A method and device for assessing biological clogging risk in nuclear power cooling water systems

Abstract

The present application relates to the technical field of biological clogging risk assessment, and discloses a method and device for biological clogging risk assessment of a nuclear power cooling water system, comprising: step one, collecting underwater image information, identifying the underwater image information, and identifying and obtaining the species and corresponding quantity of organisms in the underwater image information; step two, collecting the liquid level, water flow velocity and tension on the trash net at the drum net position; step three, performing an estimated clogging risk analysis based on the species and quantity of organisms in the underwater image information identified at different positions and time points, and obtaining a first clogging risk result; obtaining 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, comparing and analyzing the predicted force curve of the trash net with the tension on the trash net collected over a period of time, and obtaining a second clogging risk result based on the comparison and analysis result; and evaluating the biological clogging risk based on the first clogging risk result and the second clogging risk result.

Description

Technical Field

[0001] The present application relates to the technical field of biological blockage risk assessment, and in particular to a method and device for biological blockage risk assessment of a nuclear power cooling water system. Background Art

[0002] During the operation of coastal nuclear power plants, infestations of aquatic organisms (such as shrimp and jellyfish) in the seawater can frequently cause circulating water pumps to trip, triggering automatic reactor shutdowns and ultimately leading to unit downtime. Environmental fluctuations and adverse weather conditions pose a range of threats to the safety of power plant cooling systems, including blockage of coarse and fine screens, filters, condensers, and shellfish traps in critical plant water systems. Such incidents have been reported at various power plants both domestically and internationally, some leading to unit load reductions and, in severe cases, damage to filtration equipment and even reactor shutdowns. Common risk factors include shrimp, jellyfish, algae, sediment, and marine debris. In particular, shrimp and jellyfish can easily experience population explosions, preventing timely cleaning of nuclear power plant cooling water systems.

[0003] In the existing biological blockage risk assessment system, an early warning system platform is set up, and then an integrated air, space, shore and sea monitoring system is established based on the monitoring equipment installed in the seawater. The number of organisms is monitored by underwater cameras, sonar and remote sensing devices, thereby realizing effective monitoring of organisms in seawater. Among them, the image recognition scheme in the existing technology can obtain the number of aquatic organisms at the collection point, and use them as samples to judge the status of aquatic organisms in seawater. At the same time, in the existing technical scheme, a tension detection device is set on the pollution-blocking net to directly judge the risk of biological blockage in the cooling water system.

[0004] The existing biological blockage risk assessment system can issue an early warning when it detects a significant increase in aquatic organisms or a serious blockage in the trash net. However, the trash net is easily affected by environmental factors. If the threshold for the tension value comparison is set too high, the early warning process will have a serious lag. If the threshold for the tension value comparison is set too low, it is very easy to make misjudgments. At the same time, there is also a 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 risk of biological blockage 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 risk of biological blockage, the present application provides a method and device for assessing the risk of biological blockage in a nuclear power cooling water system.

[0006] In a first aspect, the present application provides a method for assessing the risk of biological blockage in a nuclear power cooling water system, which adopts the following technical solution:

[0007] A method for assessing biological clogging risk 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 identify the species and corresponding number of organisms in the underwater image information;

[0009] Step 2: Collect the liquid level, water flow velocity and tension of the trash screen at the drum net position;

[0010] Step 3. Perform an estimated clogging risk analysis based on the species and number of organisms in the underwater image information identified at different locations and time points to obtain a first clogging 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 tension of the trash net collected over a period of time, and obtain a second clogging risk result based on the comparison and analysis results; and evaluate the biological clogging risk based on the first and second clogging risk results.

[0011] By adopting the above technical solution, it is possible to make a comprehensive judgment based on the data obtained from the separate estimated blockage risk analysis and the sewage net force analysis. While judging that there are obvious risks in a single analysis process, it is also possible to judge the potential risks by combining the data of the two. 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 improve the accuracy of the risk assessment results.

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

[0013] Underwater image information is synchronously collected at different locations at preset time intervals, and the species and corresponding number of organisms at each location are identified. The average number of the same species of organisms collected at the same time point at different locations is obtained, and the average number of different species of organisms is compared with the number threshold of the corresponding species of organisms. When the average number of a certain species of organisms is greater than or equal to the corresponding number threshold, the first blockage risk result is determined to be high risk;

[0014] 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 changing trend of the mean number of each species of organism at different time points.

[0015] By adopting the above technical solution, the mean number of different types of organisms can be compared with the number threshold of the corresponding biological species. The number threshold of the corresponding biological species is obtained by comprehensively setting the relevant data when biological congestion occurs in historical data. This value is a value under the critical state, so the probability of misjudgment is low. When the mean number of a certain type of organism is greater than or equal to the corresponding number threshold, the first congestion risk result is judged to be high risk; when the mean number of all types of organisms is less than the corresponding number threshold, the first congestion risk result is obtained according to the changing trend of the mean number of each biological species at different time points. This process can avoid the problem of low judgment sensitivity.

[0016] Optionally, when the mean number of all types of organisms is less than the corresponding number threshold, the process of obtaining the first congestion risk result includes:

[0017] For any species of organisms, the mean value of the number of organisms at 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 model of the species of organisms at the value at the current time point. The comparison process includes:

[0018] pass Get the growth burst coefficient R of the i-th species at the current time point i , R i The threshold Rtr corresponding to this type i To compare:

[0019] If R i <Rtr i , then the first congestion risk result is judged to be 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 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 species i at the j+1th time point in the growth burst model.

[0021] By adopting the above technical solution, the number of organisms at multiple time points can be matched with the outbreak model of this type of organism, and their number can be predicted. The risk of biological blockage in the cooling water system can be judged based on the prediction results, which can improve the timeliness of the judgment and the accuracy of the judgment results.

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

[0023] The liquid level Hx, water velocity Vx, and corresponding tension Tx of the net at the drum position at different time points under non-clogging risk conditions in historical data were obtained. A spatial lattice was established with (Hx, Vx, Tx) as the X, Y, and Z axes of the spatial coordinate system, and the lattice was fitted to obtain a tension surface model.

[0024] 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.

[0025] By adopting the above technical solution, it is possible to obtain a predicted stress curve Tp(t) for comparison with the actual stress curve, and compare the two. Compared with the single threshold comparison method, this comparison method can greatly improve the sensitivity of judgment, reduce the probability of misjudgment, and promptly discover the stress risk of the current trash net, thereby making a timely judgment on the risk of biological blockage in the cooling water system.

[0026] Optionally, the process of obtaining the predicted stress curve of the trash screen includes:

[0027] 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.

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

[0029] Real-time collection of the tension on the trash net to obtain the real-time tension curve T(t);

[0030] By formula:

[0031]

[0032] Calculate the tensile risk value ΔT at the current time point value (t b );

[0033] ΔT value (t b ) and the preset tensile risk value ΔT thr To compare:

[0034] If ΔT value (t b )>ΔT thr , then the second congestion risk result is judged to be high risk;

[0035] Among them, t a is a historical time point, t b is the current time point, A(t b ) is the current time point tb 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.

[0036] By adopting the above technical solution, the tension risk value ΔT is obtained to judge the matching state between the real-time tension of the trash net and the predicted tension of the trash net. value (t b ), ΔT value (t b ) and the preset tensile risk value ΔT thr Compare, where the tension risk value ΔT is preset thr Set according to empirical data and satisfy ΔT thr >0, so if ΔT value (t b )>ΔT thr When , it means that the real-time pulling force on the trash screen exceeds the force under normal conditions, which indicates that the current cooling water system has a high risk of biological blockage, and the second blockage risk result is judged to be high risk.

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

[0038] The risk value U is calculated by formula (3)-(4):

[0039]

[0040] When the risk value U exceeds the preset risk value U1, the congestion risk result is judged to be high risk;

[0041] Where P is the number of 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 species at the current time point, and ΔRs is the empirical value of the growth burst coefficient.

[0042] By adopting the above technical solution, it is possible to make a comprehensive judgment based on the data obtained from the separate estimated blockage risk analysis and the stress analysis of the sewage net, and to judge the potential risks based on the data from the two, thereby reducing the problem of misjudgment or untimely monitoring, and at the same time improving the reliability of the detection results and the accuracy of the risk assessment results.

[0043] In a second aspect, the present application provides a nuclear power cooling water system biological blockage risk assessment device, which adopts the following technical solution:

[0044] A nuclear power cooling water system biological blockage risk assessment device comprises a processor, an underwater camera, a flow meter, a liquid level gauge and a tension sensor, wherein the processor runs a program of any one of the above-mentioned nuclear power cooling water system biological blockage risk assessment methods.

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

[0046] 1. The present invention conducts a comprehensive judgment based on the data obtained from the separate estimated blockage risk analysis and the stress analysis of the pollution control net. It can judge the existence of obvious risks in a single analysis process while combining the data of the two to judge the potential risks. On the one hand, the threshold value 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 improve the accuracy of the risk assessment results.

[0047] 2. The present invention can predict the number of organisms by matching the number of organisms at multiple time points with the outbreak model of the organisms of that type. The risk of biological blockage in the cooling water system can be judged based on the prediction results, thereby improving the timeliness of the judgment and the accuracy of the judgment results.

[0048] 3. The present invention compares the actual stress curve with the predicted stress curve. Compared with the single threshold comparison method, this comparison method can greatly improve the sensitivity of judgment, reduce the probability of misjudgment, and at the same time, can timely discover the stress risk of the current pollution control net, and then make a timely judgment on the risk of biological blockage in the cooling water system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the steps of the biological clogging risk assessment method for nuclear power cooling water systems. DETAILED DESCRIPTION

[0050] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0051] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0052] The present invention 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 species and corresponding number of organisms in the underwater image information; wherein the underwater camera adopts an underwater polarization camera, and the underwater polarization 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, the common aquatic organisms are shrimps, jellyfish, etc. Taking the riverside and lakeside areas as an example, the common aquatic organisms are fish, shrimp, 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 drum net at the position of the drum net; the liquid level at the drum net is measured by a level gauge, and 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 will be different, the position of the tension sensor needs to be verified 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: perform an estimated blockage risk analysis based on the species and number of organisms in the underwater image information identified at different locations 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 tension 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, based on the separate estimated blockage risk analysis and the trash net force analysis, combines the data obtained from both to make a comprehensive judgment. While judging the existence of obvious risks in a single analysis process, it can also judge potential risks by combining the data from both. On the one hand, the threshold value 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 blockage risk result includes: synchronously collecting underwater image information at different locations according to preset time intervals, wherein the preset time interval is selected and set according to the season when biological outbreaks are prone to occur. When the risk of biological outbreaks is high, the preset time interval is shortened and the monitoring frequency is increased. Through the above process, the biological species and corresponding number at each location are identified and obtained. The corresponding number can be obtained by using the counting algorithm in the existing technology, and then the average number of the same type of organisms collected at the same time point at different locations is obtained, and the average number of different types of organisms is compared with the number threshold of the corresponding biological species. The number threshold of the corresponding biological species is obtained by comprehensively setting the relevant data when biological blockage occurs in the historical data. This value is a critical value, so the probability of misjudgment is low. When the mean population of a certain type of organism is greater than or equal to the corresponding threshold population, the first congestion risk result is judged to be high risk. When the mean population of all types of organisms is less than the corresponding threshold population, the first congestion risk result is obtained based on the changing trend of the mean population of each type of organism at different time points. This process can avoid the problem of low judgment sensitivity. The specific process includes: obtaining the mean population of N consecutive time points before the current time point for any type of organism, fitting the change curve of the type of organism, and aligning and comparing the change curve of the type of organism with the growth burst model of the type of organism at the current time point. The comparison process includes:

[0056] pass Get the growth burst coefficient R of the i-th 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 low, so the impact of the change in the time point interval is not considered. 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 species i at the j+1th time point in the growth burst model, so It is possible to obtain the matching status of the i-th organism and the corresponding growth burst quantity model within a single time point interval, that is, The smaller the size, the higher the matching degree, which means the higher the probability of the outbreak of this type of organism. Therefore, by replacing R i The threshold Rtr corresponding to this type i Compare and match the threshold Rtr i The setting is selected based on the relevant data of the i-th type of organisms in the empirical data. Therefore, if Ri <Rtr i , then the first blockage risk result is judged to be high risk. Through the above process, the number of organisms at multiple time points can be matched with the outbreak model of this type of organism, and its number can be predicted. The risk of biological blockage in the cooling water system can be judged based on the prediction results, which can improve the timeliness of the judgment and the accuracy of the judgment results.

[0057] In one embodiment, the process of obtaining the second blockage risk result includes: obtaining the liquid level Hx, water flow velocity Vx and the corresponding tension Tx of the drum net position at different time points in the non-blockage risk state in the historical data, establishing a spatial lattice with (Hx, Vx, Tx) as the X, Y, and Z axes of the spatial coordinate system, and fitting the lattice to obtain a tension surface model; the surface fitting process in the above scheme can be implemented by the surface fitting module in Matlab based on the Bezier algorithm. The specific operation process is a common existing technology and will not be repeated here. The liquid level H, water flow velocity Vx and the corresponding tension Tx of the trash net at different time points are respectively established. V is brought into the tension surface model, and then the predicted force values ​​Tp of the trash net at different time points can be obtained. The predicted force values ​​Tp of the trash net at different time points are curve fitted to obtain the predicted force curve Tp(t) of the trash net. Through the above process, the predicted force curve Tp(t) can be obtained for comparison with the actual force curve. Compared with the single threshold comparison method, this comparison method can greatly improve the sensitivity of judgment, reduce the probability of misjudgment, and at the same time, can timely discover the force risk of the current trash net, and then make a timely judgment on the risk of biological blockage in the cooling water system.

[0058] In addition, the process of obtaining the second blockage risk result also includes: collecting the tension of the trash net in real time to obtain a real-time tension curve T(t);

[0059] By formula:

[0060]

[0061] Calculate the current time point t b Tensile risk value ΔT value (t b ); where t a is a historical time point, which is set according to the order of magnitude of users and historical data, t b is the current time point, and then determine t a ~t b is the analysis time interval of the acquisition, A(t b ) is the current time point t b Before a ~t bThe tension deviation in 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, which is set according to empirical data, where A(t b ) reflects t a ~t b The overall tension deviation of the time period, m(T(t)-Tp(t)) reflects the a ~t b The critical tension deviation condition of the time period is obtained. Therefore, after adjusting the deviation coefficient g, the tension risk value ΔT used to judge the matching state between the real-time tension of the trash net and the predicted tension of the trash net is obtained. value (t b ), ΔT value (t b ) and the preset tensile risk value ΔT thr Compare, where the tension risk value ΔT is preset thr Set according to empirical data and satisfy ΔT thr >0, so if ΔT value (t b )>ΔT thr When , it means that the real-time pulling force on the trash screen exceeds the force under normal conditions, which indicates that the current cooling water system has a high risk of biological blockage, and the second blockage risk result is judged to be high risk.

[0062] In addition, the process of evaluating the biological clogging risk according to the first clogging risk result and the second clogging risk result includes: calculating the risk value U by formula (3)-(4):

[0063]

[0064] Where P is the number of species, i = 1, 2, ..., P; k i is the influence coefficient of the i-th species of organisms, μ is the adjustment weight coefficient, which is selected and set according to empirical data, Rs(t b ) is the total number of organisms at the current time point t b The burst coefficient, ΔRs is the empirical value of the growth burst coefficient, which is selected from the historical data Rs(t b ), and the minimum value of all species at the current time point t is obtained through formula (3). b The burst coefficient Rs(t b), and then obtain the risk value U through calculation, and then the risk value U can be combined with the potential outbreak status of the number of biological species and the deviation between the real-time tension of the trash net and the predicted tension of the trash net to conduct a comprehensive assessment of the biological blockage risk of the nuclear power cooling water system, and compare the risk value U with the preset risk value U1, wherein the preset risk value U1 is obtained according to empirical data, so when the risk value U exceeds the preset risk value U1, the blockage risk result is judged to be high risk. Through the above comparison process, on the basis of separate estimated blockage risk analysis and trash net force analysis, a comprehensive judgment can be made based on the data obtained from the two, and the potential risks can be judged based on the data from the two, thereby reducing the problem of misjudgment or untimely monitoring, and at the same time improving the reliability of the detection results and the accuracy of the risk assessment results.

[0065] The various 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 a computing device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, the intelligent cold source hazard monitoring method based on multi-sensor fusion described above or multiple actions can be performed.

[0066] The present disclosure relates to methods, computing devices, computer-readable storage media, and / or computer program products. The computer program products may include computer-readable program instructions for executing various aspects of the present disclosure.

[0067] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can 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 thereof. More specific examples (a non-exhaustive list) of computer-readable storage media 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 disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0068] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, 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, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge computing devices. The 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 the 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-dependent 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++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, 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 via 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., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing 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 generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner, so that the computer-readable medium storing the instructions comprises an article of manufacture, which includes instructions for implementing various aspects of a multi-sensor fusion-based intelligent cold source disaster monitoring method or multiple actions.

[0071] Computer-readable program instructions can also be loaded onto a computer, other programmable data processing device, or other device, so that a series of operating steps are executed on the computer, other programmable data processing device, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing device, or other device implement an intelligent cold source disaster-causing object monitoring method or multiple actions based on multi-sensor fusion.

[0072] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable other persons skilled in the art 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, wherein the memory stores a computer program that can be loaded by the processor and execute a method.

[0074] An embodiment of the present application also discloses a nuclear power cooling water system biological blockage risk assessment device, including 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 described in any one of the above items.

[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 cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for assessing the risk of biological blockage in a nuclear power cooling water system, characterized in that: include: 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 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 net position; Step 3: Perform an estimated blockage risk analysis based on the species and number of organisms in the underwater image information identified at different locations and time points to obtain a first blockage risk result; obtain a predicted force curve for 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 for the trash net with the tensile force on the trash net collected over a period of time, and obtain a second blockage risk result based on the comparison and analysis results; Assessing the biological clogging risk based on the first clogging risk result and the second clogging risk result; The process of obtaining the first congestion risk result includes: Underwater image information is synchronously collected at different locations at preset time intervals, and the species and corresponding number of organisms at each location are identified. The average number of the same species of organisms collected at the same time point at different locations is obtained, and the average number of different species of organisms is compared with the number threshold of the corresponding species of organisms. When the average number of a certain species of organisms is greater than or equal to the corresponding number threshold, the first blockage risk result is determined 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 changing trend of the mean number of each species of organism at different time points.

2. A method for assessing biological blockage risk in a nuclear power cooling water system according to claim 1, 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 congestion risk result includes: For any species of organisms, the mean value of the number of organisms at 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 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 species 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 congestion 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 species i at the j+1th time point in the growth burst model.

3. A method for assessing biological blockage risk in a nuclear power cooling water system according to claim 2, characterized in that: The process of obtaining the second congestion risk result includes: The liquid level Hx, water velocity Vx, and corresponding tension Tx of the net at the drum position at different time points under non-clogging risk conditions in historical data were obtained. A spatial lattice was established with (Hx, Vx, Tx) as the X, Y, and Z axes of the spatial coordinate system, and the lattice was fitted to obtain a 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.

4. A method for assessing biological blockage risk in a nuclear power cooling water system according to claim 3, characterized in that: The process of obtaining the predicted stress curve of the trash screen 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.

5. A method for assessing biological blockage risk in a nuclear power cooling water system according to claim 3, characterized in that: The process of obtaining the second congestion risk result further includes: Real-time collection of the tension on the trash net to obtain the real-time tension curve T(t); By formula: ΔT value (t b )=m(T(t)-Tp(t))+g*A(t b ) / (1+g) (2) 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 congestion 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.

6. A method for assessing biological blockage risk in a nuclear power cooling water system according to claim 5, characterized in that: The process of assessing the bioclogging risk based on the first and second clogging risk results includes: The risk value U is calculated by formula (3)-(4): U=(ΔT thr -ΔT value (t b )) / μ*ΔT thr +Rs(t b ) / P*ΔRs (4) When the risk value U exceeds the preset risk value U1, the congestion risk result is judged to be high risk; Where P is the number of 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 species at the current time point, and ΔRs is the empirical value of the growth burst coefficient.

7. A nuclear power cooling water system biological clogging risk assessment device, characterized in that: The method 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 according to any one of claims 1 to 6.

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