Water supply system control method based on flow self-distribution

Abstract

The invention discloses a water supply system control method based on flow self-distribution, and particularly relates to the technical field of water supply systems, and the method comprises the steps: building a probability density function of monitoring features through historical records, determining the actual condition of the current monitoring features through a sensor, determining water supply probability information based on a Bayesian formula, and calculating the probability density function of the monitoring features; adjusting the weight of the monitoring feature through the cross entropy, monitoring the steam flow of each position in the boiler pipeline, determining the influence of the steam flow change of each position on the water supply decision of the water supply system based on historical data, and determining the water supply deviation information through the logistic regression model. Through comprehensive analysis of water supply probability information and water supply deviation information, the influence degree of each time point on a water supply system decision is quantified, through comprehensive consideration of the steam flow, the water supply flow and the steam drum water level, a neural network model is constructed, regulation and control of the water supply system are further optimized, the water supply rate can be accurately adjusted, and the water supply efficiency is improved. And the operation efficiency and safety of the boiler are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply systems, and more specifically, to a water supply system control method based on flow self-distribution. Background Art

[0002] With the widespread application of industrial boilers and steam generators, precise regulation of the water supply system is crucial to improving energy efficiency and ensuring safe operation of the boiler. The water supply system of the boiler is an important part of the boiler operation. Its main function is to provide the water required by the steam generator to ensure efficient and stable operation of the boiler. However, in actual operation, due to the influence of multiple factors such as load fluctuations, changes in environmental factors and differences in equipment status, the steam demand and water supply of the boiler fluctuate significantly. Traditional water supply control methods generally rely on fixed control strategies or simple PID control, but these methods are difficult to fully cope with the dynamic changes of the boiler system under complex working conditions, and it is not easy to assist in adjusting the water supply rate based on the historical data and actual usage of the water supply system.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a water supply system control method based on flow self-distribution to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A water supply system control method based on flow self-distribution specifically comprises the following steps:

[0007] S1: Construct the probability density function of the monitoring feature by using historical records, determine the actual situation of the current monitoring feature through sensors, determine the water supply probability information based on the Bayesian formula, and adjust the weight of the monitoring feature through cross entropy;

[0008] S2: By monitoring the steam flow at various locations in the boiler pipeline, the impact of the steam flow change at each location on the water supply decision of the water supply system is determined based on historical data, and the water supply deviation information is determined through a logistic regression model;

[0009] S3: Through comprehensive analysis of water supply probability information and water supply deviation information, a water supply assessment model is constructed to determine the water supply assessment coefficient at each time point and quantify the impact of each time point on the water supply system decision;

[0010] S4: By comprehensively considering the steam flow, feed water flow and drum water level, and combining the feed water assessment coefficient and the actual steam demand, a neural network model is constructed to further optimize the regulation of the feed water system.

[0011] In a preferred embodiment, the probability density function of the monitoring feature is constructed using historical records, including:

[0012] According to the specific demand for steam, determine the historical data of the monitoring characteristics, where the monitoring characteristics include steam flow, feed water flow and drum water level, determine the steam demand deviation in the current unit time period, and mark the steam demand deviation in the current unit time period as: XQ pc , among which, XQ pc =XXQ-YXQ, XXQ is the actual steam demand per unit time period, YXQ is the original steam demand per unit time period;

[0013] Based on the historical data of monitoring characteristics under steam demand deviation, the probability density function of each monitoring characteristic under feedwater instruction is calculated by kernel density estimation, and the probability density function of each monitoring characteristic under feedwater instruction is marked as: P(ZQ), P(GS), P(SW), P(ZQ) is the probability density function of steam flow rate under feedwater instruction at the current steam demand deviation, P(GS) is the probability density function of feedwater flow rate under feedwater instruction at the current steam demand deviation, P(ZQ) is the probability density function of drum water level under feedwater instruction at the current steam demand deviation, where, i is the index of each monitoring feature sample data in a unit time period, i = 1, 2, 3, ..., N, N is a positive integer, h zq 、h GS 、h SW is the smoothing parameter, K is the kernel function;

[0014] Based on the historical data of monitoring characteristics under steam demand deviation, the probability density function of each monitoring characteristic under no feedwater instruction is calculated by kernel density estimation, and the probability density function of each monitoring characteristic under no feedwater instruction is marked as: Q(ZQ), Q(GS), Q(SW), among which, Q(ZQ) is the probability density function of steam flow at the current steam demand deviation under no feedwater instruction, Q(GS) is the probability density function of feedwater flow at the current steam demand deviation under no feedwater instruction, and Q(SW) is the probability density function of drum water level at the current steam demand deviation under no feedwater instruction.

[0015] In a preferred embodiment, the water supply probability information is determined based on the Bayesian formula, and the weight of the monitoring feature is adjusted by cross entropy, including:

[0016] The water supply probability information is represented by the water supply probability adjustment coefficient;

[0017] The actual value of the monitoring feature of the current unit time period is brought into the probability density function, the prior probability and posterior probability of the monitoring feature at each time point in the current time period are determined, the posterior probability of water supply is calculated, and the posterior probability of water supply is marked as:

[0018]

[0019] Among them, α1, α2, and α3 are the weights of steam flow, feed water flow, and drum water level, respectively. XY is the prior probability of water supply, P gs is the posterior probability of water supply;

[0020] Calculate the posterior probability of no water supply, the calculation formula is:

[0021]

[0022] Among them, Q XY is the prior probability of no water supply, P bgs is the posterior probability of not giving water;

[0023] Among them, a weight is assigned to each monitoring feature, the prediction probability of the model is calculated through historical data, and the weight of each monitoring feature is adjusted by minimizing the cross entropy loss using gradient descent;

[0024] Calculate the water supply probability adjustment coefficient, the calculation formula is: Among them, TZ gs is the water supply probability adjustment coefficient.

[0025] In a preferred embodiment, determining water supply deviation information includes:

[0026] The water supply deviation information is expressed by the water supply influence coefficient;

[0027] The logic for obtaining the feedwater influence coefficient is as follows: determine the steam flow rate at each position of the boiler pipeline, and determine the degree of influence of the steam flow rate at each position on the feedwater system based on historical data, and determine the proportion of the influence of the steam flow rate at each position on the feedwater system in the historical data;

[0028] Calculate the water supply influence coefficient, the calculation formula is: Among them, YX gs is the water supply influence coefficient, β1, β2, ..., β j is the influence of steam flow at each position on the water supply of the water supply system, χ1, χ2, ..., χ j is the proportion of the impact of the steam flow rate at each location in the historical data on the water supply of the water supply system, LL1, LL2, ..., LL jis the steam flow rate at each location at each time point in a unit time period, and e is a natural number.

[0029] In a preferred embodiment, constructing a water supply assessment model includes:

[0030] The water supply probability adjustment coefficient and water supply influence coefficient at each time point in the unit time period are weighted and calculated to construct a water supply assessment model and generate a water supply assessment coefficient. The calculation formula of the water supply assessment coefficient is: Among them, pg t is the water supply assessment coefficient at each time point in the unit time period, λ1 and λ2 are the proportional coefficients of the water supply probability adjustment coefficient and the water supply influence coefficient, respectively, and λ1 and λ2 are both greater than 0.

[0031] In a preferred embodiment, constructing a neural network model includes:

[0032] The feedwater assessment coefficient at each time point, the steam flow at each time point, the feedwater flow at each time point, the drum water level at each time point and the steam demand deviation in the current unit time period are used as the input of the neural network model, and the feedwater warning coefficient is used as the output of the neural network model;

[0033] The input of the input layer is time series data, and the dimension of the input data is 5*k, where k is the number of each time point. The time series dependency in the time series data is captured through the RNN layer, and the features extracted by the RNN layer are mapped to the output of the water supply warning coefficient. The output layer is the water supply warning coefficient;

[0034] Historical data is used for model training. Each input sample corresponds to a water supply warning coefficient. The output layer is the target value, and the mean square error is used as the loss function.

[0035] Technical effects and advantages of the present invention:

[0036] The present invention constructs a probability density function and applies Bayesian inference, combines characteristics such as steam flow and feed water flow, quantifies the impact of each time point on the feed water decision, uses logistic regression to analyze the deviation impact of steam flow changes on the feed water system, and then optimizes the control of the feed water system. By applying the neural network model to the feed water decision of the boiler system, it can provide a more accurate and intelligent basis for adjusting the feed water rate based on capturing complex temporal dependencies and multi-dimensional input characteristics. The present invention helps to accurately adjust the feed water rate and improve the operating efficiency and safety of the boiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0038] Figure 1The present invention is a flow chart of a water supply system control method based on flow self-distribution. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1

[0041] like Figure 1 The present invention is a flow chart of a water supply system control method based on flow self-distribution, which specifically includes the following steps:

[0042] S1: Construct the probability density function of the monitoring feature by using historical records, determine the actual situation of the current monitoring feature through sensors, determine the water supply probability information based on the Bayesian formula, and adjust the weight of the monitoring feature through cross entropy;

[0043] S2: By monitoring the steam flow at various locations in the boiler pipeline, the impact of the steam flow change at each location on the water supply decision of the water supply system is determined based on historical data, and the water supply deviation information is determined through a logistic regression model;

[0044] S3: Through comprehensive analysis of water supply probability information and water supply deviation information, a water supply assessment model is constructed to determine the water supply assessment coefficient at each time point and quantify the impact of each time point on the water supply system decision;

[0045] S4: By comprehensively considering the steam flow, feed water flow and drum water level, and combining the feed water assessment coefficient and the actual steam demand, a neural network model is constructed to further optimize the regulation of the feed water system.

[0046] In this embodiment, the feed water system adopts a three-impulse control strategy, which comprehensively considers the steam flow rate, feed water flow rate and drum water level to achieve accurate control of the feed water. Through real-time monitoring and adjustment, the system can automatically allocate the water flow rate according to the change of steam load to ensure the safety and efficiency of boiler operation, including:

[0047] Steam flow measurement: Real-time monitoring of the steam output flow of the boiler as a feedforward signal to provide a reference for feedwater control. When the steam demand changes, such as load increase (increase in equipment steam consumption) or load decrease (decrease in equipment steam consumption), steam flow measurement can quickly capture these changes and provide real-time data for the control system;

[0048] Feedwater flow measurement: monitor the feedwater flow entering the boiler to ensure that it matches the steam flow. The feedwater flow matches the steam output flow of the boiler. An increase in steam flow means that the amount of evaporated water also increases, so the feedwater flow needs to be increased accordingly. Conversely, if the steam demand decreases, the feedwater flow should also be reduced.

[0049] Drum water level measurement: Continuously monitor the water level in the drum to prevent the water level from being too high or too low, which will affect the safe operation of the boiler. Accurate water level control helps maintain stable steam quality. Too low a water level will cause the boiler to produce overheated steam, while too high a water level will cause high-humidity steam, which will affect the subsequent steam utilization efficiency and the normal operation of the equipment.

[0050] The probability density function of the monitoring feature is constructed by using historical records, and the actual situation of the current monitoring feature is determined by the sensor. The water supply probability information is determined by the Bayesian formula, and the weight of the monitoring feature is adjusted by the cross entropy. The water supply probability information is represented by the water supply probability adjustment coefficient. The advantages of the water supply probability adjustment coefficient are:

[0051] By using historical records to construct the probability density function of monitoring features, the long-term operating characteristics of the boiler system can be captured, so that the system can dynamically adjust the weight of monitoring features according to historical data. In particular, when making water supply decisions, the water supply probability adjustment coefficient can be adjusted in real time according to the relationship between current sensor data and historical data.

[0052] The feedwater probability information is calculated through the Bayesian formula, and the parameter weights are adjusted in combination with the cross entropy. The feedwater probability adjustment coefficient can optimize the boiler feedwater strategy under different operating conditions. The cross entropy can quantify the accuracy of the prediction and gradually optimize the boiler control logic by minimizing the difference between the prediction and the actual data. The feedwater probability adjustment coefficient, as a feedback mechanism, can help the system identify which control parameters have a greater impact on the feedwater decision and adjust their weights, thereby improving the control effect.

[0053] In boiler control systems, overfitting and underfitting are common problems. Overfitting may cause the control strategy to over-adapt to specific data and ignore the impact of other important variables, while underfitting means that the model cannot fully capture the potential relationships in the boiler system. Through Bayesian updating and cross-entropy optimization, the feedwater probability adjustment coefficient can find a balance between historical data and real-time data, avoid overfitting and underfitting, and improve the generalization ability of the model.

[0054] The acquisition logic of the feedwater probability adjustment coefficient is as follows: according to the specific demand for steam, determine the historical data of the monitoring characteristics, where the monitoring characteristics include steam flow, feedwater flow and drum water level, determine the steam demand deviation in the current unit time period, and mark the steam demand deviation in the current unit time period as: XQpc , among which, XQ pc =XXQ-YXQ, XXQ is the actual steam demand per unit time period, YXQ is the original steam demand per unit time period;

[0055] It should be noted that the unit time period refers to a fixed time interval used to measure and record key parameters in the system. The actual steam demand per unit time period refers to the required steam consumption calculated based on system data within a specific unit time period. The original steam demand per unit time period refers to the steam consumption when the steam demand remains unchanged within the unit time period.

[0056] Based on the historical data of monitoring characteristics under steam demand deviation, the probability density function of each monitoring characteristic under feedwater instruction is calculated by kernel density estimation, and the probability density function of each monitoring characteristic under feedwater instruction is marked as: P(ZQ), P(GS), P(SW), P(ZQ) is the probability density function of steam flow rate under feedwater instruction at the current steam demand deviation, P(GS) is the probability density function of feedwater flow rate under feedwater instruction at the current steam demand deviation, P(ZQ) is the probability density function of drum water level under feedwater instruction at the current steam demand deviation, where, i is the index of each monitoring feature sample data in a unit time period, i = 1, 2, 3, ..., N, N is a positive integer, h zq 、h GS 、h SW is the smoothing parameter, K is the kernel function;

[0057] Based on the historical data of monitoring characteristics under steam demand deviation, the probability density function of each monitoring characteristic under no feedwater instruction is calculated by kernel density estimation, and the probability density function of each monitoring characteristic under no feedwater instruction is marked as: Q(ZQ), Q(GS), Q(SW), where Q(ZQ) is the probability density function of the steam flow rate at the current steam demand deviation under no feedwater instruction, Q(GS) is the probability density function of the feedwater flow rate at the current steam demand deviation under no feedwater instruction, and Q(SW) is the probability density function of the drum water level at the current steam demand deviation under no feedwater instruction;

[0058] The actual value of the monitoring feature of the current unit time period is brought into the probability density function, the prior probability and posterior probability of the monitoring feature at each time point in the current time period are determined, the posterior probability of water supply is calculated, and the posterior probability of water supply is marked as:

[0059]

[0060] Among them, α1, α2, and α3 are the weights of steam flow, feed water flow, and drum water level, respectively. XYis the prior probability of water supply, P gs is the posterior probability of water supply;

[0061] It should be noted that the weight determines the impact of each monitoring feature on the final decision. The sum of the weight values ​​is not necessarily required to be 1, but their relative size will affect the contribution of each feature to the posterior probability. If a feature is more important, give it a larger weight; if a feature is relatively unimportant, give it a smaller weight. The prior probability of water supply is determined by the historical data of the monitoring feature under steam demand deviation.

[0062] Calculate the posterior probability of no water supply, the calculation formula is:

[0063]

[0064] Among them, Q XY is the prior probability of no water supply, P bgs is the posterior probability of not giving water;

[0065] Among them, a weight is assigned to each monitoring feature, the prediction probability of the model is calculated through historical data, and the weight of each monitoring feature is adjusted by minimizing the cross entropy loss using gradient descent;

[0066] Calculate the water supply probability adjustment coefficient, the calculation formula is: Among them, TZ gs is the water supply probability adjustment coefficient.

[0067] The feedwater deviation information is determined by the feedwater influence coefficient. According to the actual steam demand, the steam flow rate at each position of the boiler pipeline is monitored to determine the deviation between the actual and required steam. The feedwater influence coefficient is determined by the logistic regression model, which includes:

[0068] Collect historical data, obtain steam flow at various locations through sensors, and determine whether water supply is carried out in each time period or the specific value of water supply flow. Usually, these data are based on the steam demand of the boiler to decide whether to start water supply;

[0069] The change of steam flow at each location is calculated based on historical data. In order to determine the impact of the change of steam flow on the feedwater decision, a regression model (such as linear regression or logistic regression) is used to establish the relationship between the change of steam flow and the feedwater decision;

[0070] Based on the results of regression analysis, the impact of steam flow changes at each location on the feedwater decision is calculated.

[0071] It should be noted that the steam flow rates at various locations may not be consistent, mainly because the flow rate in the steam pipe will be affected by the pipe layout, pressure loss, flow resistance and other environmental factors. Therefore, the steam flow rates at various locations in the water supply system may have different effects on the water supply decision, including the steam flow rate close to the boiler having a greater impact on the water supply system and can be assigned a higher weight. The pipes far away from the boiler may have an increased steam flow resistance and a reduced flow rate, so their impact on the water supply decision may be smaller and can be assigned a lower weight.

[0072] The advantages of water supply influence coefficient are:

[0073] Taking into account the spatial differences in steam flow, by assigning different weights to the steam flow at different pipeline locations, the impact of steam flow changes at each location on water supply decisions can be captured more accurately, avoiding the errors caused by ignoring spatial differences;

[0074] By calculating the proportion of each pipeline location’s influence in historical data, we can know which location has the greatest impact on water supply decisions, and thus better understand and explain the model’s decision-making process;

[0075] The weight value can be adjusted according to different working conditions, so that the model can be adaptively optimized according to actual steam flow demand, pipeline conditions and other factors;

[0076] By analyzing the impact of steam flow rate changes on feed water at each pipe location, the model can more accurately allocate water flow to ensure efficient operation of the boiler, avoid excessive or insufficient feed water, and optimize resource utilization.

[0077] The logic for obtaining the feedwater influence coefficient is as follows: determine the steam flow rate at each position of the boiler pipeline, and determine the degree of influence of the steam flow rate at each position on the feedwater system based on historical data, and determine the proportion of the influence of the steam flow rate at each position on the feedwater system in the historical data;

[0078] Calculate the water supply influence coefficient, the calculation formula is: Among them, YX gs is the water supply influence coefficient, β1, β2, ..., β j is the influence of steam flow at each position on the water supply of the water supply system, χ1, χ2, ..., χ j is the proportion of the impact of the steam flow rate at each location in the historical data on the water supply of the water supply system, LL1, LL2, ..., LL j is the steam flow rate at each location at each time point in a unit time period, and e is a natural number.

[0079] It can be seen from the formula that the larger the water supply influence coefficient is, the more likely it is that the water supply system needs to open the gate opening more quickly to increase the water supply rate.

[0080] The water supply probability adjustment coefficient and water supply influence coefficient at each time point in the unit time period are weighted and calculated to construct a water supply assessment model and generate a water supply assessment coefficient. The calculation formula of the water supply assessment coefficient is: Among them, pg t is the water supply assessment coefficient at each time point in the unit time period, λ1 and λ2 are the proportional coefficients of the water supply probability adjustment coefficient and the water supply influence coefficient, respectively, and λ1 and λ2 are both greater than 0.

[0081] It can be seen from the formula that the larger the water supply assessment coefficient is, the larger the water supply probability adjustment coefficient and the water supply influence coefficient at each time point are, indicating that it is more likely that the water supply adjustment will be required through the water supply system at this time point. Conversely, the smaller the water supply assessment coefficient is, the smaller the water supply probability adjustment coefficient and the water supply influence coefficient at each time point are, indicating that it is more likely that the water supply adjustment will not be required through the water supply system at this time point.

[0082] The feedwater assessment coefficient at each time point, the steam flow rate at each time point, the feedwater flow rate at each time point, the drum water level at each time point and the steam demand deviation in the current unit time period are taken as the input of the neural network model, and the feedwater early warning coefficient is taken as the output of the neural network model. The feedwater early warning coefficient is used to control the quantitative basis of the feedwater rate.

[0083] It should be noted that the input of the input layer in the structure of the neural network is time series data, and the dimension of the input data is 5*k, where k is the number of each time point. The time series dependency in the time series data is captured through the RNN layer, and the features extracted by the RNN layer are mapped to the output of the water supply warning coefficient. The output layer is the water supply warning coefficient;

[0084] Historical data is used for model training. Each input sample corresponds to a water supply warning coefficient. The output layer is the target value. The mean square error is used as the loss function. The water supply warning coefficient indicates the urgency of water supply adjustment under the current state.

[0085] The advantages of water supply early warning coefficient are:

[0086] The RNN layer can effectively process time series data. Through the internal loop structure, the RNN can "remember" the state of the previous time point and pass this information to the current moment, so that the neural network can better capture the time series dependencies between data, identify potential time series patterns in the water supply system, and ensure that the system can predict future water supply needs based on past conditions;

[0087] Through the training of the neural network model, the network can automatically learn the mapping relationship between the input features and the water supply warning coefficient. During the training process, by gradually minimizing the mean square error (MSE) loss function, the model can provide a high-precision water supply warning coefficient.

[0088] The neural network can adjust the water supply warning coefficient under different input conditions and can respond dynamically according to various real-time factors such as actual steam demand deviation and flow rate changes, without relying on fixed rules or linear models.

[0089] The feedwater warning coefficient obtained through the neural network can be combined with the PID controller or other control strategies to further optimize the regulation of the feedwater system and fine-tune the feedwater rate to adapt to the dynamic changes of the boiler system.

[0090] The present invention constructs a probability density function and applies Bayesian inference, combines characteristics such as steam flow and feed water flow, quantifies the impact of each time point on the feed water decision, uses logistic regression to analyze the deviation impact of steam flow changes on the feed water system, and then optimizes the control of the feed water system. By applying the neural network model to the feed water decision of the boiler system, it can provide a more accurate and intelligent basis for adjusting the feed water rate based on capturing complex temporal dependencies and multi-dimensional input characteristics. The present invention helps to accurately adjust the feed water rate and improve the operating efficiency and safety of the boiler.

[0091] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0093] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0094] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0095] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0096] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0097] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A water supply system control method based on flow self-distribution, characterized in that: The specific steps include: S1: Construct the probability density function of the monitoring feature by using historical records, determine the actual situation of the current monitoring feature through sensors, determine the water supply probability information based on the Bayesian formula, and adjust the weight of the monitoring feature through cross entropy; S2: By monitoring the steam flow at various locations in the boiler pipeline, the impact of the steam flow change at each location on the water supply decision of the water supply system is determined based on historical data, and the water supply deviation information is determined through a logistic regression model; S3: Through comprehensive analysis of water supply probability information and water supply deviation information, a water supply assessment model is constructed to determine the water supply assessment coefficient at each time point and quantify the impact of each time point on the water supply system decision; S4: By comprehensively considering the steam flow, feed water flow and drum water level, and combining the feed water assessment coefficient and the actual steam demand, a neural network model is constructed to further optimize the regulation of the feed water system.

2. A water supply system control method based on flow self-distribution according to claim 1, characterized in that: Use historical records to construct probability density functions of monitoring features, including: According to the specific demand for steam, determine the historical data of the monitoring characteristics, where the monitoring characteristics include steam flow, feed water flow and drum water level, determine the steam demand deviation in the current unit time period, and mark the steam demand deviation in the current unit time period as: XQ pc , among which, XQ pc =XXQ-YXQ, XXQ is the actual steam demand per unit time period, YXQ is the original steam demand per unit time period; Based on the historical data of monitoring characteristics under steam demand deviation, the probability density function of each monitoring characteristic under feedwater instruction is calculated by kernel density estimation, and the probability density function of each monitoring characteristic under feedwater instruction is marked as: P(ZQ), P(GS), P(SW), P(ZQ) is the probability density function of steam flow rate under feedwater instruction at the current steam demand deviation, P(GS) is the probability density function of feedwater flow rate under feedwater instruction at the current steam demand deviation, P(ZQ) is the probability density function of drum water level under feedwater instruction at the current steam demand deviation, where, i is the index of each monitoring feature sample data in a unit time period, i = 1, 2, 3, ..., N, N is a positive integer, h zq 、h GS 、h SW is the smoothing parameter, K is the kernel function; Based on the historical data of monitoring characteristics under steam demand deviation, the probability density function of each monitoring characteristic under no feedwater instruction is calculated by kernel density estimation, and the probability density function of each monitoring characteristic under no feedwater instruction is marked as: Q(ZQ), Q(GS), Q(SW), among which, Q(ZQ) is the probability density function of steam flow at the current steam demand deviation under no feedwater instruction, Q(GS) is the probability density function of feedwater flow at the current steam demand deviation under no feedwater instruction, and Q(SW) is the probability density function of drum water level at the current steam demand deviation under no feedwater instruction.

3. A water supply system control method based on flow self-distribution according to claim 2, characterized in that ,The water supply probability information is determined based on the Bayesian formula, and the weights of the monitoring features are adjusted through cross entropy, including: The water supply probability information is represented by the water supply probability adjustment coefficient; The actual value of the monitoring feature of the current unit time period is brought into the probability density function, the prior probability and posterior probability of the monitoring feature at each time point in the current time period are determined, the posterior probability of water supply is calculated, and the posterior probability of water supply is marked as: Among them, α1, α2, and α3 are the weights of steam flow, feed water flow, and drum water level, respectively. XY is the prior probability of water supply, P gs is the posterior probability of water supply; Calculate the posterior probability of no water supply, the calculation formula is: Among them, Q XY is the prior probability of no water supply, P bgs is the posterior probability of not giving water; Among them, a weight is assigned to each monitoring feature, the prediction probability of the model is calculated through historical data, and the weight of each monitoring feature is adjusted by minimizing the cross entropy loss using gradient descent; Calculate the water supply probability adjustment coefficient, the calculation formula is: Among them, TZ gs is the water supply probability adjustment coefficient.

4. A water supply system control method based on flow self-distribution according to claim 3, characterized in that: Determine feedwater deviation information, including: The water supply deviation information is expressed by the water supply influence coefficient; The logic for obtaining the feedwater influence coefficient is as follows: determine the steam flow rate at each position of the boiler pipeline, and determine the degree of influence of the steam flow rate at each position on the feedwater system based on historical data, and determine the proportion of the influence of the steam flow rate at each position on the feedwater system in the historical data; Calculate the water supply influence coefficient, the calculation formula is: Among them, YX gs is the water supply influence coefficient, β1, β2, ..., β j is the influence of steam flow at each position on the water supply of the water supply system, χ1, χ2, ..., χ j is the proportion of the impact of the steam flow rate at each location in the historical data on the water supply of the water supply system, LL1, LL2, ..., LL j is the steam flow rate at each location at each time point in a unit time period, and e is a natural number.

5. A water supply system control method based on flow self-distribution according to claim 4, characterized in that: Construct a water supply assessment model, including: The water supply probability adjustment coefficient and water supply influence coefficient at each time point in the unit time period are weighted and calculated to construct a water supply assessment model and generate a water supply assessment coefficient. The calculation formula of the water supply assessment coefficient is: Among them, pg t is the water supply assessment coefficient at each time point in the unit time period, λ1 and λ2 are the proportional coefficients of the water supply probability adjustment coefficient and the water supply influence coefficient, respectively, and λ1 and λ2 are both greater than 0.

6. A water supply system control method based on flow self-distribution according to claim 5, characterized in that: Build a neural network model, including: The feedwater assessment coefficient at each time point, the steam flow at each time point, the feedwater flow at each time point, the drum water level at each time point and the steam demand deviation in the current unit time period are used as the input of the neural network model, and the feedwater warning coefficient is used as the output of the neural network model; The input of the input layer is time series data, and the dimension of the input data is 5*k, where k is the number of each time point. The time series dependency in the time series data is captured through the RNN layer, and the features extracted by the RNN layer are mapped to the output of the water supply warning coefficient. The output layer is the water supply warning coefficient; Historical data is used for model training. Each input sample corresponds to a water supply warning coefficient. The output layer is the target value, and the mean square error is used as the loss function.

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