A fixed-point detection and early warning system for key equipment in nuclear power plants

Abstract

The invention relates to the field of nuclear power plant monitoring systems, and discloses a fixed-point detection and early warning system for key equipment in a nuclear power plant, comprising: a detection end, a feature database, an intelligent diagnosis platform and a data interaction end, wherein the detection end is used to detect the operating conditions of the key equipment and generate a detection signal; the feature database includes key equipment corresponding to each feature identifier; the collection end is used to collect leakage data information of a test point; the intelligent diagnosis platform is configured with a signal processing module and an early warning processing module, the signal processing module can judge whether the detection signal is abnormal, and when the judgment result is that the signal is abnormal, the collection end collects leakage data information of the corresponding key equipment test point; the early warning processing module can generate an early warning processing task according to the leakage data information; the data interaction end is used to receive the early warning processing task from the intelligent diagnosis platform, and send the early warning processing task to an operator.

Description

Technical Field

[0001] The invention relates to the field of nuclear power plant monitoring systems, and in particular to a fixed-point detection and early warning system for key equipment in a nuclear power plant. Background Art

[0002] A nuclear power plant is a facility that converts nuclear energy into electrical energy through appropriate devices. A nuclear power plant uses a nuclear reactor to replace the boiler of a thermal power plant. Nuclear fuel is burned in a special form in the nuclear reactor to generate heat, which converts nuclear energy into thermal energy to heat water and produce steam.

[0003] Nuclear power plants have a wide variety of equipment, complex pipeline connections, and harsh operating environment conditions. There are a large number of welded structures between the key equipment and pipelines of nuclear power plants, and welding defects will exist in many welds; at the same time, during the service period, pressure vessels and pipelines are often subjected to alternating loads due to start-up, shutdown and fluctuations in operating conditions (temperature and pressure), and are also affected by the environment (such as radiation) and media (such as corrosion), which will cause the original defects in pressure vessels and pipelines to crack and expand. Defective pressure vessels and pipelines cause cracks to initiate and expand during production, and eventually lead to failure of pressure vessels and pipelines. The direct consequence of the failure form is leakage.

[0004] The commonly used leakage monitoring methods in the prior art include: pressure boundary leakage acoustic emission monitoring, pressure boundary leakage absolute humidity monitoring and environmental radioactivity monitoring. Leak location and leakage rate estimation are achieved through pressure boundary leakage acoustic emission monitoring and pressure boundary leakage absolute humidity monitoring. However, the current pressure boundary leakage acoustic emission monitoring method can only identify and locate pressure pipeline leakage, lacks effective estimation of leakage rate, and thus cannot gain effective solution time. Summary of the invention

[0005] The present invention is intended to provide a fixed-point detection and early warning system for key equipment in a nuclear power plant to overcome the problems existing in the prior art.

[0006] To achieve the above-mentioned purpose, the basic scheme of the present invention is as follows: A fixed-point detection and early warning system for key equipment of a nuclear power plant, comprising:

[0007] A detection end, wherein the detection end is configured with a plurality of detection units, which are respectively preset at the test points of each key equipment of the nuclear power plant, and are used to detect the operating conditions of each test point of the key equipment of the nuclear power plant and generate detection signals, and each detection signal carries a characteristic identifier;

[0008] A feature database, wherein the feature database includes a key device corresponding to each feature identifier and the position coordinates of a point to be measured of the key device;

[0009] A collection end, wherein the collection end is configured with a plurality of collection units, each of the key equipment's test points is preset with a collection unit, and each collection unit is respectively associated with a detection unit of the test point, and the collection unit is used to collect leakage data information of the test point;

[0010] An intelligent diagnosis platform, wherein the intelligent diagnosis platform is connected to a detection end, a feature database and a collection end, and the intelligent diagnosis platform is configured with a signal processing module and an early warning processing module. When the intelligent diagnosis platform receives a detection signal, the signal processing module retrieves the key equipment corresponding to the feature identifier from the feature signal library according to the feature identifier carried by the detection signal and determines whether the detection signal is abnormal. When the judgment result is that the signal is abnormal, the signal processing module generates an inspection task and sends it to the collection unit corresponding to the test point corresponding to the detection signal. The inspection task is associated with an inspection item, and the inspection item includes leakage data information of the test point, and the leakage data information includes leakage source categories and leakage concentrations corresponding to the leakage source categories;

[0011] When the intelligent diagnosis platform receives leakage data information, the warning processing module generates a warning processing task according to the leakage data information, and the warning processing task includes the key equipment to be processed, the position coordinates of the key equipment to be tested, and the processing time limit;

[0012] The data interaction terminal is connected to the intelligent diagnosis platform, and is used to receive early warning processing tasks from the intelligent diagnosis platform and send early warning processing tasks to operators.

[0013] Furthermore, the inspection task includes a preset collection time period and a collection time interval of the leakage data information. When the intelligent diagnosis platform receives the leakage data information from the collection unit, the early warning processing module calculates the leakage index according to the leakage source category and the leakage concentration corresponding to the leakage source category, and calculates multiple leakage indexes according to the leakage data information obtained at different time intervals, and generates a leakage curve according to the multiple leakage indexes and the time points corresponding to each leakage index. The leakage curve is characterized by time T0 to time T1. i The actual change of leakage index at time T0 is the initial time, T i For the current moment.

[0014] Furthermore, the early warning processing module is configured with a curve model establishment strategy, which generates a leakage simulation curve for a period of time in the future according to the generated leakage curve simulation, and the leakage simulation curve is characterized by time T i To T j Simulated changes in leakage index;

[0015] The early warning processing module is configured with a leakage early warning strategy, and the leakage early warning strategy is configured with a leakage early warning critical value. The leakage early warning strategy finds the time point corresponding to the leakage early warning critical value in the leakage simulation curve according to the leakage early warning critical value to generate a processing time limit, and generates an early warning processing task according to the processing time limit and sends it to the data interaction end.

[0016] Furthermore, the early warning processing module is configured with a leakage index calculation strategy, the leakage index calculation strategy is configured with a leakage index calculation algorithm, the leakage index calculation algorithm is configured with preset weight parameters corresponding to different leakage source categories, and the preset weight parameters corresponding to each leakage data information category are different, and the leakage index calculation formula is:

[0017] Y=β∑Ai*Bi

[0018] Among them, Y is the leakage index, β is the random parameter, Ai is the preset weight parameter corresponding to the leakage source category, and Bi is the leakage concentration.

[0019] Furthermore, the random parameter β is provided with a plurality of influencing factors, wherein the influencing factors include temperature, humidity and air flow rate, and the random parameter β is generated according to each influencing factor within each preset collection time period.

[0020] Furthermore, the early warning processing module is configured with a leakage simulation curve correction strategy, the leakage simulation curve correction strategy is configured with a correction threshold, and the leakage simulation curve correction strategy compares T i To T n The leakage curve and T i To T n When the overlap is less than the correction threshold, the curve model establishment strategy is based on T0 to T n (T0<T i <T n <T j ) at the time of correction of the leakage simulation curve and obtaining the leakage simulation correction curve; the leakage warning strategy finds the time point corresponding to the leakage warning critical value in the leakage simulation correction curve according to the leakage warning critical value to generate a correction processing time limit; and generates a correction warning processing task according to the correction processing time limit and sends it to the data interaction end.

[0021] Furthermore, the data interaction terminal is configured with a warning level marking strategy, and the warning level marking strategy is configured with a warning time interval. When the data interaction terminal receives a warning processing task from the intelligent diagnosis platform, the warning level allocation strategy retrieves the processing time limit in the warning processing task, and determines whether the processing time limit falls within the warning time interval, and assigns a danger level to the warning processing task according to the determination result;

[0022] When the processing time limit falls within the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a medium-risk level task;

[0023] When the processing time limit does not fall within the warning time interval, and the processing time limit is less than the minimum time value of the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a high-risk level task;

[0024] When the processing time limit does not fall within the limited warning time interval, and the processing time limit is greater than the maximum time value of the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a low-risk level task.

[0025] Furthermore, the key equipment includes a reactor pressure vessel, a steam generator and a main pump.

[0026] Furthermore, the test points of the reactor pressure vessel are located at the top head, the bottom head, the input nozzle, the output nozzle and the welds of the pressure vessel body; the test points of the steam generator are located at the water feed inlet, the steam outlet, the connection between the generator body and the cover plate, the welds between each heat transfer tube and the tube sheet, and the connection between the generator body and the drain valve; the test points of the main pump are located at the input end and the output end of the pump body.

[0027] Compared with the prior art, the beneficial effects of this solution are:

[0028] 1. When a leak occurs at a test point of a key device, when the intelligent diagnosis platform receives an abnormal detection signal, the collection unit corresponding to the test point corresponding to the detection signal collects the leakage data information and sends it to the intelligent diagnosis platform; the intelligent diagnosis platform calculates multiple leakage indexes, generates a leakage curve according to the multiple leakage indexes and the time points corresponding to each leakage index, and then simulates and generates a leakage simulation curve for a period of time in the future according to the generated leakage curve. The leakage warning strategy of the early warning processing module finds the time point corresponding to the leakage warning critical value in the leakage simulation curve according to the leakage warning critical value to generate a processing time limit, and generates an early warning processing task according to the processing time limit and sends it to the data interaction end. The data interaction end sends the early warning processing task containing the key equipment to be processed, the position coordinates of the test point of the key equipment and the processing time limit to the operator, so that the nearest operator can be arranged to deal with the leakage in time within the processing time limit according to the early warning processing task. The operator can deal with the leakage point in time within the safe processing time limit, ensuring the operation safety of the nuclear power plant and the operator's own safety.

[0029] 2. As the operating environment of nuclear power plants is complex and changeable, random parameters are configured for the leakage index. The random parameters are randomly generated within the preset collection time period according to the temperature, humidity and air flow rate of the environment where the key equipment of the nuclear power plant is located. According to the leakage situation, the leakage curve is corrected in time, so as to correct the processing time limit in time to remind the operator of the leakage situation and the degree of danger, so that the operator can quickly and accurately deal with the leakage of key equipment within a relatively safe time. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a system architecture diagram of the present invention.

[0031] The figure marks in the drawings of the specification include: detection end 1, detection unit 11, feature database 2, collection end 3, collection unit 31, intelligent diagnosis platform 4, signal processing module 41, early warning processing module 42, data interaction end 5. DETAILED DESCRIPTION

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and through specific implementation methods:

[0033] Example:

[0034] A fixed-point detection and early warning system for key equipment of a nuclear power plant, the key equipment including but not limited to reactor pressure vessels, steam generators and main pumps;

[0035] like Figure 1 The system comprises: a detection terminal 1, the detection terminal 1 is configured with a plurality of detection units 11, the detection units 11 are respectively preset at the test points of each key equipment of the nuclear power plant, and are used to detect the operating conditions of each test point of the key equipment of the nuclear power plant and generate detection signals, and each detection signal carries a characteristic identifier;

[0036] Among them, the test points of the reactor pressure vessel are located at the top head, bottom head, input nozzle, output nozzle and the welds of the pressure vessel body; the test points of the steam generator are located at the feed water inlet, steam outlet, the connection between the generator body and the cover plate, the welds between each heat transfer tube and tube sheet, and the connection between the generator body and the drain valve; the test points of the main pump are located at the input and output ends of the pump body.

[0037] Feature database 2, which includes the key equipment corresponding to each feature identifier and the position coordinates of the key equipment's test point;

[0038] The collection terminal 3 is configured with a plurality of collection units 31. Each test point of the key equipment is preset with a collection unit 31, and each collection unit 31 is respectively associated with the detection unit 11 of the test point. The collection unit 31 is used to collect leakage data information of the test point;

[0039] Intelligent diagnosis platform 4, intelligent diagnosis platform 4 is connected to detection end 1, feature database 2 and collection end 3, intelligent diagnosis platform 4 is configured with signal processing module 41 and early warning processing module 42, when intelligent diagnosis platform 4 receives detection signal, signal processing module 41 retrieves key equipment corresponding to the feature identifier from feature signal library according to feature identifier carried by detection signal and judges whether detection signal is abnormal, when the judgment result is that signal is abnormal, signal processing module 41 generates inspection task and sends it to collection unit 31 corresponding to the detection point corresponding to detection signal, inspection task is associated with inspection item, inspection item includes leakage data information of detection point, leakage data information includes leakage source category and leakage concentration corresponding to leakage source category;

[0040] When the intelligent diagnosis platform 4 receives the leakage data information, the early warning processing module 42 generates an early warning processing task according to the leakage data information. The early warning processing task includes the key equipment to be processed, the position coordinates of the key equipment to be tested, and the processing time limit;

[0041] The data interaction terminal 5 is connected to the intelligent diagnosis platform 4, and is used to receive the early warning processing task from the intelligent diagnosis platform 4, and send the early warning processing task to the operator.

[0042] The inspection task includes a preset collection time period and a collection time interval of the leakage data information. When the intelligent diagnosis platform 4 receives the leakage data information from the collection unit 31, the early warning processing module 42 calculates the leakage index according to the leakage source category and the leakage concentration corresponding to the leakage source category, and calculates multiple leakage indexes according to the leakage data information obtained at different time intervals, and generates a leakage curve according to the multiple leakage indexes and the time points corresponding to each leakage index. The leakage curve is characterized by time T0 to time T i The actual change of leakage index at time T0 is the initial time, T i For the current moment.

[0043] The early warning processing module is configured with a curve model establishment strategy. The curve model establishment strategy generates a leakage simulation curve for a period of time in the future according to the generated leakage curve simulation. The leakage simulation curve is characterized by time T i To T j Simulated changes in leakage index;

[0044] The early warning processing module 42 is configured with a leakage early warning strategy, and the leakage early warning strategy is configured with a leakage early warning critical value. The leakage early warning strategy finds the time point corresponding to the leakage early warning critical value in the leakage simulation curve according to the leakage early warning critical value to generate a processing time limit, and generates an early warning processing task according to the processing time limit and sends it to the data interaction terminal 5.

[0045] The early warning processing module 42 is configured with a leakage index calculation strategy, which is configured with a leakage index calculation algorithm. The leakage index calculation algorithm is configured with preset weight parameters corresponding to different leakage source categories, and each leakage data information category has a different preset weight parameter. The leakage index calculation formula is:

[0046] Y=β∑Ai*Bi

[0047] Among them, Y is the leakage index, β is the random parameter, Ai is the preset weight parameter corresponding to the leakage source category, and Bi is the leakage concentration.

[0048] The random parameter β is set with multiple influencing factors, including temperature, humidity and air flow rate. The random parameter β is generated according to each influencing factor in each preset collection time period.

[0049] The early warning processing module 42 is configured with a leakage simulation curve correction strategy, which is configured with a correction threshold. The leakage simulation curve correction strategy compares T i To T n The leakage curve and T i To T n When the overlap is less than the correction threshold, the curve model establishment strategy is based on the time from T0 to T n The leakage curve at that time corrects the leakage simulation curve and obtains the leakage simulation correction curve. The leakage warning strategy finds the time point corresponding to the leakage warning critical value in the leakage simulation correction curve according to the leakage warning critical value to generate a correction processing time limit, and generates a correction warning processing task according to the correction processing time limit and sends it to the data interaction terminal 5.

[0050] Among them, T0<T i <T n <T j , T n is the correction time, T j is a certain moment in the future, and T j The time point at which the moment is greater than the leakage warning critical value.

[0051] The data interaction terminal 5 is configured with a warning level marking strategy, and the warning level marking strategy is configured with a warning time interval. When the data interaction terminal 5 receives a warning processing task from the intelligent diagnosis platform 4, the warning level allocation strategy retrieves the processing time limit in the warning processing task, and determines whether the processing time limit falls within the warning time interval, and assigns a danger level to the warning processing task according to the determination result;

[0052] When the processing time limit falls within the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a medium-risk level task;

[0053] When the processing time limit does not fall within the warning time interval, and the processing time limit is less than the minimum time value of the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a high-risk level task;

[0054] When the processing time limit does not fall within the limited warning time interval, and the processing time limit is greater than the maximum time value of the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a low-risk level task.

[0055] The specific implementation of this scheme is as follows:

[0056] A detection unit 11 and a collection unit 31 are installed at each key equipment to be tested point of the nuclear power plant. Each detection unit 11 detects the operating conditions of each key equipment to be tested point according to a preset detection time period and generates a detection signal, and sends the detection signal to the intelligent diagnosis platform 4;

[0057] When the intelligent diagnosis platform 4 receives the detection signal, the signal processing module 41 retrieves the key device corresponding to the feature identifier from the feature signal library according to the feature identifier carried by the detection signal and determines whether the detection signal is abnormal.

[0058] When the judgment result is that the signal is abnormal, the signal processing module 41 generates an inspection task and sends it to the collection unit 31 corresponding to the test point corresponding to the detection signal. The collection unit 31 collects the leakage data information of the corresponding test point according to the inspection items associated with the inspection task, and sends the leakage data information to the intelligent diagnosis platform 4.

[0059] The early warning processing module 42 of the intelligent diagnosis platform 4 calculates the leakage index according to the leakage source category and the leakage concentration corresponding to the leakage source category, and calculates multiple leakage indices according to the leakage data information obtained at different time intervals, and generates a leakage curve according to the multiple leakage indices and the time points corresponding to each leakage index, and then simulates and generates a leakage simulation curve for a period of time in the future based on the generated leakage curve. The leakage early warning strategy of the early warning processing module 42 finds the time point corresponding to the leakage early warning critical value in the leakage simulation curve according to the leakage early warning critical value to generate a processing time limit, and generates an early warning processing task according to the processing time limit and sends it to the data interaction terminal 5.

[0060] When the data interaction terminal 5 receives the early warning processing task from the intelligent diagnosis platform 4, the early warning level allocation strategy calls the processing time limit in the early warning processing task, assigns the early warning processing task a danger level, and sends the early warning processing task to the operator. The operator can arrange the nearest operator to deal with the leakage in time within the processing time limit according to the danger level of the early warning processing task, the key equipment to be processed, the location coordinates of the key equipment to be tested points and the processing time limit contained in the early warning processing task. The operator can deal with the leakage point in time within the safe processing time limit, ensuring the safe operation of the nuclear power plant and the operator's own safety.

[0061] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A fixed-point detection and early warning system for key equipment in a nuclear power plant, characterized in that: include: A detection end, wherein the detection end is configured with a plurality of detection units, which are respectively preset at the test points of each key equipment of the nuclear power plant, and are used to detect the operating conditions of each test point of the key equipment of the nuclear power plant and generate detection signals, and each detection signal carries a characteristic identifier; A feature database, wherein the feature database includes a key device corresponding to each feature identifier and the position coordinates of a point to be measured of the key device; A collection end, wherein the collection end is configured with a plurality of collection units, each of the key equipment's test points is preset with a collection unit, and each collection unit is respectively associated with a detection unit of the test point, and the collection unit is used to collect leakage data information of the test point; An intelligent diagnosis platform, wherein the intelligent diagnosis platform is connected to a detection end, a feature database and a collection end. The intelligent diagnosis platform is configured with a signal processing module and an early warning processing module. When the intelligent diagnosis platform receives a detection signal, the signal processing module retrieves the key equipment corresponding to the feature identifier from the feature signal library according to the feature identifier carried by the detection signal and determines whether the detection signal is abnormal. When the judgment result is that the signal is abnormal, the signal processing module generates an inspection task and sends it to the collection unit corresponding to the test point corresponding to the detection signal. The inspection task is associated with an inspection item, and the inspection item includes leakage data information of the test point, and the leakage data information includes leakage source categories and leakage concentrations corresponding to the leakage source categories; When the intelligent diagnosis platform receives leakage data information, the warning processing module generates a warning processing task according to the leakage data information, and the warning processing task includes the key equipment to be processed, the position coordinates of the key equipment to be tested, and the processing time limit; The data interaction terminal is connected to the intelligent diagnosis platform, and is used to receive the early warning processing tasks from the intelligent diagnosis platform and send the early warning processing tasks to the operator; The inspection task includes a preset collection time period and a collection time interval of the leakage data information. When the intelligent diagnosis platform receives the leakage data information from the collection unit, the early warning processing module calculates the leakage index according to the leakage source category and the leakage concentration corresponding to the leakage source category, and calculates multiple leakage indexes according to the leakage data information obtained at different time intervals, and generates a leakage curve according to the multiple leakage indexes and the time points corresponding to each leakage index. The leakage curve is characterized by time T0 to time T1. i The actual change of leakage index at the time, T0 is the initial time, T i For the current moment.

2. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 1, characterized in that: The early warning processing module is configured with a curve model establishment strategy, which generates a leakage simulation curve for a period of time in the future according to the generated leakage curve simulation. The leakage simulation curve is characterized by time T i To T j Simulated variation of leakage index, T j for a future time; The early warning processing module is configured with a leakage early warning strategy, and the leakage early warning strategy is configured with a leakage early warning critical value. The leakage early warning strategy finds the time point corresponding to the leakage early warning critical value in the leakage simulation curve according to the leakage early warning critical value to generate a processing time limit, and generates an early warning processing task according to the processing time limit and sends it to the data interaction end.

3. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 2, characterized in that: The early warning processing module is configured with a leakage index calculation strategy, and the leakage index calculation strategy is configured with a leakage index calculation algorithm. The leakage index calculation algorithm is configured with preset weight parameters corresponding to different leakage source categories, and the preset weight parameters corresponding to each leakage data information category are different. The leakage index calculation formula is: Y=β∑Ai*Bi Among them, Y is the leakage index, β is the random parameter, Ai is the preset weight parameter corresponding to the leakage source category, and Bi is the leakage concentration.

4. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 3, characterized in that: The random parameter β is provided with a plurality of influencing factors, wherein the influencing factors include temperature, humidity and air flow rate, and the random parameter β is generated according to each influencing factor within each preset collection time period.

5. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 2, characterized in that: The early warning processing module is configured with a leakage simulation curve correction strategy, the leakage simulation curve correction strategy is configured with a correction threshold, the leakage simulation curve correction strategy is compared with T i To T n The leakage curve and T i To T n The overlap of the leakage simulation curve at T n is the correction time. When the overlap is less than the correction threshold, the curve model establishment strategy is based on T0 to T n (T 0＜ T i＜ T n＜ T j ) when the leakage curve is corrected to obtain the leakage simulation curve and the leakage simulation correction curve is obtained; the leakage warning strategy finds the time point corresponding to the leakage warning critical value in the leakage simulation correction curve according to the leakage warning critical value to generate a correction processing time limit; and generates a correction warning processing task according to the correction processing time limit and sends it to the data interaction end.

6. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 1, characterized in that: The data interaction terminal is configured with a warning level marking strategy, and the warning level marking strategy is configured with a warning time interval. When the data interaction terminal receives a warning processing task from the intelligent diagnosis platform, the warning level allocation strategy retrieves the processing time limit in the warning processing task, and determines whether the processing time limit falls within the warning time interval, and assigns a danger level to the warning processing task according to the determination result; When the processing time limit falls within the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a medium-risk level task; When the processing time limit does not fall within the warning time interval, and the processing time limit is less than the minimum time value of the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a high-risk level task; When the processing time limit does not fall within the warning time interval, and the processing time limit is greater than the maximum time value of the warning time interval, the warning level marking strategy marks the warning level of the warning processing task as a low-risk level task.

7. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 1, characterized in that: The key equipment includes a reactor pressure vessel, a steam generator and a main pump.

8. A fixed-point detection and early warning system for key equipment in a nuclear power plant according to claim 7, characterized in that: The test points of the reactor pressure vessel are located at the top head, bottom head, input nozzle, output nozzle and the welds of the pressure vessel body; the test points of the steam generator are located at the water feed inlet, steam outlet, the connection between the generator body and the cover plate, the welds between each heat transfer tube and the tube sheet, and the connection between the generator body and the drain valve; the test points of the main pump are located at the input end and the output end of the pump body.

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