Intelligent monitoring system for process industrial pipe networkTechnical Field
The invention relates to the technical field of monitoring of flow industrial pipe networks, in particular to an intelligent monitoring system for a flow industrial pipe network.
Background
Various energy media widely used in the process industry, such as water, steam, gas of iron and steel enterprises, gas of chemical enterprises and the like, are transmitted through a pipe network, and corresponding devices are connected to the pipe network and are responsible for producing, consuming and converting the transmitted energy media or performing other various treatment operations on the energy media.
Along with the development of urban construction, intelligent pipe networks are increasingly complicated, the traditional management means cannot meet the requirements of intelligent pipe network management, the prior art only monitors related factors such as flow and pipe pressure in the pipe networks simply, and cannot control working conditions of fault pipe networks in real time, so that faults of all parts in the pipe networks are difficult to judge accurately, the pipe network leakage rate is high, waste is huge, and when the pipe network faults occur, maintenance reaction is slow, so that a large amount of resources are wasted, normal allocation of resources in the pipe networks is seriously influenced, the influence range is enlarged, and industrial production efficiency is influenced.
Disclosure of Invention
The invention aims to provide an intelligent monitoring system for a process industrial pipe network, which is used for solving the problems in the background technology.
The intelligent monitoring system for the flow industrial pipe network comprises a monitoring and early warning module, an abnormality repair module, an intelligent scheduling module, a deep learning module, a communication module and a monitoring terminal, wherein the monitoring and early warning module, the abnormality repair module, the intelligent scheduling module, the monitoring terminal and the deep learning module are connected with one another through the communication module;
The monitoring and early warning module comprises a fluid monitoring unit, a monitoring equipment monitoring unit, a monitoring program self-checking unit and an alarm unit;
the monitoring and early warning module comprises a fluid monitoring unit, a monitoring equipment monitoring unit, a monitoring program self-checking unit and an alarm unit;
The system comprises a fluid monitoring unit, a monitoring program self-checking unit, an alarm unit, a monitoring program self-checking unit, a control unit and a control unit, wherein the fluid monitoring unit is used for monitoring the fluid in a system pipe network, the monitoring device monitoring unit is used for carrying out real-time cross self-checking on the operation conditions of all hardware devices related in the system, and the monitoring program self-checking unit is used for carrying out real-time cross self-checking on the operation conditions of all non-hardware plates related in the system;
The abnormal repair module comprises an intelligent repair unit and a repair data processing unit, wherein the intelligent repair unit comprises intelligent repair equipment and an artificial repair plate which are mutually matched for use, and the intelligent repair equipment is used for performing intelligent repair work on the abnormal equipment monitored in the monitoring and early-warning module;
the intelligent scheduling module comprises a reservation scheduling unit, a quantitative scheduling unit, a risk assessment unit and an emergency scheduling unit, wherein the reservation scheduling unit is used for submitting reservation applications of relevant parameters of fluid scheduling to a remote monitoring terminal according to use requirements of fluid of the relevant management and control area in the system, the quantitative scheduling unit is used for performing intelligent quantitative scheduling distribution by the remote monitoring terminal according to actual fluid data in the system and relevant reservation applications, the risk assessment unit is used for performing scheduling risk assessment according to fluid scheduling data of the relevant management and control area in the system, and the emergency scheduling unit is used for preparing relevant emergency scheduling measures for the relevant management and control area in advance according to the fluid scheduling risk assessment data provided by the risk assessment unit.
As a preferred scheme, the fluid monitoring unit is used for monitoring the flow, the flow speed, the quality, the temperature, the pressure and the flow direction of the fluid in real time.
As a preferable scheme, the monitoring equipment monitoring unit comprises intelligent patrol equipment applied to a official network pipeline, industrial control equipment, monitoring equipment, communication equipment, overhaul equipment and intelligent scheduling equipment.
As a preferable scheme, the monitoring program self-checking unit comprises a software self-checking program applied to monitoring software, communication software, industrial control software, maintenance software and intelligent scheduling software.
As a preferred scheme, intelligent repair equipment and manual repair plate all have independent repair function, and simultaneously, intelligent repair equipment is connected with manual repair plate for maintenance personnel controls intelligent repair equipment through the relevant program on the remote monitoring terminal and carries out remote repair work.
As a preferred scheme, the abnormal repair module further comprises a repair data processing unit, wherein the repair data processing unit is used for classifying the repair data according to repair difficulty and frequency after collecting and analyzing and processing the related repair data in the system, and reporting the classified repair data to the deep learning module and the monitoring terminal through the communication module.
As a preferable scheme, the repair data processing unit is used for collecting related information data, image data and audio data related to the intelligent repair unit, wherein the information data comprises a repair log and working condition parameters.
The communication module comprises a signal transmitting unit, a signal transmitting unit and a signal receiving unit, wherein the signal transmitting unit is used for transmitting related communication signals in the system, the signal transmitting unit is used for transmitting the communication signals transmitted by the signal transmitting unit to the signal receiving unit, and the signal receiving unit is used for receiving the communication signals transmitted by the signal transmitting unit.
As a preferable scheme, the monitoring early warning module and the abnormality repair module are both connected with the deep learning module, and the deep learning module is used for performing deep learning and training according to monitoring alarm data and abnormality repair data in the system, upgrading and perfecting the system.
As a preferred scheme, the deep learning module comprises a data acquisition unit, a classification processing unit and an autonomous learning unit, wherein the data acquisition unit is used for acquiring abnormal monitoring and repairing data in the system in real time, the classification processing unit is used for classifying and processing the acquired data and sending the classified data to the corresponding autonomous learning unit, and the autonomous learning unit is used for receiving the relevant functional data processed by the classification processing unit and performing autonomous learning according to the relevant functional data.
Compared with the prior art, the invention has the beneficial effects that:
The intelligent monitoring system for the flow industrial pipe network provided by the invention is used for realizing scientific scheduling of fluid in the flow industrial pipe network through the intelligent scheduling module when in actual application, actively coping with sudden conditions in the fluid scheduling process, monitoring and alarming the fluid, monitoring equipment and monitoring programs in the flow industrial pipe network in real time through the monitoring and early warning module when the intelligent scheduling module works, repairing and processing the detected abnormal problems through the abnormality repairing module, and deeply learning various problems in the process in the flow industrial pipe network through the deep learning module, so that emergency response can be timely carried out when the emergency conditions in the system are faced, and further optimizing and upgrading the system to enable the system to be more intelligent.
Drawings
Fig. 1 is a schematic diagram of the overall structure of the present invention.
In the figure, 1, a monitoring and early warning module, 11, a fluid monitoring unit, 12, a monitoring equipment monitoring unit, 13, a monitoring program self-checking unit, 14, an alarm unit, 2, an abnormal repair module, 21, an intelligent repair unit, 211, an intelligent repair equipment, 212, an artificial repair plate, 22, a repair data processing unit, 3, an intelligent scheduling module, 31, a reservation scheduling unit, 32, a quantitative scheduling unit, 33, a risk assessment unit, 34, an emergency scheduling unit, 4, a communication module, 41, a signal transmitting unit, 42, a signal transmission unit, 43, a signal receiving unit, 5, a monitoring terminal, 6, a deep learning module, 61, a data acquisition unit, 62, a classification processing unit, 63 and an autonomous learning unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to FIG. 1, the invention provides a technical scheme that an intelligent monitoring system for a process industrial pipe network comprises a monitoring and early warning module 1, an abnormality repair module 2, an intelligent scheduling module 3, a deep learning module 6, a communication module 4 and a monitoring terminal 5, wherein the monitoring and early warning module 1, the abnormality repair module 2, the intelligent scheduling module 3, the monitoring terminal 5 and the deep learning module 6 are connected with each other through the communication module 4;
The monitoring and early warning module 1 comprises a fluid monitoring unit 11, a monitoring equipment monitoring unit 12, a monitoring program self-checking unit 13 and an alarm unit 14;
The monitoring and early warning module 1 comprises a fluid monitoring unit 11, a monitoring equipment monitoring unit 12, a monitoring program self-checking unit 13 and an alarm unit 14;
The system comprises a fluid monitoring unit 11, a monitoring equipment monitoring unit 12, a monitoring program self-checking unit 13, an alarm unit 14, a monitoring equipment monitoring unit 12 and a monitoring program self-checking unit 13, wherein the fluid monitoring unit 11 is used for monitoring the data of the fluid in a system pipe network, the monitoring equipment monitoring unit 12 is used for carrying out real-time cross self-checking on the operation conditions of all hardware equipment related in the system, the monitoring program self-checking unit 13 is used for carrying out real-time cross self-checking on the operation conditions of all non-hardware plates related in the system, and the alarm unit 14 is connected with the fluid monitoring unit 11, the monitoring equipment monitoring unit 12 and the monitoring program self-checking unit 13 and is used for reporting the abnormal conditions monitored by the monitoring equipment monitoring unit 12 and the monitoring program self-checking unit 13 and taking emergency alarm measures;
the abnormal repair module 2 comprises an intelligent repair unit 21 and a repair data processing unit 22, wherein the intelligent repair unit 21 comprises intelligent repair equipment 211 and an artificial repair plate 212 which are mutually matched for use, the intelligent repair equipment 211 is used for performing intelligent repair work on the abnormal equipment monitored in the monitoring and early warning module 1, and the artificial repair plate 212 is used for performing artificial repair work on the abnormal equipment monitored in the monitoring and early warning module 1;
The intelligent scheduling module 3 comprises a reservation scheduling unit 31, a quantitative scheduling unit 32, a risk assessment unit 33 and an emergency scheduling unit 34, wherein the reservation scheduling unit 31 is used for submitting reservation applications of relevant parameters of fluid scheduling to the remote monitoring terminal 5 according to the use requirements of fluid of the user, the quantitative scheduling unit 32 is used for the remote monitoring terminal 5 to perform intelligent quantitative scheduling distribution according to actual fluid data and relevant reservation applications in the system, the risk assessment unit 33 is used for performing scheduling risk assessment according to the fluid scheduling data of the relevant management and control area in the system, and the emergency scheduling unit 34 is used for preparing relevant emergency scheduling measures for the relevant management and control area in advance according to the fluid scheduling risk assessment data provided by the risk assessment unit 33.
As a preferred embodiment, the fluid monitoring unit 11 is configured to monitor the flow, the flow velocity, the quality, the temperature, the pressure and the flow direction of the fluid in real time, so that the monitoring system can grasp the fluid scheduling process and the state in the industrial process pipe network in real time, and provide powerful data support for the emergency that may occur in the later stage.
As a preferred embodiment, the monitoring unit 12 includes an intelligent inspection device applied to a network pipeline, an industrial control device, a monitoring device, a communication device, an overhaul device and an intelligent scheduling device, and the intelligent inspection device is used for monitoring and inspecting the related hardware devices in the system in real time, so that the monitoring system can grasp the operation condition of the hardware devices in the industrial process network in real time, and provides powerful data support for the emergency possibly occurring in the later stage.
As a preferred embodiment, the monitoring program self-checking unit 13 includes a software self-checking program applied to monitoring software, communication software, industrial control software, maintenance software, and intelligent scheduling software, and the software self-checking program is used for monitoring and inspecting the non-hardware blocks in the system, such as the related operation software and program, in real time, so that the monitoring system can grasp the operation condition of the non-hardware blocks in the industrial process pipe network in real time, and provide powerful data support for the emergency that may occur in the later period.
As a preferred embodiment, the intelligent repairing device 211 and the artificial repairing plate 212 have independent repairing functions, meanwhile, the intelligent repairing device 211 is connected with the artificial repairing plate 212, when in actual use, the abnormal problems in the system can be repaired in an emergency manner through the intelligent repairing device 211 or the artificial repairing plate 212, and when the repairing function of the intelligent repairing device 211 or the artificial repairing plate 212 is limited, an maintainer can control the intelligent repairing device 211 to carry out remote repairing work through a related program on the remote monitoring terminal 5.
As a preferred embodiment, the anomaly repair module 2 further includes a repair data processing unit 22, where the repair data processing unit 22 is configured to collect, analyze and process related repair data in the system, and then rank-divide the repair data according to repair difficulty and frequency, report the repair data after the classification to the deep learning module 6 and the monitoring terminal 5 via the communication module 4, so that the monitoring terminal 5 can more intuitively check repair history records in the system, and the deep learning module 6 can perform targeted simulation, training and learning on the repair data.
As a preferred embodiment, the repair data processing unit 22 is configured to collect related information data, image data and audio data related to the intelligent repair unit 21, where the information data includes a repair log and working condition parameters.
As a preferred embodiment, the communication module 4 includes a signal transmitting unit 41, a signal transmitting unit 42, and a signal receiving unit 43, where the signal transmitting unit 41 is configured to transmit a relevant communication signal in the system, the signal transmitting unit 42 is configured to transmit the communication signal transmitted by the signal transmitting unit 41 to the signal receiving unit 43, and the signal receiving unit 43 is configured to receive the communication signal transmitted by the signal transmitting unit 42.
As a preferred embodiment, the monitoring and early warning module 1 and the abnormality repair module 2 are both connected with the deep learning module 6, and the deep learning module 6 is used for performing deep learning, training, upgrading and perfecting the system according to the monitoring and warning data and the abnormality repair data in the system.
As a preferred embodiment, the deep learning module 6 includes a data acquisition unit 61, a classification processing unit 62 and an autonomous learning unit 63, where the data acquisition unit 61 is configured to acquire abnormality monitoring and repairing data in the system in real time, the classification processing unit 62 is configured to classify and process the acquired data, and send the classified data to the corresponding autonomous learning unit 63, and the autonomous learning unit 63 is configured to receive relevant functional data processed by the classification processing unit 62, and perform autonomous learning according to the relevant functional data.
In the embodiment, in the monitoring system, the intelligent scheduling module 3 is used for realizing scientific scheduling of the fluid in the flow industrial pipe network, specifically:
the remote monitoring terminal 5 rapidly approves the submitted reservation application and carries out intelligent quantitative dispatching allocation according to actual fluid data in the system and the related reservation application, the dispatching plan in the system is adaptively adjusted, the risk assessment is carried out according to the fluid dispatching data of the related management and control area in the system by the risk assessment unit 33, the related emergency dispatching measures are prepared in advance for the related management and control area according to the fluid dispatching risk assessment data provided by the risk assessment unit 33 by the emergency dispatching unit 34, and the emergency situation occurring in the fluid dispatching process is actively dealt with.
When the intelligent scheduling module 3 works, fluid, monitoring equipment and monitoring programs in the flow industrial pipe network are monitored and alarmed in real time through the monitoring and early warning module 1, when the alarm occurs, operators, the monitoring terminal 5 and the abnormality repairing module 2 are informed of the first time, the modes including a platform, APP, a short message, mails and the like, high-frequency alarm ranking statistics are carried out through intelligent calculation and analysis, deep mining is carried out on alarm data, data support is provided for the later deep learning module 6, automatic or artificial repairing treatment is carried out on the detected abnormal problems through the abnormality repairing module 2, and deep learning is carried out on various problems in the flow industrial pipe network through the deep learning module 6, so that emergency response can be timely carried out when the emergency occurs in the system, and further the system is optimized and upgraded, so that the system is more intelligent.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.