Large-scale public building pipeline quality real-time monitoring and early warning system

Abstract

The invention relates to the technical field of building pipeline monitoring, in particular to a large-scale public building pipeline quality real-time monitoring and early warning system, and solves the problems that when an existing large-scale public building pipeline is applied, due to the fact that the building volume is large, pipeline arrangement is complex, various data during operation of the building pipeline are inconvenient to obtain, the data size is too large, and the working efficiency is high. The system comprises a distributed data sensing system, a dual-channel data interaction system, a multi-source fusion monitoring platform and a multi-stage early warning platform, wherein the distributed data sensing system is composed of a multi-source deployment module and a terminal integration module; and the multi-source deployment module is used for deploying a multi-point sensor and identifying static and dynamic parameters in a pipeline in real time. According to the invention, multi-point monitoring and data acquisition are carried out on the large-scale public building pipeline, and the data are classified and processed, so that abnormal data and fault points can be effectively determined, and early warning can be carried out in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building pipeline monitoring, in particular to a large public building pipeline quality real-time monitoring and early warning system. BACKGROUND

[0002] The underground comprehensive pipe gallery refers to a structure and auxiliary facilities arranged below the ground of a city for accommodating two or more public facility pipelines or professional pipelines. The public facility pipelines include power, communication (including monitoring lines), radio and television, water supply, drainage, heat, gas, fire-fighting pipelines, traffic signals, emergency optical cables, etc. The auxiliary facilities include drainage, ventilation, lighting, electrical, communication, fire-fighting, safety monitoring systems and monitoring management rooms for maintaining the normal operation of the pipe gallery. The public facility pipelines are incorporated into the pipe gallery, which avoids the trouble of repeated excavation of roads due to pipeline burying or maintenance, and at the same time, avoids the corrosion of the pipelines by soil and underground water, thereby prolonging the service life of the pipelines.

[0003] In the application of the existing large public building pipelines, due to the large building volume, the pipeline arrangement is complex, it is not convenient to obtain various data during the operation of the building pipelines, and the data volume is too large, which is not conducive to timely early warning when an abnormality or failure occurs. Therefore, it does not meet the existing needs, and for this purpose, a large public building pipeline quality real-time monitoring and early warning system is proposed. SUMMARY

[0004] The purpose of the present application is to provide a large public building pipeline quality real-time monitoring and early warning system to solve the problem that the existing large public building pipelines are difficult to obtain various data during the operation of the building pipelines due to the large building volume, the complex pipeline arrangement, and the large data volume, which is not conducive to timely early warning when an abnormality or failure occurs.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a large public building pipeline quality real-time monitoring and early warning system, comprising a distributed data sensing system, a dual-channel data interaction system, a multi-source fusion monitoring platform and a multi-level early warning platform:

[0006] The distributed data sensing system is composed of a multi-source deployment module and a terminal integration module. The multi-source deployment module is used to deploy multi-point sensors and to identify static and dynamic parameters in the pipeline in real time. The multi-source deployment module includes a structure safety monitoring unit and an operation state monitoring unit. The terminal integration module is used to integrate and locally preprocess the static and dynamic data of the multi-source sensors and supports multi-interface access of RS485, RS232, AI and DI.

[0007] The double-channel data interaction system is used for near and far interaction transmission of building pipeline operation data, and comprises a near distance transmission module and a remote interaction module.

[0008] The multi-source fusion monitoring platform is used for integrating, calculating and classifying the preprocessed building pipeline data, and establishing a model for the processed data, and comprises a data integration module, a data processing module, a multi-modal pipeline monitoring model and a multi-modal pipeline prediction model.

[0009] The multi-level early warning platform is used for visual processing and automatic early warning operation of the data of the building pipeline according to the multi-modal pipeline monitoring model and the multi-modal pipeline prediction model.

[0010] Preferably, the structure safety monitoring unit is used for deploying sensors between the building and the pipeline and performing static parameter monitoring and acquisition, and comprises an inclination sensor, a hydrostatic level, a crack meter and a temperature sensor, and the operation state monitoring unit is used for deploying sensors inside the pipeline and performing dynamic parameter monitoring and acquisition, and comprises a pressure sensor, a methane detector, a liquid level meter, a flow sensor and an optical fiber strain sensor.

[0011] Preferably, the terminal integration module is composed of an edge computing gateway and a multi-port data transmission unit, the multi-port data transmission unit is used for synchronously receiving multi-source sensor data and transmitting the data processed by the edge computing gateway, and the edge computing gateway is used for inputting the multi-source sensor data received by the multi-port data transmission unit and performing localized data processing.

[0012] Preferably, the near distance transmission module and the remote interaction module are both composed of an optical fiber communication channel and a wireless transmission channel, the optical fiber communication channel comprises an optical fiber path, an amplifier, a terminal box, a signal converter and a WDM, and the wireless transmission channel comprises a modem, a switch, a wireless router, an antenna and a wireless bridge.

[0013] Preferably, the data integration module and the data processing module are composed of a data processing center, a local server and a cloud server, the local server and the cloud server are both provided with a database, and the data processing center performs classified calculation on the building pipeline data based on the local server and the cloud server and synchronously inputs the data into the database.

[0014] Preferably, the multi-modal pipeline prediction model comprises data trend change early warning and data fusion risk early warning, the data trend change early warning comprises:

[0015]

[0016] wherein ΔS is the pipeline length increment per unit time, ∑ is the total increment function per unit time, n is the number of summed terms, t is the time, and the pre-warning threshold is reached and triggers a pre-warning when ΔS > 0.5 mm / day;

[0017] The calculation formula of the data fusion risk pre-warning is:

[0018] R = w1·f(P) + w2·g(T) + w3·h(C);

[0019] w1 + w2 + w3 = 1;

[0020] wherein R is the risk evaluation value, w1, w2 and w3 are weights, f(P), g(T) and h(C) are normalized functions of pressure, temperature and corrosion rate respectively;

[0021] f(P) = (P - P max ) / (P min - P min );

[0022] wherein P is the actual pressure value, P max is the upper limit value of pressure pre-warning, and P min is the lower limit value of pressure safety;

[0023] g(T) = (T - T max ) / (T min - T min );

[0024] wherein T is the actual temperature value, T max is the upper limit value of temperature pre-warning, and T min is the lower limit value of temperature safety;

[0025] h(C) = (C - C max ) / (C min - C s );

[0026] wherein C is the corrosion rate value, C s is the upper limit value of corrosion rate pre-warning, and C s is the lower limit value of corrosion rate safety;

[0027] when 0 < R ≤ 0.25, the risk level is determined to be one, and the action of routine monitoring is performed;

[0028] when 0.25 < R ≤ 0.5, the risk level is determined to be two, and the action of strengthened inspection and real-time monitoring of sensor data is performed;

[0029] when 0.5 < R ≤ 0.75, the risk level is determined to be three, and the action of triggering an alarm and manual intervention is performed;

[0030] When 0.75 < R ≤ 1, the risk level is determined as four, the action emergency shutdown is performed, and the emergency plan is prepared.

[0031] Preferably, the multi-level early warning platform comprises an early warning threshold control module, an early warning grading module and a multi-level early warning pushing module.

[0032] Preferably, the inside of the early warning threshold control module is provided with a multi-channel threshold control switch, the early warning threshold control module adjusts the early warning threshold height through the multi-channel threshold control switch, and the early warning grading module grades according to the amount of exceeding the early warning threshold of the building pipeline data.

[0033] Preferably, the early warning threshold includes but is not limited to stress threshold and leakage risk threshold.

[0034] The formula of the stress threshold W is:

[0035] W = σ s × k s ;

[0036] Where σ s is the yield strength of the material, k s is the reduction factor, and k s takes a value of 0.65-0.85.

[0037] The formula for calculating the leakage risk threshold is:

[0038] P 警戒 = P 设计 × 0.9;

[0039] P 行动 = P 设计 × 0.75

[0040] Where P 警戒 is the alert pressure, P 设计 is the design pressure, and P 行动 is the action pressure.

[0041] P is the real-time pressure value, when 0 < P ≤ 0.75P 设计 , the risk level is one, and the action is regular monitoring.

[0042] When 0.75P 设计 < P ≤ 0.9P 设计 , the risk level is two, the action is manual inspection of the sealing and search for the leakage point.

[0043] When 0.9P 设计 < P, the risk level is three, the action is emergency pressure reduction of the pipeline and execution of safety pressure relief.

[0044] Preferably, the multi-level early warning push module is used for automatic pushing and visual processing of the graded building pipeline early warning signals, and the multi-level early warning push module is composed of an early warning platform screen, a multi-level sound and light alarm unit and an emergency platform.

[0045] Compared with the prior art, the present application has the following advantages:

[0046] 1. The multi-source deployment module in the distributed data sensing system is used for deploying sensors at multiple points and identifying static and dynamic parameters in the pipeline in real time, wherein the sensors are deployed between the building and the pipeline through the structure safety monitoring unit and the running state monitoring unit, and static and dynamic parameter monitoring and acquisition are performed, and then the terminal integration module is used for integrating and locally preprocessing the static and dynamic data of the building pipeline and transmitting the data to the double-channel data interaction system, and then the double-channel data interaction system performs near and far interaction transmission operations on the building pipeline running data through the near distance transmission module and the remote interaction module.

[0047] 2. The near distance transmission module and the remote interaction module are both composed of an optical fiber communication channel and a wireless transmission channel, which meets the real-time transmission of the static and dynamic data of the building pipeline in multiple ways, avoids interference and interruption of data transmission, the multi-source fusion monitoring platform integrates, calculates and classifies the preprocessed building pipeline data, establishes a model for the processed data, and finally performs visual processing and automatic early warning operations on the data of the building pipeline through the multi-level early warning platform multi-modal pipeline monitoring model and the multi-modal pipeline prediction model, wherein the data exceeding the threshold is automatically graded through the early warning threshold control module, the early warning grading module and the multi-level early warning push module, and the corresponding level of early warning operation is started according to the grading size. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 It is a structural schematic diagram of the whole application;

[0049] Figure 2 It is a flowchart of the whole application;

[0050] Figure 3 It is a flowchart of the monitoring and early warning system of the application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments.

[0052] Please refer to Figure 1The application provides a large public building pipeline quality real-time monitoring and early warning system, which comprises a distributed data sensing system, a double-channel data interaction system, a multi-source fusion monitoring platform and a multi-level early warning platform.

[0053] Please refer to Figure 1 and Figure 2 The distributed data sensing system is composed of a multi-source deployment module and a terminal integration module, the multi-source deployment module comprises a structure safety monitoring unit and an operation state monitoring unit, the multi-source deployment module is used for deploying sensors at multiple positions and identifying static and dynamic parameters in the pipeline in real time.

[0054] The structure safety monitoring unit is used for deploying sensors between the building and the pipeline and performing static parameter monitoring and acquisition, and comprises an inclination sensor, a static level gauge, a crack meter and a temperature sensor; the operation state monitoring unit is used for deploying sensors in the pipeline and performing dynamic parameter monitoring and acquisition, and comprises a pressure sensor, a methane detector, a liquid level meter, a flow sensor and an optical fiber strain sensor; the inclination sensor, the static level gauge, the crack meter and the temperature sensor are used for monitoring the ground settlement of the building pipeline, the pipeline inclination, the crack expansion rate of the bearing wall and the pipeline temperature in real time respectively; and the pressure sensor, the methane detector, the liquid level meter, the flow sensor and the optical fiber strain sensor are used for monitoring the pipeline pressure, the methane concentration, the liquid level, the gas-liquid conveying flow and the internal force distribution of the concrete structure of the building pipeline in real time respectively.

[0055] The terminal integration module is used for integrating and locally preprocessing dynamic and static data of the multi-source sensors and supporting multi-interface access of RS485, RS232, AI and DI, and the terminal integration module is composed of an edge computing gateway and a multi-port data transmission unit; the multi-port data transmission unit is used for synchronously receiving multi-source sensor data and transmitting data processed by the edge computing gateway; and the edge computing gateway is used for recording the multi-source sensor data received by the multi-port data transmission unit and performing localized data processing.

[0056] Please refer to Figure 1 and Figure 2The dual-channel data interaction system is used for near and far interaction transmission of building pipeline operation data, and comprises a near transmission module and a remote interaction module, both of which are composed of an optical fiber communication channel and a wireless transmission channel, the optical fiber communication channel comprises an optical fiber path, an amplifier, a terminal box, a signal converter and a WDM, and the wireless transmission channel comprises a modem, a switch, a wireless router, an antenna and a wireless bridge, through the near transmission module and the remote interaction module, real-time transmission of various modes of static and dynamic data of building pipelines is met, and data transmission is prevented from being interfered and interrupted.

[0057] Please refer to Figure 1 and Figure 2 The multi-source fusion monitoring platform is used for integrating, calculating and classifying the preprocessed building pipeline data and establishing a model for the processed data, comprising a data integration module, a data processing module, a multi-modal pipeline monitoring model and a multi-modal pipeline prediction model, the data integration module and the data processing module are composed of a data processing center, a local server and a cloud server, the local server and the cloud server are both provided with a database, and the data processing center classifies and calculates the building pipeline data based on the local server and the cloud server and synchronizes them into the database.

[0058] The multi-modal pipeline prediction model comprises data trend change early warning and data fusion risk early warning, and the data trend change early warning is:

[0059]

[0060] Wherein, ΔS is the pipeline length increment in unit time, ∑ is the total increment function in a single time, n is the number of summation terms, and t is time, when ΔS>0.5mm / day, the early warning threshold is reached and early warning is triggered.

[0061] The calculation formula of the data fusion risk early warning is:

[0062] R=w1·f(P)+w2·g(T)+w3·h(C);

[0063] w1+w2+w3=1;

[0064] Wherein, R is the risk evaluation value, w1, w2 and w3 are all weights, f(P), g(T) and h(C) are respectively normalized functions of pressure, temperature and corrosion rate;

[0065] f(P)=(P-P min ) / (P max -P min );

[0066] Wherein, P is the actual pressure value, P max is the upper limit value of pressure early warning, and P minthe lower limit of the safety pressure value;

[0067] g(T) = (T - T min ) / (T max - T min );

[0068] wherein T is the actual temperature value, T max is the upper limit of the temperature warning value, and T min is the lower limit of the safety temperature value;

[0069] h(C) = (C - C min ) / (C max - C min );

[0070] wherein C is the corrosion rate value, C max is the upper limit of the corrosion rate warning value, and C min is the lower limit of the safety corrosion rate value;

[0071] when 0 < R ≤ 0.25, the risk level is determined to be one, and the action of routine monitoring is performed;

[0072] when 0.25 < R ≤ 0.5, the risk level is determined to be two, and the action of enhanced inspection and real-time monitoring of sensor data is performed;

[0073] when 0.5 < R ≤ 0.75, the risk level is determined to be three, and the action of triggering an alarm and manual intervention is performed;

[0074] when 0.75 < R ≤ 1, the risk level is determined to be four, and the action of emergency shutdown and preparation of emergency plans is performed;

[0075] see Figure 1 and Figure 2 , the multi-level warning platform is used for visual processing and automatic warning operation of building pipeline data according to a multi-modal pipeline monitoring model and a multi-modal pipeline prediction model, and the multi-level warning platform comprises a warning threshold control module, a warning grading module and a multi-level warning pushing module, the inside of the warning threshold control module is provided with a multi-channel threshold control switch, the warning threshold control module adjusts the warning threshold height through the multi-channel threshold control switch, the warning grading module grades according to the amount of building pipeline data exceeding the warning threshold, and the multi-level warning pushing module is used for automatically pushing and visually processing the graded building pipeline warning signals, and the multi-level warning pushing module is composed of a warning platform screen, a multi-level sound and light alarm unit and an emergency platform, and the data exceeding the threshold is automatically graded and the corresponding level of warning is started according to the grading size through the warning threshold control module, the warning grading module and the multi-level warning pushing module in sequence;

[0076] The warning threshold includes but is not limited to the stress threshold and the leakage risk threshold, and the formula of the stress threshold W is:

[0077] W = σ s × k s ;

[0078] where σ s is the yield strength of the material, k s is the reduction factor, k s takes the value 0.65-0.85;

[0079] The formula for calculating the leakage risk threshold is:

[0080] P 警戒 = P 设计 × 0.9;

[0081] P 行动 = P 设计 × 0.75

[0082] where P 警戒 is the alert pressure, P 设计 is the design pressure, and P 行动 is the action pressure;

[0083] P is the real-time pressure value. When 0 < P ≤ 0.75P 设计 , the corresponding risk level is one, and the action is regular monitoring;

[0084] When 0.75P 设计 < P ≤ 0.9P 设计 , the corresponding risk level is two, and the action is manual inspection of the sealing and search for the leakage point;

[0085] When 0.9P 设计 < P, the corresponding risk level is three, and the action is emergency pressure reduction of the pipeline and execution of the safety pressure relief.

[0086] Please refer to Figure 3 , in summary, the multi-source deployment module in the distributed data sensing system is used to deploy sensors at multiple points and to identify static and dynamic parameters in the pipeline in real time. The structural safety monitoring unit and the running state monitoring unit are used to deploy sensors between the building and the pipeline and to monitor and acquire static and dynamic parameters. The inclination sensor, the static level instrument, the crack meter, and the temperature sensor are used to monitor the ground settlement, the pipeline inclination, the crack expansion rate of the bearing wall, and the pipeline temperature in real time, respectively. The pressure sensor, the methane detector, the liquid level meter, the flow sensor, and the optical fiber strain sensor are used to monitor the pipeline pressure, the methane concentration, the liquid height, the gas-liquid conveying flow, and the internal force distribution in the concrete structure in real time, respectively.

[0087] And then the terminal integration module is integrated and locally preprocessed to the static and dynamic data of the building pipeline and transmitted to the double-channel data interaction system, and then the double-channel data interaction system is operated by the near distance transmission module and the remote interaction module to the near and far interaction transmission of the building pipeline operation data, the near distance transmission module and the remote interaction module are composed of optical fiber communication channel and wireless transmission channel, which meets the real-time transmission of the static and dynamic data of the building pipeline in multiple ways, avoids the interference and interruption of data transmission, the multi-source fusion monitoring platform integrates, calculates and classifies the preprocessed building pipeline data and establishes a model for the processed data, and finally the multi-level early warning platform visually processes and automatically warns the data of the building pipeline through the multi-modal pipeline monitoring model and the multi-modal pipeline prediction model, wherein the data exceeding the threshold is automatically classified through the early warning threshold control module, the early warning grading module and the multi-level early warning pushing module, and the corresponding level of early warning operation is started according to the size of the classification.

[0088] It will be obvious to a person skilled in the art that the application is not limited to the details of the above-described exemplary embodiments and can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being defined by the appended claims rather than by the above description, and it is therefore intended that all changes and modifications that fall within the meaning and range of equivalency of the elements of the claims are encompassed by the application. No figure reference in the claims should be considered limiting of the claims to which they relate.

Claims

1. A large public building pipeline quality real-time monitoring and early warning system, characterized in that: The system comprises a distributed data sensing system, a dual-channel data interaction system, a multi-source fusion monitoring platform and a multi-level early warning platform. The distributed data sensing system is composed of a multi-source deployment module and a terminal integration module. The dual-channel data interaction system can transmit data of the building pipeline in near and remote ways. The multi-source fusion monitoring platform integrates, calculates and classifies the preprocessed building pipeline data and establishes a model for the processed data. The multi-level early warning platform can visually process and automatically warn the data of the building pipeline according to the multi-modal pipeline monitoring model and the multi-modal pipeline prediction model.

2. The real-time monitoring and early warning system for pipeline quality of large public buildings according to claim 1, characterized in that: The structure safety monitoring unit is used for deploying sensors between the building and the pipeline and monitoring and acquiring static parameters.

3. The real-time monitoring and early warning system for pipeline quality of large public buildings according to claim 1, characterized in that: The terminal integration module is composed of an edge computing gateway and a multi-port data transmission unit.

4. The large public building pipeline quality real-time monitoring and early warning system according to claim 1, characterized in that: The near-distance transmission module and the remote interaction module are both composed of an optical fiber communication channel and a wireless transmission channel.

5. The large public building pipeline quality real-time monitoring and early warning system according to claim 1, characterized in that: The data integration module and the data processing module are composed of a data processing center, a local server and a cloud server.

6. The large public building pipeline quality real-time monitoring and early warning system according to claim 5, characterized in that: The multi-modal pipeline prediction model comprises data trend change early warning and data fusion risk early warning. The data fusion risk early warning formula is R = w1·f(P) + w2·g(T) + w3·h(C); w1 + w2 + w3 = 1. ​ ​ ​ Wherein R is the risk assessment value, w1, w2 and w3 are weights, f(P), g(T) and h(C) are normalization functions of pressure, temperature and corrosion rate respectively; f(P) = (P - P min ) / (P max - P min ); where P is the actual pressure value, P max is the pressure warning upper limit value, P min is the pressure safety lower limit value; g(T) = (T - T min ) / (T max - T min ); Wherein T is the actual temperature value, T max is the temperature warning upper limit value, T min is the temperature safety lower limit value; h(C) = (C - C min ) / (C max -C min ); where C is the corrosion rate value, C max is the corrosion rate early warning upper limit value, C min is the corrosion rate safety lower limit value; When 0 < R < 0.25, the risk level is determined to be one, and the action of regular monitoring is executed; When 0.25 < R < 0.5, the risk level is determined to be two, and the action of strengthening inspection and monitoring sensor data in real time is executed; When 0.5 < R < 0.75, the risk level is determined to be three, and the action of triggering alarm and manual intervention is executed; When 0.75 < R < 1, the risk level is determined to be four, and the action of emergency shutdown and preparation of emergency plan is executed.

7. The large public building pipeline quality real-time monitoring and early warning system according to claim 6, characterized in that: The multi-level early warning platform comprises a warning threshold control module, a warning grading module and a multi-level early warning pushing module.

8. The large public building pipeline quality real-time monitoring and early warning system according to claim 7, characterized in that: The inside of the warning threshold control module is provided with a multi-channel threshold control switch, the warning threshold control module adjusts the warning threshold height through the multi-channel threshold control switch, and the warning grading module grades according to the amount of building pipeline data exceeding the warning threshold.

9. The real-time monitoring and early warning system for pipeline quality of large public buildings according to claim 8, characterized in that: The warning threshold includes but is not limited to stress threshold and leakage risk threshold, the formula of stress threshold W is: W = σ s x k s ; where σ s is the yield strength of the material, k s is a reduction factor, k s takes values between 0.65 and 0.85; The formula for calculating the leakage risk threshold is: P 警戒 = P 设计 x 0.9; P 行动 = P 设计 x 0.75 where P 警戒 is the alert pressure, P 设计 is the design pressure, P 行动 is the action pressure; P is the real-time pressure value, when 0 < P < 0.75P 设计 action regular monitoring is performed; When 0.75P 设计 ≤ P < 0.9P 设计 corresponding to risk level two, the action of manually checking the tightness and looking for leakage points is performed; When 0.9P 设计 When < P, the corresponding risk level is three, and the action corresponding to the pipeline emergency depressurization and safety relief is performed.

10. The large public building pipeline quality real-time monitoring and early warning system according to claim 9, characterized in that: The multi-level early warning pushing module is used for automatically pushing and visually processing the early warning signals of the graded building pipelines, and the multi-level early warning pushing module is composed of a warning platform screen, a multi-level sound and light alarm unit and an emergency platform.

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