A method for calculating flow meter data

Abstract

This invention discloses a method for calculating flow meter data, relating to the field of flow measurement technology. It addresses the problem of excessive computational resource consumption under complex operating conditions, which negatively impacts the overall system's operational efficiency. The method acquires the operating signal records of an onboard flow meter, filters and marks abnormal signal segments, collects timestamps and detects signal energy distribution, calculates a distortion score based on time interval differences and energy distribution, and calculates an overall distortion index by combining real-time waveform characteristics. Segmented calculation improves distortion detection accuracy and reduces resource consumption, while entropy weighting is used to assess the importance of signal segments for subsequent repair. A repair algorithm is selected based on the distortion index. During dynamic repair, errors and harmonic interference are detected and counted, the repair cycle is calculated, and early warnings are generated, improving the reliability of the calculated data.

Description

Technical Field

[0001] This invention relates to the field of flow measurement technology, and more specifically, to a method for calculating flow meter data. Background Technology

[0002] In practical applications, vehicle-mounted flow meters frequently face the problem of data distortion. This distortion can be caused by various factors, such as signal interference, transmission loss, or equipment errors, leading to a decrease in the accuracy and reliability of flow measurement. To address this issue, existing technologies have proposed various data restoration methods, using algorithms to repair and restore distorted data to improve measurement accuracy. However, existing data restoration techniques still have certain limitations when processing flow data under complex operating conditions, making it difficult to fully meet the demands of modern industry for efficient and accurate flow measurement.

[0003] The existing technology has the following shortcomings:

[0004] Current methods primarily focus on restoring network traffic data, without fully considering the characteristics of vehicle-mounted flow meter data. They emphasize ensuring the integrity of data during acquisition and transmission, but have limited ability to repair data distortion, especially when dealing with common nonlinear distortions and high-frequency noise in vehicle-mounted flow meter data. This results in the inability to achieve high-precision data restoration, leading to excessive computational resource consumption under complex operating conditions and impacting the overall system efficiency. Therefore, this paper proposes a new method for flow meter data calculation.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide a flow meter data calculation method. This method analyzes the signal fluctuation characteristics of a vehicle-mounted flow meter and combines this with a multi-dimensional data processing algorithm to extract key characteristic parameters of distorted data. A dynamic weight allocation mechanism is used to repair the distorted data, and data quality indicators are monitored in real time during the repair process to generate optimized flow measurement results. This method solves the data distortion problem of vehicle-mounted flow meters mentioned in the background art, especially the insufficient ability to repair high-frequency noise and nonlinear distortion under complex operating conditions.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for calculating flow meter data, comprising the following steps:

[0008] Step S1: Obtain the working signal record of the vehicle-mounted flow meter, filter out abnormal signal segments and mark them according to the fluctuation amplitude and frequency characteristics in the signal record; collect the timestamp information of the marked signal segments, detect the amplitude change of the input signal when the vehicle-mounted flow meter is working and calculate the signal energy distribution;

[0009] Step S2: Calculate the time interval difference of the signal segments based on the timestamp information of the marked signal segments. Calculate the distortion score of the corresponding signal segment using the entropy weight method based on the time interval difference and signal energy distribution. Detect the real-time waveform characteristics of each marked signal segment and calculate the overall distortion index of the vehicle flow meter.

[0010] Step S3: Select either a static repair algorithm or a dynamic repair algorithm to process the distorted data based on the overall distortion index. After each processing with the dynamic repair algorithm, measure the signal repair error and calculate the error change. Collect the number of peaks in the signal spectrum as harmonic interference counts and record them.

[0011] Step S4: Calculate the quality coefficient of the current signal using a neural network algorithm based on the combined signal repair error change and harmonic interference count. Adjust the execution cycle of the dynamic repair algorithm according to the quality coefficient and generate an early warning prompt.

[0012] In a preferred embodiment, in step S1, the working signal records of the vehicle flow meter are retrieved from the historical database. Each signal record contains the signal amplitude and frequency characteristics of the vehicle flow meter under normal working conditions in the historical data. The signal amplitude and frequency characteristics of the vehicle flow meter under normal working conditions are used as the reference signal segment.

[0013] The amplitude and frequency characteristics of all reference signal segments in the vehicle flow meter are recorded and the average value is calculated as the signal filtering threshold. Signal segments that exceed the signal filtering threshold are filtered out and marked.

[0014] In a preferred embodiment, in step S1, a period of time is selected as the analysis window when the vehicle-mounted flow meter is working, and the amplitude change of the input signal when the vehicle-mounted flow meter is working is detected by the signal acquisition module within the analysis window.

[0015] Calculate the signal energy distribution based on the analysis window and the amplitude change of the input signal: E = A 2 ×t;

[0016] Where A is the amplitude of the input signal when the vehicle-mounted flow meter is working, t is the time interval of the analysis window, and E is the energy distribution of the corresponding signal segment;

[0017] Within the analysis window, two time points are randomly selected to detect the timestamp information of each marked signal segment.

[0018] In a preferred embodiment, in step S2, the difference between the timestamp information detected at the next time point and the timestamp information detected at the previous time point is calculated in the analysis window to obtain the time interval difference of the marked signal segment.

[0019] The distortion rate increase coefficient of each marked signal segment is calculated based on the time interval difference and signal energy distribution, and used as the distortion score of each marked signal segment.

[0020] Calculate the distortion growth factor of the marked signal segment: C = Δt × E;

[0021] Where Δt is the time interval difference of the marked signal segments, E is the signal energy distribution of the marked signal segments, C is the distortion growth coefficient of the corresponding marked signal segments, and i is the sequence number of each marked signal segment;

[0022] The signal analysis module is used to detect the real-time waveform characteristics of each marked signal segment. The distortion growth coefficient of each marked signal segment is used as the distortion score. The distortion scores are sorted from smallest to largest. The entropy weight method is used to set the distortion weight for each marked signal segment after sorting.

[0023] In a preferred embodiment, in step S2, the distortion weight is set using the entropy weight method and the overall distortion index of the entire vehicle flow meter is calculated by combining the real-time waveform characteristics of each marked signal segment.

[0024] The specific steps are as follows: Construct a distortion matrix by using the distortion scores of each marked signal segment to compare the relative importance of each signal segment;

[0025] Normalize each column of the distortion matrix so that the sum of each column equals 1;

[0026] Calculate the weight value of each factor. The weight value is the weighted average of each column of the distortion matrix. Use the weight value of each signal segment as the quantification value of the importance of each signal segment.

[0027] The weight value of each signal segment is used as the distortion weight of the corresponding marked signal segment. The distortion weight of each marked signal segment is weighted and summed with the real-time waveform characteristics of the corresponding marked signal segment to obtain the overall distortion index of the entire vehicle flow meter.

[0028] In a preferred embodiment, in step S3, when the overall distortion index of the vehicle flow meter is lower than the distortion index threshold, the distorted data is processed using a static repair algorithm.

[0029] When the overall distortion index of the vehicle flow meter exceeds the distortion index threshold, a dynamic repair algorithm is used to process the distorted data.

[0030] In a preferred embodiment, in step S3, after each data processing is completed, the signal repair error is measured and recorded using the error analysis module, and the difference between the current measured signal repair error and the previously recorded signal repair error is used to obtain the change in signal repair error.

[0031] The signal spectrum is collected according to the set fixed sampling frequency, and the peak values ​​exceeding the preset amplitude are obtained and counted as harmonic interference counts and recorded.

[0032] In a preferred embodiment, in step S4, the quality coefficient of the current signal is calculated using a neural network algorithm by combining the change in signal repair error and the harmonic interference count: Q = k / (w1×Δe+w2×N);

[0033] Where Q is the quality coefficient of the current signal, Δe is the change in signal repair error after the current data processing, N is the harmonic interference count after the current data processing, w1 and w2 are the influence weights of the two parameters respectively, and k is the adjustment constant;

[0034] The reciprocal of the quality coefficient of the current signal is taken as the adjustment ratio of the execution cycle of the next dynamic repair algorithm, and the product of the execution cycle of the current dynamic repair algorithm and the adjustment ratio of the execution cycle is taken as the execution cycle of the next dynamic repair algorithm.

[0035] In a preferred embodiment, in step S4, the execution cycle is recorded in real time each time the dynamic repair algorithm is used to process the distorted data. When the processing time reaches the current execution cycle, the static repair algorithm is switched to process the data of the vehicle flow meter.

[0036] If the execution cycle of the next dynamic repair algorithm, calculated from the execution cycle of the current dynamic repair algorithm, is lower than the preset cycle threshold, an early warning will be issued and a prompt will be made to replace the signal acquisition module.

[0037] The technical effects and advantages of this invention are as follows:

[0038] 1. This invention filters and marks abnormal signal segments of a vehicle-mounted flow meter by acquiring its operating signal records. It collects the timestamp information of the marked signal segments and detects the signal energy distribution during their operation. Based on the time interval difference and signal energy distribution of each marked signal segment, it calculates the distortion score for each segment. Then, based on the distortion scores and real-time waveform characteristics of each segment, it calculates the overall distortion index of the vehicle-mounted flow meter. By calculating the overall distortion index segment by segment, it reduces signal processing resource consumption while improving the accuracy of distortion detection. Furthermore, it analyzes the relative importance of each signal segment using the entropy weight method, facilitating subsequent targeted repair. Different repair algorithms are selected based on the overall distortion index to process the distorted data. When using a dynamic repair algorithm, it detects signal repair errors and harmonic interference counts, calculates the execution cycle of the dynamic repair algorithm, and generates early warning prompts, thereby improving the reliability of vehicle-mounted flow meter data measurement. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the overall process of a flow meter data calculation method according to the present invention.

[0040] Figure 2 This is a logic block diagram of the distortion score calculation process in a flow meter data measurement method of the present invention.

[0041] Figure 3 This is a schematic diagram of the dynamic repair algorithm execution cycle adjustment mechanism in a flow meter data measurement method of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] Please see Figures 1 to 3 A method for calculating flow meter data, the specific operation process is as follows:

[0045] Step S1: Obtain the working signal record of the vehicle-mounted flow meter, filter out abnormal signal segments and mark them according to the fluctuation amplitude and frequency characteristics in the signal record; collect the timestamp information of the marked signal segments, detect the amplitude change of the input signal when the vehicle-mounted flow meter is working and calculate the signal energy distribution;

[0046] Step S2: Calculate the time interval difference of the signal segments based on the timestamp information of the marked signal segments. Calculate the distortion score of the corresponding signal segment using the entropy weight method based on the time interval difference and signal energy distribution. Detect the real-time waveform characteristics of each marked signal segment and calculate the overall distortion index of the vehicle flow meter.

[0047] Step S3: Select either a static repair algorithm or a dynamic repair algorithm to process the distorted data based on the overall distortion index. After each processing with the dynamic repair algorithm, measure the signal repair error and calculate the error change. Collect the number of peaks in the signal spectrum as harmonic interference counts and record them.

[0048] Step S4: Calculate the quality coefficient of the current signal using a neural network algorithm based on the combined signal repair error change and harmonic interference count. Adjust the execution cycle of the dynamic repair algorithm according to the quality coefficient and generate an early warning prompt.

[0049] In practical applications, the above method is implemented through the following modules, including the coordinated operation of a signal acquisition module, a distortion score calculation module, a dynamic repair module, a static repair module, a quality coefficient calculation module, and an early warning module to complete the measurement and repair of flow meter data.

[0050] First, the signal acquisition module is used to acquire the working signal records of the vehicle-mounted flow meter and perform preliminary screening.

[0051] In the specific implementation process, the signal acquisition module retrieves the signal amplitude and frequency characteristics of the vehicle flow meter under normal working conditions from the historical database as the reference signal segment. After statistically analyzing the amplitude and frequency characteristics of these reference signal segments, the average value is calculated and used as the signal filtering threshold.

[0052] When the vehicle-mounted flow meter is running, the signal acquisition module collects the amplitude changes of the current signal in real time and compares them with the signal filtering threshold, marking abnormal signal segments that exceed the threshold.

[0053] Meanwhile, the signal acquisition module also records the timestamp information of each abnormal signal segment and calculates the signal energy distribution by detecting the amplitude change of the input signal through the analysis window.

[0054] The time interval of the analysis window is set by the user, usually between 0.1 seconds and 1 second, to ensure that the effects of high-frequency noise can be captured.

[0055] The formula for calculating signal energy distribution is E = A. 2 ×t, where A is the amplitude of the input signal, t is the time interval of the analysis window, and E is the energy distribution value of the corresponding signal segment.

[0056] Through the above operations of the signal acquisition module, abnormal signal segments that need further processing can be initially screened out, and basic data can be provided for subsequent steps.

[0057] Next, the distortion score calculation module further analyzes the abnormal signal segments marked by the signal acquisition module and calculates their distortion scores.

[0058] The distortion score calculation module first calculates the time interval difference based on the timestamp information of the marked signal segments, and then calculates the distortion growth rate coefficient in combination with the signal energy distribution, which serves as the distortion score for each marked signal segment.

[0059] Specifically, two time points are randomly selected within the analysis window to detect the timestamp information of each marked signal segment, and the time interval difference Δt is obtained by subtracting the timestamp of the previous time point from the timestamp of the later time point.

[0060] Subsequently, the distortion scoring calculation module uses the formula C=Δt×E to calculate the distortion growth coefficient C, where Δt is the time interval difference and E is the signal energy distribution.

[0061] The distortion growth rate coefficient C reflects the rate at which signal distortion increases; a larger value indicates more severe distortion. The distortion scoring calculation module sorts the distortion growth rate coefficients of all marked signal segments from smallest to largest and uses the entropy weight method to set the distortion weights.

[0062] The specific implementation process of the entropy weight method is as follows: First, construct a distortion matrix. Form a matrix by the distortion scores of each marked signal segment and normalize each column so that the sum of each column is equal to 1.

[0063] Then, the weight value of each factor is calculated. The weight value is the weighted average of each column of the distortion matrix. Finally, the weight value of each signal segment is used as its importance quantification value.

[0064] Finally, the distortion scoring calculation module weights and sums the distortion weights of each marked signal segment with its real-time waveform characteristics to obtain the overall distortion index of the entire vehicle flow meter.

[0065] The overall distortion index comprehensively reflects the degree of distortion of the vehicle flow meter, providing a basis for subsequent selection of repair algorithms.

[0066] After determining the overall distortion index, the dynamic repair module and the static repair module select different repair strategies based on the magnitude of the distortion index.

[0067] If the overall distortion index is lower than the preset distortion index threshold, the static repair module is activated to process the distorted data; if the overall distortion index is higher than or equal to the distortion index threshold, the dynamic repair module is activated to repair it.

[0068] After each repair is completed, the dynamic repair module measures and records the signal repair error through the error analysis module.

[0069] The error analysis module subtracts the signal repair error of the current measurement from the signal repair error of the previous measurement to obtain the change in signal repair error Δe.

[0070] Meanwhile, the dynamic repair module also collects the signal spectrum according to the set fixed sampling frequency, extracts the number of peak values ​​exceeding the preset amplitude as the harmonic interference count N and records it.

[0071] The change in signal repair error and the count of harmonic interference are important indicators for evaluating the repair effect. Together, they determine the adjustment ratio of the execution cycle of the dynamic repair algorithm.

[0072] The quality coefficient calculation module is responsible for comprehensively analyzing the changes in signal repair error and harmonic interference counting, and using a neural network algorithm to calculate the quality coefficient Q of the current signal.

[0073] The quality coefficient is calculated as Q = k / (w1×Δe + w2×N), where k is the adjustment constant, and w1 and w2 are the influence weights of the signal repair error change and the harmonic interference count, respectively.

[0074] The reciprocal of the quality coefficient Q is used as the adjustment ratio for the execution cycle of the next dynamic repair algorithm, that is, the next execution cycle is equal to the current execution cycle multiplied by 1 / Q.

[0075] The dynamic repair module adjusts the execution cycle based on the calculation results to ensure that the repair process remains efficient and accurate under different levels of distortion.

[0076] In addition, the dynamic repair module records the execution cycle of each repair in real time, and switches to the static repair module for processing when the processing time reaches the current execution cycle.

[0077] If the next execution cycle calculated from the current execution cycle of the dynamic repair algorithm is lower than the preset cycle threshold, the warning module will issue a warning and prompt the signal acquisition module to be replaced.

[0078] Throughout the process, the connections and collaboration between the various modules are crucial.

[0079] The signal acquisition module transmits the marked abnormal signal segments and their timestamp information to the distortion score calculation module, which in turn provides decision-making basis for the dynamic repair module and the static repair module by calculating the distortion score and the overall distortion index.

[0080] The dynamic repair module and the static repair module select the appropriate repair strategy based on the overall distortion index and feed the repair results back to the quality coefficient calculation module.

[0081] The quality coefficient calculation module calculates the quality coefficient using a neural network algorithm and passes the result to the dynamic repair module to adjust the execution cycle.

[0082] The early warning module determines whether an early warning needs to be issued based on the execution cycle of the dynamic repair module, thus forming a closed-loop control system.

[0083] This modular design not only improves the system's flexibility but also ensures efficient collaboration between modules.

[0084] In practical applications, suppose the vehicle-mounted flow meter is installed on a heavy truck traveling on complex road conditions. Due to the long-term operation of the vehicle, the flow meter signal is affected by factors such as road bumps, engine vibration, and electromagnetic interference, resulting in high-frequency noise and nonlinear distortion.

[0085] At this point, the flow meter data calculation method provided by the present invention processes the signal step by step through modular design, ensuring that the final flow measurement result has high accuracy and stability.

[0086] First, the signal acquisition module starts working, calling the signal amplitude and frequency characteristics of the vehicle flow meter under normal conditions from the historical database as the reference signal segments, and calculating the average value of these reference signal segments to determine the signal filtering threshold.

[0087] When the vehicle-mounted flow meter is running, the signal acquisition module collects the amplitude changes of the current signal in real time and compares them with the signal filtering threshold, marking abnormal signal segments that exceed the threshold.

[0088] Meanwhile, the signal acquisition module records the timestamp information of each abnormal signal segment and calculates the signal energy distribution by detecting the amplitude change of the input signal through the analysis window.

[0089] For example, select a 0.5-second time interval in the analysis window, and combine it with the formula E=A 2 Calculate the signal energy distribution value E using the formula ×t.

[0090] If, at this time, the amplitude A of a certain signal segment is 2 volts and the time interval t is 0.5 seconds, then the energy distribution value E of that signal segment is 2. 2 ×0.5=2 Joules.

[0091] In this way, the signal acquisition module can initially screen out abnormal signal segments that need further processing and pass the timestamp information of these signal segments to the distortion score calculation module.

[0092] Next, the distortion score calculation module performs further analysis based on the abnormal signal segments marked by the signal acquisition module.

[0093] Randomly select two time points in the analysis window, denoted as t1 and t2 respectively, and calculate the time interval difference Δt = t2 - t1.

[0094] For example, if t1 is 0.2 seconds and t2 is 0.7 seconds, then Δt = 0.7 - 0.2 = 0.5 seconds.

[0095] Subsequently, the distortion scoring calculation module uses the formula C=Δt×E to calculate the distortion growth rate coefficient C.

[0096] For example, if Δt is 0.5 seconds and the signal energy distribution E is 2 joules, then the distortion growth factor C = 0.5 × 2 = 1.

[0097] The distortion growth rate coefficient C reflects the rate at which signal distortion increases; the larger the value, the more severe the distortion.

[0098] The distortion scoring calculation module sorts the distortion rate increase coefficients of all marked signal segments from smallest to largest and uses the entropy weight method to set the distortion weights.

[0099] The specific implementation process of the entropy weight method is as follows: First, construct a distortion matrix. Form a matrix by the distortion scores of each marked signal segment and normalize each column so that the sum of each column is equal to 1.

[0100] Then, the weight value of each factor is calculated. The weight value is the weighted average of each column of the distortion matrix. Finally, the weight value of each signal segment is used as its importance quantification value.

[0101] Finally, the distortion scoring calculation module weights and sums the distortion weights of each marked signal segment with its real-time waveform characteristics to obtain the overall distortion index of the entire vehicle flow meter.

[0102] For example, if the distortion weight of a certain signal segment is 0.3 and the real-time waveform characteristic is 0.5, then the comprehensive score of the signal segment is 0.3 × 0.5 = 0.15.

[0103] In this way, the distortion scoring calculation module can comprehensively evaluate the distortion level of the vehicle flow meter and transmit the overall distortion index to the dynamic repair module and the static repair module.

[0104] In practical applications, assuming the vehicle-mounted flow meter is installed on a vehicle traveling on complex road conditions, factors such as road bumps and engine vibrations can cause high-frequency noise and nonlinear distortion in the flow meter signal.

[0105] At this point, the signal acquisition module first retrieves the signal characteristics under normal working conditions from the historical database, and then filters out abnormal signal segments and marks their timestamp information based on the current signal amplitude changes.

[0106] The distortion scoring calculation module calculates the distortion growth rate coefficient based on the time interval difference of the marked signal segments and the signal energy distribution, and sets the distortion weight using the entropy weight method to finally obtain the overall distortion index.

[0107] If the overall distortion index is high, the dynamic repair module will start and adjust the execution cycle according to the change in signal repair error and harmonic interference count to gradually repair the distorted data.

[0108] During this process, the quality coefficient calculation module continuously optimizes the repair strategy, while the early warning module monitors the system status in real time to ensure the reliability of the flow meter data measurement.

[0109] Through the above process, the present invention effectively solves the problem of data distortion in vehicle-mounted flow meters under complex working conditions, and significantly improves the accuracy and stability of flow measurement.

[0110] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0111] In practical applications, assume that the vehicle-mounted flow meter is installed on a heavy truck traveling on complex road conditions.

[0112] Due to the long-term operation of vehicles, the on-board flow meter signal is affected by factors such as road bumps, engine vibration, and electromagnetic interference, resulting in high-frequency noise and nonlinear distortion in the signal.

[0113] At this point, the flow meter data calculation method provided by the present invention processes the signal step by step through modular design, ensuring that the final flow measurement result has high accuracy and stability.

[0114] First, the signal acquisition module starts working, calling the signal amplitude and frequency characteristics of the vehicle flow meter under normal conditions from the historical database as the reference signal segments, and calculating the average value of these reference signal segments to determine the signal filtering threshold.

[0115] When the vehicle-mounted flow meter is running, the signal acquisition module collects the amplitude changes of the current signal in real time and compares them with the signal filtering threshold, marking abnormal signal segments that exceed the threshold.

[0116] Meanwhile, the signal acquisition module records the timestamp information of each abnormal signal segment and calculates the signal energy distribution by detecting the amplitude change of the input signal through the analysis window.

[0117] For example, select a 0.5-second time interval in the analysis window, and combine it with the formula E=A 2 Calculate the signal energy distribution value E using the formula ×t.

[0118] If, at this time, the amplitude A of a certain signal segment is 2 volts and the time interval t is 0.5 seconds, then the energy distribution value E of that signal segment is 2. 2 ×0.5=2 Joules.

[0119] In this way, the signal acquisition module can initially screen out abnormal signal segments that need further processing and pass the timestamp information of these signal segments to the distortion score calculation module.

[0120] Next, the distortion score calculation module performs further analysis based on the abnormal signal segments marked by the signal acquisition module.

[0121] Randomly select two time points in the analysis window, denoted as t1 and t2 respectively, and calculate the time interval difference Δt = t2 - t1.

[0122] For example, if t1 is 0.2 seconds and t2 is 0.7 seconds, then Δt = 0.7 - 0.2 = 0.5 seconds.

[0123] Subsequently, the distortion scoring calculation module uses the formula C=Δt×E to calculate the distortion growth rate coefficient C.

[0124] For example, if Δt is 0.5 seconds and the signal energy distribution E is 2 joules, then the distortion growth factor C = 0.5 × 2 = 1.

[0125] The distortion growth rate coefficient C reflects the rate at which signal distortion increases; the larger the value, the more severe the distortion.

[0126] The distortion scoring calculation module sorts the distortion rate increase coefficients of all marked signal segments from smallest to largest and uses the entropy weight method to set the distortion weights.

[0127] The specific implementation process of the entropy weight method is as follows: First, construct a distortion matrix. Form a matrix by the distortion scores of each marked signal segment and normalize each column so that the sum of each column is equal to 1.

[0128] Then, the weight value of each factor is calculated. The weight value is the weighted average of each column of the distortion matrix. Finally, the weight value of each signal segment is used as its importance quantification value.

[0129] Finally, the distortion scoring calculation module weights and sums the distortion weights of each marked signal segment with its real-time waveform characteristics to obtain the overall distortion index of the entire vehicle flow meter.

[0130] For example, if the distortion weight of a certain signal segment is 0.3 and the real-time waveform characteristic is 0.5, then the comprehensive score of the signal segment is 0.3 × 0.5 = 0.15.

[0131] In this way, the distortion scoring calculation module can comprehensively evaluate the distortion level of the vehicle flow meter and transmit the overall distortion index to the dynamic repair module and the static repair module.

[0132] After determining the overall distortion index, the dynamic repair module and the static repair module select different repair strategies based on the magnitude of the distortion index.

[0133] For example, if the overall distortion index is lower than the preset distortion index threshold (e.g., 0.8), the static repair module is activated to process the distorted data; if the overall distortion index is higher than or equal to the distortion index threshold, the dynamic repair module is activated to repair it.

[0134] After each repair is completed, the dynamic repair module measures and records the signal repair error through the error analysis module.

[0135] The error analysis module subtracts the signal repair error of the current measurement from the signal repair error of the previous measurement to obtain the change in signal repair error Δe.

[0136] For example, if the signal repair error recorded last time was 0.05, and the signal repair error measured this time is 0.03, then Δe = 0.05 - 0.03 = 0.02.

[0137] Meanwhile, the dynamic repair module also collects the signal spectrum according to the set fixed sampling frequency, extracts the number of peak values ​​exceeding the preset amplitude as the harmonic interference count N and records it.

[0138] For example, if the number of peaks exceeding the preset amplitude in the signal spectrum is 3, then the harmonic interference count N = 3.

[0139] The change in signal repair error and the count of harmonic interference are important indicators for evaluating the repair effect. Together, they determine the adjustment ratio of the execution cycle of the dynamic repair algorithm.

[0140] The quality coefficient calculation module is responsible for comprehensively analyzing the changes in signal repair error and harmonic interference counting, and using a neural network algorithm to calculate the quality coefficient Q of the current signal.

[0141] The formula for calculating the quality coefficient is Q=k / (w1×Δe+w2×N), where k is an adjustment constant (e.g., 10), and w1 and w2 are the influence weights of the signal repair error change and harmonic interference count, respectively (e.g., w1=0.6, w2=0.4).

[0142] For example, if Δe = 0.02 and N = 3, then Q = 10 / (0.6 × 0.02 + 0.4 × 3) = 10 / (0.012 + 1.2) ≈ 8.26.

[0143] The reciprocal of the quality coefficient Q is used as the adjustment ratio for the execution cycle of the next dynamic repair algorithm, that is, the next execution cycle is equal to the current execution cycle multiplied by 1 / Q.

[0144] For example, if the current execution cycle is 1 second, then the next execution cycle will be 1 × (1 / 8.26) ≈ 0.12 seconds.

[0145] The dynamic repair module adjusts the execution cycle based on the calculation results to ensure that the repair process remains efficient and accurate under different levels of distortion.

[0146] In addition, the dynamic repair module records the execution cycle of each repair in real time, and switches to the static repair module for processing when the processing time reaches the current execution cycle.

[0147] If the next execution cycle calculated from the current execution cycle of the dynamic repair algorithm is lower than the preset cycle threshold (e.g., 0.1 seconds), the warning module will issue a warning and prompt the signal acquisition module to be replaced.

[0148] Throughout the process, the connections and collaboration between the various modules are crucial.

[0149] The signal acquisition module transmits the marked abnormal signal segments and their timestamp information to the distortion score calculation module, which in turn provides decision-making basis for the dynamic repair module and the static repair module by calculating the distortion score and the overall distortion index.

[0150] The dynamic repair module and the static repair module select the appropriate repair strategy based on the overall distortion index and feed the repair results back to the quality coefficient calculation module.

[0151] The quality coefficient calculation module calculates the quality coefficient using a neural network algorithm and passes the result to the dynamic repair module to adjust the execution cycle.

[0152] The early warning module determines whether an early warning needs to be issued based on the execution cycle of the dynamic repair module, thus forming a closed-loop control system.

[0153] This modular design not only improves the system's flexibility but also ensures efficient collaboration between modules.

[0154] Through the above process, the present invention effectively solves the problem of data distortion in vehicle-mounted flow meters under complex operating conditions.

[0155] For example, during vehicle operation, high-frequency noise may occur in the flow meter signal due to road bumps. The signal acquisition module can quickly filter out abnormal signal segments, the distortion scoring calculation module can accurately locate the degree of distortion by calculating the distortion growth coefficient and the overall distortion index, the dynamic repair module can optimize the repair strategy based on the change in signal repair error and harmonic interference count, and the quality coefficient calculation module can dynamically adjust the execution cycle through a neural network algorithm to ultimately ensure the accuracy and stability of the flow measurement results.

[0156] This process significantly improves the data processing capabilities of vehicle-mounted flow meters under complex operating conditions, meeting the needs of modern industry for efficient and accurate flow measurement.

[0157] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0158] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0159] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0160] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0161] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0165] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for calculating flow meter data, characterized in that: Includes the following steps: Step S1: Obtain the working signal record of the vehicle-mounted flow meter, filter out abnormal signal segments and mark them according to the fluctuation amplitude and frequency characteristics in the signal record; collect the timestamp information of the marked signal segments, detect the amplitude change of the input signal when the vehicle-mounted flow meter is working and calculate the signal energy distribution; Step S2: Calculate the time interval difference of the signal segments based on the timestamp information of the marked signal segments. Calculate the distortion score of the corresponding signal segment using the entropy weight method based on the time interval difference and signal energy distribution. Detect the real-time waveform characteristics of each marked signal segment and calculate the overall distortion index of the vehicle flow meter. In step S2, the distortion weight is set using the entropy weight method and the overall distortion index of the entire vehicle flow meter is calculated by combining the real-time waveform characteristics of each marked signal segment. The specific steps are as follows: Construct a distortion matrix by using the distortion scores of each marked signal segment to compare the relative importance of each signal segment; Normalize each column of the distortion matrix so that the sum of each column equals 1; Calculate the weight value of each factor. The weight value is the weighted average of each column of the distortion matrix. Use the weight value of each signal segment as the quantification value of the importance of each signal segment. The weight value of each signal segment is used as the distortion weight of the corresponding marked signal segment. The distortion weight of each marked signal segment is weighted and summed with the real-time waveform characteristics of the corresponding marked signal segment to obtain the overall distortion index of the entire vehicle flow meter. Step S3: Select either a static repair algorithm or a dynamic repair algorithm to process the distorted data based on the overall distortion index. After each processing with the dynamic repair algorithm, measure the signal repair error and calculate the error change. Collect the number of peaks in the signal spectrum as harmonic interference counts and record them. Step S4: Calculate the current signal quality coefficient using a neural network algorithm based on the combined signal repair error change and harmonic interference count. Adjust the execution cycle of the dynamic repair algorithm according to the quality coefficient and generate an early warning prompt. In step S4, the quality coefficient of the current signal is calculated using a neural network algorithm based on the combined signal repair error change and harmonic interference count: Q=k / (w1×Δe+w2×N); Where Q is the quality coefficient of the current signal, Δe is the change in signal repair error after the current data processing, N is the harmonic interference count after the current data processing, w1 and w2 are the influence weights of the two parameters respectively, and k is the adjustment constant; The reciprocal of the quality coefficient of the current signal is taken as the adjustment ratio of the execution cycle of the next dynamic repair algorithm, and the product of the execution cycle of the current dynamic repair algorithm and the adjustment ratio of the execution cycle is taken as the execution cycle of the next dynamic repair algorithm.

2. The flow meter data calculation method according to claim 1, characterized in that: In step S1, the working signal records of the vehicle flow meter are retrieved from the historical database. Each signal record contains the signal amplitude and frequency characteristics of the vehicle flow meter under normal working conditions in the historical data. The signal amplitude and frequency characteristics of the vehicle flow meter under normal working conditions are used as the reference signal segment. The amplitude and frequency characteristics of all reference signal segments in the vehicle flow meter are recorded and the average value is calculated as the signal filtering threshold. Signal segments that exceed the signal filtering threshold are filtered out and marked.

3. The flow meter data calculation method according to claim 2, characterized in that: In step S1, a period of time is selected as the analysis window when the vehicle-mounted flow meter is working. Within the analysis window, the signal acquisition module is used to detect the amplitude change of the input signal when the vehicle-mounted flow meter is working. Calculate the signal energy distribution based on the analysis window and the amplitude change of the input signal: E = A² × t; Where A is the amplitude of the input signal when the vehicle-mounted flow meter is working, t is the time interval of the analysis window, and E is the energy distribution of the corresponding signal segment; Within the analysis window, two time points are randomly selected to detect the timestamp information of each marked signal segment.

4. The flow meter data calculation method according to claim 3, characterized in that: In step S2, the difference between the timestamp information detected at the next time point and the timestamp information detected at the previous time point is calculated in the analysis window to obtain the time interval difference of the marked signal segment; The distortion rate increase coefficient of each marked signal segment is calculated based on the time interval difference and signal energy distribution, and used as the distortion score of each marked signal segment. Calculate the distortion growth factor of the marked signal segment: ; in, To mark the time interval difference of the signal segments, Let C represent the signal energy distribution of the marked signal segment, C be the distortion growth coefficient of the corresponding marked signal segment, and i be the sequence number of each marked signal segment. The signal analysis module is used to detect the real-time waveform characteristics of each marked signal segment. The distortion growth coefficient of each marked signal segment is used as the distortion score. The distortion scores are sorted from smallest to largest. The entropy weight method is used to set the distortion weight for each marked signal segment after sorting.

5. The flow meter data calculation method according to claim 1, characterized in that: In step S3, when the overall distortion index of the vehicle flow meter is lower than the distortion index threshold, the distorted data is processed using a static repair algorithm. When the overall distortion index of the vehicle flow meter exceeds the distortion index threshold, a dynamic repair algorithm is used to process the distorted data.

6. The flow meter data calculation method according to claim 5, characterized in that: In step S3, after each data processing is completed, the signal repair error is measured and recorded using the error analysis module, and the change in signal repair error is obtained by subtracting the measured signal repair error from the previously recorded signal repair error. The signal spectrum is collected according to the set fixed sampling frequency, and the peak values ​​exceeding the preset amplitude are obtained and counted as harmonic interference counts and recorded.

7. The flow meter data calculation method according to claim 1, characterized in that: In step S4, the execution cycle is recorded in real time each time the dynamic repair algorithm is used to process the distorted data. When the processing time reaches the current execution cycle, the static repair algorithm is switched to process the data of the vehicle flow meter. If the execution cycle of the next dynamic repair algorithm, calculated from the execution cycle of the current dynamic repair algorithm, is lower than the preset cycle threshold, an early warning will be issued and a prompt will be made to replace the signal acquisition module.

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