reliability. Based on the intersection and union of feature parameter sets and standard fault pattern features, a quantitative evaluation framework for basic matching degree is established to ensure the objectivity of fault feature similarity judgment. A dynamic compensation algorithm for temperature deviation and sensitivity factors is introduced to adaptively adjust the matching weight of temperature-sensitive errors, effectively suppressing the risk of misjudgment caused by environmental interference. By superimposing and integrating basic matching degree with temperature correction values, a dual evaluation system is formed that considers both static feature correlation and the influence of dynamic operating conditions. This ensures that fault diagnosis results not only conform to the core parameter matching rules but also reflect the conductive effects of environmental variables. This matching degree calculation model overcomes the limitations of traditional threshold judgment criteria and provides a quantifiable decision-making basis for the parallel identification and hierarchical treatment of multiple types of errors under complex operating conditions.

[0088] An error correlation network model is constructed based on historical maintenance records to quantify the strength of the induced relationship between different error types. Specifically, the following are included:

[0089] Establish a directed relationship graph of error types, where nodes represent error types, the directions of directed edges represent the inducing relationships between error types, and the weights of directed edges represent the inducing probabilities between error types.

[0090] Specifically, if error type i induces error type j, a directed edge from error type i to error type j is constructed.

[0091] Specifically, refer to Figure 4 As shown in the figure, the method for determining the weight of the directed edge is as follows:

[0092] Determine the number of records in the historical maintenance records that contain error type i;

[0093] Determine the number of records in the historical maintenance records that contain both error type i and error type j.

[0094] Compare the number of records in which error type i and error type j occur simultaneously with the number of records in which error type i occurs to obtain the probability that error type i induces error type j.

[0095] It is understandable that flow sensor errors can be causally correlated. For example, when temperature-sensitive errors, such as zero-point drift caused by thermal expansion, are not promptly compensated, stress accumulation in the sensor's mechanical structure can lead to further mechanical stress errors, such as range offset caused by diaphragm deformation. Alternatively, when signal distortion errors caused by electromagnetic interference are not effectively suppressed, the reference voltage in the analog-to-digital conversion process can fluctuate, leading to nonlinear response errors. This type of error propagation chain is particularly pronounced under dynamic conditions. For example, in high-temperature environments, temperature drift errors can appear before other errors and form compound faults through internal sensor coupling mechanisms. This method constructs a causal error correlation network through statistical mining of historical maintenance data. Its core is to quantitatively reveal the potential induction patterns between error types.

[0096] Reference Figure 5 As shown in the figure, the composite weight coefficient is generated by combining the matching evaluation value and the induced relationship strength to determine the dominant error type and the associated influencing factors, which include:

[0097] Get all error types pointing to the error type, and form a set of induced error types that constitute the error type;

[0098] Get all error types pointed to by the error type to form the induced error type set of the error type;

[0099] Based on the matching evaluation values of all error types and the induced relationship strength between each error type, the composite weight coefficient of each error type is calculated through the correlation fitting formula;

[0100] Specifically, the correlation fitting formula is:

[0101]

[0102] Among them, R i is the composite weight coefficient of the i-th error type, β is the direct matching weight, S i is the matching evaluation value of the i-th error type, N i is the set of induced error types of the i-th error type, j is N i The jth element in S j N i The matching evaluation value of the jth element in w ij is the i-th error type pair Ni The probability of inducing the jth element in U j N i The set of induced error types for the jth element in , K is N i The kth element of the set of induced error types for the jth element in w kj N i The kth element of the set of induced error types of the jth element in N i The induction probability of the jth element in is , and γ is the associated influence factor.

[0103] This composite weight coefficient calculation model innovatively applies the core concepts of the PageRank algorithm to error correlation network analysis, quantifying the impact of error propagation by simulating the weight transfer mechanism between web pages. Its design principle can be analogized as follows: each error type is treated as a network node, the induced probability corresponds to the link weight between nodes, and the composite weight coefficient represents the node's global importance in the system. The reconstructed formula primarily reflects the following PageRank characteristics:

[0104] (1) Random walk mechanism: induced error superposition term Simulate the user's browsing behavior along the directed edge, that is, the influence of the current error i is partly determined by the weight S of the downstream error j that it may trigger j and trigger probability Joint decision-making;

[0105] (2) Weight normalization: The denominator is the sum of the inverse probabilities of the induced error set Implementing a "weighted distribution of outbound links" logic similar to PageRank to avoid bias in weight calculation caused by high-in-degree nodes;

[0106] (3) Introduction of damping factor: direct matching weight β and correlation influence factor γ, where β and γ satisfy β+γ=1. In some preferred embodiments, the value range of β is 0.6-0.8 and the value range of γ is 0.2-0.4, which corresponds to the random jump probability in PageRank. It is used to balance the contribution ratio of direct matching (local evidence) and network propagation (global association) to ensure the stability of the model between strong correlation chains and independent errors. This design makes the weight evaluation of error types not only depend on their own matching degree S i , and dynamically captures its potential risks as an "error propagation hub" through the topological network, which is particularly suitable for root cause tracing and priority sorting in multiple concurrent error scenarios.

[0107] Reference Figure 6 As shown in the figure, a multi-level compensation strategy is formulated based on the composite weight coefficient, and the coordinated calibration of hardware parameter adjustment and software algorithm correction is implemented, specifically including:

[0108] Screen out the error types whose composite weight coefficients exceed the compensation threshold as compensation errors;

[0109] When the compensation error is dominated by temperature drift, dual-channel compensation is activated: the hardware side adjusts the bridge excitation voltage, and the software side corrects the flow conversion factor;

[0110] When the compensation error is the accumulated error of mechanical wear, the self-learning compensation algorithm is started to predict the wear trend curve based on historical calibration data;

[0111] When the compensation error is a strong vibration interference, it switches to high-frequency sampling mode and enables digital filtering, and triggers the installation stability alarm.

[0112] A multi-level collaborative compensation mechanism driven by composite weights significantly improves the flow sensor's error suppression performance and system robustness under complex operating conditions. A weighted selection strategy based on an error correlation network accurately identifies core error types with high propagation risk, prioritizing targeted compensation for root-cause errors such as temperature drift and mechanical wear, effectively blocking the error transmission chain. The dual-channel collaborative design of hardware parameters and software algorithms achieves deep coupling between physical-layer signal conditioning and data-layer model correction. For example, in temperature drift compensation, dynamic adjustment of the hardware excitation voltage quickly eliminates thermal stress distortion, while synchronous correction of the software conversion coefficient further ensures output linearity. A self-learning compensation algorithm for mechanical wear integrates historical trend predictions with real-time data feedback to achieve progressive calibration, avoiding the over- or under-correction issues associated with traditional fixed compensation. A coupled mechanism of high-frequency sampling and digital filtering balances signal fidelity and noise suppression requirements in strong vibration interference scenarios. This strategy transcends the limitations of a single compensation model, enabling the system to implement intelligent control features that process multiple errors in parallel and resolve primary and secondary conflicts in a hierarchical manner, significantly enhancing adaptability to all operating conditions and calibration timeliness.

[0113] Furthermore, based on the same inventive concept as the above method, this solution proposes an intelligent flow sensor testing and analysis system, comprising:

[0114] A signal excitation module is used to apply a stepped flow excitation signal to the sensor under test through a standard flow generating device;

[0115] The dynamic data acquisition module is triggered synchronously with the signal excitation module to collect the dynamic response data of the sensor in real time and generate a time domain waveform sequence;

[0116] The characteristic parameter extraction module includes a steady-state analysis unit, a transient analysis unit, and a noise analysis unit. The steady-state analysis unit calculates the rise time percentage deviation based on the full-scale response curve. The transient analysis unit establishes a zero-point drift-temperature linear model based on variable temperature environment testing. The noise analysis unit uses a wavelet decomposition algorithm to extract the high-frequency noise energy ratio index.

[0117] The error matching engine is connected to the feature parameter extraction module and has a built-in pre-stored error knowledge base and a similarity calculator. The similarity calculator performs feature intersection ratio calculations and superimposes temperature deviation compensation to output matching evaluation values for each fault mode.

[0118] The association network modeling module integrates the historical maintenance database and constructs a weighted directed graph model. The weight is calculated through the statistical calculation of the error co-occurrence probability, reflecting the induction intensity between error types.

[0119] The composite weight decision maker receives the evaluation value of the error matching engine and the induced probability data of the association network model, fuses the direct matching degree and the multi-level association influencing factors through the association fitting formula, and generates a composite weight coefficient of the error type;

[0120] The multi-level collaborative calibration device triggers the compensation strategy selection logic according to the composite weight coefficient. It includes a dual-channel temperature drift compensation unit, a mechanical wear self-learning unit and a vibration interference suppression unit. The dual-channel compensation unit synchronously adjusts the hardware bridge voltage and software conversion coefficient. The self-learning unit predicts the wear curve based on historical data and dynamically corrects the gain parameters. The vibration suppression unit switches to high-frequency sampling mode and activates the digital filter and installation stability alarm function.

[0121] Furthermore, the method according to the embodiment of the present application can also be used with the aid of Figure 7 The electronic device architecture shown in FIG. Figure 7 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, ROM 503, RAM 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as ROM 503 or hard disk 507, may store a smart flow sensor test and analysis method provided by the present application. The electronic device 500 may also include a user interface 508. Of course, Figure 7 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 7 One or more components of an electronic device are shown.

[0122] Figure 8 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 8, a computer-readable storage medium 600 according to one embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by a processor, the intelligent flow sensor testing and analysis method according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0123] In summary, the advantages of the present invention are: through step-by-step dynamic excitation testing and multi-source data fusion analysis, the systematicness and reliability of flow sensor error diagnosis are significantly improved. Based on the synchronous monitoring of multimodal flow excitation patterns and environmental parameters, the dynamic separation and precise tracing of complex errors are achieved, and the recognition ability of steady-state deviations and transient overshoots is effectively enhanced; combined with the error correlation network modeling driven by historical data, the inducing mechanism and interactive relationship of different error types can be analyzed, greatly improving the pertinence of the compensation strategy; through the coordinated optimization of hardware compensation modules and software adaptive algorithms, a multi-level error suppression mechanism is formed, which realizes the stable control of comprehensive errors under complex working conditions and reduces the system maintenance requirements. This technical solution breaks through the limitations of traditional calibration methods and provides a more adaptive error management path for industrial measurement and control scenarios.

[0124] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A test and analysis method for an intelligent flow sensor, characterized in that: include: Apply a stepped flow excitation signal to the sensor under test through a standard flow generating device, and synchronously collect the dynamic response data of the sensor; Extracting a characteristic parameter set including steady-state deviation, transient overshoot and noise interference level from the response data; Perform similarity matching calculation on the characteristic parameter set and typical fault modes in a pre-stored error knowledge base to obtain a matching evaluation value for each error type; An error correlation network model was constructed based on historical maintenance records to quantify the strength of the induced relationships between different error types. Combine the matching evaluation value and the induced relationship strength to generate a composite weight coefficient to determine the dominant error type and associated influencing factors; A multi-level compensation strategy is formulated based on the composite weight coefficient, and coordinated calibration of hardware parameter adjustment and software algorithm correction is implemented.

2. The intelligent flow sensor testing and analysis method according to claim 1, characterized in that: The step of extracting a characteristic parameter set including steady-state deviation, transient overshoot and noise interference level from the response data specifically includes: Record the full-scale response curve under constant voltage test conditions and calculate the percentage deviation of the rise time from the theoretical value; Measure the zero-point output drift in a variable temperature environment and establish a linear relationship model between temperature change and zero-point offset; The high-frequency noise components are extracted through wavelet decomposition, and the energy proportion of a specific frequency band is counted as an interference level indicator.

3. The intelligent flow sensor testing and analysis method according to claim 2, characterized in that: The performing similarity matching calculation on the characteristic parameter set and the typical fault modes in the pre-stored error knowledge base to obtain the matching evaluation value of each error type specifically includes: Calculate the number of common features between the current feature parameter and each fault mode standard feature and compare it with the number of elements in the union of the current feature parameter and each fault mode standard feature to obtain a basic matching degree; According to the degree of deviation of the test environment temperature from the standard working condition, the matching degree of the temperature-sensitive error is enhanced according to the preset proportional coefficient to obtain the temperature correction value; The basic matching degree and the temperature correction value are added together to obtain the final matching degree evaluation value.

4. The intelligent flow sensor testing and analysis method according to claim 3, characterized in that: The error association network model is constructed based on historical maintenance records to quantify the strength of the induced relationship between different error types. Specifically, the following steps are involved: Establish a directed relationship graph of error types, where nodes represent error types, the directions of directed edges represent the inducing relationships between error types, and the weights of directed edges represent the inducing probabilities between error types. Specifically, if error type i induces error type j, a directed edge from error type i to error type j is constructed.

5. The intelligent flow sensor testing and analysis method according to claim 4, characterized in that: The method for determining the directed edge weight is specifically as follows: Determine the number of records in the historical maintenance records that have error type i; Determine the number of records in the historical maintenance records that contain both error type i and error type j. Compare the number of records in which error type i and error type j occur simultaneously with the number of records in which error type i occurs to obtain the probability that error type i induces error type j.

6. The intelligent flow sensor testing and analysis method according to claim 5, characterized in that: The generation of a composite weight coefficient by combining the matching evaluation value and the induced relationship strength to determine the dominant error type and associated influencing factors specifically includes: Get all error types pointing to the error type, and form a set of induced error types that constitute the error type; Get all error types pointed to by the error type to form the induced error type set of the error type; Based on the matching evaluation values of all error types and the induced relationship strength between each error type, the composite weight coefficient of each error type is calculated through the correlation fitting formula; Specifically, the correlation fitting formula is: Among them, R i is the composite weight coefficient of the i-th error type, β is the direct matching weight, S i is the matching evaluation value of the i-th error type, N i is the set of induced error types of the i-th error type, j is N i The jth element in S j N i The matching evaluation value of the jth element in w ij is the i-th error type pair N i The probability of inducing the jth element in U j N i The set of induced error types for the jth element in , K is N i The kth element of the set of induced error types for the jth element in w kj N i The kth element of the set of induced error types of the jth element in N i The induction probability of the jth element in is , and γ is the associated influence factor.

7. The intelligent flow sensor testing and analysis method according to claim 6, characterized in that: The multi-level compensation strategy is formulated based on the composite weight coefficient, and the coordinated calibration of hardware parameter adjustment and software algorithm correction is implemented specifically including: Screen out the error types whose composite weight coefficients exceed the compensation threshold as compensation errors; When the compensation error is dominated by temperature drift, dual-channel compensation is activated: the hardware side adjusts the bridge excitation voltage, and the software side corrects the flow conversion factor; When the compensation error is the accumulated error of mechanical wear, the self-learning compensation algorithm is started to predict the wear trend curve based on historical calibration data; When the compensation error is a strong vibration interference, it switches to high-frequency sampling mode and enables digital filtering, and triggers the installation stability alarm.

8. An intelligent flow sensor testing and analysis system, characterized in that: A method for testing and analyzing an intelligent flow sensor according to any one of claims 1 to 7, comprising: A signal excitation module is used to apply a stepped flow excitation signal to the sensor under test through a standard flow generating device; The dynamic data acquisition module is triggered synchronously with the signal excitation module to collect the dynamic response data of the sensor in real time and generate a time domain waveform sequence; The characteristic parameter extraction module includes a steady-state analysis unit, a transient analysis unit, and a noise analysis unit. The steady-state analysis unit calculates the rise time percentage deviation based on the full-scale response curve. The transient analysis unit establishes a zero-point drift-temperature linear model based on variable temperature environment testing. The noise analysis unit uses a wavelet decomposition algorithm to extract the high-frequency noise energy ratio index. An error matching engine, connected to the feature parameter extraction module, has a built-in pre-stored error knowledge base and a similarity calculator. The similarity calculator performs a feature intersection ratio calculation and superimposes a temperature deviation compensation amount to output a matching evaluation value for each fault mode. The association network modeling module integrates the historical maintenance database and constructs a weighted directed graph model. The weight is calculated by the statistical calculation of the error co-occurrence probability, reflecting the induction intensity between error types. The composite weight decision maker receives the evaluation value of the error matching engine and the induced probability data of the association network model, fuses the direct matching degree and the multi-level association influencing factors through the association fitting formula, and generates a composite weight coefficient of the error type; The multi-level collaborative calibration device triggers the compensation strategy selection logic according to the composite weight coefficient, and includes a dual-channel temperature drift compensation unit, a mechanical wear self-learning unit, and a vibration interference suppression unit. The dual-channel compensation unit synchronously adjusts the hardware bridge voltage and software conversion coefficient. The self-learning unit predicts the wear curve based on historical data and dynamically corrects the gain parameters. The vibration suppression unit switches to high-frequency sampling mode and activates the digital filter and installation stability alarm function.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the intelligent flow sensor testing and analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the intelligent flow sensor testing and analysis method according to any one of claims 1 to 7 is implemented.

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