CN115479635A - Natural gas pipeline running state monitoring system based on big data - Google Patents
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Abstract
The invention relates to the technical field of natural gas pipelines, and aims to solve the problems that in the existing monitoring management of the running state of a natural gas pipeline, the running state of the natural gas pipeline is difficult to be comprehensively monitored, and the running state of the natural gas pipeline is difficult to be accurately analyzed, so that accident danger cannot be predicted in advance, and the safe operation of a gas supply system is difficult to guarantee; according to the invention, the running state of the natural gas pipeline is definitely judged from the static analysis level and the dynamic analysis level respectively, so that the running state of the natural gas pipeline is comprehensively and accurately monitored, the normal and stable running of a natural gas pipeline network is ensured, and the safe running of a gas supply system is promoted.
Description
Technical Field
The invention relates to the technical field of natural gas pipelines, in particular to a natural gas pipeline running state monitoring system based on big data.
Background
The natural gas pipeline refers to a pipeline for conveying natural gas (including associated gas produced in an oil field) from a mining place or a processing plant to an urban gas distribution center or an industrial enterprise user, and is also called a gas conveying pipeline, the natural gas pipeline is used for conveying natural gas, and is a mode for conveying a large amount of natural gas on land, and the natural gas pipeline accounts for about half of the total length of the pipeline in the world, so that the conveying stability and the operation safety of the natural gas pipeline can be mastered, and the pipeline is of great importance;
however, in the existing monitoring management of the running state of the natural gas pipeline, the one-sidedness and the error exist, the running state of the natural gas pipeline is difficult to be comprehensively monitored, and the running state of the natural gas pipeline is difficult to be accurately analyzed, so that the accident danger cannot be predicted in advance, and the safe running of a gas supply system is difficult to be ensured;
in order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to solve the problems that the running state of a natural gas pipeline is difficult to be comprehensively monitored and accurately analyzed in the existing monitoring management of the running state of the natural gas pipeline, so that accident danger cannot be predicted in advance and the safe running of a gas supply system is difficult to ensure, the running state of the natural gas pipeline is definitely judged from a static analysis layer and a dynamic analysis layer respectively, the running state of the natural gas pipeline is comprehensively analyzed, the running state of the natural gas pipeline is definitely early-warning analyzed in a text word description mode, so that the running state of the natural gas pipeline is accurately analyzed while the running state of the natural gas pipeline is comprehensively monitored and analyzed, the advance prediction of pipeline accidents is realized, the normal and stable running of a natural gas pipeline network is ensured, and the safe running of the gas supply system is promoted, and the monitoring system for the running state of the natural gas pipeline based on big data is provided.
The purpose of the invention can be realized by the following technical scheme:
a natural gas pipeline running state monitoring system based on big data comprises a data acquisition unit, a cloud data storage unit, a static analysis unit, a dynamic analysis unit, a comprehensive analysis unit, a running depth supervision unit, an early warning feedback unit and a monitoring terminal;
the data acquisition unit is used for acquiring appearance state information and running state information of each natural gas pipeline in the unit area and sending the appearance state information and the running state information to the cloud data storage unit for temporary storage;
the static analysis unit is used for calling appearance state information of each natural gas pipeline from the cloud data storage unit, judging and analyzing appearance loss, generating a basic loss severe signal, a basic loss moderate signal and a basic loss mild signal according to the appearance state information, and sending the basic loss severe signal, the basic loss moderate signal and the basic loss mild signal to the comprehensive analysis unit;
the dynamic analysis unit is used for calling the running state information of each natural gas pipeline from the cloud data storage unit, judging and analyzing the running state, generating a pipeline running stability signal and a pipeline running abnormity signal according to the running state information, and sending the pipeline running stability signal and the pipeline running abnormity signal to the comprehensive analysis unit;
the comprehensive analysis unit is used for receiving the basic loss type judgment signal and the pipeline running state type judgment signal, performing data integration analysis processing, generating a comprehensive superior qualitative signal, a comprehensive middle-level qualitative signal and a comprehensive secondary qualitative signal according to the basic loss type judgment signal and the pipeline running state type judgment signal, sending the comprehensive superior qualitative signal to the early warning feedback unit, and sending the comprehensive middle-level qualitative signal and the comprehensive secondary qualitative signal to the running depth supervision unit;
the operation depth monitoring unit is used for receiving the comprehensive qualitative judgment signals of all levels, carrying out detailed analysis and processing item by item, generating a pipeline light leakage signal, a pipeline moderate leakage signal or a pipeline severe leakage signal and a temperature severe influence signal or a temperature slight influence signal according to the signals, and sending the signals to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning analysis processing on the received various types of judgment signals and respectively sending the signals to the monitoring terminal in a text word description mode for warning explanation.
Further, the specific operation steps of the appearance loss judgment analysis processing are as follows:
acquiring appearance depreciation values, using duration and environmental interference values in appearance state information of each natural gas pipeline in a unit area in real time, and respectively marking the values as ws i 、tc i And hg i And carrying out normalization processing on the data according to a formula
Calculating the basic coefficient bas of each natural gas pipeline i Wherein e1, e2 and e3 are weighting factor coefficients of an appearance depreciation value, a use time and an environmental interference value respectively, and e1 > e3 > e2, and e1+ e2+ e3=6.8;
and comparing and analyzing the basic coefficient with a preset basic comparison reference threshold value Y1, generating a basic loss heavy signal when the basic coefficient is greater than the preset basic comparison reference threshold value Y1, generating a basic loss moderate signal when the basic coefficient is equal to the preset basic comparison reference threshold value Y1, and generating a basic loss light signal when the basic coefficient is less than the preset basic comparison reference threshold value Y1.
Further, the specific operation steps of the operation state determination and analysis processing are as follows:
acquiring the dynamic pressure, the dynamic flow and the dynamic temperature of each natural gas pipeline in a unit area in real time, and respectively marking the dynamic pressure, the dynamic flow and the dynamic temperature as dp i 、fl i And dt i And carrying out normalization analysis on the data according to a formula dyx i ＝h1*dp i +h2*fl i +h3*dt i Calculating the running coefficient dyx i Wherein h1, h2 and h3 are correction factor coefficients of dynamic pressure, dynamic flow and dynamic temperature respectively;
and comparing and analyzing the operation coefficient with a preset operation comparison reference interval Q1, generating a pipeline operation stable signal when the operation coefficient is within the preset operation comparison reference interval Q1, and generating a pipeline operation abnormal signal when the operation coefficient is out of the preset operation comparison reference interval Q1.
Further, the specific operation steps of the data integration analysis processing are as follows:
determining a signal according to the type of the basic loss to establish a set A, calibrating a basic loss heavy signal as an element 1, calibrating a basic loss medium signal as an element 2, calibrating a basic loss light signal as an element 3, wherein the element 1 belongs to the set A, the element 2 belongs to the set A, and the element 3 belongs to the set A;
determining a signal establishment set B according to the type of the pipeline running state, marking a pipeline running stable signal as an element 4, marking a pipeline running abnormal signal as an element 5, wherein the element 4 belongs to the set B, and the element 5 belongs to the set B;
and performing union analysis on the sets A and B, generating a comprehensive secondary qualitative signal if A ≦ B = {1,5} or {2,5}, generating a comprehensive superior qualitative signal if A ≦ B = {3,4}, and generating a comprehensive intermediate qualitative signal in other cases.
Further, the specific operation steps of item-by-item refining analysis processing are as follows:
acquiring inlet pressure and outlet pressure of each natural gas pipeline in a unit area in real time, and performing difference analysis on absolute values of the inlet pressure and the outlet pressure to obtain a pressure deviation value;
setting a comparison interval Q2 of the pressure deviation value, substituting the pressure deviation value p into the comparison interval Q2 for comparison and analysis, and generating a pipeline pressure normal deviation signal or a pipeline pressure abnormal deviation signal according to the comparison;
according to the pipeline pressure abnormal deviation signal, acquiring the inlet pressure and the outlet pressure of each natural gas pipeline, performing data comparison and analysis, and generating a pipeline pressure reduction signal and a pipeline pressure increase signal according to the data comparison and analysis;
according to the pipeline pressure drop signal, wall thickness values and crack values of all natural gas pipelines are called to carry out pressure drop fault analysis processing, and accordingly a pipeline slight leakage signal, a pipeline moderate leakage signal and a pipeline severe leakage signal are generated;
according to the pipeline pressure rise signal, the temperature value of each natural gas pipeline is called to carry out pressure rise factor analysis processing, and accordingly a temperature serious influence signal and a temperature slight influence signal are generated.
Further, the specific operation steps of the pressure drop fault analysis processing are as follows:
according to the pipeline pressure drop signal, acquiring the wall thickness value and the crack value of each natural gas pipeline in real time, and respectively marking the wall thickness value and the crack value as bh o And lw o And performing formulated analysis on the data, according to the formula
Determining the leakage coefficient xl o Wherein f1 and f2 are respectively proportional coefficients of a wall thickness value and a crack value;
setting gradient leakage comparison intervals F1, F2 and F3 of leakage coefficients, and substituting the leakage coefficients into the preset gradient leakage comparison intervals F1, F2 and F3 for comparison and analysis;
when the leakage coefficient is within the gradient leakage contrast interval F1, a pipeline light leakage signal is generated, when the leakage coefficient is within the gradient leakage contrast interval F2, a pipeline medium leakage signal is generated, and when the leakage coefficient is within the gradient leakage contrast interval F3, a pipeline heavy leakage signal is generated.
Further, the specific operation steps of the pressure rise factor analysis processing are as follows:
equally dividing each natural gas pipeline into k unit lengths according to the pipeline pressure rise signal, acquiring the temperature value of each unit length of each natural gas pipeline, and calibrating the temperature value as tm qk ；
Setting a temperature comparison reference threshold Y2 of the temperature magnitude, comparing and analyzing the temperature magnitude with a preset temperature comparison reference threshold Y2, generating a temperature higher signal when the temperature magnitude is greater than or equal to the temperature comparison reference threshold Y2, and generating a temperature normal signal when the temperature magnitude is smaller than the temperature comparison reference threshold Y2;
respectively counting the sum of the number of weather gas pipelines in each unit length, which are calibrated as a high temperature signal and a normal temperature signal, and respectively calibrating the sum as symbols sum1 and sum2, wherein if sum1 is greater than or equal to (k/2), a temperature serious influence signal is generated, and if sum2 > (k/2) is satisfied, a temperature slight influence signal is generated.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the method, the running state of the natural gas pipeline is definitely judged from the external characteristic static analysis level and the running state dynamic analysis level respectively by utilizing the symbolized calibration, normalized analysis and threshold comparison analysis modes, and the comprehensive monitoring and comprehensive analysis of the running state of the natural gas pipeline are realized by adopting the modes of data calibration, union budget and signal output, so that a foundation is laid for the definite judgment of the running state of the natural gas pipeline;
(2) According to the invention, the running state of the natural gas pipeline is comprehensively analyzed by using the modes of data difference analysis, interval substitution analysis and item-by-item classification comparison, and the running state of the natural gas pipeline is clearly early-warned and analyzed by using the text word description mode, so that the running state of the natural gas pipeline is comprehensively monitored and analyzed, the running state of the natural gas pipeline is accurately analyzed, the pipeline accidents are predicted in advance, the normal and stable running of a natural gas pipeline network is ensured, and the safe running of a gas supply system is promoted.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a general block diagram of the system of the present invention;
FIG. 2 is a block flow diagram of a first embodiment of the present invention;
fig. 3 is a flow chart of a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1 and 2, a natural gas pipeline running state monitoring system based on big data comprises a data acquisition unit, a cloud data storage unit, a static analysis unit, a dynamic analysis unit, a comprehensive analysis unit, a running depth supervision unit, an early warning feedback unit and a monitoring terminal;
the data acquisition unit is used for acquiring appearance state information and running state information of each natural gas pipeline in the unit area and sending the appearance state information and the running state information to the cloud data storage unit for temporary storage;
when the static analysis unit acquires the appearance state information of each natural gas pipeline from the cloud data storage unit, and the appearance loss judgment analysis processing is performed according to the appearance state information, the specific operation process is as follows:
the method comprises the steps of acquiring appearance depreciation values, the time length of putting into service and environmental interference values in appearance state information of each natural gas pipeline in a unit area in real time, and respectively marking the values as ws i 、tc i And hg i And normalizing the data according to a formula
Obtaining basic coefficient bas of each natural gas pipeline i Wherein e1, e2 and e3 are weighting factor coefficients of an appearance depreciation value, a use time and an environmental disturbance value respectively, e1 is more than e3 and more than e2, e1+ e2+ e3=6.8, i represents each natural gas pipeline, and i is a positive integer greater than or equal to 1;
it should be noted that the weighting factor coefficient is used to balance the proportion weight of each item of data in the formula calculation, thereby promoting the accuracy of the calculation result;
it should be noted that, when the appearance breaking loss value, the time spent in use and the environmental disturbance value are larger, the more serious the base loss degree of the natural gas pipeline is, the worse the monitored external operation state of the natural gas pipeline is;
comparing and analyzing the basic coefficient and a preset basic comparison reference threshold value Y1, generating a basic loss severe signal when the basic coefficient is greater than the preset basic comparison reference threshold value Y1, generating a basic loss moderate signal when the basic coefficient is equal to the preset basic comparison reference threshold value Y1, and generating a basic loss mild signal when the basic coefficient is less than the preset basic comparison reference threshold value Y1;
sending the generated basic loss severe signal, basic loss moderate signal and basic loss mild signal to a comprehensive analysis unit;
when the dynamic analysis unit calls the running state information of each natural gas pipeline from the cloud data storage unit, the running state judgment analysis processing is carried out according to the running state information, and the specific operation process is as follows:
acquiring the dynamic pressure, the dynamic flow and the dynamic temperature of each natural gas pipeline in a unit area in real time, and respectively marking the dynamic pressure, the dynamic flow and the dynamic temperature as dp i 、fl i And dt i And carrying out normalization analysis on the data according to a formula dyx i ＝h1*dp i +h2*fl i +h3*dt i Calculating the running coefficient dyx i Wherein h1, h2 and h3 are respectively correction factor coefficients of dynamic pressure, dynamic flow and dynamic temperature, h1, h2 and h3 are all natural numbers greater than 0, and the correction factor coefficients are used for correcting the deviation of each parameter in the formula calculation process, so that more accurate parameter data can be calculated;
comparing and analyzing the operation coefficient with a preset operation comparison reference interval Q1, generating a pipeline operation stable signal when the operation coefficient is within the preset operation comparison reference interval Q1, generating a pipeline operation abnormal signal when the operation coefficient is out of the preset operation comparison reference interval Q1, and sending the generated pipeline operation stable signal and the generated pipeline operation abnormal signal to a comprehensive analysis unit;
when the comprehensive analysis unit receives the basic loss severe signal, the basic loss moderate signal or the basic loss mild signal and the pipeline operation stable signal or the pipeline operation abnormal signal, the data integration analysis processing is carried out according to the basic loss severe signal, the basic loss moderate signal or the basic loss mild signal, and the specific operation process is as follows:
determining a signal establishment set A according to the type of the basic loss, calibrating a basic loss heavy signal into an element 1, calibrating a basic loss moderate signal into an element 2, calibrating a basic loss light signal into an element 3, wherein the element 1 belongs to the set A, the element 2 belongs to the set A, and the element 3 belongs to the set A;
judging a signal according to the type of the pipeline running state to establish a set B, marking a pipeline running stable signal as an element 4, marking a pipeline running abnormal signal as an element 5, wherein the element 4 belongs to the set B, and the element 5 belongs to the set B;
and performing union analysis on the sets A and B, if A { [ U ] B = {3,4}, generating a comprehensive superior qualitative signal, and sending the comprehensive superior qualitative signal to an early warning feedback unit for early warning analysis processing, wherein the specific operation process is as follows:
when the comprehensive superior qualitative signal is received, a text word of 'the running state of the natural gas pipeline is stable and no early warning operation is needed' is sent to the monitoring terminal to display the explanation.
Example two:
as shown in fig. 1 and 3, appearance state information and operation state information of each natural gas pipeline in a unit area are acquired by a data acquisition unit and are both sent to a cloud data storage unit for temporary storage;
performing appearance loss judgment analysis processing on the appearance state information of each natural gas pipeline through a static analysis unit, generating a basic loss severe signal, a basic loss moderate signal and a basic loss mild signal according to the appearance loss judgment processing, and sending the basic loss severe signal, the basic loss moderate signal and the basic loss mild signal to a comprehensive analysis unit;
the running state information of each natural gas pipeline is judged, analyzed and processed through the dynamic analysis unit, and accordingly, a pipeline running stability signal and a pipeline running abnormity signal are generated and sent to the comprehensive analysis unit;
when the comprehensive analysis unit receives the basic loss type judgment signal and the pipeline running state type judgment signal, the data integration analysis processing is carried out according to the basic loss type judgment signal and the pipeline running state type judgment signal, and the specific operation process is as follows:
determining a signal according to the type of the basic loss to establish a set A, calibrating a basic loss heavy signal as an element 1, calibrating a basic loss medium signal as an element 2, calibrating a basic loss light signal as an element 3, wherein the element 1 belongs to the set A, the element 2 belongs to the set A, and the element 3 belongs to the set A;
judging a signal according to the type of the pipeline running state to establish a set B, marking a pipeline running stable signal as an element 4, marking a pipeline running abnormal signal as an element 5, wherein the element 4 belongs to the set B, and the element 5 belongs to the set B;
performing union analysis on the sets A and B, and generating a comprehensive secondary qualitative signal if A $ B = {1,5} or {2,5}, or generating a comprehensive intermediate qualitative signal if A $ B = {1,4} or {2,4} or {3,5 };
sending the generated comprehensive middle-level qualitative signal and the comprehensive secondary-level qualitative signal to a running depth supervision unit;
when the operation depth monitoring unit receives the comprehensive middle-level qualitative signal and the comprehensive secondary-level qualitative signal, item-by-item detailed analysis processing is carried out, and the specific operation process is as follows:
s1: acquiring the inlet pressure and the outlet pressure of each natural gas pipeline in the unit area in real time, and respectively marking the inlet pressure and the outlet pressure as ep z And ex z And performing difference analysis on the absolute value according to a formula p z = ep z －ex z Firstly, calculate the pressure deviation value p z Wherein z represents each natural gas pipeline calibrated to the composite intermediate qualitative signal and the composite secondary qualitative signal, and z is a positive integer greater than or equal to 1;
s2: setting a comparison interval Q2 of the pressure deviation value, and substituting the pressure deviation value into the comparison interval Q2 for comparison and analysis;
s2-1: when the pressure deviation value is within the comparison interval Q2, generating a normal pipeline pressure deviation signal, otherwise, when the pressure deviation value is outside the comparison interval Q2, generating an abnormal pipeline pressure deviation signal;
s3: according to the pipeline pressure abnormal deviation signal, acquiring the inlet pressure and the outlet pressure of each natural gas pipeline, and performing data comparison and analysis;
s3-1: when ep j ＞ex j When it is, a pipeline pressure drop signal is generated, when ep j ＜ex j Generating pipeline pressure rising signals, wherein j represents each natural gas pipeline calibrated as pipeline pressure deviation abnormal signals, and j is a positive integer greater than or equal to 1;
s4: according to the pipeline pressure drop signal, the wall thickness value and the crack value of each natural gas pipeline are called to carry out pressure drop fault analysis and processing, and the specific operation process is as follows:
s4-1: according to the pipeline pressure drop signal, acquiring the wall thickness value and the crack value of each natural gas pipeline in real time, and respectively marking the wall thickness value and the crack value as bh o And lw o And performing a formula analysis on the obtained product according to the formula
Determining the leakage coefficient xl o Wherein f1 and f2 are proportional coefficients of a wall thickness value and a crack value respectively, f1 and f2 are both natural numbers greater than 0, o represents each natural gas pipeline calibrated as a pipeline pressure drop signal, and o is a positive integer greater than or equal to 1;
it should be noted that the wall thickness value refers to a data value of the wall thickness of the natural gas pipeline, and when the expression value of the wall thickness value is smaller, the thinner the wall thickness of the natural gas pipeline is, the greater the corrosion loss degree of the wall of the natural gas pipeline is further described; the crack quantity value refers to a data quantity value of a ratio of the area of the crack on the surface of the pipe wall of the natural gas pipeline to the surface area of the unit pipe wall, when the expression value of the crack quantity value is larger, the more cracks diffused on the surface of the pipe wall of the natural gas pipeline in the unit pipe wall surface area are explained, so that the obvious leakage condition of the wall of the natural gas pipeline is further explained, and when the expression value of the leakage coefficient is larger, the more serious leakage problem of the natural gas pipeline is explained;
s4-2: setting gradient leakage comparison intervals F1, F2 and F3 of leakage coefficients, and substituting the leakage coefficients into preset gradient leakage comparison intervals F1, F2 and F3 for comparison and analysis, wherein the gradient leakage comparison intervals F1, F2 and F3 are increased in a gradient manner;
s4-2-1: when the leakage coefficient is within the gradient leakage contrast interval F1, generating a pipeline slight leakage signal, when the leakage coefficient is within the gradient leakage contrast interval F2, generating a pipeline moderate leakage signal, and when the leakage coefficient is within the gradient leakage contrast interval F3, generating a pipeline severe leakage signal;
s5: according to the pipeline pressure rise signal, the temperature value of each natural gas pipeline is called to carry out pressure rise factor analysis processing, and the specific operation process is as follows:
s5-1: equally dividing each natural gas pipeline into k unit lengths according to the pipeline pressure rise signal, acquiring the temperature value of each unit length of each natural gas pipeline, and calibrating the temperature value as tm qk Wherein q represents each natural gas pipeline calibrated as a pipeline pressure rise signal, k represents each pipeline of unit length, and both q and k are positive integers greater than or equal to 1;
s5-2: setting a temperature comparison reference threshold Y2 of the temperature magnitude, and comparing and analyzing the temperature magnitude and a preset temperature comparison reference threshold Y2;
s5-2-1: when the temperature magnitude is greater than or equal to a temperature comparison reference threshold Y2, generating a temperature higher signal, and when the temperature magnitude is smaller than the temperature comparison reference threshold Y2, generating a temperature normal signal;
s5-3: respectively counting the sum of the number of weather gas pipelines in each unit length, which are calibrated as a temperature over-high signal and a temperature normal signal, and respectively calibrating the sum as symbols sum1 and sum2, if sum1 is greater than or equal to (k/2), generating a temperature serious influence signal, and if sum2 > (k/2), generating a temperature slight influence signal;
s6: sending the generated pipeline slight leakage signal, pipeline moderate leakage signal or pipeline severe leakage signal and temperature severe influence signal or temperature slight influence signal to an early warning feedback unit;
when the early warning feedback unit receives various types of judgment signals, early warning analysis processing is carried out according to the judgment signals, and the specific operation process is as follows:
when a pipeline severe leakage signal or a pipeline moderate leakage signal is received, a primary leakage early warning signal or a secondary leakage early warning signal is generated, and a text word of 'the natural gas pipeline has a severe leakage problem and needs leakage overhaul early warning operation' is sent to a monitoring terminal for displaying and explaining;
when a slight leakage signal of the pipeline is received, generating a three-level leakage early warning signal, and sending a text word of 'slight leakage of the natural gas pipeline, and urgent need of leakage overhaul early warning operation' to a monitoring terminal for displaying and explaining;
when a signal seriously influencing the temperature is received, a text typeface of 'seriously influencing the stable operation of a natural gas pipeline in a high-temperature environment and urgently needing cooling early warning operation' is sent to a monitoring terminal for displaying and explaining;
when a temperature slight influence signal is received, a text word of 'slightly influencing the stable operation of the natural gas pipeline in a high-temperature environment, urgently needing to perform cooling early warning operation' is sent to the monitoring terminal to be displayed and explained.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions;
such as the formula:
collecting multiple groups of sample data and setting corresponding weight factor coefficient for each group of sample data by the technicians in the field; substituting the set weight factor coefficient and the acquired sample data into formulas, forming a linear equation set by any two formulas, screening the calculated coefficients and taking the mean value to obtain values of e1, e2 and e3 which are respectively 3.8, 1 and 2;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
When the system is used, the appearance loss judgment analysis processing and the operation state judgment analysis processing are respectively carried out by collecting the appearance state information and the operation state information of each natural gas pipeline in a unit area, the operation state of the natural gas pipeline is definitely judged from an external characteristic static analysis layer and an operation state dynamic analysis layer respectively by utilizing the modes of symbolic calibration, normalized analysis and threshold comparison analysis, and the modes of data calibration, combined budget and signal output are adopted, so that the comprehensive monitoring and comprehensive analysis of the operation state of the natural gas pipeline are realized, and meanwhile, the basis is laid for the definite judgment of the operation state of the natural gas pipeline;
on the basis of comprehensive judgment and analysis of the natural gas pipelines, by acquiring the inlet pressure and the outlet pressure of each natural gas pipeline, differential analysis is carried out by utilizing data, and the modes of interval substitution analysis and item-by-item classification comparison are utilized, so that the comprehensive analysis of the running state of the natural gas pipelines is realized, the abnormal problems of the natural gas pipelines are clarified, and the clear early warning analysis is carried out on the running state of the natural gas pipelines by utilizing the text character description mode, thereby realizing the comprehensive monitoring and analysis of the running state of the natural gas pipelines, simultaneously also realizing the advance foreknowledge of pipeline accidents, ensuring the normal and stable running of the natural gas pipe network, and promoting the safe running of a gas supply system.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. A natural gas pipeline running state monitoring system based on big data is characterized by comprising a data acquisition unit, a cloud data storage unit, a static analysis unit, a dynamic analysis unit, a comprehensive analysis unit, a running depth supervision unit, an early warning feedback unit and a monitoring terminal;
the data acquisition unit is used for acquiring appearance state information and running state information of each natural gas pipeline in the unit area and sending the appearance state information and the running state information to the cloud data storage unit for temporary storage;
the static analysis unit is used for calling appearance state information of each natural gas pipeline from the cloud data storage unit, judging and analyzing appearance loss, generating a basic loss severe signal, a basic loss moderate signal and a basic loss mild signal according to the appearance state information, and sending the basic loss severe signal, the basic loss moderate signal and the basic loss mild signal to the comprehensive analysis unit;
the dynamic analysis unit is used for calling the running state information of each natural gas pipeline from the cloud data storage unit, judging and analyzing the running state, generating a pipeline running stability signal and a pipeline running abnormity signal according to the running state information, and sending the pipeline running stability signal and the pipeline running abnormity signal to the comprehensive analysis unit;
the comprehensive analysis unit is used for receiving the basic loss type judgment signal and the pipeline running state type judgment signal, performing data integration analysis processing, generating a comprehensive superior qualitative signal, a comprehensive intermediate qualitative signal and a comprehensive secondary qualitative signal according to the basic loss type judgment signal and the pipeline running state type judgment signal, sending the comprehensive superior qualitative signal to the early warning feedback unit, and sending the comprehensive intermediate qualitative signal and the comprehensive secondary qualitative signal to the running depth supervision unit;
the operation depth monitoring unit is used for receiving the comprehensive qualitative judgment signals of all levels, carrying out item-by-item refining analysis processing, generating a pipeline light leakage signal, a pipeline moderate leakage signal or a pipeline heavy leakage signal and a temperature serious influence signal or a temperature slight influence signal according to the signals, and sending the signals to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning analysis processing on the received various types of judgment signals and sending the judgment signals to the monitoring terminal for warning explanation in a text word description mode.
2. The system for monitoring the running state of the natural gas pipeline based on the big data as claimed in claim 1, wherein the specific operation steps of the appearance loss judgment analysis processing are as follows:
acquiring appearance depreciation values, the time length for putting into use and environmental interference values in appearance state information of each natural gas pipeline in a unit area in real time, and carrying out normalization processing on the values to obtain basic coefficients of each natural gas pipeline;
and comparing and analyzing the basic coefficient with a preset basic comparison reference threshold value Y1, generating a basic loss heavy signal when the basic coefficient is greater than the preset basic comparison reference threshold value Y1, generating a basic loss moderate signal when the basic coefficient is equal to the preset basic comparison reference threshold value Y1, and generating a basic loss light signal when the basic coefficient is less than the preset basic comparison reference threshold value Y1.
3. The natural gas pipeline running state monitoring system based on big data according to claim 1, characterized in that the specific operation steps of the running state judgment analysis processing are as follows:
acquiring the dynamic pressure, the dynamic flow and the dynamic temperature of each natural gas pipeline in a unit area in real time, and carrying out normalized analysis on the dynamic pressure, the dynamic flow and the dynamic temperature to obtain an operation coefficient;
and comparing and analyzing the operation coefficient with a preset operation comparison reference interval Q1, generating a pipeline operation stable signal when the operation coefficient is within the preset operation comparison reference interval Q1, and generating a pipeline operation abnormal signal when the operation coefficient is out of the preset operation comparison reference interval Q1.
4. The natural gas pipeline running state monitoring system based on big data as claimed in claim 1, wherein the specific operation steps of data integration analysis processing are as follows:
determining a signal according to the type of the basic loss to establish a set A, calibrating a basic loss heavy signal as an element 1, calibrating a basic loss medium signal as an element 2, calibrating a basic loss light signal as an element 3, wherein the element 1 belongs to the set A, the element 2 belongs to the set A, and the element 3 belongs to the set A;
determining a signal establishment set B according to the type of the pipeline running state, marking a pipeline running stable signal as an element 4, marking a pipeline running abnormal signal as an element 5, wherein the element 4 belongs to the set B, and the element 5 belongs to the set B;
and performing union analysis on the sets A and B, generating a comprehensive secondary qualitative signal if A ≦ B = {1,5} or {2,5}, generating a comprehensive superior qualitative signal if A ≦ B = {3,4}, and generating a comprehensive intermediate qualitative signal in other cases.
5. The natural gas pipeline running state monitoring system based on big data according to claim 1, characterized in that, the specific operation steps of item-by-item refined analysis processing are as follows:
acquiring inlet pressure and outlet pressure of each natural gas pipeline in a unit area in real time, and carrying out difference analysis on absolute values of the inlet pressure and the outlet pressure to obtain a pressure deviation value;
setting a comparison interval Q2 of the pressure deviation value, substituting the pressure deviation value into the comparison interval Q2 for comparison and analysis, and generating a pipeline pressure normal deviation signal or a pipeline pressure abnormal deviation signal according to the comparison;
according to the pipeline pressure abnormal deviation signal, acquiring the inlet pressure and the outlet pressure of each natural gas pipeline, performing data comparison and analysis, and generating a pipeline pressure reduction signal and a pipeline pressure increase signal according to the data comparison and analysis;
according to the pipeline pressure drop signal, wall thickness values and crack values of all natural gas pipelines are called to carry out pressure drop fault analysis processing, and accordingly a pipeline light leakage signal, a pipeline moderate leakage signal and a pipeline severe leakage signal are generated;
according to the pipeline pressure rise signal, the temperature value of each natural gas pipeline is called to carry out pressure rise factor analysis processing, and accordingly a temperature serious influence signal and a temperature slight influence signal are generated.
6. The system for monitoring the running state of the natural gas pipeline based on the big data as claimed in claim 5, wherein the specific operation steps of the pressure drop fault analysis processing are as follows:
according to the pipeline pressure drop signal, acquiring the wall thickness value and the crack value of each natural gas pipeline in real time, and respectively marking the wall thickness value and the crack value as bh o And lw o And performing formulated analysis on the data, according to the formula
Determining the leakage coefficient xl o Wherein f1 and f2 are respectively proportional coefficients of a wall thickness value and a crack value;
setting gradient leakage comparison intervals F1, F2 and F3 of leakage coefficients, and substituting the leakage coefficients into the preset gradient leakage comparison intervals F1, F2 and F3 for comparison and analysis;
when the leakage coefficient is within the gradient leakage contrast interval F1, a pipeline light leakage signal is generated, when the leakage coefficient is within the gradient leakage contrast interval F2, a pipeline medium leakage signal is generated, and when the leakage coefficient is within the gradient leakage contrast interval F3, a pipeline heavy leakage signal is generated.
7. The natural gas pipeline running state monitoring system based on the big data as claimed in claim 5, wherein the specific operation steps of the pressure rise factor analysis processing are as follows:
equally dividing each natural gas pipeline into k unit lengths according to the pipeline pressure rise signal, acquiring the temperature value of each unit length of each natural gas pipeline, and calibrating the temperature value as tm qk ；
Setting a temperature comparison reference threshold Y2 of the temperature magnitude, comparing and analyzing the temperature magnitude with a preset temperature comparison reference threshold Y2, generating a temperature higher signal when the temperature magnitude is greater than or equal to the temperature comparison reference threshold Y2, and generating a temperature normal signal when the temperature magnitude is smaller than the temperature comparison reference threshold Y2;
respectively counting the sum of the number of weather gas pipelines in each unit length, which are calibrated as a high temperature signal and a normal temperature signal, and respectively calibrating the sum as symbols sum1 and sum2, wherein if sum1 is greater than or equal to (k/2), a temperature serious influence signal is generated, and if sum2 > (k/2) is satisfied, a temperature slight influence signal is generated.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116257811A (en) * | 2023-05-16 | 2023-06-13 | 天津新科成套仪表有限公司 | Abnormality processing method based on gas flow detection deep learning |
| CN116303742A (en) * | 2023-03-17 | 2023-06-23 | 深产发城市产业信息科技(深圳)有限公司 | Full-period digital monitoring method for patch area based on big data and Internet of things |
| CN117495611A (en) * | 2024-01-03 | 2024-02-02 | 鲁东大学 | Multi-channel piping heat transfer balance control supervision system based on internet of things data processing |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116303742A (en) * | 2023-03-17 | 2023-06-23 | 深产发城市产业信息科技(深圳)有限公司 | Full-period digital monitoring method for patch area based on big data and Internet of things |
| CN116303742B (en) * | 2023-03-17 | 2023-09-19 | 深产发城市产业信息科技(深圳)有限公司 | Full-period digital monitoring method for patch area based on big data and Internet of things |
| CN116257811A (en) * | 2023-05-16 | 2023-06-13 | 天津新科成套仪表有限公司 | Abnormality processing method based on gas flow detection deep learning |
| CN116257811B (en) * | 2023-05-16 | 2023-07-25 | 天津新科成套仪表有限公司 | Abnormality processing method based on gas flow detection deep learning |
| CN117495611A (en) * | 2024-01-03 | 2024-02-02 | 鲁东大学 | Multi-channel piping heat transfer balance control supervision system based on internet of things data processing |
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