Intelligent design and evaluation method and system for water network engineering

Abstract

The application discloses a water network engineering intelligent design and evaluation method and system, and the method comprises the following steps: S1, acquiring data of a water network engineering reconstruction node; S2, constructing a node water supply feature evaluation model according to the data of the water network engineering reconstruction node, and determining a reconstruction direction; S3, constructing and selecting a suitable reconstruction scheme according to the node water supply feature evaluation model, and calculating relevant parameters and control conditions required by the reconstruction scheme to obtain a reconstruction model; and S4, constructing a control model related to the reconstruction model, and realizing intelligent reconstruction of the water network engineering. The application provides feature evaluation of a water supply network engineering, forms a reconstruction benefit evaluation mechanism of the water supply network engineering, and under the working guidance of the evaluation mechanism, a matched control system is constructed, so that not only intelligent operation of the water network system is realized, but also the goal of intensive utilization of water resources and efficient development of water energy is achieved.

Description

Technical Field

[0001] This invention relates to the fields of smart water conservancy, water network engineering, and intelligent control, specifically to intelligent design, evaluation methods, and systems for water network engineering. Background Technology

[0002] Complex water supply projects are generally defined as artificial water conservancy projects built upon natural river and lake systems that combine water diversion, distribution, usage, and stable operation, serving multiple stakeholders. These projects include almost all canal system structures such as mountain reservoirs, plain reservoirs, open water conveyance channels, hydroelectric power stations, tunnels, aqueducts, inverted siphons, river dams, pumping stations, and flood control dikes. If the guiding principles of safe water conveyance, accurate monitoring, and scientific scheduling are implemented, water supply projects can operate efficiently, economically, and scientifically.

[0003] Traditional water supply network engineering can meet the most basic needs of water diversion from the supply side, water intake from canal systems, and water distribution to the receiving side through several branch canals. However, due to the simplistic design of hardware, equipment, devices, and structures, and the fact that the control and management system is still equipped according to traditional water supply processes, it cannot perform real-time automated scheduling or operate flexibly, resulting in insufficient system stability of the entire water supply project. This not only leads to the waste of water resources and energy but also increases water supply costs and fails to meet the needs for rational development and improved utilization efficiency of water resources. Especially when it involves water situation management, control and regulation, and engineering safety for multiple water supply targets, the randomness, multi-objective nature, and real-time nature of water demand changes further exacerbate the difficulty of achieving reliable operation of complex water supply systems.

[0004] Existing control decision systems have revealed a series of problems, such as: inaccurate current situation identification, limited model parameter calculation and insufficient integration of business function systems with actual models in control, lack of buffer space in channel control systems, accumulation of deviations and errors in the control process, insufficient coordination between different working conditions, complex mutual constraints between gate control and channel hydrodynamic processes, significant changes in flow and water level caused by tailwater downstream of generator units, and control effects that do not meet the requirements of timeliness and precision.

[0005] In summary, current work on the joint control of multi-point gates in channel systems is limited in areas such as device design, structural layout, state measurement and identification, control mechanisms and drives, digital twin applications, and refined decision-making coordination. To achieve scientific water resource allocation, it is necessary to construct a zoned, hierarchical, and balanced distributed multi-point linkage control system, improving the interconnected layout of regional water supply, taking into account the needs of water use, irrigation, water diversion, and ecology, and further developing and utilizing hydropower. However, the following difficulties remain:

[0006] (1) Existing water supply network projects lack standards and guidelines for installation, operation, and maintenance. In particular, there is a lack of methods for selecting and designing equipment in terms of system architecture. Inaccurate selection of facilities and non-standard installation make it difficult to lay out and construct the proposed engineering measures. Not only is it difficult to achieve the "compact" structural requirements and to quickly and shortly stabilize water levels, but it is also difficult to integrate with natural rivers to form a natural-social dual integration, resulting in insufficient control and practical applicability. Even in the process of intelligent transformation, the lack of functional demand-driven judgment and deterministic setting of control effects based on actual needs in the evaluation mechanism of water supply network projects leads to the lack of accurate positioning and tone in the construction of regional smart water networks, making it difficult to lay out and construct the proposed engineering measures, resulting in a lack of practical methods and paths.

[0007] (2) There are difficulties in the selection and design of equipment components for water supply network projects. Traditional hardware devices are not only unable to cope with the surge waves generated by the opening and closing of gates and the roughness of channels, but also lack the necessary hydraulic characteristics and flow capacity. Furthermore, they lack the design of risk backup components and secondary regulation and storage equipment, all of which have a negative impact on control. On the other hand, from the perspective of intensive water resource utilization, there is a lack of effective measurement and regulation mechanism design methods, making it difficult to accurately measure and control instability. Moreover, the design methods of traditional open channel hydropower facilities hinder water supply security and do not meet the expectation of efficient use of hydropower.

[0008] (3) The systematic layout of water supply network projects is difficult, and it is hard to form a connection in terms of physical structure. Especially for cases where the entire line adopts gravity water conveyance and there are no external power facilities, the hydraulic process of the connection section is more complex. In particular, the imbalance of inflow and outflow can bring a lot of unpredictability to the operation and control of the system, which puts forward higher requirements for the control process. In addition, the coupling effect of gravity water supply and hydrodynamic action on the overall operation of the system must be considered during the operation and control process. It is easy to cause flooding accidents due to improper flow coordination between adjacent sections. In addition, traditional methods are also difficult to deal with the negative safety effects of different terrain and geological conditions during the operation period.

[0009] (4) Because the operation of the water supply network system involves many aspects such as hydrology and meteorology, water conditions and water regulation, control and regulation, and the safety of water conveyance structures, and has the characteristics of randomness, multi-objectives, real-time nature, and being affected by many uncertain factors, the current simple structure does not support the complex objectives of water supply-power generation-water diversion, nor can it adapt to water level fluctuations under dynamic conditions. It is impossible to achieve comprehensive control of water flow within the canal system, including main canals and branch canals. Furthermore, the lack of regulation component design and insufficient buffer space not only fail to suppress the degree of water flow fluctuation, resulting in losses in artificial canal lining, but also cause insufficient water supply security in the water-receiving area.

[0010] (5) Traditional monitoring devices and methods, due to inaccurate selection and layout of monitoring facilities, lack sufficient understanding and real-time acquisition of the control objects and their relationships in the water supply network, hindering the coordinated implementation of unsteady-state control. On the one hand, the fluctuating water level under unsteady-state control is difficult to measure, thus making it impossible to accurately measure the channel conditions, resulting in insufficient real-time acquisition of control objects and poor monitoring and evaluation, making it difficult to accurately evaluate and analyze the dynamic system, leading to an inability to accurately judge and measure the channel conditions. On the other hand, existing acquisition mechanisms also lead to insufficient data availability, with both "too much" and "too little" control support data appearing simultaneously. Among them, daily data redundancy itself has insufficient support for control, and also causes long calculation time and low decision-making efficiency. Once there are significant differences in characteristics, it will also cause inconsistent and non-convergent decision-making results, greatly hindering control decision-making. In some situations requiring emergency response, there is often a lack of data, causing decision-making deviations and errors, or even making it impossible to reach a conclusion. The lack of complete data features and panoramic stitching restricts the basic realization of intelligent control decision-making.

[0011] (6) The automation and overall coordination of control devices, methods, and systems are insufficient. Traditional water supply network devices lack controllability and execution, especially those directly connected to the receiving area via the main canal, making flow regulation extremely difficult. Furthermore, the control algorithm fails to address the complex synergistic effects between devices, particularly the mutual constraints between gates during opening and closing, which interfere with control decisions. The control process lacks an immediate correction mechanism for deviations; control errors accumulate and superimpose during joint gate control, continuously affecting the unsteady-state system control and causing deviations in steady-state determination. Comprehensive control of water level and flow is difficult to achieve, resulting in large fluctuations, which not only damage artificial channels but can also lead to disasters if control is inadequate. Power station devices used for hydropower development inevitably exacerbate water level and flow fluctuations, and there is a lack of research on how shutdowns and maintenance interrupt water flow, impacting water supply, and what gate opening adjustments should be made under coordinated flow to achieve complementarity. In terms of coordination of control situations, there are also obstacles to collaboration in emergency response and dispatch and in maintaining stable daily operations. The water flow relationship and time delay control within water supply projects have not been properly addressed, the degree of automation is insufficient, and a large amount of manual monitoring is still required.

[0012] (7) The poor control effect caused by the insufficient intelligence of traditional control systems manifests as the inability of control decision-making accuracy and timeliness to support the safe and stable operation of water supply network projects. Mechanism-driven control lacks timeliness in decision-making and is generally time-consuming. It not only fails to keep up with changes in water demand in the water receiving area in a timely manner, but also poses other risks and hidden dangers when facing complex risks due to the untimely generation and response of control schemes. Moreover, the formation process of control decisions lacks scientific verification and cannot predict the current and post-control trends. It lacks directional control for the characteristics of complex hydraulic fluctuations, strong coupling, and large time delays in water supply network projects. Furthermore, measurement errors, simulation errors, and control errors accumulate and superimpose during the control process, continuously affecting the control of unsteady systems in subsequent operation, causing deviations in the determination of steady state and making it impossible to execute steady-state control. Summary of the Invention

[0013] In view of the above-mentioned shortcomings in the prior art, the intelligent design and evaluation method for water network engineering provided by the present invention solves the problems of lack of evaluation standards in installation, operation and maintenance of existing water supply network engineering, difficulty in the selection and design of device components, difficulty in systematic layout, susceptibility to natural factors, inability to coordinate unsteady-state control, insufficient automation and overall coordination of control devices, methods and systems, and poor control effect.

[0014] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for intelligent design and evaluation of water network engineering is provided, comprising the following steps:

[0015] S1. Obtain data on nodes of the water network engineering renovation project;

[0016] S2. Construct a node water supply characteristic assessment model based on the data of the water network engineering renovation nodes, and determine the renovation direction;

[0017] S3. Based on the node water supply characteristic assessment model, construct and select a suitable renovation scheme, and calculate the relevant parameters and control conditions required for the renovation scheme to obtain the renovation model;

[0018] S4. Construct control models related to the transformation model to realize intelligent transformation of water network projects.

[0019] A system for intelligent design and evaluation of water network projects is provided, including a data acquisition module, a node water supply characteristic evaluation module, a transformation model construction module, and a control module;

[0020] The data acquisition module is used to acquire data from nodes in the water network engineering renovation project;

[0021] The node water supply characteristic assessment module is used to construct a node water supply characteristic assessment model based on the data of the nodes in the water network project renovation, and to determine the direction of renovation.

[0022] The modification model construction module is used to build an evaluation model based on the water supply characteristics of the node, select a suitable modification scheme, and calculate the relevant parameters and control conditions required for the modification scheme to obtain the modification model.

[0023] The control module is used to build control models related to the transformation model, so as to realize the intelligent transformation of water network projects.

[0024] The beneficial effects of this invention are as follows: This invention provides characteristic evaluation of water supply network engineering, forms a mechanism for evaluating the transformation benefits of water supply network engineering, provides subsequent benefit evaluation and transformation direction; provides layout design guidance, completes reasonable standard specifications for overall component design methods; constructs a matching control system to achieve intelligent control; realizes the intensive use of water resources, and also achieves the efficient development of hydropower to realize the overall stable operation of the system, avoiding the loss of artificial engineering under water flow fluctuations. Attached Figure Description

[0025] Figure 1 This is a flowchart of the present invention;

[0026] Figure 2 Schematic diagram of a water supply-power generation parallel linkage control device and system;

[0027] Figure 3 Schematic diagram of a water supply-power generation series linkage control device and system;

[0028] Figure 4 Schematic diagram of the inverted siphon control gate linkage control device and system;

[0029] Figure 5 This is a schematic diagram of the linkage control device and system for water intake and distribution points in a complex water network. Detailed Implementation

[0030] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0031] like Figure 1 As shown, the intelligent design and evaluation method for water network projects includes the following steps:

[0032] S1. Obtain data on nodes of the water network engineering renovation project;

[0033] S2. Construct a node water supply characteristic assessment model based on the data of the water network engineering renovation nodes, and determine the renovation direction;

[0034] S3. Based on the node water supply characteristic assessment model, construct and select a suitable renovation scheme, and calculate the relevant parameters and control conditions required for the renovation scheme to obtain the renovation model;

[0035] S4. Construct control models related to the transformation model to realize intelligent transformation of water network projects.

[0036] Step S2 includes the following steps:

[0037] S2-1. Based on the data of the water network engineering renovation nodes, perform principal component analysis to obtain the data after removing subjective influences;

[0038] S2-2. Perform principal component analysis on the data after removing subjective influences to obtain the dataset;

[0039] S2-3. Construct a node water supply characteristic evaluation model;

[0040] S2-4. Use the node water supply characteristic assessment model to evaluate the data in the dataset and determine the direction of the renovation.

[0041] Step S2-3 includes the following steps:

[0042] S2-3-1, According to the formula:

[0043]

[0044]

[0045]

[0046] Obtain the importance of the current category indicator. ij Quality value C of water supply characteristics ij Scoring process parameters under the influence of weights in, This indicates the maximum value within the range specified for the current category; This represents the minimum value within the range specified for the current category; Let represent the percentage score for each subcategory j of the current category i, with a total of k values; s indicates that the current score is the s-th value among these k values; hg represents a constant indicating that the score is acceptable; α ij β represents the weight obtained by the subjective weighting method. ij This represents the weights obtained using the entropy method;

[0047] S2-3-2, According to the formula:

[0048]

[0049]

[0050] The average water supply performance pjxn and the average difference pjcy are obtained; where N is the number of databases, i.e., the number of categories; S i B represents the number of data points in category i; B represents the performance metric value corresponding to subcategory j in category i; I represents the (j+1)th subcategory in category i.

[0051] S2-3-3. Based on the measured and predicted values ​​of the assessment information categories, construct a confusion matrix including false positive, false negative, and true categories. The true category represents the influencing factors that accurately correspond to the assessment category under the water supply characteristics. The false negative category represents characteristics that objectively have an impact on water supply characteristics and equipment engineering modifications, but are difficult to measure, and can and need to be modified. The false positive category represents negative water supply characteristics that are not necessarily related to equipment engineering modifications, but can be resolved through engineering modifications.

[0052] S2-3-4, According to the formula:

[0053]

[0054]

[0055]

[0056] The harmonic mean value FM of precision and recall is obtained for the purpose of evaluating the effectiveness of the retrofit; where P t P represents the number of actual classes; f N represents the number of false positives; f Represents the number of false negatives; JD represents precision; ZHL represents recall.

[0057] S2-3-5, According to the formula:

[0058] C pred =C o +I WS

[0059] I WS =A pred lg( pred +b)

[0060] Impact assessment of the continuity of the modified nodes I WS Among them, C o C represents the quality value of the water supply characteristics before prediction; pred The quality value representing the predicted water supply characteristics; d pred A represents a time-predicting variable under a persistent effect. pred denoted as the predictive constant for negative characteristics of water supply under the effect of delay; b represents the value parameter for water supply service security; lg represents the logarithm with base 10.

[0061] S2-3-6, According to the formula:

[0062] Fd i =M i ×Jz i

[0063]

[0064]

[0065] Obtain the average water supply service value of the current node. Among them, Fd i M represents the water supply service value of a single node under category i; M represents the overall water supply service value of the current node; i This indicates the area covered by the water supply for the current category; Jz i This represents the value coefficient under category i;

[0066] S2-3-7, According to the formula:

[0067]

[0068] The sensitivity coefficient MX is obtained; where Fz x The initial value of the service before node modification; Fz y This is the predicted adjustment value for the service value after node modification; This represents the performance value coefficient of the predetermined water supply characteristics before the overall transformation of nodes under category i; This represents the performance value coefficient of the predetermined water supply characteristics after the overall transformation of nodes under category i;

[0069] S2-3-8, According to the formula:

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] Normalized water security index The External Environmental Safety Index (EEI) for water supply network projects; among which, L P This indicates that the water supply network project is functioning without faults; S P This indicates that the reservoir's water storage and release functions have failed; Q P This indicates instability in the control of water diversion and regulation in the canal; GP This indicates water distribution imbalance at the water distribution point; index represents the set of unnormalized values ​​for PI; ΔES rm This represents a comparison of the value of water supply characteristic performance under continuous prediction after node modification; A T χ represents the area of ​​the administrative region; Δt represents the predicted duration; χ represents the value conversion factor of the water supply network project; ΔEC represents the change in the form of water supply and its development level before and after the renovation; GFI is the service value index and GSI is the intensity index of water supply and use.

[0076] S2-3-9, According to the formula:

[0077]

[0078]

[0079] P(X′=1|X1=1,X2=1,…,X N =1)=1,P(X′=1|else)=0

[0080] P(X′=1|X1=0,X2=0,…,X N =0)=0,P(X′=1|else)=1

[0081]

[0082]

[0083] The operational safety index (ESI) of the water supply network project was obtained; among them, Let X represent the joint probability density of all random variables of the nodes; N represents the number of nodes in the Bayesian network; π represents the parameter set; d represents the sample in the sample set; X represents a vector of random variables; x represents the corresponding element in the vector; ωi1 represents the weight assignment under each category; ESIi1 (i1 = 1, 2, 3, 4) corresponds to node X. u ;Φ i’ Y represents the engineering safety characterization constant for risk states; i’ The engineering safety characterization constant representing the risk of daily operation; P(X′=1|X1=0,X2=0,…,X N =0) and P(X′=1|X1=1,X2=1,…,X N =1) represents the engineering safety index; u represents the u-th node of the Bayesian network; j' represents the j'-th node; k represents; i1 represents the i1-th node; i' represents the i'-th category;

[0084] S2-3-10, According to the formula:

[0085] WPSI = Il ×PI+I m ×EEI+I n ×ESI

[0086] The Water Supply Security Index (WPSI) was obtained; among which, I l I m I n These represent the weights of PI, EEI, and ESI, respectively.

[0087] S2-3-11, Based on the Water Supply Security Index (WPSI), Sensitivity Coefficient (MX), and the average water service value of the current node. Impact assessment of the continuity of the modified nodes I WS Harmonic mean (FM) of precision and recall, and the importance of the current category metric. ij Quality value C of water supply characteristics ij Scoring process parameters under the influence of weights The average water supply performance pjxn and the average difference pjcy are used to construct a node water supply characteristic evaluation model.

[0088] The specific implementation method of step S3 is as follows:

[0089] S3-1. Construct four renovation schemes and select the optimal renovation scheme based on the node water supply characteristic evaluation model;

[0090] S3-2. Calculate the key parameters and control constraints of each scheme, and select the corresponding key parameters and control constraints according to the optimal modification scheme.

[0091] S3-3. Construct a modification model based on the key parameters and control constraints of the optimal modification scheme.

[0092] The four modification schemes in step S3-1 are as follows:

[0093] A scheme for the renovation of a gate-controlled water conveyance channel for a water supply-power generation parallel linkage control device and system includes: an upstream water network main stream or reservoir, diversion channels, diversion rivers, control gates, generator sets, stilling basins, downstream canals, and diversion gates; the upstream water network main stream or reservoir flows to the diversion channels and diversion rivers; the diversion channels and the upstream water network main stream or reservoir control water flow through diversion gates; the diversion rivers and diversion channels are distributed in parallel; the water flow in the diversion rivers flows to the generator sets through the control gates; the water flow through the generator sets and the water flow in the diversion channels converges into the stilling basin; the water flow in the stilling basin flows to the downstream canals;

[0094] A scheme for the renovation of a gate-controlled water conveyance channel for a water supply-power generation series linkage control device and system includes: reservoirs or main stream canals of a water network, gates, canals, axial flow turbines, overflow weirs, water intake gates, downstream canals, and multiple water intake channels; the gates control the water flow from the reservoir or main stream canal into the canal; the water flows through the canal to the overflow weir via the axial flow turbines; the overflow weir controls the water flow size, allowing excess water to flow to the downstream canal;

[0095] A gate-controlled water pipeline renovation scheme for an inverted siphon control gate linkage control device and system includes a channel, an inlet gate, a forebay, an inverted siphon, an outlet gate, and a downstream channel; the inlet gate controls the speed of water flow from the channel to the forebay; the inverted siphon connects the forebay and the outlet gate; and the outlet gate controls the speed of water flow from the inverted siphon to the downstream channel.

[0096] A renovation scheme for a complex water network water intake and distribution point linkage control device and system includes: the main stream of the water network, a regulating reservoir, multiple control gates, and multiple water outlets corresponding to the control gates; the main stream of the water network and the regulating reservoir are connected through the gates; the regulating reservoir and multiple water outlets are connected through the corresponding control gates.

[0097] The key parameters and control constraints of the four modification schemes in step S3-2 include the following parameters:

[0098] The design parameters for the channel's superelevation, overflow weir parameters, reservoir capacity, stilling basin, regulating basin parameters, long-distance water diversion device parameters, branch inlet and gate parameters, and control constraints; among which:

[0099] The channel's ultra-high design parameters include ultra-high values ​​for flow rate requirements and additional ultra-high values ​​for zero-flow water level requirements;

[0100] The parameters of reservoir capacity, stilling basin, and regulating basin include overflow weir parameters, total head of the main water diversion canal in the water supply network engineering system, water depth required for the stilling basin to achieve its energy dissipation function, water volume variation coefficient of the diversion basin, flow rate that the water diversion point needs to ensure under the condition of guaranteed water supply, regulating basin volume required under the condition of insufficient water inflow, regulating basin storage capacity required under the condition of excessive water inflow, maximum water diversion, regulating basin capacity, regulating basin superelevation, regulating basin length, regulating basin width, reservoir capacity when the reservoir capacity sample conforms to a normal distribution, and reservoir capacity when the reservoir capacity sample does not conform to a normal distribution;

[0101] The overflow weir parameters include a top width of 4m, a 0.6m seepage barrier for the earth-rock weir, a 20m spacing between transverse joints, a safety factor K≥3 calculated using the shear resistance formula, a principal tensile stress of stress deformation less than 0.2MPa, and simultaneous satisfaction of the height and width conditions of the stilling basin.

[0102] The parameters and control rules of long-distance water diversion devices include the energy loss of water per unit weight per unit flow in the siphon pipe, the straight distance of the inverted siphon pipe, the layout elevation of the pipe at a certain point, the control rules of the total head of the pressurized pipe, the pressure line, the basic equation of the inverted siphon pipe, the coupling conditions of the pipe and the sluice gate under unsteady flow discrete processing results, the turbulence generation term and the turbulence kinetic energy generation term of the average value of the steps.

[0103] The parameters of the water diversion point and gate include the gate flow rate, total flow rate, water diversion flow rate of the water diversion point, sensitivity index of the water diversion point, hydraulic sensitivity index of the gate upstream and downstream, and flow rate of the main water diversion channel.

[0104] The control constraints include the basic equations for unsteady flow calculation, the continuity equations of the eccentric scheme, incremental linearization, improved transformation of the momentum equation, control equations, and conditional constraints for solving the time-domain finite difference equations.

[0105] The specific implementation method of step S4 is as follows:

[0106] S4-1. Based on the existing water network engineering layout and the various hydrological monitoring stations deployed, obtain real-time monitoring data of the water network system;

[0107] S4-2. Equip monitoring stations with a data processing system to remove interference and noise from real-time monitoring data and perform fuzzing processing to obtain a current water network status monitoring dataset that is consistent with the data, thereby generating immediate feedback.

[0108] S4-3. Based on the results of real-time feedback, the set water supply network, the water volume target on the receiving side, and the expected water supply process path, establish a predictive control mode for the water flow status of the water network.

[0109] S4-4. Based on the established predictive control model, the fuzzy evaluation and feedback correction method of control effect is adopted to obtain the adjusted predictive fuzzy control model, thereby realizing the intelligent transformation of nodes.

[0110] like Figure 2 As shown, the gate-controlled water conveyance channel design scheme of the water supply-power generation parallel linkage control device and system includes: an upstream water network main stream or reservoir, a diversion channel, a diversion river, a control gate, a generator set, a stilling basin, a downstream river channel, and a water diversion gate; the upstream water network main stream or reservoir flows to the diversion channel and the diversion river; the water flow of the diversion channel and the upstream water network main stream or reservoir is controlled by the water diversion gate; the diversion river and the diversion channel are distributed in parallel; the water flow in the diversion river flows to the generator set through the control gate; the water flow of the generator set and the water flow of the diversion channel converge into the stilling basin; the water flow in the stilling basin flows to the downstream river channel.

[0111] like Figure 3As shown, the design scheme of the gate-controlled water conveyance channel of the water supply-power generation series linkage control device and system includes a reservoir or main stream canal of the water network, gates, canals, axial flow turbines, overflow weirs, water intake gates, downstream canals and multiple water intake channels; the water flow from the reservoir or main stream canal of the water network is controlled by the gates; the water flow in the canal is directed to the overflow weir through the axial flow turbines; the overflow weir controls the water flow size, so that excess water flows to the downstream canal.

[0112] like Figure 4 As shown, the gate-controlled water conveyance pipeline design scheme of the inverted siphon control gate linkage control device and system includes a channel, an inlet gate, a forebay, an inverted siphon, an outlet gate, and a downstream channel; the inlet gate controls the speed of water flow from the channel to the forebay; the inverted siphon connects the forebay and the outlet gate; and the outlet gate controls the speed of water flow from the inverted siphon to the downstream channel.

[0113] like Figure 5 As shown, the design scheme of the complex water network water intake and diversion linkage control device and system includes the main stream of the water network, the regulating pool, multiple control gates and multiple diversion points corresponding to the control gates; the main stream of the water network and the regulating pool are connected through the gates; the regulating pool and multiple diversion points are connected through the corresponding control gates.

[0114] In one embodiment of the present invention, the method for obtaining key parameters and control constraints includes:

[0115] The calculation method for the ultra-high design parameters of the channel is as follows:

[0116] According to the formula:

[0117]

[0118] F0 = i9 * μL

[0119] Obtain the additional superelevation F0 for zero flow level requirement and the superelevation value F for flow rate requirement. Q Where i9 is the average longitudinal slope of the channel, μ is the turbulent water surge parameter during the water conveyance process, and L is the length of the channel; The flow rate is designed for the channel; φ is the flow factor coefficient for water surge. The safety margin depends on the design flow rate and channel size;

[0120] The methods for obtaining parameters of overflow weirs, dams, and reservoir capacity are as follows:

[0121] According to the formula:

[0122]

[0123] The total head H0 of the main water diversion canal in the water supply network engineering system is obtained, where h cThe depth after the jump; q is the average diversion flow rate and the distribution flow rate Q. d The unit width flow rate is obtained under the condition of equilibrium; g is the acceleration due to gravity; φ1 represents the degree of influence of the water flow velocity on the energy dissipation depth.

[0124] According to the formula:

[0125] σh” c =h t +S

[0126] The required water depth S for the energy dissipation pool to achieve its energy dissipation function is obtained; where σ is the submersion coefficient, σ = 1.05; h t The downstream water depth; h” c The conjugate of the post-jump water depth;

[0127] Increase and determine the reaction time T based on the actual conditions of the storage level and channels;

[0128] According to the formula:

[0129] K h =Q h / Q rj

[0130] Obtain the water volume variation coefficient K. h ; where Q h Q represents the total water volume during the period of maximum daily flow. rj Total water volume during the average daily period;

[0131] According to the formula:

[0132] Q s =K h Nq / n

[0133] The required flow rate Q at the water intake / distribution point under guaranteed water supply conditions is obtained. s Where N is the number of current water-receiving areas; q is the maximum standard water supply guarantee; and n is the number of time periods per day. If the hourly calculation method is used, then n = 24 is selected.

[0134] According to the formula:

[0135] V1 = Q s T

[0136] q m =Q m T

[0137] V2 = q m -Q c T

[0138] The required equalization tank volume V1 and maximum water diversion rate q under conditions of insufficient water inflow are obtained. mWhen there is excessive water inflow, the storage capacity V2 of the tank needs to be adjusted; Q c For average water supply;

[0139] According to the formula:

[0140] V≥(1+10%)|V1-V2|

[0141] The capacity V of the regulating tank is obtained;

[0142] According to the formula:

[0143]

[0144] L≥6.9(h) c -h c )

[0145] Obtain the ultra-high D of the regulating tank v and the length L of the regulating pool; where i 10 Let L be the average longitudinal slope of the equalization pool, L be the length of the equalization pool, and R be the maximum run-up of the equalization pool.

[0146] The width B of the equalization tank is determined based on its capacity, length, and depth.

[0147] According to the formula:

[0148] s 2 = / v

[0149]

[0150]

[0151] n0 = 0 + 1

[0152] v = v0 + 1

[0153]

[0154] X~N(μ,σ 2 )

[0155] The reservoir volume μ is obtained when the reservoir capacity sample conforms to a normal distribution; where s 2 Let S represent the standard deviation, S represent the sum of squared residuals of the combined prior and current samples, S0 represent the sum of squared residuals of the prior samples, S1 represent the sum of squared residuals of the current samples, and v0 represent the previous degrees of freedom. Represents the prior sample mean. This represents the current sample mean. Let n represent the current sample mean, n0 represent the total number of prior samples, n1 represent the total number of current samples, and v represent the degrees of freedom. This represents the combined prior and current sample range. This represents the range of the prior sample. σ represents the range of the current sample, and σ represents the standard deviation of the normal distribution.

[0156] According to the formula:

[0157]

[0158]

[0159] The reservoir capacity W is obtained when the reservoir capacity sample does not conform to a normal distribution; x represents the sample element of the reservoir capacity value; r(x) is the reverse risk rate; X represents the current sample; This represents the sample mean; The values ​​are from the storage capacity coefficient table; n1 indicates that there are n1 sample elements; [·] represents the floor function;

[0160] The methods for obtaining parameters and control rules for long-distance water diversion devices are as follows:

[0161] According to the formula:

[0162]

[0163] The energy loss per unit weight of water in the inverted siphon pipe per unit flow rate is obtained. Where z represents the head of the pressurized flow in the inverted siphon pipe; x' represents the length of the pipe; γ represents the specific weight of the water; u' represents the velocity of the pressurized flow in the pipe; g represents the acceleration due to gravity; and t represents time.

[0164] The straight-line distance of the inverted siphon pipe is determined based on the distances between the upstream and downstream ends of the water intake and receiving points, and is defined as L. zx ;

[0165] According to the formula:

[0166]

[0167] Obtain the elevation of the pipeline at location point i' in, This represents the change in distance between two points; Q represents the pipe flow rate; F represents the water hammer wave in a pressurized pipe; c represents the flow coefficient.

[0168] According to the formula:

[0169]

[0170]

[0171]

[0172]

[0173] The control rules for the total head of the pressurized pipeline are obtained; where n' represents time; i 12 Indicates the location; α∈[0,1] is the weighting coefficient, representing the degree of influence of the actual situation at the location point on the pressurized flow in the pipeline;

[0174] According to the formula:

[0175]

[0176] Obtain the pressure line H;

[0177] According to the formula:

[0178]

[0179] The basic equations for an inverted siphon pipe are obtained; where R1 represents the hydraulic radius of the inverted siphon pipe.

[0180]

[0181] The turbulent flow generation term E is obtained from the coupling condition processing result of the pipeline and sluice gate under unsteady flow discretization; where a represents the gate opening; d2 represents the flow coefficient; Z represents the water level; i'3 represents the position; and c2 represents the flow coefficient through the sluice gate.

[0182] According to the formula:

[0183]

[0184]

[0185] h z =h j +h f

[0186] The total head loss h of the inverted siphon pipe is obtained. z Local head loss h j and head loss along the route h f Where ξ represents the local head loss coefficient; v represents the average flow velocity in the pipe; L represents the calculated pipe length; d1 represents the pipe diameter; and λ represents the friction head loss coefficient.

[0187] Based on hydraulic sensitivity, the required parameters and control conditions for the design of the water diversion point and gate are as follows:

[0188] According to the formula:

[0189]

[0190] The gate flow rate Q1 is obtained; where C dThe flow coefficient is represented by 'a', the gate opening is represented by 'h0', the upstream water level is represented by 'h0', and the gate width is represented by 'b1'.

[0191] According to the formula:

[0192]

[0193] The total flow rate Q0 and the flow rate Q of the i'5th water distribution port in the water distribution device are obtained. i'5 μ Q This represents a coefficient that includes the lateral contraction coefficient, submergence coefficient, and flow coefficient; h is the water depth of the regulating pool; e is the gate opening.

[0194] According to the formula:

[0195] S0=Δh / Δq

[0196] The sensitivity index S0 of the water distribution outlet is obtained; where Δh is the amplitude of the flow rate change at the water distribution outlet, and Δq is the amplitude of the head change in the regulating tank.

[0197] According to the formula:

[0198]

[0199]

[0200] get The hydraulic sensitivity indicators for the upstream and downstream of gate i6 are: Δh qian Δq represents the variation in water level upstream of the sluice gate. hou Δe represents the variation in flow rate after the gate; Δe represents the variation in gate opening.

[0201] According to the formula:

[0202]

[0203] C g ∝e / R2,C g ∝H 0' / R2

[0204]

[0205] C g ∝e / R², C f ∝h t / e

[0206] The flow rate Q of the main irrigation canal is obtained. g The free outflow flow coefficient C g and the flow coefficient C of the submerged outflow f ; where H 0’ h is the water depth of the regulating tank. tR1 is the head of the water flow at the water diversion point; R2 is the radius of the arc gate; ∝ indicates a direct proportion;

[0207] The control constraints include the fundamental equations for unsteady current computation, the governing equations, and the time-domain finite difference equations for conditional constraint solutions; among which...

[0208] The fundamental equations for unsteady flow calculations are expressed as follows:

[0209] AX = B

[0210]

[0211]

[0212] Where a', b', c', and d' represent the monitoring coefficient, control coefficient, water level coefficient, and flow coefficient, respectively; the strip matrix A is the set of parameters for complex water network engineering corresponding to different locations of control actuators under the equal volume operation mode; X is a column vector composed of water level and flow information; B is the set of sensing values ​​corresponding to the gate opening degree; n” represents the number of control actuators at the current location; e' represents the gate opening degree coefficient; and Q′ represents the flow coefficient.

[0213] The structural composition and control operation mode of the channel system are determined based on the basic equations of unsteady flow calculation.

[0214] The expression for the governing equation is:

[0215]

[0216] x2 represents the distance coordinate, t2 represents the time coordinate, A' is the cross-sectional area of ​​the equalization tank, h' is the water depth, q1 is the side inflow rate, C is the roughness coefficient of the tank bottom, R4 is the hydraulic radius, and Q2 is the flow rate.

[0217] The expression for solving the time-domain finite-difference equation under conditions and constraints is as follows:

[0218]

[0219]

[0220]

[0221]

[0222]

[0223]

[0224] These are the parameters used to describe the difference form; All The function; Δt2 is the time interval; u′ and v′ are the components of the flow velocity in the x and y directions; D1 represents the interval of the difference; CFL HD <1; h represents the total water depth; Δx3 and Δy represent the characteristic lengths in the x and y directions; n2+1 represents the n2+1th cycle step; j3 represents the j3rd node.

[0225] The process of deploying and constructing monitoring stations to acquire water supply system data includes the following steps:

[0226] According to the formula:

[0227]

[0228]

[0229]

[0230]

[0231]

[0232]

[0233]

[0234] Obtain the h′ water level, Q′ flow rate, and gate opening e of the existing water network project. 1i The relationship between the water level (h), flow rate (Q), and gate opening (e) obtained through the deployment and construction of monitoring stations. 2i The relationship is as follows: j represents the cycle step, i represents the node, and a, b, c, and d represent the conversion factors for water level and flow rate to opening degree; θ is the angle between the tangent of the lower edge of the arc gate and the horizontal direction; g is the acceleration due to gravity; B is the corresponding gate; A is the cross-sectional area of ​​the water passage; M represents the design condition; Δt represents the measurement time; Δx i This represents the change in spatial coordinates;

[0235] The location of the measuring points is modified based on the existing water network engineering relationships and the relationships obtained by setting up and constructing monitoring stations.

[0236] The monitoring stations are equipped with a data processing system to remove interference and noise from the water supply system data, and to perform fuzzification processing to obtain the status of the monitoring stations and generate real-time feedback. This includes the following steps:

[0237] The continuous information from the monitoring station is instantaneously sampled with a period TH, and the sampled value is used as an external input signal W;

[0238] The external input signal W is transformed into an input signal Y suitable for the sampler S, and then input into the sampler to obtain discrete state values ​​Y with periodic patterns. d ;

[0239] The periodic discrete state value Y d Input fuzzy controller K d The discrete signal U is obtained. d ;

[0240] Discrete signal U d The control input U is formed by a zero-order hold;

[0241] According to the formula:

[0242]

[0243] C n-1 (t)=S(t)

[0244]

[0245]

[0246]

[0247] The output value of the state variables C(t) of the discretized system model after removing disturbances and noise is obtained; where V represents the number of nodes that cause coupling disturbances to the water flow; li represents the li-th node; Indicates the parameters of the Karman filter. and Indicates the numerical value of the filter factor; C n-1 (t), C n (t), C n+1 (t) represents the state variable obtained during the application of the Active Disturbance Rejection Algorithm, T ab tj represents the inertial time constant of the water flow in the water network; x ,tj g ,tj y The constant m represents the self-regulating coefficient for eliminating fluctuations in the water flow of a small disturbance network. df This indicates large fluctuations. The control input U is converted into a real-time input signal S(t), where S1(t-τ) is the approximate input extracted by the differential tracker, and S2(t) is the differential signal. and Used to measure the relative deviation between the output current water level and flow rate of the water network channel and the actual value; t represents the measurement time point; e qy and e qh Both represent the self-adjustment coefficient; E li F represents the energy fluctuation caused by the coupling disturbance effect of water flow; liT represents the dynamic force that generates the coupling disturbance effect of water flow. w e represents the water flow time delay constant of the water network channel; h T is the self-adjusting coefficient for the sampling period; y e is the time delay constant of the self-adjustment coefficient; y This is the self-adjusting coefficient; For the state values ​​and differential signals in the time interval from n-1 to n; For the state values ​​and differential signals in the time interval from n to n+1; For the state values ​​and differential signals in the time interval from n-1 to n+1;

[0248] The output value C(t) of the state variable of the discretized system model after removing disturbances and noise is used as the real-time state of the station detection, forming an instant feedback. The instant feedback includes all parameters related to water.

[0249] Based on real-time feedback results, set water volume targets for both the supply and receiving sides, and contingency plan data, predictive fuzzy control is formed, including the following steps:

[0250] Based on the prediction model framework of Bayesian theory, a combined prediction model is constructed.

[0251] According to the formula:

[0252]

[0253]

[0254]

[0255] The fuzzy control plan P is obtained by combining prediction models. Jc ;in, This represents the weight of the water supply plan in group i2, and satisfies... Q represents the simulation plans for various prediction methods based on historical data. sj (j2) represents the corresponding actual water supply and water demand target setting value; This represents the prediction of the i2th water supply plan; m indicates that there are m prediction simulation methods; S represents the sliding surface;

[0256] According to the fuzzy control plan P JC This creates fuzzy control.

[0257] According to the formula:

[0258]

[0259]

[0260] We obtain the channel control model and the functions of upstream and downstream water depth relative to flow rate. Among them, c i (t) represents the deviation between the water level at the control point at time t and the steady-state water level e' in the canal pool i3 (i3=1,2,3) corresponding to the main water diversion canal, in meters; This represents the area of ​​the return water zone of the i-th main irrigation canal, in meters (m²). 2 ; These represent the deviations of the inflow, outflow, and intake flow rates of the i-th and 3rd water diversion canals from the steady state, respectively, in meters. 3 / s; The time delay corresponding to the i-th 3rd water diversion canal is represented in seconds; L is the weir length or gate width associated with the gate; k a k b h is the reduction factor. m h1 and h2 represent the water depths upstream and downstream of the gate, respectively; ω represents the gate opening.

[0261] The current state of the water network is obtained based on the water network channel control model and the functions of upstream and downstream water depth relative to the flow rate.

[0262] According to the formula:

[0263]

[0264] The steady-state control conditions s of the water network system are obtained. e (t); where W -1 ω1 represents the inverse matrix representing the state space of the water supply network project; ω1 represents the time variable that measures the current state input of the water network. Represents the state variables of the water network system; ∈1, ∈2, and ku are the setting parameters of the sliding surface; θ i This indicates the bounded range of the external disturbance; sat(·) represents the state of the sliding surface; This is a matrix representing the state space of the water supply network project, obtained from the time variables input from the state of the water network.

[0265] According to the formula:

[0266]

[0267]

[0268] The output u of the adjusted predictive fuzzy control model and the control decision of the steady-state water network is obtained. i (t); where (t) is the final state of the time-delay control. This is the output of time-delay control; All are system matrices of the i5th subsystem; ζ(t) represents the membership degree of the antecedent variable in set N; ζ(t) = [ζ1(t), ... ζ p [(t)] represents the antecedent variable; N is the fuzzy set; represents the prediction simulation method; x(t-τ) is the approximate input extracted by the differential tracker corresponding to the original data obtained from the monitoring station; x(t) represents the original data obtained from the monitoring station; u(t) represents the output of the control decision of the steady-state water network; ω(t) is the time variable that measures the current state input of the water network; θ i It represents the bounded range of the external disturbance; sign(·) is the sign function, which indicates the sign of (·).

[0269] Based on the adjusted predictive fuzzy control, a joint automatic fuzzy control mechanism is formed using a state-space model-based neural network to achieve intelligent transformation of nodes, including the following steps:

[0270] According to the formula:

[0271] L 2 δx(k+1)=R 2 δx(k)+Wδq(k)

[0272]

[0273]

[0274]

[0275]

[0276] The matrix solution space of the water level and flow rate constraint equations is obtained; where, L 2 R represents the coefficient matrix representing the influence of predicted conditions on water level and flow rate based on the control and operation of water network channels. 2 The matrix represents the coefficients that influence water level and flow rate under the current water network channel control operation. W′ represents the comprehensive influence coefficient matrix of lateral outflow on the water network state under time-delay control. [·] T Z represents the transpose matrix; δq(k) represents the relative steady-state outflow deviation of water intake on both sides of the water network channel; δx(k) represents the deviation of the raw data obtained by the monitoring station at time k; δx(k+1) represents the deviation of the raw data obtained by the monitoring station at time k+1; j+2 Z represents the water level at the (j+2)th cycle step. j+1 Let ε be the water level at the (j+1)th cycle step; ε be the differential error. For the incoming traffic to the node; δQ p This represents the current traffic value of the node.

[0277] According to the formula:

[0278]

[0279] Obtain a predictive model x representing the state of the water network. m (k+i6); where J represents the number of basis functions; This represents the linear weighting coefficients obtained from the basis function optimization calculation; The value of the basis function at a given time point within the sampling period is represented by j3; j3 represents the j3rd basis function; i6 represents the number of responses; G m Represents the prediction function; This represents the form of the prediction function in the i6-1 time period; This represents the form of the prediction function in the i6-2 time period; H m The control function represents the prediction function;

[0280] According to the formula:

[0281]

[0282]

[0283]

[0284] This involves decoupling the control gates of the water network channel from the upstream gates controlling the incoming water, decoupling the upstream gates controlling the incoming water from the channel / pipe head gates, and decoupling the output for flow rate; among these, This indicates the opening degree of the i7th control gate in the upstream section of the river / pipeline; This indicates the water distribution volume at the corresponding i7th gate outlet. This indicates the deviation between the water level in front of the gate at water distribution point i7 in the previous time period and the corresponding water supply target setting, where k represents the time period; KD0 and KD1 represent the proportional and integral coefficients of the PID controller for the upstream gate of the i7th river segment, respectively, with 0 representing the coefficient of the water diversion gate at the water diversion channel; These are the gate opening and the decoupling parameters for decoupling the flow output, respectively. This indicates the deviation between the water level at the diversion point i7 at time k and the corresponding water supply target setting; KP0 and KI0 represent the proportional coefficient and integral coefficient of the PID controller at the water intake gate of the water intake channel.

[0285] According to the formula:

[0286]

[0287]

[0288] The solution x'(t) of the state equation and the solution y'(t) of the output equation are obtained from the state-space expression; where e At x(0) is a homogeneous solution in the matrix solution space of the water level and flow limit equations; Ce is the product of the Laplace transform of the matrix solution space of the water level and flow rate constraint equations and the Laplace transform of the input; At x(0) is the zero-input response; Du(t) is the zero-state response;

[0289] Based on the output of the adjusted predictive fuzzy control model, the solution of the state equation, and the solution of the output equation, the control information of the opening of each gate, the water level control information of the upstream water network, and the flow control information of the downstream water network are used to control the opening of each gate, thus completing the joint automatic fuzzy control.

[0290] This invention provides a characteristic evaluation of water supply network engineering, establishes a mechanism for evaluating the transformation benefits of water supply network engineering, provides subsequent benefit evaluation and transformation direction; provides layout design guidance, completes reasonable standard specifications for overall component design methods; constructs a matching control system to achieve intelligent control; realizes the intensive utilization of water resources, achieves efficient development of hydropower, realizes the overall stable operation of the system, and avoids damage to artificial engineering under water flow fluctuations.

Claims

1. A method for intelligent design and evaluation of water network engineering, characterized in that, Includes the following steps: S1. Obtain data on nodes of the water network engineering renovation project; S2. Construct a node water supply characteristic assessment model based on the data of the water network engineering renovation nodes, and determine the renovation direction; S3. Based on the node water supply characteristic assessment model, construct and select a suitable renovation scheme, and calculate the relevant parameters and control conditions required for the renovation scheme to obtain the renovation model; S4. Construct control models related to the transformation model to realize intelligent transformation of water network projects; The construction of the node water supply characteristic evaluation model includes: According to the formula: Obtain the importance of the current category indicator Quality values ​​of water supply characteristics Scoring process parameters under the influence of weights ;in, This indicates the maximum value within the range specified for the current category; This represents the minimum value within the range specified for the current category; Indicates the current category i subcategories j The total score for each of the following is out of 100. k indivual, s This indicates that the current score is this. k The first of the values s indivual; A constant representing the passing score; This represents the weight obtained by the subjective weighting method. This represents the weights obtained using the entropy method; According to the formula: Normalized water security index External environmental safety index of water supply network projects EEI ;in, This indicates that the water supply network is functioning properly. This indicates that the reservoir's water storage and release functions have failed. This indicates instability in the control of water diversion and regulation in the canal; This indicates a water distribution imbalance at the water distribution point; express PI A set of unnormalized numerical values; This represents a comparison of the value of water supply characteristics and performance under continuous prediction after node modification; Indicates the area of ​​the administrative region; Indicates the predicted duration span; This represents the value conversion factor for water supply network projects. This indicates the changes in water supply methods and their development level before and after the renovation; GFI For service value index and GSI The intensity index of water supply and consumption; According to the formula: Obtain the operational safety index of the water supply network project ESI ;in, This represents the joint probability density of all random variables in the nodes; N This represents the number of nodes in a Bayesian network. Represents the parameter set, d Represents the samples in the sample set. X Let it represent a vector consisting of random variables. x This represents the corresponding element in the vector; This indicates the weight allocation for each category; Corresponding node ; This represents the engineering safety characterization constant for a risk state; Engineering safety characterization constants representing the risks of daily operation; and Indicates the engineering safety index; u Indicates the first u The nodes of a Bayesian network; Indicates the first One node; Indicates the first One node; Indicates the first There are several categories.

2. The intelligent design and evaluation method for water network engineering according to claim 1, characterized in that, Step S2 includes the following steps: S2-1. Based on the data of the water network engineering renovation nodes, perform principal component analysis to obtain the data after removing subjective influences; S2-2. Perform principal component analysis on the data after removing subjective influences to obtain the dataset; S2-3. Construct a node water supply characteristic evaluation model; S2-4. Use the node water supply characteristic assessment model to evaluate the data in the dataset and determine the direction of the renovation.

3. The intelligent design and evaluation method for water network engineering according to claim 2, characterized in that, Step S2-3 includes the following steps: S2-3-1, According to the formula: Obtain average water supply performance pjxn Average difference pjcy ;in, N It refers to the number of databases, i.e., the number of categories; yes i The number of data items under each category; B Indicate category i Subcategories in j The corresponding performance metrics; I Indicate category i In j +1 subcategory; S2-3-3. Based on the measured and predicted values ​​of the assessment information categories, construct a confusion matrix including false positive, false negative, and true categories. The true category represents the influencing factors that accurately correspond to the assessment category under the water supply characteristics. The false negative category represents characteristics that objectively have an impact on water supply characteristics and equipment engineering modifications, but are difficult to measure, and can and need to be modified. The false positive category represents negative water supply characteristics that are not necessarily related to equipment engineering modifications, but can be resolved through engineering modifications. S2-3-3, According to the formula: Obtain the harmonic average values ​​of precision and recall. FM An evaluation of the benefits of the renovation was conducted; among them... Indicates the number of actual classes; Indicates the number of false positives; This indicates the number of elements in the confusion matrix corresponding to the false negative class; JD Indicate precision; ZHL Indicates recall rate; S2-3-4, According to the formula: An impact assessment of the continuity of the modified nodes was obtained. ;in, A quality value representing the water supply characteristics before prediction; A quality value representing the predicted water supply characteristics; Indicates time-predicting variables under persistent influence. This represents the predictive constant for negative water supply characteristics under the effect of delay. The parameter representing the value of water supply service guarantee; lg represents the logarithm with base 10; S2-3-5, According to the formula: Obtain the average water supply service value of the current node. ;in, For a single node in the category i The value of water supply services; The overall water supply service value at the current node; This indicates the area covered by the water supply for the current category; express i Value coefficient under category; S2-3-6, According to the formula: Obtain the sensitivity coefficient MX ;in, The initial value of the service value before node modification; This is the predicted adjustment value for the service value after node modification; express i The performance value coefficient of the established water supply characteristics before the overall renovation of the nodes under the category; express i The performance value coefficient of the established water supply characteristics after the overall renovation of the nodes under the category; S2-3-7, According to the formula: Obtain water supply security index WPSI ;in, , , They represent , , The weights; S2-3-8, Based on the water supply security index WPSI Sensitivity coefficient MX Average water supply service value at the current node Impact assessment of the continuity of the modified nodes Harmonic average values ​​of precision and recall FM The importance of current category indicators Quality values ​​of water supply characteristics Scoring process parameters under the influence of weights Average water supply performance pjxn and average difference pjcy Construct a node water supply characteristic evaluation model.

4. The intelligent design and evaluation method for water network engineering according to claim 3, characterized in that, The specific implementation method of step S3 is as follows: S3-1. Construct four renovation schemes and select the optimal renovation scheme based on the node water supply characteristic evaluation model; S3-2. Calculate the key parameters and control constraints of each scheme, and select the corresponding key parameters and control constraints according to the optimal modification scheme. S3-3. Construct a modification model based on the key parameters and control constraints of the optimal modification scheme.

5. The intelligent design and evaluation method for water network engineering according to claim 4, characterized in that, The four modification schemes in step S3-1 are as follows: A scheme for the renovation of a gate-controlled water conveyance channel for a water supply-power generation parallel linkage control device and system includes: an upstream water network main stream or reservoir, diversion channels, diversion rivers, control gates, generator sets, stilling basins, downstream canals, and diversion gates; the upstream water network main stream or reservoir flows to the diversion channels and diversion rivers; the diversion channels and the upstream water network main stream or reservoir control water flow through diversion gates; the diversion rivers and diversion channels are distributed in parallel; the water flow in the diversion rivers flows to the generator sets through the control gates; the water flow through the generator sets and the water flow in the diversion channels converges into the stilling basin; the water flow in the stilling basin flows to the downstream canals; A scheme for the renovation of a gate-controlled water conveyance channel for a water supply-power generation series linkage control device and system includes: reservoirs or main stream canals of a water network, gates, canals, axial flow turbines, overflow weirs, water intake gates, downstream canals, and multiple water intake channels; the gates control the water flow from the reservoir or main stream canal into the canal; the water flows through the canal to the overflow weir via the axial flow turbines; the overflow weir controls the water flow size, allowing excess water to flow to the downstream canal; A gate-controlled water pipeline renovation scheme for an inverted siphon control gate linkage control device and system includes a channel, an inlet gate, a forebay, an inverted siphon, an outlet gate, and a downstream channel; the inlet gate controls the speed of water flow from the channel to the forebay; the inverted siphon connects the forebay and the outlet gate; and the outlet gate controls the speed of water flow from the inverted siphon to the downstream channel. A renovation scheme for a complex water network water intake and distribution point linkage control device and system includes: the main stream of the water network, a regulating reservoir, multiple control gates, and multiple water outlets corresponding to the control gates; the main stream of the water network and the regulating reservoir are connected through the gates; the regulating reservoir and multiple water outlets are connected through the corresponding control gates.

6. The intelligent design and evaluation method for water network engineering according to claim 5, characterized in that, The key parameters and control constraints of the four modification schemes in step S3-2 include the following parameters: The design parameters for the channel's superelevation, overflow weir parameters, reservoir capacity, stilling basin, regulating basin parameters, long-distance water diversion device parameters, branch inlet and gate parameters, and control constraints; among which: The channel's ultra-high design parameters include ultra-high values ​​for flow rate requirements and additional ultra-high values ​​for zero-flow water level requirements; The parameters of reservoir capacity, stilling basin, and regulating basin include overflow weir parameters, total head of the main water diversion canal in the water supply network engineering system, water depth required for the stilling basin to achieve its energy dissipation function, water volume variation coefficient of the diversion basin, flow rate that the water diversion point needs to ensure under the condition of guaranteed water supply, regulating basin volume required under the condition of insufficient water inflow, regulating basin storage capacity required under the condition of excessive water inflow, maximum water diversion, regulating basin capacity, regulating basin superelevation, regulating basin length, regulating basin width, reservoir capacity when the reservoir capacity sample conforms to a normal distribution, and reservoir capacity when the reservoir capacity sample does not conform to a normal distribution; The overflow weir parameters include a crest width of 4m, a 0.6m impermeable body within the earth-rock weir, and a 20m spacing between transverse joints. The safety factor is calculated using the shear strength formula. K ≥3, the principal tensile stress of stress deformation is less than 0.2MPa, and the height and width conditions of the stilling basin are met simultaneously; The parameters and control rules of long-distance water diversion devices include the energy loss of water per unit weight per unit flow in the siphon pipe, the straight distance of the inverted siphon pipe, the layout elevation of the pipe at a certain point, the control rules of the total head of the pressurized pipe, the pressure line, the basic equation of the inverted siphon pipe, the coupling conditions of the pipe and the sluice gate under unsteady flow discrete processing results, the turbulence generation term and the turbulence kinetic energy generation term of the average value of the steps. The parameters of the water diversion point and gate include the gate flow rate, total flow rate, water diversion flow rate of the water diversion point, sensitivity index of the water diversion point, hydraulic sensitivity index of the gate upstream and downstream, and flow rate of the main water diversion channel. The control constraints include the basic equations for unsteady flow calculation, the continuity equations of the eccentric scheme, incremental linearization, improved transformation of the momentum equation, control equations, and conditional constraints for solving the time-domain finite difference equations.

7. The intelligent design and evaluation method for water network engineering according to claim 6, characterized in that, The specific implementation method of step S4 is as follows: S4-1. Based on the existing water network engineering layout and the various hydrological monitoring stations deployed, obtain real-time monitoring data of the water network system; S4-2. Equip monitoring stations with a data processing system to remove interference and noise from real-time monitoring data and perform fuzzing processing to obtain a current water network status monitoring dataset that is consistent with the data, thereby generating immediate feedback. S4-3. Based on the results of real-time feedback, the set water supply network, the water volume target on the receiving side, and the expected water supply process path, establish a predictive control mode for the water flow status of the water network. S4-4. Based on the established predictive control model, the fuzzy evaluation and feedback correction method of control effect is adopted to obtain the adjusted predictive fuzzy control model, thereby realizing the intelligent transformation of nodes.

8. A system for applying the intelligent design and evaluation method of water network engineering as described in any one of claims 1 to 7, characterized in that, It includes a data acquisition module, a node water supply characteristic assessment module, a transformation model construction module, and a control module; The data acquisition module is used to acquire data from nodes in the water network engineering renovation project; The node water supply characteristic assessment module is used to construct a node water supply characteristic assessment model based on the data of the nodes in the water network project renovation, and to determine the direction of renovation. The modification model construction module is used to build an evaluation model based on the water supply characteristics of the node, select a suitable modification scheme, and calculate the relevant parameters and control conditions required for the modification scheme to obtain the modification model. The control module is used to build control models related to the transformation model, so as to realize the intelligent transformation of water network projects.

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