Circuit cooling and pressure reduction method and device, electronic equipment and storage medium

Abstract

The present disclosure discloses a method and device for reducing temperature and pressure of a loop, electronic equipment and a storage medium. According to the present application, the deviation information is dynamically determined based on the real-time temperature and pressure parameters of the reactor primary loop, the fuzzy controller domain parameters are adaptively adjusted, the precise association of dynamic parameters and control rules is established, the valve control instruction is automatically generated through fuzzy reasoning to adjust the opening degree, and the subjectivity and hysteresis of manual experience judgment and manual adjustment are avoided. Therefore, the technical problem that the response lag and control deviation accumulation are caused by manual adjustment and the adaptive association of dynamic parameters and control rules is not established in the existing pressurized water reactor primary loop control method, which further affects the operation safety and economy of the nuclear power plant, can be solved, the response speed and control accuracy of the temperature and pressure reduction process are improved, the control deviation is reduced, the stability of the transition condition from hot shutdown to cold shutdown is ensured, and the technical effects of strengthening the operation safety and economy of the nuclear power plant are achieved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method and apparatus for cooling and reducing the pressure of a circuit, an electronic device, and a storage medium. Background Technology

[0002] The pressurized water reactor primary loop depressurization and cooling system is a core control component for safe shutdown of nuclear power plants and is widely used in the transition from hot shutdown to cold shutdown.

[0003] The existing control methods for the primary loop of pressurized water reactors rely directly on operator experience and manual adjustment of valve openings, without establishing an adaptive correlation between dynamic parameters and control rules. This may lead to risks such as response lag and accumulation of control deviations, thereby affecting the operational safety and economy of nuclear power plants. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for cooling and depressurizing a circuit.

[0005] According to a first aspect of this disclosure, a method for cooling and reducing the pressure of a circuit is provided, comprising: Obtain real-time temperature and pressure parameters of the reactor primary loop; Based on the real-time temperature and pressure parameters and the preset safe operating boundary, the temperature deviation information and pressure deviation information are dynamically determined. The universe of discourse parameters of the fuzzy controller are adjusted based on the temperature deviation information and pressure deviation information. Based on the adjusted universe parameters and preset fuzzy rules, fuzzy reasoning is performed on the temperature deviation information and pressure deviation information to obtain control commands for adjusting the cooling and pressure reducing valves. The opening degree of the cooling and depressurization valve is adjusted according to the control command, so that the temperature and pressure of the primary circuit decrease to the target range along a preset trajectory.

[0006] Optionally, the step of dynamically determining the temperature deviation information and pressure deviation information based on the real-time temperature parameters, real-time pressure parameters, and preset safe operating boundaries includes: Based on the preset pressure-temperature relationship graph, the corresponding upper and lower temperature limits are obtained by querying the real-time pressure parameters. Calculate the first difference between the real-time temperature parameter and the upper temperature limit, and the second difference between the real-time temperature parameter and the lower temperature limit, as the temperature deviation information.

[0007] Optionally, adjusting the universe of discourse parameters of the fuzzy controller includes: When the absolute value of the temperature deviation information is less than a first threshold, a first universe scaling factor is generated based on the mean value of the temperature deviation information. When the absolute value of the pressure deviation information is less than the second threshold, a second universe scaling factor is generated based on the mean value of the pressure deviation information.

[0008] Optionally, the fuzzy inference of the temperature deviation information and pressure deviation information includes: The temperature deviation information and its rate of change, and the pressure deviation information and its rate of change are used as fuzzy inputs; The fuzzy inference results are defuzzified according to the preset weighting rules, wherein the weighting rules include assigning different weighting coefficients to the temperature tracking error, the first valve action amplitude, and the second valve action amplitude.

[0009] Optionally, adjusting the opening degree of the cooling and depressurizing valve according to the control command includes: When the real-time pressure parameter is higher than the pressure set value, the opening of the bypass discharge valve is adjusted first. When the difference between the real-time temperature parameter and the temperature limit of the safe operating boundary is less than a preset threshold, the opening of the voltage regulator spray valve is adjusted at a preset rate.

[0010] Optionally, the method further includes: Based on historical operating data or simulation data, an optimization algorithm is used to jointly optimize the universe parameters and the fuzzy rules, so that the system output driven by the control command meets the preset performance indicators.

[0011] According to a second aspect of this disclosure, a cooling and pressure-reducing device for a circuit is provided, comprising: The acquisition unit is also used to acquire real-time temperature and pressure parameters of the reactor primary loop; The determining unit is also used to dynamically determine temperature deviation information and pressure deviation information based on the real-time temperature parameters, real-time pressure parameters, and preset safe operating boundaries; The adjustment unit is also used to adjust the universe of discourse parameters of the fuzzy controller based on the temperature deviation information and the pressure deviation information. The inference unit is also used to perform fuzzy inference on the temperature deviation information and pressure deviation information based on the adjusted domain parameters and preset fuzzy rules, so as to obtain control commands for adjusting the cooling and pressure reducing valves. The control unit is also used to adjust the opening of the cooling and depressurizing valve according to the control command, so that the temperature and pressure of the primary circuit decrease to the target range along a preset trajectory.

[0012] Optionally, the determining unit is further configured to: Based on the preset pressure-temperature relationship graph, the corresponding upper and lower temperature limits are obtained by querying the real-time pressure parameters. Calculate the first difference between the real-time temperature parameter and the upper temperature limit, and the second difference between the real-time temperature parameter and the lower temperature limit, as the temperature deviation information.

[0013] Optionally, the adjustment unit is further configured to: When the absolute value of the temperature deviation information is less than a first threshold, a first universe scaling factor is generated based on the mean value of the temperature deviation information. When the absolute value of the pressure deviation information is less than the second threshold, a second universe scaling factor is generated based on the mean value of the pressure deviation information.

[0014] Optionally, the inference unit is further configured to: The temperature deviation information and its rate of change, and the pressure deviation information and its rate of change are used as fuzzy inputs; The fuzzy inference results are defuzzified according to the preset weighting rules, wherein the weighting rules include assigning different weighting coefficients to the temperature tracking error, the first valve action amplitude, and the second valve action amplitude.

[0015] Optionally, the control unit is further configured to: When the real-time pressure parameter is higher than the pressure set value, the opening of the bypass discharge valve is adjusted first. When the difference between the real-time temperature parameter and the temperature limit of the safe operating boundary is less than a preset threshold, the opening of the voltage regulator spray valve is adjusted at a preset rate.

[0016] Optional, also includes: The optimization unit is also used to perform joint optimization of the universe parameters and the fuzzy rules based on historical operating data or simulation data, so that the system output driven by the control command meets the preset performance indicators.

[0017] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0018] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0020] The cooling and depressurization method, apparatus, electronic equipment, and storage medium for the primary loop provided in this application, by dynamically determining deviation information based on real-time temperature and pressure parameters of the reactor primary loop, adaptively adjusting the parameters of the fuzzy controller's domain, establishing a precise correlation between dynamic parameters and control rules, and automatically generating valve control commands to adjust the opening degree through fuzzy inference, avoids the subjectivity and lag of manual judgment and adjustment. Therefore, it can solve the technical problems in existing pressurized water reactor primary loop control methods caused by manual adjustment and the lack of adaptive correlation between dynamic parameters and control rules, which lead to response lag and accumulation of control deviations, thus affecting the safety and economy of nuclear power plant operation. It achieves the technical effect of improving the response speed and control accuracy of the cooling and depressurization process, reducing control deviations, ensuring the stability of the transition from hot shutdown to cold shutdown, and thus enhancing the safety and economy of nuclear power plant operation.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A schematic flowchart illustrating a method for cooling and reducing voltage in a circuit according to an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a cooling and pressure reducing device for a circuit provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of a cooling and pressure-reducing device for another circuit provided in an embodiment of this disclosure; Figure 4 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0023] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] The following description, with reference to the accompanying drawings, describes a method, apparatus, electronic device, and storage medium for cooling and reducing the voltage of a circuit according to embodiments of the present disclosure.

[0025] Figure 1 This is a schematic flowchart of a method for cooling and reducing the pressure of a circuit provided in an embodiment of this disclosure.

[0026] like Figure 1 As shown, the method includes the following steps: Step 101: Obtain the real-time temperature and pressure parameters of the reactor primary loop; This step aims to obtain real-time temperature and pressure parameters of the reactor primary loop, providing fundamental data support for subsequent pressurized water reactor primary loop cooling and depressurization control. The reactor primary loop is a core component of the reactor cooling system, playing a crucial role in transferring heat generated by the reactor core. The stability of its temperature and pressure parameters directly determines the safety and efficiency of reactor operation. The core real-time temperature parameter is the average coolant temperature. The coolant circulates within the primary loop, continuously absorbing heat released from the reactor core. Temperature measuring devices positioned at key locations in the primary loop can capture real-time temperature data of the coolant at different flow cross-sections. After data integration and processing, the average coolant temperature is obtained, accurately reflecting the overall temperature state of the primary loop.

[0027] Real-time pressure parameters refer to the pressure values ​​within the primary loop system. The stability of the primary loop pressure directly affects the flow characteristics and heat transfer effect of the coolant. Continuous monitoring of the pressure within the primary loop main pipeline using high-precision pressure measurement devices allows for accurate acquisition of real-time pressure data and timely detection of minute pressure fluctuations. Considering the significant time lag characteristic of the primary loop during the transition from hot shutdown to cold shutdown, the acquisition of real-time temperature and pressure parameters must ensure timeliness and accuracy. The measurement devices must possess good response speed and anti-interference capabilities, and be able to operate stably in the complex reactor operating environment to ensure that the collected parameters accurately reflect the real-time operating status of the primary loop.

[0028] These real-time parameters serve as the input for subsequent limit calculations, adaptive domain adjustment, and fuzzy control decisions. Their reliability directly affects the control accuracy and safety of the entire cooling and depressurization control system. Only by accurately acquiring real-time temperature and pressure parameters can subsequent control links accurately determine the operating status of the primary loop, laying a solid foundation for achieving efficient and safe cooling and depressurization control.

[0029] Step 102: Based on the real-time temperature parameters, real-time pressure parameters, and preset safe operating boundaries, dynamically determine the temperature deviation information and pressure deviation information; The core of this step is to dynamically generate temperature and pressure deviation information that accurately reflects the operating status of the primary loop based on the acquired real-time temperature and pressure parameters and the preset safe operating boundaries, providing key judgment basis for subsequent control decisions.

[0030] The preset safe operating boundaries are the core benchmarks for ensuring the safe and stable operation of the reactor's primary loop. They are not fixed values, but dynamic boundaries closely related to the operating state of the primary loop. The temperature safety boundary includes an upper temperature limit and a lower temperature limit, while the pressure safety boundary includes an upper pressure limit and a lower pressure limit. The upper temperature limit and the lower temperature limit are functions of real-time pressure parameters and can be obtained through relevant interpolation calculation methods. The upper pressure limit and the lower pressure limit are functions of real-time temperature parameters and can be adaptively adjusted according to changes in the primary loop temperature to ensure that the safe operating boundaries always match the current operating conditions.

[0031] Temperature deviation information specifically includes the deviation between the average coolant temperature and the upper temperature limit, as well as the deviation between the average coolant temperature and the lower temperature limit. The calculation process uses the real-time average coolant temperature as the core parameter, comparing it with dynamically determined upper and lower temperature limits to intuitively reflect the current temperature state's proximity to the safety boundary. Pressure deviation information includes the deviation between the real-time primary loop pressure and the upper pressure limit, as well as the deviation between the real-time primary loop pressure and the lower pressure limit. Based on real-time pressure parameters and combined with dynamically updated upper and lower pressure limits, the calculation accurately captures the relative positional relationship between pressure parameters and the safety boundary.

[0032] Considering the significant time delay characteristic of the primary circuit during the transition from hot to cold shutdown, and the susceptibility of parameters to fluctuations due to various factors during operation, a dynamic determination method is adopted to obtain deviation information. This avoids the lag and inaccuracy caused by fixed deviation calculations, and tracks the dynamic relationship between parameter changes and safety boundaries in real time. This dynamic deviation calculation method can promptly detect trends of parameters approaching or deviating from safety boundaries, providing timely and accurate reference for subsequent control strategy adjustments. This ensures that the primary circuit remains within the safe operating range throughout the entire cooling and depressurization process, effectively mitigating operational risks caused by inaccurate deviation judgments, and laying a solid data foundation for precise and safe cooling and depressurization control.

[0033] Step 103: Adjust the universe of discourse parameters of the fuzzy controller based on the temperature deviation information and pressure deviation information; This study focuses on dynamically adjusting the universe of discourse (UOD) parameters of a fuzzy controller based on temperature and pressure deviation information. This adapts to the complex operating conditions during the primary circuit cooling and depressurization process of a reactor, improving the accuracy and adaptability of the control response. The UOD parameters of a fuzzy controller are the core parameters determining the range of its input and output variables, directly affecting the actual effectiveness of the control rules. Reasonable adjustment of the UOD parameters can effectively expand the adaptability of fuzzy control without increasing the number of control rules, avoiding the control lag or insufficient accuracy problems that occur with traditional fixed-UOD fuzzy controllers when operating conditions change.

[0034] Temperature deviation information includes the deviation between the average coolant temperature and the upper and lower temperature limits, while pressure deviation information includes the deviation between the primary loop pressure and the upper and lower pressure limits. This deviation information directly reflects the relative relationship between the current operating state of the primary loop and the safe operating boundary, serving as a direct basis for adjusting the domain parameters. When the upper and lower limit deviations in the temperature deviation information gradually decrease, it indicates that the primary loop temperature is approaching or even exceeding the safe limit. In this case, the domain range of the fuzzy controller needs to be reduced accordingly. By limiting the adjustment amplitude of the fuzzy control output, the rate of temperature change is slowed down, preventing the temperature parameter from exceeding the safe boundary. Conversely, when the temperature deviation increases, it indicates that the temperature state is far from the safe limit. The domain range can be expanded, giving the fuzzy control output more adjustment space and accelerating the rate at which the temperature approaches the target state. For pressure deviation information, its changing trend is consistent with the adjustment logic of the domain parameters. When the upper and lower limit deviations of pressure decrease, there is a risk that the system pressure will exceed the safe range. Reducing the domain range reduces the fuzzy control output, decreases the rate of temperature and pressure change, and ensures that the pressure parameter remains stable within the safe range. When the pressure deviation increases, the domain range is expanded to achieve rapid pressure adjustment.

[0035] This method of adjusting the universe of discourse parameters based on deviation information can accurately match the large time delay and nonlinear operating characteristics of the first loop, enabling the fuzzy controller to maintain good control performance under different operating conditions and achieve a smooth transition from coarse control to fine control. When the system is in the initial stage of changing operating conditions with large deviations, the universe of discourse is expanded to achieve a fast response. When the system approaches steady state with small deviations, the universe of discourse is narrowed to achieve precise control, effectively improving the stability and reliability of the entire cooling and depressurization control process. At the same time, it avoids the complexity of large-scale control rule base tuning and ensures the practicality and efficiency of the control strategy.

[0036] Step 104: Based on the adjusted universe parameters and preset fuzzy rules, perform fuzzy reasoning on the temperature deviation information and pressure deviation information to obtain control commands for adjusting the cooling and pressure reducing valves. The core of this step is to generate control commands that can precisely adjust the cooling and depressurization valves based on the adjusted universe of discourse parameters and preset fuzzy rules through a scientific fuzzy inference process, providing a direct execution basis for the cooling and depressurization control of the reactor's primary loop. The adjusted universe of discourse parameters are the key foundation for adapting to the current operating conditions of the primary loop. They are dynamically optimized based on temperature and pressure deviation information, ensuring a high degree of match between the input and output range of fuzzy inference and the actual operating state, avoiding inference deviations caused by traditional fixed universes of discourse, and laying a solid foundation for the accuracy of the inference process. The preset fuzzy rules are designed based on actual operating experience of the reactor's primary loop, fully considering the large time delay characteristics and nonlinear control requirements during the process from hot shutdown to cold shutdown. The system systematically analyzes the valve adjustment logic corresponding to different combinations of temperature and pressure deviations, forming a complete and practical set of inference criteria that can achieve comprehensive coverage of complex operating conditions without increasing the number of rules.

[0037] In the fuzzy inference process, temperature deviation information and pressure deviation information are first used as inputs. The fuzzification process is completed by combining the adjusted universe parameters to convert the precise deviation data into fuzzy linguistic variables. Then, rule matching and logical reasoning are performed according to the preset fuzzy rules. The operation experience and control logic contained in the rule base are fully utilized to analyze the optimal valve adjustment direction and amplitude corresponding to the current deviation state. Finally, the fuzzy inference results are converted into explicit and executable control commands through defuzzification.

[0038] This control command directly targets the adjustment operation of the cooling and depressurization valves, clearly defining the total opening requirements of the pressure regulator spray valve and bypass valve. By precisely controlling the valve opening, it effectively regulates the temperature and pressure of the primary loop, ensuring a smooth and orderly cooling and depressurization process. This fuzzy reasoning method, based on a dynamic domain and preset rules, can quickly respond to changes in primary loop parameters and promptly generate control commands adapted to the current operating conditions. This ensures both the accuracy and stability of the control process while effectively avoiding the lag and uncertainty of manual decision-making under complex operating conditions, providing reliable technical support for the primary loop system to safely and efficiently complete the cooling and depressurization task.

[0039] Step 105: Adjust the opening of the cooling and depressurization valve according to the control command, so that the temperature and pressure of the primary circuit decrease to the target range along the preset trajectory.

[0040] This step aims to precisely adjust the opening of the cooling and depressurization valves according to the generated control commands, driving the temperature and pressure of the reactor primary loop to smoothly decrease to the target range along a preset trajectory, ensuring the safety, stability, and efficiency of the cooling and depressurization process. The control commands are derived through fuzzy reasoning based on dynamically adjusted domain parameters and preset fuzzy rules, clarifying the total opening requirements of the pressurizer spray valve and bypass valve. Their accuracy directly determines the effect of valve adjustment, providing a clear execution standard for the orderly control of primary loop parameters.

[0041] As core actuators for primary loop temperature and pressure control, the cooling and depressurization valves, including the pressurizer spray valve which directly cools the primary loop coolant by spraying cooling media, and the bypass valve which reduces system pressure by discharging a portion of the loop media, are crucial for coordinated temperature and pressure control. The preset trajectory is an optimal descent path determined by considering the large time delay characteristics, nonlinear control requirements, and safety constraints of the primary loop from hot shutdown to cold shutdown. This path clarifies the rate of temperature and pressure decrease at different stages while ensuring that parameter changes remain within safe boundaries, preventing system fluctuations or equipment damage due to excessively rapid descent, and avoiding shutdown efficiency issues caused by excessively slow descent. During valve opening adjustment, the system continuously monitors real-time temperature and pressure parameters of the primary loop, dynamically feeding back deviations between actual parameter changes and the preset trajectory. If deviations occur, subsequent control links fine-tune control commands to correct valve openings, ensuring that temperature and pressure always adhere to the preset trajectory.

[0042] Because primary loop temperature and pressure are coupled, valve opening adjustments must be coordinated to ensure their optimal performance. For example, precisely controlling the spray valve opening adjusts the cooling rate, while simultaneously optimizing the bypass valve opening to balance system pressure, preventing one parameter's adjustment from causing another to exceed safe limits. This precise valve opening adjustment method based on control commands effectively avoids the subjectivity and lag of manual operation, allowing primary loop temperature and pressure to drop smoothly and efficiently to the target range. This ensures the safety and reliability of reactor shutdown, reduces maintenance costs and human error, and further improves plant availability.

[0043] In some embodiments, dynamically determining the temperature deviation information and pressure deviation information based on the real-time temperature parameters, real-time pressure parameters, and preset safe operating boundaries includes: Based on the preset pressure-temperature relationship graph, the corresponding upper and lower temperature limits are obtained by querying the real-time pressure parameters. Calculate the first difference between the real-time temperature parameter and the upper temperature limit, and the second difference between the real-time temperature parameter and the lower temperature limit, as the temperature deviation information.

[0044] When dynamically determining temperature deviation information based on real-time temperature and pressure parameters and preset safe operating boundaries, the core is to rely on a preset pressure-temperature relationship graph to accurately match the upper and lower temperature limits and calculate the difference. The pressure-temperature relationship graph is a professional graph constructed by combining long-term operating data of the reactor primary loop, safety design standards, and thermo-hydraulic characteristics. It systematically outlines the safe temperature range under different pressure conditions, intuitively reflecting the correspondence between pressure and temperature safety boundaries, and providing a reliable basis for dynamically obtaining temperature limits.

[0045] By inputting the acquired real-time pressure parameters into this relationship graph, and through precise matching methods such as interpolation calculations, the upper and lower temperature limits that perfectly match the current pressure state can be quickly retrieved. These two limits are not fixed values ​​but are adjusted synchronously with changes in real-time pressure parameters, ensuring a high degree of fit between the temperature safety boundary and the real-time operating conditions of the primary loop. Subsequently, temperature deviation information is calculated using the real-time temperature parameters as the core. Specifically, the difference between the real-time temperature parameters and the retrieved upper temperature limit is calculated to obtain the first difference value, and the difference between the real-time temperature parameters and the lower temperature limit is calculated to obtain the second difference value. These two differences together constitute complete temperature deviation information. The first difference value directly reflects the distance between the real-time temperature and the upper temperature safety limit, while the second difference value clearly reflects the relative position of the real-time temperature and the lower temperature safety limit. The combination of the two can comprehensively and accurately capture the dynamic relationship between the primary loop temperature state and the safe operating boundary.

[0046] This limit query and difference calculation method based on the pressure-temperature relationship spectrum is well adapted to the large time delay and nonlinear characteristics of the primary loop from hot shutdown to cold shutdown. It effectively avoids the problem of inaccurate deviation judgment caused by fixed limits, and allows the temperature deviation information to reflect the temperature operating status of the primary loop in a true and timely manner. This provides solid data support for the accurate adjustment of the domain parameters of the subsequent fuzzy controller and the safe and stable operation of the entire cooling and depressurization control process.

[0047] In some embodiments, adjusting the universe of discourse parameters of the fuzzy controller includes: When the absolute value of the temperature deviation information is less than a first threshold, a first universe scaling factor is generated based on the mean value of the temperature deviation information. When the absolute value of the pressure deviation information is less than the second threshold, a second universe scaling factor is generated based on the mean value of the pressure deviation information.

[0048] When adjusting the universe of discourse parameters of the fuzzy controller, the core is to determine the deviation state through a first threshold and a second threshold, and then generate a corresponding universe of discourse scaling factor based on the mean deviation, thereby achieving precise fine-tuning of the universe of discourse. The first threshold is a judgment standard set based on the safe operating range of the primary loop temperature and the control accuracy requirements, used to define whether the temperature deviation is within the range requiring fine control; the second threshold is formulated for the characteristics of the pressure parameter, clarifying the critical value at which the pressure deviation enters the fine control stage. Both have been verified through extensive operating condition simulations to ensure the rationality and practicality of the threshold division.

[0049] Temperature deviation information includes the difference between the real-time temperature and the upper and lower temperature limits. When the absolute values ​​of these differences are all less than the first threshold, it indicates that the primary loop temperature is close to the safe operating boundary or near the steady state. At this time, the average value of the temperature deviation information is needed to reflect the overall trend and concentration of the temperature deviation. This average value integrates the influence of temperature deviations in different dimensions, which can avoid the randomness of single deviation data. The first universe scaling factor generated based on this average value can accurately adjust the temperature-related universe range, so that the universe adaptively shrinks with small changes in temperature deviation, thereby achieving fine control of temperature parameters.

[0050] Pressure deviation information encompasses the difference between real-time pressure and its upper and lower limits. When the absolute values ​​of these differences are all less than the second threshold, it indicates that the system pressure is in a stable and controllable near-boundary state. By calculating the average value of the pressure deviation information, the overall level of pressure deviation can be comprehensively captured. The second universe of discourse scaling factor generated accordingly can be used to specifically adjust pressure-related universe of discourse parameters, narrowing the universe of discourse range to reduce the adjustment amplitude of fuzzy control output and slow down the rate of pressure change. This scaling factor generation method based on threshold judgment and the average deviation perfectly matches the core strategy of variable universe of discourse fuzzy control from coarse control to fine control. When the deviation is small, the universe of discourse is narrowed by the scaling factor to improve control accuracy and prevent parameters from exceeding the safety boundary. At the same time, it does not require increasing the number of control rules, effectively simplifying the design and tuning of the controller, ensuring that the temperature and pressure of the primary loop remain stable during the cooling and depressurization process, and further enhancing the safety and reliability of the control process.

[0051] In some embodiments, the fuzzy inference of the temperature deviation information and the pressure deviation information includes: The temperature deviation information and its rate of change, and the pressure deviation information and its rate of change are used as fuzzy inputs; The fuzzy inference results are defuzzified according to the preset weighting rules, wherein the weighting rules include assigning different weighting coefficients to the temperature tracking error, the first valve action amplitude, and the second valve action amplitude.

[0052] When performing fuzzy inference on the temperature and pressure deviation information, it is necessary to first define the scientifically sound fuzzification input variables, and then complete the defuzzification calculation through preset weighting rules to ensure that the inference results accurately match the cooling and pressure reduction control requirements. The selection of fuzzification inputs fully considers the large time delay and nonlinear operating characteristics of the reactor's primary loop. Temperature deviation information reflects the static relationship between the current temperature and the safety boundary, and its rate of change can capture the dynamic trend of temperature parameter changes. The combination of the two can comprehensively grasp the real-time operating status of the temperature. Pressure deviation information directly reflects the distance between the pressure and the safety limit, and its rate of change can promptly detect the pressure fluctuation pattern. Using these four types of parameters as fuzzification inputs together can achieve comprehensive monitoring of the primary loop operating status and avoid the one-sidedness of inference caused by a single parameter input.

[0053] During the fuzzification stage, these precise deviation and rate of change data are transformed into fuzzy linguistic variables, bridging the gap for subsequent logical reasoning and enabling efficient adaptation of abstract control logic to actual operating conditions. Defuzzification calculation is the key step in transforming fuzzy reasoning results into explicit control commands, relying primarily on preset weighting rules. These weighting rules are formulated based on long-term operating experience and control accuracy requirements, assigning different weighting coefficients to the temperature tracking error of the first and second valve actuation amplitudes. The weighting coefficient for temperature tracking error primarily ensures that the primary loop temperature decreases along a preset trajectory, ensuring the accuracy of the cooling process. The weighting coefficient for the first valve actuation amplitude is used to balance the effectiveness of spray valve regulation with equipment wear, preventing excessive valve actuation from affecting service life. The weighting coefficient for the second valve actuation amplitude optimizes the regulation characteristics of the bypass valve, ensuring the stability of pressure control. By reasonably allocating weight coefficients, the defuzzification calculation results can satisfy both the control objectives of temperature and pressure, and take into account the economy and safety of valve regulation. The final explicit value generated is the core basis for regulating the cooling and depressurization valve, ensuring that the fuzzy reasoning process not only meets the needs of complex working conditions, but also outputs accurate and executable control guidance, providing reliable support for subsequent valve opening adjustment.

[0054] In some embodiments, adjusting the opening degree of the cooling and depressurizing valve according to the control command includes: When the real-time pressure parameter is higher than the pressure set value, the opening of the bypass discharge valve is adjusted first. When the difference between the real-time temperature parameter and the temperature limit of the safe operating boundary is less than a preset threshold, the opening of the voltage regulator spray valve is adjusted at a preset rate.

[0055] When adjusting the opening of the cooling and pressure reducing valves according to control commands, the core principle is to adopt targeted valve adjustment strategies based on the real-time parameter status of the primary loop and the requirements for safe operation, ensuring the accuracy and safety of temperature and pressure control. The bypass discharge valve is a key actuator for primary loop pressure regulation. Its core function is to quickly reduce the internal pressure of the system by discharging a portion of the loop medium. When the real-time pressure parameter exceeds the pressure setpoint, the opening of the bypass discharge valve is adjusted first, enabling a direct and efficient response to pressure exceeding the setpoint, quickly mitigating the impact of excessive pressure on the stability of the primary loop system.

[0056] This priority adjustment logic fully considers the critical impact of pressure parameters on system safety, as well as the rapid response advantage of the bypass discharge valve in pressure regulation. It avoids equipment damage or safety risks caused by persistently high pressure, while also adapting to the coupled operating characteristics of primary loop temperature and pressure, preventing a single temperature adjustment from indirectly causing further pressure increases. The pressure regulator spray valve, as a core component of primary loop cooling, achieves temperature control by directly applying cooling medium to the coolant. When the real-time temperature parameter approaches the safe operating boundary, its opening is adjusted at a preset rate, effectively preventing rapid temperature changes from exceeding the safety limit.

[0057] The preset rate is designed based on the large time delay and nonlinear operating characteristics of the primary loop, as well as the tolerance range of the temperature safety limit. Extensive simulation verification under various operating conditions ensures that the cooling efficiency closely matches the preset trajectory while preventing system fluctuations caused by excessively rapid temperature drops due to excessively fast adjustment rates, or temperatures approaching or exceeding the limit due to excessively slow rates. This scenario-specific valve adjustment strategy makes the cooling and pressure-reducing valve actions more targeted, leveraging the functional advantages of different valves and achieving coordinated temperature and pressure control. This ensures that the primary loop remains within a safe operating range under complex conditions, while reducing the subjectivity and lag of manual operation, further improving the stability and reliability of the cooling and pressure-reducing process, and helping to reduce maintenance costs and human error.

[0058] In some embodiments, the method further includes: Based on historical operating data or simulation data, an optimization algorithm is used to jointly optimize the universe parameters and the fuzzy rules, so that the system output driven by the control command meets the preset performance indicators.

[0059] This method also includes a step of jointly optimizing the universe of discourse parameters and fuzzy rules based on historical operating data or simulation data through optimization algorithms. The core objective is to ensure that the system output driven by control commands accurately meets preset performance indicators. Historical operating data consists of massive amounts of data accumulated in the reactor primary loop during long-term actual operation, covering real-time temperature parameters, real-time pressure parameters, valve opening data, and system response results under different operating conditions. It truly reflects the correlation between various scenarios and parameters in actual operation. Simulation data is supplementary data generated by simulating typical operating conditions of the primary loop from hot shutdown to cold shutdown. It can cover extreme operating conditions or special scenarios that are difficult to encounter in actual operation. The combination of the two provides a comprehensive and rich data foundation for joint optimization, ensuring that the optimization results are adaptable to various operating conditions.

[0060] The optimization algorithm selected is a high-efficiency optimization algorithm that has been verified under operating conditions, such as a genetic algorithm. This algorithm can simulate the process of natural selection and genetic mutation to achieve efficient iterative optimization of parameters and rules. In the joint optimization process, historical operating data and simulation data are used as inputs to generate an initial parameter population containing universe parameters and fuzzy rules. The universe parameters include key parameters related to fuzzification and defuzzification, while the fuzzy rules cover the valve control logic corresponding to different combinations of deviations. Then, the system output meeting the preset performance indicators is taken as the objective, and the fitness value corresponding to each individual in the population is calculated. The preset performance indicators include the accuracy of temperature and pressure tracking of preset trajectories, the stability of parameter change rate, the smoothness of valve action, and the safety of system operation. Through the selection, crossover, and mutation operations of the algorithm, a new generation of parameter population is generated. This process is repeated iteratively until the system output meets the preset performance indicators or the indicators no longer improve. The universe parameters and fuzzy rules obtained at this time are the optimal combination.

[0061] This joint optimization approach enables the universe parameters and fuzzy rules to be highly adapted to the large time-delay nonlinear characteristics of a single loop, avoiding the control performance shortcomings caused by optimizing the universe parameters or fuzzy rules alone. This allows the fuzzy controller to output accurate and effective control commands under various operating conditions, ensuring the safety, stability and efficiency of the system operation, further enhancing the cooling and pressure reduction control effect, reducing operation and maintenance costs and human error, and improving the availability of the power plant.

[0062] Corresponding to the aforementioned method for cooling and reducing the pressure of a circuit, this invention also proposes a device for cooling and reducing the pressure of a circuit. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments, and will not be repeated here.

[0063] Figure 2 This is a schematic diagram of the structure of a cooling and voltage reduction device for a circuit provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: The acquisition unit 21 is also used to acquire the real-time temperature and pressure parameters of the reactor primary loop; The determining unit 22 is also used to dynamically determine temperature deviation information and pressure deviation information based on the real-time temperature parameters, real-time pressure parameters and preset safe operating boundaries; The adjustment unit 23 is also used to adjust the universe of discourse parameters of the fuzzy controller according to the temperature deviation information and the pressure deviation information. The reasoning unit 24 is also used to perform fuzzy reasoning on the temperature deviation information and pressure deviation information based on the adjusted domain parameters and preset fuzzy rules, so as to obtain control commands for adjusting the cooling and pressure reducing valves. The control unit 25 is also used to adjust the opening of the cooling and depressurizing valve according to the control command, so that the temperature and pressure of the primary circuit decrease to the target range along a preset trajectory.

[0064] Furthermore, in one possible implementation of this disclosure, the determining unit 22 is further configured to: Based on the preset pressure-temperature relationship graph, the corresponding upper and lower temperature limits are obtained by querying the real-time pressure parameters. Calculate the first difference between the real-time temperature parameter and the upper temperature limit, and the second difference between the real-time temperature parameter and the lower temperature limit, as the temperature deviation information.

[0065] Furthermore, in one possible implementation of this disclosure, the adjustment unit 23 is further configured to: When the absolute value of the temperature deviation information is less than a first threshold, a first universe scaling factor is generated based on the mean value of the temperature deviation information. When the absolute value of the pressure deviation information is less than the second threshold, a second universe scaling factor is generated based on the mean value of the pressure deviation information.

[0066] Furthermore, in one possible implementation of this disclosure, the inference unit 24 is further configured to: The temperature deviation information and its rate of change, and the pressure deviation information and its rate of change are used as fuzzy inputs; The fuzzy inference results are defuzzified according to the preset weighting rules, wherein the weighting rules include assigning different weighting coefficients to the temperature tracking error, the first valve action amplitude, and the second valve action amplitude.

[0067] Furthermore, in one possible implementation of this disclosure embodiment, the control unit 25 is further configured to: When the real-time pressure parameter is higher than the pressure set value, the opening of the bypass discharge valve is adjusted first. When the difference between the real-time temperature parameter and the temperature limit of the safe operating boundary is less than a preset threshold, the opening of the voltage regulator spray valve is adjusted at a preset rate.

[0068] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, it also includes: The optimization unit 26 is also used to perform joint optimization of the universe parameters and the fuzzy rules based on historical running data or simulation data and using optimization algorithms, so that the system output driven by the control command meets the preset performance indicators.

[0069] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0070] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0071] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0072] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0073] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the loop cooling and depressurization method. For example, in some embodiments, the loop cooling and depressurization method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 may be configured by any other suitable means (e.g., by means of firmware) to perform the cooling and depressurization method of the aforementioned circuit.

[0075] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0076] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0077] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0078] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0079] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0080] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0081] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0082] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for cooling and reducing the voltage of a circuit, characterized in that, include: Obtain real-time temperature and pressure parameters of the reactor primary loop; Based on the real-time temperature and pressure parameters and the preset safe operating boundary, the temperature deviation information and pressure deviation information are dynamically determined. The universe of discourse parameters of the fuzzy controller are adjusted based on the temperature deviation information and pressure deviation information. Based on the adjusted universe parameters and preset fuzzy rules, fuzzy reasoning is performed on the temperature deviation information and pressure deviation information to obtain control commands for adjusting the cooling and pressure reducing valves. The opening degree of the cooling and depressurization valve is adjusted according to the control command, so that the temperature and pressure of the primary circuit decrease to the target range along a preset trajectory.

2. The method according to claim 1, characterized in that, The dynamic determination of temperature deviation information and pressure deviation information based on the real-time temperature parameters, real-time pressure parameters, and preset safe operating boundaries includes: Based on the preset pressure-temperature relationship graph, the corresponding upper and lower temperature limits are obtained by querying the real-time pressure parameters. Calculate the first difference between the real-time temperature parameter and the upper temperature limit, and the second difference between the real-time temperature parameter and the lower temperature limit, as the temperature deviation information.

3. The method according to claim 1, characterized in that, The adjustment of the universe of discourse parameters of the fuzzy controller includes: When the absolute value of the temperature deviation information is less than a first threshold, a first universe scaling factor is generated based on the mean value of the temperature deviation information. When the absolute value of the pressure deviation information is less than the second threshold, a second universe scaling factor is generated based on the mean value of the pressure deviation information.

4. The method according to claim 1, characterized in that, The fuzzy inference of the temperature deviation information and pressure deviation information includes: The temperature deviation information and its rate of change, and the pressure deviation information and its rate of change are used as fuzzy inputs; The fuzzy inference results are defuzzified according to the preset weighting rules, wherein the weighting rules include assigning different weighting coefficients to the temperature tracking error, the first valve action amplitude, and the second valve action amplitude.

5. The method according to claim 1, characterized in that, Adjusting the opening degree of the cooling and depressurizing valve according to the control command includes: When the real-time pressure parameter is higher than the pressure set value, the opening of the bypass discharge valve is adjusted first. When the difference between the real-time temperature parameter and the temperature limit of the safe operating boundary is less than a preset threshold, the opening of the voltage regulator spray valve is adjusted at a preset rate.

6. The method according to claim 1, characterized in that, The method further includes: Based on historical operating data or simulation data, an optimization algorithm is used to jointly optimize the universe parameters and the fuzzy rules, so that the system output driven by the control command meets the preset performance indicators.

7. A cooling and pressure-reducing device for a circuit, characterized in that, include: The acquisition unit is also used to acquire real-time temperature and pressure parameters of the reactor primary loop; The determining unit is also used to dynamically determine temperature deviation information and pressure deviation information based on the real-time temperature parameters, real-time pressure parameters, and preset safe operating boundaries; The adjustment unit is also used to adjust the universe of discourse parameters of the fuzzy controller based on the temperature deviation information and the pressure deviation information. The inference unit is also used to perform fuzzy inference on the temperature deviation information and pressure deviation information based on the adjusted domain parameters and preset fuzzy rules, so as to obtain control commands for adjusting the cooling and pressure reducing valves. The control unit is also used to adjust the opening of the cooling and depressurizing valve according to the control command, so that the temperature and pressure of the primary circuit decrease to the target range along a preset trajectory.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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