Intelligent early warning method for threats in sea area of water intake of nuclear power plant

Abstract

The application provides a nuclear power plant water intake sea area threat intelligent early warning method, the method comprises the following steps: data acquisition and synchronization; data preprocessing is carried out; modeling analysis is carried out, a nuclear power plant water intake sea area threat early warning algorithm based on mechanism analysis and an intelligent early warning algorithm based on biological fuzzy characteristics are constructed; risk identification and early warning are carried out according to the processing results of the early warning algorithm, and the results are output and displayed. The method of the application can deeply mine and fully utilize the multi-source heterogeneous data collected in the front end, and through the integrated implementation of the two early warning algorithms based on mechanism analysis and biological fuzzy characteristics, the real-time effect and the medium and short-term prediction effect of the cold source monitoring and early warning can be ensured, which is an effective supplement to the previous simple real-time early warning or medium and short-term prediction.

Description

Technical Field

[0001] This invention relates to an intelligent early warning method for threats to the marine area of ​​a nuclear power plant's water intake. Background Technology

[0002] In recent years, with changes in the nearshore marine ecological environment, several coastal nuclear power plants both domestically and internationally have experienced incidents of marine organisms or foreign objects clogging their water intakes. These incidents range from causing short-term power reductions in nuclear power units to serious shutdowns, severely impacting the safety and economic efficiency of power plant operations.

[0003] In response to the safety of cold sources affecting nuclear power plant safety and power generation, the national nuclear power regulatory authorities have successively introduced a series of management measures, requiring the strengthening and improvement of cold source safety for nuclear power plants from both the design and operation management perspectives. This includes the design of a threat monitoring and early warning system for the marine area around the nuclear power plant's intake. The early warning algorithm is a crucial component of this system design, and its effectiveness and rationality directly impact the system's application results.

[0004] However, the inventors found that existing technologies cannot quantitatively predict marine biological blockage at nuclear power plant intakes and are not effective in comprehensively utilizing various monitoring data collected at the system front end. Therefore, existing methods for predicting threats to marine areas near nuclear power plant intakes need further improvement.

[0005] Current research indicates that marine organism blockage-related disasters involve many influencing factors and have complex and ever-changing internal relationships, and a relatively mature and stable early warning model for marine organism threats has not yet been formed. Summary of the Invention

[0006] It should be understood that the general description above and the detailed description below are exemplary and illustrative, and are intended to provide further explanation of this disclosure.

[0007] The technical problem to be solved by this invention is to provide two intelligent early warning algorithms based on mechanism analysis and biological fuzzy features, and to identify and warn of risks based on the processing of the early warning algorithms.

[0008] To address the aforementioned technical problems, this invention provides an intelligent early warning method for threats to the marine area near nuclear power plant intakes, characterized in that the method includes:

[0009] Step S1: Data acquisition and synchronization;

[0010] Step S2: Perform data preprocessing;

[0011] Step S3: Perform modeling and analysis to construct a threat early warning algorithm for the marine area of ​​nuclear power plant intake based on mechanism analysis and an intelligent early warning algorithm based on biological fuzzy features;

[0012] Step S4: Based on the processing results of the early warning algorithm, risk identification and early warning are performed, and the results are output and displayed.

[0013] Preferably, the present invention further provides an intelligent early warning method for threats to the marine area of ​​nuclear power plant intakes, characterized in that, in step S3, the early warning algorithm for threats to the marine area of ​​nuclear power plant intakes based on mechanism analysis further includes:

[0014] Step S21, obtain the cooling capacity requirements based on the specific operating needs of the power plant:

[0015]

[0016] in, It can provide cooling for seawater. To meet the minimum cooling requirements of nuclear power plants,

[0017]

[0018] in, The seawater flow rate Q at the intake pump inlet 水 and a function of temperature T0, C p The specific heat is given by constant pressure, ρ is the density of seawater, and T is the design drainage temperature of the heat exchanger.

[0019] Step S22: Based on the size of marine organisms observed by sonar, determine the volume fraction of marine organisms, including three cases:

[0020] Scenario 1: When sonar observes marine organisms of size D ≥ filter size, and all of them are of this type, the effective seawater cooling flow rate is:

[0021] Q η =Q 水 =(1-η)Q·w (3)

[0022] Where w is the weighting coefficient, and w = 1 or w > 1 is selected;

[0023] Scenario 2: When the sonar observes marine organisms of size D ≤ filter size, and all of them are of this type, the effective seawater flow rate is:

[0024] Q 水 =(1-ξ)Q (4)

[0025] Where ξ is the volume fraction of marine organisms smaller than the filter screen size, and the pump suction volume flow rate is Q;

[0026] Scenario 3: Effective seawater cooling flow rate when some marine organisms are larger than the filter screen size and some are smaller than the filter screen size:

[0027] Q 水=(1-α)Q·w (8)

[0028] Where w is the weighting coefficient, and w = 1 or w > 1 is selected;

[0029] Step S23, calculate the maximum cooling capacity that the introduced pump can provide:

[0030]

[0031] Step S24, calculate the early warning factor, and... As a basis for alarms; among them, a warning factor of 0-0.249 is low risk, 0.25-0.499 is medium risk, 0.5-0.749 is high risk, and greater than or equal to 0.75 is extremely high risk.

[0032] Preferably, the present invention further provides an intelligent early warning method for threats to the marine area of ​​nuclear power plant intakes, characterized in that, in step S3, the intelligent early warning algorithm based on biological fuzzy features further includes:

[0033] Step S31: Obtain marine hydrological conditions and calculate marine hydrological factor A:

[0034]

[0035] Where A1 is the magnitude of the ocean current, A2 is the direction of the ocean current, and V 监测 V represents the magnitude of the velocity component of the ocean current in the direction perpendicular to the intake. 平均 This represents the historical average ocean current speed in the current sea area.

[0036] Step S32: Assess the severity of biological blockage and calculate the blockage severity index B:

[0037]

[0038] Where B is the bioblocking influencing factor, W is the influencing factor weight, i is the index of the influencing factor, n is the number of influencing factors, and W Bi For the i-th impact factor B i The weight it occupies;

[0039] Step S33: Assess biological characteristics and calculate the biological characteristic index C:

[0040]

[0041] Where i is the index of the impact factor and n is the number of impact factors;

[0042] Step S34: Based on the values ​​of criteria layers A, B, and C, the disaster risk index D is obtained as follows:

[0043] D = A × B × C (14)

[0044] The disaster risk index D ranges from 0 to 1.

[0045] Preferably, the present invention further provides an intelligent early warning method for threats in the marine area of ​​nuclear power plant intake, characterized in that the influencing factors of the blockage severity index B include individual size B1, biological aggregation probability B2, buoyancy B3 and individual number B4, wherein the values ​​of B1 to B4 range from 0 to 1.

[0046] Preferably, the present invention further provides an intelligent early warning method for threats in the marine area of ​​nuclear power plant intake, characterized in that the influencing factors of the biological characteristic index C include the organism's environmental adaptability C1, reproductive capacity and mode C2, distribution of natural enemies C3, and survival ability C4, wherein the values ​​of C1 to C4 range from 0 to 1.

[0047] Preferably, the present invention further provides an intelligent early warning method for threats in the marine area of ​​nuclear power plant intake, characterized in that the values ​​of B1 to B4 and C1 to C4 are divided into 5 levels according to 0, 0.25, 0.5, 0.75 and 1, and the levels include no blockage, slight blockage, moderate blockage, heavy blockage and severe blockage.

[0048] Preferably, the present invention further provides an intelligent early warning method for threats in the marine area of ​​nuclear power plant intake, characterized in that the disaster risk index includes 0.000-0.249 as low risk, 0.250-0.499 as medium risk, 0.500-0.749 as high risk, and 0.750-1.000 as extremely high risk.

[0049] Preferably, the present invention further provides a method for intelligent early warning of threats to the marine area of ​​nuclear power plant intake, characterized in that,

[0050] The data in step S1 includes data collection by subsystem units consisting of multiple sensors deployed in the field environment. Each subsystem unit aggregates and synchronizes the collected data information to the database in the server deployed at the shore-based monitoring station through a data synchronization module.

[0051] Preferably, the present invention further provides an intelligent early warning method for threats in the marine area of ​​nuclear power plant intake, characterized in that step S2 further includes: performing necessary data preprocessing work, including data governance, data quality inspection, data fusion, and feature engineering, in a data preprocessing module based on ETL data tools deployed in the server.

[0052] Preferably, the present invention further provides a method for intelligent early warning of threats in the marine area of ​​a nuclear power plant intake, characterized in that the sensors include sonar, surface and underwater cameras, water flow sensors and water quality sensors.

[0053] Compared with the prior art, the present invention has the following advantages: The method of the present invention can deeply mine and make full use of multi-source heterogeneous data collected at the front end. Through the integrated implementation of two algorithms based on mechanism analysis and biological fuzzy features, the real-time effect and short-to-medium-term prediction effect of cold source monitoring and early warning can be ensured. It is an effective supplement to the previous method of simply performing real-time early warning or short-to-medium-term prediction. Attached Figure Description

[0054] Embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. Preferred embodiments of the present disclosure will now be described in detail, examples of which are illustrated in the drawings. Wherever possible, the same reference numerals will be used in all the drawings to denote the same or similar parts. Furthermore, although the terminology used in this disclosure is selected from commonly known and used terminology, some terms referenced in this disclosure may have been chosen by the applicant at his or her judgment, and their detailed meanings are explained in the relevant sections of the description herein. Moreover, this disclosure should be understood not only by the actual terms used, but also by the meaning implied by each term.

[0055] The above and other objects, features and advantages of the present invention will become apparent to those skilled in the art from the detailed description thereof, with reference to the accompanying drawings.

[0056] Figure 1 This invention relates to a flowchart of an intelligent early warning method for threats in the marine area of ​​nuclear power plant intakes;

[0057] Figure 2 yes Figure 1 Flowchart of the mechanism-based threat early warning algorithm for the marine area of ​​nuclear power plant intake, step S3;

[0058] Figure 3 yes Figure 1 A flowchart of the threat early warning algorithm for the marine area of ​​nuclear power plant intake based on biological fuzzy features in step S3;

[0059] Figure 4 The table illustrates how the constructed disaster risk assessment system predicts disaster risks. Detailed Implementation

[0060] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0061] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0062] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0063] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0064] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0065] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0066] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0067] This invention first addresses the cooling problem of nuclear power plants, combining multi-source heterogeneous measurement data to develop a relatively scientific mechanism / data-driven early warning method for marine threat alarm and prediction through demand analysis, assurance methods, and parameter characterization. Building upon this, and incorporating multi-source heterogeneous measurement data collected from the front end, an early warning model for marine biological blockage at nuclear power plant intakes is further proposed. This model is continuously refined during subsequent nuclear power plant operation, ultimately forming an effective mechanism / biological fuzzy feature / data-driven early warning model.

[0068] Figure 1 A flowchart of the intelligent early warning method for threats to the marine area of ​​nuclear power plant intakes according to the present invention is provided. The detailed steps, in conjunction with this flowchart, are explained below:

[0069] Step S1: Data acquisition and synchronization;

[0070] Specifically, it involves data acquisition through multiple sensors deployed in the field environment, such as sonar, surface and underwater cameras, water flow sensors, and water quality sensors. Each subsystem unit aggregates and synchronizes the collected data to the database on the server deployed at the shore-based monitoring station through a data synchronization module.

[0071] Step S2: Perform data preprocessing;

[0072] The data preprocessing module, based on ETL data tools, is deployed within the server to perform necessary data preprocessing tasks such as data governance, data quality checks, data fusion, and feature engineering.

[0073] Step S3: Perform modeling and analysis to construct a threat early warning algorithm for the marine area of ​​nuclear power plant intake based on mechanism analysis and an intelligent early warning algorithm based on biological fuzzy features;

[0074] Step S4: Based on the processing results of the early warning algorithm, risk identification and early warning are performed, and the results are output and displayed, including anomaly monitoring, trend prediction, etc.

[0075] In the above process, step S3 is the core of the entire early warning method. Considering the engineering scenario of the threat to the nuclear power plant's water intake sea area in this case, this step employs an early warning method for threats to the nuclear power plant's water intake sea area based on mechanism analysis and an intelligent early warning algorithm based on biological fuzzy features. The details are as follows:

[0076] The algorithm implementation process based on mechanism analysis is as follows: Figure 2 As shown, the main steps include the following:

[0077] Step S21: Obtain cooling demand based on the specific operating requirements of the nuclear power plant.

[0078] Based on the specific marine species associated with the plant site, the impact of marine organisms at the inlet on cooling capacity is mainly achieved by reducing the pump suction volumetric flow rate Q or directly reducing the effective cooling seawater flow rate Q. 水 reflect.

[0079] In order to achieve effective cooling, It can provide cooling for seawater. To meet the minimum cooling capacity required for nuclear power plant cooling, both conditions must be met:

[0080]

[0081] in,

[0082]

[0083] in, The seawater flow rate Q at the intake pump inlet 水 and a function of temperature T0;

[0084] Where C p ρ is the specific heat at constant pressure, ρ is the density of seawater, and T is the design drainage temperature of the heat exchanger.

[0085] Step S22: Determine the volume fraction of marine organisms;

[0086] This step includes three scenarios:

[0087] a. When sonar observes marine organisms of the same size D as the filter size, and all of them are of that type, it is assumed that the filter will be clogged.

[0088] Assuming η is the volume fraction of marine organisms larger than the filter screen size, then the pump suction flow rate and the effective seawater cooling flow rate are the same, which is:

[0089] Q η =Q 水 =(1-η)Q·w (3)

[0090] Where w is a weighting coefficient. When w = 1, it is assumed that the proportion of the filter area that is blocked is the same as the proportion of the pump's suction volume flow rate reduction, which is a relatively safe value.

[0091] However, the actual reduction in pump suction volume flow rate is even smaller, i.e., w>1. This weighting coefficient can be estimated by the pump's performance curve or actual operating conditions. Therefore, an interface will be provided for subsequent adjustments during the development process.

[0092] b. When sonar observes marine organisms of size D ≤ filter size, and all of them are of that type, it is assumed that the marine organisms will be inhaled, resulting in a decrease in the effective cooling seawater flow.

[0093] Assuming ξ represents the volume fraction of marine organisms smaller than the filter screen size, then when the pump's suction volumetric flow rate is Q, the effective seawater flow rate is:

[0094] Q 水 =(1-ξ)Q (4)

[0095] c. When some marine organisms are larger than the filter screen size and some are smaller than the filter screen size, i.e., falling between a and b above, the marine organism volume fraction is introduced:

[0096] α=η+ξ (5)

[0097] Based on the analysis in points a and b above, the effective seawater cooling flow rate at this point is:

[0098]

[0099] Right now:

[0100] Q 水 =(1-α)Q·w (7)

[0101] Furthermore, it effectively cools the seawater flow rate:

[0102] Q 水 =(1-α)Q·w (8)

[0103] Where w is the weighting coefficient.

[0104] Step S23: Calculate the available cooling capacity;

[0105] Ultimately, introducing a pump can provide the maximum cooling capacity:

[0106]

[0107] Step S24, calculate the early warning factor, which can be As evidence for reporting an incident;

[0108] Among them, a warning factor of 0-0.249 indicates low risk, 0.25-0.499 indicates medium risk, 0.5-0.749 indicates high risk, and 0.75 or higher indicates extremely high risk.

[0109] The risk of a marine organism causing disaster is essentially a function of the degree and probability of blockage at a nuclear power plant's intake. This invention employs the analytic hierarchy process (AHP) to establish a screening index system for disaster-causing organisms. Based on the analysis of the mechanism by which marine organisms block nuclear power plant intakes, it constructs an analytical framework and assessment model for the risk of marine organism-induced disasters.

[0110] Figure 3 A flowchart is provided for another method in step S3 for constructing a threat early warning method for the marine area of ​​nuclear power plant intake based on biological fuzzy features.

[0111] This early warning system based on biological fuzzy features mainly includes four parts: acquiring marine hydrological conditions, assessing the severity of biological blockage, evaluating biological characteristics, and calculating the final disaster risk index.

[0112] Figure 4 The parameters for constructing a threat early warning model for the marine area of ​​nuclear power plant intakes based on biological fuzzy features are listed. The basic idea is to refer to risk analysis methods and then use a multi-level fuzzy comprehensive evaluation model to propose a classification standard for disaster-causing organisms, and use a qualitative grading and assignment method to assign values ​​to each disaster-causing probability index factor.

[0113] The calculation formula is as follows:

[0114] D = A × B × C (10)

[0115] Where D is the marine organism disaster risk index, A is the marine hydrological conditions, B is the blockage severity index, and C is the biological characteristic index. The following flowchart details each step:

[0116] A qualitative grading and value assignment method is used to assign values ​​to each disaster probability index factor. Among them:

[0117] Step S31: Obtain marine hydrological conditions and calculate marine hydrological factor A:

[0118] Considering the relationship between the magnitude A1 and the direction A2 of the ocean current, the magnitude V of the velocity component of the ocean current in the direction perpendicular to the water intake is calculated using the values ​​of A1 and A2. 监测 And calculate its relationship with the historical average ocean current velocity V in the current sea area. 平均 The ratio of the two values ​​determines the value of A, that is:

[0119]

[0120] Step S32: Assess the severity of biological blockage and calculate the blockage severity index B:

[0121]

[0122] Where B is the bioblocking influencing factor, W is the influencing factor weight, i is the index of the influencing factor, n is the number of influencing factors, and W Bi For the i-th impact factor B i The weighting of the factors. The factors influencing the severity index B of blockage involve individual size B1, bioaggregation probability B2, floatability B3, and number of individuals B4. The values ​​of the factors B1-B4 range from 0 to 1, and can be divided into 5 levels: 0, 0.25, 0.5, 0.75, and 1.

[0123] For example, for individual size B1, if the individual size is less than 0.5mm, it is considered that there will be no blockage, and a value of 0 is assigned; if the individual size is greater than or equal to 0.5mm and less than 1mm, it is considered that it is not likely to cause blockage, and a value of 0.25 is assigned; if the individual size is greater than or equal to 1mm and less than 2mm, it is considered that it may cause blockage, and a value of 0.5 is assigned; if the individual size is greater than or equal to 2mm and less than 3mm, it is considered that it is likely to cause blockage, and a value of 0.75 is assigned; if the individual size is greater than 3mm, it is considered that it is very likely to cause blockage, and a value of 1 is assigned.

[0124] For the bioaccumulation probability B2, if the bioaccumulation intensity is extremely low, it is considered unlikely to cause blockage, and a value of 0 is assigned; if the bioaccumulation intensity is low, it is considered to cause mild blockage, and a value of 0.25 is assigned; if the bioaccumulation intensity is moderate, it is considered to cause moderate blockage, and a value of 0.5 is assigned; if the bioaccumulation intensity is high, it is considered to cause severe blockage, and a value of 0.75 is assigned; if the bioaccumulation intensity is extremely high, it is considered to cause severe blockage, and a value of 1 is assigned.

[0125] For buoyancy B3, if a creature is extremely unlikely to drift with the current, it is assumed not to cause blockage, so a value of 0 is assigned; if a creature is not easily drifted with the current, it is assumed not to cause blockage, so a value of 0.25 is assigned; if a creature does drift with the current, it is assumed to cause blockage, so a value of 0.5 is assigned; if a creature easily drifts with the current, it is assumed to cause blockage, so a value of 0.75 is assigned; if a creature is extremely easily drifted with the current, it is assumed to cause blockage, so a value of 1 is assigned.

[0126] For a population size B4, if the habitat density is extremely low, it is assumed that there will be no blockage, so a value of 0 is assigned; if the habitat density is low, it is assumed that blockage is not likely, so a value of 0.25 is assigned; if the habitat density is relatively high, it is assumed that blockage may occur, so a value of 0.5 is assigned; if the habitat density is high, it is assumed that blockage is likely, so a value of 0.75 is assigned; if the habitat density is extremely high, it is assumed that blockage is very likely, so a value of 1 is assigned.

[0127] Step S33: Assess biological characteristics and calculate the biological characteristic index C:

[0128]

[0129] Where i is the index of the influencing factor and n is the number of influencing factors, calculated by the above formula (13). The index layers are determined according to the biological characteristics of the aggregated marine organisms. This situation is similar to the previous one. The influencing factors of C involve the organism's environmental adaptability C1, reproductive capacity and mode C2, distribution of natural enemies C3 and survival ability C4. The value range of influencing factors C1-C4 is 0-1, which can be divided into 5 levels with reference to 0, 0.25, 0.5, 0.75 and 1.

[0130] For example, for environmental adaptability C1, if the biological environmental adaptability is extremely low, it is considered that there will be no blockage, and a value of 0 is assigned; if the biological environmental adaptability is low, it is considered that it is not easy to blockage, and a value of 0.25 is assigned; if the biological environmental adaptability is moderate, it is considered that it may cause blockage, and a value of 0.5 is assigned; if the biological environmental adaptability is high, it is considered that it is easy to cause blockage, and a value of 0.75 is assigned; if the biological environmental adaptability is extremely high, it is considered that it is extremely easy to cause blockage, and a value of 1 is assigned.

[0131] For reproductive capacity and mode C2, if an organism reproduces sexually and has a very low reproductive rate, it is considered unlikely to be blocked and assigned a value of 0; if an organism reproduces sexually and has a low reproductive rate, it is considered slightly blocked and assigned a value of 0.25; if an organism reproduces sexually and has a moderate reproductive rate, it is considered moderately blocked and assigned a value of 0.5; if an organism reproduces sexually and has a large reproductive rate, it is considered severely blocked and assigned a value of 0.75; if an organism reproduces sexually / asexually and has an extremely high reproductive rate, it is considered severely blocked and assigned a value of 1.

[0132] For the distribution of natural enemies (C3), if natural enemies are widely distributed, it is assumed that there will be no blockage, and a value of 0 is assigned; if natural enemies are distributed over a medium area, it is assumed that blockage is not likely, and a value of 0.25 is assigned; if natural enemies are scattered over a small area, it is assumed that blockage may occur, and a value of 0.5 is assigned; if natural enemies are distributed in very small numbers, it is assumed that blockage is likely, and a value of 0.75 is assigned; if the distribution of natural enemies is unclear, it is assumed that blockage is very likely, and a value of 1 is assigned.

[0133] For survivability C4, if the organism's survivability is extremely poor, it is assumed that it will not cause blockage, so a value of 0 is assigned; if the organism's survivability is poor, it is assumed that it is not likely to cause blockage, so a value of 0.25 is assigned; if the organism's survivability is moderate, it is assumed that it may cause blockage, so a value of 0.5 is assigned; if the organism's survivability is good, it is assumed that it is likely to cause blockage, so a value of 0.75 is assigned; if the organism's survivability is extremely good, it is assumed that it is very likely to cause blockage, so a value of 1 is assigned.

[0134] Step S34: Finally, based on the values ​​of criteria layers A, B, and C, the disaster risk index D is obtained as follows:

[0135] D = A × B × C (14)

[0136] The final disaster risk index D ranges from 0 to 1.

[0137] The range is as follows: 0.000-0.249 is low risk, 0.250-0.499 is medium risk, 0.500-0.749 is high risk, and 0.750-1.000 is extremely high risk.

[0138] Figure 4 The table illustrates how the constructed disaster risk assessment system predicts disaster risks.

[0139] The three factors are: Factor A, which considers ocean current size and direction to determine the severity of water intake blockage by marine organisms; Factor B, which considers individual size, likelihood of aggregation, buoyancy, and number of individuals to determine the severity of water intake blockage by marine organisms; and Factor C, which considers environmental adaptability, reproductive capacity and methods, predator distribution, and survival ability to determine the specific marine species at the nuclear power plant site.

[0140] The degree of disaster risk index can be adjusted by combining historical data on marine life surveys at specific power plant intakes.

[0141] This invention achieves intelligent early warning of threats to the sea area of ​​nuclear power plant intake through two main steps in step S4. This method can deeply mine and make full use of multi-source heterogeneous data collected at the front end. By constructing the method in an integrated manner through the two steps, the real-time effect of cold source monitoring and early warning and the effect of medium and short-term prediction can be ensured. It is an effective supplement to the previous method of simply conducting real-time early warning or medium and short-term prediction.

[0142] In summary, this invention constructs a mechanism-based early warning method for threats to the marine environment of nuclear power plant intakes. It can quantitatively determine the degree of blockage of the nuclear power plant intake by marine organisms in real time. At the same time, by combining the displayed images from video acquisition, it can cross-compare the activity range and situation of underwater marine organisms in real time, and provide early warning of marine threats to the cold source water intake of the power plant, thus ensuring the safety of the power plant.

[0143] Furthermore, by constructing a threat early warning method for the marine area of ​​nuclear power plant intake based on a biological fuzzy feature model, it is possible to predict the future development trend of marine organisms, predict the degree of risk of intake blockage, and achieve short- to medium-term prediction of threats to the marine area of ​​nuclear power plant intake, providing guidance for nuclear power plant operators to take necessary management and emergency measures in advance.

[0144] Ultimately, by processing and comprehensively utilizing the real-time monitoring data through the aforementioned quantitative methods, combined with cross-comparison of video footage, and risk prediction of the impact of short- and medium-term marine organism development trends on the degree of intake blockage, a more scientific and accurate prediction model can be constructed. This will provide technical support for the safety of nuclear power plant cold sources and a basis for response decisions in the management of nuclear power plant cold source safety.

[0145] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0146] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0147] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0148] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0149] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0150] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0151] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0152] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0153] Although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of this application. Therefore, any changes or modifications to the above embodiments within the essential spirit of this application will fall within the scope of the claims of this application.

Claims

1. A method for intelligent early warning of threats to the marine area near the intake of a nuclear power plant, characterized in that, The method includes: Step S1: Data acquisition and synchronization; Step S2: Perform data preprocessing; Step S3: Perform modeling and analysis to construct a threat early warning algorithm for the marine area of ​​nuclear power plant intake based on mechanism analysis and an intelligent early warning algorithm based on biological fuzzy features. The threat early warning algorithm for the marine area of ​​nuclear power plant intake based on mechanism analysis further includes: Step S21: Obtain the cooling demand based on the specific operating needs of the power plant, and satisfy: （1） in, It can provide cooling for seawater. To meet the minimum cooling requirements of nuclear power plants, （2） in, It is the seawater flow rate at the inlet of the water intake pump. and temperature The function, For isobaric specific heat, The density of seawater, Design the drain temperature for the heat exchanger; Step S22: Determine the volume fraction of marine organisms based on the size of the organisms observed by sonar. There are three scenarios: Scenario 1: When sonar observes the size of marine organisms... The effective seawater cooling flow rate when all organisms are of this type is: （3） in, This represents the volume fraction of marine organisms larger than the filter screen size. For the weighting coefficients, select ,or ; Scenario 2: When sonar observes the size of marine organisms... The effective seawater flow rate when all organisms are of this type is: （4） in, The volume fraction of marine organisms smaller than the filter screen size is given by the pump's suction volumetric flow rate. ; Scenario 3: Effective seawater cooling flow rate when some marine organisms are larger than the filter screen size and some are smaller than the filter screen size: （8） in, , For the weighting coefficients, select ,or ; Step S23, calculate the maximum cooling capacity that the introduced pump can provide: （9） Step S24, calculate the early warning factor, and... As evidence for reporting an incident; Specifically, a warning factor of 0-0.249 indicates low risk, 0.25-0.499 indicates medium risk, 0.5-0.749 indicates high risk, and 0.75 or higher indicates extremely high risk; and the intelligent warning algorithm based on biological fuzzy features further includes: Step S31: Obtain marine hydrological conditions and calculate marine hydrological factor A: （11） Where A1 represents the magnitude of the ocean current, and A2 represents the direction of the ocean current. This represents the magnitude of the velocity component of the ocean current in the direction perpendicular to the water intake. This represents the historical average ocean current speed in the current sea area. Step S4: Based on the processing results of the early warning algorithm, risk identification and early warning are performed, and the results are output and displayed.

2. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 1, characterized in that, In step S3, the intelligent early warning algorithm based on biological fuzzy features further includes: Step S32: Assess the severity of biological blockage and calculate the blockage severity index B: （12） Where B is the bioblocking influencing factor, W is the influencing factor weight, i is the index of the influencing factor, n is the number of influencing factors, and W Bi For the i-th impact factor B i The weight it occupies; Step S33: Assess biological characteristics and calculate the biological characteristic index C: （13） Where i is the index of the impact factor and n is the number of impact factors; Step S34: Based on the values ​​of criteria layers A, B, and C, the disaster risk index D is obtained as follows: （14） The disaster risk index D ranges from 0 to 1.

3. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 2, characterized in that, The factors influencing the blockage severity index B include individual size B1, bioaggregation probability B2, buoyancy B3, and individual quantity B4, wherein the values ​​of B1 to B4 range from 0 to 1.

4. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 3, characterized in that, The influencing factors of the biological characteristic index C include the organism's environmental adaptability (C1), reproductive capacity and mode (C2), distribution of natural enemies (C3), and survival ability (C4), wherein the values ​​of C1 to C4 range from 0 to 1.

5. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 4, characterized in that, The values ​​of B1 to B4 and C1 to C4 are divided into 5 levels according to 0, 0.25, 0.5, 0.75 and 1. The levels include no blockage, mild blockage, moderate blockage, severe blockage and serious blockage.

6. The intelligent early warning method for threats in the marine area of ​​a nuclear power plant intake as described in claim 2, characterized in that, The disaster risk index includes 0.000-0.249 as low risk, 0.250-0.499 as medium risk, 0.500-0.749 as high risk, and 0.750-1.000 as extremely high risk.

7. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 5, characterized in that, The data in step S1 includes data collection by subsystem units consisting of multiple sensors deployed in the field environment. Each subsystem unit aggregates and synchronizes the collected data information to the database in the server deployed at the shore-based monitoring station through a data synchronization module.

8. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 5, characterized in that, Step S2 further includes: performing data preprocessing work, including data governance, data quality inspection, data fusion, and feature engineering, in a data preprocessing module based on ETL data tools deployed in the server.

9. The intelligent early warning method for threats to the marine area of ​​a nuclear power plant intake as described in claim 7, characterized in that, The sensors include sonar, surface and underwater cameras, water flow sensors, and water quality sensors.

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