Biological blockage risk assessment method and device for nuclear power cooling water system

Abstract

The invention relates to the technical field of biological blockage risk assessment, and discloses a nuclear power cooling water system biological blockage risk assessment method and device, and the method comprises the steps: 1, collecting underwater image information, carrying out the recognition of the underwater image information, and carrying out the recognition of the biological types and the corresponding number in the underwater image information; 2, collecting the liquid level and the water flow speed of the position of the drum net and the pulling force borne by the trash holding net; 3, pre-estimated blockage risk analysis is carried out on the basis of the species and number of organisms in the underwater image information identified at different position points and time points, and a first blockage risk result is obtained; a predicted trash holding net stress curve is obtained based on the liquid level of the drum net position and the water flow speed collected within a period of time, the predicted trash holding net stress curve and the tensile force borne by the trash holding net collected within the period of time are compared and analyzed, and a second blocking risk result is obtained according to the comparison and analysis result; and evaluating the biological clogging risk according to the first clogging risk result and the second clogging risk result.

Description

Technical Field

[0001] This application relates to the technical field of biological clogging risk assessment, and particularly to a method and device for assessing the biological clogging risk of a nuclear power plant cooling water system. Background Art

[0002] During the operation of coastal nuclear power plants, due to the invasion of aquatic organisms in seawater (such as Acetes chinensis, jellyfish, etc.), the circulating water pump may trip, triggering the automatic shutdown of the reactor, and ultimately resulting in the shutdown of the unit. Changes in the environment and severe weather pose a series of threats to the cold source safety of power plants, including clogging of coarse and fine grids, filters, condensers, and shellfish traps in important plant water systems. There are relevant reports in various power plants at home and abroad. Some have led to a reduction in unit load, and in severe cases, the filtration equipment has been damaged, or even the unit has been shut down and the reactor has been stopped. Common risk sources include Acetes chinensis, jellyfish, algae, sediment, marine garbage, etc. For organisms such as Acetes chinensis and jellyfish, it is easy to have an explosive increase in their numbers, resulting in the problem that the nuclear power plant cooling water system is too late to clean.

[0003] In the existing biological clogging risk assessment system, an early warning system platform is set up, and an integrated space-air-shore-sea monitoring system is established based on monitoring equipment set in seawater. The number of organisms is monitored through devices such as cameras, sonars, and remote sensing set underwater, so as to effectively monitor the organisms in seawater. Among them, using the image recognition scheme in the existing technology, the aquatic organisms and their numbers at the collection point can be obtained, and they can be used as samples to judge the state of aquatic organisms in seawater. At the same time, in the existing technical solution, a device for detecting the tension is also set on the trash rack to directly judge the biological clogging risk of the cooling water system.

[0004] The existing biological clogging risk assessment system can give an early warning when it detects a significant increase in aquatic organisms or when the trash rack is already in a severely clogged state. However, the trash rack is easily affected by environmental factors. If the threshold value for comparing the tension value is set too high, there will be a serious lag in the early warning process. If the threshold value for comparing the tension value is set too low, it is very easy to have a problem of misjudgment. At the same time, there is also a problem of large error in the overall judgment based on the number of aquatic organisms collected at a single point. Therefore, how to more accurately and sensitively assess the biological clogging risk is the fundamental problem to be solved by the present invention. Summary of the Invention

[0005] In order to more accurately and sensitively assess the biological clogging risk, this application provides a method and device for assessing the biological clogging risk of a nuclear power plant cooling water system.

[0006] In a first aspect, this application provides a method for assessing the biological clogging risk of a nuclear power plant cooling water system, adopting the following technical solution:

[0007] A method for risk assessment of biological blockage in a nuclear power cooling water system, comprising:

[0008] Step 1: Collect underwater image information through an underwater camera, and identify the underwater image information based on a preset biological image recognition library to obtain the biological species and corresponding quantities in the underwater image information;

[0009] Step 2: Collect the liquid level, water flow velocity at the drum screen position, and the tensile force on the trash rack;

[0010] Step 3: Conduct a predicted blockage risk analysis based on the biological species and quantities in the underwater image information identified at different position points and time points to obtain a first blockage risk result; obtain a predicted trash rack force curve based on the liquid level and water flow velocity at the drum screen position collected over a period of time, compare and analyze the predicted trash rack force curve with the tensile force on the trash rack collected over a period of time, and obtain a second blockage risk result according to the comparison and analysis result; evaluate the biological blockage risk based on the first blockage risk result and the second blockage risk result.

[0011] By adopting the above technical solution, it is possible to make a comprehensive judgment by combining the data obtained from both on the basis of separately conducting a predicted blockage risk analysis and a trash rack force analysis. It is possible to judge the potential risks by combining the data of both while judging the obvious risks in a single analysis process. On the one hand, the threshold in the risk judgment process can be adjusted more reasonably, reducing the problems of misjudgment or untimely monitoring. On the other hand, the combined analysis of the data can improve the reliability of the detection result and the accuracy of the risk assessment result.

[0012] Optionally, the process of obtaining the first blockage risk result includes:

[0013] Collect underwater image information synchronously at different position points at preset time intervals, identify the biological species and corresponding quantities at each position point, obtain the mean value of the quantities of the same biological species collected at the same time point at different positions, compare the mean value of the quantities of different biological species with the quantity thresholds corresponding to the biological species. When the mean value of the quantity of a certain biological species is greater than or equal to the corresponding quantity threshold, judge the first blockage risk result as a high risk;

[0014] When the mean values of the quantities of all biological species are less than the corresponding quantity thresholds, obtain the first blockage risk result according to the change trend of the mean values of the quantities of each biological species at different time points.

[0015] By adopting the above technical solution, it is possible to compare the average value of the number of different types of organisms with the quantity threshold corresponding to the type of organism. The quantity threshold corresponding to the type of organism is obtained by comprehensively setting according to the relevant data when biological blockage occurs in historical data. This value is the value in the critical state. Therefore, the probability of misjudgment is relatively low. Furthermore, when the average value of the number of a certain type of organism is greater than or equal to the corresponding quantity threshold, it is determined that the first blockage risk result is a high risk; when the average values of the numbers of all types of organisms are less than the corresponding quantity thresholds, the first blockage risk result is obtained according to the change trend of the average value of the number of each type of organism at different time points. This process can avoid the problem of low judgment sensitivity.

[0016] Optionally, the process of obtaining the first blockage risk result when the average values of the numbers of all types of organisms are less than the corresponding quantity thresholds includes:

[0017] For any type of organism, obtain the average value of the number at N consecutive time points before the current time point, fit the change curve of this type of organism, and align and compare the change curve of this type of organism with the growth explosion quantity model of this type of organism at the value of the current time point. The comparison process includes:

[0018] Through Obtain the growth explosion compliance coefficient R of the i-th type of organism at the current time point i , and compare R i with the corresponding compliance threshold Rtr i of this type:

[0019] If R i < Rtr i , then it is determined that the first blockage risk result is a high risk;

[0020] where N is the number of consecutive time points before the current time point, j = 1, 2,..., N; Δt is the interval of consecutive time points, Q ij is the number of the i-th type of organism at the j-th time point, Q i(j+1) is the number of the i-th type of organism at the (j + 1)-th time point, Qt ij is the number of the i-th type of organism at the j-th time point in the growth explosion quantity model, and Qt i(j+1) is the number of the i-th type of organism at the (j + 1)-th time point in the growth explosion quantity model.

[0021] By adopting the above technical solution, it is possible to match the biological quantity at multiple time points with the explosion model of this type of organism, predict its quantity, and judge the biological blockage risk of the cooling water system through the prediction result, which can improve the timeliness of judgment and the accuracy of the judgment result.

[0022] Optionally, the process of obtaining the second blockage risk result includes:

[0023] Obtain the liquid level Hx, water flow velocity Vx at different time points in the non-clogging risk state of historical data and the corresponding tensile force Tx on the trash rack, establish a spatial lattice with (Hx, Vx, Tx) as the X, Y, and Z axes of the spatial coordinate system respectively, and fit the lattice to obtain a tensile force surface model;

[0024] Obtain the predicted force curve Tp(t) of the trash rack according to the liquid level H, water flow velocity V at different time points and the tensile force surface model.

[0025] By adopting the above technical solution, a predicted force curve Tp(t) that can be compared with the actual force curve can be obtained. Comparing the two, compared with the method of single threshold comparison, this comparison method can greatly improve the sensitivity of judgment, reduce the probability of misjudgment, and can timely detect the existing force risk of the current trash rack, thereby making a timely judgment on the biological clogging risk of the cooling water system.

[0026] Optionally, the process of obtaining the predicted force curve of the trash rack includes:

[0027] Substitute the liquid level H and water flow velocity V at the current time point into the tensile force surface model to obtain a predicted force value Tp, and fit the predicted force values Tp at several consecutive time points to obtain the predicted force curve Tp(t) of the trash rack.

[0028] Optionally, the process of obtaining the second clogging risk result further includes:

[0029] Real-time collect the tensile force on the trash rack to obtain the real-time tensile force curve T(t);

[0030] Through the formula:

[0031]

[0032] Calculate to obtain the tensile force risk value ΔT value (t b )

[0033] Compare ΔT value (t b ) with the preset tensile force risk value ΔT thr :

[0034] If ΔT value (t b ) > ΔT thr , then judge that the second clogging risk result is a high risk;

[0035] Among them, t a is the historical time point, t b is the current time point, A(t b ) is the current time point tb Before t a ~t b The tensile deviation amount in the time period, m(T(t)-Tp(t)) represents the maximum value of the time period T(t)-Tp(t), g is the deviation coefficient, g < 1, ΔT thr > 0.

[0036] By adopting the above technical solution, a tensile risk value ΔT for judging the matching state between the tensile force on the real-time trash rack and the predicted tensile force on the trash rack is obtained value (t b ), compare ΔT value (t b ) with the preset tensile risk value ΔT thr , where the preset tensile risk value ΔT thr is set according to empirical data and satisfies ΔT thr > 0. Therefore, if ΔT value (t b ) > ΔT thr , it indicates that the tensile force on the real-time trash rack exceeds the force under normal conditions, so it shows that the risk of biological blockage in the current cooling water system is relatively large, and then it is judged that the second blockage risk result is a high risk.

[0037] Optionally, the process of evaluating the biological blockage risk according to the first blockage risk result and the second blockage risk result includes:

[0038] Calculate the risk value U through formulas (3)-(4):

[0039]

[0040] When the risk value U exceeds the preset risk value U1, it is judged that the blockage risk result is a high risk;

[0041] where P is the number of biological species, i = 1, 2,..., P; k i is the influence coefficient of the i-th type of organism, μ is the weight adjustment coefficient, Rs(t b ) is the outbreak coefficient of all types of organisms at the current time point, and ΔRs is the empirical value of the growth outbreak coincidence coefficient.

[0042] By adopting the above technical solution, it is possible to make a comprehensive judgment by combining the data obtained from both on the basis of separately estimating the blockage risk analysis and the force analysis of the trash rack, and to judge the potential risk by combining the data of both, reducing the problems of misjudgment or untimely monitoring, and at the same time improving the reliability of the detection result and the accuracy of the risk assessment result.

[0043] In a second aspect, the present application provides a device for evaluating the biological blockage risk of a nuclear power cooling water system, adopting the following technical solution:

[0044] A device for assessing the risk of biological blockage in a nuclear power cooling water system, comprising a processor, an underwater camera, a flowmeter, a liquid level gauge and a tensile sensor, and a program of a method for assessing the risk of biological blockage in a nuclear power cooling water system as described in any one of the above is run in the processor.

[0045] In summary, the present application includes at least one of the following beneficial technical effects:

[0046] 1. Based on separately conducting an analysis of predicting the blockage risk and an analysis of the force on the trash rack, and combining the data obtained from both for comprehensive judgment, it can, while judging that there are obvious risks in a single analysis process, also combine the data of both to judge potential risks. On the one hand, the threshold in the risk judgment process can be adjusted more reasonably, reducing the problems of misjudgment or untimely monitoring. On the other hand, the combined analysis of data can improve the reliability of the detection results and the accuracy of the risk assessment results.

[0047] 2. The present invention can match the biological quantity at multiple time points with the outbreak model of this type of organism to predict its quantity, and judge the risk of biological blockage in the cooling water system based on the prediction results, which can improve the timeliness of judgment and the accuracy of the judgment results.

[0048] 3. By comparing the actual force curve with the predicted force curve, compared with the method of comparing a single threshold, this comparison method can greatly improve the sensitivity of judgment, reduce the probability of misjudgment, and can timely detect the existing force risk of the current trash rack, and then timely judge the risk of biological blockage in the cooling water system. Description of the Drawings

[0049] Figure 1 is a flowchart of the steps of a method for assessing the risk of biological blockage in a nuclear power cooling water system. Detailed Embodiments

[0050] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0051] In the description of this specification, the description referring to the terms "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0052] The present application embodiment discloses a method for assessing the risk of biological blockage in a nuclear power cooling water system. Figure 1 , including: step 1, collecting underwater image information through an underwater camera, and identifying the underwater image information based on a preset biological image recognition library, identifying and obtaining the biological species and corresponding quantities in the underwater image information; wherein the underwater camera adopts an underwater polarized light camera, and the underwater polarized camera has a wide dynamic range and can simultaneously capture polarization information in strong light and weak light areas, so that good imaging effects can be maintained under complex underwater lighting conditions. The preset biological image recognition library is obtained based on the common aquatic species in the seawater of the corresponding area and multiple groups of sample databases. Taking the coastal area as an example, its common aquatic organisms are hair shrimps, jellyfish, etc. Taking the riverside and lakeside areas as an example, its common aquatic organisms are fish schools, shrimps, etc. The specific image recognition algorithm can be implemented based on the commonly used technology in the existing technology.

[0053] Step 2: Collect the liquid level, water flow velocity and tension on the trash net at the drum net position; wherein, the liquid level at the drum net position is measured by a level gauge. The liquid level values ​​measured within a time range fluctuate. The average value within the time range can be obtained by taking the average value as the liquid level at the drum net position. The water flow velocity is measured by a flow meter installed underwater. Similar to the liquid level measurement method, the average value within the time range can also be obtained by taking the average value as the water flow velocity. The tension on the trash net is obtained by a tension sensor installed on the trash net. Since the tension at different positions is different, the position of the tension sensor needs to be confirmed in advance, and the relevant data at this position is used as a benchmark. It should also be noted that monitoring modules such as underwater cameras and flow meters are integrated at the bottom of the data acquisition buoy.

[0054] Step three, based on the species and number of organisms in the underwater image information identified at different positions and time points, perform an estimated blockage risk analysis to obtain a first blockage risk result; based on the liquid level and water flow velocity at the drum net position collected over a period of time, obtain a predicted force curve of the trash net, compare and analyze the predicted force curve of the trash net with the tensile force of the trash net collected over a period of time, and obtain a second blockage risk result based on the comparison and analysis results; evaluate the biological blockage risk based on the first blockage risk result and the second blockage risk result. The prediction method in this embodiment, on the basis of separately performing an estimated blockage risk analysis and a trash net force analysis, combines the data obtained from both for a comprehensive judgment. While judging that there is an obvious risk in a single analysis process, it can also judge the potential risk by combining the data of both. On the one hand, the threshold in the risk judgment process can be adjusted more reasonably, reducing the problem of misjudgment or untimely monitoring. On the other hand, the combined analysis of data can improve the reliability of the detection results and the accuracy of the risk assessment results.

[0055] Among them, the process of obtaining the first clogging risk result includes: synchronously collecting underwater image information at different position points at preset time intervals, where the preset time intervals are selected according to the seasons prone to biological outbreaks. When the biological outbreak risk is high, the preset time intervals are shortened to increase the monitoring frequency. Through the above process, the biological species and corresponding quantities at each position point are identified. The acquisition of the corresponding quantities can use the counting algorithms in the existing technologies. Furthermore, the average value of the quantities of the same biological species collected at the same time point at different positions is obtained. The average values of the quantities of different biological species are compared with the quantity thresholds corresponding to the biological species. The quantity thresholds corresponding to the biological species are obtained by comprehensively setting according to the relevant data when biological clogging occurred in historical data. This value is the value in the critical state, so the probability of misjudgment is relatively low. Furthermore, when the average value of the quantity of a certain biological species is greater than or equal to the corresponding quantity threshold, it is determined that the first clogging risk result is a high risk; when the average values of the quantities of all biological species are less than the corresponding quantity thresholds, the first clogging risk result is obtained according to the change trend of the average values of the quantities of each biological species at different time points. This process can avoid the problem of low judgment sensitivity. Its specific process includes: obtaining the average values of the quantities at N consecutive time points before the current time point for any biological species, fitting out the change curve of this biological species, and aligning and comparing the change curve of this biological species with the growth outbreak quantity model of this biological species at the value of the current time point. The comparison process includes:

[0056] By obtaining the growth outbreak compliance coefficient R of the i-th biological species at the current time point i , where N is the number of consecutive time points before the current time point, j = 1, 2,..., N; Δt is the interval between consecutive time points. It should be noted that the seasonal adjustment frequency of the time point interval is relatively low, so the influence of the change of the time point interval is not considered. Q ij is the quantity of the i-th biological species at the j-th time point, Q i(j+1) is the quantity of the i-th biological species at the (j + 1)-th time point, Qt ij is the quantity of the i-th biological species at the j-th time point in the growth outbreak quantity model, Qt i(j+1) is the quantity of the i-th biological species at the (j + 1)-th time point in the growth outbreak quantity model. Therefore, by the matching status of the i-th biological species with the corresponding growth outbreak quantity model within a single time point interval can be obtained, that is the smaller it is, the higher its matching degree, that is, it indicates that the probability of the outbreak of this type of biological species is higher. For the N time periods, therefore, by comparing R i with the corresponding compliance threshold Rtr i of this biological species, the compliance threshold Rtr i is selected and set according to the relevant data of the i-th biological species in the empirical data. Therefore, if Ri <Rtr i If so, it is determined that the first clogging risk result is a high risk. Through the above process, by matching the biological quantity at multiple time points with the outbreak model of this type of organism, the quantity can be predicted. Based on the prediction result, the biological clogging risk of the cooling water system can be judged, which can improve the timeliness of the judgment and the accuracy of the judgment result.

[0057] In one embodiment, the process of obtaining the second clogging risk result includes: obtaining the liquid level Hx, water flow velocity Vx at different time points in the non-clogging risk state of the historical data, and the corresponding tension Tx on the trash rack, and establishing a space lattice with (Hx, Vx, Tx) as the X, Y, and Z axes of the space coordinate system respectively, and fitting the lattice to obtain a tension surface model; the surface fitting process in the above solution can be implemented based on the Bezier algorithm through the surface fitting module in Matlab. The specific operation process is common prior art and will not be elaborated here. Substitute the liquid level H and water flow velocity V at different time points into the tension surface model, and then the predicted force value Tp of the trash rack at different time points can be obtained. Curve fitting is performed on the predicted force values Tp of the trash rack at different time points, and then the predicted force curve Tp(t) of the trash rack is obtained. Through the above process, a predicted force curve Tp(t) that can be compared with the actual force curve can be obtained. Comparing the two, compared with the method of comparing with a single threshold, this comparison method can greatly improve the sensitivity of the judgment, reduce the probability of misjudgment, and can timely detect the force risk existing in the current trash rack, and then timely judge the biological clogging risk of the cooling water system.

[0058] In addition, the process of obtaining the second clogging risk result further includes: real-time collecting the tension on the trash rack to obtain the real-time tension curve T(t);

[0059] Through the formula:

[0060]

[0061] Calculate to obtain the tension risk value ΔT b at the current time point t value (t b ); where t a is the historical time point, which is set according to the user and the order of magnitude of the historical data, t b is the current time point, and then determine t a ~t b as the collected analysis time interval, A(t b ) is the time before the current time point t b ~t a ~t bThe tensile deviation of the time period, m(T(t)-Tp(t)) represents t a ~t b The maximum value of the time period T(t)-Tp(t), g is the deviation coefficient, g < 1. The deviation coefficient is used to adjust the balance setting between the critical state and the overall state, and it is set according to empirical data, where A(t b ) reflects t a ~t b The overall tensile deviation condition of the time period, m(T(t)-Tp(t)) reflects t a ~t b The critical tensile deviation condition of the time period. Therefore, after adjusting by the deviation coefficient g, the tensile risk value ΔT value (t b ) for judging the matching state between the tensile force on the real-time trash rack and the predicted tensile force on the trash rack is obtained. Compare ΔT value (t b ) with the preset tensile risk value ΔT thr . Among them, the preset tensile risk value ΔT thr is set according to empirical data and satisfies ΔT thr > 0. Therefore, if ΔT value (t b ) > ΔT thr , it indicates that the tensile force on the real-time trash rack exceeds the magnitude of the force under normal conditions. Therefore, it shows that the biological clogging risk of the current cooling water system is relatively high, and then it is judged that the second clogging risk result is a high risk.

[0062] In addition, the process of evaluating the biological clogging risk based on the first clogging risk result and the second clogging risk result includes: calculating the risk value U through formulas (3)-(4):

[0063]

[0064] Among them, P is the number of biological species, i = 1, 2,..., P; k i is the influence coefficient of the i-th type of organism, μ is the weight adjustment coefficient, which is selected and set according to empirical data, Rs(t b ) is the outbreak coefficient of all types of organisms at the current time point t b , ΔRs is the empirical value of the growth outbreak coincidence coefficient, which selects the minimum value of Rs(t b ) in historical data. The outbreak coefficient Rs(t b ) of all types of organisms at the current time point t b) Then, a risk value U is obtained through calculation. Furthermore, based on the risk value U, the biological clogging risk of the nuclear power cooling water system can be comprehensively evaluated by combining the outbreak state of the quantity of potential biological species and the deviation between the actual tension on the real-time trash rack and the predicted tension on the trash rack. The risk value U is compared with a preset risk value U1, where the preset risk value U1 is obtained from empirical data. Therefore, when the risk value U exceeds the preset risk value U1, the clogging risk result is determined to be a high risk. Through the above comparison process, on the basis of separately estimating the clogging risk analysis and the force analysis of the trash rack, a comprehensive judgment can be made by combining the data obtained from both, and the potential risk can be judged by combining the data of both, reducing the problems of misjudgment or untimely monitoring. At the same time, the reliability of the detection result can be improved, and the accuracy of the risk assessment result can be enhanced.

[0065] Each of the processes and treatments described above, such as a method, can be executed by a CPU. For example, in some embodiments, a method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as a memory. In some embodiments, part or all of the computer program can be loaded and / or installed onto the computing device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more actions of a method for monitoring intelligent cold source disaster-causing substances based on multi-sensor fusion described above can be performed.

[0066] The present disclosure relates to a method, a computing device, a computer-readable storage medium, and / or a computer program product. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.

[0067] A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0068] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge computing devices. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0069] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via an Internet service provider through the Internet). In some embodiments, by using the status information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0070] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which causes a computer, a programmable data processing device, and / or other devices to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured article that includes instructions for implementing various aspects of an intelligent cold source disaster-causing object monitoring method or multiple actions based on multi-sensor fusion.

[0071] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement a multi-sensor fusion-based intelligent cold source disaster-causing object monitoring method or multiple actions.

[0072] The embodiments of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein.

[0073] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal, including a memory and a processor, and a computer program capable of being loaded and executed by the processor to implement a method is stored on the memory.

[0074] An embodiment of the present application also discloses a risk assessment device for biological blockage in a nuclear power cooling water system, including a processor, an underwater camera, a flowmeter, a liquid level gauge, and a tensile sensor. A program of a risk assessment method for biological blockage in a nuclear power cooling water system as described in any one of the above is run in the processor.

[0075] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for assessing the risk of biological clogging in a nuclear power cooling water system, characterized in that: include: Step 1: Collect underwater image information through an underwater camera, and recognize the underwater image information based on a preset biological image recognition library to identify the species and corresponding number of organisms in the underwater image information; Step 2: Collect the liquid level, water flow velocity and tension of the trash screen at the drum screen position; Step 3: perform an estimated blockage risk analysis based on the species and quantity of organisms in the underwater image information identified at different positions and time points to obtain a first blockage risk result; obtain a predicted force curve of the trash net based on the liquid level and water flow velocity at the drum net position collected over a period of time, compare and analyze the predicted force curve of the trash net with the tensile force of the trash net collected over a period of time, and obtain a second blockage risk result based on the comparison and analysis results; The biological clogging risk is evaluated based on the first clogging risk result and the second clogging risk result.

2. A method for assessing biological clogging risk in a nuclear power cooling water system according to claim 1, characterized in that: The process of obtaining the first blocking risk result includes: Underwater image information is synchronously collected at different locations at preset time intervals, the species and corresponding quantity of each location are identified, the mean of the number of the same species of organisms collected at the same time point at different locations is obtained, the mean of the number of different species of organisms is compared with the number threshold of the corresponding species of organisms, and when the mean of the number of a certain species of organisms is greater than or equal to the corresponding number threshold, the first blockage risk result is judged to be high risk; When the mean number of all species of organisms is less than the corresponding number threshold, the first congestion risk result is obtained according to the change trend of the mean number of each species of organisms at different time points.

3. A method for assessing biological clogging risk in a nuclear power cooling water system according to claim 2, characterized in that: When the mean number of all species of organisms is less than the corresponding number threshold, the process of obtaining the first blocking risk result includes: For any species of organisms, the mean value of the number of N consecutive time points before the current time point is obtained, and the change curve of the species of organisms is fitted. The change curve of the species of organisms is aligned and compared with the growth burst quantity model of the species of organisms at the value at the current time point. The comparison process includes: pass Get the growth burst coefficient R of the i-th organism at the current time point i , R i The threshold Rtr corresponding to this type i To compare: If R i <Rtr i , then the first blocking risk result is judged to be high risk; Where N is the number of consecutive time points before the current time point, j = 1, 2, ..., N; Δt is the interval between consecutive time points, Q ij is the number of species i at the jth time point, Q i(j+1) Qt is the number of species i at the j+1th time point, ij is the number of species i at the jth time point in the growth burst model, Qt i(j+1) is the number of organisms of the i-th species at the j+1-th time point in the growth burst population model.

4. A method for assessing biological clogging risk in a nuclear power cooling water system according to claim 3, characterized in that: The process of obtaining the second blocking risk result includes: The liquid level Hx, water flow velocity Vx and the corresponding tension Tx of the trash screen at different time points in the historical data under the non-blocking risk state are obtained, and a spatial lattice is established with (Hx, Vx, Tx) as the X, Y, and Z axes of the spatial coordinate system, and the lattice is fitted to obtain the tension surface model; The predicted force curve Tp(t) of the trash screen is obtained based on the liquid level H, water flow velocity V and tension surface model at different time points.

5. A method for assessing biological clogging risk in a nuclear power cooling water system according to claim 4, characterized in that: The process of obtaining the predicted stress curve of the trash-blocking net includes: The liquid level H and water flow velocity V at the current time point are brought into the tension surface model to obtain the predicted force value Tp. The predicted force values ​​Tp at several consecutive time points are fitted to obtain the predicted force curve Tp(t) of the trash net.

6. A method for assessing biological clogging risk in a nuclear power cooling water system according to claim 4, characterized in that: The process of obtaining the second blocking risk result further includes: Collect the tension of the trash-blocking net in real time to obtain the real-time tension curve T(t); By formula: Calculate the tensile risk value ΔT at the current time point value (t b ); ΔT value (t b ) and the preset tensile risk value ΔT thr To compare: If ΔT value (t b )>ΔT thr , then the second blocking risk result is judged to be high risk; Among them, t a is a historical time point, t b is the current time point, A(t b ) is the current time point t b Before a ~t b The tension deviation in the time period, m(T(t)-Tp(t)) represents the maximum value of the time period T(t)-Tp(t), g is the deviation coefficient, g<1, ΔT thr >0.

7. A method for assessing biological clogging risk in a nuclear power cooling water system according to claim 6, characterized in that: The process of assessing the bioclogging risk based on the first clogging risk result and the second clogging risk result includes: The risk value U is calculated by formula (3)-(4): When the risk value U exceeds the preset risk value U1, the blocking risk result is judged to be high risk; Where P is the number of biological species, i = 1, 2, ..., P; k i is the influence coefficient of the i-th species of organisms, μ is the adjustment weight coefficient, Rs(t b ) is the burst coefficient of all types of organisms at the current time point, and ΔRs is the empirical value of the growth burst coefficient.

8. A nuclear power cooling water system biological clogging risk assessment device, characterized in that: The invention comprises a processor, an underwater camera, a flow meter, a liquid level meter and a tension sensor, wherein the processor runs a program of a nuclear power cooling water system biological blockage risk assessment method as claimed in any one of claims 1 to 7.

Images