CN116541656A - Data identification method and system of ultrasonic flowmeter - Google Patents

Data identification method and system of ultrasonic flowmeter Download PDF

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Publication number
CN116541656A
CN116541656A CN202310398243.4A CN202310398243A CN116541656A CN 116541656 A CN116541656 A CN 116541656A CN 202310398243 A CN202310398243 A CN 202310398243A CN 116541656 A CN116541656 A CN 116541656A
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data
ultrasonic
description field
ultrasonic data
analyzed
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邵泽华
周莙焱
王峰
王川
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Chengdu Qinchuan IoT Technology Co Ltd
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Chengdu Qinchuan IoT Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/66—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

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  • Electromagnetism (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)

Abstract

According to the data identification method and system of the ultrasonic flowmeter, the target description field belonging to the abnormal event is determined from the first description field according to the relative positioning relationship between the first mapping point and the second mapping point; the method and the device can determine the target description field belonging to the abnormal event in the first description field by utilizing the mapping variable of the second mapping point pointing to the first mapping point, and delete the target description field in the ultrasonic data needing to be analyzed, so that the possibility of abnormal data identification is reduced when the data identification of the ultrasonic flowmeter is performed based on the first description field with the target description field deleted, more accurate ultrasonic data can be obtained, and the efficiency of data processing is improved when the subsequent ultrasonic data processing is performed.

Description

Data identification method and system of ultrasonic flowmeter
Technical Field
The present application relates to the technical field of data identification of ultrasonic flow meters, and in particular, to a data identification method and system of an ultrasonic flow meter.
Background
The gas ultrasonic flowmeter is a high-precision measuring instrument for detecting gas flow, and the principle is as follows: a pair of ultrasonic transducers are respectively arranged at the upstream and the downstream of the gas flow, the time difference of ultrasonic waves in the forward flow direction and the backward flow direction in the gas flow is in direct proportion to the average flow velocity of the gas, the flow velocity of the gas is calculated by calculating the relation between the propagation time difference and the propagation distance of the ultrasonic waves, and the flow of the gas is obtained by the product of the flow velocity and the area of a sound channel in the gas flow channel.
Because the data acquired during gas flow detection by adopting a single ultrasonic flowmeter is too single, the accuracy of the data is lower, and a plurality of ultrasonic flowmeters are commonly adopted for compound metering at present. In the process of data processing, abnormal data or mutual interference of data with higher similarity can occur in a plurality of ultrasonic flow meters, so that the ultrasonic data meeting the requirements cannot be accurately identified. How to reject the interference data in the ultrasonic data, so as to identify the effective data is a technical problem which is currently difficult to overcome.
Disclosure of Invention
In order to improve the technical problems in the related art, the application provides a data identification method and a data identification system of an ultrasonic flowmeter.
In a first aspect, there is provided a data identification method of an ultrasonic flow meter, the method comprising: acquiring a first description field in ultrasonic data to be analyzed and a second description field associated with the first description field in target reference ultrasonic data corresponding to the ultrasonic data to be analyzed; mapping the first description field to specified sample data, determining a first mapping point of the first description field in the specified sample data, and mapping the second description field to the specified sample data, determining a second mapping point of the second description field in the specified sample data; determining a target description field belonging to an abnormal event from the first description field according to the relative positioning relation between the first mapping point and the second mapping point; the relative positioning relation is generated based on the position change of the ultrasonic transducer when acquiring the target reference ultrasonic data and acquiring the ultrasonic data to be analyzed; deleting the target description field from the ultrasonic data to be analyzed, and determining an ultrasonic data identification result.
It can be understood that a first description field in the ultrasonic data to be analyzed and a second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed are acquired; mapping the first description field to the specified sample data, determining a first mapping point of the first description field in the specified sample data, and mapping the second description field to the specified sample data, and determining a second mapping point of the second description field in the specified sample data; determining a target description field belonging to the abnormal event from the first description field according to the relative positioning relation between the first mapping point and the second mapping point; the method and the device can determine the target description field belonging to the abnormal event in the first description field by utilizing the mapping variable pointing to the first mapping point from the second mapping point, and delete the target description field in the ultrasonic data needing to be analyzed, so that the possibility of abnormal data identification is reduced when the data identification of the ultrasonic flowmeter is performed based on the first description field with the target description field deleted, more accurate ultrasonic data can be obtained, and the efficiency of data processing is improved when the subsequent ultrasonic data processing is performed.
In an independently implemented embodiment, said determining a target description field belonging to an exception event from said first description field comprises: determining a mapping variable pointing from the second mapping point to the first mapping point according to the relative positioning relation between the first mapping point and the second mapping point; and determining a target description field belonging to the abnormal event from the first description field according to the numerical value of the mapping variable.
It can be understood that the present application may determine the object description field of the abnormal event according to the difference of the mapping points, so as to reduce the interference of the abnormal data as much as possible.
In an independent embodiment, before the acquiring the first description field in the ultrasonic data to be analyzed and the second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed, the method further includes: the target reference ultrasound data is determined for the ultrasound data to be analyzed based on specified pick requirements.
It can be understood that, before the second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed is analyzed, the data is selected, so that some interference data can be filtered, and the workload of subsequent work is reduced.
In an independently implemented embodiment, the determining the target reference ultrasound data for the ultrasound data to be analyzed based on specified pick requirements includes: identifying whether important ultrasonic data in the ultrasonic data to be analyzed meets the specified selection requirement or not; on the premise that the important ultrasonic data meets the specified selection requirement, determining the important ultrasonic data as the target reference ultrasonic data; and on the premise that important ultrasonic data in the ultrasonic data to be analyzed is identified to be not in accordance with the specified selection requirement, determining secondary ultrasonic data as the target reference ultrasonic data.
It can be appreciated that the present application can more precisely pick out the target reference ultrasound data through preset selection requirements.
In an independently implemented embodiment, further comprising: on the premise that important ultrasonic data in the ultrasonic data to be analyzed is identified to be not in accordance with the specified selection requirement, the ultrasonic data to be analyzed is determined to be new important ultrasonic data; the new important ultrasonic data is used for ultrasonic data processing of the next ultrasonic data needing to be analyzed.
It can be understood that the data which does not meet the selection requirement can be analyzed and corrected, so that the accuracy and the reliability of data analysis are improved.
In an independently implemented embodiment, the specified pick requirement includes at least one of: the difference between the ultrasonic data to be analyzed and the important ultrasonic data is smaller than the specified target difference; the number of second description fields associated with the first description fields in the important ultrasonic data reaches a specified number value; the visual angle difference between the first data acquisition direction corresponding to the ultrasonic data to be analyzed and the second data acquisition direction corresponding to the important ultrasonic data is smaller than a specified visual angle difference threshold.
It can be understood that the ultrasonic data of the present application is error-permitted, so that the ultrasonic data can be ensured not to have a missing phenomenon.
In an independent embodiment, the mapping the first description field to the specified sample data, determining a first mapping point of the first description field in the specified sample data, includes: determining data attributes of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed according to the space positioning data of the ultrasonic transducer in a target scene when acquiring the target reference ultrasonic data and the first dimension information of the ultrasonic transducer in the target scene when acquiring the ultrasonic data to be analyzed; and mapping the first description field to the specified sample data according to the data attribute, and determining a first mapping point of the first description field in the specified sample data.
It can be appreciated that the present application can analyze ultrasonic data from different dimensions, and can improve the accuracy and reliability of ultrasonic data analysis.
In an independently implemented embodiment, the mapping the second description field to the specified sample data, determining a second mapping point of the second description field in the specified sample data, includes: and mapping the second description field to the appointed sample data based on second description content of the ultrasonic transducer when acquiring the target reference ultrasonic data, and determining a second mapping point of the second description field in the appointed sample data.
In an independently implemented embodiment, further comprising: determining key description contents of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed according to a non-target description field except the target description field in the first description field, a third description field associated with the non-target description field in the target reference ultrasonic data and second description contents of the ultrasonic transducer when acquiring the target reference ultrasonic data; wherein the second description field includes the third description field.
In an independently implemented embodiment, further comprising: according to the key description content, the third description field is mapped into the ultrasonic data to be analyzed again, and a third mapping point of the third description field in the ultrasonic data to be analyzed is determined; determining abnormal ultrasonic data according to the positioning data of the third mapping point in the ultrasonic data to be analyzed and the positioning data of the non-target description field in the ultrasonic data to be analyzed; and determining a new designated deletion percentage according to the abnormal ultrasonic data. The new designated deletion percentage is used for carrying out ultrasonic data processing on the ultrasonic data which needs to be analyzed next.
In an independent embodiment, the remapping the third description field into the ultrasound data to be analyzed according to the key description, and determining a third mapping point of the third description field in the ultrasound data to be analyzed includes: determining a switching relation between a secondary ultrasonic data AI vector space corresponding to the ultrasonic data to be analyzed and a second ultrasonic data AI vector space corresponding to the appointed sample data according to the key description content; and according to the switching relation, mapping the second mapping point of the third description field in the appointed sample data to the ultrasonic data needing to be analyzed, and determining the third mapping point of the third description field in the ultrasonic data needing to be analyzed.
In a second aspect, there is provided a data identification system of an ultrasonic flow meter, comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the above-described ultrasonic flow meter data identification method.
According to the data identification method and system for the ultrasonic flowmeter, a first description field in ultrasonic data to be analyzed and a second description field associated with the first description field in target reference ultrasonic data corresponding to the ultrasonic data to be analyzed are obtained; mapping the first description field to the specified sample data, determining a first mapping point of the first description field in the specified sample data, and mapping the second description field to the specified sample data, and determining a second mapping point of the second description field in the specified sample data; determining a target description field belonging to the abnormal event from the first description field according to the relative positioning relation between the first mapping point and the second mapping point; the method and the device can determine the target description field belonging to the abnormal event in the first description field by utilizing the mapping variable pointing to the first mapping point from the second mapping point, and delete the target description field in the ultrasonic data needing to be analyzed, so that the possibility of abnormal data identification is reduced when the data identification of the ultrasonic flowmeter is performed based on the first description field with the target description field deleted, more accurate ultrasonic data can be obtained, and the efficiency of data processing is improved when the subsequent ultrasonic data processing is performed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a data identification method of an ultrasonic flowmeter according to an embodiment of the present application.
Fig. 2 is a block diagram of a data identification device of an ultrasonic flowmeter according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions described above, the following detailed description of the technical solutions of the present application is provided through the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limit the technical solutions of the present application, and the technical features of the embodiments and embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for identifying data of an ultrasonic flowmeter is shown, which may include the following steps S101-S104.
S101: and acquiring a first description field in the ultrasonic data to be analyzed and a second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed.
S102: mapping the first description field to specified sample data, determining a first mapping point of the first description field in the specified sample data, and mapping the second description field to the specified sample data, determining a second mapping point of the second description field in the specified sample data.
S103: and determining a target description field belonging to the abnormal event from the first description field according to the relative positioning relation between the first mapping point and the second mapping point.
S104: deleting the target description field from the ultrasonic data to be analyzed, and determining an ultrasonic data identification result.
According to the method, a first description field in ultrasonic data to be analyzed and a second description field corresponding to the first description field in target reference ultrasonic data are respectively mapped to appointed sample data, and a first mapping point of the first description field in the appointed sample data and a second mapping point of the second description field in the appointed sample data are determined; and then determining a target description field belonging to the abnormal event from the first description field based on the relative positioning relation between the second mapping point and the first mapping point, and deleting the target description field of the abnormal event from the first description field, so that the target description field belonging to the abnormal event can be deleted from the first description field based on the inconsistency of the motion of the description field corresponding to the abnormal event and the static object on the appointed sample data, the possibility of abnormality identification of the data of the ultrasonic flowmeter is reduced, more accurate ultrasonic data can be obtained, and the efficiency of ultrasonic data processing can be improved when the subsequent ultrasonic data processing is carried out.
The following describes the above-mentioned S101 to S104 in detail.
In the specific implementation of S101, the ultrasonic data to be analyzed may be understood as real-time ultrasonic data acquired from a plurality of ultrasonic transducers. In the process of flow statistics by acquiring ultrasonic data by a plurality of ultrasonic transducers, interference may exist between two or more ultrasonic data, for example, the ultrasonic data caused by the superposition phenomenon of ultrasonic waves is inaccurate, so that the ultrasonic data needs to be identified to ensure the accuracy of ultrasonic data acquisition.
The target reference ultrasonic data corresponding to the ultrasonic data to be analyzed can be obtained by the following method: the target reference ultrasound data is determined for the ultrasound data to be analyzed based on specified pick requirements.
The embodiment of the application provides a specific method for determining the target reference ultrasonic data for the ultrasonic data to be analyzed based on specified selection requirements, which can comprise the following execution steps.
S201: identifying whether important ultrasonic data in the ultrasonic data to be analyzed meets the specified selection requirement.
For example, important ultrasonic data may be understood as key feature data extracted from ultrasonic data, and may include ultrasonic data having an amplitude and wavelength within a set range. The ultrasonic data includes important data that can represent the core content of the ultrasonic wave, and also includes edge data and noise data that cannot represent the core content. Non-important edge data and noise data can be filtered out through the artificial intelligence model, so that important ultrasonic data can be screened out.
The specified selection requirement is understood as a preset selection condition (for example, the ultrasonic frequency is within 200kHz + -5 kHz as the selection requirement, and the corresponding ultrasonic data is selected).
S202: and on the premise that the important ultrasonic data meets the specified selection requirement, determining the important ultrasonic data as the target reference ultrasonic data.
S203: and on the premise that important ultrasonic data in the ultrasonic data to be analyzed is identified to be not in accordance with the specified selection requirement, determining secondary ultrasonic data as the target reference ultrasonic data. Secondary ultrasound data may be understood as data that is not representative of the ultrasound core content (e.g., amplitude and wavelength).
S204: determining the ultrasonic data to be analyzed as new important ultrasonic data; the new important ultrasonic data is used for ultrasonic data processing of the next ultrasonic data needing to be analyzed.
Exemplary ultrasonic data processing embodiments may include data filtering, data analysis, and data classification
In the above process, when identifying whether the current important ultrasonic data meets the specified selection requirement, determining whether the current important ultrasonic data meets the specified selection requirement according to the ultrasonic data distribution condition based on time sequence and the preset condition.
The specified selection requirements include, but are not limited to, at least one of the following (1), (2), and (3).
(1) The difference between the ultrasound data to be analyzed and the significant ultrasound data is less than a specified target difference.
For example, the target variance may be understood as a variance threshold set in advance. Such as: and may be specifically understood as a range of discrepancy permissions.
If the difference between the ultrasonic data to be analyzed and the current important ultrasonic data is smaller than the designated target difference, the current important ultrasonic data is determined to be the target reference ultrasonic data, so that the ultrasonic data to be analyzed and the target reference ultrasonic data are ensured to have enough first description fields and second description fields which can be associated, the target description field belonging to the abnormal event can be better selected from the first description fields, and after the target description field is selected from the first description fields, the other first description fields remained in the first description fields can be better utilized to carry out subsequent processing on the ultrasonic data to be analyzed.
The description field may be understood as a data feature. The ultrasonic data can be identified through artificial intelligence to obtain data characteristics. For example: when the wavelength of the ultrasonic wave is a1, the artificial intelligence can identify the characteristic of the wavelength a; when the amplitude of the ultrasonic wave is b, the artificial intelligence can recognize the characteristic of the amplitude b 1.
(2) The number of second description fields associated with the first description field in the important ultrasound data reaches a specified number value.
By way of example, the specified value may be understood as a threshold value.
For example, after extracting the description field of the ultrasonic data to be analyzed, determining the first description field of the ultrasonic data to be analyzed, extracting the description field of the target reference ultrasonic data, and determining the second description field of the target reference ultrasonic data, the first description field of the ultrasonic data to be analyzed and the second description field of the target reference ultrasonic data are associated. A first description field in the ultrasound data that needs to be analyzed and a second description field in the target reference ultrasound data that can be successfully associated with the first description field are determined. The first description field and the second description field are successfully associated, i.e. the first description field and the second description field characterize the same description field on the same object. If the number of the second description fields associated with the first description fields in the current important ultrasonic data reaches the specified number value, the current important ultrasonic data is determined to be the target reference ultrasonic data, so that the target description fields belonging to the abnormal event can be better selected from the first description fields.
(3) The visual angle difference between the first data acquisition direction corresponding to the ultrasonic data to be analyzed and the second data acquisition direction corresponding to the important ultrasonic data is smaller than a specified visual angle difference threshold.
By way of example, the data acquisition direction may be understood as the different dimensions of the analysis of the ultrasound data. .
On the premise that the difference between the first data acquisition direction corresponding to the ultrasonic data to be analyzed and the second data acquisition direction corresponding to the important ultrasonic data is smaller than a specified difference threshold, the ultrasonic data to be analyzed and the important ultrasonic data can be guaranteed to have most of the same target objects, so that a sufficient number of first description fields can be determined from the ultrasonic data to be analyzed.
With regard to S102 described above, after the specified sample data is determined in the target scene, the specific content of the specified sample data in the AI vector space corresponding to the target scene is already determined, that is, the switching relationship between the AI vector space and the specified sample data can be determined. On the premise that the ultrasonic transducer acquires the data attribute of the ultrasonic data to be analyzed in the target scene and acquires the second description content of the target reference ultrasonic data in the target scene, the first description field can be mapped to the specified sample data, the first mapping point of the first description field in the specified sample data is determined, the second description field is mapped to the specified sample data, and the second mapping point of the second description field in the specified sample data is determined.
The specified sample data may be understood as template data pre-stored in a database. The data attributes include data name, data type, data characteristics, and the like. The second description is to be understood as ultrasonic flow velocity data acquired after effective propagation in a medium such as a gas, liquid, solid solution, or the like.
Since the ultrasonic waves of the same ultrasonic data are transmitted under different scenes (such as scenes with different humidity, pressure and impurities), the ultrasonic data received by the ultrasonic transducer also have certain difference, so that determining the specified sample data in the target scene can be understood as determining that the template data prestored in the database is q1 under the air-wet environment; in the air-drying environment, the template data pre-stored in the database is determined to be q2.
Further, the AI vector space can be understood as a three-dimensional coordinate system.
For example, the embodiments of the present application take mapping the first description field to the specified sample data based on the data attribute as an example: determining the relative description content between the data acquired by the ultrasonic transducer in real time and the appointed sample data based on the description content of the appointed sample data in the AI vector space and the data attribute; determining a switching relation between the specified sample data and real-time ultrasonic data of the ultrasonic transducer when the ultrasonic data to be analyzed is acquired and an AI vector space based on the relative description content and an imaging principle of the ultrasonic transducer; and mapping the first description field into the appointed sample data according to the switching relation.
The specific process of mapping the second description field to the specified sample data is similar to the specific process of mapping the first description field to the specified sample data, and will not be described herein.
For the above S103, when determining the target description field belonging to the abnormal event from the first description field, the following manner may be adopted:
determining a mapping variable pointing from the second mapping point to the first mapping point based on the relative positioning relationship between the first mapping point and the second mapping point; based on the mapping variables, a target description field belonging to the exception event is determined from the first description field. The mapping variable can be understood as the distance between two mapping points, a distance threshold is preset, when the distance threshold is larger than the distance threshold, the data is abnormal, and when the distance threshold is smaller than or equal to the distance threshold, the data is normal.
The object description field of the anomaly event can be understood as the determined characteristics of the ultrasound anomaly data. For example: the abnormal data is error data, incomplete data, data having a high similarity, and the like found from the ultrasonic data. In a specific application scenario, data loss may exist in the process of acquiring ultrasonic related data, and if the lost related data is key data capable of representing key content, the acquired related data may be understood as error data or incomplete data; when acquiring ultrasound related data, there may be two data having extremely high similarity, so that two data may be recognized as one data at the time of recognition, resulting in a decrease in the accuracy of data processing.
In the data identification method of the ultrasonic flowmeter provided in another embodiment of the present application, the method further includes: determining key description contents of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed according to a non-target description field except the target description field in the first description field, a third description field associated with the non-target description field in the target reference ultrasonic data and second description contents of the target reference ultrasonic data acquired by the ultrasonic transducer; wherein the second description field includes the third description field.
Therefore, the content of the ultrasonic data which is acquired by the ultrasonic transducer and needs to be analyzed can be debugged, the accuracy of the determined key description content is higher, and the positioning accuracy of the ultrasonic transducer is improved.
Further, after obtaining the key description content of the ultrasonic data to be analyzed, the method further comprises: according to the key description content, the third description field is mapped into the ultrasonic data to be analyzed again, and a third mapping point of the third description field in the ultrasonic data to be analyzed is determined; determining abnormal ultrasonic data according to the positioning data of the third mapping point in the ultrasonic data to be analyzed and the positioning data of the non-target description field in the ultrasonic data to be analyzed; and determining a new designated deletion percentage according to the abnormal ultrasonic data.
The abnormal ultrasonic data can be determined by judging the relation between the relative position distance between the two positioning data and the set distance threshold value, and if the relative position distance is larger than the set distance threshold value, the abnormal ultrasonic data is represented. The specified deletion percentage refers to the ratio of the abnormal event data deleted from the ultrasonic data to be analyzed to all the ultrasonic data to be analyzed. The new designated deletion percentage is used for carrying out ultrasonic data processing on the ultrasonic data which needs to be analyzed next.
For example, if the abnormal ultrasonic data is smaller than the specified error threshold, it indicates that there is a smaller possibility of an abnormal event in the current ultrasonic data to be analyzed, and the specified deletion percentage of the next ultrasonic data to be analyzed may be correspondingly reduced or kept unchanged. If the abnormal ultrasonic data is greater than or equal to the specified error threshold value, the possibility that the abnormal event exists in the current ultrasonic data needing to be analyzed is higher, and the specified deletion percentage of the next ultrasonic data needing to be analyzed can be correspondingly increased, so that the description field belonging to the abnormal event can be more fully deleted when the next ultrasonic data needing to be analyzed is processed.
In this embodiment, when the third description field is mapped again to the ultrasonic data to be analyzed according to the key description content and the third mapping point of the third description field in the ultrasonic data to be analyzed is determined, the switching relationship between the secondary ultrasonic data AI vector space corresponding to the ultrasonic data to be analyzed and the second ultrasonic data AI vector space corresponding to the specified sample data may be determined according to the key description content; and according to the switching relation, mapping the second mapping point of the third description field in the appointed sample data to the ultrasonic data needing to be analyzed, and determining the third mapping point of the third description field in the ultrasonic data needing to be analyzed.
On the basis of the above, fig. 2 provides a data identification device 200 of an ultrasonic flowmeter, the device comprising:
a description field analysis module 210, configured to obtain a first description field in the ultrasonic data to be analyzed, and a second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed;
A mapping point determining module 220, configured to map the first description field to specified sample data, determine a first mapping point of the first description field in the specified sample data, and map the second description field to the specified sample data, determine a second mapping point of the second description field in the specified sample data;
the result determining module 230 is configured to determine, from the first description field, a target description field belonging to an abnormal event according to a relative positioning relationship between the first mapping point and the second mapping point. Wherein the relative positioning relationship is generated based on a positional change of the ultrasonic transducer when acquiring the target reference ultrasonic data and acquiring the ultrasonic data to be analyzed. Deleting the target description field from the ultrasonic data to be analyzed, and determining an ultrasonic data identification result.
On the basis of the above, the present embodiment provides a data identification system 300 of an ultrasonic flowmeter, which includes a processor 310 and a memory 320 that are in communication with each other, where the processor 310 is configured to read and execute a computer program from the memory 320 to implement the above method.
On the basis of the above, there is also provided a computer readable storage medium on which a computer program stored which, when run, implements the above method.
In summary, based on the above scheme, a first description field in the ultrasonic data to be analyzed and a second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed are obtained; mapping the first description field to the specified sample data, determining a first mapping point of the first description field in the specified sample data, and mapping the second description field to the specified sample data, and determining a second mapping point of the second description field in the specified sample data; determining a target description field belonging to the abnormal event from the first description field according to the relative positioning relation between the first mapping point and the second mapping point; the method and the device can determine the target description field belonging to the abnormal event in the first description field by utilizing the mapping variable pointing to the first mapping point from the second mapping point, and delete the target description field in the ultrasonic data needing to be analyzed, so that the possibility of abnormal data identification is reduced when the data identification of the ultrasonic flowmeter is performed based on the first description field with the target description field deleted, more accurate ultrasonic data can be obtained, and the efficiency of data processing is improved when the subsequent ultrasonic data processing is performed.
It should be appreciated that the systems and modules thereof shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only with hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software, such as executed by various types of processors, and with a combination of the above hardware circuitry and software (e.g., firmware).
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application and are not intended to limit the order in which the processes and methods of the application are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed herein and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the numbers allow for adaptive variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the present application, documents that are currently or later attached to this application for which the broadest scope of the claims to the present application is limited. It is noted that the descriptions, definitions, and/or terms used in the subject matter of this application are subject to such descriptions, definitions, and/or terms if they are inconsistent or conflicting with such descriptions, definitions, and/or terms.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of this application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present application may be considered in keeping with the teachings of the present application. Accordingly, embodiments of the present application are not limited to only the embodiments explicitly described and depicted herein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims (10)

1. A method of data identification for an ultrasonic flow meter, the method comprising:
acquiring a first description field in ultrasonic data to be analyzed and a second description field associated with the first description field in target reference ultrasonic data corresponding to the ultrasonic data to be analyzed;
mapping the first description field to specified sample data, determining a first mapping point of the first description field in the specified sample data, and mapping the second description field to the specified sample data, determining a second mapping point of the second description field in the specified sample data;
determining a target description field belonging to an abnormal event from the first description field according to the relative positioning relation between the first mapping point and the second mapping point; the relative positioning relation is generated based on the position change of the ultrasonic transducer when acquiring the target reference ultrasonic data and acquiring the ultrasonic data to be analyzed;
deleting the target description field from the ultrasonic data to be analyzed, and determining an ultrasonic data identification result.
2. The method of claim 1, wherein determining a target description field belonging to an abnormal event from the first description field comprises:
determining a mapping variable pointing from the second mapping point to the first mapping point according to the relative positioning relation between the first mapping point and the second mapping point;
and determining the target description field belonging to the abnormal event from the first description field according to the numerical value of the mapping variable.
3. The method of claim 1, wherein before the acquiring a first description field in the ultrasonic data to be analyzed and a second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed, the method further comprises: the target reference ultrasound data is determined for the ultrasound data to be analyzed based on specified pick requirements.
4. The method of claim 3, wherein determining the target reference ultrasonic data for the ultrasonic data to be analyzed based on a specified pick requirement comprises:
Identifying whether important ultrasonic data in the ultrasonic data to be analyzed meets the specified selection requirement or not;
on the premise that the important ultrasonic data meets the specified selection requirement, determining the important ultrasonic data as the target reference ultrasonic data;
and on the premise that important ultrasonic data in the ultrasonic data to be analyzed is identified to be not in accordance with the specified selection requirement, determining secondary ultrasonic data as the target reference ultrasonic data.
5. The method of data identification of an ultrasonic flow meter of claim 3, further comprising:
on the premise that important ultrasonic data in the ultrasonic data to be analyzed is identified to be not in accordance with the specified selection requirement, the ultrasonic data to be analyzed is determined to be new important ultrasonic data;
the new important ultrasonic data is used for ultrasonic data processing of the next ultrasonic data needing to be analyzed.
6. The method of claim 4, wherein the specified pick requirement comprises at least one of:
The difference between the ultrasonic data to be analyzed and the important ultrasonic data is smaller than the specified target difference; the number of second description fields associated with the first description fields in the important ultrasonic data reaches a specified number value; the visual angle difference between the first data acquisition direction corresponding to the ultrasonic data to be analyzed and the second data acquisition direction corresponding to the important ultrasonic data is smaller than a specified visual angle difference threshold.
7. The method of claim 6, wherein mapping the first description field to specified sample data, determining a first mapping point of the first description field in the specified sample data, comprises:
determining data attributes of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed according to the space positioning data of the ultrasonic transducer in a target scene when acquiring the target reference ultrasonic data and the first dimension information of the ultrasonic transducer in the target scene when acquiring the ultrasonic data to be analyzed;
And mapping the first description field to the specified sample data according to the data attribute, and determining the first mapping point of the first description field in the specified sample data.
8. The method of claim 6, wherein mapping the second description field to the specified sample data, determining a second mapping point of the second description field in the specified sample data, comprises: and based on second description content of the ultrasonic transducer when acquiring the target reference ultrasonic data, mapping the second description field to the appointed sample data, and determining the second mapping point of the second description field in the appointed sample data.
9. The method of data identification of an ultrasonic flow meter of claim 6, further comprising: determining key description contents of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed according to a non-target description field except the target description field in the first description field, a third description field associated with the non-target description field in the target reference ultrasonic data and second description contents of the target reference ultrasonic data acquired by the ultrasonic transducer; wherein the second description field includes the third description field;
Wherein, still include:
according to the key description content, the third description field is mapped into the ultrasonic data to be analyzed again, and a third mapping point of the third description field in the ultrasonic data to be analyzed is determined;
determining abnormal ultrasonic data according to the positioning data of the third mapping point in the ultrasonic data to be analyzed and the positioning data of the non-target description field in the ultrasonic data to be analyzed;
determining a new designated deletion percentage according to the abnormal ultrasonic data; the new designated deletion percentage is used for carrying out ultrasonic data processing on the ultrasonic data which needs to be analyzed next;
and mapping the third description field to the ultrasonic data to be analyzed again according to the key description content, and determining a third mapping point of the third description field in the ultrasonic data to be analyzed includes:
determining a switching relation between a secondary ultrasonic data AI vector space corresponding to the ultrasonic data to be analyzed and a second ultrasonic data AI vector space corresponding to the appointed sample data according to the key description content;
And according to the switching relation, mapping the second mapping point of the third description field in the appointed sample data to the ultrasonic data needing to be analyzed, and determining the third mapping point of the third description field in the ultrasonic data needing to be analyzed.
10. A data identification system for an ultrasonic flow meter, comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and execute the computer program to implement the method for identifying data of an ultrasonic flow meter according to any one of claims 1 to 9.
CN202310398243.4A 2023-04-14 2023-04-14 Data identification method and system of ultrasonic flowmeter Pending CN116541656A (en)

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