CN117249873B - Quality monitoring method and equipment for gas molecular analysis - Google Patents
Quality monitoring method and equipment for gas molecular analysis Download PDFInfo
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Abstract
The invention belongs to the technical field of gas analysis and environmental monitoring, and provides a quality monitoring method and equipment for gas molecular analysis, which specifically comprises the following steps: firstly, initially arranging a quality analysis early warning scene; then, obtaining a gauge pressure value and an internal pressure difference value from a quality analysis early warning scene and forming a analysis segment characterization set; calculating by using the analysis segment characterization group to obtain a pressure characterization loss value; and finally, sending out drift early warning according to the pressure sign loss magnitude order value. The correlation between the upper pressure difference and the lower pressure difference of the pipeline of the thermal type gas mass flowmeter and pipeline blockage and the diffusivity of the environmental pressure change on the drift of the thermal type gas mass flowmeter are effectively quantized, so that the sensitivity of early warning drift phenomenon is improved, reliable quantity support is provided for real-time and rapid feedback of the blockage drift phenomenon of the thermal type gas mass flowmeter, and the accuracy and stability of the thermal type mass flowmeter in a long-term use process are further ensured.
Description
Technical Field
The invention belongs to the technical field of gas analysis and environmental monitoring, and particularly relates to a quality monitoring method and equipment for gas molecular analysis.
Background
Thermal mass flowmeters are commonly used air molecular analysis tools, however, in practical application scenes, the accuracy of the thermal mass flowmeters is often affected due to the fact that pressure changes in the environment and stacking of sediments are born, and the pressure changes in the environment can cause gas density changes, so that range drift and pressure sensing distortion of the thermal mass flowmeters are generated; for pollutants and sediments, the thermal mass flowmeter is doped and adhered with foreign matters in the process of measuring work, when the thermal mass flowmeter continuously works for a long time, the pollutants or sediments are accumulated in a measuring pipeline, further a sensing element in the thermal mass flowmeter is blocked, so that the accuracy of the thermal mass flowmeter is reduced or zero drift occurs, meanwhile, the blocking object in the pipeline is unstable to move or is partially blocked, so that pressure fluctuation in the pipeline occurs, and the long-term stability and accuracy of the thermal mass flowmeter are affected; the risk of real-time data feedback precision reduction exists in the process of gas molecular analysis, so that a thermal mass flow analysis method capable of early warning interference factors in real time is needed to deal with the abnormal state of an instrument with pressure abnormality as a cause in the monitoring process for analysis and identification, and further the effectiveness and stability of the thermal mass flow meter in the long-term use process are ensured.
Disclosure of Invention
The present invention is directed to a method and apparatus for quality monitoring of gas molecular analysis, which solves one or more of the technical problems of the prior art, and provides at least one advantageous option or condition.
To achieve the above object, according to an aspect of the present invention, there is provided a quality monitoring method for gas molecular analysis, the method comprising the steps of:
s100, initially arranging a quality analysis early warning scene;
s200, obtaining a gauge pressure value and an internal pressure difference value from a quality analysis early warning scene, and combining the gauge pressure value and the internal pressure difference value into a binary set serving as an analysis segment symptom set;
s300, performing compression sign loss analysis by using the analysis segment sign group, and calculating to obtain a compression sign loss order value;
s400, sending out drift early warning according to the pressure sign loss magnitude order value.
Further, in step S100, the method for analyzing the early warning scene by the initial placement quality is: the mass analysis early warning scene comprises a pipeline for gas delivery and a plurality of double-sensitive nodes, wherein the double-sensitive nodes are arranged in the pipeline at equal distances, each double-sensitive node respectively comprises a gauge pressure sensor and a differential pressure sensor, and the distances between the starting point and the ending point of the differential pressure sensor are the same. The nodes hereinafter are equivalent to dual sensitive nodes.
Further, in step S200, the method for obtaining the gauge pressure value and the internal pressure difference value from the mass analysis early warning scene and forming the segment characterization set is as follows: a time period is set as a separation section WT, the value range of the separation section is WT epsilon [0.5,3] seconds, and the separation section is equal to the separation section hereinafter.
The gauge pressure sensor and the differential pressure sensor in the double-sensitive node measure data in real time; in any WT period, each double-sensitive node obtains the maximum value of the pressure value and the maximum value of the pressure difference, and records the maximum value and the maximum value of the pressure difference as the gauge pressure value and the internal pressure difference corresponding to the analysis section interval respectively, wherein the gauge pressure value is obtained by measuring a gauge pressure sensor in the double-sensitive node, the internal pressure difference is obtained by measuring a differential pressure sensor in the double-sensitive node, and a binary group formed by the gauge pressure value and the internal pressure difference obtained by one double-sensitive node in real time is used as the analysis section characterization group corresponding to the double-sensitive node in the analysis section interval.
Further, in step S300, the method for analyzing the compression loss by using the analysis segment syndrome set and calculating the compression loss order value is as follows: setting a variable as analysis segment to measure TN, TN epsilon [50, 200 ]]Taking a corresponding analysis segment at the current moment and TN previous analysis segment symptom groups to form a symptom group sequence; calculating a first differential pressure characteristic GSEQ of the j2 th analysis segment according to the syndrome sequence by taking j2 as the sequence number of the analysis segment j2 ：
；
Where j1 is an accumulated variable, ln () is a logarithmic function with the natural constant e as a base, XQA in the formula j2 Represents the internal pressure difference value of the j2 th element in the syndrome sequence, and Bmean (j 1) represents the average value of the internal pressure difference values of the j1 st to TN th elements in the syndrome sequence;
taking the ratio of the first differential pressure characteristic corresponding to each analysis segment in the characterization group sequence to the gauge pressure value as a sub-loss magnitude order value EG j2 The difference value of the sub-loss level of any analysis segment and the sub-loss level of the previous analysis segment is the instantaneous loss difference, the corresponding analysis segment with the maximum value in the instantaneous loss difference is obtained from the characterization sequence and is used as a height difference analysis segment, and the average value of the sub-loss levels of the height difference analysis segments is used as the pressure sign loss level.
The method for calculating the pressure loss magnitude order value has the independent operation phenomenon of the measuring points, which can cause the problem of slow data analysis and continuous abnormal behavior recognition hysteresis, but the prior art cannot solve the problem of hysteresis, so that the method has better real-time performance and timeliness for obtaining the pressure loss magnitude order value and solving the problem, and eliminates the recognition hysteresis, and therefore, the invention provides a more preferable scheme as follows:
preferably, in step S300, the method for analyzing the compression loss by using the analysis segment syndrome set and calculating the order value of the compression loss is as follows: the binary group constructed by the gauge pressure value fea1 and the internal pressure difference value fea2 of any double-sensitive node under the same analysis section is recorded as the corresponding analysis section characterization group under the analysis section; setting a time period measurement period tg, wherein the value range of the measurement period tg is tg epsilon [60,120] minutes, in the latest tg time period, setting analysis segment feature groups under the same analysis segment of different double-sensitive nodes as a column, setting analysis segment feature groups under different analysis segments of the same double-sensitive node as a row to construct a matrix and taking the matrix as a safety early warning model, and recording the number of analysis segments as tLen;
for any row in the safety early warning model, calculating the gauge pressure step value fea3 of each element: taking the element of the gauge pressure order value to be calculated as a leading element, and taking the average value of the gauge pressure values in each analysis section from the corresponding analysis section of the leading element to the analysis section which belongs to the current moment as the gauge pressure level, wherein the gauge pressure order value of the leading element is the ratio of the gauge pressure value corresponding to the leading element to the gauge pressure level;
taking any row of a safety early warning model as a temporary measuring row, sequencing each analysis section of the temporary measuring row according to the large-to-small gauge pressure value, recording the sequencing as the distance sequence of the temporary measuring row, re-sequencing each column of the safety early warning model according to the distance sequence to form a new matrix, recording as a distance sequence model EQ_Gp corresponding to the temporary measuring row, and recording the EQ_Gp as the distance sequence model EQ_Gp fea2 And EQ_Gp fea3 Respectively representing distance sequence models for selecting only the internal pressure difference value and selecting only the gauge pressure step value; because the adjacent measurement rows and the double-sensitive nodes have a one-to-one correspondence, each double-sensitive node has a corresponding distance sequence model;
the distance sequence indentation coefficient HLC of the double-sensitive node is obtained by combining the internal pressure difference value and the gauge pressure step value of the distance sequence model, and the calculation method comprises the following steps:
；
wherein i1 is an accumulation variable, exp () is an exponential function with a natural constant e as a base, and EQ_Gp (i 1) represents an i 1-th column element of the distance sequence model; RK_gp </SUB > is an order value index function, returning an order value after the corresponding elements of the current double-sensitive node are ordered from small to large in a call sequence, wherein the order value is not zero, and tLen is the total number of columns of the safety early warning model;
the ratio of the internal pressure difference value of any analysis section and the previous analysis section is recorded as the instantaneous pressure difference ratio of the analysis section; the upper quartile value of each instantaneous pressure difference ratio in the same double-sensitive node metering period is recorded as a first pressure difference ratio, and the average value of the instantaneous pressure difference ratios of each double-sensitive node in the same analysis period is recorded as a second pressure difference ratio; if the instantaneous pressure difference ratio corresponding to any analysis segment of the double-sensitive node is larger than the first pressure difference ratio and larger than the second pressure difference ratio, the analysis segment symptom group under the analysis segment is a polar deviation group, otherwise, the analysis segment symptom group under the analysis segment is defined as a normal deviation group;
all polar bias groups and all normal bias groups of the double-sensitive node are respectively constructed into polar bias sequences HRD_Ls and normal bias sequences PRD_Ls, and a compression loss value RVL is obtained by calculation according to a distance sequence indentation coefficient, a polar bias sequence and Chang Pianxu columns, and the calculation method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base, inp < > is a desired inner product function, and a return value of the desired inner product function is an average value of inner products of each analysis segment symptom group in the calling sequence.
The beneficial effects are that: from the above, the pressure characterization loss value is the quantitative calculation of the pressure difference of the pipeline and the pressure of the environment, and according to the transverse comparison between the pressure nodes at different positions, the relevance between the upper and lower pressure differences of the pipeline of the thermal type gas mass flowmeter and the pipeline blockage and the diffusivity of the ambient pressure change to the drift of the thermal type gas mass flowmeter are effectively quantized, so that the sensitivity of early warning drift phenomenon is improved, the rationality of an early warning system is increased, and reliable quantity support is provided for real-time rapid feedback of the blockage drift phenomenon of the thermal type gas mass flowmeter.
Further, in step S400, the method for sending out the drift early warning according to the magnitude order of the pressure sign loss is as follows: setting a time period as an early warning time zone (ALTF), wherein ALTF epsilon [5,10] min, in the latest ALTF period, marking the average value of all the pressure loss step values in the same analysis period as a first blockage measurement (FSTD), marking the average value and standard deviation of all the pressure loss step values in all the analysis periods as a second blockage measurement (SSTD) and a third blockage measurement (TSTD), and setting a numerical interval as a drift early warning interval (SALF), wherein SALF epsilon [ SSTD-2. TSTD, SSTD+2. TSTD ]; if the first blocking measurement under one analysis section is in a drift early warning section, the analysis section is marked as a thermal stability point, and a drift phenomenon does not occur in the thermal gas mass flowmeter; if the first blockage measurement under one analysis section is not in the drift early warning section, the analysis section is marked as a thermal unbalance time point, the thermal type gas mass flowmeter is in drift phenomenon, the thermal type gas mass flowmeter is suspended to be used, and early warning information is sent to a client of an administrator.
Preferably, all undefined variables in the present invention, if not explicitly defined, may be thresholds set manually.
The present invention also provides a quality monitoring apparatus for gas molecular analysis, the quality monitoring apparatus for gas molecular analysis comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the quality monitoring method for gas molecular analysis when the computer program is executed, the quality monitoring device for gas molecular analysis can be operated in a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud data center, and the like, and an executable system can include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to be executed in a unit of the following system:
the early warning scene arrangement unit is used for initially arranging the quality analysis early warning scene;
the analysis segment symptom acquisition unit is used for acquiring a gauge pressure value and an internal pressure difference value from a quality analysis early warning scene and forming an analysis segment symptom;
the pressure sign loss magnitude order value calculation unit is used for calculating and obtaining the pressure sign loss magnitude order value by utilizing the analysis segment sign group;
and the drift early warning unit is used for sending drift early warning according to the pressure sign loss magnitude order value.
The beneficial effects of the invention are as follows: the invention provides a quality monitoring method and equipment for gas molecular analysis, which quantizes the pressure sign loss value of a thermal type gas mass flowmeter, wherein the pressure sign loss value is the quantized calculation of the pressure difference of a pipeline and the pressure of the environment, and according to the transverse comparison between pressure nodes at different positions, the relevance between the upper and lower pressure differences of the pipeline of the thermal type gas mass flowmeter and the blockage of the pipeline and the diffusivity of the abnormal drift of the thermal type gas mass flowmeter caused by the change of the environmental pressure are effectively quantized, so that the occurrence sensitivity of early warning drift phenomenon is improved, the rationality of an early warning system is increased, and reliable quantity support is provided for real-time rapid feedback of the blockage drift phenomenon of the thermal type gas mass flowmeter.
Drawings
The above and other features of the present invention will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present invention, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of a method of quality monitoring for molecular analysis of a gas;
fig. 2 shows a block diagram of a quality monitoring apparatus for molecular analysis of a gas.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
Referring to fig. 1, which is a flowchart illustrating a quality monitoring method for gas molecular analysis, a quality monitoring method for gas molecular analysis according to an embodiment of the present invention is described below with reference to fig. 1, and includes the steps of:
s100, initially arranging a quality analysis early warning scene;
s200, obtaining a gauge pressure value and an internal pressure difference value from a quality analysis early warning scene, and combining the gauge pressure value and the internal pressure difference value into a binary set serving as an analysis segment symptom set;
s300, performing compression sign loss analysis by using the analysis segment sign group, and calculating to obtain a compression sign loss order value;
s400, sending out drift early warning according to the pressure sign loss magnitude order value.
Further, in step S100, the method for analyzing the early warning scene by the initial placement quality is: the mass analysis early warning scene comprises a pipeline for gas delivery and a plurality of double-sensitive nodes, wherein the double-sensitive nodes are arranged in the pipeline at equal distances, each double-sensitive node respectively comprises a gauge pressure sensor and a differential pressure sensor, and the distances between the starting point and the ending point of the differential pressure sensor are the same. The nodes hereinafter are equivalent to dual sensitive nodes.
Further, in step S200, the method for obtaining the gauge pressure value and the internal pressure difference value from the mass analysis early warning scene and forming the segment characterization set is as follows: a time period is set as a separation section WT, the value range of the separation section is WT epsilon [0.5,3] seconds, and the separation section is equal to the separation section hereinafter.
The gauge pressure sensor and the differential pressure sensor in the double-sensitive node measure data in real time; in any WT period, each double-sensitive node obtains the maximum value of the pressure value and the maximum value of the pressure difference, and records the maximum value and the maximum value of the pressure difference as the gauge pressure value and the internal pressure difference corresponding to the analysis section interval respectively, wherein the gauge pressure value is obtained by measuring a gauge pressure sensor in the double-sensitive node, the internal pressure difference is obtained by measuring a differential pressure sensor in the double-sensitive node, and a binary group formed by the gauge pressure value and the internal pressure difference obtained by one double-sensitive node in real time is used as the analysis section characterization group corresponding to the double-sensitive node in the analysis section interval.
Further, in step S300, the method for analyzing the compression loss by using the analysis segment syndrome set and calculating the compression loss order value is as follows: setting a variable as analysis segment to measure TN, TN epsilon [50, 200 ]]Taking the current moment and TN preceding analysis segment symptom groups to form a symptom group sequence; calculating according to the syndrome sequence by taking j2 as the sequence number of the analysis segmentFirst differential pressure characteristic GSEQ of jth 2 analysis segment j2 ：
；
Where j1 is an accumulated variable, ln () is a logarithmic function with the natural constant e as a base, XQA in the formula j2 Represents the internal pressure difference value of the j2 th element in the syndrome sequence, and Bmean (j 1) represents the average value of the internal pressure difference values of the j1 st to TN th elements in the syndrome sequence;
taking the ratio of the first differential pressure characteristic corresponding to each analysis segment in the characterization group sequence to the gauge pressure value as a sub-loss magnitude order value EG j2 The difference value of the sub-loss level of any analysis segment and the sub-loss level of the previous analysis segment is the instantaneous loss difference, the corresponding analysis segment with the maximum value in the instantaneous loss difference is obtained from the characterization sequence and is used as a height difference analysis segment, and the average value of the sub-loss levels of the height difference analysis segments is used as the pressure sign loss level.
Preferably, in step S300, the method for analyzing the compression loss by using the analysis segment syndrome set and calculating the order value of the compression loss is as follows: the binary group constructed by the gauge pressure value fea1 and the internal pressure difference value fea2 of any double-sensitive node under the same analysis section is recorded as the corresponding analysis section characterization group under the analysis section; setting a time period measurement period tg, wherein the value range of the measurement period tg is tg epsilon [60,120] minutes, in the latest tg time period, setting analysis segment feature groups under the same analysis segment of different double-sensitive nodes as a column, setting analysis segment feature groups under different analysis segments of the same double-sensitive node as a row to construct a matrix and taking the matrix as a safety early warning model, and recording the number of analysis segments as tLen;
for any row in the safety early warning model, calculating the gauge pressure step value fea3 of each element: taking the element of the gauge pressure order value to be calculated as a leading element, and taking the average value of the gauge pressure values in each analysis section from the corresponding analysis section of the leading element to the analysis section which belongs to the current moment as the gauge pressure level, wherein the gauge pressure order value of the leading element is the ratio of the gauge pressure value corresponding to the leading element to the gauge pressure level;
taking any line of the safety early warning model as a temporary measurement line, sequencing each analysis section of the temporary measurement line according to the magnitude of the gauge pressure value, and recording the sequencing as the temporary measurement lineThe distance sequence is that the safety early warning model reorders each column according to the distance sequence to form a new matrix which is recorded as a distance sequence model EQ_Gp corresponding to the adjacent measurement line, so as to form EQ_Gp fea2 And EQ_Gp fea3 Respectively representing distance sequence models for selecting only the internal pressure difference value and selecting only the gauge pressure step value; because the adjacent measurement rows and the double-sensitive nodes have a one-to-one correspondence, each double-sensitive node has a corresponding distance sequence model;
the distance sequence indentation coefficient HLC of the double-sensitive node is obtained by combining the internal pressure difference value and the gauge pressure step value of the distance sequence model, and the calculation method comprises the following steps:
；
wherein i1 is an accumulation variable, exp () is an exponential function with a natural constant e as a base, and EQ_Gp (i 1) represents an i 1-th column element of the distance sequence model; RK_gp </SUB > is an order value index function, returning to an order value after the corresponding elements of the current double-sensitive node are ordered from small to large in the call sequence, wherein the order value is not zero; wherein tLen is the total number of columns of the safety precaution model;
the ratio of the internal pressure difference value of any analysis section and the previous analysis section is recorded as the instantaneous pressure difference ratio of the analysis section; the upper quartile value of each instantaneous pressure difference ratio in the same double-sensitive node metering period is recorded as a first pressure difference ratio, and the average value of the instantaneous pressure difference ratios of each double-sensitive node in the same analysis period is recorded as a second pressure difference ratio; if the instantaneous pressure difference ratio corresponding to any analysis segment of the double-sensitive node is larger than the first pressure difference ratio and larger than the second pressure difference ratio, the analysis segment symptom group under the analysis segment is a polar deviation group, otherwise, the analysis segment symptom group under the analysis segment is defined as a normal deviation group;
all polar bias groups and all normal bias groups of the double-sensitive node are respectively constructed into polar bias sequences HRD_Ls and normal bias sequences PRD_Ls, and a compression loss value RVL is obtained by calculation according to a distance sequence indentation coefficient, a polar bias sequence and Chang Pianxu columns, and the calculation method comprises the following steps:
；
wherein exp () is an exponential function with a natural constant e as a base, inp < > is a desired inner product function, and a return value of the desired inner product function is an average value of inner products of each analysis segment symptom group in the calling sequence.
Further, in step S400, the method for sending out the drift early warning according to the magnitude order of the pressure sign loss is as follows: setting a time period as an early warning time zone (ALTF), wherein ALTF epsilon [5,10] min, in the latest ALTF period, marking the average value of all the pressure loss step values in the same analysis period as a first blockage measurement (FSTD), marking the average value and standard deviation of all the pressure loss step values in all the analysis periods as a second blockage measurement (SSTD) and a third blockage measurement (TSTD), and setting a numerical interval as a drift early warning interval (SALF), wherein SALF epsilon [ SSTD-2. TSTD, SSTD+2. TSTD ]; if the first blocking measurement under one analysis section is in a drift early warning section, the analysis section is marked as a thermal stability point, and a drift phenomenon does not occur in the thermal gas mass flowmeter; if the first blockage measurement under one analysis section is not in the drift early warning section, the analysis section is marked as a thermal unbalance time point, the thermal type gas mass flowmeter is in drift phenomenon, the thermal type gas mass flowmeter is suspended to be used, and early warning information is sent to a client of an administrator.
A mass monitoring device for gas molecular analysis according to an embodiment of the present invention is shown in fig. 2, which is a block diagram of the mass monitoring device for gas molecular analysis according to the present invention, and the mass monitoring device for gas molecular analysis according to the embodiment includes: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in an embodiment of a quality monitoring device for gas molecular analysis as described above when the computer program is executed.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the following system:
the early warning scene arrangement unit is used for initially arranging the quality analysis early warning scene;
the analysis segment symptom acquisition unit is used for acquiring a gauge pressure value and an internal pressure difference value from a quality analysis early warning scene and forming an analysis segment symptom;
the pressure sign loss magnitude order value calculation unit is used for calculating and obtaining the pressure sign loss magnitude order value by utilizing the analysis segment sign group;
and the drift early warning unit is used for sending drift early warning according to the pressure sign loss magnitude order value.
The quality monitoring device for gas molecular analysis can be operated in computing devices such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The described quality monitoring device for gas molecular analysis may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the example is merely an example of a quality monitoring device for gas molecular analysis, and is not limiting of a quality monitoring device for gas molecular analysis, and may include more or fewer components than examples, or may combine certain components, or different components, e.g., the quality monitoring device for gas molecular analysis may further include an input-output device, a network access device, a bus, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the system for operating a quality monitoring apparatus for molecular analysis of gas, and which connects various parts of the entire system for operating a quality monitoring apparatus for molecular analysis of gas using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement the various functions of the quality monitoring device for gas molecular analysis by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
Claims (5)
1. A quality monitoring method for molecular analysis of a gas, the method comprising the steps of:
s100, initially arranging a quality analysis early warning scene;
s200, obtaining a gauge pressure value and an internal pressure difference value from a quality analysis early warning scene, and combining the gauge pressure value and the internal pressure difference value into a binary set serving as an analysis segment symptom set;
s300, performing compression sign loss analysis by using the analysis segment sign group, and calculating to obtain a compression sign loss order value;
s400, sending drift early warning according to the pressure sign loss magnitude order value;
in step S300, the method for analyzing the compression loss by using the analysis segment symptom group and calculating the compression loss order value is as follows: setting upOne variable is used as analysis section to measure TN, TN epsilon [50, 200 ]]Taking the current moment and TN preceding analysis segment symptom groups to form a symptom group sequence; calculating a first differential pressure characteristic GSEQ of the j2 th analysis segment according to the syndrome sequence by taking j2 as the sequence number of the analysis segment j2 ：
；
Where j1 is an accumulated variable, ln () is a logarithmic function with the natural constant e as a base, XQA in the formula j2 Represents the internal pressure difference value of the j2 th element in the syndrome sequence, and Bmean (j 1) represents the average value of the internal pressure difference values of the j1 st to TN th elements in the syndrome sequence;
taking the ratio of the first differential pressure characteristic corresponding to each analysis segment in the characterization group sequence to the gauge pressure value as a sub-loss magnitude order value EG j2 The difference value of the sub-loss level of any analysis segment and the sub-loss level of the previous analysis segment is the instantaneous loss difference, the corresponding analysis segment with the maximum value in the instantaneous loss difference is obtained from the syndrome sequence and is used as a height difference analysis segment, and the average value of the sub-loss levels of the height difference analysis segments is used as a pressure sign loss level;
or in step S300, the method for analyzing the compression loss by using the analysis segment symptom group and calculating to obtain the compression loss order value is as follows: the binary group constructed by the gauge pressure value and the internal pressure difference value of any double-sensitive node under the same analysis section is recorded as the corresponding analysis section sign group under the analysis section; setting a time period measurement period tg, wherein the value range of the measurement period tg is in the range of [60,120] minutes, and in the latest tg time period, constructing a matrix by taking analysis segment feature groups under the same analysis segment of different double-sensitive nodes as a row and taking the analysis segment feature groups under different analysis segments of the same double-sensitive node as a row, and taking the matrix as a safety early warning model;
for any row in the safety early warning model, calculating the gauge pressure value of each element: taking the element of the gauge pressure order value to be calculated as a leading element, and taking the average value of the gauge pressure values in each analysis section from the corresponding analysis section of the leading element to the analysis section which belongs to the current moment as the gauge pressure level, wherein the gauge pressure order value of the leading element is the ratio of the gauge pressure value corresponding to the leading element to the gauge pressure level;
taking any row of the safety early warning model as a temporary measuring row, sequencing each analysis section of the temporary measuring row according to the gauge pressure value, marking the sequencing as the distance sequence of the temporary measuring row, and re-sequencing each column of the safety early warning model according to the distance sequence to form a new matrix which is marked as a distance sequence model corresponding to the temporary measuring row; calculating a distance sequence indentation coefficient of the double-sensitive node by combining the internal pressure difference value of the distance sequence model and the gauge pressure order value, and recording the ratio of the internal pressure difference value of any analysis section to the internal pressure difference value of the previous analysis section as the instantaneous pressure difference ratio of the analysis section; the upper quartile value of each instantaneous pressure difference ratio in the same double-sensitive node metering period is recorded as a first pressure difference ratio, and the average value of the instantaneous pressure difference ratios of each double-sensitive node in the same analysis period is recorded as a second pressure difference ratio; if the instantaneous pressure difference ratio corresponding to any analysis segment of the double-sensitive node is larger than the first pressure difference ratio and larger than the second pressure difference ratio, the analysis segment symptom group under the analysis segment is a polar deviation group, otherwise, the analysis segment symptom group under the analysis segment is defined as a normal deviation group; and respectively constructing all the polar bias groups and all the normal bias groups of the double-sensitive nodes into polar bias sequences and normal bias sequences, and calculating according to the distance sequence indentation coefficient, the polar bias sequences and Chang Pianxu columns to obtain the compression characteristic loss magnitude order value.
2. The method for monitoring the quality of gas molecular analysis according to claim 1, wherein in step S100, the method for initially arranging a quality analysis pre-warning scene is: the mass analysis early warning scene comprises a pipeline for gas delivery and a plurality of double-sensitive nodes, wherein the double-sensitive nodes are arranged in the pipeline at equal distances, each double-sensitive node respectively comprises a gauge pressure sensor and a differential pressure sensor, and the distances between the starting point and the ending point of the differential pressure sensor are the same.
3. The method for monitoring the quality of gas molecular analysis according to claim 1, wherein in step S200, the method for obtaining the gauge pressure value and the internal pressure difference value from the quality analysis pre-warning scene and forming the segment characterization set is as follows: setting a time period as a separation section (WT), wherein the value range of the separation section is WT epsilon [0.5,3] seconds;
the gauge pressure sensor and the differential pressure sensor in the double-sensitive node measure data in real time; in any WT period, each double-sensitive node obtains the maximum value of the pressure value and the maximum value of the pressure difference, and records the maximum value and the maximum value of the pressure difference as the gauge pressure value and the internal pressure difference corresponding to the analysis section interval respectively, wherein the gauge pressure value is obtained by measuring a gauge pressure sensor in the double-sensitive node, the internal pressure difference is obtained by measuring a differential pressure sensor in the double-sensitive node, and a binary group formed by the gauge pressure value and the internal pressure difference obtained by one double-sensitive node in real time is used as the analysis section characterization group corresponding to the double-sensitive node in the analysis section interval.
4. The method for quality monitoring of gas molecular analysis according to claim 1, wherein in step S400, the method for giving drift warning according to the pressure loss level is as follows: setting a time period as an early warning time zone (ALTF), wherein ALTF epsilon [5,10] min, in the latest ALTF period, marking the average value of all the pressure loss step values in the same analysis period as a first blockage measurement (FSTD), marking the average value and standard deviation of all the pressure loss step values in all the analysis periods as a second blockage measurement (SSTD) and a third blockage measurement (TSTD), and setting a numerical interval as a drift early warning interval (SALF), wherein SALF epsilon [ SSTD-2. TSTD, SSTD+2. TSTD ]; if the first blocking measurement under one analysis section is in a drift early warning section, the analysis section is marked as a thermal stability point, and a drift phenomenon does not occur in the thermal gas mass flowmeter; if the first blockage measurement under one analysis section is not in the drift early warning section, the analysis section is marked as a thermal unbalance time point, the thermal type gas mass flowmeter is in drift phenomenon, the thermal type gas mass flowmeter is suspended to be used, and early warning information is sent to a client of an administrator.
5. A quality monitoring device for molecular analysis of a gas, the quality monitoring device comprising: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in a quality monitoring method for gas molecular analysis according to any one of claims 1 to 4 when the computer program is executed, the quality monitoring device for gas molecular analysis being run in a computing device of a desktop computer, a notebook computer, a palm computer and a cloud data center.
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