High-entropy production area regulation and control system and method, electronic equipment, storage medium and program product

Abstract

The invention provides a high-entropy production region regulation and control system and method, electronic equipment, a storage medium and a program product. Through multi-sensor hard real-time synchronization and edge calculation, entropy production analysis delay is compressed to a millisecond level. Microsecond-level time synchronization precision is realized through laser trigger pulses, so that the time-space correlation between transient eddy current and an entropy increase process can be accurately captured. In-situ real-time measurement and dynamic regulation and control of the local entropy yield of the industrial equipment are realized, and the problems of entropy production calculation error and lag caused by data asynchronism in a traditional method are solved. The system can automatically identify the space overlapping phenomenon of the eddy current region and the high-entropy production region, and rapidly inhibit the energy dissipation source through a closed-loop control mechanism, thereby providing a reliable technical means for the real-time energy efficiency management level.

Description

Technical Field

[0001] This application relates to the field of thermodynamic engineering monitoring and energy efficiency management technology, and in particular to a high-entropy production area control system, method, electronic device, storage medium and program product. Background Technology

[0002] During the operation of industrial equipment, the entropy increase caused by energy dissipation directly affects the system's energy efficiency. Existing methods for monitoring the energy efficiency of industrial equipment rely on independently deployed infrared thermometers, differential pressure gauges, and particle image velocimetry systems. Due to the lack of a unified triggering mechanism, there are millisecond-level time differences in the temperature field, pressure field, and flow field data.

[0003] Current technologies suffer from three significant drawbacks: First, traditional monitoring uses independent sensors to collect temperature, pressure, and flow field data separately, making it difficult to fuse and calculate these data due to asynchronous acquisition times. Second, existing methods rely on offline CFD simulations to infer entropy production rates, with calculations taking hours to days, failing to capture dynamic irreversible losses such as transient eddies and turbulence. Third, existing sensors only output single physical quantity measurements, lacking the ability to directly reflect the degree of energy dissipation through entropy production rate output. Particularly in critical equipment such as turbomachinery and heat exchangers, the inability to locate the spatial coupling relationship between high-entropy production regions and eddy current regions in real time leads to delayed energy loss warnings and inaccurate control measures. Although existing technologies have attempted to improve monitoring accuracy through infrared thermometry optimization and flow field visualization, none have established a real-time multi-physics fusion entropy production rate calculation model, and even less so a dynamic control mechanism based on entropy production thermograms. Summary of the Invention

[0004] To address the aforementioned technical steady state, this application provides a high-entropy production area control system, method, electronic device, storage medium, and program product.

[0005] In a first aspect, embodiments of this application provide a laser trigger pulse module for transmitting synchronous trigger pulses to each multiphysics sensor, so as to synchronize the data acquisition time of each multiphysics sensor;

[0006] The data acquisition module is used to acquire multi-dimensional physical field data collected in real time by each multi-physics sensor;

[0007] The entropy production calculation module is used to calculate the real-time entropy production rate and vortex modulus of each entropy production region of each subsystem in the target system based on the multi-dimensional physical field data, determine the vortex region and vortex core coordinates of each subsystem based on the preset vortex modulus threshold, and generate an entropy production rate distribution heatmap of each subsystem based on the real-time entropy production rate of each entropy production region of each subsystem and the vortex region and vortex core coordinates of each subsystem.

[0008] The adjustment and control module is used to locate the high entropy production rate region in each subsystem that overlaps with the eddy region in each subsystem based on the heat map of the entropy production rate distribution of each subsystem, generate adjustment and control instructions for the high entropy production region in each subsystem, and drive the real-time entropy production rate of the eddy region of each subsystem to decrease.

[0009] In some embodiments, the multidimensional physical field data includes at least temperature gradient data;

[0010] The entropy production calculation module includes at least a sampling unit and an entropy production rate calculation unit;

[0011] The sampling unit is used for:

[0012] Spatiotemporal alignment of the multidimensional physical field data of each entropy-producing region in each subsystem is performed to obtain standard multidimensional physical field data of each entropy-producing region in each subsystem.

[0013] Based on the temperature gradient data of each entropy-producing region in each subsystem, the attention weight of each entropy-producing region in each subsystem is calculated according to the following formula:

[0014]

[0015] Wherein, α refers to the attention weight of each entropy-producing region in each subsystem. This refers to the temperature gradient data, where k refers to the preset parameter.

[0016] Based on the attention weight of each entropy-producing region in each subsystem, determine the sampling frequency and sampling density of each entropy-producing region in each subsystem;

[0017] Based on the sampling frequency and sampling density of each entropy-producing region in each subsystem, the standard multidimensional physical field data of each entropy-producing region in each subsystem are sampled to obtain the sampled data of each entropy-producing region in each subsystem.

[0018] The entropy yield calculation unit is used for:

[0019] Based on the sampled data of each entropy-producing region in each subsystem, calculate the real-time entropy production rate of each entropy-producing region in each subsystem.

[0020] In some embodiments, the sampling frequency includes at least a standard sampling frequency and a high sampling frequency, and the sampling density includes at least a standard sampling density and a high sampling density;

[0021] Based on the attention weight of each entropy-producing region in each subsystem, determine the sampling frequency and sampling density of each entropy-producing region in each subsystem, including:

[0022] If the attention weight of a certain entropy-producing region in each subsystem is less than a preset weight threshold, the sampling frequency and sampling density of the multi-dimensional physical field data of the entropy-producing region are maintained at the standard sampling frequency and standard sampling density.

[0023] If the attention weight of a certain entropy-producing region in each subsystem is greater than a preset weight threshold, the sampling frequency of the multi-dimensional physical field data of the entropy-producing region is increased to a high sampling frequency, and the sampling density of the multi-dimensional physical field data of the entropy-producing region is increased to a high sampling density.

[0024] In some embodiments, the multidimensional physical field data further includes at least flow velocity data and pressure difference data;

[0025] The process of determining the real-time entropy production rate of each entropy production region in each subsystem includes:

[0026]

[0027] Based on the temperature gradient data, the vorticity data, and the pressure difference data, the real-time entropy production rate of each entropy production region of each subsystem is calculated according to the following formula:

[0028] Where, φ visc The viscous dissipation function, S″ gen This refers to the real-time entropy production rate of each entropy production region, u refers to the flow velocity component in the x-direction, v refers to the flow velocity component in the y-direction, λ refers to thermal conductivity, and T refers to thermodynamic temperature. Temperature gradient, μ refers to dynamic viscosity, ζ refers to volumetric viscosity, and ΔP refers to pressure difference data.

[0029] In some embodiments, the adjustment and control module further includes at least a coordinate adjustment unit, the coordinate adjustment unit being used for:

[0030] Based on the real-time entropy production rate of each entropy production region of each subsystem, determine the extreme point of the entropy production rate of each entropy production region;

[0031] The vortex core position error of each subsystem is determined based on the spatial offset between the vortex core coordinates of each subsystem and the extreme point of entropy production rate.

[0032] Based on the vortex core position error, the vortex core coordinates of each subsystem are corrected to generate the corrected vortex core coordinates of each subsystem, and synchronized to the entropy production rate distribution heatmap.

[0033] In some embodiments, the adjustment and control module includes at least an instruction generation unit, the instruction generation unit being used for:

[0034] Based on the heatmap of entropy yield distribution of each subsystem, the location of each high entropy yield region is determined;

[0035] When a high-entropy productivity region is located within the turbine guide vane passage, the angle correction is determined by a PID controller according to the following formula, based on the real-time entropy productivity of this high-entropy productivity region and the optimal entropy productivity under rated operating conditions:

[0036]

[0037] Among them, K p K i K d Here, φ is the preset control coefficient, and φ is the real-time entropy yield of this high-entropy yield region. ref The optimal entropy production rate under rated operating conditions;

[0038] Based on the angle correction amount of the high-entropy productivity region, an adjustment control command for the high-entropy productivity region is generated, and the adjustment control command is used to adjust the guide vane angle of the high-entropy productivity region.

[0039] Secondly, embodiments of this application provide a method for regulating high-entropy production regions, including:

[0040] A synchronization trigger pulse is sent to each multiphysics sensor to synchronize the data acquisition time of each multiphysics sensor;

[0041] Acquire multi-dimensional physical field data collected in real time by each of the multi-physics sensors;

[0042] Based on the multi-dimensional physical field data, the real-time entropy production rate and vortex modulus of each entropy production region of each subsystem in the target system are calculated. The coordinates of the vortex region and vortex core of each subsystem are determined according to the preset vortex modulus threshold. Based on the real-time entropy production rate of each entropy production region of each subsystem and the coordinates of the vortex region and vortex core of each subsystem, a heat map of the entropy production rate distribution of each subsystem is generated.

[0043] Based on the heatmap of entropy productivity distribution of each subsystem, the high entropy productivity region in each subsystem that overlaps with the eddy region is located, and adjustment and control instructions for the high entropy productivity region in each subsystem are generated to drive the real-time entropy productivity of the eddy region of each subsystem to decrease.

[0044] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the high-entropy production region control method described above.

[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the high-entropy production region control method described above.

[0046] Fifthly, embodiments of this application provide a computer program product, which includes a computer program that, when executed by a processor, implements the high-entropy production region control method described above.

[0047] Compared with the prior art, this application has the following advantages:

[0048] This application's embodiments compress the entropy production analysis delay to the millisecond level through multi-sensor hard real-time synchronization and edge computing. Microsecond-level time synchronization accuracy is achieved through laser trigger pulses, enabling accurate capture of the spatiotemporal correlation between transient eddies and entropy increase processes. This allows for in-situ real-time measurement and dynamic control of local entropy production rates in industrial equipment, solving the entropy production calculation errors and lag problems caused by data asynchrony in traditional methods. The system can automatically identify the spatial overlap between eddy current regions and high-entropy production regions, and quickly suppress energy dissipation sources through a closed-loop control mechanism, providing a reliable technical means for real-time energy efficiency management. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the structure of the high-entropy production region regulation system proposed in the embodiments of this application;

[0050] Figure 2 This is a flowchart of the high-entropy production region regulation method proposed in the embodiments of this application;

[0051] Figure 3 This is a schematic diagram of the structure of the electronic device proposed in the embodiments of this application. Detailed Implementation

[0052] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] In existing technologies, the field of industrial equipment energy efficiency monitoring has long faced the problem of insufficient synchronization of multi-physics field data. Traditional methods rely on independently deployed infrared thermometers, differential pressure gauges, and particle image velocimetry systems. Due to the lack of a unified triggering mechanism, millisecond-level time differences exist between temperature, pressure, and flow field data. For example, in the turbine guide vane flow channel, the local entropy increase phenomenon caused by transient eddies is often difficult to accurately capture due to asynchronous data acquisition. This results in a significant deviation between the entropy production distribution obtained from offline simulation calculations and actual operating conditions, failing to provide a reliable basis for real-time energy efficiency control.

[0054] To address the aforementioned issues, a high-entropy production area control system, method, electronic device, storage medium, and program product are provided.

[0055] Reference Figure 1 This application proposes a high-entropy production region regulation system, comprising:

[0056] The laser trigger pulse module 10 is used to send synchronous trigger pulses to each multiphysics sensor so that each multiphysics sensor synchronizes its data acquisition time.

[0057] The data acquisition module 20 is used to acquire multi-dimensional physical field data collected in real time by each multi-physics sensor.

[0058] The entropy production calculation module 30 is used to calculate the real-time entropy production rate and vortex modulus of each entropy production region of each subsystem in the target system based on multi-dimensional physical field data, determine the coordinates of the vortex region and vortex core of each subsystem according to the preset vortex modulus threshold, and generate a heat map of the entropy production rate distribution of each subsystem based on the real-time entropy production rate of each entropy production region of each subsystem and the coordinates of the vortex region and vortex core of each subsystem.

[0059] The adjustment and control module 40 is used to locate the high entropy production area that overlaps with the eddy region in each subsystem based on the heat map of the entropy production rate distribution of each subsystem, generate adjustment and control instructions for the high entropy production area in each subsystem, and drive the real-time entropy production rate of the eddy region of each subsystem to decrease.

[0060] The laser trigger pulse module 10 is a device that coordinates the sampling timing of multiple sensors through short-period optical signals. Specifically, it can be implemented using a nanosecond-level laser diode array, which eliminates the data phase difference caused by clock drift in traditional independent sensors. The data acquisition module 20 is a multi-channel acquisition circuit integrating temperature, flow rate, and pressure signals, such as an FPGA-based parallel sampling architecture, used to ensure the consistency of multidimensional physical quantities in the spatiotemporal dimensions. The entropy production calculation module 30 is an edge processor embedding a vortex-entropy production coupling algorithm, which calculates the viscous dissipation function by analyzing the velocity gradient tensor and dynamically identifies the spatial coordinates of the vortex core. The adjustment and control module 40 is an execution unit with real-time feedback control function, such as a programmable logic controller combined with a PID algorithm, used to generate guide vane angle adjustment signals based on the entropy production distribution heatmap.

[0061] Specifically, during system operation, all sensors are first triggered by laser pulses to synchronously acquire data. Temperature gradient, velocity field, and pressure field data are spatiotemporally aligned and then input into the edge computing unit. The calculation module delineates the vortex region boundary based on a preset vortex modulus threshold and generates a two-dimensional thermogram by combining it with the real-time entropy production rate. When an abnormal peak in entropy production rate is detected in a certain guide vane channel region, the adjustment module automatically calculates the guide vane deflection angle correction amount and changes the channel geometry through the actuator to suppress vortex intensity, thereby reducing energy dissipation in that region.

[0062] This application's embodiments compress the entropy production analysis delay to the millisecond level through multi-sensor hard real-time synchronization and edge computing. Microsecond-level time synchronization accuracy is achieved through laser trigger pulses, enabling accurate capture of the spatiotemporal correlation between transient eddies and entropy increase processes. This allows for in-situ real-time measurement and dynamic control of local entropy production rates in industrial equipment, solving the entropy production calculation error problem caused by data asynchrony in traditional methods. The system can automatically identify the spatial overlap between eddy current regions and high-entropy production regions, and quickly suppress energy dissipation sources through a closed-loop control mechanism, providing a reliable technical means for real-time energy efficiency management.

[0063] In some embodiments, the multidimensional physical field data includes at least temperature gradient data.

[0064] The entropy production calculation module 30 includes at least a sampling unit and an entropy production rate calculation unit.

[0065] The sampling unit is used for:

[0066] Spatiotemporal alignment of the multidimensional physical field data of each entropy-producing region in each subsystem is performed to obtain the standard multidimensional physical field data of each entropy-producing region in each subsystem.

[0067] Based on the temperature gradient data of each entropy-producing region in each subsystem, the attention weight of each entropy-producing region in each subsystem is calculated according to the following formula:

[0068]

[0069] Where α refers to the attention weight of each entropy-producing region in each subsystem. This refers to the temperature gradient data, where k refers to the preset parameter.

[0070] Based on the attention weight of each entropy-producing region in each subsystem, determine the sampling frequency and sampling density of each entropy-producing region in each subsystem.

[0071] Based on the sampling frequency and sampling density of each entropy-producing region in each subsystem, the standard multidimensional physical field data of each entropy-producing region in each subsystem are sampled to obtain the sampling data of each entropy-producing region in each subsystem.

[0072] The entropy productivity calculation unit is used for:

[0073] Calculate the real-time entropy production rate of each entropy production region in each subsystem based on the sampled data of each entropy production region in each subsystem.

[0074] Temperature gradient data refers to the rate of temperature change within a region collected by sensors. Specifically, this can be achieved using an infrared thermal imaging module with multi-point measurements at a preset resolution, reflecting the energy dissipation distribution during heat conduction. Attention weight is a quantization parameter dynamically adjusted based on the temperature gradient. It can be calculated using normalization and an exponential function, used to identify high-energy-dissipation areas and guide sampling strategy optimization. Sampling frequency refers to the number of times data is collected from a specified area per unit time. This can be achieved by controlling the sensor trigger timing with a programmable clock signal, balancing data accuracy and system resource consumption. Sampling density refers to the number of data collection points per unit area or volume. This can be achieved by adjusting the spatial distribution density of the sensor array, improving the measurement resolution in key areas.

[0075] Specifically, during data acquisition, the multiphysics sensor array first performs spatiotemporal alignment on data such as temperature, flow rate, and pressure difference to eliminate coordinate offsets caused by differences in sensor response times. Attention weights for each region are calculated based on temperature gradient data; higher weight values ​​indicate more significant energy dissipation in that region. For regions with weights below a preset threshold, standard sampling frequency and density are used to reduce computational load; for regions with weights above the preset threshold, a high sampling mode is automatically switched to capture transient thermodynamic characteristics. The sampled data is input into the entropy yield calculation unit, which calculates the entropy yield value reflecting local irreversible losses by fusing the viscous dissipation function with the heat conduction equation.

[0076] This application's embodiments utilize a temperature gradient-driven dynamic sampling mechanism to increase data acquisition density in the early stages of vortex core formation, thereby reducing entropy production calculation errors for key phenomena such as boundary layer separation and secondary flow. It effectively identifies transient high-entropy production regions during industrial equipment operation, such as the formation process of localized high-temperature zones within a gas turbine combustion chamber. Through an adaptive sampling strategy, it captures abrupt temperature gradient changes on a millisecond-level timescale, providing accurate real-time data support for suppressing energy dissipation in hot-end components.

[0077] In some embodiments, the sampling frequency includes at least a standard sampling frequency and a high sampling frequency, and the sampling density includes at least a standard sampling density and a high sampling density.

[0078] Based on the attention weight of each entropy-producing region in each subsystem, determine the sampling frequency and sampling density of each entropy-producing region in each subsystem, including:

[0079] If the attention weight of a certain entropy-producing region in each subsystem is less than a preset weight threshold, the sampling frequency and sampling density of the multi-dimensional physical field data of that entropy-producing region are maintained at the standard sampling frequency and standard sampling density.

[0080] If the attention weight of a certain entropy-producing region in each subsystem is greater than a preset weight threshold, the sampling frequency of the multi-dimensional physical field data of that entropy-producing region is increased to a high sampling frequency, and the sampling density of the multi-dimensional physical field data of that entropy-producing region is increased to a high sampling density.

[0081] The standard sampling frequency refers to the periodic sampling rate that meets the basic data acquisition requirements. This can be achieved by using a fixed time interval for data acquisition, such as acquiring data once every millisecond. The high sampling frequency refers to an increased sampling rate for critical areas. This can be achieved by using hardware triggering with dynamically adjusted clock cycles, such as acquiring data once every microsecond. The standard sampling density refers to the number of basic data acquisition points per unit space. This can be achieved by using a uniformly distributed sensor array layout. The high sampling density refers to the increased density of data acquisition points for critical areas. This can be achieved by using a dynamic focusing mode of a programmable sensor array. The preset weight threshold is the critical value used to distinguish between ordinary and critical areas. This can be obtained through experimental calibration or training with a machine learning model.

[0082] Specifically, when the attention weight corresponding to the temperature gradient data in a certain entropy-producing region is lower than a preset threshold, the system maintains standard sampling parameters to reduce resource consumption. For example, in a homogeneous flow region, standard sampling frequency and density are sufficient to meet data accuracy requirements. When the attention weight exceeds the preset threshold, the system automatically switches to a high sampling mode. For example, in vortex cores or boundary layer regions, increasing the sampling frequency and density can capture transient vortex evolution processes and abrupt temperature gradient changes. This dynamic adjustment mechanism is executed in real time through edge computing units to ensure that the physical field data of high-entropy-producing regions are completely recorded.

[0083] This application's embodiments achieve optimized allocation of data acquisition resources by introducing an attention weight threshold judgment mechanism. It can automatically identify high-gradient regions and improve data acquisition accuracy during industrial equipment operation, effectively avoiding the problem of missed sampling of transient eddy current features. Simultaneously, by dynamically adjusting sampling parameters, it reduces resource consumption in non-critical areas, improving the overall system operating efficiency.

[0084] In some embodiments, the multidimensional physical field data may include at least flow velocity data and pressure difference data.

[0085] The process of determining the real-time entropy production rate of each entropy production region in each subsystem includes:

[0086] Based on temperature gradient data, vorticity data, and pressure difference data, the real-time entropy production rate of each entropy production region of each subsystem is calculated using the following formula:

[0087]

[0088] Where, φ visc The viscous dissipation function, S″ gen This refers to the real-time entropy production rate of each entropy production region, u refers to the flow velocity component in the x-direction, v refers to the flow velocity component in the y-direction, λ refers to thermal conductivity, and T refers to thermodynamic temperature. Temperature gradient, μ refers to dynamic viscosity, ζ refers to volumetric viscosity, and ΔP refers to pressure difference data.

[0089] Among them, temperature gradient data refers to the local temperature change rate data acquired by the infrared thermal imaging module, specifically implemented using a high-temperature resistant sensor array with a resolution of ±0.1℃, used to reflect the entropy production component caused by heat conduction. Flow velocity data refers to the flow velocity field distribution information obtained by the particle image velocimetry module, specifically implemented using a vibration-resistant PIV system, used to calculate the entropy production component caused by viscous dissipation. Pressure difference data refers to the pressure gradient data measured by the microchannel pressure difference sensing array, specifically implemented using a miniature sensor with a response time ≤1ms, used to reflect the impact of pressure pulsation on entropy production. Thermal conductivity λ refers to the thermal conductivity coefficient of the material, which can be obtained by matching a preset physical property parameter library. Dynamic viscosity μ and volumetric viscosity ζ refer to the viscous characteristic parameters of the fluid, which can be dynamically adjusted according to real-time temperature data.

[0090] Specifically, in calculating the real-time entropy production rate, temperature gradient data is first acquired synchronously via an infrared thermal imaging module. Combined with the flow velocity components u and v provided by the particle image velocimetry module, the spatial gradient of the velocity field is derived. ΔP data collected by the differential pressure sensing array is used to quantify the contribution of pressure pulsations to energy dissipation. The viscous dissipation function is calculated using the quadratic term of the velocity gradient tensor, reflecting the mechanical energy loss caused by fluid shearing. The product of thermal conductivity λ and the square term of the temperature gradient characterizes the irreversible loss caused by heat conduction. The product of bulk viscosity ζ and the square term of the velocity divergence is used to capture energy dissipation during fluid compression or expansion. By fusing multiphysics data and using the above formulas for real-time calculation, both thermodynamic and fluid dynamic entropy production mechanisms can be simultaneously covered.

[0091] This application embodiment utilizes multi-sensor data fusion and real-time calculation to synchronously input temperature gradient, flow velocity field, and pressure difference data into the entropy production rate calculation formula. This ensures that the calculation results simultaneously incorporate the combined effects of viscous dissipation and heat conduction, solving the problem that existing technologies cannot reflect the coupling effect of dynamic flow fields and temperature fields in real time. It enables in-situ real-time measurement of local entropy production rate in industrial equipment, accurately quantifying the energy dissipation intensity in high-gradient regions such as vortex zones and boundary layers. By fusing multi-dimensional physical field data, it avoids entropy production calculation errors caused by measurement deviations of single physical quantities, providing high-precision data support for turbine guide vane angle adjustment and flow channel structure optimization, thereby effectively reducing irreversible losses during system operation.

[0092] In some embodiments, the adjustment control module 40 further includes at least a coordinate adjustment unit, which is used for:

[0093] Based on the real-time entropy production rate of each entropy production region in each subsystem, determine the extreme point of the entropy production rate of each entropy production region.

[0094] The vortex core position error of each subsystem is determined based on the spatial offset between the vortex core coordinates and the extreme point of entropy production rate of each subsystem.

[0095] Based on the vortex core position error, the vortex core coordinates of each subsystem are corrected, generating the corrected vortex core coordinates of each subsystem, and synchronized to the entropy yield distribution heatmap.

[0096] Among them, the extreme point of entropy productivity refers to the coordinate position where the entropy productivity reaches a local maximum in the spatial distribution. This can be achieved using gradient descent or peak detection algorithms to identify the core region where entropy productivity abnormally increases. Spatial offset refers to the distance difference between the vortex core coordinates and the extreme point of entropy productivity in the spatial coordinate system. This can be achieved through Euclidean distance calculation or vector difference modulus calculation, used to quantify the degree of deviation between the vortex center and the high-entropy-productive region. Vortex core position error refers to the positioning deviation between the vortex core position derived from the spatial offset and the actual high-entropy-productive region. This can be achieved using normalization or weighted averaging methods, used to guide the magnitude and direction of coordinate correction.

[0097] Specifically, during operation, the coordinate adjustment unit first locates the peak entropy productivity point within each entropy productivity region using an extreme value search algorithm based on real-time entropy productivity data. Then, it spatially compares the original vortex core coordinates of the subsystem with the extreme points of entropy productivity, calculating the offset between them. If the offset exceeds a preset tolerance threshold, a coordinate correction is generated based on the offset direction and magnitude, and the vortex core coordinates are iteratively adjusted. The corrected vortex core coordinates are updated in real-time to the entropy productivity distribution heatmap, thereby ensuring the accurate spatial mapping between the vortex region and the high-entropy productivity region.

[0098] This application embodiment achieves online calibration of the vortex core position by dynamically matching the extreme points of real-time entropy production rate with the coordinates of the vortex core, effectively eliminating positioning errors caused by transient changes in the flow field. It solves the problem of inaccurate spatial mapping between the vortex core position and the high-entropy production region in existing technologies. The real-time coordinate correction mechanism improves the accuracy of the entropy production rate distribution heatmap, providing a reliable spatial positioning basis for the generation of subsequent adjustment and control commands, thereby ensuring the precise execution of system energy efficiency optimization measures.

[0099] In some embodiments, the adjustment and control module 40 includes at least an instruction generation unit, which is used for:

[0100] Based on the heatmap of entropy yield distribution for each subsystem, the location of each high-entropy yield region is determined.

[0101] When a high-entropy productivity region is located within the turbine guide vane passage, the angle correction is determined by a PID controller according to the following formula, based on the real-time entropy productivity of this high-entropy productivity region and the optimal entropy productivity under rated operating conditions:

[0102]

[0103] Among them, K p K i K d Here, φ is the preset control coefficient, and φ is the real-time entropy yield of this high-entropy yield region. ref This represents the optimal entropy production rate under rated operating conditions.

[0104] Based on the angle correction amount of the high-entropy yield region, an adjustment control command for the high-entropy yield region is generated. The adjustment control command is used to adjust the guide vane angle of the high-entropy yield region.

[0105] The PID controller refers to a regulator based on proportional, integral, and derivative control. Specifically, it can be implemented using an incremental digital PID algorithm, dynamically adjusting the guide vane angle based on the deviation between the real-time entropy production rate and the optimal entropy production rate. The angle correction amount refers to the mechanical angle adjustment required for the guide vane, which can be achieved by converting the output value of the PID controller into the displacement of the mechanical actuator, used to precisely correct the spatial attitude of the guide vane. Guide vane angle adjustment refers to changing the installation angle of the turbine guide vane, which can be achieved by using a servo motor to drive the guide vane rotation mechanism, adjusting the flow field distribution within the flow channel by changing the guide vane angle.

[0106] Specifically, when the entropy production rate distribution heatmap shows a high entropy production rate region located in the turbine guide vane flow channel, the real-time entropy production rate and the optimal entropy production rate under rated operating conditions are input to the PID controller. The PID controller calculates the combined output of the proportional, integral, and derivative terms based on the deviation between the two, generating a corresponding angle correction. This correction is converted into a control signal for the guide vane drive mechanism, driving the guide vane to rotate by a specific angle, thereby changing the vortex morphology and velocity distribution within the flow channel and reducing viscous and thermal dissipation in that region. For example, when high entropy production is detected in the guide vane trailing edge region, the guide vane angle is adjusted to weaken flow separation and reduce turbulent dissipation.

[0107] This application embodiment achieves dynamic angle adjustment by driving PID control with real-time entropy production data. Continuous optimization of entropy production rate is achieved through closed-loop control. It can respond in real-time to changes in entropy production rate within the flow channel, automatically adjusting the guide vane angle to suppress eddy current dissipation, effectively reducing energy loss in high-entropy production regions and improving turbine operating efficiency. This solves the technical problem of traditional guide vane adjustment lagging behind actual operating conditions, avoiding continuous performance degradation caused by a fixed guide vane angle.

[0108] Reference Figure 2 This application proposes a method for regulating high-entropy production regions, including:

[0109] S10. Send a synchronization trigger pulse to each multiphysics sensor to synchronize the data acquisition time of each multiphysics sensor.

[0110] S20. Acquire multi-dimensional physical field data collected in real time by each multi-physics sensor.

[0111] S30. Based on multi-dimensional physical field data, calculate the real-time entropy production rate and vortex modulus of each entropy production region of each subsystem in the target system. Determine the coordinates of the vortex region and vortex core of each subsystem based on the preset vortex modulus threshold. Generate a heat map of the entropy production rate distribution of each subsystem based on the real-time entropy production rate of each entropy production region of each subsystem and the coordinates of the vortex region and vortex core of each subsystem.

[0112] S40. Based on the heat map of entropy productivity distribution of each subsystem, locate the high entropy productivity region in each subsystem that overlaps with the eddy region, generate adjustment and control instructions for the high entropy productivity region in each subsystem, and drive the real-time entropy productivity of the eddy region in each subsystem to decrease.

[0113] In some embodiments, the multidimensional physical field data includes at least temperature gradient data.

[0114] The real-time entropy production rate for each entropy production region is determined through the following steps:

[0115] Spatiotemporal alignment of the multidimensional physical field data of each entropy-producing region in each subsystem is performed to obtain the standard multidimensional physical field data of each entropy-producing region in each subsystem.

[0116] Based on the temperature gradient data of each entropy-producing region in each subsystem, the attention weight of each entropy-producing region in each subsystem is calculated according to the following formula:

[0117]

[0118] Where α refers to the attention weight of each entropy-producing region in each subsystem. This refers to the temperature gradient data, where k refers to the preset parameter.

[0119] Based on the attention weight of each entropy-producing region in each subsystem, determine the sampling frequency and sampling density of each entropy-producing region in each subsystem.

[0120] Based on the sampling frequency and sampling density of each entropy-producing region in each subsystem, the standard multidimensional physical field data of each entropy-producing region in each subsystem are sampled to obtain the sampling data of each entropy-producing region in each subsystem.

[0121] Calculate the real-time entropy production rate of each entropy production region in each subsystem based on the sampled data of each entropy production region in each subsystem.

[0122] In some embodiments, the sampling frequency includes at least a standard sampling frequency and a high sampling frequency, and the sampling density includes at least a standard sampling density and a high sampling density.

[0123] If the attention weight of a certain entropy-producing region in each subsystem is less than a preset weight threshold, the sampling frequency and sampling density of the multi-dimensional physical field data of that entropy-producing region are maintained at the standard sampling frequency and standard sampling density.

[0124] If the attention weight of a certain entropy-producing region in each subsystem is greater than a preset weight threshold, the sampling frequency of the multi-dimensional physical field data of that entropy-producing region is increased to a high sampling frequency, and the sampling density of the multi-dimensional physical field data of that entropy-producing region is increased to a high sampling density.

[0125] In some embodiments, the multidimensional physical field data may include at least flow velocity data and pressure difference data.

[0126] The process of determining the real-time entropy production rate of each entropy production region in each subsystem includes:

[0127] Based on temperature gradient data, vorticity data, and pressure difference data, the real-time entropy production rate of each entropy production region of each subsystem is calculated using the following formula:

[0128]

[0129] Where, φ visc The viscous dissipation function, S gen This refers to the real-time entropy production rate of each entropy production region, u refers to the flow velocity component in the x-direction, v refers to the flow velocity component in the y-direction, λ refers to thermal conductivity, and T refers to thermodynamic temperature. Temperature gradient, μ refers to dynamic viscosity, ζ refers to volumetric viscosity, and ΔP refers to pressure difference data.

[0130] In some embodiments, the adjustment control module 40 further includes at least a coordinate adjustment unit, which is used for:

[0131] Based on the real-time entropy production rate of each entropy production region in each subsystem, determine the extreme point of the entropy production rate of each entropy production region.

[0132] The vortex core position error of each subsystem is determined based on the spatial offset between the vortex core coordinates and the extreme point of entropy production rate of each subsystem.

[0133] Based on the vortex core position error, the vortex core coordinates of each subsystem are corrected, generating the corrected vortex core coordinates of each subsystem, and synchronized to the entropy yield distribution heatmap.

[0134] In some embodiments, the adjustment and control module 40 includes at least an instruction generation unit, which is used for:

[0135] Based on the heatmap of entropy yield distribution for each subsystem, the location of each high-entropy yield region is determined.

[0136] When a high-entropy productivity region is located within the turbine guide vane passage, the angle correction is determined by a PID controller according to the following formula, based on the real-time entropy productivity of this high-entropy productivity region and the optimal entropy productivity under rated operating conditions:

[0137]

[0138] Among them, K p K i K d Here, φ is the preset control coefficient, and φ is the real-time entropy yield of this high-entropy yield region. ref This represents the optimal entropy production rate under rated operating conditions.

[0139] Based on the angle correction amount of the high-entropy yield region, an adjustment control command for the high-entropy yield region is generated. The adjustment control command is used to adjust the guide vane angle of the high-entropy yield region.

[0140] Reference Figure 3 This application also provides an electronic device, including:

[0141] processor.

[0142] Memory is used to store processor-executable instructions.

[0143] The processor is configured to execute instructions to implement any high-entropy production region control method.

[0144] In this embodiment, the computer device includes a processor, memory, and network interface connected via a system bus.

[0145] The computer device's processor provides computational and control capabilities. Its memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The computer device's database stores data samples. Its network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements any high-entropy production region control method.

[0146] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] Fourthly, embodiments of this application also provide a computer-readable storage medium, which, when the instructions in the computer-readable storage medium are executed by the processor of a terminal, enables the terminal to execute any high-entropy production region control method.

[0148] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0149] Optionally, a readable storage medium can be coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components within the device.

[0150] Fifthly, embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements any high-entropy production region control method.

[0151] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0156] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0157] As the method embodiments are basically similar to the system embodiments, the description is relatively simple, and relevant parts can be found in the description of the system embodiments.

[0158] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0159] The above provides a detailed description of a high-entropy production region control system, method, electronic device, storage medium, and program product provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A high exergy region regulation system, characterized in that, The method comprises the following steps: a laser trigger pulse module is used to emit a synchronous trigger pulse to each multi-physical field sensor to synchronize the data acquisition time of each multi-physical field sensor; a data acquisition module is used to acquire multi-dimensional physical field data collected by each multi-physical field sensor in real time; an entropy production calculation module is used to calculate the real-time entropy production rate and vorticity module length of each entropy production region of each subsystem in the target system according to the multi-dimensional physical field data, determine the vortex region and vortex core coordinates of each subsystem according to a preset vorticity module length threshold, and generate an entropy production rate distribution thermodynamic map of each subsystem according to the real-time entropy production rate of each entropy production region of each subsystem, the vortex region and vortex core coordinates of each subsystem. an adjustment control module is used to locate a high-entropy production rate region in each subsystem that spatially overlaps with the vortex region according to the entropy production rate distribution thermodynamic map of each subsystem, generate an adjustment control instruction for the high-entropy production region in each subsystem, and drive the real-time entropy production rate of the vortex region of each subsystem to decrease.

2. The system of claim 1, wherein, The multi-dimensional physical field data at least comprises temperature gradient data; The entropy production calculation module at least comprises a sampling unit and an entropy production rate calculation unit; The sampling unit is used to: spatially and temporally align the multi-dimensional physical field data of each entropy production region in each subsystem to obtain standard multi-dimensional physical field data of each entropy production region in each subsystem; calculate the attention weight of each entropy production region in each subsystem according to the temperature gradient data of each entropy production region in each subsystem according to the following formula: Wherein, a indicates the attention weight of each entropy generation region in each subsystem, Indicating temperature gradient data, k indicates a preset parameter: determine the sampling frequency and sampling density of each entropy production region in each subsystem according to the attention weight of each entropy production region in each subsystem; sample the standard multi-dimensional physical field data of each entropy production region in each subsystem according to the sampling frequency and sampling density of each entropy production region in each subsystem to obtain sampling data of each entropy production region in each subsystem. The entropy production rate calculation unit is used to: calculate the real-time entropy production rate of each entropy production region in each subsystem according to the sampling data of each entropy production region in each subsystem.

3. The system of claim 2, wherein, The sampling frequency at least comprises a standard sampling frequency and a high sampling frequency, and the sampling density at least comprises a standard sampling density and a high sampling density; determining the sampling frequency and sampling density of each entropy production region in each subsystem according to the attention weight of each entropy production region in each subsystem comprises: in the case that the attention weight of a certain entropy production region in each subsystem is less than a preset weight threshold, maintaining the sampling frequency and sampling density of the multi-dimensional physical field data of the entropy production region at a standard sampling frequency and a standard sampling density; in the case that the attention weight of a certain entropy production region in each subsystem is greater than a preset weight threshold, increasing the sampling frequency of the multi-dimensional physical field data of the entropy production region to a high sampling frequency, and increasing the sampling density of the multi-dimensional physical field data of the entropy production region to a high sampling density.

4. The system of claim 2, wherein, The multi-dimensional physical field data at least further comprises flow velocity data and pressure difference data. The determination process of the real-time entropy generation rate of each entropy generation region in each subsystem comprises: According to the temperature gradient data, the vorticity data and the pressure difference data, the real-time entropy generation rate of each entropy generation region in each subsystem is calculated according to the following formula: where φ visc denotes the viscous dissipation function, S" gen denotes the real-time entropy production rate of each entropy production region, u denotes the flow velocity component of the flow velocity field in the x direction, v denotes the flow velocity component of the flow velocity field in the y direction, λ denotes the thermal conductivity, T denotes the thermodynamic temperature, denotes the temperature gradient, μ denotes the dynamic viscosity, ζ denotes the bulk viscosity, and ΔP denotes the pressure difference data.

5. The system of claim 1, wherein, The adjustment control module further comprises a coordinate adjustment unit, which is configured to: According to the real-time entropy generation rate of each entropy generation region in each subsystem, the extreme point of the entropy generation rate of each entropy generation region is determined; According to the spatial offset between the vortex core coordinate of each subsystem and the extreme point of the entropy generation rate, the vortex core position error of each subsystem is determined; According to the vortex core position error, the vortex core coordinate of each subsystem is corrected to generate the corrected vortex core coordinate of each subsystem, and the corrected vortex core coordinate is synchronized to the entropy generation rate distribution thermodynamic map.

6. The system of claim 1, wherein, The adjustment control module comprises an instruction generation unit, which is configured to: According to the entropy generation rate distribution thermodynamic map of each subsystem, the position of each high-entropy generation rate region is determined; When a high-entropy generation rate region is located in the turbine guide vane flow passage, the angle correction amount is determined by the PID controller according to the real-time entropy generation rate of the high-entropy generation rate region and the optimal entropy generation rate under the rated operating condition according to the following formula: wherein K p , K i , K d are preset control coefficients, φ is the real-time entropy generation rate of the high-entropy generation rate region, and φ ref is the optimal entropy generation rate under the rated operating condition. According to the angle correction amount of the high-entropy generation rate region, the adjustment control instruction of the high-entropy generation rate region is generated, which is used to adjust the guide vane angle of the high-entropy generation rate region.

7. A high entropy region regulation method, characterized in that, Comprise: Synchronous trigger pulses are transmitted to each multi-physical field sensor to synchronize the data acquisition time of each multi-physical field sensor; Multi-dimensional physical field data collected by each multi-physical field sensor in real time is obtained; According to the multi-dimensional physical field data, the real-time entropy generation rate and the vorticity module length of each entropy generation region in each subsystem of the target system are calculated, the vortex region and the vortex core coordinate of each subsystem are determined according to a preset vorticity module length threshold, and the entropy generation rate distribution thermodynamic map of each subsystem is generated according to the real-time entropy generation rate of each entropy generation region in each subsystem, the vortex region and the vortex core coordinate of each subsystem. According to the entropy generation rate distribution thermodynamic map of each subsystem, the high-entropy generation rate region in each subsystem that spatially overlaps with the vortex region is located, and the adjustment control instruction for the high-entropy generation region in each subsystem is generated to drive the real-time entropy generation rate of the vortex region of each subsystem to decrease.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the high-entropy generation region regulation method of claim 7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the high-entropy generation region regulation method of claim 7.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to realize the high-entropy generation region regulation method of claim 7.

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