Intelligent cooperative switching control system and method for multi-diameter valve flow channel

Abstract

The invention discloses an intelligent cooperative switching control system and method for multi-path valve runners, and the method comprises the steps: collecting all-condition operation data of a multi-path valve system, extracting valve switching characteristic parameters, and building a runner mapping relation between paths; a cascade action chain containing time sequence dependence is constructed based on the mapping relation and the switching characteristics, and the action sequence and the trigger condition of each drift diameter are determined; by analyzing the pressure distribution and the flow velocity change in the cascade action chain, predicting the water hammer strength and designing a reverse pressure wave parameter, and generating a water hammer counteracting control instruction; high-low pressure area distribution in the flow field is identified, a flow field complementary cooperation scheme is constructed, and a pressure balance control strategy is generated; analyzing a pressure wave propagation path to determine a superposition elimination point, extracting pressure pulse resonance characteristics and generating time sequence control parameters; and the sequential control parameters are matched with the cascade action chain, synchronous control nodes are determined through delay compensation, and a time-sharing control sequence is generated to achieve intelligent cooperative switching of the multi-drift-diameter valve flow channels.

Description

Technical Field

[0001] The present invention relates to the field of fluid control technology, and in particular to an intelligent coordinated switching control system and method for multi-path valve flow channels. Background Art

[0002] In complex fluid transport systems, multi-port valves are widely used in fields such as petrochemicals, natural gas transportation, and water conservancy projects, fulfilling critical functions such as flow distribution, pressure regulation, and flow direction switching. However, traditional valve control methods typically employ independent control strategies, with each valve operating independently based on local measurement signals and lacking an overall coordination mechanism. This control approach is prone to problems such as sudden pressure changes, flow fluctuations, and pipeline vibration during valve operation, which not only affects system operational stability but also poses safety risks such as equipment damage.

[0003] Existing technologies for multi-valve systems face numerous limitations: They are unable to effectively predict and control transient flow phenomena caused by valve actuation; they struggle to coordinate pressure and flow across different flow paths; they lack the ability to comprehensively analyze and control the system's dynamic response characteristics; and they lack optimal timing for valve actuation, which can easily lead to adverse interactions. Therefore, there is an urgent need for a new control method that can coordinate the actuation of multiple valves, effectively control transients, and optimize overall system performance, thereby improving the safety and operational efficiency of fluid delivery systems. Summary of the Invention

[0004] The present invention provides an intelligent collaborative switching control system and method for multi-path valve flow channels. This system achieves collaborative control of multi-path valves by establishing a cascade relationship and timing optimization mechanism for valve actions. The system collects system operating data and extracts switching characteristics to construct a cascade action chain with timing dependencies. It predicts water hammer intensity through pressure and flow rate analysis and designs reverse pressure waves for active cancellation. It identifies pressure distribution characteristics to construct a flow field complementation scheme. It generates timing control parameters based on resonance characteristics. Finally, it implements intelligent collaborative switching control through delay compensation and synchronization node establishment.

[0005] The first aspect of the present invention provides an intelligent coordinated switching control system and method for multi-path valve flow channels, comprising:

[0006] Collecting real-time fluid parameters of the multi-path valve, performing feature analysis on the real-time fluid parameters to identify the current flow channel working mode, and constructing a flow channel state transfer matrix using the flow channel working mode;

[0007] Performing reverse path deduction through the flow channel state transfer matrix to generate a candidate path set, performing pressure wave timing analysis on the candidate path set to identify leveraged switching points, generating a time-series leveraged switching sequence based on the leveraged switching points, and constructing a cascade action chain based on the time-series leveraged switching sequence;

[0008] Extracting the pressure gradient distribution and flow velocity variation curve of each path from the cascade action chain, predicting the water hammer intensity distribution based on the pressure gradient distribution, determining the water hammer occurrence sequence based on the flow velocity variation curve, designing reverse pressure wave parameters based on the water hammer intensity distribution and the water hammer occurrence sequence, and generating water hammer compensation control instructions using the reverse pressure wave parameters;

[0009] Obtaining pressure distribution characteristics of each path in the cascade action chain, identifying high-pressure areas and low-pressure areas based on the pressure distribution characteristics, constructing a flow field complementary and collaborative scheme based on the distribution of the high-pressure areas and the low-pressure areas, and generating a pressure balance control strategy using the flow field complementary and collaborative scheme;

[0010] constructing a multi-valve coordinated control sequence based on the water hammer offset control instruction and the pressure balance control strategy, performing pressure wave propagation path analysis through the multi-valve coordinated control sequence to determine a pressure wave superposition elimination point, extracting a pressure pulse resonance feature from the pressure wave superposition elimination point, and generating a timing control parameter based on the pressure pulse resonance feature;

[0011] The timing control parameters are matched with the cascade action chain to generate a synchronous control node, the action moment of each path is determined based on the synchronous control node, and a time-sharing control sequence is generated according to the action moment to complete the intelligent coordinated switching control of the multi-path valve flow channel.

[0012] A second aspect of the present invention provides an intelligent coordinated switching control system for multi-path valve flow channels, comprising:

[0013] A flow state recognition module is used to collect real-time fluid parameters of the multi-path valve, perform feature analysis on the real-time fluid parameters to identify the current flow channel working mode, and construct a flow channel state transition matrix using the flow channel working mode;

[0014] a path planning module configured to perform reverse path deduction using the flow channel state transition matrix to generate a candidate path set, perform pressure wave timing analysis on the candidate path set to identify leveraged switching points, generate a sequential leveraged switching sequence based on the leveraged switching points, and construct a cascade action chain based on the sequential leveraged switching sequence;

[0015] a water hammer control module, configured to extract the pressure gradient distribution and flow velocity variation curve of each path from the cascade action chain, predict the water hammer intensity distribution based on the pressure gradient distribution, determine the water hammer occurrence timing based on the flow velocity variation curve, design reverse pressure wave parameters based on the water hammer intensity distribution and the water hammer occurrence timing, and generate water hammer compensation control instructions using the reverse pressure wave parameters;

[0016] a collaborative control module, configured to obtain pressure distribution characteristics of each path in the cascade action chain, identify high-pressure areas and low-pressure areas based on the pressure distribution characteristics, construct a flow field complementary collaborative scheme based on the distribution of the high-pressure areas and the low-pressure areas, and generate a pressure balance control strategy using the flow field complementary collaborative scheme;

[0017] a timing optimization module for constructing a multi-valve coordinated control timing based on the water hammer offset control instruction and the pressure balance control strategy, performing pressure wave propagation path analysis through the multi-valve coordinated control timing to determine a pressure wave superposition elimination point, extracting a pressure pulse resonance feature from the pressure wave superposition elimination point, and generating a timing control parameter based on the pressure pulse resonance feature;

[0018] The execution control module is used to perform timing matching between the timing control parameters and the cascade action chain to generate a synchronous control node, determine the action moment of each path based on the synchronous control node, generate a time-sharing control sequence according to the action moment, and complete the intelligent coordinated switching control of the multi-path valve flow channel.

[0019] The beneficial effects of the present invention are reflected in the following points: First, by constructing a cascade action chain, the time-sequential management of the multi-path valve action is realized, and the action sequence and trigger conditions of each valve are determined according to the flow channel mapping relationship and switching characteristics, and the originally independent control actions are transformed into an orderly cascade process, reducing the mutual interference and pressure disturbance in the valve switching process. Secondly, an active control strategy is adopted to deal with transient pressure changes. By predicting the water hammer intensity and generating reverse pressure wave parameters, destructive interference is generated before the pressure shock arrives; at the same time, a flow field complementary and coordinated scheme is constructed based on the distribution of high and low pressure areas, and the pressure potential energy difference is used to achieve natural flow, thereby reducing the energy consumption of the system. Finally, the control timing is optimized by extracting the resonance characteristics of the pressure pulse to avoid exciting the natural frequency of the system; a synchronous control node is established and delay compensation is performed to ensure the precise coordination of the actions of multiple valves; the generated time-sharing control sequence can be applied to industrial control systems, thereby improving the automation level and operational safety of the multi-path valve system.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein illustrate specific examples of the technical solutions described in the present invention, and together with the specific implementation methods constitute a part of the specification, and are used to explain the technical solutions, principles and effects of the present invention.

[0022] Unless otherwise specified or defined, the same reference numerals in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference numerals.

[0023] Figure 1 It is a flow chart of an intelligent coordinated switching control system and method for a multi-path valve flow channel of the present invention.

[0024] Figure 2 It is a structural block diagram of an intelligent coordinated switching control system for multi-path valve flow channels of the present invention. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0026] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0027] The technical solutions of the embodiments of this application are introduced below.

[0028] like Figure 1 As shown, the embodiment of the present invention provides an intelligent coordinated switching control system and method for a multi-path valve flow channel, including the following steps S110 to S160:

[0029] Step S110 , collecting real-time fluid parameters of the multi-path valve, performing feature analysis on the real-time fluid parameters to identify the current flow channel working mode, and constructing a flow channel state transfer matrix using the flow channel working mode.

[0030] Specifically, sensors installed at the inlet and outlet of each flow channel and key nodes of a multi-port valve collect real-time fluid parameters. These fluid parameters include basic physical quantities such as pressure, flow rate, velocity, and temperature. Pressure sensors are placed at the inlet and outlet of each port, with a sampling frequency of 100 Hz to capture detailed pressure fluctuations. Flow meters are installed along the centerline of the main flow channel, achieving a measurement accuracy of ±0.5%. Temperature sensors monitor the impact of fluid temperature changes on flow characteristics. The data acquisition system utilizes a distributed architecture, with each sensor connected to the data acquisition unit via a fieldbus for multi-point synchronous sampling. The collected raw data undergoes filtering to eliminate high-frequency noise, and the data curve is smoothed using a moving average method with a window length of 10 sampling points. Data preprocessing also includes outlier removal and missing value interpolation to ensure data quality. A time synchronization mechanism ensures temporal consistency of data at each measurement point, with timestamp accuracy reaching milliseconds. The real-time data cache utilizes a circular buffer structure, retaining the last five minutes of historical data. Multi-point collaborative acquisition obtains complete real-time fluid parameters including pressure time series P(t), flow time series Q(t), flow velocity distribution v(x, t) and temperature field T(x, t).

[0031] Multi-dimensional feature analysis is performed on the collected real-time fluid parameters to identify the current flow channel operating mode. Feature extraction calculates the average pressure, pressure fluctuation amplitude, pressure gradient, and pulsation frequency from the pressure time series P(t). From the flow time series Q(t), the instantaneous flow rate, average flow rate, and flow distribution ratio r_i = Q_i / Q_total are extracted, where Q_i is the flow rate of the i-th path and Q_total is the total flow rate. Flow velocity uniformity is evaluated from the velocity distribution v(x, t). The temperature field T(x, t) analyzes the impact of temperature gradients on flow. Time domain analysis identifies periodic patterns within a 5-minute historical window, and frequency domain analysis uses fast Fourier transforms to extract the main frequency components. Principal component analysis is used to reduce the dimensionality of the extracted multi-dimensional feature parameters, including pressure, flow rate, velocity, and temperature. The top four principal components with a contribution exceeding 85% are selected to construct the feature vector F = [f_1, f_2, f_3, f_4], where f_i represents the i-th principal component. Based on the feature vector F, a pre-trained support vector machine classifier is used to identify the operating mode. The operating modes are categorized into single-path mode (M1), dual-path parallel mode (M2), multi-path diversion mode (M3), and switching transition mode (M4). Sub-modes are further subdivided into operating conditions, such as M2, which is divided into uniform diversion (M2a) and master-slave diversion (M2b). The real-time recognition process runs continuously, updating every 100ms to obtain the current flow channel operating mode M(t).

[0032] The flow channel state transition matrix is ​​constructed using the sequence of continuously identified flow channel operating modes to quantify the transition relationships between different operating modes. The state set is defined as S = {M1, M2a, M2b, M3, M4}, which includes all major operating modes and key submodes. A transition event sequence is constructed by recording mode changes at consecutive moments. When mode M(t_k) at time t_k changes to mode M(t_{k+1}) at time t_{k+1}, a transition from state i to state j is recorded. The number of occurrences of each type of transition event within the time window, N_{ij}, is counted, where i represents the starting state and j represents the target state. The transition probability is calculated as p_{ij} = N_{ij} / Σ_j N_{ij}, which is the number of transitions from state i to state j divided by the total number of transitions from state i. A 5×5 state transition matrix P is constructed, where the matrix element p_{ij} represents the probability of transitioning from state i to state j. Dangerous transition paths are identified based on expert knowledge. For example, if a direct transition from M1 to M3 could result in severe water hammer, the corresponding p_{13} is set to 0. Matrix normalization ensures that the sum of the elements in each row is 1, that is, Σ_j p_{ij} = 1, satisfying the probability distribution constraint. Time-varying characteristics are implemented using a sliding window mechanism, with matrix parameters updated hourly to adapt to changing operating conditions. Steady-state analysis obtains the steady-state probability vector π by solving π=πP, where π_i represents the probability of the system being in state i during long-term operation. After statistical modeling and probabilistic normalization, the flow channel state transition matrix P is constructed.

[0033] In step S120, reverse path deduction is performed through the flow channel state transfer matrix to generate a candidate path set, and pressure wave timing analysis is performed on the candidate path set to identify the leverage switching points. A timing leverage switching sequence is generated based on the leverage switching points, and a cascade action chain is constructed based on the timing leverage switching sequence.

[0034] In some embodiments, the reverse path deduction through the flow channel state transition matrix is ​​used to generate a candidate path set, including: extracting the switching cost between states based on the flow channel state transition matrix; obtaining the total path cost by reversely accumulating from the target state according to the switching cost; and screening out multiple paths with the minimum cost based on the total path cost to form a candidate path set.

[0035] Based on the flow channel state transition matrix P, a systematic method is used to extract the switching cost between any state pair. The cost calculation comprehensively considers the multi-dimensional influencing factors of the switching process. The pressure shock cost is calculated by estimating the maximum pressure change caused by the state transition, and the shock intensity is predicted using the water hammer formula ΔP = ρ × c × Δv. The flow disturbance cost quantifies the degree of flow instability during the switching process and is calculated by integrating the flow rate change rate. The energy loss cost assesses the irreversible energy dissipation during the switching process, including valve throttling losses and pipeline friction losses. The time cost reflects the stabilization time required to complete the state transition. Fast and smooth transitions have lower time costs. The comprehensive cost value is obtained through weighted summation, with the weight coefficient set according to the priority of the specific operating conditions. Special switching paths, such as emergency shutdown, require additional safety cost assessment. State pairs with zero probability in the transition matrix P are assigned infinite costs, indicating that the transition is prohibited. Standardization of cost values ​​ensures comparability between different cost types.

[0036] Based on the extracted switching cost, reverse dynamic programming is performed starting from the set target state to calculate the total path cost. The algorithm initializes the target state's reach cost to zero, indicating that the target state has already been reached without additional cost. The first round of calculation processes all predecessor states that can directly reach the target state, and their reach costs are updated to the cost of direct switching. The iterative process propagates forward. For any state, its optimal reach cost is determined by examining all reachable successor states, and the path with the lowest cost is selected. The optimal decision records the best successor state selected for each state for path reconstruction. Convergence is determined by comparing the cost changes between adjacent iterations. Iterations are terminated when the cost update for all states is less than a threshold. Multiple starting points are handled by calculating the reach cost for each possible starting state separately. Circular dependencies are broken by introducing virtual nodes to ensure algorithm convergence. Special treatment of boundary states ensures the integrity of the calculation. After the reverse accumulation is completed, the minimum total cost distribution from each starting state to the target state is obtained.

[0037] Based on the calculated total path cost, a multi-criteria screening process is performed to generate a high-quality candidate path set. The primary screening criterion is to minimize the total cost. All possible paths are sorted in ascending order according to J_total, and the top 10 paths with the lowest cost are initially selected. Path reconstruction is accomplished by tracing the optimal decision chain from the starting state to the target state, recording the complete state transition sequence. Path length constraints eliminate lengthy paths exceeding eight steps to avoid excessive execution complexity. Path diversity is assessed by calculating the edit distance between paths to ensure that the candidate set includes different switching strategies. A critical state coverage check prioritizes paths that pass through important intermediate modes. Time feasibility verification ensures that the cumulative switching time per step does not exceed the total time limit. A risk assessment identifies paths containing high-risk switching and appropriately disperses them. A stability score considers the smoothness of each switching step in the path. Finally, the five paths with the highest overall scores are retained as the candidate set. Each candidate path fully records key information, including the state sequence, step cost, and cumulative time, to form the candidate path set.

[0038] In some embodiments, the pressure wave timing analysis of the candidate path set to identify the switching point that can be leveraged includes: extracting the pressure wave propagation characteristics of each path based on the candidate path set; performing timing energy analysis on the pressure wave propagation characteristics to obtain an energy overlapping area; and determining the switching point that can be leveraged through the energy overlapping area.

[0039] Based on the state transition sequence of each candidate path, a pre-established state-valve mapping table is used to convert the abstract state into specific valve opening and closing actions, thereby extracting the propagation characteristic parameters of the pressure wave. The state mapping table defines the valve operation sequence corresponding to each mode switch. For example, switching from M1 to M2a requires the opening of valve V2 and the adjustment of valve V3. The pressure wave generation mechanism is analyzed to determine the initial disturbance intensity caused by each state switch, and the pressure jump value is calculated using fluid dynamics equations. Propagation path mapping converts the abstract state switch into specific valve actions and pipeline flow changes. This mapping process is achieved by querying a pre-established state-network topology database, which records the changes in pipeline network connectivity and corresponding flow path changes caused by each state switch. Wave velocity calculation takes into account the differences in material, diameter, and fluid properties of different pipe sections to generate a segmented wave velocity distribution. The propagation time matrix records the propagation delay from each disturbance source to each monitoring point. Reflection points are identified through pipeline network topology analysis, including valve locations, pipe diameter jumps, and T-junctions. Reflection intensity is calculated based on the degree of acoustic impedance mismatch to form a reflection coefficient distribution. Attenuation characteristics are obtained by fitting historical data to obtain an empirical attenuation curve. Phase relationship analysis analyzes the relative phases of pressure waves generated by different sources. In-phase superposition enhances, while out-of-phase superposition weakens. Spectral characteristics extract the dominant frequency component of the pressure wave. A complete set of propagation characteristic parameters covers the entire process of pressure wave generation, propagation, reflection, and attenuation.

[0040] Time-series energy analysis is performed on the extracted pressure wave propagation characteristics to identify the energy distribution and interaction of different pressure waves in space and time. Energy density calculation converts pressure fluctuations into energy per unit volume. Time-series unfolding plots the pressure wave energy generated by each switching action on the time axis, forming an energy evolution curve. Spatial distribution analysis discretizes the pipe network by gridding and calculates the energy flux of each grid cell. Multi-source superposition uses the principle of linear superposition to calculate the total energy field. Energy overlap regions are identified by setting an energy density threshold; regions exceeding the threshold are marked as high-energy regions. Time window analysis searches for overlap of energy peaks within a range of ±100 ms, with the degree of overlap quantified using a cross-correlation function. Positive overlap produces an energy enhancement effect, reaching 1.6-2.0 times that of a single wave. Negative overlap leads to energy cancellation, forming an energy trough. The stability of the overlap region is assessed by duration, with stable overlap lasting for more than 50 ms. The spatial extent is determined by the energy diffusion radius, typically 2-3 pipe diameters. The space-time coordinates of the energy overlap region constitute candidate areas for leverage.

[0041] By analyzing the identified energy overlap regions, specific leveraged switching points are determined. The leveraged switching criterion is set as the energy in the overlap region exceeding 1.5 times the single-wave energy and lasting longer than 30ms. This threshold allows for a safety margin of 1.6-2.0 times the theoretical maximum enhancement effect. The switching points are spatially located near nodes with the highest energy density, typically corresponding to critical valves or diversion points. The timing is located in the middle and late stages of the energy rise, when energy has accumulated sufficiently but has not yet begun to decay. The leveraged switching strength is rated as strong, medium, and weak based on the amount of available energy. A directional compatibility check ensures that the leveraged switching direction aligns with the desired switching direction to avoid reverse obstruction. The stability score considers the temporal stability and spatial concentration of the energy overlap. Accessibility verification verifies the physical feasibility of switching at that spatiotemporal point. Prioritization comprehensively considers leverage strength, stability, and accessibility to generate a ranked list. Two to four high-quality leveraged switching points are identified for each path, and their location, timing, energy, and rating are recorded. The identification of leveraged switching points identifies key control points for energy utilization.

[0042] Based on the identified leveraged switching points, an optimized time-sequenced leveraged switching sequence is generated. The primary principle of sequence generation is to maximize energy efficiency. The original continuous switching sequence is decomposed into discrete actions executed at leveraged points. Timing alignment is achieved by adjusting the switching moments to align with the leveraged points, allowing for a time window of ±20ms. Action decomposition breaks down complex state transitions into multiple sub-actions, which are allocated based on their energy requirements and the energy supply of available leveraged points. A sub-action can utilize one or more nearby leveraged points. The energy allocation strategy matches the energy requirements of each sub-action with the energy supply capacity of the leveraged points to ensure a balanced supply and demand. Switching sequence optimization considers the mutual influence of preceding and following actions, allowing residual energy from preceding actions to be utilized by subsequent actions. Time intervals are set to ensure sufficient stabilization time between adjacent actions, typically 100-200ms. Sequence integrity checks ensure that all necessary state transitions are covered. Redundant action identification eliminates unnecessary intermediate switches, simplifying the execution sequence. Alternative solutions are generated to prepare alternative leveraged points for each key action in the main sequence. The final temporal leveraging switching sequence contains action, time, position and borrowed energy information.

[0043] In some embodiments, constructing a cascade action chain based on the timing-leveraged switching sequence includes: extracting the residual energy value of each switching point based on the timing-leveraged switching sequence; identifying the available energy window based on the residual energy value; determining the optimal triggering time based on the energy window; and connecting the timing-leveraged switching sequence in series according to the optimal triggering time to form a cascade action chain.

[0044] Based on the energy utilization at each switching point in the sequential assistance switching sequence, the residual energy distribution after execution is calculated. The residual energy value, E_residual = E_assist - E_consumed, is calculated by subtracting the actual consumption from the available energy. The consumed energy is estimated based on the resistance characteristics of the switching action. The energy transfer efficiency considers the transmission loss of the residual energy from the previous action to the downstream, with a typical efficiency of 0.7-0.85. The spatiotemporal diffusion model predicts the propagation range and decay of the residual energy. The residual energy quality assessment distinguishes between high-quality and low-quality energy. The cumulative effect analysis combines the residual energy of multiple actions to form a usable energy pool. The energy lifespan assesses the effective utilization time window of the residual energy; energy beyond this time limit is no longer usable. The spatial distribution map shows the distribution density of the residual energy in the pipeline network. The directional analysis determines the main flow direction of the residual energy. The energy account management tracks the energy balance at each node. The residual energy analysis forms a complete spatiotemporal energy distribution map, E_residual(x, t). Based on the income and expenditure records of energy account management, a continuous energy spatiotemporal distribution function E_residual(x,t) is generated through spatial interpolation and temporal smoothing algorithms. The spatial dimension uses bilinear interpolation of the energy values ​​of the grid nodes, and the temporal dimension uses cubic spline interpolation to ensure the continuity of the distribution function.

[0045] Based on the spatiotemporal distribution of residual energy, E_residual(x, t), available energy windows are identified. An energy window is defined as a spatiotemporal region where the residual energy density exceeds a utilization threshold. In the temporal dimension, a window begins when energy accumulation exceeds a threshold and ends when energy decay falls below a threshold. In the spatial dimension, a window covers a network area with sufficient energy density, typically a limited range centered on the energy source. The effectiveness of a window is scored based on energy density, duration, and spatial coverage. Overlapping windows are merged using a set union to form a larger available region. Window stability is assessed using the energy fluctuation coefficient; windows with fluctuations less than 20% are considered stable. Reachability constraints eliminate physically unusable window regions. Window capacity estimates the maximum energy demand for supported actions. Timing constraints ensure that window occurrence times match planned action times. Prioritization determines the order of utilization based on window quality and matching.

[0046] Based on the identified energy window set, an optimization algorithm is used to determine the optimal triggering timing for each action. The goal of triggering timing selection is to maximize energy utilization and action success rate. Energy utilization is measured by the ratio of actual energy acquired to the available energy in the window. Action success rate considers the reliability and stability of executing the action at that timing. Time constraints ensure that the triggering moment falls within the validity period of the energy window W_i, with a 20-30ms safety margin reserved to prevent edge effects. Multi-action coordination is achieved by establishing a timing dependency graph to ensure a reasonable interval between successive actions. Trigger point selection favors the stable segment of the energy rising edge to avoid instability at the peak point. Spatial matching ensures consistency between the trigger location and the action execution location. Conflict detection prevents multiple actions from competing for the same energy window. A dynamic adjustment mechanism allows fine-tuning of the triggering moment based on real-time conditions. Robustness analysis assesses the sensitivity of the triggering timing to perturbations. The optimization calculation yields the optimal triggering timing set {t_trigger_1, t_trigger_2, ..., t_trigger_n} for each action.

[0047] Based on the optimal triggering timing {t_trigger_i}, discrete switching actions are connected in series to form a causal cascade action chain. The cascade structure design enables the output of the preceding action to become the input of the downstream action, forming a relay transmission of energy and information. The definition of an action node N_i includes four elements: trigger time t_trigger_i, execution location x_i, energy requirement E_need_i, and output characteristics E_out_i. Connections are represented by edges: energy flow and timing dependency, forming a directed graph structure. The cascade effect G_ij quantifies the gain effect of node i on node j. Critical path identification uses graph algorithms to identify the main paths that influence overall performance. Parallel branch processing allows for the concurrent execution of some actions, improving efficiency. Synchronization points ensure that parallel branches converge at critical nodes. A fault-tolerant mechanism inserts checkpoints in the cascade chain, enabling recovery from failures. This designed cascade structure achieves efficient energy utilization and coordinated action coordination, ultimately constructing a cascade action chain C = {N_1 → N_2 → ... → N_n}.

[0048] Step S130: Extract the pressure gradient distribution and flow velocity change curve of each path from the cascade action chain, predict the water hammer intensity distribution based on the pressure gradient distribution, determine the water hammer occurrence timing through the flow velocity change curve, design the reverse pressure wave parameters based on the water hammer intensity distribution and water hammer occurrence timing, and use the reverse pressure wave parameters to generate water hammer compensation control instructions.

[0049] Specifically, based on the constructed cascaded action chain, the flow parameter changes generated in each path when each action node is executed are analyzed. The switching information contained in the action node directly affects the flow field distribution, including the triggering time, execution location, and energy level. Pressure gradient extraction is accomplished by placing virtual monitoring points in each path. The pressure values ​​at these virtual monitoring points are calculated using a fluid dynamics interpolation algorithm based on the actual sensor data collected in step S110. The spacing between monitoring points is set to 5-10 times the pipe diameter. The pressure changes caused by each node action propagate through the path, forming a dynamic pressure field. The pressure gradient calculation ∇P = ∂P / ∂x uses the central difference format to improve computational accuracy. The flow velocity curve is related to the pressure change using the Bernoulli equation, Δv = √(2×ΔP / ρ). The coupling effect of multiple paths is described using the mass conservation and momentum conservation equations. The time resolution is set to 10ms to capture fast transient processes. The spatial interpolation method uses the cubic spline method to ensure gradient continuity. The cumulative effect of cascaded actions is calculated by superimposing the contributions of each node. Propagation delay is handled using a time-weighted superposition method, determining the delay time based on the triggering moment and propagation distance of each action node to ensure the timing accuracy of the pressure gradient calculation. Based on this extraction process, pressure gradient distribution and flow velocity change curves for each path are generated.

[0050] Based on the extracted pressure gradient distribution, water hammer theory is used to predict the potential water hammer intensity for each flow path. Water hammer mechanism analysis identifies locations with abrupt pressure gradient changes as potential water hammer sources. Hazardous areas are marked when the absolute value of the gradient exceeds a critical threshold. Water hammer intensity is calculated using the Joukowski formula ΔH = a × Δv / g, where ΔH is the water hammer pressure head, a is the pressure wave velocity, Δv is the velocity jump, and g is the gravitational acceleration. The pressure wave velocity takes into account the combined effects of fluid compressibility and pipeline elasticity. The spatial characteristics of the intensity distribution exhibit a pattern of decay from the water hammer source toward both ends. The temporal evolution follows the wave equation, with an initial rapid rise followed by a gradual decay. The cumulative effect of multiple water hammer sources is evaluated using the linear superposition principle: H_total = Σ H_i, where H_i is the intensity generated by the i-th water hammer source. The hazard level classification categorizes water hammer intensity into three levels: mild, moderate, and severe, corresponding to different multiples of the working pressure. Pipeline pressure capacity verification ensures that the predicted maximum pressure does not exceed the allowable value. The water hammer duration is estimated as t_duration = 2L / a, where L is the characteristic length of the pipe and a is the pressure wave velocity.

[0051] By analyzing the temporal characteristics of the velocity curve, the timing of water hammer occurrence in each flow path is determined. The water hammer triggering moment is identified by detecting velocity mutation points. When the velocity change rate exceeds a set threshold, the water hammer onset time t_hammer is recorded. The threshold is set based on the velocity variation range during normal operation and is typically 3-5 times the normal rate of change. The timing relationship between multiple flow paths is determined through cross-correlation analysis, and the time difference corresponding to the correlation peak reflects the water hammer propagation delay. Propagation path tracing begins at the trigger point and calculates the arrival time at each node along the pipe network topology. The wavefront arrival time t_arrival = t_hammer + L / a, where t_hammer is the water hammer onset time, L is the propagation distance, and a is the pressure wave velocity. The timing of the reflected wave is determined by the round-trip time 2L / a. Periodic oscillations are identified through spectral analysis. The dominant frequency f = a / (4L) corresponds to the fundamental oscillation mode of the pipeline, where f is the oscillation frequency. Key moments are marked, including the initial impact, first reflection, and secondary reflection.

[0052] In some embodiments, the design of reverse pressure wave parameters in combination with the water hammer intensity distribution and the water hammer occurrence sequence includes: extracting the peak pressure amplitude based on the water hammer intensity distribution; determining the pressure wave arrival time according to the water hammer occurrence sequence; calculating the required reverse pressure value based on the peak pressure amplitude; and matching the required reverse pressure value with the pressure wave arrival time to generate reverse pressure wave parameters.

[0053] Based on the predicted water hammer intensity distribution, the peak pressure amplitude is extracted at each location within each flow path. A peak search algorithm traverses the pressure curve within a time window to identify local maxima. Peak determination criteria include the mathematical conditions of zero first-order derivative and negative second-order derivative. For multi-peak processing, the global maximum is selected as the primary peak, while secondary peaks are recorded for reference. Spatially distributed peaks are extracted along the pipeline axis, forming a continuous peak envelope. Statistical characteristics of the peaks, including maximum value, mean value, and standard deviation, are used to comprehensively assess the overall level and distribution of pressure surges. Abnormal peaks are identified and specially marked using the 3σ criterion to prevent extreme values ​​from influencing the overall analysis results. The time difference between the peak occurrence and the water hammer triggering moment reflects the pressure buildup process from the initial disturbance to the peak. The peak duration assesses the length of time that the pressure remains at a high level, which directly influences the design of the control strategy. Comparing peak values ​​across different flow paths identifies the most dangerous locations in the system and areas requiring the most protection. A safety margin is considered to be an additional 10%-20% reserve based on the peak value.

[0054] Based on the determined water hammer occurrence sequence, the propagation process and arrival time of the pressure wave in the pipe network are calculated. The propagation model uses the characteristic line method, tracing the trajectory of the wavefront along the pressure wave propagation path. The arrival time calculation considers both direct and reflected waves. The direct wave arrival time is t_arrive_direct = t_hammer + L / a, and the reflected wave arrival time is t_arrive_reflect = t_hammer + 2L / a, where t_hammer is the water hammer occurrence time, L is the propagation distance, and a is the pressure wave velocity. The complexity of the pipe network topology is addressed using graph theory algorithms to calculate the shortest propagation path between any two nodes. Wave distribution at branch nodes follows the principles of acoustic propagation, with the transmission and reflection coefficients determined by the impedance matching of the pipes. Multipath propagation processing requires identifying all possible propagation paths and calculating the arrival time for each path. Time accuracy is maintained at the millisecond level, fully meeting the response requirements of industrial control systems. Phase relationship analysis is used to determine the relative phase difference between pressure waves along different paths. Attenuation correction during propagation accounts for the impact of energy loss on arrival time. The influence of boundary conditions is adjusted accordingly through the endpoint reflection coefficient.

[0055] Based on the extracted peak pressure amplitude, the backpressure required to produce destructive interference is calculated. The core principle of backpressure design is based on the destructive interference phenomenon in physics. When two pressure waves of equal amplitude and opposite phases are superimposed, they achieve the ideal effect of complete cancellation. The ideal backpressure calculation formula is P_counter = -H_peak × α, where P_counter is the backpressure value, H_peak is the peak pressure, and α is a compensation factor to account for the non-ideal characteristics of the actual control system. The compensation factor is set between 0.8 and 1.0 to ensure sufficient cancellation while avoiding overcompensation and the generation of new reverse water hammer. The segmented compensation strategy decomposes the complete pressure curve into multiple independent segments, each of which is designed with a corresponding backpressure value based on its characteristics. Nonlinear correction is used to address the nonlinear propagation effects of large-amplitude pressure waves. The nonlinear correction formula is P_counter = -H_peak × α × (1 + β × H_peak / P_rated), where β is the nonlinear enhancement factor, ensuring effective cancellation at high pressures, and P_rated is the rated pressure of the pipeline. Safety constraints ensure that the calculated backpressure does not exceed the actual regulation capability of the system. The multi-point collaborative design approach generates reverse pressure simultaneously at multiple key locations in the pipeline network, enhancing the overall cancellation effect. Pressure gradient matching requires not only matching the pressure amplitude but also the pressure change rate.

[0056] The calculated reverse pressure value is precisely time-spaced and matched with the determined arrival time to generate complete reverse pressure wave parameters. The parameter matching process strictly adheres to the principle of causality, ensuring that reverse pressure is generated at the appropriate time before the water hammer pressure wave reaches the target location. A time lead of 50-100ms is set to compensate for the mechanical response delay of the actuator and the transmission delay of the control signal. Spatial correspondence is established to precisely align the reverse pressure application location with the water hammer peak. The parameter assembly process requires the definition of four key elements: the spatial location of the control point (such as valve number or pipeline mileage), the triggering time, the desired pressure value, and the duration of pressure maintenance. Waveform design requires the selection of an appropriate pressure relief curve type, with commonly used waveforms including step, ramp, and sinusoidal. The waveform generates the desired pressure pulse by rapidly adjusting the valve opening. The relationship between valve opening and pressure change is determined by a pre-calibrated characteristic curve. Complex waveforms are achieved by combining multiple step signals, with the duration of each segment calculated based on the desired waveform's frequency characteristics. Precise phase control ensures a 180° phase difference between the reverse pressure wave and the forward water hammer wave at the target location. A redundant design incorporates backup parameter sets at key system locations to enhance control reliability. Each control point is configured with complete action parameters, including when and where to actuate, how much, and how long to actuate. Together, these parameters form the execution plan for water hammer mitigation.

[0057] Using the designed reverse pressure wave parameters, water hammer mitigation control commands are generated that can be directly executed by the field control system. The command conversion process maps abstract physical parameters into a specific signal format that the controller can recognize and execute. The core components of valve control commands include the valve opening adjustment amount, adjustment rate, and precise timing. The valve opening is calculated based on the functional relationship between the flow coefficient Cv and pressure using the formula ΔCv = ΔQ / √(ΔP / SG), ensuring accurate flow regulation. The adjustment rate setting fully considers the physical limitations of the actuator. For emergency water hammer mitigation, a rapid adjustment mode is employed, enabling an opening change rate of 50%-100% per second, ensuring effective pressure regulation within 100ms. Multi-valve coordinated control is achieved through carefully designed timing and a unified clock synchronization mechanism. High-precision timestamps and preset trigger delay compensation ensure strict millisecond-level synchronization of valve operations. Priority management assigns the water hammer mitigation command the highest system priority, ensuring rapid response in emergency situations. Safety interlock logic prevents command conflicts or dangerous over-limit operations. The command format strictly adheres to the standard communication protocol for industrial control systems. An execution feedback channel is set up to monitor the effectiveness of control command execution in real time. A fault handling mechanism ensures that a backup control solution can be promptly activated if the primary command fails. The generated water hammer offset control command precisely adjusts valve opening and operation timing to generate a reverse pressure wave before the water hammer pressure wave arrives, achieving a destructive interference effect, effectively reducing pressure shock in the pipeline system and protecting equipment safety.

[0058] Step S140, obtain the pressure distribution characteristics of each path in the cascade action chain, identify the high-pressure area and the low-pressure area based on the pressure distribution characteristics, construct a flow field complementary coordination scheme based on the distribution of the high-pressure area and the low-pressure area, and use the flow field complementary coordination scheme to generate a pressure balance control strategy.

[0059] Based on the execution of the water hammer compensation control instruction, the abstract action nodes in the cascade action chain are converted into specific valve opening control sequences. This action conversion process is achieved by querying a pre-established action-valve mapping database, which records the specific valve operating parameters corresponding to each action type. After the valve control sequence executes, the pressure changes in each port are monitored in real time using the pressure sensor network deployed in step S110. The acquisition frequency is maintained at 100 Hz, and the real-time pressure distribution of each port after the action is executed is obtained. Pressure distribution feature extraction is performed using the pressure sensor network deployed at key nodes of each port. The sensor density is determined by the complexity of the flow field, with a typical spacing of 5-10 pipe diameters. The pressure distribution of each port reflects the residual pressure field after water hammer compensation control, consisting of steady-state and dynamic components. Characteristic parameters include pressure mean, pressure gradient, pressure fluctuation amplitude, and pressure distribution skewness. The spatial distribution is expanded into a continuous pressure field using a three-dimensional interpolation method, converting discrete measurement point data into a continuous pressure field. Temporal features are analyzed using a moving time window with a window width of 500 ms and an update frequency of 100 ms. The topological structure of the pressure field is identified through isobar analysis. Areas with dense isobars indicate large pressure gradients. Pressure coupling between multiple paths is assessed using the cross-correlation coefficient; a correlation coefficient greater than 0.7 indicates strong coupling. Anomalous pressure points are identified using a local outlier detection algorithm to prevent sensor failures from impacting the analysis. The pressure distribution nonuniformity index (NI) = σ_p / P_avg quantifies the degree of pressure distribution nonuniformity, where σ_p is the pressure standard deviation and P_avg is the average pressure. The pressure distribution characteristic data for each path comprehensively describes the flow field pressure state after the cascade action.

[0060] Based on the acquired pressure distribution characteristics, high-pressure and low-pressure areas in the flow field are identified through threshold segmentation and cluster analysis. A high-pressure area is defined as a continuous region with pressure values ​​exceeding the mean pressure plus 1.5-2.0 standard deviations. A low-pressure area is defined as a continuous region with pressure values ​​below the mean pressure minus 1.0-1.5 standard deviations. Region boundaries are determined using a pressure gradient operator, with boundary points marked when the absolute value of the gradient exceeds a set threshold. Connected domain analysis merges scattered high-pressure or low-pressure points into complete regions, with a minimum region area set at twice the square of the pipe diameter. Regional features extracted include center location, area size, mean pressure, and pressure extremes. Causes of high-pressure areas are identified through analysis, including channel contraction, valve throttling, or multi-stream confluence. Mechanisms of low-pressure areas include channel expansion, diversion points, or vortex regions. Regional stability is assessed through time series analysis, with stable regions exhibiting positional changes of less than 10% over the observation period. The migration speed and direction of dynamic regions are determined through centroid tracking. Interactions between regions are assessed through potential energy analysis, demonstrating that high-pressure regions drive low-pressure regions. The distribution map of high-pressure and low-pressure areas formed by the identification results clearly shows the pressure imbalance in the flow field, which provides an opportunity for energy redistribution.

[0061] In some embodiments, constructing a flow field complementary and collaborative scheme based on the distribution of the high-pressure area and the low-pressure area includes: extracting the pressure difference value between each area based on the distribution of the high-pressure area and the low-pressure area; determining the natural direction of pressure flow based on the pressure difference value; designing an auxiliary channel based on the natural direction of the pressure flow to obtain an optimized flow channel layout; and forming a flow field complementary and collaborative scheme through the optimized flow channel layout.

[0062] Based on the identified high- and low-pressure areas, the pressure differential between each pair of zones is systematically calculated. The pressure differential calculation formula, ΔP_ij = P_h_i - P_l_j - ΔP_loss, takes into account the average pressure of the high-pressure zone, P_h_i, the average pressure of the low-pressure zone, P_l_j, and the estimated long-range loss, ΔP_loss, between the two zones. Long-range loss is estimated using the Darcy-Weisbach formula: ΔP_loss = f × (L / D) × (ρv² / 2), where f is the friction coefficient, L is the flow length, D is the equivalent diameter, ρ is the fluid density, and v is the flow velocity. Effective pressure differential screening eliminates pairs with pressure differentials less than 0.05 times the working pressure to ensure that the driving force is sufficient to overcome flow resistance. The time-varying characteristics of the pressure differential are obtained through continuous monitoring, reflecting the dynamic characteristics of the pressure field. The spatially distributed pressure differential gradient indicates the direction of the most dramatic pressure change; a larger gradient indicates greater driving potential. The diagonal elements of the pressure differential matrix are zero, while the off-diagonal elements record the pressure differentials for all possible pairs of zones. Statistical analysis characterizes the probability distribution of differential pressure. The mean reflects the average driving capacity, and the variance indicates the stability of the differential pressure. Extreme differential pressures identify high-energy transfer pathways of particular concern. The differential pressure analysis results not only quantify the driving potential but also reveal the most valuable energy transfer pathways.

[0063] Based on the calculated pressure differential distribution, fluid mechanics principles are applied to determine the natural flow direction driven by pressure. The basic principle for flow direction is to flow from high-pressure areas to low-pressure areas, creating a natural release path for pressure potential energy. The direction vector is calculated as F_ij = -∇P × A_eff, where ∇P is the pressure gradient and A_eff is the effective flow area. The negative sign indicates flow in the direction of decreasing pressure. Streamline tracing starts from the center of the high-pressure area and proceeds in the direction of the maximum drop in the local pressure gradient. The length of each step is determined by the local flow velocity, and the tracing ends at the boundary of the low-pressure area. Path curvature constraints ensure smooth changes in flow direction. The radius of curvature must be no less than five times the pipe diameter to avoid local losses and flow separation caused by sharp turns. Flow distribution in multi-path scenarios follows the principle of minimum resistance, with the flow rate of each path proportional to its conductance. Flow around obstacles is calculated using potential flow theory. The velocity potential function satisfies the Laplace equation, and the no-penetration boundary condition is satisfied at the obstacle surface. Boundary layer effects reduce the flow velocity near the wall, and a logarithmic velocity distribution is used to correct the flow velocity in the mainstream. The three-dimensional flow is simplified into two main components: horizontal and vertical. The determination of the flow direction field provides theoretical guidance for the design of auxiliary channels.

[0064] Based on the determined natural direction of pressure flow, a flow regulation strategy is designed between channels. This regulation strategy does not physically add new channels, but rather creates a "virtual connection" between channels through intelligent valve opening coordination. This is achieved by synchronously adjusting the openings of multiple channels: when high pressure is detected in a channel, the opening of that channel is slightly reduced, while the openings of other channels are correspondingly increased, maintaining a constant total flow while redistributing pressure. Auxiliary channels provide dedicated pathways for the natural flow of pressure, and their design directly impacts the synergistic effect. Channel routing uses an improved optimal path algorithm to search for the optimal path in three-dimensional space. The heuristic function considers both distance and pressure drop. Path constraints include avoiding critical equipment, not crossing load-bearing structures, and maintaining a safe distance from existing pipelines. Channel dimensions are determined by flow continuity. The design flow rate is based on pressure differential and time requirements, allowing the flow rate to be limited to an economic flow rate range, from which the required cross-sectional area is calculated. The hydraulic diameter is optimized by selecting among circular, square, and elliptical cross-sections, taking into account flow resistance, manufacturing cost, and space constraints. The branching and converging design adheres to the principle of momentum conservation. Branching angles do not exceed 45°, and converging uses Y-shaped or curved transitions. Channel materials are selected based on fluid characteristics, pressure rating, and service life requirements; stainless steel, carbon steel, or composite materials are commonly used. Support and fixation take into account thermal expansion and contraction, as well as vibration, using a combination of elastic supports and guide brackets. The optimized layout of the auxiliary channel system ensures efficient connectivity between high- and low-pressure zones.

[0065] Through a designed optimization strategy, the opening control of each channel is coordinated to form a complete flow field complementary and synergistic solution. The core of this solution is to establish a "virtual complementary network" between channels: the pressure status of each channel is shared in real time to form a pressure sensing matrix; the optimal opening distribution for each channel is calculated based on this pressure sensing matrix; and a coordinated control algorithm is used to synchronously adjust the openings of multiple channels to achieve dynamic pressure balance. Solution integration is not a simple channel stacking, but a systematic flow field reconstruction. Flow distribution optimization establishes the objective function min Σ(f_i×L_i×ΔP_i), where f_i is the friction coefficient of the i-th segment, L_i is the pipe length, and ΔP_i is the pressure drop. Linear programming is used to solve the optimal flow distribution for each channel. Constraints include node flow balance, upper and lower pressure limits, and flow velocity not exceeding the maximum allowable value. Control valve type selection and characteristic matching consider flow range, pressure drop characteristics, and response speed. Valve placement follows the principle of proximity, with preference given to branching and confluence points. The directional nature of check valves ensures flow follows the designed path and prevents pressure reversal. Monitoring instruments, including pressure transmitters, flow meters, and thermometers, are deployed at key nodes to form a monitoring network. The startup procedure is designed to consist of three phases: preheating, low-flow trial operation, and normal operation. Emergency response plans cover abnormal operating conditions such as overpressure, leakage, and blockage. The implementation of a collaborative solution transforms previously independent flow paths into a mutually supportive integrated system.

[0066] Using the constructed flow field complementary synergy scheme, an implementable pressure balance control strategy is generated. The control objective function is set as J = w_1×(P_max-P_min) + w_2×E_total + w_3×Σ|dP / dt|, where J is the overall objective function value, w_1, w_2, and w_3 are weight coefficients, P_max and P_min are the maximum and minimum pressures in the system, respectively, E_total is the total system energy consumption, dP / dt is the rate of change of pressure at each monitoring point, and Σ represents the summation of all monitoring points. The first term (P_max-P_min) minimizes the pressure range to achieve uniform pressure distribution; the second term, E_total, reduces total energy consumption and improves operating economy; and the third term, Σ|dP / dt|, suppresses pressure fluctuations and enhances system stability. The weight coefficients are adjusted based on the importance of the operating condition, with typical values ​​of w_1=0.5, w_2=0.3, and w_3=0.2. Control variables include the valve opening of each channel, pump speed, and the auxiliary channel activation status. The model predictive control algorithm solves the optimization problem at each sampling moment. The prediction time domain is set to 10 times the control period to cover the main dynamic processes, and the control time domain is set to 3 times the control period to balance the computational complexity and control performance. The state-space model is obtained through system identification and includes pressure dynamics, flow coupling, and time-lag characteristics. Constraint processing uses soft constraint technology to allow short-term violations of non-critical constraints. The feedback correction link compensates for model errors and unmodeled dynamics. The hierarchical control architecture decomposes complex problems, with the upper layer optimizing the steady-state operating point and the lower layer tracking the dynamic trajectory. Anomaly detection is based on residual analysis and statistical process control. Fault tolerance maintains basic functions through control reconstruction. The pressure balance control strategy generated based on the flow field complementary collaborative scheme realizes intelligent pressure management of multi-diameter valve systems.

[0067] Step S150, construct a multi-valve collaborative control sequence based on the water hammer offset control instruction and the pressure balance control strategy, perform pressure wave propagation path analysis through the multi-valve collaborative control sequence to determine the pressure wave superposition elimination point, extract the pressure pulse resonance characteristics from the pressure wave superposition elimination point, and generate timing control parameters based on the pressure pulse resonance characteristics.

[0068] Specifically, water hammer cancellation control instructions and pressure balancing control strategies are integrated to construct a multi-valve coordinated control sequence. Water hammer cancellation control instructions include the action time, pressure value, and duration of each control point. The pressure balancing control strategy provides optimized trajectories for control variables such as valve opening and pump speed. The core of this sequence construction is to coordinate the two control actions to avoid mutual interference and maximize synergy. The timeline uses millisecond-level precision, with the control system's sampling period of 100ms as the basic time unit. Priority allocation assigns water hammer cancellation instructions a high priority, allowing them to be executed first at critical moments, while pressure balancing adjustments run continuously as background control. A conflict detection algorithm identifies duplicate instructions for the same actuator and resolves them through time window merging or staggered execution. A synergistic design schedules pre- and post-pressure balancing adjustments before and after the water hammer cancellation action to enhance control effectiveness. The spatial distribution of multiple valves takes into account upstream and downstream relationships, with upstream valves actuating earlier than downstream ones. Propagation delay is calculated based on pipeline length and wave velocity. A hybrid parallel and serial arrangement allows for parallel control of independent paths and serial coordination of coupled paths. The fault-tolerant timing design inserts buffer time between key actions to prevent timing disruptions caused by execution delays. The constructed multi-valve coordinated control sequence includes the precise timing and execution order of all control actions.

[0069] By constructing a multi-valve coordinated control sequence, the propagation paths and interactions of pressure waves in the pipeline network are analyzed. Each control action generates a pressure disturbance, which propagates in the form of pressure waves. Propagation path analysis begins at each valve position and tracks the trajectory of the pressure waves along the pipeline. Wave velocity calculation takes into account fluid properties and pipeline elasticity, with typical values ​​ranging from 1000 to 1400 m / s. Multi-source pressure waves are identified using a timing table to determine the actuation timing of each valve and calculate the pressure waves generated by each source. Branching and merging during the propagation process occur at nodes such as tees and crosses, following the principles of acoustic propagation. Reflections occur at pipeline ends, valve closing positions, and sudden changes in pipe diameter, with the reflection coefficient determined by impedance matching. Superposition effect analysis uses the principle of linear superposition: when multiple pressure waves intersect at the same spatial and temporal point, the pressure amplitudes are algebraically added. The cancellation condition determines when two equal-amplitude, anti-phase waves completely cancel each other, with a phase difference of nearly 180°. A spatiotemporal scan is performed across the gridded pipeline network, calculating the pressure superposition results at each grid point at different times. The cancellation effect is quantified by the ratio of the pressure amplitude after superposition to the original amplitude. The pressure wave superposition cancellation points are concentrated in specific spatial locations and time windows, and the pressure pulsation at these points is effectively suppressed.

[0070] In some embodiments, extracting the pressure pulse resonance characteristics from the pressure wave superposition elimination point includes: monitoring the pressure pulse frequency based on the pressure wave superposition elimination point; comparing the pressure pulse frequency with the system natural frequency to obtain frequency matching; identifying the resonance enhancement interval based on the frequency matching; and extracting amplitude and phase information from the resonance enhancement interval as the pressure pulse resonance characteristics.

[0071] A pressure pulse frequency monitoring scheme is established based on the location information of the pressure wave superposition cancellation points. Monitoring points are selected where the superposition cancellation effect is significant but residual pulsation still exists, as these locations are most sensitive to frequency characteristics. Data acquisition utilizes high-speed sampling. Sampling theorem requires the sampling frequency to be at least twice the highest frequency of interest; in practice, it is set to 10 times to improve accuracy. Filter preprocessing removes DC components and low-frequency drift, retaining pulse components between 1Hz and 500Hz. The frequency extraction algorithm utilizes an adaptive notch filter bank to track changes in key frequency components in real time. Instantaneous frequency calculation uses a Hilbert transform to obtain an analytical representation of the signal, and the instantaneous frequency is derived from the phase derivative. Frequency stability assessment calculates the standard deviation of the frequency within a specific time window; stable frequency components are more likely to cause resonance. The spatial distribution of multiple frequency points reflects the differences in pulse characteristics across different regions. Frequency evolution tracking records the temporal trends of key frequency components. Abnormal frequency detection identifies frequency components that suddenly appear or disappear. The monitored pressure pulse frequency spectrum provides the foundational data for resonance analysis.

[0072] The monitored pressure pulse frequency is systematically compared and analyzed against the predetermined system natural frequencies. The system natural frequency database contains the characteristic frequencies of the pipeline acoustic modes, structural vibration modes, and fluid-structure interaction modes. The frequency matching formula is M = 1 - |f_pulse - f_natural| / f_natural, where f_pulse is the pulse frequency and f_natural is the closest natural frequency. An M close to 1 indicates a high match. Multimodal matching accounts for the possibility that a pulse frequency may approach multiple natural frequencies simultaneously. Weights are assigned based on the participation factors of each mode, with modes with larger participation factors having a more significant impact. Frequency drift compensation considers the impact of operating conditions such as temperature and pressure on the natural frequency, and corrects the natural frequency value in real time. The time-varying characteristics of the matching degree are analyzed using a sliding window with a window width of 1 second and an update period of 100ms. Statistical distribution analysis of the probability density function of the matching degree identifies the frequency range where resonance is most likely to occur. A threshold setting marks cases with a matching degree greater than 0.9 as potential resonance.

[0073] Exemplarily, identifying the resonance enhancement interval based on the frequency matching degree includes: evaluating the frequency proximity coefficient based on the frequency matching degree; predicting the amplitude amplification factor based on the proximity coefficient; determining the resonance starting point based on the moment when the amplitude amplification factor exceeds a threshold; and marking the moment from the resonance starting point to the moment when the amplitude amplification factor falls back as the resonance enhancement interval.

[0074] First, based on the frequency matching degree M, a more refined frequency proximity coefficient is calculated. A nonlinear transformation is applied to the proximity coefficient K = M² / (1-M²+ε), where ε=0.01 prevents the denominator from being zero. This transformation amplifies variations in high matching degrees. Multi-frequency superposition is handled through vector synthesis, with K_total = √(ΣK_i²) accounting for the combined impact of multiple frequencies. A temperature correction factor compensates the K value based on the actual temperature, increasing the correction factor by 2% for every 10°C increase in temperature. Pressure correction similarly accounts for the impact of operating pressure on resonance characteristics. Historical data fitting establishes an empirical relationship between the K value and the actual resonance intensity. A confidence interval calculation provides an uncertainty range for the K value, which is used for risk assessment. Temporal smoothing uses a moving average to eliminate rapid fluctuations in the K value. Spatial interpolation estimates the K value distribution between monitoring points. Next, based on the proximity coefficient K, the amplitude amplification factor under resonance conditions is predicted. The prediction model is based on the response characteristics of a single-degree-of-freedom resonant system. The amplification factor A = 1 / √((1-K)² +(2ζK)²), where ζ is the system damping ratio. The damping ratio is estimated through free vibration attenuation tests or the half-power bandwidth method, with a typical value of 0.02-0.05. Nonlinear correction is taken into account for large amplitudes: A_corrected = A×(1-β×A²), where β is the nonlinear coefficient. Multimodal coupling is handled through modal superposition, and the total amplification factor is the vector sum of the contributions of each mode. Confidence assessment assesses the reliability of the prediction based on historical prediction error statistics. Parameter sensitivity analysis identifies key factors affecting prediction accuracy. Real-time correction adjusts model parameters based on the deviation between the measured amplitude and the predicted value. The prediction time domain is extended to 10 sampling periods into the future to provide early warning. The predicted amplitude amplification factor is then used to determine the onset of resonance. The resonance onset is determined using a dual criterion: the amplification factor A exceeds the set threshold A_th=3 and the growth rate dA / dt>0. The threshold is adaptively adjusted based on the background vibration level: A_th = 3 + 2×σ_background, where σ_background is the standard deviation of the background vibration. The precise location of the starting point is achieved through a reverse search, finding the moment when A first exceeds 0.9×A_th. The resonance development process is divided into three stages: rapid growth, quasi-steady-state, and decay. Peak identification not only focuses on the maximum value but also records sub-peaks for pattern analysis. Duration estimation is based on the system's quality factor (Q) and the excitation characteristics. The end of resonance is determined when A falls below 1.5 for three sampling cycles. Finally, the resonance enhancement interval is marked from the resonance onset to the moment when the amplitude gain decreases. This is achieved by continuously monitoring the amplitude gain A(t). When A(t) decreases monotonically from the peak value and maintains a downward trend for more than five consecutive sampling cycles, the decay begins. The end of the interval is defined as the first moment when A(t) falls below the threshold A_th. Stability is confirmed by requiring A(t) to remain below the threshold for the next three sampling cycles.The interval duration is the time difference between the end and start times, with typical values ​​ranging from half a second to three seconds. The energy integral within the interval, E_resonance = ∫[t_start to t_end] A²(t)dt, quantifies the overall level of resonance intensity. Multiple interval processing: When a single excitation generates multiple resonance peaks, each is marked as a subinterval and its characteristics are calculated. Interval overlap detection prevents confusion between adjacent resonance intervals, assigning the overlapping portion to the interval with stronger energy. An interval database records all identified resonance enhancement intervals and their characteristic parameters.

[0075] From the identified resonance enhancement intervals, amplitude and phase information are extracted as complete resonance features. Amplitude feature extraction includes peak amplitude, average amplitude, amplitude envelope, and amplitude modulation depth. Peak amplitude directly reads the maximum value within the interval, while average amplitude is obtained through integral averaging. The amplitude envelope is extracted through Hilbert transform, reflecting slow changes in amplitude. Phase features focus on initial phase, phase drift rate, and phase jumps. The initial phase is determined by the signal phase at the start of the interval. Phase drift is derived by linearly fitting the slope of the phase-time curve. Instantaneous features include the joint distribution of instantaneous frequency and instantaneous amplitude. Statistical features calculate the higher-order moments of the amplitude distribution to assess the stability of the resonance. Spatial coherence analyzes the phase relationship between resonances at different locations. Time-frequency features obtain the time-frequency energy distribution of the resonance through wavelet transform. Feature vector construction combines all features into a high-dimensional vector for pattern recognition.

[0076] Based on the extracted pressure pulse resonance characteristics, optimized timing control parameters are generated. The goal of parameter generation is to suppress harmful resonances and utilize beneficial resonances to achieve stable and efficient system operation. The suppression strategy targets resonances with excessive amplitude by adjusting the control timing to disrupt the resonance conditions. A timing offset of Δt = T_resonance / 4 creates a 90° phase difference between the excitation and response, where T_resonance is the resonance period. Frequency modulation fine-tunes the valve actuation rate to shift the excitation frequency by at least 5% from the resonant frequency. Amplitude modulation reduces control intensity in the resonance region, minimizing energy input. Phase control introduces a specific phase difference between multiple valves, allowing the pressure waves generated by each valve to suppress each other. A strategy is used to identify beneficial low-amplitude resonances, enhancing transmission efficiency through synchronized control. Parameter optimization utilizes a genetic algorithm, with the objective function combining resonance suppression effectiveness and control energy consumption. Constraints ensure that parameters are within the actuator's capabilities. Robust design considers parameter uncertainty and maintains performance near nominal values. The generated parameter table includes actuation timing correction, rate adjustment coefficient, and amplitude scaling factor for each valve. The resulting timing control parameters proactively manage pressure pulse resonance.

[0077] In step S160, the timing control parameters are matched with the cascade action chain to generate a synchronous control node, the action time of each path is determined based on the synchronous control node, and a time-sharing control sequence is generated according to the action time to complete the intelligent coordinated switching control of the multi-path valve flow channel.

[0078] In some embodiments, the timing matching of the timing control parameters with the cascade action chain to generate a synchronization control node includes: performing delay characteristic analysis on the timing control parameters to identify fixed delay components and variable delay components; comparing the fixed delay components and the variable delay components with the cascade action chain to obtain timing deviations; performing compensation calculations based on the timing deviations to determine the optimal synchronization moment; and marking the control point corresponding to the optimal synchronization moment as a synchronization control node.

[0079] Conduct an in-depth analysis of the delay characteristics of the timing control parameters to distinguish different types of delay components. The action-time correction within the obtained timing control parameters inherently includes pre-compensation for system delays, requiring analysis for both adequacy and accuracy. Fixed delay component analysis identifies the constant compensation component within the correction. This component corresponds to the physical propagation time t_prop = L / a (where t_prop is the propagation time, L is the propagation distance, and a is the speed of sound of the pressure wave), the inherent actuator delay (typically 20-50ms), and the signal processing delay (approximately 10ms). Comparing the correction component within the parameters with theoretically calculated values ​​assesses the rationality of the compensation. Variable delay component analysis analyzes the dynamically adjusted portion of the parameters. The rate adjustment coefficient affects the action completion time, thereby generating variable delay. When the rate coefficient is less than 1, the action slows down, increasing the equivalent delay by Δt_var = (1 / k_rate - 1) × t_nominal, where k_rate is the rate adjustment coefficient and t_nominal is the nominal action time. While the amplitude scaling factor primarily affects control intensity, it also indirectly affects response time by changing valve travel. Parameter coupling analysis analyzes the combined delay characteristics of multiple control parameters acting simultaneously, revealing the mutual influence between them. Statistical features extract the distribution patterns of parameters under different operating conditions, showing that fixed compensation components remain stable while variable components exhibit a normal distribution. This delay analysis identifies both considered and under-considered delay factors within the control parameters.

[0080] The identified fixed and variable delay components are compared in detail with the timing requirements of the cascaded action chain. The cascaded action chain defines the ideal action timing, including the time intervals between nodes and causal dependencies. The timing construction process converts the abstract action nodes into a specific time schedule. The time intervals between nodes are determined by the difference in triggering times, and the causal dependencies are reflected in the order of the nodes. The comparison method uses timing diagram alignment to overlay the actual delays onto the ideal timing. Fixed delay comparison directly calculates the offset Δt_fixed = t_actual_fixed - t_ideal, where t_actual_fixed is the actual time including the fixed delay, and t_ideal is the ideal design time. Variable delay comparison considers its randomness and calculates the expected deviation and variance. Cumulative effect analysis analyzes the propagation and amplification of delays in multi-stage cascades. Critical path analysis identifies the control paths with the greatest delay impact. Timing margin assessment assesses whether the time margin reserved in the original design is sufficient to cover the actual delay. Deviation types include constant offset, proportional offset, and random jitter. Spatial distribution analysis analyzes delay differences at different locations, with delays typically greater at the far end. The time-varying characteristic considers the drift trend of the delay over the running time. The comparison results form a deviation matrix, which records the timing deviation of each node under different working conditions.

[0081] Based on the acquired timing deviation, a system performs compensation calculations to determine the optimal synchronization time that eliminates or minimizes the deviation. The compensation strategy uses a combination of feedforward and feedback. Feedforward compensation is based on known fixed delays, while feedback compensation accounts for variable delays. Fixed delay compensation directly advances the time by the corresponding amount. The compensated time is t_comp = t_orig - Δt_fixed, where t_orig is the original time. Variable delay compensation uses statistical prediction: t_comp = t_orig - (μ_var + k × σ_var), where μ_var is the mean of the variable delay, σ_var is the standard deviation, and k is the confidence factor, with a value of 1.96 corresponding to a 95% confidence level. Multi-node synchronization uses the least squares method, with the objective function min Σ(t_i_actual - t_i_target)². The time of each node is adjusted to minimize the total deviation. Constraints include maintaining causality and limiting execution capacity. Robust design ensures synchronization is maintained within a delay variation of ±20%. Sensitivity analysis identifies the parameters that most impact synchronization. The result of the compensation calculation is a set of optimized control moments that fully consider the influence of various delay factors.

[0082] The calculated optimal synchronization time is mapped to the control system and marked as a synchronized control node. The marking process first locates each optimal synchronization time on the timeline with an accuracy of 1ms. Node attributes include key information such as node ID, timestamp, list of participating paths, and synchronization type. Synchronization types are divided into strong and weak synchronization. Strong synchronization requires that the time deviation of each path action does not exceed ±5ms, while weak synchronization allows a deviation of ±20ms. Spatial mapping associates time nodes with specific control devices and measurement point locations. Priority is assigned based on the node's impact on system performance, with pressure balance-related nodes receiving high priority and auxiliary function nodes receiving low priority. Trigger conditions can be set in two modes: time triggering and event triggering. Under normal circumstances, triggering is based on time, while special operating conditions can be triggered by events. Node association establishes logical relationships between synchronized nodes, including predecessor, successor, and mutually exclusive relationship types. Visualization uses a Gantt chart to intuitively display the distribution of synchronized nodes on the timeline. Through a systematic marking process, a complete set of synchronized control nodes is formed, each containing precise time and space information.

[0083] Based on the generated synchronization control nodes, the action timing of each path in coordinated control is precisely determined. Synchronization control nodes define the time anchor points at which multiple paths need to coordinate their actions, and the specific action times of each path are scheduled around these anchor points. Time calculation first determines the absolute time of each synchronization node, establishing a unified time coordinate with the system startup time as the zero point. Path priority is determined based on fluid dynamic characteristics and control objectives. The primary path, responsible for the primary flow regulation task, is prioritized, while auxiliary paths coordinate with the primary path. Propagation delay compensation is calculated based on the physical length and pressure wave velocity of each path to ensure that downstream actions are executed after upstream influences arrive. Parallel actions identify actions that can be executed simultaneously under the same synchronization node, improving control efficiency. Serialization constraints ensure that causally dependent actions are executed in the correct order, with subsequent actions initiated only after the preceding action completes. The action window sets the permissible execution time range [t_min, t_max] for each action, which can be fine-tuned based on real-time conditions. A safety interval maintains a minimum of 50ms between adjacent actions to prevent interference caused by overlapping actions. Special timing arrangements are designed for special operating conditions such as system startup, normal operation, and operating mode switching. The determination of the action time of each path transforms the abstract synchronization requirement into a specific execution schedule.

[0084] A time-sharing control sequence is generated based on the determined action moments, enabling intelligent coordinated switching control of multi-path valve flow paths. Time-sharing control breaks down the continuous control process into discrete time slices, each set to 100ms, consistent with the control system's sampling period. Sequence encoding uses a structured format, with each instruction containing five elements: a timestamp, path identifier, action type, target value, and duration. Action types cover basic control functions such as valve opening adjustment, flow setting, and pressure regulation. The target value is calculated based on the S150's amplitude scaling factor and current operating conditions to ensure appropriate control action intensity. The gradual control strategy breaks down large adjustments into multiple small steps, with each step varying no more than 10% to avoid system shock. Parallel instructions allow control instructions to be issued simultaneously for multiple paths within the same time slice, improving response speed. Conditional logic determines subsequent actions based on the results of the previous time slice, achieving closed-loop control. Sequence integrity is ensured through head and tail markers and a checksum, ensuring error-free transmission. Control instructions are sent to each actuator through the fieldbus, and the actuator executes accurately according to the timestamp, ultimately completing the intelligent coordinated switching control of the multi-diameter valve flow channel.

[0085] In order to implement the intelligent coordinated switching control method of the multi-path valve flow channel corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The following is a block diagram of a multi-path valve flow channel intelligent coordinated switching control system 200 provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The multi-path valve flow channel intelligent coordinated switching control system 200 provided in an embodiment of the present application includes:

[0086] The flow state identification module 201 is used to collect real-time fluid parameters of the multi-path valve, perform feature analysis on the real-time fluid parameters to identify the current flow channel working mode, and construct a flow channel state transition matrix using the flow channel working mode;

[0087] A path planning module 202 is configured to perform reverse path deduction using the flow channel state transition matrix to generate a candidate path set, perform pressure wave timing analysis on the candidate path set to identify leveraged switching points, generate a sequential leveraged switching sequence based on the leveraged switching points, and construct a cascade action chain based on the sequential leveraged switching sequence;

[0088] a water hammer control module 203 configured to extract the pressure gradient distribution and flow velocity variation curve of each path from the cascade action chain, predict the water hammer intensity distribution based on the pressure gradient distribution, determine the water hammer occurrence sequence based on the flow velocity variation curve, design reverse pressure wave parameters based on the water hammer intensity distribution and the water hammer occurrence sequence, and generate water hammer compensation control instructions using the reverse pressure wave parameters;

[0089] A collaborative control module 204 is configured to obtain pressure distribution characteristics of each path in the cascade action chain, identify high-pressure areas and low-pressure areas based on the pressure distribution characteristics, construct a flow field complementary collaborative scheme based on the distribution of the high-pressure areas and the low-pressure areas, and generate a pressure balance control strategy using the flow field complementary collaborative scheme;

[0090] a timing optimization module 205 for constructing a multi-valve coordinated control timing sequence based on the water hammer offset control instruction and the pressure balance control strategy, performing pressure wave propagation path analysis through the multi-valve coordinated control timing sequence to determine a pressure wave superposition elimination point, extracting a pressure pulse resonance feature from the pressure wave superposition elimination point, and generating a timing control parameter based on the pressure pulse resonance feature;

[0091] The execution control module 206 is used to perform timing matching between the timing control parameters and the cascade action chain to generate a synchronous control node, determine the action moment of each path based on the synchronous control node, generate a time-sharing control sequence according to the action moment, and complete the intelligent coordinated switching control of the multi-path valve flow path.

[0092] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0093] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. An intelligent coordinated switching control method for multi-path valve flow channels, characterized in that: include: Collecting real-time fluid parameters of the multi-path valve, performing feature analysis on the real-time fluid parameters to identify the current flow channel working mode, and constructing a flow channel state transfer matrix using the flow channel working mode; Performing reverse path deduction through the flow channel state transfer matrix to generate a candidate path set, performing pressure wave timing analysis on the candidate path set to identify leveraged switching points, generating a time-series leveraged switching sequence based on the leveraged switching points, and constructing a cascade action chain based on the time-series leveraged switching sequence; Extracting the pressure gradient distribution and flow velocity variation curve of each path from the cascade action chain, predicting the water hammer intensity distribution based on the pressure gradient distribution, determining the water hammer occurrence sequence based on the flow velocity variation curve, designing reverse pressure wave parameters based on the water hammer intensity distribution and the water hammer occurrence sequence, and generating water hammer compensation control instructions using the reverse pressure wave parameters; Obtaining pressure distribution characteristics of each path in the cascade action chain, identifying high-pressure areas and low-pressure areas based on the pressure distribution characteristics, constructing a flow field complementary and collaborative scheme based on the distribution of the high-pressure areas and the low-pressure areas, and generating a pressure balance control strategy using the flow field complementary and collaborative scheme; constructing a multi-valve coordinated control sequence based on the water hammer offset control instruction and the pressure balance control strategy, performing pressure wave propagation path analysis through the multi-valve coordinated control sequence to determine a pressure wave superposition elimination point, extracting a pressure pulse resonance feature from the pressure wave superposition elimination point, and generating a timing control parameter based on the pressure pulse resonance feature; The timing control parameters are matched with the cascade action chain to generate a synchronous control node, the action moment of each path is determined based on the synchronous control node, and a time-sharing control sequence is generated according to the action moment to complete the intelligent coordinated switching control of the multi-path valve flow channel.

2. The method according to claim 1, characterized in that The generating of a candidate path set by performing reverse path deduction through the flow channel state transition matrix includes: Extracting switching cost values ​​between states based on the flow channel state transition matrix; Obtaining a total path cost by reversely accumulating the switching cost value from the target state; Based on the total path cost, multiple paths with the lowest cost are screened out to form a candidate path set.

3. The method according to claim 1, characterized in that The performing pressure wave timing analysis on the candidate path set to identify the leveraged switching point includes: extracting pressure wave propagation characteristics of each path based on the candidate path set; Performing time-series energy analysis on the pressure wave propagation characteristics to obtain energy overlapping areas; The energy overlap region determines the switching point that can be leveraged.

4. The method according to claim 1, wherein The step of constructing a cascade action chain based on the timing-based switching sequence includes: Extracting the remaining energy value of each switching point based on the timing-based switching sequence; identifying an available energy window based on the remaining energy value; Determining an optimal triggering time based on the energy window; According to the optimal triggering timing, the timing leveraging switching sequence is connected in series to form a cascade action chain.

5. The method according to claim 1, characterized in that The designing of reverse pressure wave parameters in combination with the water hammer intensity distribution and the water hammer occurrence sequence includes: extracting a peak pressure amplitude based on the water hammer intensity distribution; determining the arrival time of the pressure wave according to the water hammer occurrence sequence; calculating a required reverse pressure value based on the peak pressure amplitude; The required reverse pressure value is matched with the pressure wave arrival time to generate reverse pressure wave parameters.

6. The method according to claim 1, characterized in that The flow field complementary synergistic scheme constructed according to the distribution of the high-pressure area and the low-pressure area includes: Extracting the pressure difference value between each area based on the distribution of the high pressure area and the low pressure area; determining a natural direction of pressure flow according to the pressure differential value; Designing auxiliary channels based on the natural direction of the pressure flow to obtain an optimized flow channel layout; A flow field complementary and coordinated solution is formed by optimizing the flow channel layout.

7. The method according to claim 1, characterized in that The extracting of the pressure pulse resonance feature from the pressure wave superposition elimination point comprises: monitoring the pressure pulse frequency based on the pressure wave superposition elimination point; Comparing the pressure pulse frequency with the system natural frequency to obtain a frequency matching degree; identifying a resonance enhancement interval according to the frequency matching degree; Amplitude and phase information are extracted from the resonance enhancement interval as pressure pulse resonance features.

8. The method according to claim 1, characterized in that The step of performing time sequence matching between the timing control parameter and the cascade action chain to generate a synchronization control node includes: Performing delay characteristic analysis on the timing control parameters to identify fixed delay components and variable delay components; Comparing the fixed delay component and the variable delay component with the cascade action chain to obtain a timing deviation; Perform compensation calculation based on the timing deviation to determine the optimal synchronization time; The control point corresponding to the optimal synchronization moment is marked as a synchronization control node.

9. The method according to claim 7, characterized in that The identifying the resonance enhancement interval according to the frequency matching degree includes: evaluating a frequency closeness coefficient based on the frequency matching degree; predicting an amplitude magnification factor based on the proximity coefficient; Determining a resonance starting point based on the moment when the amplitude amplification factor exceeds a threshold; The time from the resonance starting point to the moment when the amplitude amplification factor falls back is marked as the resonance enhancement interval.

10. An intelligent coordinated switching control system for multi-path valve flow channels, characterized in that: include: A flow state recognition module is used to collect real-time fluid parameters of the multi-path valve, perform feature analysis on the real-time fluid parameters to identify the current flow channel working mode, and construct a flow channel state transition matrix using the flow channel working mode; a path planning module configured to perform reverse path deduction using the flow channel state transition matrix to generate a candidate path set, perform pressure wave timing analysis on the candidate path set to identify leveraged switching points, generate a sequential leveraged switching sequence based on the leveraged switching points, and construct a cascade action chain based on the sequential leveraged switching sequence; a water hammer control module, configured to extract the pressure gradient distribution and flow velocity variation curve of each path from the cascade action chain, predict the water hammer intensity distribution based on the pressure gradient distribution, determine the water hammer occurrence timing based on the flow velocity variation curve, design reverse pressure wave parameters based on the water hammer intensity distribution and the water hammer occurrence timing, and generate water hammer compensation control instructions using the reverse pressure wave parameters; a collaborative control module, configured to obtain pressure distribution characteristics of each path in the cascade action chain, identify high-pressure areas and low-pressure areas based on the pressure distribution characteristics, construct a flow field complementary collaborative scheme based on the distribution of the high-pressure areas and the low-pressure areas, and generate a pressure balance control strategy using the flow field complementary collaborative scheme; a timing optimization module for constructing a multi-valve coordinated control timing based on the water hammer offset control instruction and the pressure balance control strategy, performing pressure wave propagation path analysis through the multi-valve coordinated control timing to determine a pressure wave superposition elimination point, extracting a pressure pulse resonance feature from the pressure wave superposition elimination point, and generating a timing control parameter based on the pressure pulse resonance feature; The execution control module is used to perform timing matching between the timing control parameters and the cascade action chain to generate a synchronous control node, determine the action moment of each path based on the synchronous control node, generate a time-sharing control sequence according to the action moment, and complete the intelligent coordinated switching control of the multi-path valve flow channel.

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