Nuclear power plant monitoring data processing method, device, computer equipment and storage medium

Abstract

The present invention relates to the field of nuclear power informatization technology, and discloses a method, device, computer equipment and storage medium for processing monitoring data of a nuclear power plant. The method includes obtaining first monitoring data of a first nuclear power unit, determining a first target device corresponding to a first abnormal parameter when the first abnormal parameter exists in the first monitoring data, and obtaining device attribute information of the first target device; when it is determined that the device attribute information is common device attribute information, determining a second target device corresponding to the device attribute information from a second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit different from the first nuclear power unit in the same nuclear power plant; obtaining second monitoring data corresponding to the second target device, and when it is confirmed that a second abnormal parameter exists in the second monitoring data, determining an abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter. The present invention realizes the sharing of group reactor information of multiple units, helps with unified command, and improves the comprehensiveness of decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear power informatization, and particularly to a method, device, computer device and storage medium for processing monitoring data of a nuclear power plant. Background Art

[0002] A nuclear power unit is a basic power generation unit composed of a reactor and its supporting steam turbine generator set, as well as systems and facilities required to maintain their normal operation and ensure safety. With the development of nuclear power technology and the increase in power generation demand, a nuclear power plant usually includes multiple nuclear power units. The normal operation of a nuclear power plant depends on the condition monitoring of nuclear power units.

[0003] In existing nuclear power plants, the unit unit or single reactor management mode is generally adopted to monitor the condition of nuclear power units, which consumes a large amount of manpower and material resources, is not conducive to the improvement of production efficiency, and restricts the operation and maintenance management level of nuclear power plants. There is a scale advantage among multiple nuclear power units in the same nuclear power plant, and there are no large differences in the technical points of the units. There is a basis for integrated management of multiple unit groups, and there are many common events in the group. However, in the face of common events in the group, the existing management mode cannot integrate monitoring data at the group level and monitor key parameters of the group, resulting in repeated processing of abnormal events between different units. At the same time, there is a lack of cross-professional and cross-unit online support during the event processing process, and it is also impossible to achieve group information sharing and unified command among multiple units, affecting the comprehensiveness of problem decision-making and the efficiency of event processing. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer device and storage medium for processing monitoring data of a nuclear power plant to solve the problem that the existing nuclear power plant uses the independent management method of unit units to monitor the condition of nuclear power units, which is not conducive to the handling of common events and affects the comprehensiveness of decision-making and the efficiency of event processing.

[0005] A method for processing monitoring data of a nuclear power plant includes:

[0006] Obtain first monitoring data of a first nuclear power unit, and determine whether there is a first abnormal parameter in the first monitoring data;

[0007] If there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all unit devices of the first nuclear power unit, and obtain device attribute information of the first target device;

[0008] When it is determined that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit;

[0009] Obtain second monitoring data corresponding to the second target device, and when it is confirmed that there is a second abnormal parameter in the second monitoring data, determine an abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter.

[0010] A nuclear power plant monitoring data processing device includes:

[0011] A parameter abnormality judgment module, configured to obtain first monitoring data of a first nuclear power unit and judge whether there is a first abnormal parameter in the first monitoring data;

[0012] A device attribute information obtaining module, configured to, if there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all the unit devices of the first nuclear power unit, and obtain the device attribute information of the first target device;

[0013] A common device determination module, configured to, when it is determined that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit;

[0014] An abnormal handling scheme generation module, configured to obtain second monitoring data corresponding to the second target device, and when it is confirmed that there is a second abnormal parameter in the second monitoring data, determine an abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter.

[0015] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the above-mentioned nuclear power plant monitoring data processing method is implemented.

[0016] A computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the nuclear power plant monitoring data processing method as described above.

[0017] In the above nuclear power plant monitoring data processing method, device, computer equipment and storage medium, when it is determined that there is a first abnormal parameter in the first monitoring data of the first nuclear power unit, it is first determined whether the first target device corresponding to the first abnormal parameter is a common device, and then when it is determined to be a common device, a second target device that is a common device with the first target device is determined from the second nuclear power unit. In this way, based on the group reactor information sharing among multiple units such as the first nuclear power unit and the second nuclear power unit of the nuclear power plant, it is possible to pre-judge whether there is an abnormality in the second target device according to the second monitoring data in the second nuclear power unit, realizing the linkage processing of the nuclear power plant monitoring data at the device level, reducing the lag of abnormal monitoring, and making the abnormal response and processing more timely. In addition, an abnormal handling scheme is jointly determined according to the first abnormal parameter and the second abnormal parameter, which is beneficial to unified command for common device abnormal events between the first nuclear power unit and the second nuclear power unit, improves the comprehensiveness of problem decision-making and the efficiency of abnormal event handling, and effectively improves the abnormal recognition effect of the monitoring data. Brief Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 is a flowchart of a nuclear power plant monitoring data processing method according to an embodiment of the present invention;

[0020] Figure 2 is a structural diagram of a nuclear power plant monitoring data processing device according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Embodiments

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0023] In one embodiment, as Figure 1 shown, a nuclear power plant monitoring data processing method is provided, including the following steps S10 - S40:

[0024] S10. Obtain the first monitoring data of the first nuclear power unit, and determine whether there is a first abnormal parameter in the first monitoring data.

[0025] Understandably, in the application scenario of monitoring the status of nuclear power units in a nuclear power plant, the monitoring system establishes a communication connection with the monitoring terminals pre-set in each nuclear power unit. Among them, the monitoring system can be implemented by an independent server or a server cluster composed of multiple servers. The monitoring terminal can be a sensor that collects the operating parameters of the equipment or a camera. When the nuclear power plant includes at least two nuclear power units, the monitoring system can monitor the status of each nuclear power unit simultaneously.

[0026] When the monitoring system interacts with the nuclear power unit in terms of data, all nuclear power units can be divided into the first nuclear power unit and the second nuclear power unit according to different data sources. The first nuclear power unit is the nuclear power unit used to represent the sending source of the detection data currently being analyzed, that is, the target unit. The second nuclear power unit is the nuclear power unit used to represent the nuclear power units other than the sending source of the detection data currently being analyzed, that is, other non-target units in the same nuclear power plant. The first monitoring data refers to the monitoring data sent by the first nuclear power unit to the monitoring system, including the operating parameters of any unit equipment in the first nuclear power unit. For the operating parameters of all unit equipment in the nuclear power unit, the normal parameter standards corresponding to the nuclear power unit in the normal operating state are pre-set. The first abnormal parameter refers to the parameter found to be abnormal after analyzing the first monitoring data, that is, the parameter that does not conform to the normal parameter standard of the unit equipment.

[0027] S20. If there is a first abnormal parameter in the first monitoring data, determine the first target device corresponding to the first abnormal parameter from all the unit equipment of the first nuclear power unit, and obtain the device attribute information of the first target device.

[0028] Understandably, the first monitoring data conducts data interaction in units of unit equipment, and each operating parameter in the first monitoring data corresponds to a unit equipment. When there is a first abnormal parameter in the first monitoring data, the first target device corresponding to the first abnormal parameter can be determined from all the unit equipment of the first nuclear power unit. The first target device refers to the unit equipment in the first nuclear power unit with abnormal operating parameters. The device attribute information is used to record the characteristic information of the device itself, including the model, manufacturer, installation location, maintenance records, operation manuals, etc. After determining the first target device, obtain the device attribute information of the first target device to facilitate subsequent analysis and processing based on the device attribute information.

[0029] S30. When it is determined that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit.

[0030] Understandably, for multiple nuclear power units of the same nuclear power plant, the coincidence rate between the unit devices of each nuclear power unit is very high, and it is very easy to have the situation that the same unit devices are installed at the same installation position. After obtaining the device attribute information of the first target device, the number of nuclear power units to which the device with this device attribute information belongs can be queried according to the device attribute information. When there are two or more nuclear power units to which the device belongs, it can be determined that the device attribute information is common device attribute; when there is only one nuclear power unit to which the device belongs, it can be determined that the device attribute information is not common device attribute. The common device attribute refers to the device attribute information with the same characteristics in different nuclear power units, such as device characteristics with the same functions, types, maintenance requirements, etc. When it is determined that the device attribute information is common device attribute, the second target device corresponding to the device attribute information can be determined from all the unit devices of the second nuclear power unit according to the device attribute information. The second target device refers to the unit device in the second nuclear power unit that has the same device attribute information as the first target device.

[0031] S40. Obtain second monitoring data corresponding to the second target device, and when it is confirmed that there are second abnormal parameters in the second monitoring data, determine an abnormal handling plan according to the first abnormal parameter and the second abnormal parameter.

[0032] Understandably, although the first target device and the second target device belong to different nuclear power units respectively, due to having the same device attribute information, when the first target device has an abnormality, the second target device may also have an abnormality. In order to timely determine whether the second target device has an abnormality, it is necessary to obtain the second monitoring data corresponding to the second target device. The second monitoring data refers to the operation parameters corresponding to the second target device sent by the second nuclear power unit to the monitoring system, and does not include the operation parameters of other devices in the second nuclear power unit except the second target device, so that targeted analysis of the second target device can be carried out. The second abnormal parameter refers to the parameter with an abnormality screened out after analyzing the second monitoring data, that is, the parameter that does not meet the normal parameter standard of the second target device.

[0033] The monitoring system not only acts as an observer but also as a decision-maker. Through a real-time monitoring mechanism, the monitoring system can trigger an alarm and subsequent processing procedures immediately when abnormal parameters are detected. When it is confirmed that there are second abnormal parameters in the second monitoring data, it indicates that both the first target device and the second target device are abnormal. Therefore, it is necessary to jointly determine an abnormal handling plan based on the first abnormal parameter and the second abnormal parameter. The abnormal handling plan refers to a strategy for restoring the operating parameters of the unit equipment in the nuclear power plant from abnormal to normal, such as inspection, repair, or replacement. At this time, the abnormal handling plan includes a strategy for eliminating the first abnormal parameter of the first target device and a strategy for eliminating the second abnormal parameter of the second target device.

[0034] In this embodiment, when it is determined that there is a first abnormal parameter in the first monitoring data of the first nuclear power unit, first, it is judged whether the first target device corresponding to the first abnormal parameter is a common device. Then, when it is determined to be a common device, a second target device that is a common device with the first target device is determined from the second nuclear power unit. This embodiment can pre-judge whether there is an abnormality in the second target device based on the shared information of multiple units such as the first nuclear power unit and the second nuclear power unit of the nuclear power plant, realizing the linkage processing of the monitoring data of the nuclear power plant at the device level, reducing the lag of abnormal monitoring, and making the abnormal response and processing more timely. In addition, this embodiment jointly determines an abnormal handling plan based on the first abnormal parameter and the second abnormal parameter. For the abnormal events of common devices between the first nuclear power unit and the second nuclear power unit, it is conducive to unified command, improving the comprehensiveness of problem decision-making and the efficiency of abnormal event handling, and effectively improving the abnormal recognition effect of monitoring data. This embodiment establishes a data sharing platform between multiple nuclear power units to more quickly discover common problems across units, and at the same time establishes a collaborative processing mechanism to ensure that when abnormal situations across units are discovered, the personnel and materials of the nuclear power plant can be quickly coordinated for processing.

[0035] In one embodiment, the first monitoring data includes at least one parameter type and the monitoring data corresponding to each parameter type; in step S10, that is, judging whether there is a first abnormal parameter in the first monitoring data, includes:

[0036] S101. Input the first monitoring data into a preset parameter analysis model for analysis and processing to obtain all parameter types in the first monitoring data and the type monitoring curves corresponding to each parameter type;

[0037] S102. Read the type standard curves corresponding to each parameter type in the preset parameter analysis model, compare the type monitoring curves and the type standard curves corresponding to the same parameter type, and determine the curve deviation values corresponding to each parameter type;

[0038] S103. Determine whether the curve deviation value reaches the preset deviation threshold corresponding to the same parameter type;

[0039] S104. If the curve deviation value reaches the preset deviation threshold corresponding to the same parameter type, determine that there is a first abnormal parameter in the first monitoring data, and determine the monitoring data corresponding to all the curve deviation values that reach the preset deviation threshold as the first abnormal parameter.

[0040] Understandably, there are multiple different unit devices in a nuclear power unit, and the same unit device may also have multiple aspects of operating parameters at the same time, such as the flow rate, outlet temperature, and outlet pressure of the heat exchange device. The first monitoring data includes at least one parameter type and the monitoring data corresponding to each parameter type. The parameter type is information used to characterize the characteristics between operating parameters.

[0041] In the process of determining whether there is a first abnormal parameter in the first monitoring data, it is necessary to first input the first monitoring data into a preset parameter analysis model for analysis and processing. The preset parameter analysis model refers to a neural network model that has been pre-trained to analyze the monitoring data of various parameter types. The input of the preset parameter analysis model is the monitoring data, and the output is the abnormal parameters in the monitoring data. The preset parameter analysis model will analyze and process the first monitoring data to identify all the parameter types in the first monitoring data. For each parameter type, a corresponding type monitoring curve will also be generated. The type monitoring curve refers to a trajectory curve used to characterize the change of operating parameters of different parameter types over time. The abscissa of the type monitoring curve corresponds to time, and the ordinate corresponds to the parameter type of the operating parameter. The sampling frequencies of the operating parameters are different, and the accuracies of the curves are also different. The higher the sampling frequency, the higher the accuracy.

[0042] The preset parameter analysis model also pre-stores type standard curves corresponding to each parameter type. The type standard curves are used to characterize the trajectory curves of the operating parameters of different parameter types over time under the normal operating state of the nuclear power unit. After obtaining the type monitoring curve, read the type standard curve, compare the type monitoring curve corresponding to the same parameter type with the type standard curve, and calculate the curve deviation value between the two curves. The curve deviation value is used to quantify the degree of deviation of the monitoring data from the type standard curve.

[0043] For each parameter type, there is a preset deviation threshold. Next, it is necessary to determine whether the calculated curve deviation value reaches the preset deviation threshold corresponding to the same parameter type. The preset deviation threshold is a critical value preset for determining whether the deviation in the monitoring data reaches an abnormal level. The preset deviation threshold can be a deviation interval value. For example, the preset deviation threshold for temperature is set to (-2°C, 2°C). If the curve deviation value corresponding to any parameter type reaches (or exceeds) the preset deviation threshold, it can be determined that there is a first abnormal parameter in the first monitoring data, and the monitoring data corresponding to all the curve deviation values that reach the preset deviation threshold is determined as the first abnormal parameter. If all the curve deviation values do not reach the preset deviation threshold corresponding to the same parameter type, it is determined that there is no first abnormal parameter in the first monitoring data.

[0044] This embodiment realizes the automation of the abnormal analysis process based on the preset parameter analysis model, which saves time and effort and improves the efficiency and accuracy of monitoring data processing. At the same time, due to being based on the preset parameter analysis model and the type standard curve, it also has a certain degree of flexibility and scalability, and can be learned and optimized according to different application scenarios.

[0045] In one embodiment, in step S101, that is, before inputting the first monitoring data into the preset parameter analysis model for analysis and processing, it includes:

[0046] S1011. Obtain the historical monitoring data of the first nuclear power unit in the normal operation state, and the historical monitoring data includes parameter type labels;

[0047] S1012. Clean and denoise the historical monitoring data, and classify it according to the parameter type labels to obtain the sample monitoring data of each parameter type;

[0048] S1013. Establish a backpropagation neural network based on the input layer, hidden layer, and output layer, and determine the established backpropagation neural network as the initial parameter analysis model;

[0049] S1014. Train the initial parameter analysis model according to the sample monitoring data of all parameter types to obtain the trained parameter analysis model;

[0050] S1015. Generate type standard curves corresponding to each parameter type based on the trained parameter analysis model, and obtain the preset parameter analysis model according to all the type standard curves and the trained parameter analysis model.

[0051] Understandably, before analyzing and processing the monitoring data through the preset parameter analysis model, it is necessary to first use the historical monitoring data as sample data for model training to obtain the preset parameter analysis model. The historical monitoring data refers to all the monitoring data of the nuclear power unit within the specified historical time period, where the specified historical time period can be the three months before the current time point or the previous year. First, obtain the historical monitoring data of the first nuclear power unit in the normal operation state. These historical monitoring data contain parameter type tags, which are used to identify the parameter types corresponding to each item of historical monitoring data. Then, clean and denoise the historical monitoring data to remove incorrect or invalid data to reduce the impact of noise on subsequent model training. Classify the data according to the parameter type tags to obtain the sample monitoring data of each parameter type. The sample monitoring data is the data obtained after preprocessing (cleaning and denoising) the historical monitoring data.

[0052] Next, it is necessary to establish an initial parameter analysis model. The initial parameter analysis model is a neural network model that has not been trained with sample data. Based on the input layer, hidden layer, and output layer, establish a backpropagation neural network (abbreviated as BPNN), and determine the established backpropagation neural network as the initial parameter analysis model. Use the sample monitoring data of all parameter types to train the initial parameter analysis model. During the training process, the backpropagation neural network adjusts the network weights through the backpropagation algorithm and adjusts the model parameters through iterative optimization to minimize the error between the predicted value and the actual value. In addition, based on the trained backpropagation neural network, generate a type standard curve for each parameter type. Finally, combine all the type standard curves and the trained backpropagation neural network to obtain the preset parameter analysis model.

[0053] This embodiment makes full use of the historical monitoring data to train the established backpropagation neural network, and obtains the preset parameter analysis model based on all the type standard curves and the trained backpropagation neural network, enabling the model to accurately identify the abnormal parameters in the first monitoring data and ensuring the monitoring data analysis ability of the model.

[0054] In one embodiment, in step S1015, that is, after obtaining the preset parameter analysis model according to all the type standard curves and the trained parameter analysis model, it includes:

[0055] S10151. Obtain the new historical monitoring data of the first nuclear power unit in the normal operation state according to the preset time period, and judge whether there are new parameter type tags in the new historical monitoring data;

[0056] S10152. If there is a newly added parameter type tag, screen and process the newly added historical monitoring data according to the newly added parameter type tag to obtain the newly added sample monitoring data of each newly added parameter type;

[0057] S10153. Update and train the preset parameter analysis model according to the newly added sample monitoring data to obtain an updated preset parameter analysis model, and the updated preset parameter analysis model includes newly added type standard curves corresponding to each newly added parameter type.

[0058] Understandably, in the actual operation and maintenance requirements of nuclear power units, some new unit equipment may be installed. At this time, it is necessary to monitor the operating parameters corresponding to the new unit equipment, or it is necessary to monitor new types of operating parameters for the existing unit equipment. At this time, the preset parameter analysis model needs to be adjusted. The preset time period is a fixed time period preset for adjusting the preset parameter analysis model using new historical monitoring data. For example, the default setting is one month.

[0059] Obtain the newly added historical monitoring data of the first nuclear power unit in the normal operation state according to the preset time period. The newly added historical monitoring data refers to the monitoring data generated by the first nuclear power unit in the normal operation state within the preset time period. Check the newly added historical monitoring data to determine whether there is a newly added parameter type tag. The newly added parameter type tag refers to the tag of the parameter type that is newly added in the newly added historical monitoring data compared with the previous historical time period. These newly added parameter types may be introduced with the upgrade, transformation of the nuclear power unit or the update of the monitoring technology. When there is no newly added parameter type tag, there is no need to update the preset parameter analysis model. When there is a newly added parameter type tag, screen and process the newly added historical monitoring data according to these newly added parameter type tags, and extract the newly added sample monitoring data corresponding to each newly added parameter type from the newly added historical monitoring data. The newly added sample monitoring data is the data obtained after preprocessing (cleaning and noise reduction processing) of the newly added historical monitoring data. Add the newly added sample monitoring data to the training set, and re-run the training algorithm to update and train the backpropagation neural network in the preset parameter analysis model to obtain an updated backpropagation neural network. Generate newly added type standard curves corresponding to these newly added parameter types based on the updated backpropagation neural network. The newly added type standard curves are used to characterize the trajectory curve of the operating parameters of the newly added parameter types changing with time in the normal operation state of the nuclear power unit. Combine the existing type standard curves, the newly added type standard curves and the updated backpropagation neural network to obtain an updated preset parameter analysis model.

[0060] In this embodiment, by periodically updating the preset parameter analysis model, the applicability of the preset parameter analysis model is ensured, so that abnormal parameters can be continuously and effectively identified, the accuracy and reliability of abnormal monitoring are improved, and the safe operation of the nuclear power unit is guaranteed.

[0061] In one embodiment, in step S20, that is, after obtaining the device attribute information of the first target device, it includes:

[0062] S201. Determine the device function attribute and device type attribute of the first target device according to the device attribute information;

[0063] S202. Search for the unit number information associated with the device function attribute and device type attribute in the preset unit device database, and determine whether the unit number information is unique;

[0064] S203. If the unit number information is not unique, determine that the device attribute information is common device attribute.

[0065] Understandably, the device attribute information includes the device function attribute and device type attribute of the unit device. The device function attribute is used to characterize the functional characteristics of the unit device, including technical specifications and maintenance record information. The device type attribute is used to characterize the type characteristics of the unit device, including the model, manufacturer, installation location, etc. of the device. The preset unit device database is a pre-generated database for storing the association relationship between all unit devices in the nuclear power plant and each nuclear power unit. One unit device corresponds to a combination of a device function attribute and a device type attribute, but a combination of a device function attribute and a device type attribute can correspond to one nuclear power unit, or two or more nuclear power units. The unit number information is specific number information used to clearly identify and distinguish different nuclear power units, which can be numbers or letters.

[0066] The device function attribute and device type attribute of the first target device can be extracted from the device attribute information, the unit number information associated with the device function attribute and device type attribute is searched in the preset unit device database, and it is determined whether the unit number information is unique. When the unit number information is not unique, it indicates that there are unit devices with the same device function attribute and device type attribute in other nuclear power units. Therefore, the device attribute information is determined to be common device attribute.

[0067] In this embodiment, the device function attribute and device type attribute are used as search conditions, which effectively determines the uniqueness of the first target device among all nuclear power units, and can accurately determine whether the first target device is a common device, so as to facilitate subsequent adoption of different abnormal handling schemes.

[0068] In one embodiment, after step S202, that is, after determining whether the unit number information is unique, the following steps are further included:

[0069] S2021. If the unit number information is unique, determine that the device attribute information is not the common device attribute;

[0070] S2022. Determine a first device processing solution corresponding to the first target device according to the first abnormal parameter, and determine the first device processing solution as the abnormal processing solution.

[0071] Understandably, in the process of determining whether the unit number information is unique, when the unit number information is unique, it indicates that there are no unit devices with the same device function attribute and device type attribute in other nuclear power units. Therefore, it is determined that the device attribute information is not the common device attribute. At this time, it is not necessary to further analyze the monitoring data of other nuclear power units, and the first device processing solution corresponding to the first target device can be directly determined according to the first abnormal parameter, and the first device processing solution is determined as the abnormal processing solution. The first device processing solution is a strategy for restoring the operating parameters of the first target device from abnormal to normal.

[0072] This embodiment excludes the possibility that the abnormality of the first target device is a common problem when the unit number information is unique, and can quickly and effectively determine the processing solution when an abnormal parameter is found, thus ensuring the normal operation and safety of the nuclear power unit.

[0073] In one embodiment, after step S40, that is, after obtaining the second monitoring data corresponding to the second target device, the following steps are further included:

[0074] S401. Input the second monitoring data into a preset parameter analysis model for analysis and processing, and determine whether there is a second abnormal parameter in the second monitoring data according to the analysis result;

[0075] S402. If it is confirmed that there is no second abnormal parameter in the second monitoring data, determine a first device processing solution corresponding to the first target device according to the first abnormal parameter, and determine the first device processing solution as the abnormal processing solution.

[0076] Understandably, when determining whether the second target device is operating normally, it is generally necessary to input the second monitoring data into a preset parameter analysis model for analysis and processing, and determine whether there are second abnormal parameters in the second monitoring data according to the analysis results. When there are no second abnormal parameters in the second monitoring data, it indicates that the second target device is operating normally and no processing needs to be performed on the second target device. At this time, only the first target device has an abnormality. Therefore, directly determine the first device processing plan corresponding to the first target device according to the first abnormal parameter, and determine the first device processing plan as the abnormal processing plan.

[0077] In this embodiment, when ensuring the normal operation of the second target device, directly determining the first device processing plan corresponding to the first target device can effectively handle the abnormality of the first target device and ensure the timeliness of abnormal processing.

[0078] In one embodiment, the first abnormal parameter includes an abnormal prompt parameter and an abnormal warning parameter; in step S402, that is, determining the first device processing plan corresponding to the first target device according to the first abnormal parameter includes:

[0079] S4021. When it is confirmed that the first abnormal parameter is an abnormal prompt parameter, determine the device maintenance strategy corresponding to the first target device according to the first abnormal parameter, and determine the device maintenance strategy as the first device processing plan;

[0080] S4022. When it is confirmed that the first abnormal parameter is an abnormal warning parameter, determine the device replacement strategy corresponding to the first target device according to the first abnormal parameter, and determine the device replacement strategy as the first device processing plan.

[0081] Understandably, when determining whether the curve deviation value reaches the preset deviation threshold corresponding to the same parameter type, different threshold ranges can be further divided according to the severity of the impact of the deviation on the operation. The preset deviation threshold includes a preset prompt deviation threshold and a preset warning deviation threshold, and the preset prompt deviation threshold is less than the preset warning deviation threshold. The preset prompt deviation threshold is a critical value pre-set for determining that the deviation in the monitoring data has a mild impact on the unit operation (such as the parts of the unit equipment are fatigued but still can perform functions), and the preset warning deviation threshold is a critical value pre-set for determining that the deviation in the monitoring data has a severe impact on the unit operation (such as the parts of the unit equipment are damaged and the functions cannot be realized). When the curve deviation value reaches the preset prompt deviation threshold corresponding to the same parameter type and has not reached the preset warning deviation threshold, the corresponding first abnormal parameter is an abnormal prompt parameter, and the abnormal prompt parameter indicates that the deviation value of the first abnormal parameter reaches the preset prompt deviation threshold. When the curve deviation value reaches the preset warning deviation threshold corresponding to the same parameter type, the corresponding first abnormal parameter is an abnormal warning parameter, and the abnormal warning parameter indicates that the deviation value of the first abnormal parameter reaches the preset warning deviation threshold.

[0082] The severity of the impact of the deviation of the first abnormal parameter on the unit operation is different, and the adopted processing strategies are also different. When it is confirmed that the first abnormal parameter is an abnormal prompt parameter, it indicates that there are certain problems or faults in the first target device currently, but these problems or faults may not immediately cause the device to completely fail. At this time, determine the equipment maintenance strategy corresponding to the first target device according to the first abnormal parameter, and determine the equipment maintenance strategy as the first device processing plan. The equipment maintenance strategy refers to the processing method of checking and repairing the unit equipment to meet the normal operation requirements of the unit, such as adjusting the position. When it is confirmed that the first abnormal parameter is an abnormal warning parameter, it indicates that serious problems or faults will occur in the first target device soon, and these problems or faults may cause the device to completely fail and urgent measures need to be taken immediately. At this time, determine the equipment replacement strategy corresponding to the first target device according to the first abnormal parameter, and determine the equipment replacement strategy as the first device processing plan. The equipment replacement strategy refers to the processing method of replacing the unit equipment to meet the normal operation requirements of the unit.

[0083] In an embodiment, during the power-up process of the nuclear power unit, the deaerator level parameter of the level transmitter Y1ADG004MN fluctuates greatly. Comparing with the stable levels of Y1ADG001 / 002 / 003MN, the level parameter is an abnormal parameter. When the level parameter is an abnormal warning parameter, it is confirmed that the level transmitter Y1ADG004MN is faulty (instrument damaged), and the equipment replacement strategy is determined as the equipment processing plan.

[0084] In this embodiment, different processing strategies are adopted for abnormal parameters with different degrees of influence as the abnormal handling scheme, which helps to handle device abnormalities more efficiently and accurately, and improve the availability of the device and the rationality of abnormal handling.

[0085] In one embodiment, in step S40, that is, determining the abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter includes:

[0086] S403. Search for a list of abnormal handling suggestions associated with the common device attributes in a preset device knowledge base according to the device attribute information;

[0087] S404. Obtain a first handling suggestion that matches the first abnormal parameter and a second handling suggestion that matches the second abnormal parameter from the list of abnormal handling suggestions;

[0088] S405. Generate a first device handling scheme corresponding to the first target device according to the first handling suggestion, generate a second device handling scheme corresponding to the second target device according to the second handling suggestion, and determine the first device handling scheme and the second device handling scheme as the abnormal handling scheme.

[0089] Understandably, the first target device and the second target device have the same device attribute information. At this time, the device attribute information is the common device attribute, which can be used to search for handling suggestions associated with the common device attributes in the preset device knowledge base. The preset device knowledge base is a database that stores common problems and solutions of devices in advance in units of unit devices, and contains abnormal handling suggestions associated with various device attributes. The preset device knowledge base is a structured database, stored in units of unit devices. The device attribute information of each unit device corresponds to a device attribute handling suggestion data group, and the device attribute handling suggestion data group is used to represent the association relationship between the unit device corresponding to the device attribute information and the list of abnormal handling suggestions for the operating parameters in the unit device. The list of abnormal handling suggestions records all abnormal parameters of the operating parameters in the unit device and the corresponding abnormal handling suggestions for each abnormal parameter.

[0090] In the preset device knowledge base, the monitoring system locates one or more relevant lists of abnormal handling suggestions based on the common device attributes of the first target device and the second target device. Next, the monitoring system searches for matching handling suggestions from the list of abnormal handling suggestions according to specific abnormal parameters (the first abnormal parameter and the second abnormal parameter), and obtains the first handling suggestion matching the first abnormal parameter and the second handling suggestion matching the second abnormal parameter respectively. The first handling suggestion refers to the recommended strategy for restoring the first abnormal parameter from abnormal to normal, and the second handling suggestion refers to the recommended strategy for restoring the second abnormal parameter from abnormal to normal. The recommended strategy can be a device maintenance strategy or a device replacement strategy. According to the found handling suggestions, the monitoring system generates a final device handling plan. These device handling plans include specific operation steps, required tools and spare parts, expected results, etc. The monitoring system generates a first device handling plan for handling the first target device based on the first handling suggestion, generates a second device handling plan for handling the second target device based on the second handling suggestion, and determines the first device handling plan and the second device handling plan as the final abnormal handling plan.

[0091] In this embodiment, by searching for handling suggestions in the preset device knowledge base according to device attribute information and determining the abnormal handling plan, the automatic mapping from device abnormal parameters to specific handling plans is realized, greatly improving the efficiency of abnormal troubleshooting and handling. At the same time, based on the preset device knowledge base, it not only has reliability and accuracy, but also helps to establish a collaborative handling mechanism to ensure that when cross-unit abnormalities are found, the personnel and materials of the nuclear power plant can be quickly coordinated for handling.

[0092] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0093] In one embodiment, a nuclear power plant monitoring data processing device is provided, and this nuclear power plant monitoring data processing device corresponds one-to-one with the nuclear power plant monitoring data processing method in the above embodiment. As Figure 2 shown, this nuclear power plant monitoring data processing device includes a parameter abnormality judgment module 10, a device attribute information acquisition module 20, a common device determination module 30, and an abnormal handling plan generation module 40. The detailed descriptions of each functional module are as follows:

[0094] The parameter abnormality judgment module 10 is configured to obtain the first monitoring data of the first nuclear power unit and judge whether there is a first abnormal parameter in the first monitoring data;

[0095] The device attribute information acquisition module 20 is configured to, if there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all the unit devices of the first nuclear power unit, and acquire the device attribute information of the first target device;

[0096] The common device determination module 30 is configured to, when determining that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit;

[0097] The abnormal handling solution generation module 40 is configured to acquire second monitoring data corresponding to the second target device, and when confirming that there is a second abnormal parameter in the second monitoring data, determine an abnormal handling solution according to the first abnormal parameter and the second abnormal parameter.

[0098] In one embodiment, the parameter abnormal judgment module 10 includes:

[0099] The first monitoring data analysis unit is configured to input the first monitoring data into a preset parameter analysis model for analysis and processing, to obtain all the parameter types in the first monitoring data and the type monitoring curves corresponding to each parameter type;

[0100] The curve deviation value determination unit is configured to read the type standard curves corresponding to each parameter type in the preset parameter analysis model, compare the type monitoring curves and the type standard curves corresponding to the same parameter type, and determine the curve deviation values corresponding to each parameter type;

[0101] The curve deviation value comparison unit is configured to determine whether the curve deviation value reaches a preset deviation threshold corresponding to the same parameter type;

[0102] The first abnormal parameter determination unit is configured to, if the curve deviation value reaches the preset deviation threshold corresponding to the same parameter type, determine that there is a first abnormal parameter in the first monitoring data, and determine the monitoring data corresponding to all the curve deviation values that reach the preset deviation threshold as the first abnormal parameter.

[0103] In one embodiment, the parameter abnormal judgment module 10 further includes:

[0104] The historical monitoring data acquisition unit is configured to acquire the historical monitoring data of the first nuclear power unit in a normal operation state, where the historical monitoring data includes parameter type labels;

[0105] A sample monitoring data determination unit, configured to clean and denoise the historical monitoring data, and classify the data according to the parameter type tags, so as to obtain the sample monitoring data of each parameter type;

[0106] An initial parameter analysis model establishment unit, configured to establish a backpropagation neural network based on an input layer, a hidden layer, and an output layer, and determine the established backpropagation neural network as the initial parameter analysis model;

[0107] A model training unit, configured to train the initial parameter analysis model according to the sample monitoring data of all parameter types, so as to obtain a trained parameter analysis model;

[0108] A preset parameter analysis model generation unit, configured to generate type standard curves corresponding to each parameter type based on the trained parameter analysis model, and obtain the preset parameter analysis model according to all the type standard curves and the trained parameter analysis model.

[0109] In one embodiment, the parameter anomaly judgment module 10 further includes:

[0110] A new historical monitoring data acquisition unit, configured to acquire new historical monitoring data of the first nuclear power unit in a normal operation state according to a preset time period, and judge whether there are new parameter type tags in the new historical monitoring data;

[0111] A new sample monitoring data determination unit, configured to, if there are new parameter type tags, screen the new historical monitoring data according to the new parameter type tags, so as to obtain the new sample monitoring data of each new parameter type;

[0112] A model update training unit, configured to update and train the preset parameter analysis model according to the new sample monitoring data, so as to obtain an updated preset parameter analysis model, where the updated preset parameter analysis model includes new type standard curves corresponding to each new parameter type.

[0113] In one embodiment, the device attribute information acquisition module 20 includes:

[0114] A device attribute information parsing unit, configured to determine the device function attribute and device type attribute of the first target device according to the device attribute information;

[0115] A unit for acquiring unit number information, configured to search for unit number information associated with the device function attribute and device type attribute in a preset unit device database, and judge whether the unit number information is unique;

[0116] A common device attribute determination unit, configured to, if the unit number information is not unique, determine the device attribute information as the common device attribute.

[0117] In one embodiment, the device attribute information acquisition module 20 further includes:

[0118] A common device attribute negation unit, configured to determine that the device attribute information is not a common device attribute if the unit number information is unique;

[0119] A first abnormal handling solution generation unit, configured to determine a first device handling solution corresponding to the first target device according to the first abnormal parameter, and determine the first device handling solution as an abnormal handling solution.

[0120] In one embodiment, the abnormal handling solution generation module 40 includes:

[0121] A second monitoring data analysis unit, configured to input the second monitoring data into a preset parameter analysis model for analysis and processing, and determine whether there is a second abnormal parameter in the second monitoring data according to the analysis result;

[0122] A second abnormal handling solution generation unit, configured to determine a first device handling solution corresponding to the first target device according to the first abnormal parameter and determine the first device handling solution as an abnormal handling solution if it is confirmed that there is no second abnormal parameter in the second monitoring data.

[0123] In one embodiment, the abnormal handling solution generation module 40 further includes:

[0124] A device maintenance processing unit, configured to determine a device maintenance strategy corresponding to the first target device according to the first abnormal parameter and determine the device maintenance strategy as the first device handling solution when it is confirmed that the first abnormal parameter is an abnormal prompt parameter;

[0125] A device replacement processing unit, configured to determine a device replacement strategy corresponding to the first target device according to the first abnormal parameter and determine the device replacement strategy as the first device handling solution when it is confirmed that the first abnormal parameter is an abnormal warning parameter.

[0126] In one embodiment, the abnormal handling solution generation module 40 further includes:

[0127] An abnormal handling suggestion list acquisition unit, configured to search for an abnormal handling suggestion list associated with the common device attribute in a preset device knowledge base according to the device attribute information;

[0128] A handling suggestion acquisition unit, configured to acquire a first handling suggestion matching the first abnormal parameter and a second handling suggestion matching the second abnormal parameter from the abnormal handling suggestion list;

[0129] An abnormal handling solution third generation unit, configured to generate a first device handling solution corresponding to the first target device according to the first handling suggestion, generate a second device handling solution corresponding to the second target device according to the second handling suggestion, and determine the first device handling solution and the second device handling solution as the abnormal handling solution.

[0130] For the specific limitations of the nuclear power plant monitoring data processing device, reference can be made to the limitations of the nuclear power plant monitoring data processing method in the above text, which will not be elaborated here. Each module in the above nuclear power plant monitoring data processing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0131] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database of the computer device is used to store the data involved in the nuclear power plant monitoring data processing method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a nuclear power plant monitoring data processing method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0132] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. When the processor executes the computer-readable instructions, the following steps are implemented:

[0133] Obtain the first monitoring data of the first nuclear power unit, and determine whether there is a first abnormal parameter in the first monitoring data;

[0134] If there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all the unit devices of the first nuclear power unit, and obtain the device attribute information of the first target device;

[0135] When it is determined that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit;

[0136] Obtain second monitoring data corresponding to the second target device, and when it is confirmed that there is a second abnormal parameter in the second monitoring data, determine an abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter.

[0137] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media. Computer-readable instructions are stored on the readable storage media, and when the computer-readable instructions are executed by one or more processors, the following steps are implemented:

[0138] Obtain first monitoring data of the first nuclear power unit, and determine whether there is a first abnormal parameter in the first monitoring data;

[0139] If there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all the unit devices of the first nuclear power unit, and obtain the device attribute information of the first target device;

[0140] When it is determined that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit;

[0141] Obtain second monitoring data corresponding to the second target device, and when it is confirmed that there is a second abnormal parameter in the second monitoring data, determine an abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter.

[0142] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above-mentioned embodiments can be completed by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0144] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for processing nuclear power plant monitoring data, characterized in that, Including: Obtain the first monitoring data of the first nuclear power unit, and determine whether there is a first abnormal parameter in the first monitoring data; If there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all the unit devices of the first nuclear power unit, and obtain the device attribute information of the first target device; When determining that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all the unit devices of the second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit; Obtain the second monitoring data corresponding to the second target device, and when it is confirmed that there is a second abnormal parameter in the second monitoring data, determine an abnormal handling scheme according to the first abnormal parameter and the second abnormal parameter; Wherein, the first monitoring data includes at least one parameter type and the monitoring data corresponding to each parameter type; The determining whether there is a first abnormal parameter in the first monitoring data includes: Input the first monitoring data into a preset parameter analysis model for analysis and processing to obtain all the parameter types in the first monitoring data and the type monitoring curves corresponding to each parameter type; Read the type standard curves corresponding to each parameter type in the preset parameter analysis model, compare the type monitoring curves and the type standard curves corresponding to the same parameter type, and determine the curve deviation values corresponding to each parameter type; Judge whether the curve deviation value reaches a preset deviation threshold corresponding to the same parameter type; If the curve deviation value reaches the preset deviation threshold corresponding to the same parameter type, determine that there is a first abnormal parameter in the first monitoring data, and determine the monitoring data corresponding to all the curve deviation values that reach the preset deviation threshold as the first abnormal parameter; After obtaining the device attribute information of the first target device, it includes: Determine the device function attribute and device type attribute of the first target device according to the device attribute information; Search for the unit number information associated with the device function attribute and device type attribute in the preset unit device database, and judge whether the unit number information is unique; If the unit number information is not unique, determine that the device attribute information is common device attribute.

2. The method for processing nuclear power plant monitoring data according to claim 1, wherein Before inputting the first monitoring data into the preset parameter analysis model for analysis and processing, it includes: Obtain the historical monitoring data of the first nuclear power unit in the normal operation state, and the historical monitoring data includes parameter type labels; Clean and denoise the historical monitoring data, and classify it according to the parameter type labels to obtain the sample monitoring data of each parameter type; Establish a backpropagation neural network based on the input layer, hidden layer and output layer, and determine the established backpropagation neural network as the initial parameter analysis model; Train the initial parameter analysis model according to the sample monitoring data of all parameter types to obtain the trained parameter analysis model; Generate type standard curves corresponding to each parameter type based on the trained parameter analysis model, and obtain the preset parameter analysis model according to all the type standard curves and the trained parameter analysis model.

3. The method for processing nuclear power plant monitoring data according to claim 2, wherein, After obtaining the preset parameter analysis model according to all the type standard curves and the trained parameter analysis model, it includes: Obtain the newly added historical monitoring data of the first nuclear power unit in the normal operation state according to a preset time period, and determine whether there is a newly added parameter type label in the newly added historical monitoring data; If there is a newly added parameter type label, filter and process the newly added historical monitoring data according to the newly added parameter type label to obtain the newly added sample monitoring data of each newly added parameter type; Update and train the preset parameter analysis model according to the newly added sample monitoring data to obtain an updated preset parameter analysis model, and the updated preset parameter analysis model includes newly added type standard curves corresponding to each newly added parameter type.

4. The method for processing nuclear power plant monitoring data according to claim 1, wherein After determining whether the unit number information is unique, it also includes: If the unit number information is unique, determine that the device attribute information is not a common device attribute; Determine a first device processing plan corresponding to the first target device according to the first abnormal parameter, and determine the first device processing plan as the abnormal processing plan.

5. The method for processing nuclear power plant monitoring data according to claim 1, characterized in that, After obtaining the second monitoring data corresponding to the second target device, it also includes: Input the second monitoring data into the preset parameter analysis model for analysis and processing, and determine whether there is a second abnormal parameter in the second monitoring data according to the analysis result; If it is confirmed that there is no second abnormal parameter in the second monitoring data, determine a first device processing plan corresponding to the first target device according to the first abnormal parameter, and determine the first device processing plan as the abnormal processing plan.

6. The method for processing nuclear power plant monitoring data according to any one of claims 4 or 5, characterized in that, The first abnormal parameter includes an abnormal prompt parameter and an abnormal warning parameter; Determining the first device processing plan corresponding to the first target device according to the first abnormal parameter includes: When it is confirmed that the first abnormal parameter is an abnormal prompt parameter, determine a device maintenance strategy corresponding to the first target device according to the first abnormal parameter, and determine the device maintenance strategy as the first device processing plan; When it is confirmed that the first abnormal parameter is an abnormal warning parameter, determine a device replacement strategy corresponding to the first target device according to the first abnormal parameter, and determine the device replacement strategy as the first device processing plan.

7. The method for processing nuclear power plant monitoring data according to claim 1, wherein Determining the abnormal processing plan according to the first abnormal parameter and the second abnormal parameter includes: Search for a list of abnormal processing suggestions associated with the common device attribute in the preset device knowledge base according to the device attribute information; Obtain a first processing suggestion matching the first abnormal parameter and a second processing suggestion matching the second abnormal parameter from the list of abnormal processing suggestions; Generate a first device processing solution corresponding to the first target device according to the first processing suggestion, generate a second device processing solution corresponding to the second target device according to the second processing suggestion, and determine the first device processing solution and the second device processing solution as the exception handling solution.

8. A nuclear power plant monitoring data processing device, characterized in that, Including: A parameter exception judgment module, configured to obtain first monitoring data of a first nuclear power unit and judge whether there is a first abnormal parameter in the first monitoring data; A device attribute information acquisition module, configured to, if there is a first abnormal parameter in the first monitoring data, determine a first target device corresponding to the first abnormal parameter from all unit devices of the first nuclear power unit, and acquire device attribute information of the first target device; A common device determination module, configured to, when determining that the device attribute information is common device attribute, determine a second target device corresponding to the device attribute information from all unit devices of a second nuclear power unit, where the second nuclear power unit includes at least one nuclear power unit that belongs to the same nuclear power plant as the first nuclear power unit and is different from the first nuclear power unit; An exception handling solution generation module, configured to obtain second monitoring data corresponding to the second target device, and when confirming that there is a second abnormal parameter in the second monitoring data, determine an exception handling solution according to the first abnormal parameter and the second abnormal parameter; Wherein, the parameter exception judgment module includes: A first monitoring data analysis unit, configured to input the first monitoring data into a preset parameter analysis model for analysis and processing, to obtain all parameter types in the first monitoring data and type monitoring curves corresponding to each parameter type; A curve deviation value determination unit, configured to read type standard curves corresponding to each parameter type in the preset parameter analysis model, compare the type monitoring curves and the type standard curves corresponding to the same parameter type, and determine curve deviation values corresponding to each parameter type; A curve deviation value comparison unit, configured to judge whether the curve deviation value reaches a preset deviation threshold corresponding to the same parameter type; A first abnormal parameter determination unit, configured to, if the curve deviation value reaches the preset deviation threshold corresponding to the same parameter type, determine that there is a first abnormal parameter in the first monitoring data, and determine the monitoring data corresponding to all curve deviation values reaching the preset deviation threshold as the first abnormal parameter; The device attribute information acquisition module includes: A device attribute information parsing unit, configured to determine the device function attribute and the device type attribute of the first target device according to the device attribute information; A unit number information acquisition unit, configured to search for unit number information associated with the device function attribute and the device type attribute in a preset unit device database, and judge whether the unit number information is unique; A common device attribute determination unit, configured to, if the unit number information is not unique, determine that the device attribute information is common device attribute.

9. A computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that, When the processor executes the computer-readable instructions, it implements the nuclear power plant monitoring data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the nuclear power plant monitoring data processing method according to any one of claims 1 to 7.

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