Intelligent fluid flow regulation and control method and system based on environmental perception

Abstract

The invention relates to the field of flow regulation and control, and discloses an intelligent fluid flow regulation and control method and system based on environmental perception, and the method comprises the following steps: measuring the real-time flow characteristics of a fluid in a flow channel through a Doppler ultrasonic flowmeter, and constructing a fluid flow prediction model; and the fluid flow change condition in the target flow channel is predicted through the fluid flow prediction model, a fluid flow regulation and control scheme is generated based on the fluid flow change condition in the target flow channel, and finally scheme regulation and control are achieved. The performance of the fluid regulation and control system can be improved, resources are reasonably utilized, the environment is protected, energy is saved, consumption is reduced, and safe operation of the system can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of flow control, and in particular to a method and system for intelligently controlling fluid flow based on environmental perception. Background Art

[0002] If the fluid is not regulated in the flow channel, it is easy to cause the following hazards, including but not limited to, if the flow is not regulated, the fluid system may have too large or too small flow, resulting in system performance degradation and waste of fluid resources. For example, in water resource management, if intelligent regulation is not performed, it may lead to excessive consumption or waste of water resources. At the same time, in industrial production, the failure to regulate the flow may lead to excessive discharge of wastewater and exhaust gas, causing environmental pollution, which will not only affect the ecological balance, but also threaten human health. By optimizing the flow control strategy, the stability, response speed and accuracy of the system can be improved, thereby improving the overall performance. At the same time, intelligent regulation can reasonably allocate fluid resources to avoid excessive consumption or waste. For example, in some application scenarios, such as water resource management, intelligent regulation can ensure the effective use of water resources and reduce losses and waste. Therefore, it is of great significance to intelligently regulate the flow of fluid in the flow channel through environmental perception methods. It can not only improve system performance, rationally use resources, protect the environment and save energy and reduce consumption, but also ensure the safe operation of the system. Failure to regulate may lead to hazards such as system performance degradation, resource waste, environmental pollution and safety hazards. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for intelligently controlling fluid flow based on environmental perception.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention provides a method for intelligently controlling fluid flow based on environmental perception, comprising the following steps:

[0006] The real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flow meter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid;

[0007] Based on the real-time flow characteristics of the fluid and combined with the elastic network regression algorithm, a flow prediction model of the fluid in the target flow channel is constructed;

[0008] The fluid flow prediction model is used to predict the change of fluid flow in the target flow channel, a fluid flow control scheme is generated based on the change of fluid flow in the target flow channel, and the fluid flow control scheme is controlled and output.

[0009] Furthermore, in a preferred embodiment of the present invention, the real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flowmeter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid, specifically:

[0010] Determine the flow channel that needs to be regulated, mark it as the target flow channel, introduce the historical data network, determine all sensor installation positions in the target flow channel, retrieve the historical flow rates of different sensor installation positions in the target flow channel, and determine the maximum tolerable flow rates of different sensor installation positions in the target flow channel;

[0011] If there is a sensor installable position where the historical flow is greater than the corresponding maximum tolerable flow, the corresponding sensor installable position is calibrated as a candidate flow monitoring position, and in the candidate flow monitoring position, the historical time limit in which the historical flow is greater than the corresponding maximum tolerable flow is determined, and the candidate flow monitoring position output with a history higher than the preset value is selected and calibrated as the target flow monitoring position;

[0012] Installing Doppler ultrasonic flowmeters at all target flow monitoring positions, presetting real-time information monitoring time, and using Doppler ultrasonic flowmeters to monitor real-time information of the fluid at the target flow monitoring position within the real-time information monitoring time, wherein the real-time information of the fluid includes the real-time water level, fluid flow velocity, and fluid flow rate at the target flow monitoring position;

[0013] Performing information format unification processing on the real-time information of the fluid to obtain real-time information of the fluid in a unified format, and extracting the real-time flow characteristics of the fluid in the real-time information of the fluid in a unified format in combination with the real-time information monitoring time;

[0014] The real-time flow characteristics of the fluid include the fluctuation rate of the fluid water level during the real-time information monitoring time and the change rate of the fluid flow rate during the real-time information monitoring time.

[0015] Furthermore, in a preferred embodiment of the present invention, the flow prediction model of the fluid in the target flow channel is constructed based on the real-time flow characteristics of the fluid in combination with the elastic network regression algorithm, specifically:

[0016] The real-time flow characteristics of the fluid are synchronized with the timestamp, that is, the fluctuation rate of the fluid water level within the real-time information monitoring time and the change rate of the fluid flow within the real-time information monitoring time are synchronized with the timestamp in the real-time flow characteristics of the fluid to obtain the timestamp synchronized fluid flow characteristics;

[0017] Constructing the time series features of the timestamp synchronized fluid flow features, and extracting the lag term features and the sliding average in the time series features of the timestamp synchronized fluid flow features, and calibrating them as fluid simulation condition features;

[0018] Introducing fluid dynamics software, importing the fluid simulation condition characteristics into the fluid dynamics software for fluid simulation processing, so that the fluid dynamics software can simulate the real-time flow characteristics of the fluid in the target flow channel;

[0019] Run the fluid dynamics software, introduce the elastic network regression algorithm into the fluid dynamics software, and build a flow prediction model for the fluid in the target flow channel.

[0020] Furthermore, in a preferred embodiment of the present invention, the elastic network regression algorithm is introduced into the fluid dynamics software to construct a flow prediction model of the fluid in the target flow channel, specifically:

[0021] In combination with the elastic network regression algorithm, the fluid dynamics software is run to obtain the fluid simulation information in the fluid dynamics software, and the initial regularization parameter and the initial mixing parameter are set according to the fluid simulation information, and the maximum number of iterations and the tolerance threshold of the change amount used in the elastic network regression algorithm are set based on the initial regularization parameter and the initial mixing parameter;

[0022] Based on the elastic network regression algorithm, the weight coefficients of the fluid simulation information are initialized, and the elements in the weight coefficients are used as variable elements. The optimal solution of all variable elements is evaluated by minimizing the cost function method, and the weight coefficients are iteratively updated by the optimal solution obtained by evaluation.

[0023] If the change amount of the weight coefficient during iterative update is greater than the change tolerance value, or the number of iterative updates of the weight coefficient is equal to the maximum number of iterations, then the iterative update of the weight coefficient by the optimal solution obtained by evaluation is stopped to obtain the final weight coefficient;

[0024] The final weight coefficient is applied to the fluid dynamics software to construct a mathematical model for predicting the flow rate of the fluid in the target flow channel, which is calibrated as a fluid flow prediction model.

[0025] Furthermore, in a preferred embodiment of the present invention, the fluid flow prediction model is used to predict the change of the fluid flow in the target flow channel, a fluid flow control scheme is generated based on the change of the fluid flow in the target flow channel, and the fluid flow control scheme is controlled and outputted, specifically:

[0026] Preset a prediction timestamp, import the real-time flow characteristics of the fluid into the fluid flow prediction model, and control the operation of the fluid flow prediction model to generate the real-time flow of the fluid at different target flow monitoring positions after the prediction timestamp;

[0027] Combined with the maximum tolerable flow rate at different target flow monitoring positions, the real-time flow rate of the fluid at different target flow monitoring positions after the prediction timestamp is analyzed. If there is no target flow monitoring position where the real-time flow rate of the fluid after the prediction timestamp is greater than the corresponding maximum tolerable flow rate, there is no need to regulate the fluid flow rate of the target flow channel.

[0028] If there is a target flow monitoring position where the real-time flow of the fluid after the prediction timestamp is greater than the corresponding maximum tolerable flow, the corresponding target flow monitoring position is marked as the abnormal position of fluid flow prediction;

[0029] Acquire a three-dimensional map of the flow channel, mark a fluid flow prediction abnormal position in the three-dimensional map of the flow channel, and construct a topological map of the three-dimensional map of the flow channel with the fluid flow prediction abnormal position marked, and mark it as a target flow channel topological map;

[0030] In the target flow channel topology map, abnormal locations predicted for fluid flow are marked as abnormal topology nodes, and associated regions of different abnormal topology nodes in the target flow channel are extracted from the target flow channel topology map and marked as abnormal associated flow channel sub-regions;

[0031] Determine whether there is a fluid control valve or a fluid output pump in the abnormal associated flow channel sub-area, and if so, generate a first fluid flow control scheme;

[0032] Wherein, the first fluid flow control scheme is that if there is a fluid control valve in the abnormal associated flow channel sub-area, the fluid control valve is controlled in the abnormal associated flow channel sub-area to automatically adjust the opening and closing until the real-time fluid flow at the abnormal position of the fluid flow prediction after the prediction timestamp is not greater than the corresponding maximum tolerable flow;

[0033] If there is a fluid output pump in the abnormal associated flow channel sub-area, the fluid output pump is controlled in the abnormal associated flow channel sub-area to automatically adjust the fluid output until the real-time fluid flow rate of the fluid flow prediction abnormal position after the prediction timestamp is not greater than the corresponding maximum tolerable flow rate;

[0034] If there is no fluid control valve or fluid output pump in the abnormally associated flow channel sub-region, an environmental factor association impact analysis is performed in the abnormally associated flow channel sub-region, and a circulation flow control plan is generated based on the environmental factor association impact analysis result.

[0035] Furthermore, in a preferred embodiment of the present invention, the environmental factor correlation impact analysis is performed in the abnormally correlated flow channel sub-region, and a flow control scheme is generated based on the environmental factor correlation impact analysis result, specifically:

[0036] If there is no fluid control valve or fluid output pump in the abnormal associated flow channel sub-region, the surrounding environmental factors of the abnormal associated flow channel sub-region are obtained, and the surrounding environmental factors of the abnormal associated flow channel sub-region are integrated into the target flow channel topology map for topological update.

[0037] After the topology is updated in the target flow channel topology map, the occupancy rate of the surrounding environment factors of the abnormally associated flow channel sub-region in the abnormally associated flow channel sub-region is calculated in the target flow channel topology map, and the standard occupancy rate is preset;

[0038] If the occupancy rate of the surrounding environmental factors of the abnormally associated flow channel sub-region in the abnormally associated flow channel sub-region is not less than the standard occupancy rate, the corresponding abnormally associated flow channel sub-region is marked as a type of abnormally associated flow channel sub-region, and the fluid flow prediction abnormal position corresponding to the type of abnormally associated flow channel sub-region is obtained and marked as a type of fluid flow prediction abnormal position;

[0039] A big data network is introduced to retrieve and output a scheme for controlling the surrounding environmental factors in a type of fluid flow prediction abnormal position so that the occupancy rate of the surrounding environmental factors in the abnormal associated flow channel sub-region is less than the standard occupancy rate, and the corresponding scheme is calibrated as the second fluid flow control scheme;

[0040] If there is an abnormally associated flow channel sub-area in which the occupancy rate of the surrounding environmental factors in the abnormally associated flow channel sub-area is not less than the standard occupancy rate, but the real-time fluid flow rate of the fluid flow prediction abnormal position corresponding to the abnormally associated flow channel sub-area after the prediction timestamp is still greater than the corresponding maximum tolerable flow rate, then the corresponding fluid flow prediction abnormal position is calibrated as a Class II fluid flow prediction abnormal position, and a method for reducing the fluid flow rate at the Class II fluid flow prediction abnormal position is retrieved and output in the big data network, and at the same time, the method for reducing the fluid flow rate at the Class II fluid flow prediction abnormal position is calibrated as the third fluid flow control scheme.

[0041] The second aspect of the present invention further provides a fluid flow intelligent control system based on environmental perception, the fluid flow intelligent control system includes a memory and a processor, the memory stores a fluid flow intelligent control method, and when the fluid flow intelligent control method is executed by the processor, the following steps are implemented:

[0042] The real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flow meter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid;

[0043] Based on the real-time flow characteristics of the fluid and combined with the elastic network regression algorithm, a flow prediction model of the fluid in the target flow channel is constructed;

[0044] The fluid flow prediction model is used to predict the change of fluid flow in the target flow channel, a fluid flow control scheme is generated based on the change of fluid flow in the target flow channel, and the fluid flow control scheme is controlled and output.

[0045] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the real-time flow characteristics of the fluid in the flow channel are measured by a Doppler ultrasonic flowmeter, which is used to construct a fluid flow prediction model, and the fluid flow prediction model is used to predict the change of the fluid flow in the target flow channel, and a fluid flow control scheme is generated based on the change of the fluid flow in the target flow channel, and finally the scheme control is realized. The present invention can improve the performance of the fluid control system, reasonably utilize resources, protect the environment, save energy and reduce consumption, and can also ensure the safe operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A flow chart of a fluid flow intelligent control method based on environmental perception is shown;

[0048] Figure 2 A flow chart of a method for generating and outputting a fluid flow control scheme based on a fluid flow prediction model is shown;

[0049] Figure 3 The program view of the fluid flow intelligent control system based on environmental perception is shown. DETAILED DESCRIPTION

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

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0052] Figure 1 A flow chart of a fluid flow intelligent control method based on environmental perception is shown, comprising the following steps:

[0053] S102: measuring the real-time information of the fluid in the flow channel by using a Doppler ultrasonic flow meter, and performing information mining on the real-time information of the fluid to extract the real-time flow characteristics of the fluid;

[0054] S104: Based on the real-time flow characteristics of the fluid and in combination with the elastic network regression algorithm, a flow prediction model of the fluid in the target flow channel is constructed;

[0055] S106: Predicting the change of fluid flow in the target flow channel by using the fluid flow prediction model, generating a fluid flow control scheme based on the change of fluid flow in the target flow channel, and controlling and outputting the fluid flow control scheme.

[0056] Furthermore, in a preferred embodiment of the present invention, the real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flowmeter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid, specifically:

[0057] Determine the flow channel that needs to be regulated, mark it as the target flow channel, introduce the historical data network, determine all sensor installation positions in the target flow channel, retrieve the historical flow rates of different sensor installation positions in the target flow channel, and determine the maximum tolerable flow rates of different sensor installation positions in the target flow channel;

[0058] If there is a sensor installable position where the historical flow is greater than the corresponding maximum tolerable flow, the corresponding sensor installable position is calibrated as a candidate flow monitoring position, and in the candidate flow monitoring position, the historical time limit in which the historical flow is greater than the corresponding maximum tolerable flow is determined, and the candidate flow monitoring position output with a history higher than the preset value is selected and calibrated as the target flow monitoring position;

[0059] Installing Doppler ultrasonic flowmeters at all target flow monitoring positions, presetting real-time information monitoring time, and using Doppler ultrasonic flowmeters to monitor real-time information of the fluid at the target flow monitoring position within the real-time information monitoring time, wherein the real-time information of the fluid includes the real-time water level, fluid flow velocity, and fluid flow rate at the target flow monitoring position;

[0060] Performing information format unification processing on the real-time information of the fluid to obtain real-time information of the fluid in a unified format, and extracting the real-time flow characteristics of the fluid in the real-time information of the fluid in a unified format in combination with the real-time information monitoring time;

[0061] The real-time flow characteristics of the fluid include the fluctuation rate of the fluid water level during the real-time information monitoring time and the change rate of the fluid flow rate during the real-time information monitoring time.

[0062] It should be noted that the target flow channel is a channel for fluid flow, including but not limited to wastewater circulation pipes and rivers in factories. In the target flow channel, there are some locations that are easily affected by excessive flow, such as corners and outlets of factory wastewater circulation pipes. If the flow is too high, it is easy to cause wastewater to burst the dam. Therefore, it is necessary to set fluid monitoring sensors, that is, Doppler ultrasonic flowmeters, at locations where the flow is prone to excessive flow, to monitor the real-time information of the fluid at the target flow monitoring position in real time. The real-time information of the fluid contains key information that reflects the flow characteristics of the fluid, such as flow change trends, water level fluctuations, etc., which can be extracted through signal processing and data mining technology.

[0063] Furthermore, in a preferred embodiment of the present invention, the flow prediction model of the fluid in the target flow channel is constructed based on the real-time flow characteristics of the fluid in combination with the elastic network regression algorithm, specifically:

[0064] The real-time flow characteristics of the fluid are synchronized with the timestamp, that is, the fluctuation rate of the fluid water level within the real-time information monitoring time and the change rate of the fluid flow within the real-time information monitoring time are synchronized with the timestamp in the real-time flow characteristics of the fluid to obtain the timestamp synchronized fluid flow characteristics;

[0065] Constructing the time series features of the timestamp synchronized fluid flow features, and extracting the lag term features and the sliding average in the time series features of the timestamp synchronized fluid flow features, and calibrating them as fluid simulation condition features;

[0066] Introducing fluid dynamics software, importing the fluid simulation condition characteristics into the fluid dynamics software for fluid simulation processing, so that the fluid dynamics software can simulate the real-time flow characteristics of the fluid in the target flow channel;

[0067] Run the fluid dynamics software, introduce the elastic network regression algorithm into the fluid dynamics software, and build a flow prediction model for the fluid in the target flow channel.

[0068] It should be noted that the time stamp synchronization of the real-time flow characteristics of the fluid is to synchronize the fluctuation rate of the fluid water level within the real-time information monitoring time and the change rate of the fluid flow within the real-time information monitoring time, that is, to control the changes of the two at the same time point to be in a corresponding relationship. The advantage is that the data analysis is more accurate and scientific. After obtaining the timestamp synchronized fluid flow characteristics, the characteristics are processed to remove redundant and irrelevant characteristics, and the fluid dynamics software is introduced. Combined with the above characteristics, more detailed information on the fluid flow is obtained, that is, the real-time flow characteristics of the fluid in the target flow channel are simulated. The elastic network is a linear regression method that combines the advantages of ridge regression (L2 regularization) and lasso regression (L1 regularization). It can handle multicollinearity problems in high-dimensional data and allow the coefficients of some features to be zero, thereby realizing feature selection, that is, achieving the purpose of data prediction. Therefore, the elastic network regression algorithm is introduced in the fluid dynamics software to construct a flow prediction model.

[0069] Furthermore, in a preferred embodiment of the present invention, the elastic network regression algorithm is introduced into the fluid dynamics software to construct a flow prediction model of the fluid in the target flow channel, specifically:

[0070] In combination with the elastic network regression algorithm, the fluid dynamics software is run to obtain the fluid simulation information in the fluid dynamics software, and the initial regularization parameter and the initial mixing parameter are set according to the fluid simulation information, and the maximum number of iterations and the variation tolerance threshold used in the elastic network regression algorithm are set based on the initial regularization parameter and the initial mixing parameter;

[0071] Based on the elastic network regression algorithm, the weight coefficients of the fluid simulation information are initialized, and the elements in the weight coefficients are used as variable elements. The optimal solution of all variable elements is evaluated by minimizing the cost function method, and the weight coefficients are iteratively updated by the optimal solution obtained by evaluation.

[0072] If the change amount of the weight coefficient during iterative update is greater than the change tolerance value, or the number of iterative updates of the weight coefficient is equal to the maximum number of iterations, then the iterative update of the weight coefficient by the optimal solution obtained by evaluation is stopped to obtain the final weight coefficient;

[0073] The final weight coefficient is applied to the fluid dynamics software to construct a mathematical model for predicting the flow rate of the fluid in the target flow channel, which is calibrated as a fluid flow prediction model.

[0074] It should be noted that since the elastic network regression algorithm can construct a model for predicting fluid flow, the elastic network regression algorithm is combined with the fluid dynamics software to run and set the initial regularization parameter and the initial mixing parameter, which are used to control the strength and type of the regularization term, and to set the maximum number of iterations and the tolerance threshold of the change in the elastic network regression algorithm. The maximum number of iterations is the number of times the algorithm is updated, and the tolerance threshold of the change is the amplitude of the coefficient change in the algorithm, both of which are stop conditions for controlling the iterative process. For the weight coefficient, each element in the weight coefficient is used as a variable in turn, and the other elements are fixed as the result of the previous iteration, that is, a constant. The method for evaluating the optimal solution is to solve the optimal solution of the variable by minimizing the cost function. Finally, when the change in the iterative update of the weight coefficient is greater than the tolerance value of the change, or the number of iterative updates of the weight coefficient is equal to the maximum number of iterations, the optimal solution obtained by evaluating the weight coefficient is stopped to iteratively update the weight coefficient. After obtaining the updated weight coefficient, the fluid flow prediction model can be constructed.

[0075] Figure 2 A flow chart of a method for generating and outputting a fluid flow control scheme based on a fluid flow prediction model is shown, comprising the following steps:

[0076] S202: predicting the change of fluid flow in the target flow channel by using a fluid flow prediction model, generating a fluid flow control scheme based on the change of fluid flow in the target flow channel, and controlling and outputting the fluid flow control scheme;

[0077] S204: Performing an environmental factor correlation impact analysis in the abnormally correlated flow channel sub-region, and generating a flow control scheme based on the environmental factor correlation impact analysis result.

[0078] Furthermore, in a preferred embodiment of the present invention, the fluid flow prediction model is used to predict the change of the fluid flow in the target flow channel, a fluid flow control scheme is generated based on the change of the fluid flow in the target flow channel, and the fluid flow control scheme is controlled and outputted, specifically:

[0079] Preset a prediction timestamp, import the real-time flow characteristics of the fluid into the fluid flow prediction model, and control the operation of the fluid flow prediction model to generate the real-time flow of the fluid at different target flow monitoring positions after the prediction timestamp;

[0080] Combined with the maximum tolerable flow rate at different target flow monitoring positions, the real-time flow rate of the fluid at different target flow monitoring positions after the prediction timestamp is analyzed. If there is no target flow monitoring position where the real-time flow rate of the fluid after the prediction timestamp is greater than the corresponding maximum tolerable flow rate, there is no need to regulate the fluid flow rate of the target flow channel.

[0081] If there is a target flow monitoring position where the real-time flow of the fluid after the prediction timestamp is greater than the corresponding maximum tolerable flow, the corresponding target flow monitoring position is marked as the abnormal position of fluid flow prediction;

[0082] Acquire a three-dimensional map of the flow channel, mark a fluid flow prediction abnormal position in the three-dimensional map of the flow channel, and construct a topological map of the three-dimensional map of the flow channel with the fluid flow prediction abnormal position marked, and mark it as a target flow channel topological map;

[0083] In the target flow channel topology map, abnormal locations predicted for fluid flow are marked as abnormal topology nodes, and associated regions of different abnormal topology nodes in the target flow channel are extracted from the target flow channel topology map and marked as abnormal associated flow channel sub-regions;

[0084] Determine whether there is a fluid control valve or a fluid output pump in the abnormal associated flow channel sub-area, and if so, generate a first fluid flow control scheme;

[0085] Wherein, the first fluid flow control scheme is that if there is a fluid control valve in the abnormal associated flow channel sub-area, the fluid control valve is controlled in the abnormal associated flow channel sub-area to automatically adjust the opening and closing until the real-time fluid flow at the abnormal position of the fluid flow prediction after the prediction timestamp is not greater than the corresponding maximum tolerable flow;

[0086] If there is a fluid output pump in the abnormal associated flow channel sub-area, the fluid output pump is controlled in the abnormal associated flow channel sub-area to automatically adjust the fluid output until the real-time fluid flow rate of the fluid flow prediction abnormal position after the prediction timestamp is not greater than the corresponding maximum tolerable flow rate;

[0087] If there is no fluid control valve or fluid output pump in the abnormally associated flow channel sub-region, an environmental factor association impact analysis is performed in the abnormally associated flow channel sub-region, and a circulation flow control plan is generated based on the environmental factor association impact analysis result.

[0088] It should be noted that the purpose of setting the prediction timestamp is to obtain the flow change of the fluid within this time. Based on the fluid flow prediction model, the real-time flow of the fluid at different target flow monitoring positions after the prediction timestamp can be obtained to analyze whether the fluid needs to be regulated. The real-time flow of the fluid at different target flow monitoring positions after the prediction timestamp is analyzed to determine its relationship with the maximum tolerable flow at the corresponding position, determine whether flow regulation is required, and generate different fluid flow regulation schemes accordingly. After determining the abnormal position of the fluid flow prediction, the radiation influence range of different abnormal positions of the fluid flow prediction is different. By constructing a flow channel topology map, the range of the associated influence of different nodes can be known, that is, the radiation influence range of different abnormal positions of the fluid flow prediction, so the target flow channel topology map is constructed, and the abnormal associated flow channel sub-area corresponding to the abnormal position of the fluid flow prediction is determined. If there are devices such as valves and water pumps that physically reduce the fluid flow in the abnormal associated flow channel sub-area, the fluid flow can be physically regulated by these devices to control the real-time flow of the fluid at the abnormal position of the fluid flow prediction after the prediction timestamp to be not greater than the corresponding maximum tolerable flow. If the above equipment does not exist, in order to determine whether environmental factors affect the abnormal fluid flow, it is necessary to conduct an environmental factor correlation impact analysis and generate a flow control plan based on the results of the environmental factor correlation impact analysis.

[0089] Furthermore, in a preferred embodiment of the present invention, the environmental factor correlation impact analysis is performed in the abnormally correlated flow channel sub-region, and a flow control scheme is generated based on the environmental factor correlation impact analysis result, specifically:

[0090] If there is no fluid control valve or fluid output pump in the abnormal associated flow channel sub-region, the surrounding environmental factors of the abnormal associated flow channel sub-region are obtained, and the surrounding environmental factors of the abnormal associated flow channel sub-region are integrated into the target flow channel topology map for topological update.

[0091] After the topology is updated in the target flow channel topology map, the occupancy rate of the surrounding environment factors of the abnormally associated flow channel sub-region in the abnormally associated flow channel sub-region is calculated in the target flow channel topology map, and the standard occupancy rate is preset;

[0092] If the occupancy rate of the surrounding environmental factors of the abnormally associated flow channel sub-region in the abnormally associated flow channel sub-region is not less than the standard occupancy rate, the corresponding abnormally associated flow channel sub-region is marked as a type of abnormally associated flow channel sub-region, and the fluid flow prediction abnormal position corresponding to the type of abnormally associated flow channel sub-region is obtained and marked as a type of fluid flow prediction abnormal position;

[0093] A big data network is introduced to retrieve and output a scheme for controlling the surrounding environmental factors in a type of fluid flow prediction abnormal position so that the occupancy rate of the surrounding environmental factors in the abnormal associated flow channel sub-region is less than the standard occupancy rate, and the corresponding scheme is calibrated as the second fluid flow control scheme;

[0094] If there is an abnormally associated flow channel sub-area in which the occupancy rate of the surrounding environmental factors in the abnormally associated flow channel sub-area is not less than the standard occupancy rate, but the real-time fluid flow rate of the fluid flow prediction abnormal position corresponding to the abnormally associated flow channel sub-area after the prediction timestamp is still greater than the corresponding maximum tolerable flow rate, then the corresponding fluid flow prediction abnormal position is calibrated as a Class II fluid flow prediction abnormal position, and a method for reducing the fluid flow rate at the Class II fluid flow prediction abnormal position is retrieved and output in the big data network, and at the same time, the method for reducing the fluid flow rate at the Class II fluid flow prediction abnormal position is calibrated as the third fluid flow control scheme.

[0095] It should be noted that if there is no fluid control valve or fluid output pump in the abnormal associated flow channel sub-area, it is necessary to perform an environmental factor correlation impact analysis to determine whether the environmental factors affect the abnormal fluid flow. If the environmental factors are integrated into the target flow channel topology map, the environmental factors will be covered in the topology map, that is, the influence range of the environmental factors in the abnormal associated flow channel sub-area is generated. If the size of the coverage, that is, the occupancy rate of the surrounding environmental factors of the abnormal associated flow channel sub-area in the abnormal associated flow channel sub-area is greater than the preset value, it proves that the corresponding abnormal associated flow channel sub-area is greatly affected by the environmental factors, and it is necessary to control the environmental factors of the corresponding fluid flow prediction abnormal position, including but not limited to temperature control, wind speed control, etc. If it is not greater than the preset value, the fluid flow rate is directly reduced, including but not limited to installing obstructions or diversion equipment in the flow channel to achieve fluid flow rate reduction.

[0096] like Figure 3 As shown, the second aspect of the present invention further provides a fluid flow intelligent control system based on environmental perception, the fluid flow intelligent control system includes a memory 31 and a processor 32, the memory 31 stores a fluid flow intelligent control method, and when the fluid flow intelligent control method is executed by the processor 32, the following steps are implemented:

[0097] The real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flow meter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid;

[0098] Based on the real-time flow characteristics of the fluid and combined with the elastic network regression algorithm, a flow prediction model of the fluid in the target flow channel is constructed;

[0099] The fluid flow prediction model is used to predict the change of fluid flow in the target flow channel, a fluid flow control scheme is generated based on the change of fluid flow in the target flow channel, and the fluid flow control scheme is controlled and output.

[0100] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A fluid flow intelligent control method based on environmental perception, characterized in that: The following steps are involved: The real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flow meter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid; Based on the real-time flow characteristics of the fluid and combined with the elastic network regression algorithm, a flow prediction model of the fluid in the target flow channel is constructed; The fluid flow prediction model is used to predict the change of fluid flow in the target flow channel, a fluid flow control scheme is generated based on the change of fluid flow in the target flow channel, and the fluid flow control scheme is controlled and output.

2. The method for intelligently controlling fluid flow based on environmental perception according to claim 1, characterized in that: The real-time information of the fluid in the flow channel is measured by a Doppler ultrasonic flow meter, and the real-time information of the fluid is mined to extract the real-time flow characteristics of the fluid, specifically: Determine the flow channel that needs to be regulated, mark it as the target flow channel, introduce the historical data network, determine all sensor installation positions in the target flow channel, retrieve the historical flow rates of different sensor installation positions in the target flow channel, and determine the maximum tolerable flow rates of different sensor installation positions in the target flow channel; If there is a sensor installable position where the historical flow is greater than the corresponding maximum tolerable flow, the corresponding sensor installable position is calibrated as a candidate flow monitoring position, and in the candidate flow monitoring position, the historical time limit in which the historical flow is greater than the corresponding maximum tolerable flow is determined, and the candidate flow monitoring position output with a history higher than the preset value is selected and calibrated as the target flow monitoring position; Installing Doppler ultrasonic flowmeters at all target flow monitoring positions, presetting real-time information monitoring time, and using Doppler ultrasonic flowmeters to monitor real-time information of the fluid at the target flow monitoring position within the real-time information monitoring time, wherein the real-time information of the fluid includes the real-time water level, fluid flow velocity, and fluid flow rate at the target flow monitoring position; Performing information format unification processing on the real-time information of the fluid to obtain real-time information of the fluid in a unified format, and extracting the real-time flow characteristics of the fluid in the real-time information of the fluid in a unified format in combination with the real-time information monitoring time; The real-time flow characteristics of the fluid include the fluctuation rate of the fluid water level during the real-time information monitoring time and the change rate of the fluid flow rate during the real-time information monitoring time.

3. The method for intelligently controlling fluid flow based on environmental perception according to claim 1 is characterized in that: The flow prediction model of the fluid in the target flow channel is constructed based on the real-time flow characteristics of the fluid and combined with the elastic network regression algorithm, specifically: The real-time flow characteristics of the fluid are synchronized with the timestamp, that is, the fluctuation rate of the fluid water level within the real-time information monitoring time and the change rate of the fluid flow within the real-time information monitoring time are synchronized with the timestamp in the real-time flow characteristics of the fluid to obtain the timestamp synchronized fluid flow characteristics; Constructing the time series features of the timestamp synchronized fluid flow features, and extracting the lag term features and the sliding average in the time series features of the timestamp synchronized fluid flow features, and calibrating them as fluid simulation condition features; Introducing fluid dynamics software, importing the fluid simulation condition characteristics into the fluid dynamics software for fluid simulation processing, so that the fluid dynamics software can simulate the real-time flow characteristics of the fluid in the target flow channel; Run the fluid dynamics software, introduce the elastic network regression algorithm into the fluid dynamics software, and build a flow prediction model for the fluid in the target flow channel.

4. The method for intelligently controlling fluid flow based on environmental perception according to claim 3 is characterized in that: The elastic network regression algorithm is introduced into the fluid dynamics software to construct a flow prediction model for the fluid in the target flow channel, specifically: In combination with the elastic network regression algorithm, the fluid dynamics software is run to obtain the fluid simulation information in the fluid dynamics software, and the initial regularization parameter and the initial mixing parameter are set according to the fluid simulation information, and the maximum number of iterations and the variation tolerance threshold used in the elastic network regression algorithm are set based on the initial regularization parameter and the initial mixing parameter; Based on the elastic network regression algorithm, the weight coefficients of the fluid simulation information are initialized, and the elements in the weight coefficients are used as variable elements. The optimal solution of all variable elements is evaluated by minimizing the cost function method, and the weight coefficients are iteratively updated by the optimal solution obtained by evaluation. If the change amount of the weight coefficient during iterative update is greater than the change tolerance value, or the number of iterative updates of the weight coefficient is equal to the maximum number of iterations, then the iterative update of the weight coefficient by the optimal solution obtained by evaluation is stopped to obtain the final weight coefficient; The final weight coefficient is applied to the fluid dynamics software to construct a mathematical model for predicting the flow rate of the fluid in the target flow channel, which is calibrated as a fluid flow prediction model.

5. The method for intelligently controlling fluid flow based on environmental perception according to claim 1, characterized in that: The method of predicting the change of fluid flow in the target flow channel by the fluid flow prediction model, generating a fluid flow control scheme based on the change of fluid flow in the target flow channel, and controlling and outputting the fluid flow control scheme is specifically as follows: Preset a prediction timestamp, import the real-time flow characteristics of the fluid into the fluid flow prediction model, and control the operation of the fluid flow prediction model to generate the real-time flow of the fluid at different target flow monitoring positions after the prediction timestamp; Combined with the maximum tolerable flow rate at different target flow monitoring positions, the real-time flow rate of the fluid at different target flow monitoring positions after the prediction timestamp is analyzed. If there is no target flow monitoring position where the real-time flow rate of the fluid after the prediction timestamp is greater than the corresponding maximum tolerable flow rate, there is no need to regulate the fluid flow rate of the target flow channel. If there is a target flow monitoring position where the real-time flow of the fluid after the prediction timestamp is greater than the corresponding maximum tolerable flow, the corresponding target flow monitoring position is marked as the abnormal position of fluid flow prediction; Acquire a three-dimensional map of the flow channel, mark a fluid flow prediction abnormal position in the three-dimensional map of the flow channel, and construct a topological map of the three-dimensional map of the flow channel with the fluid flow prediction abnormal position marked, and mark it as a target flow channel topological map; In the target flow channel topology map, abnormal locations predicted for fluid flow are marked as abnormal topology nodes, and associated regions of different abnormal topology nodes in the target flow channel are extracted from the target flow channel topology map and marked as abnormal associated flow channel sub-regions; Determine whether there is a fluid control valve or a fluid output pump in the abnormal associated flow channel sub-area, and if so, generate a first fluid flow control scheme; Wherein, the first fluid flow control scheme is that if there is a fluid control valve in the abnormal associated flow channel sub-area, the fluid control valve is controlled in the abnormal associated flow channel sub-area to automatically adjust the opening and closing until the real-time fluid flow at the abnormal position of the fluid flow prediction after the prediction timestamp is not greater than the corresponding maximum tolerable flow; If there is a fluid output pump in the abnormal associated flow channel sub-area, the fluid output pump is controlled in the abnormal associated flow channel sub-area to automatically adjust the fluid output until the real-time fluid flow rate of the fluid flow prediction abnormal position after the prediction timestamp is not greater than the corresponding maximum tolerable flow rate; If there is no fluid control valve or fluid output pump in the abnormally associated flow channel sub-region, an environmental factor association impact analysis is performed in the abnormally associated flow channel sub-region, and a circulation flow control plan is generated based on the environmental factor association impact analysis result.

6. The method for intelligently controlling fluid flow based on environmental perception according to claim 5 is characterized in that: The environmental factor correlation impact analysis is performed in the abnormally correlated flow channel sub-area, and a flow control scheme is generated based on the environmental factor correlation impact analysis result, specifically: If there is no fluid control valve or fluid output pump in the abnormal associated flow channel sub-region, the surrounding environmental factors of the abnormal associated flow channel sub-region are obtained, and the surrounding environmental factors of the abnormal associated flow channel sub-region are integrated into the target flow channel topology map for topological update. After the topology is updated in the target flow channel topology map, the occupancy rate of the surrounding environment factors of the abnormally associated flow channel sub-region in the abnormally associated flow channel sub-region is calculated in the target flow channel topology map, and the standard occupancy rate is preset; If the occupancy rate of the surrounding environmental factors of the abnormally associated flow channel sub-region in the abnormally associated flow channel sub-region is not less than the standard occupancy rate, the corresponding abnormally associated flow channel sub-region is marked as a type of abnormally associated flow channel sub-region, and the fluid flow prediction abnormal position corresponding to the type of abnormally associated flow channel sub-region is obtained and marked as a type of fluid flow prediction abnormal position; A big data network is introduced to retrieve and output a scheme for controlling the surrounding environmental factors in a type of fluid flow prediction abnormal position so that the occupancy rate of the surrounding environmental factors in the abnormal associated flow channel sub-region is less than the standard occupancy rate, and the corresponding scheme is calibrated as the second fluid flow control scheme; If there is an abnormally associated flow channel sub-area whose occupancy rate of the surrounding environmental factors in the abnormally associated flow channel sub-area is not less than the standard occupancy rate, but the real-time fluid flow rate of the fluid flow prediction abnormal position corresponding to the abnormally associated flow channel sub-area after the prediction timestamp is still greater than the corresponding maximum tolerable flow rate, then the corresponding fluid flow prediction abnormal position is calibrated as a Class II fluid flow prediction abnormal position, and a method for reducing the fluid flow rate at the Class II fluid flow prediction abnormal position is retrieved and output in the big data network, and at the same time, the method for reducing the fluid flow rate at the Class II fluid flow prediction abnormal position is calibrated as the third fluid flow control scheme.

7. Fluid flow intelligent control system based on environmental perception, characterized in that: The fluid flow intelligent control system includes a memory and a processor, wherein the memory stores a fluid flow intelligent control method program. When the fluid flow intelligent control method program is executed by the processor, the steps of the fluid flow intelligent control method based on environmental perception as described in any one of claims 1-6 are implemented.

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