CN117270437A - Hydropower station information data monitoring system based on distributed type - Google Patents
Hydropower station information data monitoring system based on distributed type Download PDFInfo
- Publication number
- CN117270437A CN117270437A CN202311337080.5A CN202311337080A CN117270437A CN 117270437 A CN117270437 A CN 117270437A CN 202311337080 A CN202311337080 A CN 202311337080A CN 117270437 A CN117270437 A CN 117270437A
- Authority
- CN
- China
- Prior art keywords
- data
- node
- module
- heartbeat
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 108
- 238000001514 detection method Methods 0.000 claims abstract description 120
- 230000002159 abnormal effect Effects 0.000 claims abstract description 46
- 238000013079 data visualisation Methods 0.000 claims abstract description 26
- 238000007405 data analysis Methods 0.000 claims abstract description 14
- 238000013500 data storage Methods 0.000 claims abstract description 14
- 238000004891 communication Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 63
- 238000011156 evaluation Methods 0.000 claims description 60
- 239000011159 matrix material Substances 0.000 claims description 19
- 230000005856 abnormality Effects 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 abstract description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 16
- 230000005540 biological transmission Effects 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 2
- 206010033799 Paralysis Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The utility model provides a hydropower station information data monitoring system based on distributing type, relates to data monitoring technical field, including the control center, control center communication connection has node distribution planning module, data acquisition module, data storage module, data analysis module, heartbeat detection module, fault-tolerant automatic switch module and data visualization module; the node distribution planning module is used for setting a main node and a plurality of redundant nodes; the data acquisition module is used for acquiring data; the data storage module is used for storing historical data; the data analysis module is used for marking the state of each main node; the heartbeat detection module judges the main node in the abnormal state through heartbeat response detection; the fault-tolerant automatic switching module is used for performing automatic switching operation on the main node and the related redundant nodes; the data visualization module is used for carrying out visual display on the monitoring data of each flow subsequence, and the efficiency and the accuracy of monitoring the hydropower station information data are remarkably improved.
Description
Technical Field
The invention relates to the technical field of data monitoring, in particular to a hydropower station information data monitoring system based on distribution.
Background
Hydropower stations are comprehensive engineering facilities for converting water energy into electric energy, also called hydropower plants, and include a series of hydropower station buildings and various hydropower station equipment installed for producing electric energy by utilizing water energy. The hydropower station flows high-water-level water into a factory building through a water diversion system to push a hydroelectric generating set to generate electric energy, and then the electric energy is input into a power grid through a step-up transformer, a switch station, a power transmission line and the like. When the hydropower station operates, the hydropower station can be influenced by compensation adjustment among different rivers, so that besides the maintenance of the hydropower station is needed to be paid attention to, the hydropower station is also an important means for ensuring the normal operation of the hydropower station.
The comparison document CN114442543A is a computer monitoring method suitable for hydropower station fault early warning, and the invention monitors the running state data of the hydropower station in real time through the computer monitoring system, and can diagnose the fault and early warn the fault according to the running state data, thereby improving the efficiency of fault diagnosis and further improving the running safety and reliability of the hydropower station.
The comparison document CN112817998A is used for improving the data comparison and synchronization level and the operation efficiency of related systems, ensuring the accuracy and consistency of the related system data, avoiding the problems of incorrect alarming or incorrect numerical value display, misplacement of control command and the like caused by inconsistent data, and improving the effectiveness of resource use.
The data transmission is required to be high in efficiency and accuracy when the data of the hydropower station unit equipment operation is monitored, the redundancy is low, the reliability is low in the existing hydropower station operation system, when the main line node fails, the whole system is paralyzed, and meanwhile, the data monitoring accuracy is not high.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a distributed hydropower station based information data monitoring system which comprises a monitoring center, wherein the monitoring center is in communication connection with a node distribution planning module, a data acquisition module, a data storage module, a data analysis module, a heartbeat detection module, a fault-tolerant automatic switching module and a data visualization module;
the node distribution planning module is used for acquiring flow characteristics of each position in the hydropower station, setting a plurality of data monitoring points at each key position according to the flow characteristics, and dividing the data monitoring points into a main node and a plurality of redundant nodes;
the data acquisition module and the data monitoring points are in distributed wireless communication through a 5G network, and are used for acquiring data acquired by a plurality of main nodes and transmitting the data to the data analysis module;
the data storage module is used for storing historical data of each data monitoring point location;
the data analysis module is used for marking the state of each main node according to the monitoring data of each flow subsequence, generating the abnormal detection information of the main node about the abnormal state, and sending the abnormal detection information to the heartbeat detection module;
the heartbeat detection module detects whether a main node in an abnormal state has a fault or network congestion or not through heartbeat response, judges whether operation equipment to which the main node belongs has a fault or not, generates corresponding fault information and sends the corresponding fault information to the fault-tolerant automatic switching module;
the fault-tolerant automatic switching module is used for automatically switching the main node and related redundant nodes after receiving the fault information of the main node in the fault state and the network congestion state;
the data visualization module is used for constructing a hydropower station data visualization model, and the monitoring data of each flow subsequence is visualized and displayed through the hydropower station data visualization model.
Further, the process of obtaining the flow characteristics of each position in the hydropower station by the node distribution planning module comprises the following steps:
the method comprises the steps of obtaining the functional characteristics of operation equipment at each position of a current hydropower station, extracting flow information according to the functional characteristics, splitting the operation process of the hydropower station according to the flow information, and dividing the operation process into a plurality of flow subsequences.
Further, the process of setting a plurality of data monitoring points on each key position by the node distribution planning module according to the flow characteristics includes:
selecting an evaluation index according to each functional characteristic in each flow subsequence, setting index weight of the evaluation index according to historical data, setting importance evaluation level and preset importance level, and judging a membership matrix of each flow subsequence to the importance evaluation level through fuzzy comprehensive evaluation;
obtaining a fuzzy comprehensive evaluation result according to the membership matrix and the index weight, obtaining importance evaluation levels of all flow subsequences of the hydropower station according to the fuzzy comprehensive evaluation result, comparing the importance evaluation levels of the flow subsequences with preset importance levels, setting data monitoring points for the flow subsequences with the importance evaluation levels larger than the preset importance levels, and determining the number of the data monitoring points and the data monitoring index of the data monitoring points according to the importance evaluation levels and the evaluation index of the flow subsequences.
Further, the process of setting the index weight of the evaluation index by the node distribution planning module according to the historical data includes:
the method comprises the steps of obtaining historical data monitoring results of a plurality of historical periods of evaluation indexes of each flow subsequence and corresponding historical evaluation index threshold ranges from a data storage module, comparing the historical data monitoring results with the corresponding historical evaluation index threshold ranges, obtaining abnormal accumulation times in the historical data monitoring results which do not accord with the corresponding historical evaluation index threshold ranges, and determining index weights of the evaluation indexes according to the abnormal accumulation times.
Further, the process of marking the state of each master node by the data analysis module according to the monitoring data of each flow subsequence, generating the abnormality detection information of the master node about the abnormal state, and transmitting the abnormality detection information to the heartbeat detection module includes:
acquiring real-time environment parameters of all the main nodes, acquiring threshold ranges of data monitoring indexes of all the main nodes under different environment parameters from a data storage module, carrying out consistency matching on the real-time environment parameters of all the main nodes and the threshold ranges of the data monitoring indexes of all the main nodes under the same environment parameters, and selecting the threshold ranges of the data monitoring indexes consistent with the real-time environment parameters of all the main nodes;
acquiring monitoring index data of each main node, and comparing the monitoring index data with a threshold range of a corresponding data monitoring index;
if the monitoring index data is not in the threshold range of the corresponding data monitoring index, marking the main node to which the monitoring index data belongs as an abnormal state, and sending abnormal detection information to a heartbeat detection module.
Further, the heartbeat detection module detects whether the main node in the abnormal state has a fault or network congestion and whether the running equipment of the main node has a fault through heartbeat response, and the process of judging whether the running equipment of the main node has the fault comprises the following steps:
setting a heartbeat interval time and a response delay threshold, wherein the heartbeat detection module sends heartbeat signals to a main node in an abnormal state at intervals from the moment of receiving abnormal detection information, and the main node sends corresponding heartbeat feedback signals to the heartbeat detection module after receiving the heartbeat signals;
and the heartbeat detection module takes the sum of the heartbeat interval time and the response delay threshold value as a heartbeat detection window, and judges whether the main node fails or network congestion according to whether the heartbeat feedback signal is successfully received in the heartbeat detection window.
Further, the process of judging whether the main node fails or network congestion by the heartbeat detection module according to whether the heartbeat feedback signal is successfully received in the heartbeat detection window includes:
setting a heartbeat interval time and a response delay threshold, wherein the heartbeat detection module sends heartbeat signals to a main node in an abnormal state at intervals from the moment of receiving abnormal detection information, and the main node sends corresponding heartbeat feedback signals to the heartbeat detection module after receiving the heartbeat signals;
the heartbeat detection module takes the sum of the heartbeat interval time and the response delay threshold value as a heartbeat detection window, and judges whether the main node fails or network congestion according to whether a heartbeat feedback signal is successfully received in the heartbeat detection window;
setting a heartbeat detection period, wherein the heartbeat detection period consists of a plurality of continuous heartbeat detection windows, and after the main node sends a heartbeat signal to the main node in an abnormal state, if a corresponding heartbeat feedback signal is received in the heartbeat detection window, a response success window label is added on the heartbeat detection window;
if the heartbeat detection module does not detect the response success window label in the heartbeat detection period, marking the main node as a node fault state, generating node fault information and sending the node fault information to a fault-tolerant automatic switching module;
if the heartbeat detection module detects the response success window label in the heartbeat detection period, setting a proportion threshold value, acquiring the numerical proportion of the number of the response success window labels to the number of the heartbeat detection windows in the heartbeat detection period, and comparing the numerical proportion with the proportion threshold value;
if the numerical proportion is greater than or equal to a proportion threshold value, marking the main node as a normal state, and marking operation equipment to which the main node belongs as an equipment fault state;
and if the numerical proportion is smaller than the proportion threshold value, marking the main node as a network congestion state, generating node network congestion information and sending the node network congestion information to a fault-tolerant automatic switching module.
Further, after receiving the fault information of the main node in the fault state, the fault-tolerant automatic switching module performs automatic switching operation on the main node and related redundant nodes, and the process includes:
when node fault information of a main node in a node fault state is acquired, acquiring position information of the main node in the node fault state based on the node fault information, acquiring a redundant node with the nearest position distance to the main node in the node fault state, and marking the redundant node as a new main node if the redundant node is in a normal state after the heartbeat detection module detects the redundant node;
and if the redundant node is in a fault state, eliminating the redundant node and repeating the main node selection operation.
Further, the process of performing the automatic switching operation on the master node and the related redundant nodes after the fault tolerant automatic switching module receives the fault information of the master node in the network congestion state includes:
when node network congestion information of a main node in a network congestion state is acquired, acquiring position information of the main node in the network congestion state based on the node network congestion information, acquiring a redundant node which is closest to the position of the main node in the network congestion state, and marking the redundant node as a slave node if the redundant node is in a normal state after the redundant node is detected by a heartbeat detection module, wherein the slave node is used for distributing data flow of the main node;
and if the redundant node distributes the data traffic of the master node, the master node still keeps the network congestion state, and the slave node selecting operation is repeated until the master node is in a normal state.
Further, the data visualization module builds a hydropower station data visualization model, and the process of visually displaying the monitoring data of each flow subsequence through the hydropower station data visualization model comprises the following steps:
acquiring physical entities of operation equipment at all positions in a physical space of a current hydropower station, acquiring multi-source heterogeneous data of all data monitoring points in the operation process of the current hydropower station, preprocessing a data format to acquire a twin data set, mapping the physical entities of the hydropower station operation equipment to a digital space through three-dimensional modeling processing, acquiring an assembly connection relation of the physical entities in the physical space, and matching the twin data with a three-dimensional model in the digital space according to the assembly connection relation of the physical entities in the physical space to acquire a data twin model;
different twin data scenes of each flow subsequence in the data twin model are obtained, color assignment is carried out on the twin data scenes, the twin data scenes after the color assignment are stored in a digital space, and a three-dimensional model in the data twin model is combined with the current twin data scenes of the corresponding flow subsequences to generate a hydropower station data visualization model.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the hydropower station information data monitoring function is distributed on a plurality of nodes, the fault-tolerant automatic switching module automatically switches the main node and related redundant nodes after receiving the fault information of the main node in a fault state and a network congestion state, the distributed architecture improves the reliability and expandability of the system, even if one node breaks down, other nodes can still work normally, and compared with the prior art that manual intervention is required for the conversion of the monitoring node, the automatic switching operation of the fault node greatly improves the monitoring efficiency and accuracy of the hydropower station.
2. The monitoring data of each flow subsequence is visually displayed through the hydropower station data visual model, so that operation and maintenance personnel are helped to process and manage the data through functions such as data display and the like, the operation and maintenance personnel are helped to timely master the operation state of the hydropower station, and the efficiency and the accuracy of hydropower station information data monitoring are improved.
Drawings
Fig. 1 is a schematic diagram of a distributed hydropower station information data monitoring system according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, clearly and completely describes the technical solutions of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1, the distributed hydropower station-based information data monitoring system comprises a monitoring center, wherein the monitoring center is in communication connection with a node distribution planning module, a data acquisition module, a data storage module, a data analysis module, a heartbeat detection module, a fault-tolerant automatic switching module and a data visualization module;
the node distribution planning module is used for acquiring flow characteristics of each position in the hydropower station, setting a plurality of data monitoring points at each key position according to the flow characteristics, and dividing the data monitoring points into a main node and a plurality of redundant nodes;
the data acquisition module and the data monitoring points are in distributed wireless communication through a 5G network, and are used for acquiring data acquired by a plurality of main nodes and transmitting the data to the data analysis module;
the data storage module is used for storing historical data of each data monitoring point location;
the data analysis module is used for marking the state of each main node according to the monitoring data of each flow subsequence, generating the abnormal detection information of the main node about the abnormal state, and sending the abnormal detection information to the heartbeat detection module;
the heartbeat detection module detects whether a main node in an abnormal state has a fault or network congestion or not through heartbeat response, judges whether operation equipment to which the main node belongs has a fault or not, generates corresponding fault information and sends the corresponding fault information to the fault-tolerant automatic switching module;
the fault-tolerant automatic switching module is used for automatically switching the main node and related redundant nodes after receiving the fault information of the main node in the fault state and the network congestion state;
the data visualization module is used for constructing a hydropower station data visualization model, and the monitoring data of each flow subsequence is visualized and displayed through the hydropower station data visualization model.
It should be further noted that, in the implementation process, the process of obtaining the flow characteristics of each position in the hydropower station by the node distribution planning module includes:
the method comprises the steps of obtaining the functional characteristics of operation equipment at each position of a current hydropower station, extracting flow information according to the functional characteristics, splitting the operation process of the hydropower station according to the flow information, and dividing the operation process into a plurality of flow subsequences.
It should be further noted that, in the implementation process, the process of setting, by the node distribution planning module, a plurality of data monitoring points at each key position according to the flow characteristic includes:
selecting an evaluation index according to each functional characteristic in each flow subsequence, setting index weight of the evaluation index according to historical data, setting importance evaluation level and preset importance level, and judging a membership matrix of each flow subsequence to the importance evaluation level through fuzzy comprehensive evaluation;
obtaining a fuzzy comprehensive evaluation result according to the membership matrix and the index weight, obtaining importance evaluation levels of all flow subsequences of the hydropower station according to the fuzzy comprehensive evaluation result, comparing the importance evaluation levels of the flow subsequences with preset importance levels, setting data monitoring points for the flow subsequences with the importance evaluation levels larger than the preset importance levels, and determining the number of the data monitoring points and the data monitoring index of the data monitoring points according to the importance evaluation levels and the evaluation index of the flow subsequences.
It should be further noted that, in the implementation process, the functional characteristics of the hydropower station operation device include:
the hydraulic turbine and the generator are responsible for converting water energy into mechanical energy and driving the generator to generate electric energy;
circuit breaker and switch: the system is used for controlling and protecting the power generation equipment and the electrical equipment of the power transmission line. The circuit breaker is used for isolating the circuit and cutting off the current, and the switch is used for controlling the on-off of the circuit;
a transformer: the high-voltage power generation device is used for converting high voltage generated by the generator into high-voltage power required by the power transmission line so as to transmit the power to a user through the power transmission line;
gate and valve: the control equipment is used for adjusting water flow and water level, can control the guiding and diversion of water, and ensures that the water energy is fully utilized and the balance of supply and demand is coordinated;
and (3) a water pump: the water level control device is used for lifting the water level or increasing the water flow so as to enhance the power generation capacity of the generator set.
It should be further noted that, in the implementation process, the monitoring indexes of each hydropower station operation device include: the water level, water pressure, flow, temperature, vibration, etc. measure and monitor various parameters of the hydropower station operation to provide real-time data for operation management and fault diagnosis.
It should be further noted that, in the specific implementation process, the process of setting the index weight of the evaluation index according to the historical data includes:
acquiring historical data monitoring results of a plurality of historical periods of the evaluation indexes of each flow subsequence and corresponding historical evaluation index threshold ranges from a data storage module, comparing the historical data monitoring results with the corresponding historical evaluation index threshold ranges, and acquiring the abnormal accumulation times SN in the range that the historical data monitoring results do not accord with the corresponding historical evaluation index threshold ranges i According to the number of abnormal accumulation times SN i Determining index weight W of evaluation index i Index weight W i The calculation formula of (2) is W i ＝a 1 SN i The method comprises the steps of carrying out a first treatment on the surface of the Wherein a is 1 Is a weight factor.
It should be further noted that, in the implementation process, the process of obtaining the fuzzy comprehensive evaluation result according to the membership matrix and the index weight includes:
the weight index matrix and the membership matrix of each index data of the flow subsequence are fused through the following formula to obtain a fuzzy comprehensive evaluation result matrix of the flow subsequence, and a fuzzy comprehensive evaluation result is obtained according to the fuzzy comprehensive evaluation result matrix;
wherein, the formula is:
M＝αM 1 +βM 2 ；
wherein M is a fuzzy comprehensive evaluation result matrix of the flow subsequence, and M 1 A weight index matrix for each index data, M 2 And for the membership degree matrix, "+" indicates that the weight index matrix of each item of index data is added with elements at corresponding positions of the membership degree matrix, and alpha and beta are weighting parameters for controlling balance between the weight index matrix of each item of index data and the membership degree matrix in the fuzzy comprehensive evaluation result matrix of the flow subsequence.
It should be further noted that, in the implementation process, the process of marking the state of each master node by the data analysis module according to the monitoring data of each flow subsequence, generating the abnormality detection information about the master node in the abnormal state, and sending the abnormality detection information to the heartbeat detection module includes:
acquiring real-time environment parameters of all the main nodes, acquiring threshold ranges of data monitoring indexes of all the main nodes under different environment parameters from a data storage module, carrying out consistency matching on the real-time environment parameters of all the main nodes and the threshold ranges of the data monitoring indexes of all the main nodes under the same environment parameters, and selecting the threshold ranges of the data monitoring indexes consistent with the real-time environment parameters of all the main nodes;
acquiring monitoring index data of each main node, and comparing the monitoring index data with a threshold range of a corresponding data monitoring index to acquire the monitoring index data;
if the monitoring index data is not in the threshold range of the corresponding data monitoring index, marking the main node to which the monitoring index data belongs as an abnormal state, and sending abnormal detection information to a heartbeat detection module.
It should be further noted that, in the implementation process, the process of determining whether the primary node in the abnormal state has a fault or network congestion and whether the running device to which the primary node belongs has a fault by the heartbeat detection module through heartbeat response detection includes:
setting a heartbeat interval time and a response delay threshold, wherein the heartbeat detection module sends heartbeat signals to a main node in an abnormal state at intervals from the moment of receiving abnormal detection information, and the main node sends corresponding heartbeat feedback signals to the heartbeat detection module after receiving the heartbeat signals;
and the heartbeat detection module takes the sum of the heartbeat interval time and the response delay threshold value as a heartbeat detection window, and judges whether the main node fails or network congestion according to whether the heartbeat feedback signal is successfully received in the heartbeat detection window.
It should be further noted that, in the implementation process, the process that the heartbeat detection module determines whether the master node fails or is congested according to whether the heartbeat feedback signal is successfully received in the heartbeat detection window includes:
setting a heartbeat interval time and a response delay threshold, wherein the heartbeat detection module sends heartbeat signals to a main node in an abnormal state at intervals from the moment of receiving abnormal detection information, and the main node sends corresponding heartbeat feedback signals to the heartbeat detection module after receiving the heartbeat signals;
the heartbeat detection module takes the sum of the heartbeat interval time and the response delay threshold value as a heartbeat detection window, and judges whether the main node fails or network congestion according to whether a heartbeat feedback signal is successfully received in the heartbeat detection window;
setting a heartbeat detection period, wherein the heartbeat detection period consists of a plurality of continuous heartbeat detection windows, and after the main node sends a heartbeat signal to the main node in an abnormal state, if a corresponding heartbeat feedback signal is received in the heartbeat detection window, a response success window label is added on the heartbeat detection window;
if the heartbeat detection module does not detect the response success window label in the heartbeat detection period, marking the main node as a node fault state, generating node fault information and sending the node fault information to a fault-tolerant automatic switching module;
if the heartbeat detection module detects the response success window label in the heartbeat detection period, setting a proportion threshold value, acquiring the numerical proportion of the number of the response success window labels to the number of the heartbeat detection windows in the heartbeat detection period, and comparing the numerical proportion with the proportion threshold value;
if the numerical proportion is greater than or equal to a proportion threshold value, marking the main node as a normal state, and marking operation equipment to which the main node belongs as an equipment fault state;
and if the numerical proportion is smaller than the proportion threshold value, marking the main node as a network congestion state, generating node network congestion information and sending the node network congestion information to a fault-tolerant automatic switching module.
It should be further noted that, in the implementation process, after the fault tolerant automatic switching module receives the fault information of the main node in the fault state, the process of performing the automatic switching operation on the main node and the related redundant nodes includes:
when node fault information of a main node in a node fault state is acquired, acquiring position information of the main node in the node fault state based on the node fault information, acquiring a redundant node with the nearest position distance to the main node in the node fault state, and marking the redundant node as a new main node if the redundant node is in a normal state after the heartbeat detection module detects the redundant node;
and if the redundant node is in a fault state, eliminating the redundant node and repeating the main node selection operation.
It should be further noted that, in the implementation process, after the fault-tolerant automatic switching module receives the fault information of the master node in the network congestion state, the process of performing the automatic switching operation on the master node and the related redundant nodes includes:
when node network congestion information of a main node in a network congestion state is acquired, acquiring position information of the main node in the network congestion state based on the node network congestion information, acquiring a redundant node which is closest to the position of the main node in the network congestion state, and marking the redundant node as a slave node if the redundant node is in a normal state after the redundant node is detected by a heartbeat detection module, wherein the slave node is used for distributing data flow of the main node;
and if the redundant node distributes the data traffic of the master node, the master node still keeps the network congestion state, and the slave node selecting operation is repeated until the master node is in a normal state.
It should be further noted that, in the implementation process, the process of constructing the hydropower station data visualization model by the data visualization module and visually displaying the monitoring data of each flow subsequence through the hydropower station data visualization model includes:
acquiring physical entities of operation equipment at all positions in a physical space of a current hydropower station, acquiring multi-source heterogeneous data of all data monitoring points in the operation process of the current hydropower station, preprocessing a data format to acquire a twin data set, mapping the physical entities of the hydropower station operation equipment to a digital space through three-dimensional modeling processing, acquiring an assembly connection relation of the physical entities in the physical space, and matching the twin data with a three-dimensional model in the digital space according to the assembly connection relation of the physical entities in the physical space to acquire a data twin model;
different twin data scenes of each flow subsequence in the data twin model are obtained, color assignment is carried out on the twin data scenes, the twin data scenes after the color assignment are stored in a digital space, and a three-dimensional model in the data twin model is combined with the current twin data scenes of the corresponding flow subsequences to generate a hydropower station data visualization model.
It should be further noted that, in the implementation process, the different twin data scenarios of each flow sub-sequence include: marking the running equipment of the flow sub-sequence as scene information of a fault state according to a heartbeat detection module, and automatically switching the main node and the redundant node of the flow sub-sequence according to a fault-tolerant automatic switching module.
It should be further noted that, in the implementation process, the process of performing color assignment on the twin data scene includes: marking the twin data scene as red according to the scene information of the fault state of the running equipment to which the flow sub-sequence belongs by the heartbeat detection module, marking the twin data scene as orange according to the scene information of the automatic switching of the main node and the redundant node of the flow sub-sequence by the fault-tolerant automatic switching module, and marking the twin data scene as other different colors according to the quantity information of the main node and the slave node of the flow sub-sequence.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (10)
1. The distributed hydropower station information data monitoring system comprises a monitoring center, and is characterized in that the monitoring center is in communication connection with a node distribution planning module, a data acquisition module, a data storage module, a data analysis module, a heartbeat detection module, a fault-tolerant automatic switching module and a data visualization module;
the node distribution planning module is used for acquiring flow characteristics of each position in the hydropower station, setting a plurality of data monitoring points at each key position according to the flow characteristics, and dividing the data monitoring points into a main node and a plurality of redundant nodes;
the data acquisition module and the data monitoring points are in distributed wireless communication through a 5G network, and are used for acquiring data acquired by a plurality of main nodes and transmitting the data to the data analysis module;
the data storage module is used for storing historical data of each data monitoring point location;
the data analysis module is used for marking the state of each main node according to the monitoring data of each flow subsequence, generating the abnormal detection information of the main node about the abnormal state, and sending the abnormal detection information to the heartbeat detection module;
the heartbeat detection module detects whether a main node in an abnormal state has a fault or network congestion or not through heartbeat response, judges whether operation equipment to which the main node belongs has a fault or not, generates corresponding fault information and sends the corresponding fault information to the fault-tolerant automatic switching module;
the fault-tolerant automatic switching module is used for automatically switching the main node and related redundant nodes after receiving the fault information of the main node in the fault state and the network congestion state;
the data visualization module is used for constructing a hydropower station data visualization model, and the monitoring data of each flow subsequence is visualized and displayed through the hydropower station data visualization model.
2. The distributed hydropower station information data monitoring system according to claim 1, wherein the process of obtaining the flow characteristics of each position in the hydropower station by the node distribution planning module comprises:
the method comprises the steps of obtaining the functional characteristics of operation equipment at each position of a current hydropower station, extracting flow information according to the functional characteristics, splitting the operation process of the hydropower station according to the flow information, and dividing the operation process into a plurality of flow subsequences.
3. The distributed hydropower station information data monitoring system according to claim 2, wherein the process of setting a plurality of data monitoring points on each key position by the node distribution planning module according to the flow characteristics comprises:
selecting an evaluation index according to each functional characteristic in each flow subsequence, setting index weight of the evaluation index according to historical data, setting importance evaluation level and preset importance level, and judging a membership matrix of each flow subsequence to the importance evaluation level through fuzzy comprehensive evaluation;
obtaining a fuzzy comprehensive evaluation result according to the membership matrix and the index weight, obtaining importance evaluation levels of all flow subsequences of the hydropower station according to the fuzzy comprehensive evaluation result, comparing the importance evaluation levels of the flow subsequences with preset importance levels, setting data monitoring points for the flow subsequences with the importance evaluation levels larger than the preset importance levels, and determining the number of the data monitoring points and the data monitoring index of the data monitoring points according to the importance evaluation levels and the evaluation index of the flow subsequences.
4. A distributed hydropower station information data monitoring system according to claim 3, wherein the process of setting the index weight of the evaluation index by the node distribution planning module according to the history data includes:
the method comprises the steps of obtaining historical data monitoring results of a plurality of historical periods of evaluation indexes of each flow subsequence and corresponding historical evaluation index threshold ranges from a data storage module, comparing the historical data monitoring results with the corresponding historical evaluation index threshold ranges, obtaining abnormal accumulation times in the historical data monitoring results which do not accord with the corresponding historical evaluation index threshold ranges, and determining index weights of the evaluation indexes according to the abnormal accumulation times.
5. The distributed hydropower station information data monitoring system according to claim 4, wherein the process of marking the state of each master node according to the monitoring data of each flow subsequence by the data analysis module, generating abnormality detection information about the master node in an abnormal state, and transmitting the abnormality detection information to the heartbeat detection module includes:
acquiring real-time environment parameters of all the main nodes, acquiring threshold ranges of data monitoring indexes of all the main nodes under different environment parameters from a data storage module, carrying out consistency matching on the real-time environment parameters of all the main nodes and the threshold ranges of the data monitoring indexes of all the main nodes under the same environment parameters, and selecting the threshold ranges of the data monitoring indexes consistent with the real-time environment parameters of all the main nodes;
acquiring monitoring index data of each main node, and comparing the monitoring index data with a threshold range of a corresponding data monitoring index;
if the monitoring index data is not in the threshold range of the corresponding data monitoring index, marking the main node to which the monitoring index data belongs as an abnormal state, and sending abnormal detection information to a heartbeat detection module.
6. The distributed hydropower station information data monitoring system according to claim 5, wherein the heartbeat detection module detects whether a failure or network congestion occurs in a master node in an abnormal state and whether an operation device to which the master node belongs fails through heartbeat response, and the process of judging whether the operation device to which the master node belongs fails comprises:
setting a heartbeat interval time and a response delay threshold, wherein the heartbeat detection module sends heartbeat signals to a main node in an abnormal state at intervals from the moment of receiving abnormal detection information, and the main node sends corresponding heartbeat feedback signals to the heartbeat detection module after receiving the heartbeat signals;
and the heartbeat detection module takes the sum of the heartbeat interval time and the response delay threshold value as a heartbeat detection window, and judges whether the main node fails or network congestion according to whether the heartbeat feedback signal is successfully received in the heartbeat detection window.
7. The distributed hydropower station information data monitoring system according to claim 6, wherein the process of judging whether the master node fails or has network congestion by the heartbeat detection module according to whether the heartbeat feedback signal is successfully received in the heartbeat detection window comprises:
setting a heartbeat interval time and a response delay threshold, wherein the heartbeat detection module sends heartbeat signals to a main node in an abnormal state at intervals from the moment of receiving abnormal detection information, and the main node sends corresponding heartbeat feedback signals to the heartbeat detection module after receiving the heartbeat signals;
the heartbeat detection module takes the sum of the heartbeat interval time and the response delay threshold value as a heartbeat detection window, and judges whether the main node fails or network congestion according to whether a heartbeat feedback signal is successfully received in the heartbeat detection window;
setting a heartbeat detection period, wherein the heartbeat detection period consists of a plurality of continuous heartbeat detection windows, and after the main node sends a heartbeat signal to the main node in an abnormal state, if a corresponding heartbeat feedback signal is received in the heartbeat detection window, a response success window label is added on the heartbeat detection window;
if the heartbeat detection module does not detect the response success window label in the heartbeat detection period, marking the main node as a node fault state, generating node fault information and sending the node fault information to a fault-tolerant automatic switching module;
if the heartbeat detection module detects the response success window label in the heartbeat detection period, setting a proportion threshold value, acquiring the numerical proportion of the number of the response success window labels to the number of the heartbeat detection windows in the heartbeat detection period, and comparing the numerical proportion with the proportion threshold value;
if the numerical proportion is greater than or equal to a proportion threshold value, marking the main node as a normal state, and marking operation equipment to which the main node belongs as an equipment fault state;
and if the numerical proportion is smaller than the proportion threshold value, marking the main node as a network congestion state, generating node network congestion information and sending the node network congestion information to a fault-tolerant automatic switching module.
8. The distributed hydropower station information data monitoring system according to claim 7, wherein the process of performing the automatic switching operation on the master node and the related redundant nodes after the fault tolerant automatic switching module receives the fault information of the master node in the fault state includes:
when node fault information of a main node in a node fault state is acquired, acquiring position information of the main node in the node fault state based on the node fault information, acquiring a redundant node with the nearest position distance to the main node in the node fault state, and marking the redundant node as a new main node if the redundant node is in a normal state after the heartbeat detection module detects the redundant node;
and if the redundant node is in a fault state, eliminating the redundant node and repeating the main node selection operation.
9. The distributed hydropower station information data monitoring system according to claim 8, wherein the process of performing the automatic switching operation on the master node and the related redundant nodes after the fault tolerant automatic switching module receives the fault information of the master node in the network congestion state includes:
when node network congestion information of a main node in a network congestion state is acquired, acquiring position information of the main node in the network congestion state based on the node network congestion information, acquiring a redundant node which is closest to the position of the main node in the network congestion state, and marking the redundant node as a slave node if the redundant node is in a normal state after the redundant node is detected by a heartbeat detection module, wherein the slave node is used for distributing data flow of the main node;
and if the redundant node distributes the data traffic of the master node, the master node still keeps the network congestion state, and the slave node selecting operation is repeated until the master node is in a normal state.
10. The distributed hydropower station information data monitoring system according to claim 9, wherein the data visualization module constructs a hydropower station data visualization model, and the process of visually displaying the monitored data of each flow subsequence through the hydropower station data visualization model comprises:
acquiring physical entities of operation equipment at all positions in a physical space of a current hydropower station, acquiring multi-source heterogeneous data of all data monitoring points in the operation process of the current hydropower station, preprocessing a data format to acquire a twin data set, mapping the physical entities of the hydropower station operation equipment to a digital space through three-dimensional modeling processing, acquiring an assembly connection relation of the physical entities in the physical space, and matching the twin data with a three-dimensional model in the digital space according to the assembly connection relation of the physical entities in the physical space to acquire a data twin model;
different twin data scenes of each flow subsequence in the data twin model are obtained, color assignment is carried out on the twin data scenes, the twin data scenes after the color assignment are stored in a digital space, and a three-dimensional model in the data twin model is combined with the current twin data scenes of the corresponding flow subsequences to generate a hydropower station data visualization model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311337080.5A CN117270437A (en) | 2023-10-16 | 2023-10-16 | Hydropower station information data monitoring system based on distributed type |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311337080.5A CN117270437A (en) | 2023-10-16 | 2023-10-16 | Hydropower station information data monitoring system based on distributed type |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117270437A true CN117270437A (en) | 2023-12-22 |
Family
ID=89217704
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311337080.5A Pending CN117270437A (en) | 2023-10-16 | 2023-10-16 | Hydropower station information data monitoring system based on distributed type |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117270437A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117560300A (en) * | 2023-12-28 | 2024-02-13 | 广东云百智联科技有限公司 | Intelligent internet of things flow prediction and optimization system |
| CN117575372A (en) * | 2024-01-16 | 2024-02-20 | 湘江实验室 | Knowledge graph-based supply chain quality management system |
-
2023
- 2023-10-16 CN CN202311337080.5A patent/CN117270437A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117560300A (en) * | 2023-12-28 | 2024-02-13 | 广东云百智联科技有限公司 | Intelligent internet of things flow prediction and optimization system |
| CN117560300B (en) * | 2023-12-28 | 2024-04-30 | 广东云百智联科技有限公司 | Intelligent internet of things flow prediction and optimization system |
| CN117575372A (en) * | 2024-01-16 | 2024-02-20 | 湘江实验室 | Knowledge graph-based supply chain quality management system |
| CN117575372B (en) * | 2024-01-16 | 2024-04-12 | 湘江实验室 | Knowledge graph-based supply chain quality management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117270437A (en) | Hydropower station information data monitoring system based on distributed type | |
| CN102638100B (en) | District power network equipment abnormal alarm signal association analysis and diagnosis method | |
| EP3140898B1 (en) | Method and system for monitoring distribution systems | |
| CN113299042B (en) | Safety early warning system for frequency conversion equipment of industrial electrical appliance | |
| CN110210701A (en) | A kind of grid equipment risk perceptions method | |
| CN106154084A (en) | Network load exception and operation risk real-time monitoring and early warning method | |
| CN102737287B (en) | Regional power grid on-line power supply risk assessment system | |
| CN102737286A (en) | Online risk analysis system and method for regional power grid | |
| CN111190375B (en) | Intelligent monitoring system and monitoring method for hydropower station equipment | |
| CN103259333B (en) | Intelligent alarm and comprehensive analysis of fault system | |
| CN110346673A (en) | It is a kind of meter and the rational intelligent distribution network methods of risk assessment of network topology | |
| CN104966147A (en) | Power grid operating risk analyzing method in view of base state and accident state | |
| CN106655158A (en) | Smart grid based distribution network self-healing control system and method | |
| CN201928086U (en) | Intelligent grid comprehensive alarm and cooperative processing system | |
| CN209676004U (en) | A kind of the wireless monitor detector and system of electric line safe operation | |
| CN105224667A (en) | Based on multistation end fault diagnosis and the aid decision-making method of electrical network intelligent monitoring information | |
| CN104880629A (en) | Remote diagnosis method for motion behavior of protection element at dispatching terminal | |
| CN114969876A (en) | Electrical safety monitoring method and device, computer equipment and storage medium | |
| CN110287543A (en) | Method for predicting service life of relay protection device | |
| CN109980782A (en) | A kind of the wireless monitor detector and system of electric line safe operation | |
| CN114139742A (en) | Power distribution network management and control system and management and control method | |
| CN103616877A (en) | Monitoring diagnostic method and system for energy pipe network | |
| CN109412266A (en) | A kind of nuclear power plant's transforming plant protecting monitoring system | |
| CN108683253A (en) | A kind of power transmission and transforming equipment panorama monitoring method | |
| CN116455060B (en) | Intelligent monitoring and early warning method and system for working condition of power grid equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |