Method and device for determining operating status of intelligent detection system

Abstract

The present application discloses a method and device for determining the operating state of an intelligent detection system. The method includes: obtaining measurement data and identification information, wherein the measurement data includes pressure data of the system to be measured; when the identification information indicates that the measurement data does not include static pressure data, the differential pressure data is processed according to a first strategy to obtain a first processing result, and the first operating state of the pressure pipe and the second operating state of the first type of intelligent pressure transmitter are determined according to the first processing result; when the identification information indicates that the measurement data includes static pressure data, the differential pressure data is processed according to the first strategy to obtain a first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, and the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for determining the operating status of an intelligent detection system. Background Art

[0002] Smart transmitters are commonly used devices in the field of industrial process control. They are used to convert signals collected by sensors into standard signals. For example, they convert physical quantities such as temperature and pressure into current or voltage signals, and transmit them to the control system for monitoring and control. Smart transmitters have some built-in self-diagnostic fault items. When a device fails, a corresponding fault code will be generated. However, these self-diagnostic fault items are only for data transmission or circuit failures, and there are fewer fault diagnoses for pressure pipe blockages. In the related art, the method for diagnosing pressure pipe blockage faults can only diagnose pressure pipe blockages for a single model of pressure transmitter, which has the problems of large limitations, low accuracy, and low efficiency.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and device for determining the operating status of an intelligent detection system, so as to at least solve the technical problems in the related art that only a single model of intelligent pressure transmitter can be diagnosed for the blockage of the guide pressure tube, resulting in large limitations on the diagnostic method, low accuracy of the diagnostic results, and low diagnostic efficiency.

[0005] According to one aspect of an embodiment of the present application, a method for determining the operating status of an intelligent detection system is provided, wherein the intelligent detection system includes: an intelligent pressure transmitter and a pressure pipe; the method includes: obtaining measurement data and identification information, wherein the measurement data includes pressure data of a system to be measured, and the identification information is at least used to indicate the type of measurement data, and the types include: static pressure data and differential pressure data, and the measurement data is measured by an intelligent pressure transmitter, and the intelligent pressure transmitter is connected to the system to be measured through a pressure pipe; when the identification information indicates that the measurement data does not include static pressure data, determining that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures differential pressure data; processing the differential pressure data according to a first strategy to obtain a first processing result, and determining a first operating state and a first differential pressure pipe according to the first processing result. A second operating state of a type of intelligent pressure transmitter, wherein the first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; when the identification information indicates that the measurement data includes static pressure data, the intelligent pressure transmitter is determined to be a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data; the differential pressure data is processed according to the first strategy to obtain a first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0006] Optionally, the differential pressure data is processed according to a first strategy to obtain a first processing result, including: denoising the differential pressure data to obtain denoised differential pressure data; determining a target data sequence in the denoised differential pressure data, wherein the differential pressure data in the target data sequence is measured by the intelligent pressure transmitter under stable operation; performing sliding average processing on the target data sequence to obtain an eigenvalue sequence; generating an eigenvalue difference sequence based on the eigenvalue sequence; and determining the first processing result based on the eigenvalue sequence and the eigenvalue difference sequence.

[0007] Optionally, determining a target data sequence in the denoised differential pressure data includes: arranging multiple denoised differential pressure data according to corresponding timestamps to obtain a data sequence; for each denoised differential pressure data, determining a first sequence located on the left side of the denoised differential pressure data and a second sequence located on the right side of the denoised differential pressure data in the data sequence; determining a score value of the denoised differential pressure data based on the dissimilarity between the first sequence and the second sequence; and determining a sequence consisting of multiple denoised differential pressure data whose score values ​​are less than a preset value and which are continuously arranged in the data sequence as a target data sequence.

[0008] Optionally, a sliding average process is performed on the target data sequence to obtain a characteristic value sequence, including: dividing the target data sequence into multiple short sequences, wherein the number of denoised differential pressure data contained in each short sequence is the same; the number of denoised differential pressure data contained in each short sequence is less than the number of denoised differential pressure data contained in the target data sequence; for each short sequence, sliding average process is performed on the short sequence according to a preset sliding window to obtain multiple average values, wherein the number of average values ​​is the same as the number of denoised differential pressure data contained in the short sequence, and a reference sequence is generated based on the multiple average values, wherein, when the first position sequence number is the same as the second position sequence number, there is a corresponding relationship between the denoised differential pressure data indicated by the first position sequence number in the short sequence and the average value indicated by the second position sequence number in the reference sequence, the first position sequence number is used to indicate the arrangement order of the denoised differential pressure data in the short sequence, and the second position sequence number is used to indicate the arrangement order of the average values ​​in the reference sequence; determining the number of times the short sequence passes through the reference sequence, and determining the number as the characteristic value of the short sequence; and generating a characteristic value sequence based on multiple characteristic values ​​corresponding to the multiple short sequences.

[0009] Optionally, a first processing result is determined based on the eigenvalue sequence and the eigenvalue difference sequence, including: determining a first mean value and a first variance of the eigenvalue sequence, and determining a second mean value and a second variance of the difference in the eigenvalue difference sequence; determining a first type of evaluation index based on the eigenvalue sequence and the first variance, and determining a second type of evaluation index based on the eigenvalue difference sequence and the second variance, wherein both the first type of evaluation index and the second type of evaluation index are used to represent the degree of oscillation of the measurement data, and the degree of oscillation is used to determine whether the pressure pipe is blocked; the first processing result is determined based on the eigenvalue sequence, the first type of evaluation index, the eigenvalue difference sequence, and the second type of evaluation index.

[0010] Optionally, the degree of oscillation includes: the number of oscillations and the rate of change of the number of oscillations. The first type of evaluation indicators includes: the first upper limit value of the number of oscillations, the first lower limit value of the number of oscillations, and the second type of evaluation indicators includes: the second upper limit value of the rate of change of the number of oscillations, and the second lower limit value of the rate of change of the number of oscillations; the first processing result is determined according to the eigenvalue sequence, the first type of evaluation indicators, the eigenvalue difference sequence and the second type of evaluation indicators, including: determining the first maximum value and the first minimum value in the eigenvalue sequence, and determining the second maximum value and the second minimum value in the eigenvalue difference sequence; respectively comparing the first maximum value with the first upper limit value, comparing the first minimum value with the first lower limit value, comparing the second maximum value with the second upper limit value, and comparing the second minimum value with the second lower limit value to obtain a comparison result, wherein the comparison result is the first processing result.

[0011] Optionally, determining the first operating state of the pressure guiding tube according to the first processing result includes: determining that the first operating state is blockage of the pressure guiding tube when the comparison result includes at least one of the following: the first maximum value is greater than or equal to the first upper limit value, the first minimum value is less than or equal to the first lower limit value, the second maximum value is greater than or equal to the second upper limit value, and the second minimum value is less than or equal to the second lower limit value; determining that the first operating state is not blockage of the pressure guiding tube when the comparison result includes all of the following comparison results: the first maximum value is less than the first upper limit value, the first minimum value is greater than the first lower limit value, the second maximum value is less than the second upper limit value, and the second minimum value is greater than the second lower limit value.

[0012] Optionally, after obtaining the first processing result, it also includes: when the first processing result is that the pressure pipe is blocked, respectively determining a first difference between the first maximum value and the first upper limit value, a second difference between the first minimum value and the first lower limit value, a third difference between the second maximum value and the second upper limit value, and a fourth difference between the second minimum value and the second lower limit value; determining multiple exceedances according to the first difference, the second difference, the third difference, the fourth difference, the first variance and the second variance, wherein the numerical value of the exceedance is used to indicate the blockage probability of the pressure pipe, and the sign of the exceedance is used to indicate the cause of the blockage of the pressure pipe; determining multiple absolute values ​​of the multiple exceedances, and determining the exceedance corresponding to the largest absolute value as the first target exceedance, and outputting the first target exceedance as alarm information, wherein the absolute value of the first target exceedance is the blockage probability.

[0013] Optionally, the second operating state of the first type of intelligent pressure transmitter is determined according to the first processing result, including: classifying the over-limits corresponding to the same sequence type into a group to obtain multiple groups, wherein the sequence types include: eigenvalue sequence and eigenvalue difference sequence; for each group, determining the over-limit with the largest absolute value as the second target over-limit; for each second target over-limit, determining the first mapping interval corresponding to the second target over-limit according to the sign and value of the second target over-limit, and determining the first mapping value of the second target over-limit in the first mapping interval, wherein the first mapping intervals corresponding to over-limits with different signs are different; obtaining multiple first mapping values ​​corresponding to multiple second target over-limits, and determining the average value of the multiple first mapping values ​​as the first evaluation index for quantifying the second operating state.

[0014] Optionally, the static pressure data and the differential pressure data are processed according to a second strategy to obtain a second processing result, including: determining a high-pressure characteristic quantity corresponding to a target moment based on the static pressure data and the differential pressure data generated at the target moment, wherein the target moment is any moment at which the static pressure data and the differential pressure data are simultaneously generated; and, determining a low-pressure characteristic quantity at each target moment based on the static pressure data; and, determining a differential pressure characteristic quantity at each target moment based on the differential pressure data; determining a blockage coefficient based on multiple high-pressure characteristic quantities, multiple low-pressure characteristic quantities, and multiple differential pressure characteristic quantities corresponding to multiple target moments, wherein the blockage coefficient is the second processing result, and the blockage coefficient is used to quantify the degree of blockage of the pressure pipe.

[0015] Optionally, a high-pressure characteristic quantity corresponding to the target moment is determined based on the static pressure data and differential pressure data generated at the target moment, including: for each target moment, determining the target time interval to which the target moment belongs, and determining all target moments included in the target time interval, wherein the number of target moments included in the target time interval is a preset number; determining the high-pressure characteristic quantity based on the static pressure data and differential pressure data at all target moments; determining the low-pressure characteristic quantity at each target moment based on the static pressure data, including: determining the low-pressure characteristic quantity based on the static pressure data at all target moments included in the target time interval; determining the differential pressure characteristic quantity at each target moment based on the differential pressure data, including: determining the low-pressure characteristic quantity based on the differential pressure data at all target moments included in the target time interval.

[0016] Optionally, the blockage coefficient is determined based on multiple high-pressure characteristic quantities, multiple low-pressure characteristic quantities and multiple differential pressure characteristic quantities, including: determining a first correlation based on the multiple high-pressure characteristic quantities and the multiple differential pressure characteristic quantities, and determining a second correlation based on the multiple low-pressure characteristic quantities and the multiple differential pressure characteristic quantities, wherein both the first correlation and the second correlation are used to quantify the pressure change in the pressure pipe; and determining the blockage coefficient based on the first correlation and the second correlation.

[0017] Optionally, the first operating state of the pressure-conducting pipe is determined according to the second processing result, including: determining a value interval to which the blockage coefficient belongs, and determining a second mapping interval corresponding to the value interval, wherein different value intervals correspond to different second mapping intervals; determining a second mapping value of the blockage coefficient in the corresponding second mapping interval, wherein the second mapping value is used to quantitatively evaluate the first operating state, the absolute value of the second mapping value is the blockage probability, and the sign of the second mapping value is used to indicate the location that causes the blockage of the pressure-conducting pipe.

[0018] Optionally, the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, including: obtaining multiple first mapping values ​​generated according to the first processing result; and determining the average value of the absolute values ​​of the multiple first mapping values ​​and the absolute value of the blockage coefficient as a second evaluation index for quantifying the third operating state.

[0019] According to another aspect of an embodiment of the present application, a device for determining the operating status of an intelligent detection system is also provided, including: an acquisition module, used to acquire measurement data and identification information, wherein the measurement data includes pressure data of the system to be measured, and the identification information is at least used to indicate the type of measurement data, and the types include: static pressure data and differential pressure data, and the measurement data is measured by an intelligent pressure transmitter in the intelligent detection system, and the intelligent pressure transmitter is connected to the system to be measured through a pressure pipe in the intelligent detection system; a first determination module is used to determine that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures differential pressure data when the identification information indicates that the measurement data does not include static pressure data; the differential pressure data is processed according to a first strategy to obtain a first processing result, and a first operating state and a first differential pressure pipe are determined according to the first processing result. The second operating state of the intelligent pressure transmitter of the second type, wherein the first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; a second determination module is used to determine that the intelligent pressure transmitter is a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data when the identification information indicates that the measurement data contains static pressure data; the differential pressure data is processed according to the first strategy to obtain a first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0020] In an embodiment of the present application, measurement data and identification information are obtained, wherein the measurement data includes pressure data of the system to be measured, and the identification information is at least used to indicate the type of measurement data, which includes: static pressure data and differential pressure data. The measurement data is measured by an intelligent pressure transmitter, and the intelligent pressure transmitter is connected to the system to be measured through a pressure pipe; when the identification information indicates that the measurement data does not contain static pressure data, the intelligent pressure transmitter is determined to be a first type of intelligent pressure transmitter that only measures differential pressure data; the differential pressure data is processed according to a first strategy to obtain a first processing result, and a first operating state of the pressure pipe and a second operating state of the first type of intelligent pressure transmitter are determined according to the first processing result, wherein the first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; when the identification information indicates that the measurement data contains static pressure data, the intelligent pressure transmitter is determined to be a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data; the differential pressure data is processed according to the first strategy to obtain a first processing result, The static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, and the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation. The type of the intelligent pressure transmitter is determined by judging whether the data collected by the intelligent pressure transmitter contains static pressure data. For different types of intelligent pressure transmitters, corresponding data processing strategies are used to process and analyze the collected data, thereby achieving the purpose of judging whether the pressure pipe connected to the intelligent pressure transmitter is blocked and determining the operating status of the intelligent pressure transmitter, thereby achieving the technical effect of diagnosing pressure pipe blockage for any type of intelligent pressure transmitter, improving the accuracy of the diagnostic method, and improving the accuracy of the diagnostic results, thereby solving the technical problems that the related technology can only diagnose pressure pipe blockage for a single type of intelligent pressure transmitter, resulting in large limitations in the diagnostic method, low accuracy of the diagnostic results, and low diagnostic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a method for determining the operating status of an intelligent detection system according to an embodiment of the present application;

[0023] Figure 2is a flowchart of a method for determining the operating status of an intelligent detection system according to an embodiment of the present application;

[0024] Figure 3 is a structural diagram of a device for determining the operating status of an intelligent detection system according to an embodiment of the present application;

[0025] Figure 4 is a work flow chart of a device for determining the operating status of an intelligent detection system according to an embodiment of the present application;

[0026] Figure 5 is a workflow diagram of a first determination module 32 according to an embodiment of the present application;

[0027] Figure 6 It is a workflow diagram of the second determination module 34 according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0031] Highway Addressable Remote Transducer (HART) is an open communication protocol for field smart instruments and control room devices.

[0032] In the related art, only a single type of pressure transmitter can be used to diagnose the blockage of the pressure pipe, and the influence of the actual working condition changes of the pressure transmitter during operation on the diagnosis result is not considered; therefore, there is a problem of inaccurate diagnosis results. In order to solve this problem, the embodiments of the present application provide a relevant solution, which is described in detail below.

[0033] According to an embodiment of the present application, a method embodiment of a fault diagnosis method for an intelligent transmitter is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a method for determining the operating status of an intelligent detection system is shown. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0035] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0036] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the operating state of the intelligent detection system in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the method for determining the operating state of the above-mentioned intelligent detection system is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0037] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0039] The embodiment of the present application provides a method for determining the operating state of an intelligent detection system that can be operated in the above operating environment. Figure 2 is a flowchart of the steps of the method for determining the operating status of the intelligent detection system provided in an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0040] Step S202, obtaining measurement data and identification information, wherein the measurement data includes pressure data of the system to be measured, and the identification information is at least used to indicate the type of measurement data, which includes: static pressure data and differential pressure data. The measurement data is measured by an intelligent pressure transmitter, and the intelligent pressure transmitter is connected to the system to be measured through a pressure pipe.

[0041] The method provided in the embodiment of the present application is applied to an intelligent detection system composed of an intelligent pressure transmitter and a pressure pipe, wherein the intelligent transmitter can be any type of transmitter (a type of intelligent pressure transmitter that only measures differential pressure data, or a type of transmitter that simultaneously measures differential pressure data and static pressure data), and the pressure pipe is used to connect the system to be tested and the intelligent pressure transmitter; the intelligent detection system may also include an intelligent instrument, and the intelligent pressure transmitter converts the signal detected by the sensor into an electrical signal, and displays the information represented by the electrical signal through the intelligent instrument connected thereto. In step S202, the measurement data obtained by the intelligent pressure transmitter during the measurement of the system to be tested can be collected from the intelligent instrument through the HART protocol, and the measurement data includes the pressure data of the system to be tested, and the identification information corresponding to each measurement data, and the identification information is used to mark whether the type of pressure data is differential pressure data or static pressure data.

[0042] Step S204, when the identification information indicates that the measurement data does not contain static pressure data, determine that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures differential pressure data; process the differential pressure data according to the first strategy to obtain a first processing result, and determine a first operating state of the pressure pipe and a second operating state of the first type of intelligent pressure transmitter according to the first processing result, wherein the first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0043] In this embodiment, detecting the operating state of the intelligent detection system includes: detecting the operating state of the pressure pipe and detecting the operating state of the intelligent pressure transmitter. In step S204, if the intelligent pressure transmitter is determined to be a type of intelligent pressure transmitter (i.e., a first type of intelligent pressure transmitter) that can only measure differential pressure data according to the identification information in the measurement data obtained in step S202, the differential pressure data in the measurement data is processed and analyzed by the first strategy, wherein the first strategy is a data processing strategy, and no matter what the case, as long as the data to be processed is differential pressure data, the first strategy is used to analyze and process the data to be processed. In this embodiment, the (first) operating state of the pressure pipe connected to the first type of intelligent pressure transmitter is determined to be blocked or unblocked according to the result of the differential pressure data processing and analysis (i.e., the first processing result), and it is determined whether the (second) operating state of the first type of intelligent pressure transmitter is normal operation, abnormal operation, or stopped operation caused by a blockage fault.

[0044] Optionally, the differential pressure data is processed according to a first strategy to obtain a first processing result, including: denoising the differential pressure data to obtain denoised differential pressure data; determining a target data sequence in the denoised differential pressure data, wherein the differential pressure data in the target data sequence is measured by the intelligent pressure transmitter under stable operation; performing sliding average processing on the target data sequence to obtain an eigenvalue sequence; generating an eigenvalue difference sequence based on the eigenvalue sequence; and determining the first processing result based on the eigenvalue sequence and the eigenvalue difference sequence.

[0045] In this embodiment, the strategy for separately processing the differential pressure data (i.e., the first strategy) includes the following processing steps: first, in order to remove the noise signal generated in the actual field, the differential pressure data is denoised. For example, the wavelet denoising method can be used to perform three-layer wavelet decomposition on the differential pressure data, perform threshold processing on the decomposed wavelet coefficients, and perform wavelet reconstruction on the processed wavelet coefficients to obtain the denoised differential pressure data. Next, in order to prevent the operating conditions of the circuit where the intelligent pressure transmitter is located from changing during data collection, which may cause a large change in the differential pressure data and thus affect the diagnostic effect, the differential pressure data after noise reduction (i.e., denoised differential pressure data) is divided into operating conditions, and the steady-state segment data (i.e., target data sequence) obtained by measuring the intelligent pressure transmitter under stable operating conditions is selected; further, the steady-state segment data (i.e., target data sequence) is subjected to sliding average processing to obtain an eigenvalue sequence, and the eigenvalue data is subjected to differential processing to obtain an eigenvalue differential sequence; both the eigenvalue sequence and the eigenvalue differential sequence can be used to evaluate the degree of oscillation of the measured data; and the degree of oscillation of the measured data can be used to judge whether the pressure pipe is blocked, wherein the eigenvalue sequence is used to indicate the number of oscillations of the measured data during the measurement process of the intelligent pressure transmitter; the eigenvalue differential sequence is used to indicate the rate of change of the number of oscillations of the measured data during the measurement process of the intelligent pressure transmitter. By processing the eigenvalue sequence and the eigenvalue differential sequence, the result of processing the differential pressure data alone (i.e., the first processing result) can be obtained.

[0046] According to an optional embodiment of the present application, a target data sequence is determined in the denoised differential pressure data, including: arranging multiple denoised differential pressure data according to corresponding timestamps to obtain a data sequence; for each denoised differential pressure data, determining a first sequence located on the left side of the denoised differential pressure data and a second sequence located on the right side of the denoised differential pressure data in the data sequence; determining a score value of the denoised differential pressure data according to the dissimilarity between the first sequence and the second sequence; and determining a sequence consisting of multiple denoised differential pressure data whose score values ​​are less than a preset value and which are continuously arranged in the data sequence as the target data sequence.

[0047] As mentioned in the above embodiment, in the process of processing the differential pressure data, it is necessary to screen and obtain the steady-state segment data (i.e., the target data sequence). This embodiment provides a method for screening the steady-state segment data. First, the denoised differential pressure data is arranged according to the timestamp corresponding to the denoised differential pressure data to obtain a data sequence, wherein the timestamp is used to record the generation time of the denoised differential pressure data, and the generation time of each denoised differential pressure data is the time when the corresponding differential pressure data is collected. Next, when screening the steady-state segment data from the data sequence, the score value of each denoised differential pressure data is determined, and the sequence composed of multiple denoised differential pressure data whose score value is less than the preset value and arranged continuously in the data sequence is determined as the steady-state segment data (i.e., the target data sequence); wherein, in this embodiment, the score value of each denoised differential pressure data can be determined by calculating the dissimilarity between the data sequence on the left side of each denoised differential pressure data (i.e., the first sequence) and the data sequence on the right side (i.e., the second sequence). The dissimilarity of the two sequences (the first sequence and the second sequence) can be calculated by the formula: dissimilarity = Calculated, among which, represents the probability distribution of the denoised differential pressure data contained in the first sequence, represents the probability distribution of the denoised differential pressure data contained in the second sequence, represent and The divergence between ; represent and The divergence between In this embodiment, the dissimilarity value of the sequences (the first sequence and the second sequence) on the left and right sides of each denoised differential pressure data may be used as the score value of the denoised differential pressure data.

[0048] According to another optional embodiment of the present application, a target data sequence is subjected to sliding average processing to obtain a characteristic value sequence, including: dividing the target data sequence into multiple short sequences, wherein the number of denoised differential pressure data contained in each short sequence is the same; the number of denoised differential pressure data contained in each short sequence is less than the number of denoised differential pressure data contained in the target data sequence; for each short sequence, performing sliding average processing on the short sequence according to a preset sliding window to obtain multiple average values, wherein the number of average values ​​is the same as the number of denoised differential pressure data contained in the short sequence, and generating a reference sequence according to the multiple average values, wherein, when the first position sequence number is the same as the second position sequence number, there is a corresponding relationship between the denoised differential pressure data indicated by the first position sequence number in the short sequence and the average value indicated by the second position sequence number in the reference sequence, the first position sequence number is used to indicate the arrangement order of the denoised differential pressure data in the short sequence, and the second position sequence number is used to indicate the arrangement order of the average values ​​in the reference sequence; determining the number of times the short sequence passes through the reference sequence, and determining the number as the characteristic value of the short sequence; generating a characteristic value sequence according to the multiple characteristic values ​​corresponding to the multiple short sequences.

[0049] In this embodiment, when the steady-state segment data (i.e., the target data sequence) is subjected to sliding average processing, the target data sequence is divided into equal windows. Specifically, the target data sequence is divided into multiple short sequences of equal length (containing the same number of denoised differential pressure data), wherein the number of denoised differential pressure data contained in each short sequence should be smaller than the number of denoised differential pressure data contained in the target data sequence. A sliding average process is performed on each short sequence using a sliding window of a preset size (i.e., a preset sliding window), and a replacement value of each denoised differential pressure data is determined according to the preset sliding window. Each denoised differential pressure data in the short sequence is replaced by a corresponding replacement value to obtain a characteristic value sequence; the replacement value of each denoised differential pressure data is the average value calculated when the preset sliding window traverses the denoised differential pressure data. Since the preset sliding window traverses the short sequence in sequence, and an average value is generated when the preset sliding window passes through each denoised differential pressure data, when the sliding average process is performed on each short sequence, the number of average values ​​generated is the same as the number of denoised differential pressure data contained in the short sequence, and each denoised differential pressure data will have a corresponding average value (i.e., replacement value). If these average values ​​are arranged in sequence into a sequence (i.e., a reference sequence), and the position number of each average value in the reference sequence (i.e., the first position number) and the position number of the corresponding denoised differential pressure data in the short sequence (i.e., the second position number) must also be the same. In this embodiment, each short sequence is subjected to sliding average processing to generate a reference sequence corresponding to each short sequence, the number of times each short sequence passes through the corresponding reference sequence is determined as the characteristic value of each short sequence, and a characteristic value sequence is generated according to multiple characteristic values ​​corresponding to multiple short sequences.

[0050] Optionally, determining the number of times a short sequence passes through a reference sequence includes: for each short sequence, determining the position number of each denoised differential pressure data, and determining the target average value indicated by the position number of the denoised differential pressure data in the reference sequence; comparing the denoised differential pressure data with the target average value to obtain a comparison result for each denoised differential pressure data, wherein the comparison results include: greater than the target average value, less than the target average value; traversing multiple comparison results in sequence according to the arrangement order of the denoised differential pressure data in the short sequence, and determining the number of times two adjacent comparison results are different as the number of times the short sequence passes through the reference sequence.

[0051] As mentioned in the previous embodiment, the number of times each short sequence passes through the corresponding reference sequence is determined as the characteristic value of each short sequence. In this embodiment, by comparing the data at the same position number in the short sequence and the reference sequence (denoised differential pressure data and target average value), in this embodiment, when the position number of a certain average value in the reference sequence is the same as the position number of a certain denoised differential pressure data in the short sequence, this average value is recorded as the average value corresponding to this "certain denoised differential pressure data" (i.e., target average value), and each denoised differential pressure data has and only has one corresponding target average value. The number of times each short sequence passes through the reference sequence is determined based on the comparison result of the data at the same position number in the short sequence and the reference sequence (denoised differential pressure data and target average value). Specifically, when two adjacent comparison results are different, it is determined that there is an intersection between the short sequence and the reference sequence (i.e., the short sequence passes through the reference sequence once). In this embodiment, when comparing the data at the same position number in the short sequence and the reference sequence (denoised differential pressure data and target average value), there are two results: the value of the denoised differential pressure data is greater than the target average value, and the value of the denoised differential pressure data is less than the target average value. For a short sequence, the comparison results of all denoised differential pressure data in the short sequence are obtained, and the number of times two adjacent comparison results are different is determined, which is the characteristic value of the short sequence; in the above scheme, whether the comparison results are adjacent depends on whether the arrangement positions of the corresponding denoised differential pressure data in the short sequence are adjacent, that is, the two comparison results corresponding to two adjacent denoised differential pressure data in the short sequence can be called two adjacent comparison results. It should be noted that when performing sliding window filtering to calculate the baseline (i.e., the baseline sequence), the first three points and the last three points of the window need to be eliminated to prevent the boundary effect from affecting the baseline.

[0052] According to an optional embodiment of the present application, a eigenvalue difference sequence is generated according to a eigenvalue sequence, including: determining the difference between two adjacent data in sequence according to the arrangement order of the data in the eigenvalue sequence; arranging multiple differences according to a target arrangement order to obtain a eigenvalue difference sequence, wherein the target arrangement order is the order in which the differences are generated.

[0053] In the solution provided in the embodiment of the present application, whether the pressure guiding pipe is blocked is jointly determined based on the characteristic value sequence and the characteristic value difference sequence, wherein the characteristic value difference sequence is obtained by performing difference processing on the characteristic value sequence. Specifically, in the present embodiment, the process of performing difference processing on the characteristic value is as follows: the difference between two characteristic values ​​adjacent to each other in the characteristic value sequence is determined in sequence according to the arrangement order of multiple characteristic values ​​in the characteristic value sequence, and the data sequence generated by arranging these differences in the order in which they are generated (i.e., the target order) is the characteristic value difference sequence; wherein the order in which the difference is generated is determined by the two characteristic values ​​that generate the difference, For example, if the eigenvalue sequence is [4,5,8], then the eigenvalues ​​determined according to the order of arrangement of multiple eigenvalues ​​in the eigenvalue sequence are 1 (1=5-4) and 3 (3=8-5), respectively. Since eigenvalue 4 and eigenvalue 5 are ranked higher than eigenvalue 8 and eigenvalue 5 in the eigenvalue sequence, when the eigenvalue difference sequence is generated according to difference 1 and difference 3, the arrangement order of difference 1 in the eigenvalue difference sequence is higher than the arrangement order of difference 3 in the eigenvalue difference sequence. Since there are only these two differences, the eigenvalue difference sequence corresponding to the eigenvalue sequence [4,5,8] is [1,3].

[0054] According to some optional embodiments of the present application, a first processing result is determined based on an eigenvalue sequence and an eigenvalue difference sequence, including: determining a first mean value and a first variance of the eigenvalue sequence, and determining a second mean value and a second variance of the difference in the eigenvalue difference sequence; determining a first type of evaluation index based on the eigenvalue sequence and the first variance, and determining a second type of evaluation index based on the eigenvalue difference sequence and the second variance, wherein both the first type of evaluation index and the second type of evaluation index are used to represent the degree of oscillation of the measurement data, and the degree of oscillation is used to determine whether the pressure guiding pipe is blocked; determining the first processing result based on the eigenvalue sequence, the first type of evaluation index, the eigenvalue difference sequence, and the second type of evaluation index.

[0055] In some embodiments, the eigenvalue sequence and the eigenvalue difference sequence are processed, specifically including: respectively determining the average value (i.e., the first average value) of the eigenvalue sequence, the variance (i.e., the first variance) of the eigenvalue sequence, the average value (i.e., the second average value) of the eigenvalue difference sequence, and the variance (i.e., the second variance) of the eigenvalue difference sequence; wherein the first average value and the first variance are obtained by processing the data contained in the eigenvalue sequence, for example, the average value of all the data contained in the eigenvalue sequence is recorded as the first average value Based on these data and The determined variance is recorded as the first variance The second mean value and the second variance are obtained by processing the data contained in the eigenvalue difference sequence. For example, the average value of all the data contained in the eigenvalue difference sequence is recorded as the second mean value. Based on these data and The determined variance is recorded as the second variance For these two sets of data ( and As a group, and for another group) can be further processed to obtain two types of evaluation indicators (first type of evaluation indicators and second type of evaluation indicators) for evaluating the oscillation degree of the measurement data of the intelligent transmitter; finally, the first type of evaluation indicators, the second type of evaluation indicators, the data in the eigenvalue sequence and the data in the eigenvalue difference sequence can be processed to obtain the data processing result (i.e., the first processing result) obtained when the differential pressure data is processed alone.

[0056] Optionally, the degree of oscillation includes: the number of oscillations and the rate of change of the number of oscillations. The first type of evaluation indicators includes: the first upper limit value of the number of oscillations, the first lower limit value of the number of oscillations, and the second type of evaluation indicators includes: the second upper limit value of the rate of change of the number of oscillations, and the second lower limit value of the rate of change of the number of oscillations; the first processing result is determined according to the eigenvalue sequence, the first type of evaluation indicators, the eigenvalue difference sequence and the second type of evaluation indicators, including: determining the first maximum value and the first minimum value in the eigenvalue sequence, and determining the second maximum value and the second minimum value in the eigenvalue difference sequence; respectively comparing the first maximum value with the first upper limit value, comparing the first minimum value with the first lower limit value, comparing the second maximum value with the second upper limit value, and comparing the second minimum value with the second lower limit value to obtain a comparison result, wherein the comparison result is the first processing result.

[0057] As mentioned in the above embodiment, the first type of evaluation index and the second type of evaluation index are both used to evaluate the oscillation degree of the measurement data of the smart transmitter to determine whether the pressure pipe is blocked; in this embodiment, the oscillation degree of the measurement data of the smart transmitter specifically includes: the number of oscillations and the rate of change of the number of oscillations; both of these two types can determine whether the pressure pipe is blocked, wherein, according to the first average value and the first variance The first type of evaluation index generated is used to judge whether the pressure pipe is blocked based on the number of oscillations; based on the second average value and the second variance The second type of evaluation index generated is used to judge whether the pressure pipe is blocked from the aspect of the change rate of the oscillation frequency. The first type of evaluation index mentioned above includes the upper limit value of the oscillation frequency (i.e., the first upper limit value) and the lower limit value of the oscillation frequency (i.e., the first lower limit value); the first type of evaluation index is composed of and The upper limit of the number of oscillations (i.e., the first upper limit) and the lower limit of the number of oscillations (i.e., the first lower limit) are generated by and Generated, specific, first upper limit value (upper1) = 、The first lower limit value (lower1) = The first type of evaluation indicators mentioned above include the upper limit value of the change rate of the number of oscillations (i.e., the first upper limit value) and the lower limit value of the change rate of the number of oscillations (i.e., the second lower limit value); the second type of evaluation indicators are and The upper limit value of the oscillation frequency change rate (i.e., the second upper limit value) and the lower limit value of the oscillation frequency change rate (i.e., the second lower limit value) are generated by and Generated, specific, second upper limit value (upper2) = , the second lower limit value (lower2) = The above upper1 and lower1 can also be and Other calculations are performed, upper2 and lower2 can also be and It is obtained by performing other calculations. When processing the first type of evaluation index, the second type of evaluation index, the data in the eigenvalue sequence and the data in the eigenvalue difference sequence, first, extract the maximum value (i.e., the first maximum value) and the minimum value (i.e., the first minimum value) in the eigenvalue sequence, and extract the maximum value (i.e., the second maximum value) and the minimum value (i.e., the second minimum value) in the eigenvalue difference sequence; next, compare the first type of evaluation index generated according to the eigenvalue sequence with the eigenvalue sequence, and compare the second type of evaluation index generated according to the eigenvalue difference sequence with the eigenvalue difference. Specifically, compare upper1 with the first maximum value, lower1 with the first minimum value, upper2 with the second maximum value, and lower2 with the second minimum value, respectively, and four comparison results can be obtained. These four comparison results are the results when only the differential pressure data is processed (i.e., the first processing result).

[0058] According to an optional embodiment of the present application, the first operating state of the pressure guiding tube is determined according to the first processing result, including: when the comparison result includes at least one of the following, determining that the first operating state is blockage of the pressure guiding tube: the first maximum value is greater than or equal to the first upper limit value, the first minimum value is less than or equal to the first lower limit value, the second maximum value is greater than or equal to the second upper limit value, and the second minimum value is less than or equal to the second lower limit value; when the comparison result includes all of the following comparison results, determining that the first operating state is not blockage of the pressure guiding tube: the first maximum value is less than the first upper limit value, the first minimum value is greater than the first lower limit value, the second maximum value is less than the second upper limit value, and the second minimum value is greater than the second lower limit value.

[0059] In the case where only differential pressure data exists in the measured data, the (second) operating state of a type of intelligent pressure transmitter (i.e., the first type of intelligent pressure transmitter) that can only measure differential pressure data, and the operating state of the pressure pipe connected to the first type of intelligent pressure transmitter (i.e., the first operating state) are determined according to the processing result of the differential pressure data (i.e., the first processing result), wherein the result of processing only the differential pressure data (only the first processing result) mentioned in the above embodiment contains multiple comparison results. In this embodiment, as long as there is a comparison result among the multiple comparison results included in the first processing result indicating that the value of the data of the pressure pipe is greater than or equal to the value of the evaluation index, the operating state of the pressure pipe (i.e., the first operating state) is confirmed to be blocked; only when all the comparison results included in the first processing result indicate that the value of the data of the pressure pipe is less than the value of the evaluation index, the operating state of the pressure pipe (i.e., the first operating state) is confirmed to be unblocked. In the above scheme, the first maximum value, the first minimum value, the second maximum value, and the second minimum value represent the value of the data of the pressure pipe, and the first upper limit value, the first lower limit value, the second upper limit value, and the second lower limit value represent the value of the evaluation index.

[0060] According to some other optional embodiments of the present application, after obtaining the first processing result, it also includes: when the first processing result is that the pressure pipe is blocked, respectively determining a first difference between the first maximum value and the first upper limit value, a second difference between the first minimum value and the first lower limit value, a third difference between the second maximum value and the second upper limit value, and a fourth difference between the second minimum value and the second lower limit value; determining multiple exceedances according to the first difference, the second difference, the third difference, the fourth difference, the first variance and the second variance, wherein the numerical value of the exceedance is used to indicate the blockage probability of the pressure pipe, and the sign of the exceedance is used to indicate the cause of the blockage of the pressure pipe; determining multiple absolute values ​​of the multiple exceedances, and determining the exceedance corresponding to the largest absolute value as the first target exceedance, and outputting the first target exceedance as alarm information, wherein the absolute value of the first target exceedance is the blockage probability.

[0061] In other embodiments, when the pressure pipe is determined to be blocked based on the result of processing only the differential pressure data (only the first processing result), an alarm message can also be output, wherein the alarm message can not only be used to instruct the user to check the pressure pipe, but also inform the user of the blockage probability of the pressure pipe; if the alarm message contains the blockage probability of the pressure pipe, the blockage probability needs to be determined based on the eigenvalue sequence and the eigenvalue difference sequence before the alarm message is output. The method of determining the blockage probability based on the eigenvalue sequence and the eigenvalue difference sequence, for example, extracts the first maximum value (max1) and the first minimum value (min1) in the eigenvalue sequence, and the first variance of the eigenvalue sequence. , and upper1 and lower1 of the first type of evaluation indicators generated according to the eigenvalue sequence; and extract the second maximum value (max2) and the second minimum value (min2) of the eigenvalue difference sequence, and the second variance of the eigenvalue difference sequence , and upper2 and lower2 in the second type of evaluation indicators generated according to the eigenvalue difference sequence; further, when calculating the congestion probability, it is also based on the two directions of the number of oscillations and the rate of change of the number of oscillations. In terms of the number of oscillations, the two excesses of exceeding the upper limit and exceeding the lower limit are determined, and in terms of the rate of change of the number of oscillations, the two excesses of exceeding the upper limit and exceeding the upper limit are also determined; the absolute value of the excess with the largest absolute value among the above-mentioned multiple excesses (that is, the first target excess) is output as the congestion probability; wherein each excess is signed, and the sign carried by the excess is used to indicate the excess corresponding to the excess. In the direction of the limit, different signs correspond to different directions of over-limit, and different directions of over-limit correspond to different causes of blockage; for example, in the present embodiment, the sign of the over-limit can be divided into two types: positive and negative. The positive sign represents exceeding the upper limit, and the negative sign represents exceeding the lower limit. Exceeding the upper limit means that the number of oscillations of the measured data is too many, and the corresponding causes of blockage of the pressure pipe include: excessive load of the intelligent pressure transmitter, environmental interference, etc.; exceeding the lower limit means that the number of oscillations of the measured data is too few, and the corresponding causes of blockage of the pressure pipe include: sensor failure in the intelligent pressure transmitter, etc.; therefore, the possible causes of the blockage of the pressure pipe can be determined based on the sign of each over-limit. In the above scheme, in terms of the number of oscillations, the two over-limits of exceeding the upper limit and exceeding the lower limit are determined. Specifically, the various types of data generated according to the characteristic value sequence (max1, min1, upper1, lower1, ) are processed to determine the two excess limits, for example, the excess upper limit in terms of the number of oscillations (a1) = ,in, That is, the first difference; the lower limit of the number of oscillations (b1) = ,in, That is, the second difference; the upper limit of the rate of change of the number of oscillations (a2) = , where (max2-upper2) is the third difference; the lower limit of the rate of change of the number of oscillations (b2) = , where (lower2- min2) is the fourth difference.

[0062] It should be noted that, in the embodiment of the present application, after determining the (first) operating state of the pressure guiding tube according to the comparison result, the diagnosis of the pressure guiding tube is completed and the diagnosis result of the pressure guiding tube is output; and since the diagnosis result of the pressure guiding tube will further affect the diagnosis result of the intelligent pressure transmitter, the over-limit value will not exceed 100% and will not be less than -100%. If it is greater than 100%, 100% is output, and if it is less than -100%, -100% is output.

[0063] According to another optional embodiment of the present application, the second operating state of the first type of intelligent pressure transmitter is determined according to the first processing result, including: classifying the over-limits corresponding to the same sequence type into a group to obtain multiple groups, wherein the sequence types include: eigenvalue sequence and eigenvalue difference sequence; for each group, determining the over-limit with the largest absolute value as the second target over-limit; for each second target over-limit, determining the first mapping interval corresponding to the second target over-limit according to the sign and value of the second target over-limit, and determining the first mapping value of the second target over-limit in the first mapping interval, wherein the first mapping intervals corresponding to over-limits with different signs are different; obtaining multiple first mapping values ​​corresponding to multiple second target over-limits, and determining the average value of the multiple first mapping values ​​as the first evaluation index for quantifying the second operating state.

[0064] The embodiment of the present application also determines the operating state (i.e., the second operating state) of the intelligent pressure transmitter based on the result of processing the measurement data. Specifically, for the first type of intelligent pressure transmitter that can only measure differential pressure data, the operating state of the (first type) intelligent pressure transmitter is determined by processing multiple over-limits generated in the previous embodiment. According to the scheme mentioned in the above embodiment, it can be known that multiple over-limits are generated based on the eigenvalue sequence and the eigenvalue difference sequence. In this embodiment, when processing multiple over-limits, first, the multiple over-limits are classified according to the sequence type corresponding to each over-limit, and the over-limits generated by the same sequence type are classified into one group. In this embodiment, the over-limits generated according to the eigenvalue sequence are classified into one group, and the over-limits generated according to the eigenvalue difference sequence are classified into another group; next, the over-limit with the largest absolute value in each group is screened out as the over-limit (i.e., the second target over-limit) for determining the (second) operating state of the (first type) intelligent pressure transmitter; further, the average value of the multiple (first) mapping values ​​corresponding to the multiple second target over-limits is used as the evaluation index (i.e., the first evaluation index) for evaluating the second operating state of the first type of intelligent pressure transmitter. In the above scheme, before obtaining the first evaluation index, it is necessary to determine the corresponding (first) mapping value according to the numerical value of each second target excess. Specifically, the (first) mapping interval corresponding to the second target excess can be determined according to the symbol carried by each second target excess and the numerical value of the excess, and the (first) mapping value in the corresponding (first) mapping interval can be determined according to the numerical value of the second target excess, wherein different symbols correspond to different (first) mapping intervals, and the same symbol can correspond to one (first) mapping interval or to multiple (first) mapping intervals indicating different mapping value ranges. For example, the following mapping rules are pre-set (the mapping rules specify the mapping relationship between the over-limit range and the mapping value interval): the over-limit range [-100%, 0%) is mapped to the mapping value interval [0, 0.6), the over-limit range [0%, 60%) is mapped to the mapping value interval [0.6, 0.8), and the over-limit range [60%, 100%] is mapped to the mapping value interval [0.8, 1]. When grouping according to the symbol carried by the over-limit, the two groups obtained are: [-100%, 0%] and [0%, 100%]; the first mapping intervals are [0, 0.6), [0.6, 0.8) and [0.8, 1] respectively; the first mapping interval corresponding to the second target over-limit with a negative sign is [0, 0.6), and the first mapping interval corresponding to the second target over-limit with a positive sign is [0.6, 0.8) and [0.8, 1].If the excess limits obtained by processing the measured data are a1, b1, a2, and b2 in the previous embodiment, then in this embodiment, a1 and b1 generated according to the eigenvalue sequence are classified into one group, and a2 and b2 generated according to the eigenvalue difference sequence are classified into another group, and the excess limit with the largest absolute value is screened out from a1 and b1 and recorded as the second target excess limit c1, and the excess limit with the largest absolute value is screened out from a2 and b2 and recorded as the second target excess limit c2; the mapping value of c1 in its corresponding (first) mapping interval [0, 0.6) is recorded as d1, and the corresponding mapping value of c2 in its corresponding (first) mapping interval [0.6, 0.8) or [0.8, 1] is determined and recorded as d2, then the evaluation index (i.e., the first evaluation index Final) for evaluating the second operating state of the first type of intelligent pressure transmitter is:. It should be noted that, when the group in which the second target over-limit value belongs corresponds to multiple (first) mapping intervals, each (first) mapping interval must correspond to a different over-limit range. Therefore, the over-limit range to which the second target over-limit value belongs can be determined first (such as [-100%, 0%), [0%, 60%) and [60%, 100%]), and then the corresponding (first) mapping value interval is further determined according to the over-limit range to which it belongs, and finally the corresponding (first) mapping value is determined.

[0065] It should be noted that in this embodiment, the (second) operating state of the first type of intelligent pressure transmitter can be determined according to the value of Final, and different second operating states correspond to different value ranges. For example, when Final is in the range of 0-0.6, it means that the health of the transmitter is normal; when Final is in the range of 0.6-0.8, it means that the health of the transmitter is abnormal (the first type of intelligent pressure transmitter is operating abnormally); when Final is in the range of 0.8-1, it means that the transmitter may be faulty. Output the health index.

[0066] Step S206, when the identification information indicates that the measurement data includes static pressure data, determine that the intelligent pressure transmitter is a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data; process the differential pressure data according to the first strategy to obtain a first processing result, and process the static pressure data and the differential pressure data according to the second strategy to obtain a second processing result, determine a first operating state of the pressure pipe according to the second processing result, and determine a third operating state of the second type of intelligent pressure transmitter according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0067] In step S206, if it is determined that the intelligent pressure transmitter is a type of intelligent pressure transmitter that can simultaneously measure differential pressure data and static pressure data (i.e., a second type of intelligent pressure transmitter) according to the identification information in the measurement data obtained in step S202, the first strategy and the second strategy are applied simultaneously, and the differential pressure data in the measurement data is processed and analyzed by the first strategy, and the static pressure data and the differential pressure data are processed and analyzed by the second strategy, wherein the second strategy is also a data processing strategy for processing and analyzing the differential pressure data and the static pressure data at the same time. In this embodiment, for the second type of intelligent pressure transmitter, the (first) operating state of the pressure pipe is determined to be blocked or unblocked according to the analysis and processing results of the differential pressure data and the static pressure data (i.e., the second processing result); and the (third) operating state of the second type of intelligent pressure transmitter is determined to be normal operation, abnormal operation, or stopped operation caused by a blockage fault according to the processing results of the differential pressure data alone (i.e., the first processing result) and the results of the joint analysis and processing of the differential pressure data and the static pressure data (i.e., the second processing result).

[0068] Optionally, the static pressure data and the differential pressure data are processed according to a second strategy to obtain a second processing result, including: determining a high-pressure characteristic quantity corresponding to a target moment based on the static pressure data and the differential pressure data generated at the target moment, wherein the target moment is any moment at which the static pressure data and the differential pressure data are simultaneously generated; and, determining a low-pressure characteristic quantity at each target moment based on the static pressure data; and, determining a differential pressure characteristic quantity at each target moment based on the differential pressure data; determining a blockage coefficient based on multiple high-pressure characteristic quantities, multiple low-pressure characteristic quantities, and multiple differential pressure characteristic quantities corresponding to multiple target moments, wherein the blockage coefficient is the second processing result, and the blockage coefficient is used to quantify the degree of blockage of the pressure pipe.

[0069] In this embodiment, the data processing strategy (i.e., the second strategy) for simultaneously processing and analyzing the differential pressure data and the static pressure data includes the following steps: first, determining the high-pressure side data and the low-pressure side data of the system to be measured according to the static pressure data and the differential pressure data measured by the second type of intelligent pressure transmitter, wherein the low-pressure side data and the high-pressure side data are both obtained by processing the static pressure data and the differential pressure data. For example, the low-pressure side pressure data ( ), high pressure side pressure data ( ). Next, the differential pressure data, high-pressure side pressure data and low-pressure side pressure data are differentiated twice in the time dimension to obtain the characteristic quantity of the differential pressure data (i.e., the differential pressure characteristic quantity), the characteristic quantity of the low-pressure side pressure data (i.e., the low-pressure characteristic quantity) and the characteristic quantity of the high-pressure side pressure data (i.e., the high-pressure characteristic quantity). Since the differentiation is performed in the time dimension, there are differential pressure characteristic quantities, high-pressure characteristic quantities and low-pressure characteristic quantities at each moment, wherein the differential pressure characteristic quantity, high-pressure characteristic quantity and low-pressure characteristic quantity at each moment are determined based on the measurement data generated at the same moment (i.e., measured by the intelligent pressure transmitter at the same moment). For example, the low-pressure characteristic quantity at moment i ,in, is the low-pressure side pressure data generated at time i, yes The low-pressure side pressure data generated at all times, yes The low-pressure side pressure data generated at time i; the high-pressure characteristic quantity at time i ,in, is the high-pressure side pressure data generated at time i, yes High-pressure side pressure data generated at all times, yes High-pressure side pressure data generated at time i; differential pressure characteristic quantity at time i ,in, is the differential pressure data generated at time i, yes Differential pressure data generated at all times, yes The differential pressure data generated at the time i, Moment and Furthermore, a blockage coefficient can be determined based on multiple characteristic quantities (high pressure characteristic quantity, low pressure characteristic quantity and differential pressure characteristic quantity) at multiple target moments. The blockage coefficient is a processing result (i.e., a second processing result) obtained by simultaneously processing the differential pressure data and the static pressure data using the second strategy. The blockage coefficient can indicate the blockage degree of the pressure pipe.

[0070] According to an optional embodiment of the present application, a high-pressure characteristic quantity corresponding to the target moment is determined based on the static pressure data and differential pressure data generated at the target moment, including: for each target moment, determining the target time interval to which the target moment belongs, and determining all target moments included in the target time interval, wherein the number of target moments included in the target time interval is a preset number; determining the high-pressure characteristic quantity based on the static pressure data and differential pressure data at all target moments; determining the low-pressure characteristic quantity at each target moment based on the static pressure data, including: determining the low-pressure characteristic quantity based on the static pressure data at all target moments included in the target time interval; determining the differential pressure characteristic quantity at each target moment based on the differential pressure data, including: determining the low-pressure characteristic quantity based on the differential pressure data at all target moments included in the target time interval.

[0071] In the above example, when determining the characteristic quantity of target moment i, the measurement data generated from moment i to moment (i-2) is used. In other examples, the measurement data generated from moment i to moment (in) (n is an arbitrary integer less than i) can also be used to determine the characteristic quantity of target moment i. That is, when determining the characteristic quantity of a certain moment based on the measurement data generated at different moments, the measurement data of the (target) moments used can be set according to actual needs. In this embodiment, the measurement data can be classified into different (target) time intervals in order from early to late in terms of the time of generation. The number of measurement data used to determine the characteristic quantity (high pressure characteristic quantity, low pressure characteristic quantity and differential pressure characteristic quantity) of any target moment is limited by limiting the number of target moments included in the target time interval. For example, if the number of target moments included in the target time interval to which the target moment i belongs is set to m, then the measurement data generated at these m moments are data used to determine the characteristic quantities (high pressure characteristic quantities, low pressure characteristic quantities and differential pressure characteristic quantities) of the target moment i. The target moments included in the target time interval are continuous in the time dimension, then all the target moments in the target time interval can be (im) moment, (i-(m-1))...(i-m+(m-1)) moment, (i-(mm)) moment, i moment. At this time, the measurement data generated from (im) moment to i moment is data used to determine the characteristic quantities (high pressure characteristic quantities, low pressure characteristic quantities and differential pressure characteristic quantities) of the target moment i. All target moments in the target time interval may also be moment i, moment (i+1), moment (i+2)...moment (i+m). In this case, the measurement data generated from moment i to moment (i+m) is the data used to determine the characteristic quantities (high pressure characteristic quantities, low pressure characteristic quantities and differential pressure characteristic quantities) of target moment i; wherein the high pressure characteristic quantity is jointly determined by the static pressure data and the differential pressure data, the low pressure characteristic quantity is determined by the static pressure data, and the differential pressure characteristic quantity is determined by the differential pressure data.

[0072] According to another optional embodiment of the present application, the blockage coefficient is determined according to multiple high-pressure characteristic quantities, multiple low-pressure characteristic quantities and multiple differential pressure characteristic quantities, including: determining a first correlation according to the multiple high-pressure characteristic quantities and the multiple differential pressure characteristic quantities, and determining a second correlation according to the multiple low-pressure characteristic quantities and the multiple differential pressure characteristic quantities, wherein both the first correlation and the second correlation are used to quantify the pressure change in the pressure guiding pipe; determining the blockage coefficient according to the first correlation and the second correlation.

[0073] In this embodiment, the blocking coefficient (Blcokage) = ,in, The correlation between the low-pressure characteristic quantity and the differential-pressure characteristic quantity (i.e., the second correlation) is determined based on the plurality of low-pressure characteristic quantities and the plurality of differential-pressure characteristic quantities; The correlation between the high pressure characteristic quantity and the differential pressure characteristic quantity (ie, the first correlation) is determined based on a plurality of high pressure characteristic quantities and a plurality of differential pressure characteristic quantities. and The formula can be used Determine, among which, in determining hour, In the formula, X represents the low pressure characteristic quantity, and Y represents the differential pressure characteristic quantity. represents the low pressure characteristic at any time i, represents the differential pressure characteristic at any time i, Represents the average value of multiple low-pressure characteristic quantities, Represents the average of multiple differential pressure characteristic quantities, is the standard deviation of the data set composed of multiple low-pressure characteristic quantities, is the standard deviation of the data set composed of multiple differential pressure characteristic quantities. hour, In the equation, X represents the high pressure characteristic quantity, and Y represents the differential pressure characteristic quantity. represents the high pressure characteristic at any time i, represents the differential pressure characteristic at any time i, Represents the average value of multiple high-pressure characteristic quantities, Represents the average of multiple differential pressure characteristic quantities, is the standard deviation of the data set composed of multiple high-pressure characteristic quantities, It is the standard deviation of a data set consisting of multiple differential pressure characteristic quantities.

[0074] According to some optional embodiments of the present application, the first operating state of the pressure-conducting pipe is determined according to the second processing result, including: determining the value interval to which the blockage coefficient belongs, and determining the second mapping interval corresponding to the value interval, wherein different value intervals correspond to different second mapping intervals; determining a second mapping value of the blockage coefficient in the corresponding second mapping interval, wherein the second mapping value is used to quantitatively evaluate the first operating state, the absolute value of the second mapping value is the blockage probability, and the sign of the second mapping value is used to indicate the location that causes the blockage of the pressure-conducting pipe.

[0075] In the case where both static pressure data and differential pressure data exist in the measurement data, the operating state (i.e., the first operating state) of the pressure pipe connected to the intelligent pressure transmitter (i.e., the second type of intelligent pressure transmitter) capable of simultaneously measuring static pressure data and differential pressure data is determined according to the result (i.e., the second processing result) obtained by simultaneously processing the static pressure data and the differential pressure data. In this embodiment, the blockage coefficient is used to determine whether the pressure pipe is blocked. Specifically, the blockage coefficient can be converted into a blockage probability (i.e., the second mapping value) according to a preset mapping rule, and then whether the pressure pipe is blocked can be further determined; the value range of the blockage coefficient (Blcokage) is [-1,1] wherein the mapping relationship between the blockage coefficient and the blockage probability is defined in the preset mapping rule: a blockage coefficient with a value of [-1, 1] is mapped to a blockage probability of [-100%, 100%]; for example, the mapping relationship between the blockage coefficient and the blockage probability is recorded as Y=X*100%, wherein X represents the blockage coefficient and Y represents the blockage probability; therefore, in this embodiment, when determining whether the pressure pipe is blocked by the blockage coefficient, the blockage probability of the intelligent pressure transmitter can be determined according to the mapping relationship between the blockage coefficient and the blockage probability, and then the operating status of the pressure pipe can be further determined according to the blockage probability. The pressure conducting pipe includes a high-pressure side and a low-pressure side. In this embodiment, if the blocking probability obtained after the blocking coefficient is mapped is a negative number (that is, the sign of the second mapping value is a negative sign), it means that the location where the blocking occurs is the low-pressure side of the pressure conducting pipe. At this time, the absolute value of the blocking probability (that is, the second mapping value) represents the probability of blocking on the low-pressure side; if the blocking probability obtained after the blocking coefficient is mapped is a positive number (that is, the sign of the second mapping value is a positive sign), it means that the location where the blocking occurs is the high-pressure side of the pressure conducting pipe. At this time, the absolute value of the blocking probability (that is, the second mapping value) represents the probability of blocking on the high-pressure side; in this embodiment, the following rule is followed when the operating state of the pressure conducting pipe is quantitatively evaluated by the (second) mapping value: the closer the diagnostic index (that is, the second mapping value) is to -100%, the higher the blocking probability of the low-pressure side is, and the closer the diagnostic index (that is, the second mapping value) is to 100%, the higher the blocking probability of the high-pressure side is.

[0076] According to some other optional embodiments of the present application, the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, including: obtaining multiple first mapping values ​​generated according to the first processing result; and determining the average value of the absolute values ​​of the multiple first mapping values ​​and the absolute value of the blockage coefficient as a second evaluation index for quantifying the third operating state.

[0077] For the intelligent pressure transmitter that can measure static pressure data and differential pressure data at the same time (i.e., the second type of intelligent pressure transmitter), it is necessary to jointly determine its operating state (i.e., the third operating state) based on the result of processing the differential pressure data alone (i.e., the first processing result) and the result of processing the differential pressure data and static pressure data simultaneously (i.e., the second processing result). In other embodiments, the average value of the multiple first mapping values ​​generated according to the first processing result and the blockage coefficient (i.e., the second processing result) is determined as the evaluation index (i.e., the second evaluation index) for measuring the second type of intelligent pressure transmitter. For example, in the above example: multiple over-limits are determined according to the first processing result, and the first mapping values ​​d1 and d2 are further generated, then the second evaluation index ,in, ,in, represents the blocking coefficient ( ) is the absolute value of; in this embodiment, is a numerical value, which indicates different operating states (i.e., third operating states) of the second type of intelligent pressure transmitter in different numerical ranges. For example, In the range of 0-0.6, it means that the health of the transmitter is normal and the operating status is normal operation; 0.6-0.8 means the health of the transmitter is abnormal and the operation status is abnormal operation; 0.8-1 indicates that the transmitter may be faulty and the operating state is stopped. The health index (i.e. the second evaluation index) is output. ).

[0078] Through the above steps, it is possible to diagnose the blockage of the pressure pipe of any type of intelligent pressure transmitter. When preprocessing the data of the intelligent transmitter with single differential pressure data, the working condition segmentation is performed, and the historical data in the dynamic stage is effectively eliminated, thereby avoiding the misjudgment of the algorithm in the dynamic process. For the intelligent transmitter containing static pressure and differential pressure data, a correlation diagnosis algorithm is added on the basis of the single differential pressure diagnosis algorithm to improve the accuracy of the diagnosis result. In addition, the health index of the intelligent pressure transmitter is generated according to the diagnosis result of the pressure pipe, thereby achieving the technical effect of judging the health status of the intelligent pressure transmitter.

[0079] Figure 3 It is a structural diagram of the device for determining the operating status of the intelligent detection system, such as Figure 3As shown, the device for determining the operating state of the intelligent detection system includes: an acquisition module 30, which is used to acquire measurement data and identification information, wherein the measurement data includes pressure data of the system to be tested, and the identification information is at least used to indicate the type of the measurement data, and the types include: static pressure data and differential pressure data, and the measurement data is measured by an intelligent pressure transmitter in the intelligent detection system, and the intelligent pressure transmitter is connected to the system to be tested through a pressure pipe in the intelligent detection system; a first determination module 32, which is used to determine that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures differential pressure data when the identification information indicates that the measurement data does not contain static pressure data; the differential pressure data is processed according to a first strategy to obtain a first processing result, and the first operating state of the pressure pipe and the first type of intelligent pressure transmitter are determined according to the first processing result. a second operating state, wherein the first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; a second determining module 34 is used to determine that the intelligent pressure transmitter is a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data when the identification information indicates that the measurement data includes static pressure data; the differential pressure data is processed according to the first strategy to obtain a first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0080] It should be noted that Figure 3 The preferred implementation of the illustrated embodiment can be found in Figure 2 Related description of the illustrated embodiment.

[0081] Figure 4 It is a working flow chart of the device for determining the operating status of the intelligent detection system, such as Figure 4 As shown, the determination device starts working and obtains HART data of the system to be tested through the acquisition module 30, wherein HART data refers to the measurement data and self-diagnosis data collected from the smart instrument through the HART protocol, and the measurement data in the smart instrument is the pressure data obtained by the smart pressure transmitter to measure the pressure of the system to be tested connected to it through the pressure pipe, and the smart instrument and the smart pressure transmitter are in the same smart detection system. Figure 4As shown, after the acquisition module 30 acquires the HART data, the data processing method to be called is determined by judging whether the HART data contains static pressure data. Specifically, if the HART data does not contain static pressure data, the single differential pressure data diagnosis algorithm (i.e., the first strategy) is executed by the first determination module 32. The first determination module 32 executes the single differential pressure data diagnosis algorithm (i.e., the first strategy) to obtain a data processing result, which can be used to diagnose the (first) operating state of the pressure pipe connected to the first type of intelligent pressure transmitter (i.e., the intelligent pressure transmitter that only measures differential pressure data), and is the diagnosis result of the pressure pipe; the first determination module 32 will further determine the evaluation index (i.e., the first evaluation index) for indicating the operating state of the first type of intelligent pressure transmitter according to the diagnosis result of the pressure pipe, and determine the (second) operating state of the single differential pressure intelligent pressure transmitter (i.e., the first type of intelligent pressure transmitter) according to the first evaluation index. In this embodiment, the first evaluation index is also called the comprehensive diagnosis result of the single differential pressure data. If the HART data contains static pressure data, the differential pressure-static pressure data diagnostic algorithm (i.e., the second strategy) is executed by the second determination module 34. The second determination module 34 executes the second strategy to obtain a data processing result, which can be used to diagnose the (first) operating state of the pressure pipe connected to the second type of intelligent pressure transmitter (i.e., an intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data), and is the diagnosis result of the pressure pipe. The second determination module 34 will further determine the evaluation index (i.e., the second evaluation index) used to indicate the operating state of the second type of intelligent pressure transmitter based on the diagnosis result of the pressure pipe, and determine the (third) operating state of the second type of intelligent pressure transmitter based on the second evaluation index. In this embodiment, the second evaluation index is also referred to as a comprehensive diagnostic result containing static pressure data. Figure 4 As shown, no matter which data processing method is used, the device for determining the operating status of the intelligent detection system will eventually output a diagnostic result, including: outputting the diagnostic result of the pressure pipe, outputting the diagnostic result of the (first or second type) intelligent pressure transmitter (i.e., the first evaluation index or the second evaluation index).

[0082] Figure 5 is a work flow chart of the first determination module 32. The first determination module 32 executes the steps of the single differential pressure data diagnosis algorithm (ie, the first strategy) as follows: Figure 5 As shown, first, the differential pressure data is preprocessed, for example, denoising is performed by a wavelet denoising method; next, the denoised differential pressure data is segmented under working conditions: the differential pressure data measured by the first type of intelligent pressure transmitter under steady-state conditions (i.e., steady-state segment data) is screened; further, the steady-state segment data is subjected to sliding average processing, and the diagnostic results of the pressure pipe and the diagnostic results of the first type of intelligent pressure transmitter can be determined based on the data after the sliding average processing.

[0083] Figure 6 is a work flow chart of the second determination module 34. The second determination module 34 executes the steps of the differential pressure-static pressure data diagnosis algorithm (ie, the second strategy) as follows: Figure 6 As shown, firstly, the differential pressure data and the static pressure data are preprocessed respectively, for example, the differential pressure data and the static pressure data are denoised respectively by a wavelet denoising method; next, the high-pressure side pressure data, the low-pressure side pressure data and the differential pressure data are generated according to the denoised differential pressure data and the static pressure data; further, the second determination module 34 determines the correlation between the differential pressure data and the high-pressure side data (i.e., the first correlation), the correlation between the differential pressure data and the low-pressure side data (i.e., the second correlation) according to the above-mentioned generated data, and determines the blockage coefficient for judging whether the second type of intelligent pressure transmitter is blocked / the probability of blockage according to the above-mentioned first correlation and second correlation; the second determination module 34 finally determines and outputs the diagnosis result according to the blockage coefficient, and the diagnosis result includes: the diagnosis result of the pressure pipe and the diagnosis result of the second type of intelligent pressure transmitter.

[0084] An embodiment of the present application also provides a non-volatile storage medium, in which a computer program is stored, wherein the method for determining the operating status of the above intelligent detection system is executed by running the computer program on a device where the non-volatile storage medium is located.

[0085] The non-volatile storage medium is used to store a program that performs the following functions: obtaining measurement data and identification information, wherein the measurement data includes pressure data of the system to be measured, and the identification information is at least used to indicate the type of the measurement data, and the types include: static pressure data and differential pressure data, and the measurement data is measured by an intelligent pressure transmitter, and the intelligent pressure transmitter is connected to the system to be measured through a pressure pipe; when the identification information indicates that the measurement data does not contain static pressure data, determining that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures differential pressure data; processing the differential pressure data according to a first strategy to obtain a first processing result, and determining a first operating state of the pressure pipe and a second operating state of the first type of intelligent pressure transmitter according to the first processing result, wherein, The first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; when the identification information indicates that the measurement data includes static pressure data, the intelligent pressure transmitter is determined to be a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data; the differential pressure data is processed according to the first strategy to obtain a first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0086] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above method for determining the operating status of the intelligent detection system through the computer program.

[0087] The processor in the above electronic device is used to run a program that performs the following functions: obtain measurement data and identification information, wherein the measurement data includes pressure data of the system to be measured, and the identification information is at least used to indicate the type of the measurement data, and the types include: static pressure data and differential pressure data, and the measurement data is measured by an intelligent pressure transmitter, and the intelligent pressure transmitter is connected to the system to be measured through a pressure pipe; when the identification information indicates that the measurement data does not contain static pressure data, determine that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures differential pressure data; process the differential pressure data according to a first strategy to obtain a first processing result, and determine a first operating state of the pressure pipe and a second operating state of the first type of intelligent pressure transmitter according to the first processing result, wherein, The first operating state includes one of the following: blocked, unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; when the identification information indicates that the measurement data includes static pressure data, the intelligent pressure transmitter is determined to be a second type of intelligent pressure transmitter that simultaneously measures static pressure data and differential pressure data; the differential pressure data is processed according to the first strategy to obtain a first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, the first operating state of the pressure pipe is determined according to the second processing result, and the third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation.

[0088] An embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method for determining the operating status of the above intelligent detection system.

[0089] It should be noted that the various modules in the device for determining the operating status of the above-mentioned intelligent detection system can be program modules (for example, a set of program instructions for implementing a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0090] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0091] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0094] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0096] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining the operating status of an intelligent detection system, characterized in that: The intelligent detection system includes: an intelligent pressure transmitter and a pressure pipe; the method includes: Acquire measurement data and identification information, wherein the measurement data includes pressure data of the system to be measured, and the identification information is at least used to indicate the type of the measurement data, and the type includes: static pressure data and differential pressure data, and the measurement data is measured by the intelligent pressure transmitter, and the intelligent pressure transmitter is connected to the system to be measured through the pressure pipe; In the case where the identification information indicates that the static pressure data is not included in the measurement data, determining that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures the differential pressure data; processing the differential pressure data according to a first strategy to obtain a first processing result, and determining a first operating state of the pressure pipe and a second operating state of the first type of intelligent pressure transmitter according to the first processing result, wherein the first operating state includes one of the following: blocked and unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; The first strategy is a data processing strategy for processing and analyzing the differential pressure data. The differential pressure data is processed according to the first strategy to obtain a first processing result, including: performing denoising processing on the differential pressure data to obtain denoised differential pressure data; determining a target data sequence in the denoised differential pressure data, wherein the differential pressure data in the target data sequence is measured by the intelligent pressure transmitter under stable operation; performing sliding average processing on the target data sequence to obtain a characteristic value sequence; generating a characteristic value difference sequence according to the characteristic value sequence; and determining the first processing result according to the characteristic value sequence and the characteristic value difference sequence; Determining the first processing result according to the eigenvalue sequence and the eigenvalue difference sequence includes: comparing a first type of evaluation index generated according to the eigenvalue sequence with the eigenvalue sequence, and comparing a second type of evaluation index generated according to the eigenvalue difference sequence with the eigenvalue difference, and obtaining the comparison result as the first processing result, wherein the first type of evaluation index includes: a first upper limit value of the number of oscillations, a first lower limit value of the number of oscillations, and the second type of evaluation index includes: a second upper limit value of the change rate of the number of oscillations, and a second lower limit value of the change rate of the number of oscillations; In the case where the identification information indicates that the measurement data includes the static pressure data, the intelligent pressure transmitter is determined to be a second type of intelligent pressure transmitter that simultaneously measures the static pressure data and the differential pressure data; the differential pressure data is processed according to the first strategy to obtain the first processing result, and the static pressure data and the differential pressure data are processed according to the second strategy to obtain the second processing result, a first operating state of the pressure pipe is determined according to the second processing result, and a third operating state of the second type of intelligent pressure transmitter is determined according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation; Among them, the second strategy is a data processing strategy for simultaneously processing and analyzing the differential pressure data and the static pressure data; the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, including: determining the high-pressure characteristic quantity corresponding to the target moment according to the static pressure data and the differential pressure data generated at the target moment, wherein the target moment is any moment at which the static pressure data and the differential pressure data are simultaneously generated; and, determining the low-pressure characteristic quantity at each of the target moments according to the static pressure data; and, determining the differential pressure characteristic quantity at each of the target moments according to the differential pressure data; determining a blockage coefficient according to a plurality of the high-pressure characteristic quantities, a plurality of the low-pressure characteristic quantities, and a plurality of the differential pressure characteristic quantities corresponding to a plurality of the target moments, wherein the blockage coefficient is the second processing result, and the blockage coefficient is used to quantify the degree of blockage of the pressure pipe.

2. The method according to claim 1, characterized in that Determining a target data sequence in the de-noised differential pressure data comprises: Arrange the plurality of de-noised differential pressure data according to corresponding timestamps to obtain a data sequence; For each of the de-noised differential pressure data, determining in the data sequence a first sequence located on the left side of the de-noised differential pressure data and a second sequence located on the right side of the de-noised differential pressure data; Determining a score value of the denoised differential pressure data according to a dissimilarity between the first sequence and the second sequence; A sequence consisting of a plurality of the denoised differential pressure data having score values ​​less than a preset value and arranged continuously in the data sequence is determined as the target data sequence.

3. The method according to claim 1, characterized in that: The target data sequence is subjected to sliding average processing to obtain a eigenvalue sequence, including: Dividing the target data sequence into a plurality of short sequences, wherein the number of denoised differential pressure data included in each of the short sequences is the same, and the number of denoised differential pressure data included in each of the short sequences is smaller than the number of denoised differential pressure data included in the target data sequence; For each of the short sequences, performing the sliding average processing on the short sequence according to a preset sliding window to obtain a plurality of average values, wherein the number of the average values ​​is the same as the number of denoised differential pressure data included in the short sequence; Generate a reference sequence according to the multiple average values, wherein, when the first position number is the same as the second position number, there is a corresponding relationship between the denoised differential pressure data indicated by the first position number in the short sequence and the average value indicated by the second position number in the reference sequence, the first position number is used to indicate the arrangement order of the denoised differential pressure data in the short sequence, and the second position number is used to indicate the arrangement order of the average value in the reference sequence; Determine the number of times the short sequence passes through the reference sequence, and determine the number as a characteristic value of the short sequence; The eigenvalue sequence is generated according to the multiple eigenvalues ​​corresponding to the multiple short sequences.

4. The method according to claim 1, characterized in that Determining the first processing result according to the eigenvalue sequence and the eigenvalue difference sequence includes: Determine a first mean value and a first variance of the eigenvalue sequence, and determine a second mean value and a second variance of the differences in the eigenvalue difference sequence; Determine a first type of evaluation index according to the eigenvalue sequence and the first variance, and determine a second type of evaluation index according to the eigenvalue difference sequence and the second variance, wherein both the first type of evaluation index and the second type of evaluation index are used to represent the oscillation degree of the measurement data, and the oscillation degree is used to determine whether the pressure guiding tube is blocked; The first processing result is determined according to the eigenvalue sequence, the first type of evaluation index, the eigenvalue difference sequence and the second type of evaluation index.

5. The method according to claim 4, characterized in that The degree of oscillation includes: the number of oscillations and the rate of change of the number of oscillations; Determining the first processing result according to the eigenvalue sequence, the first type of evaluation index, the eigenvalue difference sequence and the second type of evaluation index includes: Determine a first maximum value and a first minimum value in the eigenvalue sequence, and determine a second maximum value and a second minimum value in the eigenvalue difference sequence; The first maximum value is compared with the first upper limit value, the first minimum value is compared with the first lower limit value, the second maximum value is compared with the second upper limit value, and the second minimum value is compared with the second lower limit value to obtain a comparison result, wherein the comparison result is the first processing result.

6. The method according to claim 5, characterized in that Determining a first operating state of the pressure guiding pipe according to the first processing result includes: When the comparison result includes at least one of the following, the first operating state is determined to be a pressure guiding pipe blockage: the first maximum value is greater than or equal to the first upper limit value, the first minimum value is less than or equal to the first lower limit value, the second maximum value is greater than or equal to the second upper limit value, and the second minimum value is less than or equal to the second lower limit value; When the comparison result includes all of the following comparison results, it is determined that the first operating state is that the pressure guiding tube is not blocked: the first maximum value is less than the first upper limit value, the first minimum value is greater than the first lower limit value, the second maximum value is less than the second upper limit value, and the second minimum value is greater than the second lower limit value.

7. The method according to claim 5, characterized in that After obtaining the first processing result, the method further includes: When the first processing result is that the pressure guiding pipe is blocked, respectively determining a first difference between the first maximum value and the first upper limit value, a second difference between the first minimum value and the first lower limit value, a third difference between the second maximum value and the second upper limit value, and a fourth difference between the second minimum value and the second lower limit value; Determine a plurality of excesses according to the first difference, the second difference, the third difference, the fourth difference, the first variance, and the second variance, wherein the value of the excess is used to indicate a probability of blockage of the pressure guiding pipe, and the sign of the excess is used to indicate a cause of blockage of the pressure guiding pipe; Determine multiple absolute values ​​of the multiple excesses, determine the excess corresponding to the largest absolute value as a first target excess, and output the first target excess as warning information, wherein the absolute value of the first target excess is a jam probability.

8. The method according to claim 7, characterized in that Determining a second operating state of the first type of intelligent pressure transmitter according to the first processing result includes: Classifying the excess limits corresponding to the same sequence type into one group to obtain a plurality of groups, wherein the sequence type includes: the eigenvalue sequence and the eigenvalue difference sequence; For each of the groups, determining the excess with the largest absolute value as the second target excess; for each second target excess, determining a first mapping interval corresponding to the second target excess according to a sign and a value of the second target excess, and determining a first mapping value of the second target excess in the first mapping interval, wherein the first mapping intervals corresponding to the excesses of different signs are different; A plurality of the first mapping values ​​corresponding to a plurality of the second target excesses are obtained, and an average value of the plurality of the first mapping values ​​is determined as a first evaluation index for quantifying the second operating state.

9. The method according to claim 1, characterized in that: Determining a high pressure feature value corresponding to the target time according to the static pressure data and the differential pressure data generated at the target time includes: For each of the target moments, determining the target time interval to which the target moment belongs, and determining all the target moments included in the target time interval, wherein the number of the target moments included in the target time interval is a preset number; Determining the high pressure characteristic value according to the static pressure data and the differential pressure data at all the target moments; Determining the low pressure characteristic quantity at each target moment according to the static pressure data includes: determining the low pressure characteristic quantity according to the static pressure data at all target moments included in the target time interval; Determining the differential pressure characteristic quantity at each target moment according to the differential pressure data includes: determining the low pressure characteristic quantity according to the differential pressure data at all target moments included in the target time interval.

10. The method according to claim 1, characterized in that Determining a blockage coefficient according to the plurality of high pressure characteristic quantities, the plurality of low pressure characteristic quantities and the plurality of differential pressure characteristic quantities comprises: Determine a first correlation according to the plurality of high-pressure characteristic quantities and the plurality of differential-pressure characteristic quantities, and determine a second correlation according to the plurality of low-pressure characteristic quantities and the plurality of differential-pressure characteristic quantities, wherein both the first correlation and the second correlation are used to quantify pressure changes in the pressure guiding pipe; The congestion coefficient is determined according to the first correlation and the second correlation.

11. The method according to claim 1, characterized in that Determining a first operating state of the pressure guiding pipe according to the second processing result includes: Determine the value interval to which the congestion coefficient belongs, and determine the second mapping interval corresponding to the value interval, wherein the second mapping intervals corresponding to different value intervals are different; Determine a second mapping value of the blockage coefficient in the corresponding second mapping interval, wherein the second mapping value is used to quantitatively evaluate the first operating state, the absolute value of the second mapping value is the blockage probability, and the sign of the second mapping value is used to indicate the position that causes the pressure guiding pipe to be blocked.

12. The method according to claim 1, characterized in that Determining a third operating state of the second-type intelligent pressure transmitter according to the first processing result and the second processing result includes: Acquire a plurality of first mapping values ​​generated according to the first processing result; An average value of the absolute values ​​of the plurality of first mapping values ​​and the absolute value of the congestion coefficient is determined as a second evaluation index for quantifying the third operating state.

13. A device for determining the operating status of an intelligent detection system, characterized in that: include: an acquisition module, used to acquire measurement data and identification information, wherein the measurement data includes pressure data of the system to be tested, and the identification information is at least used to indicate the type of the measurement data, and the type includes: static pressure data and differential pressure data, and the measurement data is measured by an intelligent pressure transmitter in the intelligent detection system, and the intelligent pressure transmitter is connected to the system to be tested through a pressure pipe in the intelligent detection system; A first determination module is used to determine that the intelligent pressure transmitter is a first type of intelligent pressure transmitter that only measures the differential pressure data when the identification information indicates that the static pressure data is not included in the measurement data; process the differential pressure data according to a first strategy to obtain a first processing result, and determine a first operating state of the pressure pipe and a second operating state of the first type of intelligent pressure transmitter according to the first processing result, wherein the first operating state includes one of the following: blocked and unblocked, and the second operating state includes one of the following: normal operation, abnormal operation, and stopped operation; Wherein, the first strategy is a data processing strategy for processing and analyzing the differential pressure data, and the differential pressure data is processed according to the first strategy to obtain a first processing result, including: performing denoising processing on the differential pressure data to obtain denoised differential pressure data; determining a target data sequence in the denoised differential pressure data, wherein the differential pressure data in the target data sequence is measured by the intelligent pressure transmitter under stable operation; performing sliding average processing on the target data sequence to obtain a characteristic value sequence; generating a characteristic value difference sequence according to the characteristic value sequence; determining the first processing result according to the characteristic value sequence and the characteristic value difference sequence; determining the first processing result according to the characteristic value sequence and the characteristic value difference sequence, including: comparing a first type of evaluation index generated according to the characteristic value sequence with the characteristic value sequence, and comparing a second type of evaluation index generated according to the characteristic value difference sequence with the characteristic value difference, and the comparison result obtained is the first processing result, wherein the first type of evaluation index includes: a first upper limit value of the number of oscillations, a first lower limit value of the number of oscillations, and the second type of evaluation index includes: a second upper limit value of the rate of change of the number of oscillations, a second lower limit value of the rate of change of the number of oscillations; a second determination module, configured to determine, when the identification information indicates that the measurement data includes the static pressure data, that the intelligent pressure transmitter is a second type of intelligent pressure transmitter that simultaneously measures the static pressure data and the differential pressure data; process the differential pressure data according to the first strategy to obtain the first processing result, and process the static pressure data and the differential pressure data according to the second strategy to obtain the second processing result, determine the first operating state of the pressure pipe according to the second processing result, and determine the third operating state of the second type of intelligent pressure transmitter according to the first processing result and the second processing result, wherein the third operating state includes one of the following: normal operation, abnormal operation, and stopped operation; Among them, the second strategy is a data processing strategy for simultaneously processing and analyzing the differential pressure data and the static pressure data; the static pressure data and the differential pressure data are processed according to the second strategy to obtain a second processing result, including: determining the high-pressure characteristic quantity corresponding to the target moment according to the static pressure data and the differential pressure data generated at the target moment, wherein the target moment is any moment at which the static pressure data and the differential pressure data are simultaneously generated; and, determining the low-pressure characteristic quantity at each of the target moments according to the static pressure data; and, determining the differential pressure characteristic quantity at each of the target moments according to the differential pressure data; determining a blockage coefficient according to a plurality of the high-pressure characteristic quantities, a plurality of the low-pressure characteristic quantities, and a plurality of the differential pressure characteristic quantities corresponding to a plurality of the target moments, wherein the blockage coefficient is the second processing result, and the blockage coefficient is used to quantify the degree of blockage of the pressure pipe.

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