CN112329181A - Big data monitoring method for pipeline leakage - Google Patents
Big data monitoring method for pipeline leakage Download PDFInfo
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- CN112329181A CN112329181A CN202011098380.9A CN202011098380A CN112329181A CN 112329181 A CN112329181 A CN 112329181A CN 202011098380 A CN202011098380 A CN 202011098380A CN 112329181 A CN112329181 A CN 112329181A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/14—Pipes
Abstract
The invention provides a big data monitoring method for pipeline leakage, which comprises the following steps: monitoring pipeline information of N target pipelines in a preset pipeline; judging whether leakage points exist in the N target pipelines or not according to the pipeline information; if the leakage position exists, the leakage position of the leakage point is positioned based on a pre-stored leakage positioning model and a pipeline trend model; meanwhile, the leakage position is calibrated and transmitted to the monitoring terminal for displaying. By relying on a big data technology, monitoring the pipeline and by means of a leakage positioning model and a pipeline trend model, leakage positions of leakage points can be determined conveniently and effectively in time, and processing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data monitoring method for pipeline leakage.
Background
Present common pipeline includes the oil transportation pipeline, rivers transportation pipeline, various pipelines such as natural gas transportation pipeline, at the in-process of transportation, it is in the state totally sealed to need the pipeline, however, because pipeline service life overlength, perhaps external influence factor etc. make the condition of leaking appear in the pipeline, and, present monitor the pipeline, more common monitoring mode is, rely on artifical target pipeline department to correspond, accomplish the monitoring to the target pipeline, but its monitoring process, seriously rely on artifically, seriously waste monitoring time, and because its not intelligent, lead to the position information that can not in time acquire the leakage point, reduce to its treatment effeciency.
Disclosure of Invention
The invention provides a big data monitoring method for pipeline leakage, which is used for conveniently and effectively determining the leakage position of a leakage point in time by depending on a big data technology, monitoring a pipeline and passing through a leakage positioning model and a pipeline trend model so as to improve the processing efficiency.
The invention provides a pipeline leakage monitoring method based on big data, which comprises the following steps:
monitoring pipeline information of N target pipelines in a preset pipeline;
judging whether leakage points exist in the N target pipelines or not according to the pipeline information;
if the leakage position exists, the leakage position of the leakage point is positioned based on a pre-stored leakage positioning model and a pipeline trend model;
and meanwhile, calibrating the leakage position, and transmitting the leakage position to a monitoring terminal for displaying.
In one possible way of realisation,
when leakage points exist in the N target pipelines, performing first alarm based on the monitoring terminal, and recording the warning duration of the first alarm;
when the first alarm is carried out, judging whether the monitoring terminal receives an alarm stop instruction input by monitoring personnel;
if yes, stopping the first alarm;
otherwise, continuing to execute the first alarm, stopping the first alarm when the recorded warning time length is consistent with the preset time length, and executing the first alarm again based on the preset time period.
In one possible way of realisation,
judging whether the monitoring terminal receives the alarm stop instruction input by the monitoring personnel, and further comprising the following steps:
when the monitoring terminal receives an alarm stopping instruction input by a monitoring person, determining the authorization level of the monitoring person inputting the alarm stopping instruction based on an authorization level database, and judging whether the authorization level of the monitoring person inputting the alarm stopping instruction is in a preset authorization level or not;
if yes, acquiring a permission verification identifier in an alarm stop instruction input by the monitoring personnel, and meanwhile, judging whether a permission verification path corresponding to the permission verification identifier exists in a permission verification database based on a prestored permission verification database;
if the authority verification path exists, outputting an authority verification identification interface corresponding to the authority verification path according to the authority verification path, so that the monitoring personnel can input a confirmation instruction on the authority verification identification interface;
stopping the first alarm according to the confirmation instruction received by the authority verification identification interface;
otherwise, continuing to execute the first alarm.
In one possible way of realisation,
the process of judging whether leakage points exist in the N target pipelines or not according to the pipeline information comprises the following steps:
monitoring the monitoring results of the monitoring point set of the pipeline centers of the N target pipelines, and constructing a pipeline center monitoring curve F of each target pipelinei＝{fi,i1,i1＝1,2,3,...,I1}；
Wherein, i ═ 1,2, 3.., N; i1 represents the number of monitoring points of the monitoring points; f. ofi,i1Indicating the pressure value of the i1 th monitoring point in the i target pipeline; gi,i1(q1，q2) Indicating that the ith target pipeline is 1 thUltrasonic emission signal q of monitoring point1And an ultrasonic receiving signal q2The ultrasonic wave estimation function of (1); t1 denotes sending an ultrasonic emission signal q1Launch time to target pipe; t2 represents the time when the ultrasonic reception signal reflected by the target pipe is received; δ represents an average signal of the transmission ultrasonic signal and the reception ultrasonic signal;
calculating a comprehensive pressure evaluation result A of each target pipeline based on the pressure values in the pipeline center monitoring curve;
B＝max{|fi,i1-fi,i1+1|,i1＝1,2,3,...,I1}；
wherein f isi,i1+1Indicating the pressure value of the i1+1 monitoring point in the i target pipeline; b represents the maximum difference value in the pressure difference values of adjacent monitoring points in the ith target pipeline;
evaluating whether the comprehensive pressure evaluation result A is larger than a preset evaluation value or not based on a pressure evaluation database, and if so, performing first calibration on a target pipeline with abnormal pressure;
determining abnormal monitoring points according to the B, and carrying out second calibration on the abnormal monitoring points;
and transmitting the target pipeline subjected to the first calibration and the monitoring point subjected to the second calibration to a monitoring terminal for displaying.
In one possible way of realisation,
according to the pipeline information, the process of judging whether leakage points exist in the N target pipelines further comprises the following steps:
determining a first pipeline, an intermediate pipeline and a tail pipeline in the N target pipelines based on a pipeline trend model, wherein the number of the first pipelines is m1, the number of the intermediate pipelines is m2, and the number of the tail pipelines is m3, wherein m1+ m2+ m3 is N;
monitoring first monitoring results of first monitoring point sets on two sides of the first pipeline connected with the middle pipeline, and constructing each monitoring point setFirst side monitoring curve F of individual first pipelinej＝{fj,j1,j1＝1,2,3,...,J1}；
Wherein j is 1,2, 3.., m 1; j1 represents the number of monitoring points of the first monitoring point; f. ofj,j1A first monitoring value of a j1 th first monitoring point at two sides of the j first pipeline connected with the middle pipeline;
monitoring second monitoring results of second monitoring point sets on two sides of the tail pipeline connected with the middle pipeline, and constructing a second side monitoring curve F of each tail pipelinek＝{fk,k1,k1＝1,2,3,...,K1}；
Wherein k is 1,2, 3.., m 2; k1 represents the number of monitoring points of the second monitoring point; f. ofk,k1A second monitoring value of a k1 th second monitoring point at two sides of the k tail pipeline connected with the middle pipeline;
determining whether the joint of the first pipeline and the middle pipeline leaks or not according to the first side monitoring curve and the second side monitoring curve, and simultaneously determining whether the joint of the tail pipeline and the middle pipeline leaks or not;
and if the leakage occurs, performing second alarm based on the monitoring terminal.
In one possible way of realisation,
after the leakage position of the leakage point is located, the method further comprises the following steps:
calculating the leakage probability of each target pipeline with leakage;
when the leakage probability is greater than the preset probability, setting the corresponding target pipeline as an easy-to-send alarm pipeline, and simultaneously monitoring the easy-to-send alarm pipeline in real time;
otherwise, monitoring the target pipeline which is not easy to alarm based on preset monitoring time.
In one possible way of realisation,
before monitoring the pipeline information of N target pipelines in the preset pipeline, the method further comprises the following steps:
establishing a pipeline map related to the target pipeline;
transmitting the pipeline map to a monitoring terminal for display, wherein the pipeline map comprises: the pipeline direction of the target pipelines, the connecting nodes of the adjacent target pipelines, the starting position and the ending position of each target pipeline and corresponding alarm signals when each target pipeline leaks;
and receiving an editing instruction input by a monitoring person based on the monitoring terminal, editing the pipeline map, and displaying the edited pipeline map in real time based on the monitoring terminal.
In one possible way of realisation,
based on the pre-stored leakage positioning model and the pipeline trend model, before positioning the leakage position of the leakage point, the method further comprises the following steps:
constructing a positioning correction model;
according to the positioning correction model, correcting a prestored leakage positioning data model and a pipeline trend model of the preset pipeline to obtain a corrected leakage positioning model and a corrected pipeline trend model;
x＝(L+△L)+(v+△v)△t/2；
wherein x is the position distance between a preset leakage point and a monitoring point of a target pipeline; l is the total length of the target pipeline; the delta L is a length correction value of the target pipeline; v is the transmission speed of the medium pressure wave of the pipeline of the target pipeline; the delta v is a transmission speed correction value of a pipeline medium pressure wave of the preset pipeline; and delta t is the time difference of receiving the pressure wave at the adjacent monitoring points.
In one possible way of realisation,
in the process of monitoring the pipeline information of the N target pipelines in the preset pipeline, the method further comprises the following steps:
sensing and acquiring flow values of N target pipelines;
sensing and acquiring N target pipeline temperature values;
and judging whether the flow value and the temperature value exceed the corresponding preset standard ranges or not based on a pre-stored standard monitoring database, and if so, executing a third alarm based on the monitoring terminal.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a pipeline leakage monitoring method based on big data according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The invention provides a pipeline leakage monitoring method based on big data, as shown in fig. 1, comprising the following steps:
step 1: monitoring pipeline information of N target pipelines in a preset pipeline;
step 2: judging whether leakage points exist in the N target pipelines or not according to the pipeline information;
and step 3: if the leakage position exists, the leakage position of the leakage point is positioned based on a pre-stored leakage positioning model and a pipeline trend model;
and 4, step 4: and meanwhile, calibrating the leakage position, and transmitting the leakage position to a monitoring terminal for displaying.
In this embodiment, the preset pipeline includes a plurality of target pipelines (e.g., petroleum pipelines, water pipelines, natural gas pipelines, etc.), and since the lengths of the target pipelines are limited, N target pipelines need to be spliced in the process of building the pipeline, so that the pipeline can be divided into three parts, namely a first pipeline, a second intermediate pipeline, and a third tail pipeline;
in this embodiment, during the monitoring of the target pipeline, the position of the calibrated leakage point needs to be transmitted to the monitoring terminal (computer), but in the transmission process, the related information of the leakage point is transmitted to the related monitoring terminal by adopting the principle of proximity, for example, the position of the leakage point is in the Q monitoring area, so that the leakage point is transmitted to the monitoring terminal related to the Q monitoring area.
The method for positioning the position of the leakage point of the target pipeline can be characterized in that a negative pressure wave method is used as a basic method, a pipeline transient model is utilized, and the position of the leakage point of the preset pipeline is positioned in a pressure or flow positioning mode, wherein the pipeline transient model can be established based on parameters such as medium viscosity, density, pipeline diameter, elastic modulus and the like;
the monitoring terminal in this embodiment may be implemented as any one or more of a notebook, a PC, and a computer.
The monitoring personnel of the monitoring terminal executes corresponding operation according to the position information of the leakage point, and the executed operation can be that the monitoring personnel maintain the pipeline on the leakage site.
The beneficial effects of the above technical scheme are: the leakage position of the leakage point can be conveniently and effectively determined in time by relying on a big data technology, monitoring the pipeline and monitoring the leakage position through the leakage positioning model and the pipeline trend model, and the processing efficiency is improved.
The invention provides a big data monitoring method for pipeline leakage,
when leakage points exist in the N target pipelines, performing first alarm based on the monitoring terminal, and recording the warning duration of the first alarm;
when the first alarm is carried out, judging whether the monitoring terminal receives an alarm stop instruction input by monitoring personnel;
if yes, stopping the first alarm;
otherwise, continuing to execute the first alarm, stopping the first alarm when the recorded warning time length is consistent with the preset time length, and executing the first alarm again based on the preset time period.
The first alarm can be one or a combination of more of sound, light, vibration and the like;
the preset duration can be set by default of a monitoring system or set manually;
the preset time period is set to improve the effectiveness of alarming and further achieve the purpose of reminding.
The first alarm may correspond to alarm information, and may include: target pipeline information corresponding to the leakage point, position information of the leakage point, alarm time and the like.
The beneficial effects of the above technical scheme are: the reliability of knowing the alarm condition by the monitoring personnel is improved through the stop instruction input by the monitoring personnel.
The invention provides a big data monitoring method for pipeline leakage, which is used for judging whether a monitoring terminal receives an alarm stopping instruction input by a monitoring person or not, and further comprises the following steps:
when the monitoring terminal receives an alarm stopping instruction input by a monitoring person, determining the authorization level of the monitoring person inputting the alarm stopping instruction based on an authorization level database, and judging whether the authorization level of the monitoring person inputting the alarm stopping instruction is in a preset authorization level or not;
if yes, acquiring a permission verification identifier in an alarm stop instruction input by the monitoring personnel, and meanwhile, judging whether a permission verification path corresponding to the permission verification identifier exists in a permission verification database based on a prestored permission verification database;
if the authority verification path exists, outputting an authority verification identification interface corresponding to the authority verification path according to the authority verification path, so that the monitoring personnel can input a confirmation instruction on the authority verification identification interface;
stopping the first alarm according to the confirmation instruction received by the authority verification identification interface;
otherwise, continuing to execute the first alarm.
In this embodiment of the present invention,
the above-mentioned monitor personnel that correspond to the target pipeline of monitoring terminal management monitoring carry out the level and authorize, as follows:
for example, when the preset authorization level is an I level, it is determined that the authority of the monitoring person a1 is verified, and when the monitoring person a1 has the operation authority related to the alarm, an alarm stop instruction input to the monitoring person a1 is executed.
The setting of the authority verification path is to facilitate the popping of the authority verification identification interface, so that monitoring personnel can input a confirmation instruction for confirmation;
for example: and displaying the permission verification identification interface, judging whether to execute an alarm stopping instruction, and judging yes, wherein the permission verification identification interface is used for finishing the operation by inputting a confirmation instruction, namely clicking yes.
After the alarm stopping instruction input by the monitoring personnel is verified, the monitoring personnel can input a confirmation instruction, so that the reliability of the monitoring personnel with the authority to execute the stopping instruction is ensured, the monitoring personnel can know the alarm condition, and the misoperation of the monitoring personnel with the authority can be effectively avoided;
the alarm stopping instruction input by the monitoring personnel comprises a unique verification identifier capable of identifying the monitoring personnel, namely an authority verification identifier in the stopping instruction.
The beneficial effects of the above technical scheme are: through carrying out the level to monitoring personnel and authorizing, avoid irrelevant personnel, carry out the operation in violation of rules and regulations to alarm information, verify according to authorization level and authority, can ensure that only the monitoring personnel who accords with the condition can only handle the alarm condition, reduce the possibility of maloperation, reduce the unnecessary loss, improve monitoring efficiency indirectly.
The invention provides a big data monitoring method for pipeline leakage, which comprises the following steps of judging whether leakage points exist in N target pipelines according to pipeline information:
monitoring pipeline centers of N target pipelinesAnd constructing a pipeline center monitoring curve F of each target pipelinei＝{fi,i1,i1＝1,2,3,...,I1}；
Wherein, i ═ 1,2, 3.., N; i1 represents the number of monitoring points of the monitoring points; f. ofi,i1Indicating the pressure value of the i1 th monitoring point in the i target pipeline; gi,i1(q1，q2) Representing the ultrasonic emission signal q to the i1 th monitoring point in the i target pipe1And an ultrasonic receiving signal q2The ultrasonic wave estimation function of (1); t1 denotes sending an ultrasonic emission signal q1Launch time to target pipe; t2 represents the time when the ultrasonic reception signal reflected by the target pipe is received; δ represents an average signal of the transmission ultrasonic signal and the reception ultrasonic signal;
calculating a comprehensive pressure evaluation result A of each target pipeline based on the pressure values in the pipeline center monitoring curve;
B＝max{|fi,i1-fi,i1+1|,i1＝1,2,3,...,I1}；
wherein f isi,i1+1Indicating the pressure value of the i1+1 monitoring point in the i target pipeline; b represents the maximum difference value in the pressure difference values of adjacent monitoring points in the ith target pipeline;
evaluating whether the comprehensive pressure evaluation result A is larger than a preset evaluation value or not based on a pressure evaluation database, and if so, performing first calibration on a target pipeline with abnormal pressure;
determining abnormal monitoring points according to the B, and carrying out second calibration on the abnormal monitoring points;
and transmitting the target pipeline subjected to the first calibration and the monitoring point subjected to the second calibration to a monitoring terminal for displaying.
In this embodiment, the monitoring point is set at the center of the pipeline, because if the pipeline is a liquid to be transported, the probability of damage to the center of gravity is high due to the overlong pipeline, and therefore, it is particularly important to monitor the center of the pipeline.
The beneficial effects of the above technical scheme are: through the monitoring point set at monitoring pipeline center to confirm the pressure value of every monitoring point according to ultrasonic wave intelligence, and then found pipeline center monitoring curve, and then the comprehensive pressure assessment result of every target conduit of intelligent calculation, through comparison analysis, unusual pipeline is markd to intelligence and unusual monitoring point is markd to intelligence, and monitor personnel of monitor terminal in time look over, and be convenient for handle it in time.
The invention provides a big data monitoring method for pipeline leakage, which is used for judging whether leakage points exist in N target pipelines or not according to the pipeline information, and further comprises the following steps:
determining a first pipeline, an intermediate pipeline and a tail pipeline in the N target pipelines based on a pipeline trend model, wherein the number of the first pipelines is m1, the number of the intermediate pipelines is m2, and the number of the tail pipelines is m3, wherein m1+ m2+ m3 is N;
monitoring first monitoring results of first monitoring point sets on two sides of the first pipeline connected with the middle pipeline, and constructing a first side monitoring curve F of each first pipelinej＝{fj,j1,j1＝1,2,3,...,J1}；
Wherein j is 1,2, 3.., m 1; j1 represents the number of monitoring points of the first monitoring point; f. ofj,j1A first monitoring value of a j1 th first monitoring point at two sides of the j first pipeline connected with the middle pipeline;
monitoring second monitoring results of second monitoring point sets on two sides of the tail pipeline connected with the middle pipeline, and constructing a second side monitoring curve F of each tail pipelinek＝{fk,k1,k1＝1,2,3,...,K1}；
Wherein k is 1,2, 3.., m 2; k1 represents the number of monitoring points of the second monitoring point; f. ofk,k1Showing the k-th tail pipe being connected to the middle pipe on both sidesA second monitor value of the k1 th second monitor point;
determining whether the joint of the first pipeline and the middle pipeline leaks or not according to the first side monitoring curve and the second side monitoring curve, and simultaneously determining whether the joint of the tail pipeline and the middle pipeline leaks or not;
and if the leakage occurs, performing second alarm based on the monitoring terminal.
In this embodiment, the monitoring points are provided at the joints of the first pipeline and the middle pipeline and the joints of the tail pipeline and the middle pipeline, because if the pipelines are liquid for transmission, in the transmission process, the joints of the first pipeline and the tail pipeline and the middle pipeline are very general monitoring positions, and therefore, the joints are monitored.
In this embodiment, the first monitoring point may be disposed at a position where the first monitoring point is desired to be connected to the first pipeline, and the second monitoring point may be disposed at a position where the second monitoring point is desired to be connected to the second pipeline;
in this embodiment, the first monitoring result, or the second monitoring result, may be a pressure-related result, and the monitored value is also pressure-related, such as the pressure of the water flow in the pipe against the pipe wall.
In this embodiment, the second alarm may correspond to an alarm message, and may include: target pipeline information corresponding to the leakage point, position information of the leakage point, alarm time and the like.
The beneficial effects of the above technical scheme are: through the monitoring point of monitoring first pipeline and tail pipeline to the monitoring value of every monitoring point is confirmed to intelligence, and then founds relevant monitoring curve, through the monitoring value, confirms whether relevant junction appears leaking, if appear, carries out the second and reports to the police, is convenient for in time effectively remind, and in time effective processing.
The invention provides a big data monitoring method for pipeline leakage, which comprises the following steps of after positioning the leakage position of a leakage point:
calculating the leakage probability of each target pipeline with leakage;
when the leakage probability is greater than the preset probability, setting the corresponding target pipeline as an easy-to-send alarm pipeline, and simultaneously monitoring the easy-to-send alarm pipeline in real time;
otherwise, monitoring the target pipeline which is not easy to alarm based on preset monitoring time.
The preset monitoring time can be default of the system;
in this embodiment, for example, the step of counting the leakage probability of the target pipeline is as follows: and setting the target pipeline within 24 hours of preset time, and if the alarm frequency corresponding to the target pipeline exceeds 5 times, determining that the target pipeline is a pipeline which is easy to send an alarm according to the counted leakage probability.
The beneficial effects of the above technical scheme are: through carrying out the continuity monitoring to the pipeline that easily sends out the warning, be in order can and know the leakage condition of this department, the timely processing of being convenient for, through carrying out the intervallic monitoring to the pipeline that is difficult for reporting to the police, be in order to save time cost.
The invention provides a big data monitoring method for pipeline leakage, which comprises the following steps before monitoring pipeline information of N target pipelines in a preset pipeline:
establishing a pipeline map related to the target pipeline;
transmitting the pipeline map to a monitoring terminal for display, wherein the pipeline map comprises: the pipeline direction of the target pipelines, the connecting nodes of the adjacent target pipelines, the starting position and the ending position of each target pipeline and corresponding alarm signals when each target pipeline leaks;
and receiving an editing instruction input by a monitoring person based on the monitoring terminal, editing the pipeline map, and displaying the edited pipeline map in real time based on the monitoring terminal.
The editing of the pipeline map may be setting a pipeline output point, setting an input point, setting an inflection point and a length of the pipeline, calibrating a reference object of the pipeline, adjusting a position of the reference object of the pipeline, and editing the map corresponding to the pipeline, such as zooming in and zooming out.
The beneficial effects of the above technical scheme are: through receiving the editing instruction, be convenient for edit the pipeline map, through showing the pipeline map, make things convenient for monitoring personnel in time to know, improve monitoring efficiency indirectly.
The invention provides a big data monitoring method for pipeline leakage, which is based on a pre-stored leakage positioning model and a pipeline trend model, and also comprises the following steps before positioning the leakage position of a leakage point:
constructing a positioning correction model;
according to the positioning correction model, correcting a prestored leakage positioning data model and a pipeline trend model of the preset pipeline to obtain a corrected leakage positioning model and a corrected pipeline trend model;
x＝(L+△L)+(v+△v)△t/2；
wherein x is the position distance between a preset leakage point and a monitoring point of a target pipeline; l is the total length of the target pipeline; the delta L is a length correction value of the target pipeline; v is the transmission speed of the medium pressure wave of the pipeline of the target pipeline; the delta v is a transmission speed correction value of a pipeline medium pressure wave of the preset pipeline; and delta t is the time difference of receiving the pressure wave at the adjacent monitoring points.
The beneficial effects of the above technical scheme are: the correction processing is used for improving the precision of the leakage positioning model and the pipeline trend model and further improving the accuracy of obtaining the position of the leakage point.
The invention provides a big data monitoring method for pipeline leakage, which comprises the following steps in the process of monitoring pipeline information of N target pipelines in a preset pipeline:
sensing and acquiring flow values of N target pipelines;
sensing and acquiring N target pipeline temperature values;
and judging whether the flow value and the temperature value exceed the corresponding preset standard ranges or not based on a pre-stored standard monitoring database, and if so, executing a third alarm based on the monitoring terminal.
The working principle of this embodiment is: sensing and acquiring flow values (such as water flow) of N target pipelines, sensing and acquiring temperature values (such as water temperature) of the N target pipelines, judging whether the flow values and the temperature values exceed corresponding preset standard ranges based on a prestored standard monitoring database (such as a temperature range and a flow range related to water), and if so, executing a third alarm (such as voice alarm) based on the monitoring terminal;
the preset standard range may be default for a system related to monitoring, or may be set manually.
The beneficial effects of the above technical scheme are: the temperature inside the pipeline is monitored, the pipeline is prevented from being damaged due to overhigh temperature, and the phenomenon that the fluidity of related objects in the pipeline is reduced due to overlow temperature is avoided.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A big data monitoring method for pipeline leaks, comprising:
monitoring pipeline information of N target pipelines in a preset pipeline;
judging whether leakage points exist in the N target pipelines or not according to the pipeline information;
if the leakage position exists, the leakage position of the leakage point is positioned based on a pre-stored leakage positioning model and a pipeline trend model;
and meanwhile, calibrating the leakage position, and transmitting the leakage position to a monitoring terminal for displaying.
2. The big data monitoring method for pipeline leakage according to claim 1, wherein when it is determined that there are leakage points in the N target pipelines, a first alarm is performed based on the monitoring terminal, and the alarm duration of the first alarm is recorded;
when the first alarm is carried out, judging whether the monitoring terminal receives an alarm stop instruction input by monitoring personnel;
if yes, stopping the first alarm;
otherwise, continuing to execute the first alarm, stopping the first alarm when the recorded warning time length is consistent with the preset time length, and executing the first alarm again based on the preset time period.
3. The big data monitoring method for pipeline leakage according to claim 2, wherein in the process of judging whether the monitoring terminal receives an alarm stop instruction input by a monitoring person, the method further comprises:
when the monitoring terminal receives an alarm stopping instruction input by a monitoring person, determining the authorization level of the monitoring person inputting the alarm stopping instruction based on an authorization level database, and judging whether the authorization level of the monitoring person inputting the alarm stopping instruction is in a preset authorization level or not;
if yes, acquiring a permission verification identifier in an alarm stop instruction input by the monitoring personnel, and meanwhile, judging whether a permission verification path corresponding to the permission verification identifier exists in a permission verification database based on a prestored permission verification database;
if the authority verification path exists, outputting an authority verification identification interface corresponding to the authority verification path according to the authority verification path, so that the monitoring personnel can input a confirmation instruction on the authority verification identification interface;
stopping the first alarm according to the confirmation instruction received by the authority verification identification interface;
otherwise, continuing to execute the first alarm.
4. The big data monitoring method for pipeline leakage according to claim 1, wherein the process of determining whether there are leakage points in the N target pipelines according to the pipeline information comprises:
monitoring the monitoring results of the monitoring point set of the pipeline centers of the N target pipelines, and constructing a pipeline center monitoring curve F of each target pipelinei＝{fi,i1,i1＝1,2,3,...,I1}；
Wherein, i ═ 1,2, 3.., N; i1 represents the number of monitoring points of the monitoring points; f. ofi,i1Indicating the pressure value of the i1 th monitoring point in the i target pipeline; gi,i1(q1，q2) Representing the ultrasonic emission signal q to the i1 th monitoring point in the i target pipe1And an ultrasonic receiving signal q2The ultrasonic wave estimation function of (1); t1 denotes sending an ultrasonic emission signal q1Launch time to target pipe; t2 represents the time when the ultrasonic reception signal reflected by the target pipe is received; δ represents an average signal of the transmission ultrasonic signal and the reception ultrasonic signal;
calculating a comprehensive pressure evaluation result A of each target pipeline based on the pressure values in the pipeline center monitoring curve;
B＝max{|fi,i1-fi,i1+1|,i1＝1,2,3,...,I1}；
wherein f isi,i1+1Indicating the pressure value of the i1+1 monitoring point in the i target pipeline; b represents the maximum difference value in the pressure difference values of adjacent monitoring points in the ith target pipeline;
evaluating whether the comprehensive pressure evaluation result A is larger than a preset evaluation value or not based on a pressure evaluation database, and if so, performing first calibration on a target pipeline with abnormal pressure;
determining abnormal monitoring points according to the B, and carrying out second calibration on the abnormal monitoring points;
and transmitting the target pipeline subjected to the first calibration and the monitoring point subjected to the second calibration to a monitoring terminal for displaying.
5. The big data monitoring method for pipeline leakage according to claim 1, wherein in the process of determining whether there are leakage points in the N target pipelines according to the pipeline information, the method further comprises:
determining a first pipeline, an intermediate pipeline and a tail pipeline in the N target pipelines based on a pipeline trend model, wherein the number of the first pipelines is m1, the number of the intermediate pipelines is m2, and the number of the tail pipelines is m3, wherein m1+ m2+ m3 is N;
monitoring first monitoring results of first monitoring point sets on two sides of the first pipeline connected with the middle pipeline, and constructing a first side monitoring curve F of each first pipelinej＝{fj,j1,j1＝1,2,3,...,J1}；
Wherein j is 1,2, 3.., m 1; j1 represents the number of monitoring points of the first monitoring point; f. ofj,j1A first monitoring value of a j1 th first monitoring point at two sides of the j first pipeline connected with the middle pipeline;
monitoring second monitoring results of second monitoring point sets on two sides of the tail pipeline connected with the middle pipeline, and constructing a second side monitoring curve F of each tail pipelinek＝{fk,k1,k1＝1,2,3,...,K1}；
Wherein k is 1,2, 3.., m 2; k1 represents the number of monitoring points of the second monitoring point; f. ofk,k1A second monitoring value of a k1 th second monitoring point at two sides of the k tail pipeline connected with the middle pipeline;
determining whether the joint of the first pipeline and the middle pipeline leaks or not according to the first side monitoring curve and the second side monitoring curve, and simultaneously determining whether the joint of the tail pipeline and the middle pipeline leaks or not;
and if the leakage occurs, performing second alarm based on the monitoring terminal.
6. The big data monitoring method for pipeline leakage according to claim 1, wherein after locating the leakage location of the leakage point, further comprising:
calculating the leakage probability of each target pipeline with leakage;
when the leakage probability is greater than the preset probability, setting the corresponding target pipeline as an easy-to-send alarm pipeline, and simultaneously monitoring the easy-to-send alarm pipeline in real time;
otherwise, monitoring the target pipeline which is not easy to alarm based on preset monitoring time.
7. The big data monitoring method for pipeline leakage according to claim 1, wherein before monitoring pipeline information of N target pipelines in a preset pipeline, the method further comprises:
establishing a pipeline map related to the target pipeline;
transmitting the pipeline map to a monitoring terminal for display, wherein the pipeline map comprises: the pipeline direction of the target pipelines, the connecting nodes of the adjacent target pipelines, the starting position and the ending position of each target pipeline and corresponding alarm signals when each target pipeline leaks;
and receiving an editing instruction input by a monitoring person based on the monitoring terminal, editing the pipeline map, and displaying the edited pipeline map in real time based on the monitoring terminal.
8. The big data monitoring method for pipeline leakage according to claim 1, wherein before locating the leakage position of the leakage point based on a pre-stored leakage location model and a pipeline strike model, further comprising:
constructing a positioning correction model;
according to the positioning correction model, correcting a prestored leakage positioning data model and a pipeline trend model of the preset pipeline to obtain a corrected leakage positioning model and a corrected pipeline trend model;
x＝(L+△L)+(v+△v)△t/2；
wherein x is the position distance between a preset leakage point and a monitoring point of a target pipeline; l is the total length of the target pipeline; the delta L is a length correction value of the target pipeline; v is the transmission speed of the medium pressure wave of the pipeline of the target pipeline; the delta v is a transmission speed correction value of a pipeline medium pressure wave of the preset pipeline; and delta t is the time difference of receiving the pressure wave at the adjacent monitoring points.
9. The big data monitoring method for pipeline leakage according to claim 1, wherein in the process of monitoring pipeline information of N target pipelines in a preset pipeline, the method further comprises:
sensing and acquiring flow values of N target pipelines;
sensing and acquiring N target pipeline temperature values;
and judging whether the flow value and the temperature value exceed the corresponding preset standard ranges or not based on a pre-stored standard monitoring database, and if so, executing a third alarm based on the monitoring terminal.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113586804A (en) * | 2021-06-28 | 2021-11-02 | 深圳市燃气集团股份有限公司 | Pipeline monitoring protection method and system |
| CN113883423A (en) * | 2021-10-19 | 2022-01-04 | 山东腾威石油装备有限公司 | Novel pipe network repair reinforcing method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113586804A (en) * | 2021-06-28 | 2021-11-02 | 深圳市燃气集团股份有限公司 | Pipeline monitoring protection method and system |
| CN113883423A (en) * | 2021-10-19 | 2022-01-04 | 山东腾威石油装备有限公司 | Novel pipe network repair reinforcing method |
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