CN110926433A - Marine disaster early warning system for coastal nuclear power station - Google Patents
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Abstract
A marine disaster early warning system for a coastal nuclear power station, the system comprising: the multi-source data acquisition module is used for transmitting the acquired multi-source heterogeneous data to the data processing module through a network; the data processing module is used for identifying, quantizing and normalizing the multi-source heterogeneous data obtained by the multi-source data acquisition module and filtering abnormal interference data; the data management module is used for storing the processed monitoring data and the processed historical data; the analysis module is used for calculating a marine organism disaster value in real time by substituting the processed monitoring data in a prediction network architecture constructed by combining the existing historical data information; and the decision support module is used for determining a corresponding scheme and measures under the disaster grade and automatically uploading the scheme and measures to the mobile terminal of the associated personnel. The system fills the blank of early disaster early warning of the current coastal nuclear power cold source protection system in China, and better meets the monitoring and early warning requirements of the water intake of the coastal nuclear power station.
Description
Technical Field
The invention relates to the technical field of marine disaster early warning, in particular to an early warning system for marine disaster-causing substances of a coastal nuclear power station.
Background
In recent years, a large amount of garbage, jellyfish, fish, seaweed and the like in sea areas enter cold source water intake ports of coastal nuclear power stations along with tides and storms, a large amount of floating impurities enter water intake open channels and water intake pump rooms, so that filtering equipment is blocked, and power reduction and even sudden shutdown of power station units are further caused. In order to reduce huge economic loss caused by safe operation of oceans on coastal nuclear facilities, omnibearing monitoring and early warning of sea areas around a cold source water intake of a coastal nuclear power station become problems to be solved for the safety of the cold source of the nuclear power station.
At present, the coastal nuclear power cold source protection in China mainly adopts a method of combining manual regular cleaning and salvage and manual inspection tour, and belongs to a passive protection stage; in addition, in the face of complex and variable marine environments, a single monitoring means cannot deal with marine disasters caused by multi-parameter coupling, and multi-parameter omnibearing monitoring and analysis are required; meanwhile, the cold source protection lacks early warning for the threat of potential marine disasters and cannot timely make early preparation for dealing with the occurrence of the disasters.
Disclosure of Invention
The invention aims to provide an early warning system for marine disaster-causing substances of a coastal nuclear power station, which widens the application of a new technology in the field of cold source protection of the coastal nuclear power station, fills the blank of early disaster early warning of the current domestic coastal nuclear power cold source protection system, and better meets the monitoring and early warning requirements of a water intake of the coastal nuclear power station.
Specifically, the invention provides a marine disaster early warning system for a coastal nuclear power station, which comprises a multi-source data acquisition module, a data processing module, a data management module, an analysis module and a decision support module, wherein the multi-source data acquisition module is used for acquiring a marine disaster early warning signal; wherein
The multi-source data acquisition module is used for acquiring multi-source heterogeneous data at least comprising marine environment information, meteorological information, water surface object detection information and underwater object detection information, and transmitting the acquired multi-source heterogeneous data to the data processing module through a network;
the data processing module is used for identifying, quantifying and normalizing the multi-source heterogeneous data obtained by the multi-source data acquisition module, and filtering the abnormal interference data to obtain processed monitoring data;
the data management module is used for storing the processed monitoring data and historical data;
the analysis module is used for predicting and calculating a marine organism quantitative value by substituting the processed monitoring data in a prediction network architecture constructed by combining the historical data information;
and the decision support module is used for searching a corresponding disaster grade according to the quantitative value when the marine organism disaster quantitative value calculated by the analysis module exceeds a certain preset safety threshold value, further determining a corresponding scheme and measures under the disaster grade, and automatically pushing the disaster grade and the corresponding scheme to the associated personnel terminal equipment through a network.
Further, the multi-source data acquisition module specifically comprises a flow velocity meter, a water quality analyzer, a meteorological instrument, a high-definition camera, a sonar and manual statistical data, and is used for acquiring multi-dimensional comprehensive data of a cold source water intake; the multi-source data acquisition module generates multi-source heterogeneous data comprising water flow, flow direction, PH, dissolved oxygen, water temperature, wind speed, wind direction, wave height, wave direction, water surface image and underwater echo image, and the data acquired by various acquisition instruments have different formats.
Further, the multi-source heterogeneous data obtained by normalizing the multi-source data acquisition module is data which are in different formats and different dimensions and are mapped to the data between [ -11 ] through a normalization function and can be processed by a computer;
the filtering processing of the abnormal interference data is to eliminate or weaken the influence degree of data mutation caused by external interference or abnormal work of monitoring equipment in the multi-source monitoring data, simultaneously judge whether the data acquired by each data acquisition module at the same sampling time point is missing or not, and perform interpolation processing through front and rear sampling points if the data acquired is missing.
Further, the data management module comprises organization data storage, historical data query, phase data change trend and a real-time data and historical data comparison change curve.
Further, the analysis module acquires the change condition of the marine environment data before the marine disaster causing thing disaster occurs according to the historical data; predicting and calculating a quantitative value of certain multi-source heterogeneous data according to the change condition of the marine environment data and the real-time information of the multi-source data acquisition module; and comparing the predicted result with the actual result, and updating the predicted network structure parameters if a larger difference exists so as to realize the self-learning capability of the network.
Furthermore, the analysis module comprises an analysis module of a specific sea area, and is used for adjusting the weighting coefficient of the network through the historical data of the specific sea area to obtain the quantized value of the specific sea area, so that the system has the self-adaptability of the application sea area.
Further, the decision support module is used for pushing corresponding solutions under different disaster levels, and comprises: during primary early warning, pushing a cold source protection group to pay attention to potential threat information; during secondary early warning, pushing on-site verification and confirmation information of a cold source protection group; and during three-level early warning, pushing the organization personnel of the cold source protection group to clean and salvage, and simultaneously making power-reducing operation preparation information of the unit.
Further, the early warning information and the support scheme pushed by the decision support module can be sent to a computer or a mobile phone of a specific person through a network.
The invention has the advantages that:
1. the application of the new technology in the field of cold source protection of the coastal nuclear power station is expanded;
2. the multi-dimensional monitoring of the surrounding sea area environment of the cold source is realized through the multi-source data acquisition module comprising parameters such as hydrology, water quality, meteorology, water surface images, underwater imaging and the like, and the integrity of system data collection is enhanced;
3. the data processing module is adopted to preprocess the acquired data through a normalization function, a filtering algorithm and an interpolation algorithm, so that the anti-interference capability of the system and the accuracy of the data are improved;
4. the analysis module can abstract the internal weight coefficient among the quantitative indexes of each factor through multi-layer depth iterative operation according to a large amount of historical data information, so that the accuracy of the system is improved;
5. the analysis module can compare the prediction result with the actual result, and automatically adjust the network structure parameters if the difference exceeds a set range, so that the self-learning capability is good;
6. and the decision support module automatically pushes the treatment measures and suggestions under the corresponding disaster grade according to the analysis result, so that the working efficiency of cold source protection personnel is greatly improved.
Drawings
FIG. 1 is a schematic diagram of the marine disaster early warning system of a coastal nuclear power station according to the present invention;
FIG. 2 is a schematic diagram of a marine disaster early warning system of a coastal nuclear power plant according to an embodiment of the present invention;
fig. 3 is a schematic view illustrating a process of quantizing image information of a marine disaster early warning system of a coastal nuclear power plant according to an embodiment of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
Referring to fig. 1, the marine disaster early warning system of the coastal nuclear power station of the present invention includes a multi-source data acquisition module, a data processing module, a data management module, an analysis module, and a decision support module; wherein
The multi-source data acquisition module is used for acquiring multi-source heterogeneous data at least comprising marine environment information, meteorological information, water surface object detection information and underwater object detection information, and transmitting the acquired multi-source heterogeneous data to the data processing module through a network;
the data processing module is used for identifying, quantifying and normalizing the multi-source heterogeneous data obtained by the multi-source data acquisition module, and filtering the abnormal interference data to obtain processed monitoring data;
data acquired by the multi-source data acquisition module must be processed by the data processing module to form data which is quantized, normalized and recognizable to a computer.
The data management module is used for storing the processed monitoring data and historical data;
the analysis module is used for constructing a prediction network architecture by combining the historical data information and predicting and calculating a marine organism quantitative value by substituting the processed monitoring data; meanwhile, the prediction network architecture and related parameters constructed by the analysis module can also be stored in the data management module, so that bidirectional connection is realized between the two modules.
And the decision support module is used for searching a corresponding disaster grade according to the quantitative value when the marine organism disaster quantitative value calculated by the analysis module exceeds a certain preset safety threshold value, further determining a corresponding scheme and measures under the disaster grade, and automatically pushing the disaster grade and the corresponding scheme to the associated personnel terminal equipment through a network. Therefore, the decision of the decision support module is stored in the database management module as historical information, and the two modules are connected in a bidirectional mode. The terminal equipment of the personnel can feed back the executed historical process and the execution result to the database management module.
More specifically, fig. 2 shows a detailed composition schematic diagram of the marine disaster early warning system of the coastal nuclear power plant of the present invention. The system mainly comprises: and the multi-source data acquisition module 1 is used for acquiring marine environment monitoring information and transmitting the acquired multi-source heterogeneous data to the data processing module through a network.
The marine environment information in the multi-source heterogeneous data mainly comprises: hydrological data 1.1 obtained by a flow velocity instrument, water quality data 1.2 obtained by a water quality analyzer, meteorological data 1.3 from a meteorological station, water surface images 1.4 obtained by a camera, underwater imaging 1.5 data obtained by sonar, image information, and statistical data 1.6 obtained by manual fishing.
The multi-source data acquisition module generates multi-source heterogeneous data including water flow, flow direction, PH, dissolved oxygen, water temperature, wind speed, wind direction, wave height, wave direction, water surface image and underwater echo image, and the data acquired by various acquisition instruments have different formats
Specifically, due to the difference of interface protocols and transmission modes between different instruments and equipment, the integration of the multi-source monitoring equipment is difficult, and therefore, the networking transmission of data information is realized by adopting a standard Ethernet interface unified interface protocol.
The system also comprises a data processing module 2, which is used for identifying, quantifying and normalizing the multi-source heterogeneous data obtained by the multi-source data acquisition module and filtering abnormal interference data.
Specifically, due to the inconsistency of data formats and dimensions between different devices, for example, the difference between numerical data and image information, speed units and weight units seriously affects the calculation speed and precision of the system, so that the image information is converted into quantized numerical data by using an image recognition technology; taking image information quantization as an example, firstly obtaining a picture of a single frame image, extracting high-quality picture information through picture preprocessing, then converting the picture information into a two-dimensional matrix, counting the number of RGB (red, green and blue) of the image according to the size of each element value in the two-dimensional matrix, and converting the RGB into actual quantized values of area and density according to area and density coefficients corresponding to RGB with different sizes, wherein the specific flow is shown in FIG. 3.
After quantification, mapping the difference of different dimensions between [ -11 ] through a normalization function to realize standardization; each item of data in the multi-source collected data is processed respectively, a discrete numerical filter is used for eliminating or weakening the influence degree of data mutation caused by external interference or abnormal work of monitoring equipment by adopting a filtering algorithm 2.3, meanwhile, whether the collected data of each data collection module at the same sampling time point is missing or not is judged, and if the collected data is missing, interpolation processing is carried out on sampling points before and after a difference value processing algorithm 2.2.
Sample data is processed using a data normalization function 2.1, which is,
wherein x isgIs normalized data, x is the original data, xmin，xmaxThe maximum and minimum values in the same sample data.
The system also comprises a data storage module 3 which is used for storing the processed monitoring data and historical data, and is also used for dynamically displaying the change trend of each item of real-time monitoring data and generating a comparison change curve with the historical data.
Specifically, the method comprises the functions of data storage 3.1, data query 3.2, visualization contrast curve 3.3 and the like, real-time recording and storage are carried out on the processed multi-source monitoring data and the processed historical data according to specified logic setting, the accuracy and the integrity of the data are ensured, normalization processing is carried out on the existing various monitoring equipment data including pictures, data and tables according to corresponding formats, and finally all the data are stored into an existing database according to information such as categories, time and the like for unified management so as to facilitate historical information query and algorithm use.
And the analysis module 4 is used for constructing a prediction network by combining the historical data extraction 4.1 module with a prediction network architecture constructed by existing historical data information, namely constructing a prediction network 4.2, calculating a marine organism quantitative value in real time by substituting the processed multi-source monitoring data, and outputting the result by using the prediction result output module 4.3.
The construction of a contrast optimization prediction network 4.4 is specifically described, the quantitative value of marine life in the embodiment takes the artificial salvage amount as an example, the inherent incidence relation of existing marine disaster factors in a water intake sea area is obtained according to historical data and image information of hydrology 1.0, water quality 1.1, weather 1.2, water surface detection 1.3 and underwater detection 1.4 which are obtained by multi-source monitoring equipment, a network structure with certain confidence coefficient is obtained, data obtained by the multi-source monitoring equipment in real time is substituted into the network, the quantitative value of the artificial salvage amount is predicted, and the marine disaster is evaluated. As an improvement of the scheme, the quantized value of the predicted manual salvage quantity is compared with the actual manual statistical salvage quantity 1.5, if a large difference exists, the predicted network structure parameters are updated, the self-learning capability of the network is realized, and therefore the accuracy of the network prediction data is improved.
More specifically, the invention can adopt a neural network method to construct the prediction network model.
The neural network model training comprises the following steps:
(1) generating random numbers by using a system clock, initializing a weight value w and an offset value b of each layer of neural units, initializing a system random function rand and the weight value w and the offset value b into a function,
rand('state',sum(100*clock)) (1)
w＝(0.1～0.5)*rand(m,n)-0.1 (2)
b＝(0.1～0.5)*rand(m,1)-0.1 (3)
wherein, formula (1) is a system clock random function, formula (2) is a random initial value of a weighted value w on the basis of formula (1), and formula (3) is a random initial value of an offset value b on the basis of formula (1); complement the meaning of state, clock, m, n
(2) Initializing neural network parameters, including maximum training times maxEpoch of the network, learning rate LearnRate of updating step length of weight value and bias value, noise intensity noiseInternity (0-1) for preventing system overfitting, and Error range of a neural network training target Error; setting the network to start training from 1 to maxEpoch;
(3) the network hidden layer matrix A, the output layer matrix B, the network error E and the corresponding actual sample output matrix R are calculated according to the following formula,
A＝logsig(w1*S+b1)
B＝w2*A+b2
E＝R-B
wherein, w1As weight value, w, of the hidden layer2Is the weighted value of the output layer, b1For hidden layer bias values, b2Is the output layer bias value, S is the sample matrix;
(4) calculating error change delta1 and delta2 of the hidden layer and the output layer according to the following formulas, and further iteratively calculating correction values of weight values and offset values of each layer and new weight values and offset values, wherein the network parameter correction values are used for avoiding divergence and overfitting phenomena, and a noise disturbance item is added, so that the direction of current parameter value modification is not completely determined by the gradient direction under the current sample, but is determined by the parameter value modification direction of the last time and the gradient direction of the current time together, and the concrete formula is,
delta2＝E
delta1＝w2*delta2*A*(1-A)
dw2＝delta2*A
db2＝delta2
dw1＝delta1*S
db1＝delta1
wherein dw1 represents a corrected value of the hidden layer weight value, dw2 represents a corrected value of the output layer weight value, db1 represents a corrected value of the hidden layer bias value, db2 represents a corrected value of the output layer bias value, and τ represents the number of iterations;
(5) the sum of squared errors E after correction is calculated according to the following formulasqrIn order to realize the purpose,
Esqr＝sumsqr(E)
(6) determining whether the Error square sum is within a set Error range Error through a condition judgment function, if so, finishing network training, and otherwise, continuing to calculate the maximum iterative calculation times of each group of sample data to be less than maxEpoch;
(7) and (5) circulating the steps 1) to 5), training the next group of sample data until all the sample data training is finished.
In this embodiment, when marine disaster-causing quantization prediction needs to be performed on a certain sea area, the weight coefficient of the multi-source monitoring data is adjusted according to the historical data of the sea area, so as to obtain a disaster-causing quantization model applicable to the sea area.
The decision support module 5 comprises an evaluation analysis module 5.1, a disaster grade and scheme module 5.2, an information push module 5.3 and a client interaction module 5.4. The disaster grade and scheme module 5.2 is used for searching a corresponding disaster grade according to the quantized value when the value of the marine organism salvage amount calculated by the evaluation analysis module 5.1 of the prediction network exceeds a certain preset safety threshold value, further determining a corresponding scheme and measures under the disaster grade, and the information pushing module 5.3 automatically pushes the disaster grade and the corresponding scheme to the related personnel terminal equipment through the network, wherein the terminal equipment comprises a client interaction module 5.4, and bidirectional communication is realized.
Specifically, before a disaster happens, the system can obtain the degree of disaster development in a period of time in the future by using the existing prediction algorithm according to collected multi-source monitoring data, give an early warning level and a corresponding decision support scheme, meanwhile, the system integrates monitoring data such as image data, numerical data and the like, and a worker can quickly and efficiently take corresponding measures by inquiring various data and comparing the data with the current early warning level at any time and any place through a computer or a mobile phone APP.
The present invention is not limited to the above embodiments, and those skilled in the art can implement the present invention in other various embodiments according to the disclosure of the embodiments and the drawings, and therefore, all designs that can be easily changed or modified by using the design structure and thought of the present invention fall within the protection scope of the present invention.
Claims (8)
1. A marine disaster early warning system for a coastal nuclear power station is characterized by comprising a multi-source data acquisition module, a data processing module, a data management module, an analysis module and a decision support module; wherein
The multi-source data acquisition module is used for acquiring multi-source heterogeneous data at least comprising marine environment information, meteorological information, water surface object detection information and underwater object detection information, and transmitting the acquired multi-source heterogeneous data to the data processing module through a network;
the data processing module is used for identifying, quantifying and normalizing the multi-source heterogeneous data obtained by the multi-source data acquisition module, and filtering the abnormal interference data to obtain processed monitoring data;
the data management module is used for storing the processed monitoring data and historical data;
the analysis module is used for constructing a prediction network architecture by combining the historical data information and predicting and calculating a marine organism quantitative value by substituting the processed monitoring data;
and the decision support module is used for searching a corresponding disaster grade according to the quantitative value when the marine organism disaster quantitative value calculated by the analysis module exceeds a certain preset safety threshold value, further determining a corresponding scheme and measures under the disaster grade, and automatically pushing the disaster grade and the corresponding scheme to the associated personnel terminal equipment through a network.
2. The early warning system for the marine disaster causing objects of the coastal nuclear power station according to claim 1, wherein the multi-source data acquisition module specifically comprises a current meter, a water quality analyzer, a weather meter, a high-definition camera, a sonar and manual statistical data, and is used for acquiring multi-dimensional comprehensive data of a cold source water intake; the multi-source data acquisition module generates multi-source heterogeneous data comprising water flow, flow direction, PH, dissolved oxygen, water temperature, wind speed, wind direction, wave height, wave direction, water surface image and underwater echo image, and the data acquired by various acquisition instruments have different formats.
3. The marine disaster early warning system for the coastal nuclear power plant as claimed in claim 1, wherein the multi-source heterogeneous data obtained by normalizing the multi-source data acquisition module is data which has different formats and different dimensions and is mapped to the data between [ -11 ] through a normalization function and can be processed by a computer;
the filtering processing of the abnormal interference data is to eliminate or weaken the influence degree of data mutation caused by external interference or abnormal work of monitoring equipment in the multi-source monitoring data, simultaneously judge whether the data acquired by each data acquisition module at the same sampling time point is missing or not, and perform interpolation processing through front and rear sampling points if the data acquired is missing.
4. The marine disaster early warning system for the coastal nuclear power stations as claimed in claim 1, wherein the data management module comprises organization data storage, historical data query, phase data change trend, and real-time data and historical data comparison change curve.
5. The marine disaster early warning system for the coastal nuclear power station as claimed in claim 1, wherein the analysis module obtains a marine environment data change situation before a marine disaster occurs according to the historical data; predicting and calculating a quantitative value of certain multi-source heterogeneous data according to the change condition of the marine environment data and the real-time information of the multi-source data acquisition module; and comparing the predicted result with the actual result, and updating the predicted network structure parameters if a larger difference exists so as to realize the self-learning capability of the network.
6. The pre-warning system for marine disaster stricken objects in coastal nuclear power stations as claimed in claim 5, wherein the analysis module comprises an analysis module for specific sea area, which is used for adjusting the weighting coefficient of the network through historical data of the specific sea area to obtain the quantized value of the specific sea area, so that the system has adaptivity to the application sea area.
7. The marine disaster early warning system for coastal nuclear power stations as claimed in claim 1, wherein the decision support module is used for pushing corresponding solutions under different disaster levels, and comprises: during primary early warning, pushing a cold source protection group to pay attention to potential threat information; during secondary early warning, pushing on-site verification and confirmation information of a cold source protection group; and during three-level early warning, pushing the organization personnel of the cold source protection group to clean and salvage, and simultaneously making power-reducing operation preparation information of the unit.
8. The marine disaster early warning system for the coastal nuclear power plant as claimed in claim 7, wherein the early warning information and the supporting scheme pushed by the decision support module can be sent to a computer or a mobile phone of a specific person through a network.
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