CN112460492A - Toughness-evaluation-based collaborative toughness-enhanced gas safety control device - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses a toughness evaluation-based collaborative toughness-enhanced gas safety control device, which comprises: the system comprises a gas laser sensor, a signal transmission module, a front-end processor, a central processor, a call center host, a mobile field terminal and a mobile GPS receiving terminal; the gas laser sensor is arranged in a gas pipeline valve well and used for detecting gas leaked from a gas pipeline and gas leaked from a valve in the valve well; the signal transmission module is in communication connection with the gas laser sensor and is used for receiving a signal from the gas laser sensor and transmitting the signal to the front-end processor. The control device considers the resistance, the absorption capacity, the adaptability, the restoring force and the learning force of the whole system of the pipe network body and the linkage measure from the whole system level, and realizes the whole safety control of the gas pipe network from the closed loop process of alarming, toughness evaluation and toughness enhancement.
Description
Technical Field
The invention belongs to the technical field of public safety, and particularly relates to a toughness-evaluation-based collaborative toughness-enhanced gas safety control device.
Background
Natural gas pipe networks in China are being rapidly constructed and developed, but compared with other underground pipe networks in cities, such as water supply pipe networks, drainage pipe networks, electric power pipe networks and other pipe network systems, gas pipe network systems are more serious in life and property loss due to the fact that gas is flammable and explosive, and if leakage accidents happen to the pipe networks, disasters caused by untimely treatment are larger. At present, urban underground gas pipe network systems in China enter the accident high-incidence period. As the gas pipe network in partial areas of China is long in the years, internal corrosion and external corrosion are easy to occur, and in addition, urban construction, vehicle rolling, geological settlement, joint defects and welding defects are added, the accidents of leakage of the direct-buried natural gas pipeline often occur. In recent years, the number of accidents caused by gas leakage is countless internationally, life and property safety is seriously threatened, and the harmfulness is extremely high.
In recent years, the demand of domestic natural gas is increasing continuously, the urban gas pipe network also enters a rapid development period, and the underground natural gas pipe network is also denser. Once underground gas leakage occurs, subsequent operations such as detection and positioning are more complicated due to the intensive distribution of underground natural gas pipelines, and disaster accidents are more serious due to the fact that the subsequent operations are more complicated. However, the natural gas pipeline accidents frequently occur due to the fact that part of the natural gas pipeline ages for a long time, corrosion inside and outside the gas pipeline, damage of a third party and the like.
Pipeline corrosion perforation in a gas leakage accident is one of the most common situations of natural gas pipeline leakage. The direct-buried gas pipeline can generate small hole leakage due to corrosion and other reasons, the initial stage of the small hole leakage is difficult to find, the detection is difficult, and the real-time monitoring is also difficult. However, under the condition of sufficient time for leakage and diffusion, the leaked natural gas is collected to form high concentration and is diffused to the surrounding area of the soil, once the leaked natural gas enters a drainage pipeline or an inspection well and other closed spaces, the leaked natural gas is easy to explode after the concentration reaches the lower explosion limit and reaches an ignition point, and the safety of life and property of people is endangered. In cities, gas pipelines are difficult to avoid adjacent crossing with municipal pipelines such as drainage pipelines, rainwater pipelines, domestic sewage pipelines and the like, and the underground areas are wide and are high-risk areas with serious consequences such as casualties and the like caused by gas leakage, diffusion, collection and safety accidents.
At present, a large number of experts, scholars and research institutions at home and abroad perform theoretical research, numerical simulation and experimental research on the leakage diffusion rule of flammable and explosive gas in the atmospheric environment, and the research on the gas in the atmosphere is mature. However, there are few systematic studies on the diffusion law of gas leakage in soil, and there are more rare cases of studies on the diffusion law of gas in soil based on large-scale experimental methods. In most cases, the soil covering surface of the urban direct-buried gas pipeline is cement or asphalt. When the fuel gas is diffused to the surface of cement or asphalt through soil, the fuel gas is difficult to directly penetrate and then diffuse to the atmospheric environment, so that the research on the leakage diffusion rule of the fuel gas in the soil has important practical significance.
In conclusion, the safety of the urban gas pipe network is closely related to the national economic development and social prosperity and stability, and the accident prevention of the urban gas pipe network is a world problem in the public safety field worldwide.
With the acceleration of urban construction in recent years, other underground pipelines and facility structures are gradually increased, so that a large amount of adjacent underground structure spaces exist around the buried gas pipeline, such as underground supermarkets, underground parking lots and other municipal pipelines, such as drainage pipelines, heat pipelines, power pipelines, various inspection wells, inspection wells and the like. The gas in the soil always seeks the most convenient path to diffuse to the earth surface, once the adjacent underground spaces exist near the leaked buried natural gas pipeline, the natural gas can not diffuse to the earth surface from the leakage port, but diffuses along the adjacent underground spaces, high-concentration mixed gas of gas and air is formed in the closed spaces such as the well chambers or the pipe ditches, and once the explosion limit is reached, the explosion is extremely easy to generate.
The safety problems of the gas pipe network have very complex characteristics, such as various disasters of gas, water supply and drainage, heating power, electric power and the like often appear in a coupling mode, risks are hidden and transferred among systems, accident linkage, consequence superposition and other complex characteristics are difficult to find and control.
According to the physical communication attribute of the underground space, the adjacent underground spaces of two types of underground gas pipelines, namely communication and non-communication, are researched, and the underground spaces with poor communication, such as an electric power well, a communication well, a water supply well, a heat supply well, a gas well and a fire-fighting well, are the same. Such wells are relatively independent from wells and may be considered to be unconnectorized subterranean spaces. The other is the connection underground space. The underground space such as a sewage well, a rainwater well and the like is communicated, the underground spaces are connected through a communication pipeline, and compared with the underground space which is not communicated, combustible gas can diffuse to an inspection well from any position of the pipeline and is easy to explode when meeting an ignition source.
At present, various monitoring and alarming technologies are based on simple monitoring concentration judgment, the resisting force, the absorbing force, the adaptability, the restoring force and the learning force of a pipe network body and a linkage measure whole system are not considered from the whole system level, and the whole safety control of a gas pipe network is not realized from the closed-loop process of alarming, toughness evaluation and toughness enhancement, so that an integrated system and a method capable of realizing real-time monitoring, toughness evaluation and coordinated toughness need to be developed.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a toughness-evaluation-based collaborative toughness-enhanced gas safety control device.
In order to achieve the purpose, the invention adopts the following technical scheme:
a coordinated toughness enhancement gas safety management and control device based on toughness evaluation, the management and control device comprises: the system comprises a gas laser sensor, a signal transmission module, a front-end processor, a central processor, a call center host, a mobile field terminal and a mobile GPS receiving terminal; the gas laser sensor is arranged in a gas pipeline valve well and used for detecting gas leaked from a gas pipeline and gas leaked from a valve in the valve well; the signal transmission module is in communication connection with the gas laser sensor and is used for receiving a signal from the gas laser sensor and transmitting the signal to the front-end processor; the front-end processor is used for receiving the signal from the signal transmission module and analyzing the received signal so as to monitor the operation of the gas pipeline, the analysis is dynamic toughness evaluation preprocessing, and if alarm information is generated after the analysis, the alarm information is transmitted to the central processor; the central processor receives the alarm information sent by the front-end processor, carries out secondary toughness enhancement evaluation based on a dynamic toughness evaluation preprocessing result of the front-end processor, then executes a scheduling optimization strategy according to the secondary toughness enhancement evaluation result, sends a maintenance task signal to the mobile GPS receiving terminal through the call center host connected with the central processor, and simultaneously sends related maintenance task data to the mobile field terminal.
Preferably, the gas laser sensor is a laser type methane concentration sensor.
Preferably, the mobile GPS receiving terminal is an emergency vehicle with a GPS receiving device.
Preferably, the mobile field terminal is a control device in the field.
Preferably, the working method of the management and control device includes the following steps: step one, detecting the concentration of fuel gas by a fuel gas laser sensor, and entering the next step when the concentration is detected to rise; secondly, the front-end processor receives signals of the gas laser sensor and carries out dynamic toughness evaluation pretreatment; step three, judging whether the system meets the toughness requirement, if so, entering the step four, and if not, entering the step five; fourthly, starting and continuously monitoring the fan on site; and fifthly, when the system does not meet the toughness requirement, the front-end processor sends related alarm information to the central processor, the central processor performs secondary toughness enhancement evaluation and executes a scheduling optimization strategy according to the toughness enhancement evaluation result, the central processor sends a signal to the mobile GPS receiving terminal through the call center host, and meanwhile, the central processor sends related maintenance task data to the mobile field terminal.
Preferably, the dynamic toughness evaluation preprocessing process in the front-end processor in the second step is specifically as follows: step 201, collecting gas leakage monitoring data and toughness monitoring data, and acquiring monitoring data and monitoring curves of confirming gas leakage by one monitoring point or multiple monitoring points as sample data; 202, eliminating abnormal data, and performing abnormal value processing operation on the monitoring data and the monitoring curve characteristic information of each monitoring point in the sample set; step 203, noise reduction and filtering, namely performing abnormal value processing operation on the monitoring data and the monitoring curve characteristic information of each monitoring point in the sample set; step 204, extracting characteristic waveform parameters; step 205, cross checking, and then simultaneously entering step 206 and step 208; step 206, sending the data after cross checking into a training set, and then entering step 207; step 207, training a classification model of the support vector machine, and then entering step 210; step 208, sending the data after cross checking into a test set, and then entering step 209; step 209, judging whether the performance metric meets the requirement, if so, entering step 210, otherwise, returning to step 207; step 210, combining the results of step 207 and step 209 to obtain model parameters; step 210, inputting the trained model by the model parameters; and step 211, outputting a classification result.
Preferably, in step 201, 15 characteristic parameters describing the methane concentration fluctuation of the detection site are selected: maximum, minimum, mean, peak, rectified mean, rate of change, standard deviation, kurtosis, root mean square, form factor, peak factor, pulse factor, margin factor, peak time, valley time.
Preferably, the trained model in step 210 is obtained as follows: step 2121, acquiring monitoring time sequence data of the monitoring points for 30 days; step 2122, eliminating abnormal data; step 2123, noise reduction and filtering; 2124, extracting characteristic waveform parameters; step 2125, normalization; and step 2126, obtaining the trained model.
Preferably, in the fifth step, the performing, by the central processing unit, the secondary toughness enhancement evaluation is to form a corresponding toughness evaluation grade based on the toughness evaluation result of each leakage point by the single front-end processor, then perform basic probability assignment on the toughness evaluation result of each leakage point, and perform evidence fusion through global data to realize the overall toughness evaluation result.
Preferably, in the fifth step, the central processing unit performs a scheduling optimization strategy after performing the secondary toughness enhancement evaluation, and the scheduling optimization strategy specifically includes the following steps: step 501, confirming that a leakage accident exists; 502, obtaining a secondary toughness enhancement evaluation result; step 503, starting a call center host; step 504, the call center host sends a signal to the emergency vehicle with the GPS receiving device; step 505, judging whether the emergency vehicle is closest to the emergency vehicle and has no task, if so, entering step 506, otherwise, returning to step 504; step 506, judging whether personnel and tools exist in the nearest service station, if so, entering step 507, and otherwise, carrying out personnel allocation; step 507, site disposal; step 508, judge whether need to construct, if yes, enter step 509, otherwise process and finish; 509, allocating personnel; and step 510, performing site construction.
Compared with the prior art, the invention has the beneficial effects that: the control device considers the resistance, the absorption capacity, the adaptability, the restoring force and the learning force of the whole system of the pipe network body and the linkage measure from the whole system level, and realizes the whole safety control of the gas pipe network from the closed loop process of alarming, toughness evaluation and toughness enhancement.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a coordinated toughness-enhanced gas safety control device based on toughness evaluation.
FIG. 2 is a flow chart of the operation of the coordinated toughness enhanced gas safety control device based on the toughness evaluation.
Fig. 3 is a diagram of a dynamic toughness evaluation preprocessing process in a front-end processor.
FIG. 4 is a diagram of a secondary toughness enhancement evaluation process of a central processing unit.
FIG. 5 is a diagram of a schedule optimization strategy performed by the central processor.
Fig. 6 is a contrast map before and after filtering.
A gas line-1; a fan-2; methane detector valve well-3; front-end processor-4; a call center host-5; mobile GPS receiving terminal-6; a central processor-7; and (4) moving the field terminal-8.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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 obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
As shown in fig. 1, the coordinated toughness enhanced gas safety management and control device based on toughness evaluation disclosed in this embodiment includes a gas laser sensor, a signal transmission module, a front-end processor, a central processing unit, a call center host, a mobile field terminal, and a mobile GPS receiving terminal.
The gas laser sensor is installed in a gas pipeline valve well and used for detecting gas leaked by a gas pipeline and gas leaked by a valve in the valve well.
The signal transmission module is in communication connection with the gas laser sensor and is used for receiving a signal from the gas laser sensor and transmitting the signal to the front-end processor.
The front-end processor is used for receiving the signals from the signal transmission module and analyzing the received signals so as to monitor the operation of the gas pipeline, the analysis is dynamic toughness evaluation preprocessing, and alarm information is transmitted to the central processor.
The central processor receives the related alarm information sent by the front-end processor, performs secondary toughness enhancement evaluation based on a dynamic toughness evaluation preprocessing result of the front-end processor, executes a scheduling optimization strategy according to the toughness enhancement evaluation result, and sends a signal to the mobile GPS receiving terminal through the call center host, wherein the mobile GPS receiving terminal can be an emergency vehicle with a GPS receiving device, meanwhile, the central processor sends related maintenance task data to a mobile field terminal, the mobile field terminal can be a field control device, and the central processor executes coordinated toughness evaluation after executing the scheduling optimization strategy.
The laser type methane concentration sensor is preferably selected as the gas laser sensor, and the laser type methane concentration sensor is reasonably arranged by optimizing distribution, so that the leakage detection, toughness evaluation and coordinated toughness enhancement linkage process of the urban-level gas pipe network can be realized, and the integral toughness enhancement of the urban-level gas pipe network is realized.
As shown in fig. 2, the working process of the coordinated toughness-enhanced gas safety control device based on the toughness evaluation is as follows:
step one, the gas laser sensor detects the gas concentration, and the next step is carried out when the gas laser sensor detects the gas concentration rise, in the embodiment, the laser type methane concentration sensor is adopted, so that the step two is carried out when the laser type methane concentration sensor detects the methane concentration rise;
secondly, the front-end processor receives signals of the gas laser sensor and carries out dynamic toughness evaluation pretreatment;
step three, judging whether the system meets the toughness requirement, if so, entering the step four, and if not, entering the step five;
fourthly, starting and continuously monitoring the fan on site;
step five, when the system does not meet the toughness requirement, the front-end processor sends related alarm information to the central processor, the central processor performs secondary toughness enhancement evaluation and executes a scheduling optimization strategy according to the toughness enhancement evaluation result, and sends a signal to a mobile GPS receiving terminal through a call center host, wherein the mobile GPS receiving terminal can be an emergency vehicle with a GPS receiving device, and meanwhile, the central processor sends related maintenance task data to a mobile field terminal, and the mobile field terminal can be a field control device;
and step six, the central processor executes coordinated toughness evaluation after executing the scheduling optimization strategy.
According to the embodiment of the invention, the gas laser sensor, the signal transmission module, the front-end processor, the central processor, the call center host, the mobile field terminal and the mobile GPS receiving terminal are cooperatively operated, so that scientific and effective online monitoring, dynamic toughness evaluation and toughness enhancement cooperation are carried out on the leakage risk of the gas pipe network, and the safety and toughness of the urban gas pipe network are ensured.
The main toughness judgment indexes comprise:
resistance force: underground space structure, ground environment, adjacent space, pipe type, monitoring and early warning power, hazard source condition, traffic condition;
absorption force: soil material, pipe soil coupling coefficient, leakage point hole size and humidity, wherein the soil material can be divided into sand soil, clay, asphalt and cement;
adaptability: concentration variations, leakage of acoustic source, diffusion rate;
restoring force: economic loss, population composition, fan capacity, maintenance mobility, type of nearby pipe networks, emergency plan compilation and drilling, emergency rescue team and personnel training;
learning ability: historical maintenance data, historical cases, leakage monitoring and early warning, collaboration capability, leadership, responsibility system, routing inspection and the like.
As shown in fig. 3, the dynamic toughness evaluation preprocessing process in the front-end processor in the second step is specifically as follows:
step 201, collecting gas leakage monitoring data and toughness monitoring data, and acquiring monitoring data and monitoring curves of confirming gas leakage by one monitoring point or multiple monitoring points as sample data;
202, eliminating abnormal data, performing abnormal value processing operation on the monitoring data and the monitoring curve characteristic information of each monitoring point in the sample set, preferably forming characteristics such as a threshold value, a characteristic value change rate, an average value of a sliding time window and the like based on historical data, and eliminating obvious abnormal data;
step 203, denoising and filtering, namely performing abnormal value processing operation on the monitoring data and the monitoring curve characteristic information of each monitoring point in the sample set, preferably, denoising and filtering based on wavelet change and Kalman filtering algorithm, and improving the signal quality;
step 204, extracting characteristic waveform parameters, preferably, mainly extracting characteristics such as a maximum value, a minimum value, an average value, a peak value, a rectified average value, a change rate, a standard deviation, a kurtosis, a root mean square, a form factor, a peak value factor, a pulse factor, a margin factor, a peak time, a trough time and the like;
step 205, cross checking, namely repeatedly using data, segmenting the obtained sample data, combining the segmented sample data into different training sets and test sets, wherein the training sets are used for training the model, the test sets are used for evaluating the quality of the model, and then, the step 206 and the step 208 are simultaneously carried out;
step 206, sending the data after cross checking into a training set, and then entering step 207;
step 207, training a classification model of the support vector machine, namely, taking a sample data feature set acquired on site as an input variable of the support vector machine, specifying a toughness output result of each sample, establishing correct mapping of input and output by adjusting coefficient vectors of a classification function of the classifier, obtaining the coefficient vectors of the classification function of the final classifier after all samples are trained, and then entering step 210;
step 208, sending the data after cross checking into a test set, and then entering step 209;
step 209, determining whether the performance metric meets the requirement, specifically, determining whether the classification accuracy meets a threshold value for relief, if so, entering step 210, otherwise, returning to step 205;
step 210, combining the results of step 207 and step 209 to obtain model parameters, namely coefficient vectors of the classifier classification function;
step 211, inputting the trained model according to the model parameters;
and step 212, outputting the classification result to obtain a toughness evaluation result value of the front-end processor.
Further, in step 201, as a specific example, 15 characteristic parameters describing the methane concentration fluctuation of the detection site are selected: maximum, minimum, mean, peak, rectified mean, rate of change, standard deviation, kurtosis, root mean square, form factor, peak factor, pulse factor, margin factor, peak time, valley time. The specific introduction and calculation methods are as follows:
<1>maximum value: maximum value X of monitoring datamax。Xmax＝xi(t)
<2>Minimum value: minimum value X of monitoring datamin。Xmin＝xi(t)
<3>Average value: describing a stationary component of the monitored data
<4>Peak value: usually referred to as the unimodal maximum of the vibration waveform, since it is a time-unstable parameter, the different times vary widely. Therefore, the following method is adopted in the mechanical fault diagnosis system to improve the stability of the peak index: in the total length of a signal sample, 10 numbers with the maximum absolute value are found, and the arithmetic mean value of the 10 numbers is used as the peak value because of the leakage of natural gasThe concentration change in the process also greatly fluctuates at different times, so the same calculation method is adopted.
<5>Rectification average value: means the average of the absolute values of the signals
<6>Rate of change:
<7>standard deviation: is the arithmetic square root of variance, and the standard deviation reflects the degree of dispersion of a data set
<8>Kurtosis: is a numerical statistic, moment, reflecting the distribution characteristics of the vibration signal
<9>Root mean square: with the effective value describing the energy of the vibration signal
<10>Form factor: dimensionless quantity, which is the ratio of the RMS value of the signal to the rectified mean value
<11>Crest factor: the ratio of the amplitude of the calculated waveform to the root mean square represents the extreme of the peak in the waveform
<12>Pulse factor: is a system for detecting whether there is an impact in a signalIndex meter
<13>Margin factor: is the ratio of the peak value of the signal to the square root amplitude
Wherein the square root amplitude
<14> Peak time: the time point corresponding to the single peak maximum of the vibration waveform.
Further, the trained model in step 212 is obtained as follows:
2121, acquiring monitoring time sequence data of a monitoring point for 30 days, namely original data of a historical monitoring record;
step 2122, eliminating abnormal data, and referring to step 202;
step 2123, noise reduction filtering, refer to step 203;
step 2124, extracting characteristic waveform parameters, referring to step 204;
step 2125, normalization, namely processing the characteristic values with different ranges and units into dimensionless data in an interval of 0-1;
and step 2126, obtaining the trained model.
As shown in fig. 4, in the fifth step, performing secondary toughness enhancement evaluation by the central processing unit forms corresponding toughness evaluation grades based on the toughness evaluation results of the single front-end processor for each leakage point, then performs basic probability assignment on the toughness evaluation results of each leakage point, performs evidence fusion through global data, and achieves an overall toughness evaluation result, that is, obtains the basic probability assignment based on the sample evidence distance and the reliability function, and performs evidence fusion calculation based on different basic probability assignments and the evidence synthesis formula.
As shown in fig. 5, in the fifth step, the central processing unit performs the secondary toughness enhancement evaluation and then executes the scheduling optimization strategy, and the scheduling center performs the comprehensive scheduling task ordering mainly according to the size of the secondary toughness enhancement evaluation result value, the scheduling vehicle information (position, task state), the service station information (personnel, task state, tool) and the engineering construction team information (personnel, task state, tool, etc.). The scheduling optimization strategy specifically comprises the following steps:
step 501, confirming that a leakage accident exists;
502, obtaining a secondary toughness enhancement evaluation result;
step 503, starting a call center host;
step 504, the call center host sends a maintenance task signal to the emergency vehicle with the GPS receiving device;
step 505, judging whether the emergency vehicle is closest to the emergency vehicle and has no task, if so, entering step 506, otherwise, returning to step 504;
step 506, judging whether personnel and tools exist in the nearest service station, if so, entering step 507, and otherwise, carrying out personnel allocation;
step 507, site disposal;
step 508, judge whether need to construct, if yes, enter step 509, otherwise process and finish;
509, allocating personnel;
and step 510, performing site construction.
And sixthly, the central processing unit executes coordinated toughness evaluation after executing the scheduling optimization strategy, wherein the coordinated toughness evaluation is processed according to the evaluation algorithm flow of the front-end processor and the central processing unit.
The verification process of the method is as follows:
the used data is acquired from a month, the sensor value with methane concentration fluctuation exists in the month, the data is audited, namely the alarm reason is successfully judged, and a corresponding label is given.
The principle of the method is that the mean value is used for replacing each value in the original data, a point is selected, then a plurality of points in the neighborhood of the point are used for calculating the mean value of all the points, and then the mean value is given to the current point. This neighborhood is referred to as a "window" in signal processing. The larger the window is, the smoother the output result is, but it may also wipe out the useful signal characteristics, so the window size is determined according to the actual signal and noise characteristics, and the algorithm window size selects 20 points around the domain, as shown in the noise reduction graph 6. As can be seen from the first graph, the background of the signal is quite noisy, the fluctuating characteristics are submerged in the noise, the later machine learning classification is not convenient, and the relatively smooth waveform is obtained after filtering, so that the leakage diagnosis of the natural gas pipeline is convenient.
The preprocessed data are utilized, an SVM is adopted for model training, the result is shown in the following table 1, it can be seen that kernel functions 'RBF' and 'sigmoid' of the SVM correspond to different kernel functions, when penalty parameters are all 0.8, the performance of the sigmoid kernel function is obviously superior to that of a Gaussian (RBF) kernel function, but the SVM under the two different kernel functions has excellent performance on the recall ratio of natural gas pipeline leakage and the precision ratio of biogas gathering. When the SVM is set under a specific condition, the classifier can meet the requirements of projects, high accuracy, high gas pipeline leakage recall ratio and high biogas gathering precision ratio.
TABLE 1
Although the present invention has been described in detail with respect to the above embodiments, it will be understood by those skilled in the art that modifications or improvements based on the disclosure of the present invention may be made without departing from the spirit and scope of the invention, and these modifications and improvements are within the spirit and scope of the invention.
Claims (10)
1. A coordinated toughness reinforcing gas safety management and control device based on toughness aassessment, its characterized in that, the management and control device includes: the system comprises a gas laser sensor, a signal transmission module, a front-end processor, a central processor, a call center host, a mobile field terminal and a mobile GPS receiving terminal;
the gas laser sensor is arranged in a gas pipeline valve well and used for detecting gas leaked from a gas pipeline and gas leaked from a valve in the valve well;
the signal transmission module is in communication connection with the gas laser sensor and is used for receiving a signal from the gas laser sensor and transmitting the signal to the front-end processor;
the front-end processor is used for receiving the signal from the signal transmission module and analyzing the received signal so as to monitor the operation of the gas pipeline, the analysis is dynamic toughness evaluation preprocessing, and if alarm information is generated after the analysis, the alarm information is transmitted to the central processor; the central processor receives the alarm information sent by the front-end processor, carries out secondary toughness enhancement evaluation based on a dynamic toughness evaluation preprocessing result of the front-end processor, then executes a scheduling optimization strategy according to the secondary toughness enhancement evaluation result, sends a maintenance task signal to the mobile GPS receiving terminal through the call center host connected with the central processor, and simultaneously sends related maintenance task data to the mobile field terminal.
2. The toughness-evaluation-based synergistic toughness-enhanced gas safety management and control device according to claim 1, wherein the gas laser sensor is a laser-type methane concentration sensor.
3. The coordinated toughness enhanced gas safety control device based on toughness evaluation as claimed in claim 1, wherein the mobile GPS receiving terminal is an emergency vehicle with a GPS receiving device.
4. The coordinated toughness-enhanced gas safety management and control device based on toughness evaluation as claimed in claim 1, wherein the mobile field terminal is a control device on site.
5. The coordinated toughness enhanced gas safety control device based on toughness evaluation according to claim 1, wherein the working method of the control device comprises the following steps:
step one, detecting the concentration of fuel gas by a fuel gas laser sensor, and entering the next step when the concentration is detected to rise;
secondly, the front-end processor receives signals of the gas laser sensor and carries out dynamic toughness evaluation pretreatment;
step three, judging whether the system meets the toughness requirement, if so, entering the step four, and if not, entering the step five;
fourthly, starting and continuously monitoring the fan on site;
and fifthly, when the system does not meet the toughness requirement, the front-end processor sends related alarm information to the central processor, the central processor performs secondary toughness enhancement evaluation and executes a scheduling optimization strategy according to the toughness enhancement evaluation result, the central processor sends a signal to the mobile GPS receiving terminal through the call center host, and meanwhile, the central processor sends related maintenance task data to the mobile field terminal.
6. The toughness-evaluation-based collaborative toughness-enhanced gas safety management and control device according to claim 5, wherein in the second step, the dynamic toughness evaluation preprocessing process in the front-end processor is specifically as follows:
step 201, collecting gas leakage monitoring data and toughness monitoring data, and acquiring monitoring data and monitoring curves of confirming gas leakage by one monitoring point or multiple monitoring points as sample data;
202, eliminating abnormal data, and performing abnormal value processing operation on the monitoring data and the monitoring curve characteristic information of each monitoring point in the sample set;
step 203, noise reduction and filtering, namely performing abnormal value processing operation on the monitoring data and the monitoring curve characteristic information of each monitoring point in the sample set;
step 204, extracting characteristic waveform parameters;
step 205, cross checking, and then simultaneously entering step 206 and step 208;
step 206, sending the data after cross checking into a training set, and then entering step 207;
step 207, training a classification model of the support vector machine, and then entering step 210;
step 208, sending the data after cross checking into a test set, and then entering step 209;
step 209, judging whether the performance metric meets the requirement, if so, entering step 210, otherwise, returning to step 207;
step 210, combining the results of step 207 and step 209 to obtain model parameters;
step 210, inputting the trained model by the model parameters;
and step 211, outputting a classification result.
7. The coordinated toughness enhanced gas safety control device based on toughness evaluation as claimed in claim 6, wherein in step 201, 15 characteristic parameters describing methane concentration fluctuation of detection site are selected: maximum, minimum, mean, peak, rectified mean, rate of change, standard deviation, kurtosis, root mean square, form factor, peak factor, pulse factor, margin factor, peak time, valley time.
8. The coordinated toughness enhanced gas safety control device based on toughness evaluation according to claim 7, wherein the trained model in step 210 is obtained through the following process:
step 2121, acquiring monitoring time sequence data of the monitoring points for 30 days;
step 2122, eliminating abnormal data;
step 2123, noise reduction and filtering;
2124, extracting characteristic waveform parameters;
step 2125, normalization;
and step 2126, obtaining the trained model.
9. The coordinated toughness enhancement gas safety control device based on toughness assessment according to claim 8, wherein in the fifth step, the central processing unit performs secondary toughness enhancement assessment based on the toughness assessment results of the single front-end processor on each leakage point to form a corresponding toughness assessment grade, then performs basic probability assignment on the toughness assessment results of each leakage point, and performs evidence fusion through global data to realize an overall toughness assessment result.
10. The coordinated toughness enhanced gas safety control device based on toughness evaluation according to claim 9, wherein in the fifth step, the central processing unit performs secondary toughness enhancement evaluation and then executes a scheduling optimization strategy, and the scheduling optimization strategy specifically includes the following steps:
step 501, confirming that a leakage accident exists;
502, obtaining a secondary toughness enhancement evaluation result;
step 503, starting a call center host;
step 504, the call center host sends a signal to the emergency vehicle with the GPS receiving device;
step 505, judging whether the emergency vehicle is closest to the emergency vehicle and has no task, if so, entering step 506, otherwise, returning to step 504;
step 506, judging whether personnel and tools exist in the nearest service station, if so, entering step 507, and otherwise, carrying out personnel allocation;
step 507, site disposal;
step 508, judge whether need to construct, if yes, enter step 509, otherwise process and finish;
509, allocating personnel;
and step 510, performing site construction.
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