Data recognition method and system for ultrasonic flowmeter

Abstract

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

Description

Technical Field

[0001] This application relates to the field of data recognition technology for ultrasonic flow meters, and more specifically, to a data recognition method and system for ultrasonic flow meters. Background Technology

[0002] Ultrasonic gas flow meters are high-precision measuring instruments used for gas flow detection. Their principle is as follows: a pair of ultrasonic transducers are set up upstream and downstream of the gas flow. The time difference of ultrasonic waves in the downstream and upstream directions of the airflow is proportional to the average flow velocity of the gas. The gas flow velocity is calculated by calculating the relationship between the propagation time difference of ultrasonic waves and the propagation distance. The gas flow rate is obtained by multiplying the flow velocity by the area of ​​the sound channel in the airflow channel.

[0003] Because the data collected using a single ultrasonic flow meter for gas flow detection is too limited and lacks precision, multiple ultrasonic flow meters are often used for composite measurement. However, during data processing, abnormal or highly similar data may interfere with each other, preventing accurate identification of valid ultrasonic data. How to remove interfering data from the ultrasonic data and identify valid data remains a challenging technical problem. Summary of the Invention

[0004] To address the technical problems existing in related technologies, this application provides a data identification method and system for ultrasonic flow meters.

[0005] In a first aspect, a data identification method for an ultrasonic flow meter is provided. The method includes: acquiring a first descriptive field from ultrasonic data to be analyzed, and a second descriptive field from target reference ultrasonic data corresponding to the ultrasonic data to be analyzed, which is associated with the first descriptive field; mapping the first descriptive field to specified sample data to determine a first mapping point of the first descriptive field in the specified sample data, and mapping the second descriptive field to the specified sample data to determine a second mapping point of the second descriptive field in the specified sample data; determining a target descriptive field belonging to an abnormal event from the first descriptive field based on the relative positioning relationship between the first mapping point and the second mapping point; wherein the relative positioning relationship is generated based on the positional change of the ultrasonic transducer when acquiring the target reference ultrasonic data and acquiring the ultrasonic data to be analyzed; deleting the target descriptive field from the ultrasonic data to be analyzed, and determining the ultrasonic data identification result.

[0006] It is understood that the process involves obtaining a first description field from the ultrasonic data to be analyzed, and a second description field from the target reference ultrasonic data associated with the first description field; mapping the first description field to specified sample data to determine the first mapping point of the first description field in the specified sample data, and mapping the second description field to specified sample data to determine the second mapping point of the second description field in the specified sample data; determining the target description field belonging to the abnormal event from the first description field based on the relative positioning relationship between the first mapping point and the second mapping point; deleting the target description field from the ultrasonic data to be analyzed, and determining the ultrasonic data identification result. This application can use the mapping variable from the second mapping point to the first mapping point to determine the target description field belonging to the abnormal event in the first description field and delete the target description field in the ultrasonic data to be analyzed. This reduces the possibility of data identification anomalies when identifying ultrasonic flow meter data based on the first description field with the target description field deleted, thereby obtaining more accurate ultrasonic data and improving the efficiency of data processing in subsequent ultrasonic data processing.

[0007] In one standalone embodiment, determining the target description field belonging to the abnormal event from the first description field includes: 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; and determining the target description field belonging to the abnormal event from the first description field based on the value of the mapping variable.

[0008] Understandably, this application can determine the target description field of abnormal events based on the differences in mapping points, thereby minimizing interference from abnormal data.

[0009] In one standalone embodiment, before obtaining the first description field in the ultrasound data to be analyzed and the second description field in the target reference ultrasound data corresponding to the ultrasound data to be analyzed that is associated with the first description field, the method further includes: determining the target reference ultrasound data for the ultrasound data to be analyzed based on specified selection requirements.

[0010] It is understandable that this application selects data before analyzing the second description field associated with the first description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed. This can filter out some interfering data and reduce the workload of subsequent work.

[0011] In one standalone embodiment, determining the target reference ultrasonic data for the ultrasonic data to be analyzed based on specified selection criteria includes: identifying whether important ultrasonic data in the ultrasonic data to be analyzed meets the specified selection criteria; if the important ultrasonic data meets the specified selection criteria, determining the important ultrasonic data as the target reference ultrasonic data; and if the important ultrasonic data does not meet the specified selection criteria, determining secondary ultrasonic data as the target reference ultrasonic data.

[0012] Understandably, this application can more accurately select target reference ultrasonic data through preset selection criteria.

[0013] In one standalone embodiment, the method further includes: identifying the ultrasonic data to be analyzed as new important ultrasonic data if the important ultrasonic data in the ultrasonic data to be analyzed does not meet the specified selection requirements; the new important ultrasonic data is used to perform ultrasonic data processing on the next ultrasonic data to be analyzed.

[0014] It is understood that this application can analyze and correct data that does not meet the selection requirements, thereby improving the accuracy and reliability of data analysis.

[0015] In one standalone embodiment, the specified selection criteria include at least one of the following: the difference between the ultrasound data to be analyzed and the important ultrasound data is less than a specified target difference; the number of second description fields associated with the first description field in the important ultrasound data reaches a specified number; and the angular difference between the first data acquisition direction corresponding to the ultrasound data to be analyzed and the second data acquisition direction corresponding to the important ultrasound data is less than a specified angular difference threshold.

[0016] It is understood that the ultrasonic data in this application has a tolerance for error, which ensures that there are no missing ultrasonic data.

[0017] In one standalone embodiment, mapping the first description field to specified sample data and determining the first mapping point of the first description field in the specified sample data includes: determining the data attributes of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed, based on the spatial positioning data of the ultrasonic transducer in the 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 based on the data attributes to determine the first mapping point of the first description field in the specified sample data.

[0018] It is understood that this application can analyze ultrasonic data from different dimensions, thereby improving the accuracy and reliability of ultrasonic data analysis.

[0019] In one standalone embodiment, mapping the second description field to the specified sample data and determining the second mapping point of the second description field in the specified sample data includes: mapping the second description field to the specified sample data based on the second description content of the ultrasonic transducer when acquiring the target reference ultrasonic data, and determining the second mapping point of the second description field in the specified sample data.

[0020] In one standalone embodiment, the method further includes: determining key description content of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed, based on non-target description fields in the first description field other than the target description field, a third description field in the target reference ultrasonic data associated with the non-target description field, and a second description content of the ultrasonic transducer when acquiring the target reference ultrasonic data; wherein the second description field includes the third description field.

[0021] In one standalone embodiment, the method further includes: mapping the third description field back to the ultrasound data to be analyzed based on the key description content, thereby determining a third mapping point for the third description field in the ultrasound data to be analyzed; identifying abnormal ultrasound data based on the location data of the third mapping point in the ultrasound data to be analyzed and the location data of the non-target description field in the ultrasound data to be analyzed; and determining a new specified deletion percentage based on the abnormal ultrasound data. The new specified deletion percentage is used for ultrasound data processing on the next set of ultrasound data to be analyzed.

[0022] In one independently implemented embodiment, the step of remapping the third description field to the ultrasound data to be analyzed based on the key description content, and determining the third mapping point of the third description field in the ultrasound data to be analyzed, includes: determining the switching relationship between the secondary ultrasound data AI vector space corresponding to the ultrasound data to be analyzed and the second ultrasound data AI vector space corresponding to the specified sample data, based on the key description content; and mapping the second mapping point of the third description field in the specified sample data to the ultrasound data to be analyzed based on the switching relationship, thereby determining the third mapping point of the third description field in the ultrasound data to be analyzed.

[0023] Secondly, a data identification system for an ultrasonic flow meter is provided, comprising a processor and a memory that communicate with each other. The processor is used to read a computer program from the memory and execute it to implement the above-described ultrasonic flow meter data identification method.

[0024] The ultrasonic flowmeter data identification method and system provided in this application obtain a first description field from 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; map the first description field to specified sample data to determine a first mapping point of the first description field in the specified sample data, and map the second description field to specified sample data to determine a second mapping point of the second description field in the specified sample data; based on the relative positioning relationship between the first mapping point and the second mapping point, determine the target description field belonging to an abnormal event from the first description field; delete the target description field from the ultrasonic data to be analyzed, and determine the ultrasonic data identification result. This application can use the mapping variable from the second mapping point to the first mapping point to determine the target description field belonging to an abnormal event in the first description field and delete the target description field in the ultrasonic data to be analyzed, thereby reducing the possibility of data identification anomalies when performing ultrasonic flowmeter data identification based on the first description field with the target description field deleted, thereby obtaining more accurate ultrasonic data and improving the efficiency of data processing in subsequent ultrasonic data processing. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a data identification method for an ultrasonic flow meter provided in an embodiment of this application.

[0027] Figure 2 This is a block diagram of a data identification device for an ultrasonic flow meter provided in an embodiment of this application. Detailed Implementation

[0028] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0029] Please see Figure 1 This paper illustrates a data identification method for an ultrasonic flow meter, which may include the technical solutions described in steps S101-S104.

[0030] S101: Obtain the first description field from the ultrasonic data to be analyzed, and the second description field from the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed, which is associated with the first description field.

[0031] S102: Map the first description field to the specified sample data, determine the 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 the second mapping point of the second description field in the specified sample data.

[0032] S103: Based on the relative positioning relationship between the first mapping point and the second mapping point, determine the target description field belonging to the abnormal event from the first description field.

[0033] S104: Delete the target description field from the ultrasonic data that needs to be analyzed, and determine the ultrasonic data identification result.

[0034] This application embodiment maps a first description field in the ultrasonic data to be analyzed and a second description field in the target reference ultrasonic data corresponding to the first description field to specified sample data, respectively. It determines a first mapping point of the first description field in the specified sample data and a second mapping point of the second description field in the specified sample data. Then, based on the relative positioning relationship between the second mapping point and the first mapping point, it determines the target description field belonging to the abnormal event from the first description field and deletes the target description field of the abnormal event from the first description field. This reduces the possibility of abnormal data identification by the ultrasonic flowmeter, thereby obtaining more accurate ultrasonic data and improving the efficiency of subsequent ultrasonic data processing.

[0035] The above S101 to S104 are explained in detail below.

[0036] Regarding S101 above, in specific implementation, the ultrasonic data to be analyzed can be understood as real-time ultrasonic data acquired from multiple ultrasonic transducers. During the process of acquiring ultrasonic data from multiple ultrasonic transducers for flow statistics, interference may exist between two or more ultrasonic data points, such as the superposition of ultrasonic waves causing inaccurate ultrasonic data. Therefore, it is necessary to identify the ultrasonic data to ensure the accuracy of ultrasonic data acquisition.

[0037] The target reference ultrasonic data corresponding to the ultrasonic data to be analyzed can be obtained in the following way: based on specified selection requirements, the target reference ultrasonic data is determined for the ultrasonic data to be analyzed.

[0038] This application provides a method for determining the target reference ultrasonic data for the ultrasonic data to be analyzed based on specified selection requirements, which may include the following execution steps.

[0039] S201: Identify whether important ultrasonic data in the ultrasonic data to be analyzed meet the specified selection requirements.

[0040] For example, important ultrasonic data can be understood as key feature data extracted from ultrasonic data, which may include ultrasonic data with amplitude and wavelength within a set range. Ultrasonic data contains important data that represents the core content of the ultrasound, as well as peripheral data and noise data that cannot represent the core content. Artificial intelligence models can filter out unimportant peripheral and noise data, thereby identifying the important ultrasonic data.

[0041] Specifying selection criteria can be understood as pre-set selection conditions (for example, using the ultrasonic frequency range of 200kHz±5kHz as the selection criterion to select the corresponding ultrasonic data).

[0042] S202: Provided that the important ultrasonic data meets the specified selection requirements, the important ultrasonic data is determined as the target reference ultrasonic data.

[0043] S203: If the important ultrasonic data in the ultrasonic data to be analyzed does not meet the specified selection requirements, the secondary ultrasonic data is determined as the target reference ultrasonic data. Secondary ultrasonic data can be understood as data that cannot represent the core content of ultrasound (such as amplitude and wavelength).

[0044] S204: The ultrasonic data that needs to be analyzed is identified as new important ultrasonic data; the new important ultrasonic data is used to perform ultrasonic data processing on the next ultrasonic data that needs to be analyzed.

[0045] For example, specific processing methods for ultrasonic data may include data filtering, data analysis, and data classification.

[0046] In the above process, when identifying whether the current important ultrasonic data meets the specified selection requirements, it is determined based on the time-series distribution of ultrasonic data and preset conditions.

[0047] The specified selection requirements include, but are not limited to, at least one of (1), (2), and (3) below.

[0048] (1) The difference between the ultrasonic data to be analyzed and the important ultrasonic data is less than the specified target difference.

[0049] For example, the target difference can be understood as a pre-set difference threshold. Specifically, it can be understood as the permissible range of difference.

[0050] If the difference between the ultrasound data to be analyzed and the current important ultrasound data is less than the specified target difference, then the current important ultrasound data is determined as the target reference ultrasound data. This ensures that there are a sufficient number of first description fields and second description fields that can be correlated between the ultrasound data to be analyzed and the target reference ultrasound data. This allows for better selection of target description fields belonging to abnormal events from the first description fields. Furthermore, after selecting the target description fields from the first description fields, the remaining first description fields can be better utilized for subsequent processing of the ultrasound data to be analyzed.

[0051] The description field can be understood as data features. Artificial intelligence can be used to identify and extract these features from ultrasonic data. For example, when the wavelength of an ultrasonic wave is a1, artificial intelligence can identify the feature of wavelength a; when the amplitude of an ultrasonic wave is b, artificial intelligence can identify the feature of amplitude b1.

[0052] (2) The number of second description fields associated with the first description field in the important ultrasonic data reaches a specified number.

[0053] For example, the specified value can be understood as a threshold.

[0054] For example, after extracting descriptive fields from the ultrasound data to be analyzed to determine the first descriptive field in the ultrasound data to be analyzed, and extracting descriptive fields from the target reference ultrasound data to determine the second descriptive field in the target reference ultrasound data, the first descriptive field in the ultrasound data to be analyzed and the second descriptive field in the target reference ultrasound data are then associated. This process identifies the first descriptive field in the ultrasound data to be analyzed and the second descriptive field in the target reference ultrasound data that can be successfully associated with the first descriptive field. Successful association of the first and second descriptive fields means that they represent the same descriptive field on the same object. If the number of second descriptive fields associated with the first descriptive field in the current important ultrasound data reaches a specified value, then the current important ultrasound data is identified as the target reference ultrasound data, thereby enabling better selection of target descriptive fields belonging to abnormal events from the first descriptive fields.

[0055] (3) The difference in perspective 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 less than the specified perspective difference threshold.

[0056] For example, data acquisition directions can be understood as different dimensions for analyzing ultrasonic data.

[0057] Provided 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 less than a specified difference threshold, it can be guaranteed that the ultrasonic data to be analyzed and the important ultrasonic data have a large number of the same target objects, thereby ensuring that a sufficient number of first description fields can be determined from the ultrasonic data to be analyzed.

[0058] Regarding S102 above, after determining the specified sample data 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. Given that the ultrasonic transducer acquires the data attributes of the ultrasonic data to be analyzed in the target scene, and that the second description content of the target reference ultrasonic data in the target scene is determined, the first description field can be mapped to the specified sample data, determining the first mapping point of the first description field in the specified sample data; and the second description field can be mapped to the specified sample data, determining the second mapping point of the second description field in the specified sample data.

[0059] The specified sample data can be understood as template data pre-stored in the database. Data attributes include data name, data type, and data characteristics. The second description can be understood as ultrasonic flow velocity data obtained after effective propagation in media such as gas, liquid, solid, and solid solution.

[0060] Since the ultrasonic transducer receives ultrasonic data that transmits the same ultrasonic data under different scenarios (such as different humidity, pressure, and impurities), the ultrasonic data received by the ultrasonic transducer will also have certain differences. Therefore, determining the specified sample data in the target scenario can be understood as determining the template data pre-stored in the database as q1 in a humid environment and determining the template data pre-stored in the database as q2 in a dry environment.

[0061] Furthermore, the AI ​​vector space can be understood as a three-dimensional coordinate system.

[0062] For example, this application embodiment takes mapping a first description field to specified sample data based on data attributes as an example: based on the description content of the specified sample data in the AI ​​vector space and the data attributes, the relative description content between the real-time data acquired by the ultrasonic transducer and the specified sample data is determined; based on the relative description content and the imaging principle of the ultrasonic transducer, the switching relationship between the specified sample data and the real-time ultrasonic data when the ultrasonic transducer acquires the ultrasonic data to be analyzed is determined in the AI ​​vector space; according to the switching relationship, the first description field is mapped to the specified sample data.

[0063] 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 repeated here.

[0064] Regarding S103 above, when determining the target description field belonging to the abnormal event from the first description field, the following method can be used:

[0065] Based on the relative positioning relationship between the first and second mapping points, a mapping variable is determined pointing from the second mapping point to the first mapping point. Based on the mapping variable, a target description field belonging to the abnormal 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 is greater than the distance threshold, it indicates that the data is abnormal, and when it is less than or equal to the distance threshold, it indicates that the data is normal.

[0066] The target description field for anomalies can be understood as the identified characteristics of abnormal ultrasound data. For example, abnormal data refers to erroneous, incomplete, or highly similar data found in ultrasound data. In specific application scenarios, data loss may occur during the acquisition of ultrasound-related data. If the lost data is crucial data that reflects key information, the acquired data can be considered erroneous or incomplete. Furthermore, when acquiring ultrasound-related data, two highly similar data points may exist, potentially leading to them being identified as a single data point, thus reducing the accuracy of data processing.

[0067] In another embodiment of the ultrasonic flow meter data identification method provided in this application, the method further includes: determining the key description content of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed based on the non-target description field in the first description field other than the target description field, the third description field in the target reference ultrasonic data associated with the non-target description field, and the second description content of the target reference ultrasonic data acquired by the ultrasonic transducer; wherein the second description field includes the third description field.

[0068] This allows for the adjustment of the ultrasonic data acquired by the ultrasonic transducer that needs to be analyzed, resulting in higher accuracy in determining key descriptive information and improving the positioning accuracy of the ultrasonic transducer.

[0069] Furthermore, after obtaining the key descriptive content of the ultrasonic data to be analyzed, the method further includes: mapping the third descriptive field back to the ultrasonic data to be analyzed based on the key descriptive content, and determining the third mapping point of the third descriptive field in the ultrasonic data to be analyzed; determining abnormal ultrasonic data based on the positioning data of the third mapping point in the ultrasonic data to be analyzed and the positioning data of the non-target descriptive field in the ultrasonic data to be analyzed; and determining a new specified deletion percentage based on the abnormal ultrasonic data.

[0070] Abnormal ultrasonic data can be identified by comparing the relative distance between two sets of positioning data with a set distance threshold. If the distance is greater than a certain threshold, the data is considered abnormal. The specified deletion percentage refers to the ratio of abnormal event data deleted from the ultrasonic data to be analyzed out of all ultrasonic data to be analyzed. This new specified deletion percentage is used for ultrasonic data processing of the next set of ultrasonic data to be analyzed.

[0071] For example, if the abnormal ultrasound data is less than the specified error threshold, it indicates that the probability of an abnormal event in the current ultrasound data to be analyzed is relatively low. The specified deletion percentage for the next ultrasound data to be analyzed can be reduced accordingly, or the specified deletion percentage for the next ultrasound data to be analyzed can be kept unchanged. If the abnormal ultrasound data is greater than or equal to the specified error threshold, it indicates that the probability of an abnormal event in the current ultrasound data to be analyzed is relatively high. The specified deletion percentage for the next ultrasound data to be analyzed can be increased accordingly, thereby enabling more thorough deletion of descriptive fields belonging to abnormal events when processing the next ultrasound data to be analyzed.

[0072] In this embodiment, when mapping the third description field back to the ultrasound data to be analyzed based on the key description content, and determining the third mapping point of the third description field in the ultrasound data to be analyzed, the switching relationship between the secondary ultrasound data AI vector space corresponding to the ultrasound data to be analyzed and the second ultrasound data AI vector space corresponding to the specified sample data can be determined based on the key description content. According to the switching relationship, the second mapping point of the third description field in the specified sample data is mapped to the ultrasound data to be analyzed, and the third mapping point of the third description field in the ultrasound data to be analyzed is determined.

[0073] Based on the above, Figure 2 A data identification device 200 for an ultrasonic flow meter is provided, the device comprising:

[0074] The description field analysis module 210 is used to obtain a first description field in the ultrasonic data to be analyzed, and a second description field in the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed that is associated with the first description field.

[0075] The mapping point determination module 220 is used 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 to 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.

[0076] The result determination module 230 is used to determine the target description field belonging to the abnormal event from the first description field based on the relative positioning relationship between the first mapping point and the second mapping point. The relative positioning relationship is based on the positional changes of the ultrasonic transducer when acquiring the target reference ultrasonic data and the ultrasonic data to be analyzed. The target description field is then deleted from the ultrasonic data to be analyzed to determine the ultrasonic data identification result.

[0077] Based on the above, this embodiment provides a data identification system 300 for an ultrasonic flow meter, including a processor 310 and a memory 320 that communicate with each other. The processor 310 is used to read a computer program from the memory 320 and execute it to implement the above method.

[0078] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method during runtime.

[0079] In summary, based on the above scheme, the first description field in the ultrasonic data to be analyzed and the second description field in the target reference ultrasonic data associated with the first description field are obtained; the first description field is mapped to specified sample data to determine the first mapping point of the first description field in the specified sample data, and the second description field is mapped to specified sample data to determine the second mapping point of the second description field in the specified sample data; based on the relative positioning relationship between the first mapping point and the second mapping point, the target description field belonging to the abnormal event is determined from the first description field; the target description field is deleted from the ultrasonic data to be analyzed to determine the ultrasonic data identification result. This application can use the mapping variable from the second mapping point to the first mapping point to determine the target description field belonging to the abnormal event in the first description field and delete the target description field in the ultrasonic data to be analyzed. Therefore, when identifying ultrasonic flow meter data based on the first description field after deleting the target description field, the possibility of data identification anomalies is reduced, thereby obtaining more accurate ultrasonic data and improving the efficiency of data processing in subsequent ultrasonic data processing.

[0080] It should be understood that the systems and modules described above can be implemented in various ways. For example, in some embodiments, the systems and modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and systems described above can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a 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 systems and modules of this application can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0081] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

[0082] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0083] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0084] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0085] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0086] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0087] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely through software solutions, such as installing the described system on existing servers or mobile devices.

[0088] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0089] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are open to adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters are taken into account a specified number of significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of application in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0090] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this application, the entire contents of that patent are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this application, as well as documents that limit the broadest scope of the claims in this application (currently or subsequently appended to this application). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or terminology used in the supplementary materials of this application and the content of this application, the descriptions, definitions, and / or terminology used in this application shall prevail.

[0091] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other modifications may also fall within the scope of this application. Therefore, alternative configurations of the embodiments of this application are considered as examples and not limitations, and are regarded as consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly described and illustrated in this application.

[0092] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A data identification method for an ultrasonic flow meter, characterized in that, The method includes: Obtain the first description field from the ultrasonic data to be analyzed, and the second description field from the target reference ultrasonic data corresponding to the ultrasonic data to be analyzed, which is associated with the first description field. Map the first description field to specified sample data to 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 to determine a second mapping point of the second description field in the specified sample data. Based on the relative positioning relationship between the first mapping point and the second mapping point, a target description field belonging to the abnormal event is determined from the first description field; wherein, the relative positioning relationship 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; The target description field is deleted from the ultrasonic data that needs to be analyzed, and the ultrasonic data identification result is determined. The first description field and the second description field are both data features in ultrasonic data, including wavelength features and amplitude features.

2. The data identification method for an ultrasonic flow meter according to claim 1, characterized in that, The step of determining the target description field belonging to the abnormal event from the first description field includes: determining the 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 value of the mapping variable, the target description field belonging to the abnormal event is determined from the first description field.

3. The data identification method for an ultrasonic flow meter according to claim 1, characterized in that, Before obtaining the first description field in the ultrasound data to be analyzed and the second description field in the target reference ultrasound data corresponding to the ultrasound data to be analyzed, which is associated with the first description field, the method further includes: determining the target reference ultrasound data for the ultrasound data to be analyzed based on specified selection requirements.

4. The data identification method for an ultrasonic flow meter according to claim 3, characterized in that, The step of determining the target reference ultrasonic data for the ultrasonic data to be analyzed based on specified selection requirements includes: identifying whether important ultrasonic data in the ultrasonic data to be analyzed meet the specified selection requirements. Provided that the important ultrasonic data meets the specified selection requirements, the important ultrasonic data is determined as the target reference ultrasonic data; If the important ultrasonic data in the ultrasonic data to be analyzed does not meet the specified selection requirements, the secondary ultrasonic data is determined as the target reference ultrasonic data. Important ultrasonic data includes ultrasonic data with amplitude and wavelength within a set range; Secondary ultrasound data are data that cannot represent the core content of ultrasound.

5. The data identification method for an ultrasonic flow meter according to claim 3, characterized in that, Also includes: If the important ultrasonic data in the ultrasonic data to be analyzed does not meet the specified selection requirements, the ultrasonic data to be analyzed is identified as new important ultrasonic data. The new, important ultrasonic data is used for ultrasonic data processing in preparation for the next ultrasonic data to be analyzed.

6. The data identification method for an ultrasonic flow meter according to claim 4, characterized in that, The specified selection criteria include at least one of the following: the difference between the ultrasound data to be analyzed and the important ultrasound data is less than a specified target difference; the number of second description fields associated with the first description field in the important ultrasound data reaches a specified number; and the angular difference between the first data acquisition direction corresponding to the ultrasound data to be analyzed and the second data acquisition direction corresponding to the important ultrasound data is less than a specified angular difference threshold.

7. The data identification method for an ultrasonic flow meter according to claim 6, characterized in that, The step of mapping the first description field to specified sample data and determining the first mapping point of the first description field in the specified sample data includes: determining the data attributes of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed based on the spatial positioning data of the ultrasonic transducer in the target scene when acquiring the target reference ultrasonic data and the first spatial dimension information of the ultrasonic transducer in the target scene when acquiring the ultrasonic data to be analyzed. Based on the data attributes, the first description field is mapped to the specified sample data, and the first mapping point of the first description field in the specified sample data is determined; Data attributes include data name, data type, and data characteristics.

8. The data identification method for an ultrasonic flow meter according to claim 6, characterized in that, The step of mapping the second description field to the specified sample data and determining the second mapping point of the second description field in the specified sample data includes: based on the second description content of the ultrasonic transducer when acquiring the target reference ultrasonic data, mapping the second description field to the specified sample data and determining the second mapping point of the second description field in the specified sample data; The second description is the ultrasonic velocity data obtained after effective propagation in gaseous, liquid, solid, or solid solution media.

9. The data identification method for an ultrasonic flow meter according to claim 6, characterized in that, Also includes: Based on the non-target description fields in the first description field other than the target description field, the third description field in the target reference ultrasonic data associated with the non-target description field, and the second description content of the target reference ultrasonic data acquired by the ultrasonic transducer, the key description content of the ultrasonic transducer when acquiring the ultrasonic data to be analyzed is determined; wherein, the second description field includes the third description field. This also includes: Based on the key description content, the third description field is mapped again to the ultrasonic data that needs to be analyzed, and the third mapping point of the third description field in the ultrasonic data that needs to be analyzed is determined. Abnormal ultrasonic data are determined based on the location data of the third mapping point in the ultrasonic data to be analyzed and the location data of the non-target description field in the ultrasonic data to be analyzed. Based on the abnormal ultrasound data, a new specified deletion percentage is determined; wherein, the new specified deletion percentage is used to process the ultrasound data for the next ultrasound data to be analyzed. The step of mapping the third description field back to the ultrasound data to be analyzed based on the key description content, and determining the third mapping point of the third description field in the ultrasound data to be analyzed, includes: determining the switching relationship between the secondary ultrasound data AI vector space corresponding to the ultrasound data to be analyzed and the second ultrasound data AI vector space corresponding to the specified sample data based on the key description content. Based on the switching relationship, the second mapping point of the third description field in the specified sample data is mapped to the ultrasound data to be analyzed, thereby determining the third mapping point of the third description field in the ultrasound data to be analyzed. The second description is the ultrasonic velocity data obtained after effective propagation in gaseous, liquid, solid, or solid solution media.

10. A data recognition system for an ultrasonic flow meter, characterized in that, The device includes a processor and a memory that communicate with each other, the processor being configured to read a computer program from the memory and execute it to implement the ultrasonic flowmeter data identification method according to any one of claims 1-9.

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