Intelligent Decision-making Method and System for the Operating State of a Rectifying Grid Based on Data Fusion

Abstract

The present invention relates to the field of a rectifying grid, and discloses a smart decision-making method and system for the working state of a rectifying grid based on data fusion, including the following steps: By deploying an electromagnetic micro flow velocity sensor array inside the inverted siphon section of a water conveyance pipeline, calculating the flow state of the fluid inside the inverted siphon section of the water conveyance pipeline, combining with a simulation form, calculating the optimal opening and closing angle of the guide vanes inside the rectifying grid connected to the inverted siphon section of the water conveyance pipeline, and finally through temperature control processing, realizing the combination of the shape memory alloy guide vanes and the rectifying grid, and realizing the control of the optimal opening and closing angle of the guide vanes. The present invention can dynamically adjust the opening and closing angle of the guide vanes according to real-time flow velocity data to achieve dynamic flow state matching, improve the rectifying efficiency. At the same time, it can optimize the working state of the rectifying grid in real time, reduce the head loss, suppress the vortex, and improve the overall performance of the system.

Description

Technical Field

[0001] The present invention relates to the field of flow straightening grids, in particular to an intelligent decision-making method and system for the working state of flow straightening grids based on data fusion. Background Art

[0002] In long-distance water diversion and regulation projects, the water conveyance line is long, the diversion flow rate is large, the terrain conditions passed through are complex, and the flow patterns are complex and changeable under the coupling of multi-condition water flows. There are multiple water flow forms coexisting, such as non-pressure tunnels, pressure tunnels, inverted siphons, and the connection between pressure flow and non-pressure flow. Special and complex hydraulic phenomena such as stagnant air masses and the alternation of open-channel and full-flow are extremely likely to occur, affecting the water conveyance efficiency of the project and even causing abnormal hydraulic oscillations. Therefore, it is urgent to propose engineering measures to avoid adverse water flow phenomena, such as improved flow straightening grid devices, to provide a scientific basis for the operation and dispatching and hydraulic control of the water conveyance system.

[0003] The traditional grid-type flow straightening device has a simple structure and low manufacturing cost, but there are obvious deficiencies in practical applications: 1) Due to the limitation of its grid structure, the traditional grid-type flow straightening device will encounter greater resistance when water flows through the grid. This not only increases the energy consumption during water conveyance but also may cause a drop in system pressure, affecting the operation efficiency of the entire system. 2) The traditional grid-type flow straightening device cannot respond to changes in fluid parameters in a timely manner and has a regulation lag. When the fluid passes through the grid bars, parameters such as flow velocity, water temperature, and impurity content change at any time. The traditional grid-type flow straightening device lacks real-time dynamic regulation ability, and its flow straightening effect will be greatly reduced. 3) The traditional flow straightening grid technology has poor adaptability when facing complex flow patterns. Complex flow patterns often cover situations such as large fluctuations in flow velocity, disordered flow directions, and uneven internal pressure distribution of the fluid. Because the grid bar spacing and angle are fixed and cannot be adjusted according to the changes in the flow pattern, when the flow velocity fluctuates greatly, the fixed grid is difficult to match the changing flow velocity, and turbulence is easily generated when the fluid passes through, forming fluid fluctuations; when the flow direction is disordered, the grid cannot guide specifically, resulting in the fluid colliding wantonly inside the device, exacerbating the disorder of the flow; and the uneven internal pressure distribution will cause pressure differences in different regions of the fluid, thereby triggering pressure pulsation phenomena. These continuous fluid fluctuations and pressure pulsations will cause the internal components of the equipment to withstand irregular impacts and vibrations, resulting in continuous wear of the surface material and a gradual decrease in structural strength, seriously shortening the service life of the equipment. At the same time, the performance of the equipment is unstable due to wear and it is difficult to work in coordination with other equipment under established parameters.

[0004] Considering the above shortcomings of the existing flow straightening grids, an intelligent flow straightening grid control device for the inverted siphon section of the water diversion and regulation project has been developed through physical model tests, numerical simulations, and deep learning algorithms, that is, an intelligent decision-making method and system for the working state of flow straightening grids based on data fusion is proposed. Summary of the Invention

[0005] The present invention overcomes the deficiencies of the prior art and provides an intelligent decision-making method and system for the operating state of a straightening grid based on data fusion.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The first aspect of the present invention provides an intelligent decision-making method for the operating state of a straightening grid based on data fusion, including the following steps:

[0008] Determine the straightening grid that matches the siphon section of the water conveyance pipeline, and deploy an electromagnetic micro flow velocity sensor array inside the siphon section of the water conveyance pipeline to test the water flow state of the siphon section of the water conveyance pipeline;

[0009] Construct a simulation model of the internal flow field of the straightening grid to simulate the turbulent flow state of the fluid, and calculate the optimal opening angle of the guide vanes of the qualified target straightening grid in real time based on the turbulent flow state of the fluid;

[0010] Based on the optimal opening angle of the guide vanes of the qualified target straightening grid, combined with the shape memory alloy guide vanes, perform temperature control processing on the guide vanes of the qualified target straightening grid.

[0011] Further, in a preferred embodiment of the present invention, the step of determining the straightening grid that matches the siphon section of the water conveyance pipeline and deploying an electromagnetic micro flow velocity sensor array inside the siphon section of the water conveyance pipeline to test the water flow state of the siphon section of the water conveyance pipeline is specifically as follows:

[0012] Obtain the water conveyance pipeline, determine the siphon section in the water conveyance pipeline, and label it as the siphon section of the water conveyance pipeline;

[0013] Calculate the bending angles at different positions of the siphon section of the water conveyance pipeline, preset a bending angle threshold, and label the position of the siphon section of the water conveyance pipeline where the bending angle is maintained within the bending angle threshold as the sensor deployment position;

[0014] Install electromagnetic micro flow velocity sensors at all sensor deployment positions, obtain signal modules in all electromagnetic micro flow velocity sensors, control the signal modules of all electromagnetic micro flow velocity sensors to emit connection signals, and obtain the connection signals received by the controller module from different electromagnetic micro flow velocity sensors;

[0015] Perform Kalman filtering on the connection signals, and perform signal interaction on the filtered connection signals in the controller module to construct an electromagnetic micro flow velocity sensor array;

[0016] Combined with the electromagnetic micro flow velocity sensor array, test the water flow state of the siphon section of the water conveyance pipeline.

[0017] Further, in a preferred embodiment of the present invention, the combined electromagnetic micro flow velocity sensor array is used to test the water flow state of the inverted siphon section of the water conveyance pipeline, specifically as follows:

[0018] Control all electromagnetic micro flow velocity sensors to operate. Among them, the operation of the electromagnetic micro flow velocity sensor is to control the electromagnetic micro flow velocity sensor to deploy a magnetic field in the inverted siphon section of the water conveyance pipeline, and to monitor in real time the cutting rate of the magnetic induction lines in the inverted siphon section of the water conveyance pipeline;

[0019] Based on the cutting rate of the magnetic induction lines in the inverted siphon section of the water conveyance pipeline, calculate the magnitude of the induced electromotive force output by the electromagnetic micro flow velocity sensor. Combine the magnitude of the induced electromotive force output by the electromagnetic micro flow velocity sensor to calculate the real-time fluid flow velocity at different sensor deployment positions;

[0020] Obtain the straightening grid connected to the inverted siphon section of the water conveyance pipeline, calibrate it as the target straightening grid, connect the electromagnetic micro flow velocity sensor array to the target straightening grid, and update the real-time fluid flow velocity at different sensor deployment positions;

[0021] Conduct a test on the eddy current suppression response time in the target straightening grid. Combine the test results to calculate the eddy current suppression response time of the target straightening grid. At the same time, preset the maximum response time. If the eddy current suppression response time of the target straightening grid is greater than the maximum response time, then perform firmware update on the target straightening grid, and at the same time perform firmware update on the electromagnetic micro flow velocity sensor array connected to the target straightening grid until the eddy current suppression response time of the target straightening grid is not greater than the maximum response time to obtain a qualified target straightening grid.

[0022] Further, in a preferred embodiment of the present invention, the construction of an internal flow field simulation model of the straightening grid to simulate the turbulent flow state of the fluid, and based on the turbulent flow state of the fluid, calculate in real time the optimal opening angle of the guide vanes of the qualified target straightening grid, specifically as follows:

[0023] Introduce a laser scanning device to scan and analyze the dimensional data of the inverted siphon section of the water conveyance pipeline to obtain the dimensional data of the inverted siphon section of the water conveyance pipeline, and combine the bending angles at different positions of the inverted siphon section of the water conveyance pipeline to determine the installation position of the qualified target straightening grid at the same time, and output the specification parameters of the inverted siphon section of the water conveyance pipeline;

[0024] Based on the specification parameters of the inverted siphon section of the water conveyance pipeline, establish a three-dimensional geometric model in three-dimensional geometric modeling software, calibrate it as the target three-dimensional model, and determine the position and boundary of the guide vanes in the target three-dimensional model;

[0025] Among them, the boundary includes the fluid inlet and fluid outlet of the inverted siphon section of the water conveyance pipeline;

[0026] Combined with the target three-dimensional model and the corresponding positions and boundary parameters of the flow guiding vanes, a turbulence model of the target three-dimensional model is constructed, calibrated as the target three-dimensional turbulence model. At the same time, grid division is performed on the target three-dimensional turbulence model for calculating the quantization index thresholds of the flow field in the target three-dimensional turbulence model;

[0027] Among them, the quantization index thresholds of the flow field in the target three-dimensional turbulence model include the flow velocity uniformity threshold and the head loss coefficient threshold;

[0028] Combined with the quantization index thresholds of the flow field in the target three-dimensional turbulence model, the target three-dimensional turbulence model is subjected to turbulent flow simulation operation, and combined with the results of the turbulent flow simulation operation, the optimal opening and closing angles of the flow guiding vanes of the qualified target rectifying grid are calculated in real time.

[0029] Further, in a preferred embodiment of the present invention, the turbulent flow simulation operation of the target three-dimensional turbulence model and the real-time calculation of the optimal opening and closing angles of the flow guiding vanes of the qualified target rectifying grid by combining the results of the turbulent flow simulation operation are specifically as follows:

[0030] The target three-dimensional turbulence model is subjected to turbulent flow simulation operation, and orthogonal experiments are performed on the model corresponding to the qualified target rectifying grid during the turbulent flow simulation operation to generate different flow field characteristic data sets, wherein the fluid flow velocities at the boundaries and the opening and closing angles of the flow guiding vanes in different flow field characteristic data sets are different;

[0031] Based on different flow field characteristic data sets, a CFD predicted flow field is generated, and combined with the real-time fluid flow velocities at different sensor deployment positions, the boundary conditions of the CFD predicted flow field are corrected to generate a target CFD predicted flow field;

[0032] Through the Gaussian regression algorithm, principal component analysis is performed on the fluid flow velocity data in the target CFD predicted flow field, and based on the quantization index thresholds of the flow field in the target three-dimensional turbulence model, the objective function of the algorithm is constructed;

[0033] During the principal component analysis process, combined with the objective function of the algorithm, the optimal opening and closing angles of the flow guiding vanes at different fluid flow velocities are generated, wherein the optimal opening and closing angles of the flow guiding vanes need to ensure that the quantization indexes output at different fluid flow velocities are maintained within the quantization index thresholds;

[0034] If there is no opening and closing angle of the flow guiding vane to maintain the quantization index output by the fluid flow velocity within the quantization index threshold, the target CFD predicted flow field is dynamically error-corrected by the extended Kalman filter algorithm, and a genetic algorithm is introduced during the output process of the extended Kalman filter algorithm for secondary correction and convergence of the boundary conditions of the target CFD predicted flow field until there is an opening and closing angle of the flow guiding vane to maintain the quantization index output by the fluid flow velocity within the quantization index threshold.

[0035] Further, in a preferred embodiment of the present invention, based on the optimal opening and closing angle of the qualified target flow rectifying grid, combined with the shape memory alloy flow guiding vane, temperature control processing is performed on the flow guiding vane of the qualified target flow rectifying grid, specifically as follows:

[0036] Obtain the shape memory alloy flow guiding vane and determine the material parameters of the shape memory alloy flow guiding vane;

[0037] Introduce a big data network. Based on the material parameters of the shape memory alloy flow guiding vane in the big data network, retrieve the corresponding recovery states of the shape memory alloy flow guiding vane when different temperatures are applied, and construct a flow guiding vane - temperature change map;

[0038] Obtain the control module of the qualified target flow rectifying grid, replace the shape memory alloy flow guiding vane into the qualified target flow rectifying grid, and connect a heating module in parallel in the control module of the qualified target flow rectifying grid. At the same time, import the flow guiding vane - temperature change map into the control module of the qualified target flow rectifying grid;

[0039] Based on the control module of the qualified target flow rectifying grid, perform temperature control processing on the shape memory alloy flow guiding vane. Among them, the temperature control processing of the shape memory alloy flow guiding vane is that according to the real - time temperature of the fluid flowing through the qualified target flow rectifying grid, the shape memory alloy flow guiding vane automatically follows the temperature for plastic processing. After the shape memory alloy flow guiding vane is plastically deformed, if the shape memory alloy flow guiding vane needs to control the opening and closing angle to be greater than the current opening and closing angle, continue to perform automatic plastic processing according to the real - time temperature of the fluid flowing through the qualified target flow rectifying grid;

[0040] If the shape memory alloy flow guiding vane needs to control the opening and closing angle to be less than the current opening and closing angle, then in combination with the flow guiding vane - temperature change map, control the heating module through the control module to apply the temperature corresponding to the optimal opening and closing angle of the shape memory alloy flow guiding vane on the shape memory alloy flow guiding vane.

[0041] The second aspect of the present invention also provides an intelligent decision - making system for the working state of the flow rectifying grid based on data fusion. The working - state intelligent decision - making system includes a memory and a processor. The memory stores an intelligent decision - making method for the working state. When the intelligent decision - making method for the working state is executed by the processor, the following steps are realized:

[0042] Determine the flow rectifying grid that matches the inverted siphon section of the water conveyance pipeline, and deploy an electromagnetic micro - flow velocity sensor array inside the inverted siphon section of the water conveyance pipeline to test the water flow state of the inverted siphon section of the water conveyance pipeline;

[0043] Construct an internal flow field simulation model of the flow rectifying grid to simulate the turbulent flow state of the fluid, and calculate the optimal opening and closing angle of the qualified target flow rectifying grid in real - time based on the turbulent flow state of the fluid;

[0044] Based on the optimal opening and closing angle of the guide vanes of the qualified target flow straightener, combined with the shape memory alloy guide vanes, temperature control treatment is carried out on the guide vanes of the qualified target flow straightener.

[0045] The technical defects existing in the background art solved by the present invention are as follows. The present invention has the following beneficial effects: By deploying an electromagnetic micro flow velocity sensor array inside the inverted siphon section of the water conveyance pipeline, calculating the flow state of the fluid in the inverted siphon section of the water conveyance pipeline, and combining with the form of simulation, calculating the optimal opening and closing angle of the guide vanes in the flow straightener connected to the inverted siphon section of the water conveyance pipeline. Finally, through temperature control treatment, the combination of the shape memory alloy guide vanes and the flow straightener is realized, and the control of the optimal opening and closing angle of the guide vanes is achieved. The present invention can dynamically adjust the opening and closing angle of the guide vanes according to real-time flow velocity data to achieve dynamic flow state matching and improve the rectification efficiency. At the same time, it can optimize the working state of the flow straightener in real time, reduce the head loss, suppress the eddy current, and improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 Shows the flowchart of the intelligent decision-making method for the working state of the flow straightener based on data fusion;

[0048] Figure 2 Shows the flowchart of the method for calculating the optimal opening and closing angle of the guide vanes of the qualified target flow straightener;

[0049] Figure 3 Shows the program view of the intelligent decision-making system for the working state of the flow straightener based on data fusion. DETAILED DESCRIPTION OF THE INVENTION

[0050] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0051] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0052] Figure 1The flowchart of the intelligent decision-making method for the operating state of the flow rectifier based on data fusion is shown, including the following steps:

[0053] S102: Determine the flow rectifier that matches the inverted siphon section of the water conveyance pipeline, and deploy an electromagnetic micro flow velocity sensor array inside the inverted siphon section of the water conveyance pipeline to test the water flow state of the inverted siphon section of the water conveyance pipeline;

[0054] S104: Build a simulation model of the internal flow field of the flow rectifier to simulate the turbulent flow state of the fluid, and calculate the optimal opening angle of the guide vanes of the qualified target flow rectifier in real time based on the turbulent flow state of the fluid;

[0055] S106: Based on the optimal opening angle of the guide vanes of the qualified target flow rectifier, combined with the shape memory alloy guide vanes, perform temperature control processing on the guide vanes of the qualified target flow rectifier.

[0056] Further, in a preferred embodiment of the present invention, the step of determining the flow rectifier that matches the inverted siphon section of the water conveyance pipeline and deploying an electromagnetic micro flow velocity sensor array inside the inverted siphon section of the water conveyance pipeline to test the water flow state of the inverted siphon section of the water conveyance pipeline is specifically as follows:

[0057] Obtain the water conveyance pipeline, determine the inverted siphon section in the water conveyance pipeline, and label it as the inverted siphon section of the water conveyance pipeline;

[0058] Calculate the bending angles at different positions of the inverted siphon section of the water conveyance pipeline, preset a bending angle threshold, and label the position of the inverted siphon section of the water conveyance pipeline where the bending angle is maintained within the bending angle threshold as the sensor deployment position;

[0059] Install electromagnetic micro flow velocity sensors at all sensor deployment positions, obtain signal modules in all electromagnetic micro flow velocity sensors, control the signal modules of all electromagnetic micro flow velocity sensors to emit connection signals, and obtain the connection signals received by the controller module from different electromagnetic micro flow velocity sensors;

[0060] Perform Kalman filtering on the connection signals, and perform signal interaction on the filtered connection signals in the controller module to build an electromagnetic micro flow velocity sensor array;

[0061] Combine the electromagnetic micro flow velocity sensor array to test the water flow state of the inverted siphon section of the water conveyance pipeline.

[0062] It should be noted that there is a certain lag in the flow velocity monitoring of traditional rectifying grids, etc. The main reason is that the monitoring feedback of the water flow velocity is relatively slow. The micro flow velocity sensor array can monitor the water flow velocity in real time and transmit the data to the control system. The micro flow velocity sensor array can achieve accurate measurement of the water flow velocity. After comparing various micro flow velocity sensors, the electromagnetic micro flow velocity sensor is more suitable for various common water conservancy structures, such as the water conveyance channels of large water conservancy projects, etc., which have strict requirements for water flow resistance, complex flow patterns and require rapid response adjustment systems. Before installing the sensor, it is necessary to determine the installation position of the sensor. The sensor needs to be installed at the diameter mutation and bending sections of the inverted siphon section of the water conveyance pipeline to calculate the flow velocity distribution characteristics. Therefore, the bending angles of different positions of the inverted siphon section of the water conveyance pipeline are calculated to obtain the installation position. Since the fluid flow velocities at different positions may be different, it is necessary to analyze by combining the fluid flow velocities at all positions. To construct an electromagnetic micro flow velocity sensor array, the signals of different sensors need to be fused and analyzed. At the same time, the collected data may have a serious problem of data noise. Therefore, it is necessary to combine the Kalman filtering algorithm to perform Kalman filtering on the signal to achieve the purpose of noise reduction. The electromagnetic micro flow velocity sensor has the advantages of smaller water flow resistance, faster response speed, not being affected by fluid impurities and bubbles, being able to accurately measure the flow velocity and direction under complex flow patterns, high accuracy, being able to accurately measure the flow velocity in a complex environment and moderate maintenance cost compared with piezoelectric, ultrasonic and thermal sensors. Therefore, the electromagnetic micro flow velocity sensor is selected.

[0063] Further, in a preferred embodiment of the present invention, the water flow state of the inverted siphon section of the water conveyance pipeline is tested by combining the electromagnetic micro flow velocity sensor array, specifically:

[0064] Control all electromagnetic micro flow velocity sensors to operate. Among them, the operation of the electromagnetic micro flow velocity sensor is to control the electromagnetic micro flow velocity sensor to arrange a magnetic field in the inverted siphon section of the water conveyance pipeline and monitor the cutting rate of the magnetic induction lines in the inverted siphon section of the water conveyance pipeline in real time;

[0065] Based on the cutting rate of the magnetic induction lines in the inverted siphon section of the water conveyance pipeline, calculate the magnitude of the induced electromotive force output by the electromagnetic micro flow velocity sensor. Combine the magnitude of the induced electromotive force output by the electromagnetic micro flow velocity sensor to calculate the real-time fluid flow velocity at different sensor deployment positions;

[0066] Obtain the rectifying grid connected to the inverted siphon section of the water conveyance pipeline, calibrate it as the target rectifying grid, connect the electromagnetic micro flow velocity sensor array to the target rectifying grid, and update the real-time fluid flow velocity at different sensor deployment positions;

[0067] Perform a test on the eddy current suppression response time in the target rectifying grid. Based on the test results, calculate the eddy current suppression response time of the target rectifying grid. At the same time, preset the maximum response time. If the eddy current suppression response time of the target rectifying grid is greater than the maximum response time, update the firmware of the target rectifying grid and update the firmware of the electromagnetic micro flow velocity sensor array connected to the target rectifying grid until the eddy current suppression response time of the target rectifying grid is not greater than the maximum response time, and obtain a qualified target rectifying grid.

[0068] It should be noted that the electromagnetic micro flow velocity sensor works based on the principle of electromagnetic induction. Its core is to make the fluid flow in a magnetic field. When the fluid cuts the magnetic induction line as a conductor, an induced electromotive force will be generated. By detecting the magnitude of this electromotive force, the flow velocity of the fluid can be calculated. Whether it is a complex river channel with fast flowing water and vortices, or a water conservancy project with poor water quality and a large amount of sediment, it can accurately and quickly measure the flow velocity and direction, providing an accurate and reliable basis for the stable operation of the water conservancy structure. The target rectifying grid is an intelligent rectifying grid device. An electromagnetic micro flow velocity sensor array is connected inside the target rectifying grid, significantly reducing the time delay from abnormal flow state identification to eddy current suppression response. This low-delay characteristic enables the intelligent rectifying grid to quickly capture abnormal situations and make timely adjustments, improving the control efficiency of abnormal flow states. With an internal micro flow velocity sensor array, it can monitor the water flow in real time with high precision, significantly improving the monitoring efficiency of the water flow. This high-precision monitoring ability enables the intelligent rectifying grid to more accurately identify abnormal flow states, providing reliable data support for subsequent eddy current suppression.

[0069] The advantages of the present invention are that the micro flow velocity sensor array can monitor the water flow velocity in real time and provide high-precision data support. It changes the situation of traditional rectifying grids with lagging and inaccurate real-time data acquisition, adopts independent data acquisition and transmission, and transmits the data to the control center in real time, providing reliable data support for subsequent intelligent control.

[0070] Furthermore, in a preferred embodiment of the present invention, based on the optimal opening and closing angle of the guide vane of the qualified target rectifying grid, combined with the shape memory alloy guide vane, temperature control treatment is performed on the qualified target rectifying grid. Specifically:

[0071] Obtain the shape memory alloy guide vane and determine the material parameters of the shape memory alloy guide vane;

[0072] Introduce a big data network. Based on the material parameters of the shape memory alloy guide vane in the big data network, retrieve the corresponding recovery states of the shape memory alloy guide vane when different temperatures are applied, and construct a guide vane-temperature change map;

[0073] The control module for obtaining a qualified target rectifying grid replaces the shape memory alloy flow guide vane into the qualified target rectifying grid, and a heating module is connected in parallel in the control module of the qualified target rectifying grid. Meanwhile, the flow guide vane - temperature change map is imported into the control module of the qualified target rectifying grid;

[0074] Based on the control module of the qualified target rectifying grid, temperature control is performed on the shape memory alloy flow guide vane. Among them, the temperature control of the shape memory alloy flow guide vane is that according to the real - time temperature of the fluid flowing through the qualified target rectifying grid, the shape memory alloy flow guide vane automatically follows the temperature for plastic treatment. After the shape memory alloy flow guide vane is plasticized, if the required opening - closing angle of the shape memory alloy flow guide vane is greater than the current opening - closing angle, continue to perform automatic plasticization according to the real - time temperature of the fluid flowing through the qualified target rectifying grid;

[0075] If the required opening - closing angle of the shape memory alloy flow guide vane is less than the current opening - closing angle, in combination with the flow guide vane - temperature change map, the control module controls the heating module to apply the temperature corresponding to the best opening - closing angle of the shape memory alloy flow guide vane to the shape memory alloy flow guide vane.

[0076] It should be noted that the shape memory alloy (Shape Memory Alloy) has unique shape memory effect and super - elasticity, can deform under external force, and can return to its original shape when the temperature changes. This device deforms the shape memory alloy flow guide vane to the best angle by controlling the temperature. The shape memory alloy flow guide vane can automatically adjust the opening - closing angle of the flow guide vane according to the change of water temperature. After the shape memory alloy undergoes plastic deformation at low temperature and is heated to a certain specific temperature, it can return to the shape before deformation. That is, if a larger opening - closing angle of the flow guide vane is required, the opening - closing angle can be directly adjusted automatically according to the temperature. If a smaller opening - closing angle is required, heating is needed to restore the opening - closing angle. Because the water temperature changes with different flow velocities, the opening - closing angle analysis of the flow guide vane can also be realized according to the flow velocity.

[0077] This intelligent device innovatively applies memory metal to the rectifying grid. The shape memory alloy (SMA) has unique shape memory effect and super - elasticity, and can automatically adjust the opening - closing angle of the flow guide vane according to the change of water temperature. Through the thermosensitive characteristics of the SMA material, the automatic adjustment function of the flow guide vane is realized, reducing manual intervention and improving the intelligent level of the system.

[0078] Figure 2 The method flow chart for calculating the best opening - closing angle of the flow guide vane of the qualified target rectifying grid is shown, including the following steps:

[0079] S202: Construct a simulation model of the internal flow field of the rectifying grid to simulate the turbulent flow state of the fluid, and calculate the best opening - closing angle of the flow guide vane of the qualified target rectifying grid in real - time based on the turbulent flow state of the fluid;

[0080] S204: Conduct a turbulence simulation run on the target three-dimensional turbulence model, and in combination with the results of the turbulence simulation run, calculate in real time the optimal opening angle of the guide vanes of the qualified target fairing grille.

[0081] Further, in a preferred embodiment of the present invention, the method of constructing a simulation model of the internal flow field of the fairing grille to simulate the turbulent flow state of the fluid and calculating in real time the optimal opening angle of the guide vanes of the qualified target fairing grille based on the turbulent flow state of the fluid is specifically as follows:

[0082] Introduce a laser scanning device to scan and analyze the dimensional data of the inverted siphon section of the water conveyance pipeline, obtain the dimensional data of the inverted siphon section of the water conveyance pipeline, and in combination with the bending angles at different positions of the inverted siphon section of the water conveyance pipeline, determine the installation position of the qualified target fairing grille at the same time, and output the specification parameters of the inverted siphon section of the water conveyance pipeline;

[0083] Based on the specification parameters of the inverted siphon section of the water conveyance pipeline, establish a three-dimensional geometric model in three-dimensional geometric modeling software, calibrate it as the target three-dimensional model, and determine the position and boundary of the guide vanes within the target three-dimensional model;

[0084] Among them, the boundary includes the fluid inlet and fluid outlet of the inverted siphon section of the water conveyance pipeline;

[0085] In combination with the target three-dimensional model and the corresponding guide vane position and boundary parameters, construct a turbulence model of the target three-dimensional model, calibrate it as the target three-dimensional turbulence model, and at the same time perform mesh division on the target three-dimensional turbulence model to calculate the quantization index threshold of the flow field in the target three-dimensional turbulence model;

[0086] Among them, the quantization index threshold of the flow field in the target three-dimensional turbulence model includes the flow velocity uniformity threshold and the head loss coefficient threshold;

[0087] In combination with the quantization index threshold of the flow field in the target three-dimensional turbulence model, conduct a turbulence simulation run on the target three-dimensional turbulence model, and in combination with the results of the turbulence simulation run, calculate in real time the optimal opening angle of the guide vanes of the qualified target fairing grille.

[0088] It should be noted that computational fluid dynamics (CFD) is a numerical method used to simulate fluid flow and related physical processes. A control algorithm is developed to dynamically adjust the working state of the flow rectifying grid according to real-time flow velocity data. By means of CFD simulation, an internal flow field model of the flow rectifying grid is established. According to the real-time flow velocity data provided by the sensor array, the optimal opening and closing angles of the guide vanes and the working state of the flow rectifying grid are quickly calculated to achieve dynamic flow state matching. First, a three-dimensional model of the inverted siphon section of the water conveyance pipeline needs to be established, so the specification parameters of the inverted siphon section of the water conveyance pipeline need to be obtained, which can be obtained through laser scanning analysis. Secondly, the positions and boundaries of the guide vanes need to be determined because boundary conditions are required for constructing the CFD flow field. The purpose of constructing a turbulence model is to enhance the ability to capture separated flow and swirling flow, and the purpose of grid division is to improve the efficiency when calculating the flow field. The quantization index thresholds of the flow field include the flow velocity uniformity threshold and the head loss coefficient threshold, both of which are key indicators for controlling the opening and closing angles of the guide vanes.

[0089] Further, in a preferred embodiment of the present invention, the target three-dimensional turbulence model is subjected to turbulence simulation operation, and in combination with the results of the turbulence simulation operation, the optimal opening and closing angles of the qualified target flow rectifying grid are calculated in real time, specifically as follows:

[0090] The target three-dimensional turbulence model is subjected to turbulence simulation operation, and during the turbulence simulation operation, an orthogonal experiment is carried out on the model corresponding to the qualified target flow rectifying grid to generate different flow field characteristic data sets, wherein the fluid flow velocities at the boundaries and the opening and closing angles of the guide vanes in different flow field characteristic data sets are different;

[0091] Based on different flow field characteristic data sets, a CFD predicted flow field is generated. Combining the real-time flow velocities of the fluid at different sensor deployment positions, the boundary conditions of the CFD predicted flow field are corrected to generate a target CFD predicted flow field;

[0092] By means of the Gaussian regression algorithm, principal component analysis is carried out on the flow velocity data of the fluid in the target CFD predicted flow field, and based on the quantization index thresholds of the flow field in the target three-dimensional turbulence model, an objective function of the algorithm is constructed;

[0093] During the principal component analysis process, in combination with the objective function of the algorithm, the optimal opening and closing angles of the guide vanes at different fluid flow velocities are generated, wherein the optimal opening and closing angles of the guide vanes need to ensure that the quantization indexes output at different fluid flow velocities are maintained within the quantization index thresholds;

[0094] If there is no opening / closing angle of the flow guide vane to keep the quantization index of the fluid velocity output within the quantization index threshold, the extended Kalman filter algorithm is used to dynamically correct the error of the target CFD predicted flow field, and the genetic algorithm is introduced during the output process of the extended Kalman filter algorithm to perform secondary correction and convergence of the boundary conditions of the target CFD predicted flow field until there is an opening / closing angle of the flow guide vane to keep the quantization index of the fluid velocity output within the quantization index threshold.

[0095] It should be noted that during the turbulent flow simulation operation, orthogonal experiments are performed on the models corresponding to the qualified target fairing grids, and different flow field characteristic data sets can be generated. The purpose is to determine the optimal opening / closing angle corresponding to different flow field characteristic data and use it to generate the CFD predicted flow field. At the same time, the flow field boundary conditions are corrected by combining the known flow velocity data, such as local vorticity, pressure pulsation amplitude, etc. The method for making an intelligent decision on the opening / closing angle of the flow guide vane is to quantify the prediction uncertainty through the Gaussian process regression algorithm and perform principal component analysis on the flow velocity field data through feature dimension reduction to realize the judgment of the opening / closing angle control of the flow guide vane at different flow velocities. The objective function is the key data for dynamically optimizing and controlling the opening / closing angle. If the CFD simulation evaluation result shows that the predicted working state can achieve a good fairing effect, the control system will generate corresponding control instructions according to the predicted working state. After the fairing grid is adjusted to the new working state, the change of the flow field is continuously monitored in real time through the sensor. If it is found that the actual fairing effect does not match the expectation, for example, the flow velocity distribution is still uneven or there are large eddies, the control system will use these feedback information as new data to input to the target CFD predicted flow field for adjustment, that is, the extended Kalman filter algorithm is used to dynamically correct the error of the target CFD predicted flow field, and the genetic algorithm is introduced during the output process of the extended Kalman filter algorithm to perform secondary correction and convergence of the boundary conditions of the target CFD predicted flow field until there is an opening / closing angle of the flow guide vane to keep the quantization index of the fluid velocity output within the quantization index threshold. The extended Kalman filter algorithm dynamically corrects the CFD predicted flow field error through real-time data to improve the flow field prediction accuracy, and the genetic algorithm can converge quickly and improve the efficiency.

[0096] The innovation of the present invention is to dynamically adjust the opening / closing angle of the flow guide vane according to the real-time flow velocity data to achieve dynamic flow state matching and improve the fairing efficiency.

[0097] As Figure 3 shown, the second aspect of the present invention also provides a smart decision-making system for the working state of the fairing grid based on data fusion. The working state smart decision-making system includes a memory 31 and a processor 32. The memory 31 stores the working state smart decision-making method. When the working state smart decision-making method is executed by the processor 32, the following steps are realized:

[0098] Determine a flow straightening grid that matches the inverted siphon section of the water conveyance pipeline, and deploy an electromagnetic micro flow velocity sensor array inside the inverted siphon section of the water conveyance pipeline for testing the water flow state of the inverted siphon section of the water conveyance pipeline;

[0099] Construct a simulation model of the internal flow field of the flow straightening grid to simulate the turbulent flow state of the fluid, and calculate the optimal opening angle of the guide vanes of the qualified target flow straightening grid in real time based on the turbulent flow state of the fluid;

[0100] Based on the optimal opening angle of the guide vanes of the qualified target flow straightening grid, combined with the shape memory alloy guide vanes, perform temperature control treatment on the guide vanes of the qualified target flow straightening grid.

[0101] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A smart decision-making method for the operating state of a rectifying grid based on data fusion, characterized in that The following steps are involved: Determine a rectifier grid that matches the inverted siphon section of the water pipeline, and deploy an electromagnetic micro-flow velocity sensor array in the inverted siphon section of the water pipeline to test the water flow state of the inverted siphon section of the water pipeline; Obtain a rectifier grid connected to the inverted siphon section of the water pipeline, calibrate it as a target rectifier grid, connect an electromagnetic micro flow velocity sensor array to the target rectifier grid, and update the real-time flow velocity of the fluid at different sensor deployment locations; Performing an eddy current suppression response time test on a target rectifier grid, calculating the eddy current suppression response time of the target rectifier grid based on the test results, and presetting a maximum response time. If the eddy current suppression response time of the target rectifier grid is greater than the maximum response time, then performing a firmware update on the target rectifier grid and simultaneously performing a firmware update on an electromagnetic micro-flow velocity sensor array connected to the target rectifier grid until the eddy current suppression response time of the target rectifier grid is no greater than the maximum response time, thereby obtaining a qualified target rectifier grid; Construct a flow field simulation model inside the rectifier to simulate the turbulent flow state of the fluid, and calculate the optimal guide vane opening and closing angle of the qualified target rectifier in real time based on the turbulent flow state of the fluid; Based on the optimal guide vane opening and closing angle of the qualified target rectifier grid and combined with the shape memory alloy guide vane, the guide vane temperature control treatment is performed on the qualified target rectifier grid.

2. The intelligent decision-making method for the working state of the rectifying grid based on data fusion according to claim 1, wherein The determining of the rectifier grid matching the inverted siphon section of the water pipeline and the deployment of an electromagnetic micro flow velocity sensor array in the inverted siphon section of the water pipeline for testing the water flow state of the inverted siphon section of the water pipeline are specifically as follows: Obtain a water pipeline, determine an inverted siphon section in the water pipeline, and mark it as the inverted siphon section of the water pipeline; Calculate the bending angles of the inverted siphon section of the water pipeline at different locations, preset a bending angle threshold, and calibrate the position of the inverted siphon section of the water pipeline where the bending angle is maintained within the bending angle threshold as the sensor deployment location; Install electromagnetic micro flow rate sensors in all sensor deployment locations, obtain signal modules in all electromagnetic micro flow rate sensors, control the signal modules of all electromagnetic micro flow rate sensors to transmit connection signals, and obtain controller modules to receive connection signals from different electromagnetic micro flow rate sensors; Performing Kalman filtering on the connection signal, and performing signal interaction on the filtered connection signal in the controller module to construct an electromagnetic micro flow velocity sensor array; Combined with the electromagnetic micro flow velocity sensor array, the water flow state test of the inverted siphon section of the water pipeline is carried out.

3. The intelligent decision-making method for the working state of the rectifying grid based on data fusion according to claim 2, wherein The electromagnetic micro flow velocity sensor array is used to test the water flow state of the inverted siphon section of the water pipeline, specifically: Controlling the operation of all electromagnetic micro flow rate sensors, wherein the operation of the electromagnetic micro flow rate sensors is to control the magnetic field layout of the electromagnetic micro flow rate sensors in the inverted siphon section of the water pipeline and to monitor in real time the cutting rate of the magnetic flux lines in the inverted siphon section of the water pipeline; Based on the cutting rate of the magnetic flux lines in the inverted siphon section of the water pipeline, the induced electromotive force output by the electromagnetic micro flow velocity sensor is calculated. Combined with the induced electromotive force output by the electromagnetic micro flow velocity sensor, the real-time flow velocity of the fluid in different sensor deployment locations is calculated.

4. The intelligent decision-making method for the operating state of a rectifying grid based on data fusion according to claim 1, characterized in that The internal flow field simulation model of the constructed rectifying grid is used to simulate the turbulent flow state of the fluid, and the optimal opening angle of the guide vanes of the qualified target rectifying grid is calculated in real time based on the turbulent flow state of the fluid. Specifically: A laser scanning device is introduced to scan and analyze the dimensional data of the siphon section of the water conveyance pipeline, obtain the dimensional data of the siphon section of the water conveyance pipeline, and combine the bending angles at different positions of the siphon section of the water conveyance pipeline to determine the installation position of the qualified target rectifying grid at the same time, and output the specification parameters of the siphon section of the water conveyance pipeline; Based on the specification parameters of the siphon section of the water conveyance pipeline, a three-dimensional geometric model is established in the three-dimensional geometric modeling software, calibrated as the target three-dimensional model, and the position and boundary of the guide vanes are determined within the target three-dimensional model; Among them, the boundary includes the fluid inlet and fluid outlet of the siphon section of the water conveyance pipeline; Combined with the target three-dimensional model and the corresponding guide vane position and boundary parameters, a turbulent model of the target three-dimensional model is constructed, calibrated as the target three-dimensional turbulent model, and at the same time, grid division is performed on the target three-dimensional turbulent model to calculate the quantization index threshold of the flow field in the target three-dimensional turbulent model; Among them, the quantization index threshold of the flow field in the target three-dimensional turbulent model includes the flow velocity uniformity threshold and the head loss coefficient threshold; Combined with the quantization index threshold of the flow field in the target three-dimensional turbulent model, the target three-dimensional turbulent model is subjected to turbulent simulation operation, and combined with the turbulent simulation operation results, the optimal opening angle of the guide vanes of the qualified target rectifying grid is calculated in real time.

5. The intelligent decision-making method for the operating state of a rectifying grid based on data fusion according to claim 4, characterized in that The operation of performing turbulent simulation on the target three-dimensional turbulent model and calculating the optimal opening angle of the guide vanes of the qualified target rectifying grid in real time in combination with the turbulent simulation operation results is specifically as follows: Perform turbulent simulation operation on the target three-dimensional turbulent model, and perform orthogonal experiments on the model corresponding to the qualified target rectifying grid during the turbulent simulation operation to generate different flow field characteristic data sets. Among them, the fluid flow velocity at the boundary and the opening angle of the guide vanes in different flow field characteristic data sets are different; Based on different flow field characteristic data sets, a CFD predicted flow field is generated, combined with the real-time flow velocity of the fluid at different sensor deployment positions, the boundary conditions of the CFD predicted flow field are corrected, and a target CFD predicted flow field is generated; Through the Gaussian regression algorithm, principal component analysis is performed on the flow velocity data of the fluid in the target CFD predicted flow field, and based on the quantization index threshold of the flow field in the target three-dimensional turbulent model, the objective function of the algorithm is constructed; During the principal component analysis process, combined with the objective function of the algorithm, the optimal opening angle of the guide vanes at different fluid flow velocities is generated. Among them, the optimal opening angle of the guide vanes needs to ensure that the quantization indexes output at different fluid flow velocities are maintained within the quantization index threshold; If there is no opening angle of the guide vanes that can maintain the quantization index of the fluid flow velocity within the quantization index threshold, the extended Kalman filter algorithm is used to dynamically correct the error of the target CFD predicted flow field, and the genetic algorithm is introduced during the output process of the extended Kalman filter algorithm to perform secondary correction and convergence of the boundary conditions of the target CFD predicted flow field until there is an opening angle of the guide vanes that can maintain the quantization index of the fluid flow velocity within the quantization index threshold.

6. The intelligent decision-making method for the operating state of the rectifying grid based on data fusion according to claim 1, characterized in that, Based on the optimal opening angle of the qualified target rectifying grid and combined with the shape memory alloy deflector, temperature control processing is performed on the qualified target rectifying grid. Specifically: Obtain the shape memory alloy deflector and determine the material parameters of the shape memory alloy deflector; Introduce a big data network. Based on the material parameters of the shape memory alloy deflector in the big data network, retrieve the corresponding recovery states of the shape memory alloy deflector when different temperatures are applied, and construct a deflector-temperature change map; Obtain the control module of the qualified target rectifying grid, replace the shape memory alloy deflector into the qualified target rectifying grid, and connect a heating module in parallel in the control module of the qualified target rectifying grid. At the same time, import the deflector-temperature change map into the control module of the qualified target rectifying grid; Based on the control module of the qualified target rectifying grid, perform temperature control processing on the shape memory alloy deflector. Among them, the temperature control processing of the shape memory alloy deflector is that according to the real-time temperature of the fluid flowing through the qualified target rectifying grid, the shape memory alloy deflector automatically follows the temperature for plastic processing. After the shape memory alloy deflector is plasticized, if the shape memory alloy deflector needs to control the opening angle to be greater than the current opening angle, continue to perform automatic plasticization according to the real-time temperature of the fluid flowing through the qualified target rectifying grid; If the shape memory alloy deflector needs to control the opening angle to be less than the current opening angle, then in combination with the deflector-temperature change map, control the heating module through the control module to apply the temperature corresponding to the optimal opening angle of the shape memory alloy deflector on the shape memory alloy deflector.

7. An intelligent decision-making system for the operating state of a rectifying grid based on data fusion, characterized in that The intelligent decision-making system includes a memory and a processor. The intelligent decision-making method program is stored in the memory. When the intelligent decision-making method program is executed by the processor, the steps of the intelligent decision-making method described in any one of claims 1-6 are implemented.

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