Intelligent ultrasonic volume correction instrument calibration method and device

Abstract

The application discloses a kind of intelligent ultrasonic volume correction instrument verification method and device, method includes: according to the data type of detection data, construct verification three-dimensional model, three-dimensional model is segmented into multiple regions;The volume correction instrument of being detected is detected, and the first dynamic error score of static error score is calculated, the static error score is analyzed, determines static verification data and first supplementary detection data, and supplementary detection is carried out by first supplementary detection data;The second dynamic error score of each region is calculated, and the region to be verified is determined;Supplementary detection is carried out by second supplementary detection data;The first dynamic error score of second supplementary detection data is calculated, and the third dynamic error score is determined in conjunction with the first dynamic error score of detection data, and the third dynamic error score is analyzed according to, determines dynamic verification data.The application is verified by static and dynamic flow comprehensively, and the verification accuracy of ultrasonic volume correction instrument is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fluid metering, and more particularly to a smart ultrasonic volume correction instrument calibration method and device. BACKGROUND

[0002] Ultrasonic volume correction instruments are widely used in industrial production, scientific research and other fields for accurate measurement and correction of the volume of liquids or gases. The measurement accuracy directly affects the reliability of the data related to production and research. However, the existing ultrasonic volume correction instrument calibration methods have problems such as complicated calibration process, low calibration accuracy, and inability to comprehensively test the performance of ultrasonic volume correction instruments. For example, the traditional method is not accurate in collecting and analyzing ultrasonic signals during the calibration process, and it is difficult to accurately determine the error of the ultrasonic volume correction instrument under different environmental conditions and measurement ranges, resulting in calibration results that cannot truly reflect the actual performance of the ultrasonic volume correction instrument, thereby affecting its use effect and reliability in actual application.

[0003] In addition, the traditional method is usually static calibration, which only calibrates the temperature / pressure sensor separately and does not verify the correction performance of the whole machine under dynamic flow.

[0004] Therefore, there is an urgent need for a new calibration method to improve the accuracy and efficiency of ultrasonic volume correction instrument calibration. SUMMARY

[0005] In view of the above problems, the purpose of the present application is to provide a smart ultrasonic volume correction instrument calibration method and device, which can improve the calibration speed and accuracy of the ultrasonic volume correction instrument.

[0006] The first aspect of the present application provides a smart ultrasonic volume correction instrument calibration method, comprising:

[0007] obtaining historical detection data;

[0008] determining detection data i based on the historical detection data, and establishing a detection data set;

[0009] constructing a calibration three-dimensional model according to the data type of the detection data i, and dividing the three-dimensional model into a plurality of regions with the same specification based on a preset segmentation rule;

[0010] adjusting the environmental temperature by the detection data i, detecting the volume correction instrument to be detected, and calculating the static error score and the first dynamic error score of the detection data;

[0011] analyzing the static error score to determine static calibration data and first supplementary detection data, and performing supplementary detection by the first supplementary detection data;

[0012] Calculate the second dynamic error score of each area based on the first dynamic error score to determine the area to be verified;

[0013] Determining detection data of the region vertices of the region to be verified as second supplementary detection data, and performing supplementary detection using the second supplementary detection data;

[0014] Calculate a first dynamic error score of the second supplementary detection data, determine a third dynamic error score in combination with the first dynamic error score of the detection data, and perform analysis based on the third dynamic error score to determine dynamic verification data.

[0015] In this solution, the ambient temperature is adjusted according to the detection data i, the volume corrector is tested, and the static error score of the detection data is calculated, including:

[0016] The ambient temperature is adjusted according to the test data i. After the ambient temperature stabilizes, the same pressure signal is applied to the volume corrector under test and the standard volume tube synchronously through a dual-channel precision pressure controller.

[0017] Collect the first stable output value V of the volume corrector under test 1(i) And the second stable output value V of the standard volume tube 2(i) ;

[0018] Calculate the static error score Q 1(i) ;

[0019] .

[0020] In this solution, analyzing the static error score to determine static verification data and first supplementary detection data includes:

[0021] The static error score Q 1(i) and the first error score threshold Q a and the second error score threshold Q b Make a comparison;

[0022] When Q 1(i) ≥Q b When the detected volume corrector is abnormal, it is determined that the detected volume corrector has an abnormality;

[0023] When Q 1(i) ≤Q a When , no processing is done;

[0024] When Q a <Q 1(i) <Q b When the static error score Q 1(i) The corresponding detection data i is marked;

[0025] The labeled detection data is pairwise analyzed to determine first supplementary detection data, a static error score of the first supplementary detection data is calculated, first supplementary detection data with a static error score between a first error score threshold and a second error score threshold is marked, and the labeled detection data is iteratively calculated until there is no labeled detection data.

[0026] In the scheme, the labeled detection data is pairwise analyzed to determine first supplementary detection data, including:

[0027] All detection data is input into a three-dimensional model for testing;

[0028] The labeled detection data is pairwise analyzed, and the selected labeled detection data is determined as first labeled detection data j a and second labeled detection data j b .

[0029] A long radius is determined according to a pixel distance between the first labeled detection data j a and the second labeled detection data j b .

[0030] The long radius is input into a preset ellipsoidal region construction model, a short radius is determined by a product of the long radius and a preset radius ratio, and an ellipsoidal region E (ab) is generated.

[0031] When the ellipsoidal region E (ab) has no other detection data, the center coordinates of the first labeled detection data j a and the second labeled detection data j b are calculated, and the first supplementary detection data is determined.

[0032] Conversely, the influence score of each other detection data and the labeled detection data in the ellipsoidal region E (ab) is calculated, and the other detection data with the largest influence score is determined as the first detection data of the labeled detection data.

[0033] The center coordinates of the labeled detection data and the corresponding first detection data are calculated, and the first supplementary detection data is determined.

[0034] The scheme further includes:

[0035] The average value of the static error scores of the detection data corresponding to the region vertex coordinates is calculated, and the region static error score is determined.

[0036] The region with a region static error score between a first error score threshold and a second error score threshold is marked.

[0037] The occurrence frequency ratio of the historical detection data in the marked region is counted to determine the evaluation coefficient corresponding to each marked region.

[0038] The region static error score of each marking area is multiplied by the corresponding evaluation coefficient respectively, and the calculation result is accumulated to determine a static error comprehensive evaluation score;

[0039] When the static error comprehensive evaluation score is greater than a first preset evaluation score, it is determined that the volume correction instrument has an abnormality.

[0040] In the scheme, further comprising:

[0041] The ambient temperature is adjusted by a preset parameter adjustment speed e;

[0042] A first dynamic output value V of the volume correction instrument in the ambient temperature adjustment process is collected 3(e) And a second dynamic output value V of the standard volume tube 4(e) ;

[0043] A first dynamic error score Q is calculated 2(e) ;

[0044] ;

[0045] The detection data before and after adjustment and the corresponding first dynamic error score are input to a verification three-dimensional model to determine the display color of the line segment between the detection data before and after adjustment;

[0046] The verification three-dimensional model is color gradient processed according to the display color of the line segment between the detection data to determine the first dynamic error score between each detection data.

[0047] In the scheme, the second dynamic error score of each region is calculated by the first dynamic error score to determine the region to be verified, comprising:

[0048] The detection data corresponding to the region vertex of the current region is obtained, and the first dynamic error score between each region vertex in the current region is calculated;

[0049] The weighted average value of all first dynamic error scores is calculated to determine the second dynamic error score of the current region;

[0050] After the calculation of all regions is completed, the regions with the second dynamic error score greater than the corresponding preset dynamic evaluation score threshold are iteratively segmented and the second dynamic error score is calculated;

[0051] When the preset iterative segmentation times are met, the region with the second dynamic error score greater than the corresponding preset dynamic threshold is determined as the region to be verified.

[0052] In the scheme, the first dynamic error score of the second supplementary detection data is calculated, the third dynamic error score is determined in combination with the first dynamic error score of the detection data, analysis is performed according to the third dynamic error score, and the dynamic verification data is determined, comprising:

[0053] The first dynamic error score of the second supplementary detection data is calculated.

[0054] The first dynamic error scores of the detection data and the second supplementary detection data are multiplied by the corresponding adjustment time respectively, the calculation results are accumulated, and the third dynamic error score Q3 is obtained.

[0055] When the third dynamic error score Q3 is greater than the third error score threshold, it is determined that the object volume correction instrument has an abnormality.

[0056] Conversely, the display color of the verification three-dimensional model is adjusted according to the first dynamic error score of the second supplementary detection data.

[0057] In the scheme, further comprising:

[0058] The second supplementary detection data is reacquired by displaying the verification three-dimensional model after the color adjustment, and the supplementary detection is continued.

[0059] The second aspect of the application provides an intelligent ultrasonic volume correction instrument verification device, wherein the intelligent ultrasonic volume correction instrument verification device comprises an intelligent ultrasonic volume correction instrument verification method program, and the intelligent ultrasonic volume correction instrument verification method program is executed by a processor to realize the steps of the intelligent ultrasonic volume correction instrument verification method.

[0060] The application discloses an intelligent ultrasonic volume correction instrument verification method and device, which comprises the following steps: constructing a verification three-dimensional model according to the data type of detection data, dividing the three-dimensional model into multiple regions; detecting the object volume correction instrument, calculating a static error score and a first dynamic error score, analyzing the static error score, determining static verification data and first supplementary detection data, and performing supplementary detection through the first supplementary detection data; calculating a second dynamic error score of each region to determine a region to be verified; performing supplementary detection through second supplementary detection data; calculating a first dynamic error score of the second supplementary detection data, determining a third dynamic error score in combination with the first dynamic error score of the detection data, and analyzing the third dynamic error score to determine dynamic verification data. The application performs verification through static and dynamic flow, and improves the verification accuracy of the ultrasonic volume correction instrument. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 A flowchart of an intelligent ultrasonic volume correction instrument verification method provided by the application is shown;

[0062] Figure 2 A flow chart of a static error score calculation method provided by the present application is shown.

[0063] Figure 3 A flow chart of a first supplementary detection data calculation method provided by the present application is shown. DETAILED DESCRIPTION

[0064] In order to enable a more clear understanding of the above-mentioned objects, features and advantages of the present application, the present application will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0065] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, however, the present application can also be implemented in other manners different from those described herein, and therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below.

[0066] Figure 1 A flow chart of a smart ultrasonic volume correction instrument verification method provided by the present application is shown.

[0067] As shown in Figure 1 The present application discloses a smart ultrasonic volume correction instrument verification method, comprising:

[0068] S102, historical detection data is acquired;

[0069] S104, detection data i is determined based on the historical detection data, and a detection data set is established;

[0070] S106, a verification three-dimensional model is constructed according to the data type of the detection data i, and the three-dimensional model is segmented into a plurality of regions with the same specification based on a preset segmentation rule;

[0071] S108, the environmental temperature is adjusted through the detection data i, the volume correction instrument is detected, and the static error score and the first dynamic error score of the detection data are calculated;

[0072] S110, the static error score is analyzed, the static verification data and the first supplementary detection data are determined, and supplementary detection is performed through the first supplementary detection data;

[0073] S112, the second dynamic error score of each region is calculated through the first dynamic error score, and the region to be verified is determined;

[0074] S114, the detection data of the region vertex of the region to be verified is determined as the second supplementary detection data, and supplementary detection is performed through the second supplementary detection data;

[0075] S116, a first dynamic error score of the second supplementary detection data is calculated, a third dynamic error score is determined in combination with the first dynamic error score of the detection data, analysis is performed according to the third dynamic error score, and the dynamic calibration data is determined.

[0076] According to the embodiment of the present application, the historical detection data is obtained by collecting the detection data used in the historical ultrasonic volume correction instrument calibration process and performing data cleaning (including processing missing values, correcting abnormal values, and unifying data formats). The detection data is obtained by pressure sensors, temperature sensors, and flow sensors. The data types of the detection data include pressure signals p, temperature t, and flow v. The system divides each detection data into data intervals with the same value range. Assuming that each detection data is divided into n data intervals, the historical detection data can be divided into n x n x n distribution intervals. The historical detection data is analyzed, the historical detection data is divided according to the value range of each detection data, and a plurality of historical detection data is selected as the detection data i of the current volume correction instrument based on the distribution proportion of the historical detection data in each distribution interval. Among them, the distribution interval with a larger distribution proportion selects more detection data i, and the detection data i is determined by random selection.

[0077] The calibration three-dimensional model is located in a three-dimensional coordinate system, and the three coordinate axes of the three-dimensional coordinate system are divided into pressure signals p, temperature t, and flow v. The calibration three-dimensional model is divided into a plurality of regions with the same size according to the system preset segmentation rule, each region is a cubic structure, and each region stores 8 region vertices.

[0078] During the calibration of the volume correction instrument by the detection data i, the temperature t in the detection data i is used to adjust the environmental temperature of the volume correction instrument, and a double-channel precision pressure controller is used to apply the same pressure signal to the volume correction instrument and the standard volume tube simultaneously to simulate the actual work of the volume correction instrument. After the environmental temperature is stabilized, the first stable output value of the volume correction instrument and the second stable output value of the standard volume tube are collected, the static error score is calculated, and the first dynamic output value of the volume correction instrument and the second dynamic output value of the standard volume tube are collected during the temperature adjustment process (based on the system preset parameter adjustment speed to adjust the environmental temperature), and the first dynamic error score is calculated.

[0079] Firstly, the static error score of the detection data i is analyzed, and the static error score is compared with the first error score threshold and the second error score threshold, when the static error score is greater than or equal to the second error score threshold, it is determined that the current volume correction instrument exists abnormity, and the detection is ended, otherwise, the next detection data is continuously detected. In the detection process, the detection data between the first error score threshold and the second error score threshold is marked. After all the detection data analysis is completed, the first supplementary detection data is determined by analyzing the marked detection data two by two, and the first supplementary detection data is input to the detection data set through the supplementary detection. The static error score of the first supplementary detection data continues to determine the next group of first supplementary detection data, and after multiple iteration calculations, there is no marked detection data, and the first supplementary detection data obtained in the supplementary detection process is input to the detection data set. In addition, since the volume correction instrument is detected by sampling, the detection error of the volume correction instrument under each detection data cannot be accurately obtained, the static error of each region is evaluated by the regional static error score, and the volume correction instrument is assisted in the detection by calculating the static error comprehensive evaluation score.

[0080] Then, the dynamic error is evaluated by adjusting the first dynamic error scores corresponding to the front and rear detection data, a new detection three-dimensional model is established, the front and rear detection data and the corresponding first dynamic error scores are input into the detection three-dimensional model, the display color of the line segment between the front and rear detection data is determined, the display color of other pixel coordinates is determined by the known display color of the pixel coordinates, and the display color of each pixel coordinate in the detection three-dimensional model is determined. The first dynamic error score corresponding to the two detection data is determined by the display color of the center point between the detection data. The second dynamic error score of the current region is determined by calculating the weighted average value of the first dynamic error scores between the region vertices in the same region. The region whose second dynamic error score is greater than the corresponding preset dynamic evaluation score threshold is iteratively segmented and the second dynamic error score is calculated, and when the preset iteration segmentation times are met, the region whose second dynamic error score is greater than the corresponding preset dynamic threshold is determined as the verification region. The detection data of the region vertex of the verification region is determined as the second supplementary detection data, and the supplementary detection is performed. The first dynamic error score of the second supplementary detection data is obtained by the supplementary detection, the first dynamic error scores of the detection data and the second supplementary detection data are multiplied by the corresponding adjustment time respectively, the calculation results are accumulated, and the third dynamic error score is obtained. The volume correction instrument is assisted in the detection by the third dynamic error score.

[0081] In addition, the detection method used in the present application is not only suitable for the detection of the volume correction instrument matched with the gas ultrasonic flowmeter, but also suitable for the detection of the volume correction instrument matched with the gas waist wheel flowmeter and the gas turbine flowmeter.

[0082] Figure 2 A flow chart of the static error score calculation method provided by the present application is shown.

[0083] As shown in Figure 2 , according to the embodiment of the present application, the static error score of the detection data is calculated by detecting the volume correction instrument under the adjusted ambient temperature, comprising:

[0084] S202, the ambient temperature is adjusted according to the detection data i, and after the ambient temperature is stable, the same pressure signal is synchronously applied to the volume correction instrument and the standard volume tube by the double-channel precision pressure controller;

[0085] S204, the first stable output value V 1(i) of the volume correction instrument and the second stable output value V 2(i) of the standard volume tube are collected;

[0086] S206, the static error score Q 1(i) is calculated;

[0087] .

[0088] It should be noted that first, the ambient temperature of the volume correction instrument is adjusted by the temperature t in the detection data i, so that the volume correction instrument and the standard volume tube are in a constant temperature field corresponding to the temperature t, and then the same pressure signal is synchronously applied to the volume correction instrument and the standard volume tube by the double-channel precision pressure controller, the same flow rate v is input, and the first stable output value V 1(i) of the volume correction instrument and the second stable output value V 2(i) of the standard volume tube are obtained.

[0089] After the static error score Q 1(i) of the detection data i is determined, the static error score Q 1(i) of the detection data i is input to the calibration three-dimensional model to determine the pixel color of the pixel coordinate corresponding to the detection data i. The calibration three-dimensional model is subjected to color gradient processing according to the pixel color of the pixel coordinate corresponding to the detection data i, and the static error scores corresponding to other detection data are determined according to the pixel color of each pixel coordinate.

[0090] According to the embodiment of the present application, the static error score is analyzed to determine the static calibration data and the first supplementary detection data, comprising:

[0091] The static error score Q 1(i) is compared with the first error score threshold Q a and the second error score threshold Q b respectively;

[0092] When Q 1(i) ≥ Q b , it is determined that the object volume correction instrument is abnormal;

[0093] When Q 1(i) ≤ Q a , no processing is performed;

[0094] When Q a < Q 1(i) < Q b , the static error score Q 1(i) of the first supplementary detection data is calculated, and the detection data i corresponding to the static error score is marked;

[0095] The marked detection data is pairwise analyzed to determine the first supplementary detection data, the static error score of the first supplementary detection data is calculated, the first supplementary detection data whose static error score is between the first error score threshold and the second error score threshold is marked, and the iteration calculation is performed until there is no marked detection data.

[0096] It should be noted that the first error score threshold Q a and the second error score threshold Q b are determined by the system according to the error accuracy of the object volume correction instrument, the first error score threshold Q a is smaller than the second error score threshold Q b . For example, if the error accuracy of the object volume correction instrument is ≤ ± 0.5%, the second error score threshold Q b is 0.5%, and the value of the first error score threshold Q a is 0-0.5%, and the specific value of the first error score threshold Q a is determined by the system. When the static error score of any detection data is greater than or equal to the second error score threshold, it is determined that the current object volume correction instrument is abnormal, and the detection is ended; otherwise, the next detection data is detected. In the detection process, the detection data between the first error score threshold and the second error score threshold is marked. After all the detection data is analyzed, the marked detection data is pairwise analyzed, the first supplementary detection data obtained is used for supplementary detection, and the iteration calculation is performed until all the first supplementary detection data is determined, and the detection data set is supplemented. In addition, in the pairwise analysis of the marked detection data, the detection data with a pixel distance less than the minimum analysis pixel distance set by the system is filtered.

[0097] Figure 3 A flowchart of a first supplementary detection data calculation method provided by the application is shown.

[0098] As Figure 3As shown, according to the embodiment of the present application, the mark detection data is analyzed two by two to determine the first supplementary detection data, including:

[0099] S302, input all detection data into the three-dimensional model for testing;

[0100] S304, analyze the mark detection data two by two, and determine the selected mark detection data as the first mark detection data j a and the second mark detection data j b ;

[0101] S306, determine the long radius according to the pixel distance between the first mark detection data j a and the second mark detection data j b ;

[0102] S308, input the long radius into the preset ellipsoid region construction model, determine the short radius by the product of the long radius and the preset radius ratio, and generate the ellipsoid region E (ab) ;

[0103] S310, when the ellipsoid region E (ab) does not exist other detection data, calculate the center coordinates of the first mark detection data j a and the second mark detection data j b , and determine the first supplementary detection data;

[0104] S312, otherwise, calculate the influence score of each other detection data and the mark detection data in the ellipsoid region E (ab) , and determine the other detection data with the largest influence score as the first detection data of the mark detection data;

[0105] S314, calculate the center coordinates of the mark detection data and the corresponding first detection data, and determine the first supplementary detection data.

[0106] It should be noted that the ellipsoid region construction model is used to construct the ellipsoid region, and the radius ratio between the long radius and the short radius of the ellipsoid region is set by the system. The first mark detection data j a and the second mark detection data j bThe pixel distance of the three-dimensional model corresponding to the pixel coordinates is determined, a long radius is determined by multiplying the long radius by a preset radius ratio, a short radius is determined, an elliptical region is drawn, the elliptical region is rotated according to the long radius, and an ellipsoid region is generated. The ellipsoid region has two marker detection data, including first marker detection data and second marker detection data, and each marker detection region corresponds to a first detection data. The static error scores of other detection data and the pixel distances between other detection data and the marker detection data are multiplied by the corresponding error calculation weights, respectively, the calculation results are accumulated, and the influence scores of the other detection data and the marker detection data are determined.

[0107] According to the embodiment of the application, the method further comprises:

[0108] The average value of the static error scores of the detection data corresponding to the vertex coordinates of the region is determined as a region static error score.

[0109] The region is marked when the region static error score is between the first error score threshold and the second error score threshold.

[0110] The occurrence frequency ratio of the historical detection data in the marked region is determined as an evaluation coefficient corresponding to each marked region.

[0111] The region static error scores of each marked region are multiplied by the corresponding evaluation coefficients, respectively, the calculation results are accumulated, and a static error comprehensive evaluation score is determined.

[0112] When the static error comprehensive evaluation score is greater than a first preset evaluation score, it is determined that the volume correction instrument has an abnormality.

[0113] It should be noted that, since the volume correction instrument is subjected to sampling inspection, the detection error of the volume correction instrument under each detection data cannot be accurately obtained, and the static error comprehensive evaluation is performed through the region static error scores of each region. A new three-dimensional model is established, the historical detection data are input into the new three-dimensional model, the occurrence frequency of the historical detection data in each marked region is counted, the ratio of the occurrence frequency of the historical detection data in the marked region to the total number of the historical detection data is calculated, the occurrence frequency ratio of the historical detection data in the marked region is determined, and the evaluation coefficient corresponding to each marked region is determined.

[0114] The first preset evaluation score is set by a person skilled in the art according to actual needs.

[0115] According to the embodiment of the application, the method further comprises:

[0116] The environmental temperature is adjusted by the preset parameter adjustment speed e.

[0117] The first dynamic output value V of the volume correction instrument is collected during the adjustment of the environmental temperature. 3(e)and the second dynamic output value V of the standard volume tube 4(e) ;

[0118] calculating the first dynamic error score Q 2(e) ;

[0119] ;

[0120] inputting the detection data before and after adjustment and the corresponding first dynamic error score into the verification three-dimensional model to determine the display color of the line segment between the detection data before and after adjustment;

[0121] performing color gradient processing on the verification three-dimensional model according to the display color of the line segment between the detection data to determine the first dynamic error score between each detection data.

[0122] It should be noted that the preset parameter adjustment speed e is set by the system and is determined by simulating the temperature change speed of the volume correction instrument in the actual use process. By establishing a new verification three-dimensional model, the detection data before and after adjustment and the corresponding first dynamic error score are input into the verification three-dimensional model to determine the display color of the line segment between the detection data before and after adjustment, and the display color gradient processing is performed on other pixel coordinates according to the pixel coordinates of the known display color to determine the display color corresponding to each pixel coordinate in the verification three-dimensional model. Each display color corresponds to a unique first dynamic error score, and the corresponding first dynamic error score is obtained by detecting the display color of the center point between the detection data to determine the first dynamic error score between the two detection data.

[0123] According to the embodiment of the application, the second dynamic error score of each region is calculated by the first dynamic error score to determine the region to be verified, including:

[0124] obtaining the detection data corresponding to the region vertex of the current region, and calculating the first dynamic error score between each region vertex in the current region;

[0125] calculating the weighted average value of all first dynamic error scores to determine the second dynamic error score of the current region;

[0126] After all regions are calculated, the regions with a second dynamic error score greater than the corresponding preset dynamic evaluation score threshold are iteratively segmented and the second dynamic error score is calculated;

[0127] When the preset number of iterative segmentations is met, the region with a second dynamic error score greater than the corresponding preset dynamic threshold is determined as the region to be verified.

[0128] It should be noted that the first dynamic error score between the region vertices in the current region is determined by multiplying the first dynamic error score between the region vertices in the current region by the corresponding weighted weight determined by the minimum pixel distance between the region center point and the line segment between the region vertices, and the calculation result is accumulated to determine the second dynamic error score of the current region. The preset dynamic evaluation score threshold is set by the system, and the specific value of the preset dynamic evaluation score threshold is determined by the number of iterations. The more the number of iterations, the higher the value of the preset dynamic evaluation score threshold. By repeatedly segmenting the region whose second dynamic error score is greater than the corresponding preset dynamic evaluation score threshold until the number of iterations meets the preset number of iterations, the region whose second dynamic error score is greater than the corresponding preset dynamic threshold at this time is determined as the region to be verified.

[0129] The preset number of iterations is set by a person skilled in the art according to actual needs.

[0130] According to the embodiment of the application, the first dynamic error score of the second supplementary detection data is calculated, the third dynamic error score is determined in combination with the first dynamic error score of the detection data, the third dynamic error score is analyzed, and the dynamic calibration data is determined, including:

[0131] The first dynamic error score of the second supplementary detection data is calculated;

[0132] The first dynamic error scores of the detection data and the second supplementary detection data are multiplied by the corresponding adjustment time, and the calculation result is accumulated to obtain the third dynamic error score Q3;

[0133] When the third dynamic error score Q3 is greater than the third error score threshold, it is determined that the volume correction instrument to be detected has an abnormality;

[0134] Conversely, the display color of the calibration three-dimensional model is adjusted according to the first dynamic error score of the second supplementary detection data.

[0135] It should be noted that the first dynamic error score of the second supplementary detection data is determined by supplementally detecting the volume correction instrument to be detected by the second supplementary detection data. The adjustment time is the time used for adjusting the ambient temperature from the pre-detection adjustment data to the post-detection adjustment data. When the third dynamic error score Q3 is less than or equal to the third error score threshold, the display color of the corresponding pixel coordinates in the calibration three-dimensional model is updated by the second supplementary detection data. Based on the updated display color, the display colors of other pixel coordinates in the calibration three-dimensional model are synchronously updated, thereby improving the calibration accuracy.

[0136] The third error score threshold is set by a person skilled in the art according to actual needs.

[0137] According to the embodiment of the present application, further comprising:

[0138] The second supplementary detection data is reacquired by displaying the color-adjusted verification three-dimensional model, and the supplementary detection is continued.

[0139] It should be noted that the supplementary detection is performed by acquiring the second supplementary detection data multiple times, the detection sample of the sampling detection is supplemented, and the detection accuracy of the volume correction instrument verification is improved. When the to-be-verified area does not exist in the verification three-dimensional model or the number of supplementary detections reaches the system set number, the detection is ended.

[0140] The second aspect of the present application provides an intelligent ultrasonic volume correction instrument verification device, which comprises an intelligent ultrasonic volume correction instrument verification method program. When the intelligent ultrasonic volume correction instrument verification method program is executed by a processor, the steps of the intelligent ultrasonic volume correction instrument verification method are realized.

[0141] In addition, the intelligent ultrasonic volume correction instrument verification device provided by the present application can also simultaneously verify multiple volume correction instruments. Multiple volume correction instruments can be fixed on the intelligent ultrasonic volume correction instrument verification device. The double-channel precision pressure controller is replaced by a multi-channel precision pressure controller. The standard volume tube and each detected volume correction instrument are connected by the multi-channel precision pressure controller. The same pressure signal is synchronously applied to each detected volume correction instrument and the standard volume tube, so as to realize the synchronous verification of multiple volume correction instruments. During the synchronous verification of multiple volume correction instruments, the detection data (including the detection data, the first compensation detection data and the second supplementary detection data) are comprehensively analyzed and determined by the system based on the verification data (including the static verification data and the dynamic verification data) of all detected volume correction instruments.

[0142] The information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) involved in the present application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions. For example, the "historical detection data" and the like involved in the present disclosure are acquired under sufficient authorization.

[0143] The application discloses a kind of intelligent ultrasonic volume correction instrument verification method and device, method includes: according to the data type of detection data, construct verification three-dimensional model, three-dimensional model is divided into multiple regions;The volume correction instrument of being detected is detected, the static error score and the first dynamic error score are calculated, the static error score is analyzed, determines static verification data and first supplementary detection data, supplementary detection is carried out by first supplementary detection data;The second dynamic error score of each region is calculated, and the region to be verified is determined;Supplementary detection is carried out by second supplementary detection data;The first dynamic error score of second supplementary detection data is calculated, and the third dynamic error score is determined in combination with the first dynamic error score of detection data, according to the third dynamic error score, analysis is carried out, and dynamic verification data is determined.The application is verified by static and dynamic flow, and the verification accuracy of ultrasonic volume correction instrument is improved.

[0144] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division mode, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0146] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can be a unit alone, or two or more units can be integrated in one unit; the integrated unit can be realized in the form of hardware or hardware plus software functional unit.

[0147] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by relevant hardware of program instructions, and the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the above-mentioned method embodiments when executed; and the foregoing storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0148] Alternatively, the integrated unit of the present application can also be stored in a computer readable storage medium if it is realized in the form of a software function module and sold or used as an independent product. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes: a mobile storage device, a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

Claims

1. A method for calibrating an intelligent ultrasonic volume corrector, characterized in that: include: Obtain historical test data; Determine detection data i based on the historical detection data, and establish a detection data set; The data types of the detection data i include pressure signal p, temperature t, and flow v; A three-dimensional verification model is constructed according to the data type of the detection data i, and the three-dimensional model is divided into a plurality of regions of equal size based on a preset segmentation rule; the three-dimensional verification model is located in a three-dimensional coordinate system, and the three coordinate axes of the three-dimensional coordinate system are divided into pressure signal p, temperature t, and flow rate v; Adjusting the ambient temperature according to the detection data i, detecting the detected volume corrector, and calculating a static error score and a first dynamic error score of the detection data; Analyzing the static error score to determine static verification data and first supplementary detection data, and performing supplementary detection using the first supplementary detection data; Calculate the second dynamic error score of each area based on the first dynamic error score to determine the area to be verified; Determining detection data of the region vertices of the region to be verified as second supplementary detection data, and performing supplementary detection using the second supplementary detection data; Calculating a first dynamic error score of the second supplementary detection data, determining a third dynamic error score in combination with the first dynamic error score of the detection data, and performing analysis based on the third dynamic error score to determine dynamic verification data; The step of adjusting the ambient temperature by using the detection data i, detecting the detected volume corrector, and calculating the static error score of the detection data includes: The ambient temperature is adjusted according to the test data i. After the ambient temperature stabilizes, the same pressure signal is applied to the volume corrector under test and the standard volume tube synchronously through a dual-channel precision pressure controller. Collect the first stable output value V of the volume corrector under test 1(i) And the second stable output value V of the standard volume tube 2(i) ; Calculate the static error score Q 1(i) ; ； The calculation method of the first dynamic error score is specifically as follows: Adjust the ambient temperature by adjusting the speed e through the preset parameters; The first dynamic output value V of the volume corrector under test is collected during the ambient temperature adjustment process. 3(e) And the second dynamic output value V of the standard volume tube 4(e) ; Calculate the first dynamic error score Q 2(e) ; ； The calculation method of the second dynamic error score is specifically as follows: Obtain detection data corresponding to the region vertices of the current region, and calculate a first dynamic error score between the region vertices in the current region; Calculating a weighted average of all first dynamic error scores to determine a second dynamic error score for the current area; The calculation method of the third dynamic error score is specifically as follows: calculating a first dynamic error score for the second supplementary detection data; The first dynamic error scores of the detection data and the second supplementary detection data are respectively multiplied by the corresponding adjustment time, and the calculation results are accumulated to obtain a third dynamic error score Q3.

2. The intelligent ultrasonic volume correction instrument calibration method according to claim 1, characterized in that: The analyzing the static error score to determine static verification data and first supplementary detection data includes: The static error score Q 1(i) and the first error score threshold Q a and the second error score threshold Q b Make a comparison; When Q 1(i) ≥Q b When the detected volume corrector is abnormal, it is determined that there is an abnormality in the detected volume corrector; When Q 1(i) ≤Q a When , no processing is done; When Q a <Q 1(i) <Q b When the static error score Q 1(i) The corresponding detection data i is marked; Perform pairwise analysis on the marked detection data to determine the first supplementary detection data, calculate the static error score of the first supplementary detection data, mark the first supplementary detection data whose static error score is between the first error score threshold and the second error score threshold, and perform multiple iterative calculations until there is no marked detection data.

3. The intelligent ultrasonic volume correction instrument calibration method according to claim 2, characterized in that: The performing pairwise analysis on the marker detection data to determine the first supplementary detection data includes: Input all test data into the verification 3D model; Perform pairwise analysis on the marker detection data and determine the selected marker detection data as the first marker detection data j a and the second marker detection data j b ; Detect data j according to the first mark a and the second marker detection data j b The pixel distance between them determines the major radius; The long radius is input into the preset ellipsoid area to build the model, and the short radius is determined by multiplying the long radius and the preset radius ratio to generate the ellipsoid area E. (ab) ; When the ellipsoid area E (ab) When there is no other detection data, calculate the first mark detection data j a and the second marker detection data j b The center coordinates of , determine the first supplementary detection data; Conversely, calculate the ellipsoid area E (ab) Influence scores of each other detection data and the marked detection data are calculated, and the other detection data with the largest influence score is determined as the first detection data of the marked detection data; The center coordinates of the mark detection data and the corresponding first detection data are calculated to determine the first supplementary detection data.

4. The intelligent ultrasonic volume correction instrument calibration method according to claim 2, characterized in that: Also includes: Calculate the average static error score of the detection data corresponding to the vertex coordinates of the region to determine the regional static error score; Marking a region where the regional static error score is between a first error score threshold and a second error score threshold; The percentage of occurrences of historical detection data in the marked area is counted to determine the evaluation coefficient corresponding to each marked area; Multiply the regional static error score of each marked area by the corresponding evaluation coefficient, accumulate the calculation results, and determine the comprehensive evaluation score of the static error; When the static error comprehensive evaluation score is greater than a first preset evaluation score, it is determined that an abnormality exists in the inspected volume corrector.

5. The intelligent ultrasonic volume correction instrument calibration method according to claim 1, characterized in that: Also includes: Inputting the detection data before and after adjustment and the corresponding first dynamic error score into the verification three-dimensional model, and determining the display color of the line segment between the detection data before and after adjustment; The three-dimensional model is subjected to color gradient processing according to the display colors of the line segments between the detection data, and a first dynamic error score between each detection data is determined.

6. The intelligent ultrasonic volume correction instrument calibration method according to claim 1, characterized in that: Determining the area to be verified includes: After all regions are calculated, continue to iteratively segment and calculate the second dynamic error score for regions where the second dynamic error score is greater than the corresponding preset dynamic evaluation score threshold; When the preset number of iterative segmentation times is met, the region whose second dynamic error score is greater than the corresponding preset dynamic threshold is determined as the region to be verified.

7. The intelligent ultrasonic volume correction instrument calibration method according to claim 1, characterized in that: The analyzing according to the third dynamic error score to determine dynamic verification data includes: When the third dynamic error score Q3 is greater than a third error score threshold, it is determined that an abnormality exists in the inspected volume corrector; On the contrary, the display color of the verified three-dimensional model is adjusted according to the first dynamic error score of the second supplementary detection data.

8. The intelligent ultrasonic volume correction instrument calibration method according to claim 1, characterized in that: Also includes: The second supplementary inspection data is reacquired by displaying the color-adjusted verification three-dimensional model, and the supplementary inspection is continued.

9. An intelligent ultrasonic volume correction instrument calibration device, characterized in that: The intelligent ultrasonic volume corrector calibration device includes an intelligent ultrasonic volume corrector calibration method program. When the intelligent ultrasonic volume corrector calibration method program is executed by a processor, the steps of an intelligent ultrasonic volume corrector calibration method as described in any one of claims 1 to 8 are implemented.

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