CN112462657A - Big data acquisition, analysis, early warning and positioning system and method for intelligent pipe network - Google Patents
Big data acquisition, analysis, early warning and positioning system and method for intelligent pipe network Download PDFInfo
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- G05B19/00—Programme-control systems
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Abstract
The system comprises a cloud server, an aboveground detection device and an underground detection device, wherein the underground detection device comprises data detection optical fibers distributed along a pipeline, a sensor group is arranged on the data detection optical fibers according to a set position, the data detection optical fibers are in communication connection with the cloud server through an optical fiber monitoring and collecting terminal, and the aboveground detection device is in communication connection with the cloud server; and the cloud server acquires and processes the detection data to realize detection positioning and early warning of the leakage point of the pipe network. Through laying the special optic fibre that can adapt to the pipe network environment on the pipe network, realize the real-time acquisition of data and carry out big data analysis to netted pipeline, obtain the monitoring data of pipe network, can quick discernment and location to the trouble to real-time early warning has improved the intelligent control of pipe network.
Description
Technical Field
The disclosure relates to the technical field of pipe network monitoring, in particular to a big data acquisition, analysis, early warning and positioning system and method for an intelligent pipe network.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the progress of science and technology, the supply of living energy such as water, electricity, coal, heating and the transportation of traditional energy are generally realized by arranging a pipe network underground. The leakage of the underground pipe network can cause large-area water and gas supply or heat cut, pipe explosion accidents can occur in serious cases, the personal safety, property and life of people are greatly influenced, and the uncertain factors influencing social stability are formed, so that the underground pipe network needs to keep long-time stable and safe operation, even if leakage points occur, the leakage points can be found out in advance, accurately positioned and repaired in time, and public safety accidents are avoided.
The inventor finds that the maintenance of the existing pipe network, particularly the maintenance of the underground pipe network, is almost zero, and the existing pipe network can be excavated for maintenance when problems occur, so that the best maintenance opportunity is missed and even public safety accidents occur. The maintenance method is rough, old and heavy, the overhead pipe network mostly adopts a manual inspection mode, the underground pipe network basically excavates the road surface for inspection and positioning after the occurrence of accidents, the maintenance period is long, the cost is high, the efficiency is low, and the real-time detection, prevention and early warning of the whole pipe network cannot be realized; the accurate positioning of the fault position cannot be realized; the pressure, flow, stress, temperature, pipe network life, displacement, pH value and other data of the pipe network cannot be acquired and analyzed, so that the life of people is seriously influenced, and the intellectualization of the pipe network is imperative.
Disclosure of Invention
The intelligent pipe network big data acquisition, analysis, early warning and positioning system and method are provided, through arranging special sensing optical fibers capable of adapting to the pipe network environment and sensors such as pressure, flow, stress, temperature displacement and pH value on the pipe network, real-time data acquisition and big data analysis are realized on a net-shaped pipeline, monitoring data of the pipe network are obtained, faults such as leakage points, stress abnormity and the like can be rapidly identified and positioned, rapid repair and real-time early warning are realized, the service life of the pipe network can be monitored, and intelligent monitoring of the pipe network is improved.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
one or more embodiments provide a big data acquisition, analysis, early warning and positioning system for a smart pipe network, which comprises a cloud server, an aboveground detection device and an underground detection device, wherein the underground detection device comprises data detection optical fibers distributed along a pipeline, a sensor group is arranged on the data detection optical fibers according to a set position, the data detection optical fibers are in communication connection with the cloud server through an optical fiber monitoring and acquisition terminal, and the aboveground detection device is in communication connection with the cloud server; and the cloud server acquires and processes the detection data to realize detection positioning and early warning of the leakage point of the pipe network.
Furthermore, the leakage detection optical fiber sequentially comprises a protective outer tube, a first stainless steel protective layer, a water blocking layer, a second stainless steel protective layer, an inner sheath and an optical fiber core arranged in the middle from outside to inside.
Further, first layer stainless steel protective layer includes two-layerly, is first stainless steel wire stranding layer and second stainless steel wire stranding layer from outside to inside in proper order, and first stainless steel wire stranding layer and second stainless steel wire stranding layer are including distributing many stainless steel wire rope on the circumference respectively.
Further, the second layer stainless steel protective layer includes stainless steel weaving layer and stainless steel spiral layer from outside to inside in proper order, and stainless steel spiral layer includes many stainless steel wire spiral winding in the inner sheath outside, the stainless steel weaving layer is woven into netted and cladding in stainless steel spiral layer outside by the stainless steel wire.
Further, the sensor group comprises a first sensor group arranged on the pipeline, the first sensor group transmits data through optical fibers, and the first sensor group comprises a flowmeter, an optical fiber temperature sensor, a strain sensor and an optical fiber pressure sensor;
or the sensor group also comprises sensors which are arranged in the soil trench and used for detecting the soil trench environment, wherein the sensors comprise acid and alkalinity sensors, humidity sensors and displacement sensors.
Further, the ground detection device comprises a rainfall sensor, a wind direction sensor and a wind speed sensor;
or, the optical fiber monitoring and collecting terminal: the system comprises a sensor, a data processing unit and a data processing unit, wherein the sensor is used for simultaneously acquiring sensor signals of temperature, strain, pressure, displacement and flow of a pipe network, and demodulating, locally storing and uploading data;
or the system further comprises a data processing center, wherein the data processing center is in communication connection with the cloud server;
or the mobile terminal is in communication connection with the data processing center or the cloud server.
One or more embodiments provide a big data acquisition, analysis, early warning and positioning method for an intelligent pipe network, which comprises the following steps:
acquiring environment detection data of a pipe network layout area, and analyzing and processing the environment detection data to obtain a pipeline fault risk assessment result;
acquiring detection data of a pipe network pipeline, performing leak detection analysis by adopting a self-adaptive self-learning big data analysis method, acquiring abnormal data of pipe network operation, and determining an abnormal area;
and positioning the leakage position of the pipeline aiming at the abnormal area and the risk evaluation result, and sending an early warning signal according to the fault position.
Further, a self-adaptive self-learning big data analysis method is adopted for leak detection and analysis, and the specific steps are as follows:
acquiring the pressure, flow rate and temperature of a pipe network pipeline and preprocessing the pressure, flow rate and temperature;
and acquiring the probability of pipeline leakage of each region by adopting data processed by a zero forcing algorithm, a steepest descent algorithm, an LMS algorithm, an RLS algorithm or a blind equalization algorithm, and taking the region with high leakage probability as an abnormal region.
Further, the method further comprises a step of visual display, which specifically comprises the following steps:
drawing a pipe network map;
calibrating a key monitoring area according to a risk analysis result;
and acquiring the leakage position of the pipeline, and displaying the leakage position on a pipeline network map.
Further, the method also comprises the step of monitoring the whole life cycle of the pipe network, and specifically comprises the following steps:
setting an operation time threshold value of each section of pipe network;
accumulating the running time of each section of pipe network;
and when the running time reaches a set time threshold, sending out alarm information, and displaying the alarm information on a visual display interface of the pipe network.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) the system can acquire all data in real time, and can early warn abnormal data by analyzing the data, and simultaneously display a route map deployed by the pipe network and installation position maps of all sensors and see the running conditions of all the sensors in real time. The pipe network system is intelligently monitored through the detection data on the ground and underground, and the accuracy and timeliness of monitoring of the pipe network system are improved.
(2) This is disclosed to be changed the armor protection of optical fiber that leaks hunting into two-layer stainless steel wire rope protection, has improved the protective strength, pliability and the tensile strength of optical fiber, is favorable to the steady operation of optical fiber, has guaranteed the accurate collection of data.
(3) According to the method, the environmental data of the pipeline are detected, and the abnormal data can be early-warned through data abnormality; the position of leakage or stress abnormal position of the pipeline can be positioned, so that problems can be found and timely treatment is facilitated; the system can monitor, analyze and adjust the pressure, temperature, flow and heat loss rate of the pipeline in real time, and provides data support for energy conservation and consumption reduction; the early warning and positioning can be carried out on the settlement position; the pH value of the environment for laying the pipe network can be detected and the service life of the pipe network can be evaluated, the stability and the safety of the operation of the pipe network are effectively improved, and the problem of pain points of a smart city is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure.
Fig. 1 is a schematic diagram of a smart pipe network big data acquisition, analysis, early warning and positioning system in embodiment 1 of the present disclosure;
FIG. 2 is a schematic structural diagram of a leak detection optical fiber according to embodiment 1 of the present disclosure;
FIG. 3 is a schematic diagram of the sensor arrangement of embodiment 1 of the present disclosure on a pipeline;
FIG. 4 is a flow chart of a method of embodiment 2 of the present disclosure;
the system comprises a ground detection device 1, a ground detection device 2, a leakage detection and stress optical fiber 3, a sensor group 4, a soil ditch 51, an outer protection pipe 52, a heat preservation layer 53, a working steel pipe 6 and a sensing optical fiber;
21. the protective outer tube, 22, first stainless steel wire stranded layer, 23, second stainless steel wire stranded layer, 24, water-blocking layer, 25, stainless steel braid layer, 26, stainless steel spiral layer, 27, inner sheath, 28, optic fibre core, 31, optic fibre temperature sensor.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments in the present disclosure may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
Example 1
In the technical solution disclosed in one or more embodiments, as shown in fig. 1, a smart pipe network big data acquisition, analysis, early warning and positioning system includes a cloud server, an above-ground detection device 1 and an underground detection device, where the underground detection device includes a data detection optical fiber 2 arranged along a pipeline, a sensor group 3 is arranged on the data detection optical fiber 2 according to a set position, the data detection optical fiber 2 is in communication connection with the cloud server through an optical fiber monitoring and acquisition terminal, and the above-ground detection device 1 is in communication connection with the cloud server; and the cloud server acquires and processes the detection data to realize detection positioning and early warning of the leakage point of the pipe network.
In the embodiment, the data detection optical fibers 2 and the sensor groups 3 are distributed along the network of the underground pipe network, and meanwhile, the overground detection device 1 is arranged on the ground, so that the pipe network system can be intelligently monitored by combining the detection data on the ground and underground, and the accuracy, safety and timeliness of monitoring of the pipe network system are improved.
According to a further technical scheme, the ground detection device 1 comprises a rainfall detection sensor, a wind direction detection sensor and a wind speed sensor.
Optionally, the ground detection device 1 is arranged along the extending direction of the pipeline according to the set interval distance, so that the detection of the ground environment of the underground pipe network arrangement position can be realized, and the corrosion or aging degree of the underground pipe network caused by the influence of the ground environment can be favorably judged.
Optical fiber monitoring acquisition terminal: the system is used for simultaneously acquiring various optical fiber sensor signals of leakage points, stress, temperature, pressure, displacement, flow, pH value of a pipe network environment in a laying environment and the like of a pipe network, acquiring and processing data, and uploading the data to a cloud server provided with a big data analysis system for analysis and storage.
Specifically, the model of the optical fiber monitoring and collecting terminal can be HDTS, HDTS-1100 and the like, the optical fiber monitoring and collecting terminal provided in this embodiment can synchronously measure 1 to 24 channels, and the synchronous scanning frequency can reach 5000 Hz. The method has the characteristics of high precision, reliable data, quick data acquisition, self-learning and self-analysis of the system and the like, and can accurately distinguish the interference of the external complex environment on the acquired data.
In this embodiment, the pipe network is network structure, and wherein the data detection optic fibre 2 that lay in sensor quantity is huge, and the while collection of multichannel data can not be realized to ordinary optic fibre, for the precision and the degree of accuracy that improve the data acquisition of this embodiment, the structure of optic fibre has been improved to this embodiment, is replaced by two-layer stainless steel wire rope protection by current armor protection.
In some embodiments, as shown in fig. 2, the data detection fiber 2 comprises, in order from outside to inside, a protective outer tube 21, a first stainless steel protective layer, a water blocking layer 24, a second stainless steel protective layer, an inner sheath 27, and a fiber core 28 disposed in the middle.
This embodiment has set up two-layer stainless steel protective layer in data detection optic fibre 2's optical fiber structure, and first layer stainless steel protective layer has improved protection intensity and pliability, has stopped the cracked risk of optic fibre core 28, and the second floor stainless steel protective layer blocks electromagnetic interference and ray influence and protects optic fibre simultaneously.
Further technical scheme, first layer stainless steel protective layer includes two-layerly, is first stainless steel twist lay 22 and second stainless steel twist lay 23 from outside to inside in proper order, and first stainless steel twist lay 22 and second stainless steel twist lay 23 are including distributing many stainless steel wire rope on the circumference respectively, and every includes many stainless steel wires respectively, and the stainless steel wire rope's of first stainless steel twist lay 22 internal diameter is greater than the stainless steel wire rope's of second stainless steel twist lay 23 internal diameter.
Further technical scheme, second layer stainless steel protective layer includes stainless steel weaving layer 25 and stainless steel spiral layer 26 from outside to inside in proper order, and stainless steel spiral layer 26 includes that many stainless steel wire spiral winding are in inner sheath 27 outside, stainless steel weaving layer 25 is woven into netted and cladding in stainless steel spiral layer 26 outside by the stainless steel wire.
In the embodiment, each optical fiber comprises 8 parts, the outermost part is a PE outer protective pipe which is a protective outer pipe 21, the service life of the material can reach 50 years, the first layer inside the material is stainless steel wire ropes, the total number of the stainless steel wire ropes is 8, the stainless steel wire ropes are uniformly distributed around the round optical fiber, and each stainless steel wire rope is formed by twisting 16-48 stainless steel wire ropes, so that the protective strength and the flexibility are greatly improved, and the risk of breaking the optical fiber core 28 is avoided; the second layer inside is 8 stainless steel wire ropes which are uniformly distributed around the round optical fiber, each stainless steel wire rope is formed by twisting 8-32 stainless steel wires, and the optical fiber core 28 is protected by the second layer; the third layer inside is a water-resistant layer to prevent water seepage caused by the cutting of the PE layer; the fourth inward layer is a stainless steel braided layer 25 which blocks electromagnetic interference and ray influence and protects the optical fiber; the fifth layer is a stainless steel spiral layer 26 which blocks electromagnetic interference and ray influence and protects the optical fiber; the sixth layer is a PE inner sheath 27 which is waterproof, reduces friction between the optical fiber and other outer layers and protects the optical fiber; the innermost layer is the fiber core 28.
According to a further technical scheme, the sensor group 3 comprises a first sensor group arranged on the pipeline, the first sensor group transmits data through the data detection optical fiber 2, and sensors on the pipeline comprise but are not limited to a flowmeter, an optical fiber temperature sensor 31, a strain sensor, an optical fiber pressure sensor and an optical fiber displacement sensor.
In a further technical scheme, the sensor group 3 further comprises sensors arranged in the soil trench 4 for detecting the soil trench environment, including but not limited to a ph sensor, a humidity sensor and a displacement sensor. The environmental data of the soil trench 4 are detected, the service life of the pipeline can be pre-judged, advance prediction is realized, and quick positioning can be carried out according to the pre-judged result when leakage detection occurs.
Optionally, the optical fiber temperature sensor includes a housing and an internal circuit, the housing is made of stainless steel 304, the temperature sensitive element and the transmission line inside the sensor are all single-mode optical fibers, and the optical fiber temperature sensor monitors and acquires medium temperature change conditions and real-time temperature data in a pipe network for a long time, and can stably work for 40 years for a long time.
The pressure sensitive element and the transmission line in the optical fiber pressure sensor are all single-mode optical fibers, are uncharged, are not influenced by electromagnetic interference and rays, monitor and collect medium pressure change conditions and real-time pressure data in a pipe network for a long time, and can stably work for 40 years for a long time.
Optionally, the optical fiber temperature sensor 31 may also be an optical fiber fluorescence temperature sensor.
The fiber grating adopted by the fiber displacement sensor is taken as a sensing element and comprises a fiber cladding, a movable TFF I, a white light diode and the like.
The optical fiber displacement sensor can adopt 304 stainless steel and temperature self-compensation double-optical fiber packaging technology, and has the characteristics of high resolution, high precision, stable performance, corrosion resistance, long service life, simple and convenient installation and the like. The method is suitable for monitoring the displacement deformation change conditions and data acquisition of pipe networks with different depths in a severe environment for a long time.
The optical fiber temperature sensor 31 is arranged on the pipeline and transmits data through the data detection optical fiber 2, the concrete arrangement mode of the arrangement on the pipeline can be as shown in fig. 3, the pipeline comprises an outer protection pipe 51, a heat insulation layer 52 and a working steel pipe 53, an opening pipe penetrating through the outer protection pipe 51, the heat insulation layer 52 and the working steel pipe 53 is arranged, a sealing structure and a locking mechanism are arranged at the opening, the sensitive part of the optical fiber temperature sensor 31 extends into the opening pipe to detect the data, and the data transmission end of the optical fiber temperature sensor 31 is connected to a cloud server.
The sensing optical fiber 6 arranged at the tail end of the optical fiber temperature sensor 31 is a sensor-carried optical fiber which is connected with the data detection optical fiber 2.
The cloud server is provided with a big data acquisition and analysis system, and big data processing is realized on the acquired data.
The technical scheme further comprises a data processing center for realizing optical fiber monitoring and data processing. Taking a heat supply pipeline as an example, the data processing center is arranged in a heat exchange station or an independent control room and is a local server, the data processing center collects all data for processing, and uploads all the data to a cloud server provided with a big data collection and analysis system through the internet or 5G for data analysis and storage.
The data of the cloud server is transmitted to a data center electronic screen through the Internet or a 5G network for display, and maintenance personnel in corresponding areas are arranged for field maintenance of the pipeline according to analysis early warning data sent by the cloud server.
The technical scheme is that the system further comprises a mobile terminal, wherein the mobile terminal is in communication connection with a cloud server or a data processing center; optionally, the mobile terminal may be a smart phone, a tablet computer, or a computer. Specifically, the intelligent mobile phone can be used as a maintenance worker, and the data center sends early warning information of pipeline monitoring to the mobile terminal of related personnel after data early warning occurs, so that rapid maintenance is realized.
The early warning information comprises the position of pipeline leakage, pipeline number, fault condition description, damage to the pipeline caused by external stress on the pipeline, heat loss rate, pressure, temperature, flow data abnormity and the like.
Example 2
The embodiment provides a big data acquisition, analysis, early warning and positioning method for an intelligent pipe network, as shown in fig. 4, the method can be implemented in a cloud server, and comprises the following steps:
step 1, acquiring environment detection data of a pipe network layout area, and analyzing and processing the environment detection data to obtain a pipeline fault risk assessment result;
and 3, positioning the leakage position of the pipeline according to the abnormal area and the risk evaluation result, and sending an early warning signal according to the fault position.
In the step 1, acquiring environment detection data of a pipe network layout area, and analyzing and processing the environment detection data to obtain a pipeline fault risk assessment result; the influence of the external environment on the service life of the pipeline can be analyzed and dealt with in time.
And 2, acquiring detection data of the pipeline of the pipe network, analyzing leakage and stress data by adopting a self-adaptive self-learning big data analysis method in combination with a pipeline fault risk evaluation result, removing invalid data, early warning abnormal operation data of the pipe network, and determining an abnormal area and abnormal position positioning.
The detection data of the pipe network are acquired by acquiring data information such as temperature, pressure and flow inside the pipeline, the temperature, the pressure and the like inside the pipeline are increased and decreased in time according to actual conditions, and unnecessary energy consumption is reduced.
The service life of the pipeline is evaluated by analyzing the environmental data according to the pipeline; the data collected by the sensing optical fiber is analyzed, the abnormal data is early warned, and meanwhile, the fault position is positioned, so that data support is provided for timely and accurate maintenance; the collected data such as temperature, pressure, flow and the like are monitored and analyzed, and the heat loss rate and whether each data is abnormal or not are displayed in real time. The system can realize leakage detection, positioning and early warning of the pipe network with the net structure, find problems in time, accurately position fault points, greatly improve the stability, safety and maintenance work efficiency of the pipe network operation, reduce the maintenance cost and reduce the time for stopping the operation of the pipeline.
Optionally, the environment detection data includes overground environment detection data of the pipeline laying network and environment detection data of the soil trench 4.
Optionally, the environment detection data of the soil trench 4 includes the ph value and humidity of the soil and the displacement of the pipeline. Optionally, the above-ground environment detection data may include rainfall, wind direction, and wind speed detection data.
Specifically, the correlation between the environmental detection data and the damage or corrosion of the pipeline may be analyzed by using a correlation analysis method, and a change curve of the damage or corrosion of the pipeline may be obtained according to the environmental detection data. And carrying out risk analysis on the pipe network of each area according to the change curve to obtain a risk analysis result.
Further, a key monitoring area is defined according to the risk analysis result, and key monitoring is performed on the key monitoring area by improving the data acquisition frequency.
In the step 2, a self-adaptive self-learning big data analysis method is adopted for leakage detection analysis, so that self-adaptive self-learning self-resolution of leakage detection can be realized. Underground pipe networks are complicated and complicated, underground environments are changeable, and other pipe networks or underground hot springs and the like can mislead collected data. The method is characterized in that whether the temperature of the underground hot spring or other pipe networks is increased to cause big data abnormity is distinguished according to the heat supply time period of the heat supply pipe network as a standard. The self-adaptive self-learning big data analysis method effectively avoids the occurrence of false alarms.
Specifically, the self-adaptive self-learning big data analysis method may adopt a zero forcing algorithm, a steepest descent algorithm, an LMS algorithm, an RLS algorithm, or a blind equalization algorithm, and the specific steps may be as follows:
and 21, acquiring and preprocessing the detection data of the pipe network pipeline, and preprocessing and converting all the acquired data in a data processing center.
The detection data of the pipeline comprises data such as pipeline pressure, flow, temperature and the like.
And step 22, transmitting all data to a cloud server provided with a big data acquisition and analysis system through the Internet or a 5G network for data analysis. And acquiring the probability of pipeline leakage of each region by adopting data processed by a zero forcing algorithm, a steepest descent algorithm, an LMS algorithm, an RLS algorithm or a blind equalization algorithm, and taking the region with high leakage probability as an abnormal region.
The data after the analysis and the processing can be displayed, early-warned and positioned on an electronic screen of the data center in various chart forms.
Further, the method also comprises the process of monitoring the stress and the temperature of the pipeline in real time, drawing a stress change curve and a temperature change curve according to the stress and temperature data acquired in real time, and taking the area of which the temperature and the stress exceed the safety range as a pre-judgment area; and comparing the pre-judging area with the abnormal area, taking the overlapping area as a serious abnormal area, and taking the other areas of the pre-judging area and the abnormal area as general abnormal areas.
Further technical solution, the method further includes a step of visual display, which may specifically be as follows:
step 41, drawing a pipe network map;
and the pipeline deployment map can be drawn on the Google map according to the actual pipeline trend when the data detection optical fibers 2 are deployed along the pipe network.
Step 42, calibrating a key monitoring area according to the risk analysis result;
the method can be realized by not calibrating the high-risk area and the low-risk area through colors, or establishing a hyperlink to associate the risk analysis result of the area with the area, and a user can obtain the risk analysis result through request or click.
And 43, acquiring the leakage position of the pipeline, and displaying the leakage position on a pipeline network map.
The principle of leak point positioning: when the data detection optical fiber 2 is deployed along the pipeline, a pipeline deployment map is drawn on the Google map according to the actual pipeline trend, when a leakage point occurs, the temperature of the part rises suddenly, the position of the optical fiber can be determined in the map by the aid of a positioning function of the optical fiber, the error is 0.2-2 m, and the error within 12 m can be accepted because one heat preservation pipe is 12 m.
The further technical scheme also comprises the step of monitoring the whole life cycle of the pipe network, and specifically comprises the following steps:
and step 54, sending out early warning reminding when the maximum time threshold is reduced to be equal to the set early warning threshold, and displaying the early warning reminding on a visual display interface of the pipe network.
According to the scheme, the service life early warning reminding of the pipeline can be realized, the pipeline is transmitted to the big data analysis system for the factory time through the wms system of a company when the pipeline leaves a factory, then the service life of the pipeline is counted down in the big data system, for example, the service life of the pipeline is 30 years, the system reminds an owner that the service life of the pipeline is remained for 5 years by 25 years, and the owner needs to replace the pipeline in time.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. Big data acquisition analysis early warning positioning system of wisdom pipe network, characterized by: the system comprises a cloud server, an aboveground detection device and an underground detection device, wherein the underground detection device comprises data detection optical fibers distributed along a pipeline, a sensor group is arranged on the data detection optical fibers according to a set position, the data detection optical fibers are in communication connection with the cloud server through an optical fiber monitoring and collecting terminal, and the aboveground detection device is in communication connection with the cloud server; and the cloud server acquires and processes the detection data to realize detection positioning and early warning of the leakage point of the pipe network.
2. The intelligent pipe network big data acquisition, analysis, early warning and positioning system as claimed in claim 1, wherein: the leakage detection optical fiber sequentially comprises a protective outer tube, a first stainless steel protective layer, a water blocking layer, a second stainless steel protective layer, an inner sheath and an optical fiber core arranged in the middle from outside to inside.
3. The intelligent pipe network big data acquisition, analysis, early warning and positioning system as claimed in claim 2, wherein: the first layer stainless steel protective layer comprises two layers, and is a first stainless steel wire stranded layer and a second stainless steel wire stranded layer from outside to inside in sequence, and the first stainless steel wire stranded layer and the second stainless steel wire stranded layer respectively comprise a plurality of stainless steel wire ropes distributed on the circumference.
4. The intelligent pipe network big data acquisition, analysis, early warning and positioning system as claimed in claim 2, wherein: the stainless steel protective layer of second layer includes stainless steel weaving layer and stainless steel spiral layer from outside to inside in proper order, and the stainless steel spiral layer includes that many stainless steel wire spiral winding are in the inner sheath outside, the stainless steel weaving layer is woven into netted and cladding in stainless steel spiral layer outside by the stainless steel wire.
5. The intelligent pipe network big data acquisition, analysis, early warning and positioning system as claimed in claim 1, wherein: the sensor group comprises a first sensor group arranged on the pipeline, the first sensor group transmits data through optical fibers, and the first sensor group comprises a flowmeter, an optical fiber temperature sensor, a strain sensor and an optical fiber pressure sensor;
or the sensor group also comprises sensors which are arranged in the soil trench and used for detecting the soil trench environment, wherein the sensors comprise acid and alkalinity sensors, humidity sensors and displacement sensors.
6. The intelligent pipe network big data acquisition, analysis, early warning and positioning system as claimed in claim 1, wherein: the ground detection device comprises a rainfall sensor, a wind direction sensor and a wind speed sensor;
or, the optical fiber monitoring and collecting terminal: the system comprises a sensor, a data processing unit and a data processing unit, wherein the sensor is used for simultaneously acquiring sensor signals of temperature, strain, pressure, displacement and flow of a pipe network, and demodulating, locally storing and uploading data;
or the system further comprises a data processing center, wherein the data processing center is in communication connection with the cloud server;
or the mobile terminal is in communication connection with the data processing center or the cloud server.
7. The big data acquisition, analysis, early warning and positioning method for the intelligent pipe network is characterized by comprising the following steps of:
acquiring environment detection data of a pipe network layout area, and analyzing and processing the environment detection data to obtain a pipeline fault risk assessment result;
acquiring detection data of a pipe network pipeline, performing leak detection analysis by adopting a self-adaptive self-learning big data analysis method, acquiring abnormal data of pipe network operation, and determining an abnormal area;
and positioning the leakage position of the pipeline aiming at the abnormal area and the risk evaluation result, and sending an early warning signal according to the fault position.
8. The intelligent pipe network big data acquisition, analysis, early warning and positioning method as claimed in claim 7, wherein:
the method adopts a self-adaptive self-learning big data analysis method to carry out leak detection analysis, and comprises the following specific steps:
acquiring the pressure, flow rate and temperature of a pipe network pipeline and preprocessing the pressure, flow rate and temperature;
and acquiring the probability of pipeline leakage of each region by adopting data processed by a zero forcing algorithm, a steepest descent algorithm, an LMS algorithm, an RLS algorithm or a blind equalization algorithm, and taking the region with high leakage probability as an abnormal region.
9. The intelligent pipe network big data acquisition, analysis, early warning and positioning method as claimed in claim 7, wherein:
the method further comprises a step of visual display, which is specifically as follows:
drawing a pipe network map;
calibrating a key monitoring area according to a risk analysis result;
and acquiring the leakage position of the pipeline, and displaying the leakage position on a pipeline network map.
10. The intelligent pipe network big data acquisition, analysis, early warning and positioning method as claimed in claim 7, wherein: the method also comprises the steps of monitoring the whole life cycle of the pipe network, and specifically comprises the following steps:
setting an operation time threshold value of each section of pipe network;
accumulating the running time of each section of pipe network;
and when the running time reaches a set time threshold, sending out alarm information, and displaying the alarm information on a visual display interface of the pipe network.
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