Multi-drift-diameter valve system capable of intelligently and cooperatively switching runners and control method of multi-drift-diameter valve system

Abstract

The invention discloses a multi-drift-diameter valve system capable of intelligently and cooperatively switching runners and a control method of the multi-drift-diameter valve system, and belongs to the technical field of fluid control. The system comprises a cooperative controller, a plurality of valve bodies, a plurality of valve cores, a plurality of sensing arrays and a plurality of driving modules, each valve body is provided with a plurality of flow channel interfaces, the flow channel interface of each valve body is connected with the flow channel interface of the next valve body through a pipeline to form a flow channel network, the valve cores are rotatably arranged in each valve body, and the driving modules are connected with the valve cores. The valve element is connected with the driving module and used for being communicated with different flow channel connectors, a sensing array is arranged at an inlet of each flow channel connector and used for obtaining inlet pressure and instantaneous flow of the flow channel connector, the valve element is connected with the driving module, and the sensing arrays and the driving module are connected with the cooperative controller. Through energy field reconstruction and virtual impedance active control, essential smooth switching of multiple flow channels is achieved, energy consumption and mechanical wear are remarkably reduced, and the self-adaptive cooperative capability is achieved.

Description

Technical Field

[0001] This invention relates to the field of fluid control technology, specifically to a multi-port valve system with intelligent cooperative switching of flow channels and its control method. Background Technology

[0002] In complex fluid transport systems, such as petrochemicals, centralized heating, and intelligent building air conditioning networks, multi-port valves are widely used to change fluid flow direction, regulate flow rate, and distribute pressure. Traditional multi-port valve control strategies are mainly divided into two categories: one is mechanical motion decomposition based on independent or sequential control, that is, driving each valve port to move sequentially or individually according to preset instructions, converting the switching instructions into the valve core rotation sequence and the opening and closing sequence of each valve; the other is setpoint control based on independent PID controllers, that is, each valve independently adjusts according to the measurement value of local sensors to maintain the setpoint.

[0003] However, the aforementioned traditional methods have the following inherent drawbacks: First, there is a lack of awareness of the overall energy state of the system. Traditional methods only collect scattered pressure and flow signals, which are isolated from each other and cannot form a unified understanding of the energy flow distribution within the system. The controller cannot know from which channel the current energy flows to which channel, the magnitude of the transmission power, or even the degree of disorder in the energy distribution, resulting in a lack of global information support for control decisions.

[0004] Second, the control objective remains at the mechanical level, failing to address the fundamental energy source. Traditional methods directly decompose switching commands into a sequence of mechanical actions of actuators (such as rotating the main valve core to a certain angle, or opening a fine-tuning valve to a certain degree), using mechanical position and timing as the control objective. This approach ignores the fact that flow channel switching is essentially a process of redistribution of fluid energy within the system; mechanical actions are merely a means, not an end. Due to the lack of direct control over the energy evolution process, the switching process is highly susceptible to triggering severe pressure fluctuations, resulting in water hammer, which can lead to pipeline vibration, noise, and even equipment damage.

[0005] Third, the system impedance characteristics cannot be dynamically reconfigured; it can only passively respond to fluctuations. In traditional methods, the valve impedance is fixed by its physical structure, and the control algorithm can only adjust the impedance by changing the valve opening, but it cannot change the dynamic characteristics of the actuator itself (such as damping and stiffness). When faced with transient shocks caused by switching, the system can only passively withstand the pressure wave, at most mitigating it through slow action or by adding hardware such as accumulators, but it cannot actively suppress the generation of shocks at the source.

[0006] Fourth, there is a lack of real-time coordination and global optimization among multiple actuators. The main valve core and each fine-tuning choke valve are usually driven by independent controllers or independent control loops, and their coordination depends on pre-set timing logic. This open-loop, time-based coordination method cannot cope with the dynamic changes in actual operating conditions. Once the response of a certain actuator deviates, the entire coordination process will become untuned, causing the energy field evolution to deviate from the expected trend, and even triggering system instability.

[0007] Fifth, the system has poor robustness and is sensitive to sensor failures. Existing control systems rely heavily on the accuracy of sensor inputs. If a pressure or flow sensor fails, it can lead to decreased control accuracy, or even control logic disorder or malfunctions. Traditional solutions usually involve adding redundant hardware, but this significantly increases cost and system complexity.

[0008] In summary, existing multi-port valve control systems suffer from several technical problems, including an inability to perceive the overall system energy state, control objectives remaining at the mechanical level, an inability to actively reconfigure impedance characteristics, weak multi-actuator coordination, and poor robustness to sensor failures. These issues lead to severe hydraulic shocks, high energy dissipation, significant equipment wear, and low system reliability during flow channel switching. Therefore, there is an urgent need for an intelligent valve system and its control method capable of sensing, planning, and actively reshaping the multi-channel switching process from its energy source. Summary of the Invention

[0009] The purpose of this invention is to provide a multi-port valve system and its control method with intelligent collaborative switching of flow channels. Through energy field reconstruction and virtual impedance active control, it achieves inherently smooth switching of multiple flow channels, significantly reduces energy consumption and mechanical wear, and has adaptive collaborative capabilities.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A multi-bore valve system with intelligent collaborative flow channel switching includes: a collaborative controller and multiple valve bodies, valve cores, sensor arrays, and a drive module. Each valve body is provided with multiple flow channel interfaces, and each flow channel interface of the valve body is connected to the flow channel interface of the next valve body through a pipeline, forming a flow channel network. The valve core is rotatably installed inside each valve body for connecting different flow channel interfaces. A sensor array is provided at the inlet of each flow channel interface for acquiring the inlet pressure and instantaneous flow rate of the flow channel interface. The valve core is connected to the drive module, and the sensor array and drive module are connected to the collaborative controller. The collaborative controller includes: The state perception and energy field construction unit is used to construct the current fluid energy field distribution matrix based on the inlet pressure and instantaneous flow rate; The dynamic path planning and energy shaping unit is used to generate a continuous energy field migration trajectory based on the target flow channel switching command and the current fluid energy field distribution matrix. Virtual impedance adaptation and transient shaping unit, used to calculate virtual impedance adaptation vector based on energy field migration trajectory; A drive control unit is used to drive the drive module according to the virtual impedance adaptation vector.

[0011] Furthermore, the drive module includes a main drive motor, which is connected to the valve core and used to drive and control the rotation of the valve core. The main drive motor is also connected to the drive control unit.

[0012] Furthermore, each of the flow channel interfaces is also provided with a fine-tuning flow control valve, the drive motor of which is connected to the drive control unit.

[0013] This invention also provides a control method for a multi-port valve system with intelligent cooperative switching flow channels, applied to the aforementioned multi-port valve system with intelligent cooperative switching flow channels, comprising: Step 1: Collect the inlet pressure and instantaneous flow rate values ​​of each flow channel interface in real time using a sensor array; Step 2: Calculate the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate using the state sensing and energy field construction unit, and construct the current fluid energy field distribution matrix; Step 3: Receive the target flow channel switching command through the dynamic path planning and energy shaping unit, parse the command into the target fluid energy field distribution matrix, and generate the energy field migration trajectory by solving the optimization problem; Step 4: Based on the virtual impedance adaptation and transient shaping unit, establish the Jacobian relationship and solve for the virtual impedance adaptation vector according to the energy field migration trajectory and the current system state; Step 5: The drive control unit generates a control instruction set based on the virtual impedance adaptation vector, and synchronously drives the main drive motor and the slave drive motor to complete the flow channel switching.

[0014] Furthermore, in step 1, after collecting the inlet pressure value and instantaneous flow rate value of each flow channel interface, the sensor array is subjected to fault diagnosis and data reconstruction based on the adaptive fault detection and data reconstruction mechanism with analytical redundancy.

[0015] Furthermore, in step 2, the state sensing and energy field construction unit calculates the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate, and constructs the current fluid energy field distribution matrix, specifically as follows: For each flow channel Its real-time hydraulic impedance Defined as the ratio of transient pressure to flow rate at the flow channel interface, it is: ; In the formula, For the first Each flow channel in The inlet pressure value at any given moment. For the first Each flow channel in The instantaneous flow rate at any given moment; Construct the current fluid energy field distribution matrix, where the off-diagonal elements of the current fluid energy field distribution matrix are... Indicates in At any moment, from the flow channel Flowing towards the channel Transient fluid power, diagonal element Indicates flow channel Its own net power storage rate.

[0016] Furthermore, in step 3, the target flow channel switching command is received through the dynamic path planning and energy shaping unit, the command is parsed into the target fluid energy field distribution matrix, and the energy field migration trajectory is generated by solving the optimization problem, specifically as follows: The dynamic path planning and energy shaping unit receives the target flow channel switching command from the upper controller, analyzes it, and obtains the target fluid energy field distribution matrix; Obtain the target energy field vector from the target fluid energy field distribution matrix, and obtain the current energy field vector from the current fluid energy field distribution matrix; Construct an objective function based on the target energy field vector and the current energy field vector. ,for: ; In the formula, For a moment The energy field vector form; For smoothness constraints; This is a power dissipation penalty term; This is a penalty term for the increase in entropy of the energy field; The preset weighting coefficients, Let the target energy field vector be the final state. The target energy field vector represents the target state. Solving the objective function using numerical optimization algorithms Thus, the energy field migration trajectory is obtained.

[0017] Further, in step 4, based on the virtual impedance adaptation and transient shaping unit, a Jacobian relationship is established and the virtual impedance adaptation vector is solved according to the energy field migration trajectory and the current system state, specifically as follows: Obtain the current real energy field vector, the real physical impedance matrix, and its rate of change matrix; obtain the expected energy field rate of change at the current moment from the energy field migration trajectory. Establish the mapping relationship between the actuator position vector and the impedance matrix; Calculate the Jacobian matrix based on the mapping relationship; The current actual energy field change rate is calculated using the Jacobian matrix based on the current actual actuator position change rate vector. Subtracting the expected rate of change of the energy field from the actual rate of change of the energy field yields the error vector of the rate of change of the energy field. The error vector of the energy field change rate is attributed to the change rate of the virtual actuator position that needs to be added, and a system of linear equations is established. The virtual actuator position change rate is converted into the desired additional damping value to obtain the virtual impedance adaptation vector.

[0018] In summary, the present invention has at least one of the following beneficial technical effects: 1. Achieving inherently smooth flow channel switching: By elevating the control objective from mechanical action to energy field evolution and utilizing virtual impedance to actively reconstruct network characteristics, this invention fundamentally avoids drastic pressure fluctuations during flow channel switching, rather than passively offsetting them after an impact occurs. This enables multi-port valve systems to achieve seamless switching under harsh conditions of high pressure and high flow, completely eliminating the hazards of water hammer.

[0019] 2. Significantly reduced system energy consumption and mechanical wear: The trajectory optimization in step B introduces a power dissipation penalty term, guiding energy to preferentially transfer through low flow resistance paths, reducing unnecessary frictional losses. Simultaneously, the smooth switching process avoids severe pressure shocks and flow pulsations, significantly reducing pipeline vibration, valve core wear, and actuator fatigue, thus extending the overall system lifespan.

[0020] 3. High adaptability and robustness: The fault diagnosis and reconfiguration mechanism ensures that the system can maintain basic functions even when some sensors fail, enhancing the reliability of field operation. The real-time online calculation of virtual damping relies on current measured data, enabling the control algorithm to adapt to system aging, changes in operating conditions, and boundary condition drift, always maintaining optimal control performance.

[0021] 4. Achieving Global Optimal Coordination of Multiple Actuators: Unlike independent control or simple master-slave coordination, this invention, through a unified energy field model and virtual impedance distribution mechanism, places the wide-range rotation of the main valve core and the precise adjustment of each fine-tuning choke valve within the same optimization framework. Parallel command output ensures precise coordination of all actuators in time and space, achieving true global intelligent control.

[0022] 5. Provides a clear status visualization and monitoring interface: The fluid energy field distribution matrix itself is an intuitive digital description of the system's operating status. Operators or upper-level monitoring systems can observe the changes in each element in the matrix to grasp the flow direction, magnitude, and distribution of energy within the system in real time, providing an unprecedented information dimension for fault early warning, energy efficiency analysis, and operation and maintenance decisions. Attached Figure Description

[0023] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0025] This invention provides an intelligent collaborative switching multi-bore valve system, comprising: a collaborative controller and multiple valve bodies, valve cores, a sensor array, and a drive module. Each valve body is provided with multiple flow channel interfaces, and each flow channel interface of the valve body is connected to the flow channel interface of the next valve body through a pipeline, forming a flow channel network. The valve core is rotatably disposed inside each valve body for connecting different flow channel interfaces. A sensor array is provided at the inlet of each flow channel interface for acquiring the inlet pressure and instantaneous flow rate of the flow channel interface. The valve core is connected to the drive module, and the sensor array and the drive module are connected to the collaborative controller. The collaborative controller includes: The state perception and energy field construction unit is used to construct the current fluid energy field distribution matrix based on the inlet pressure and instantaneous flow rate; The dynamic path planning and energy shaping unit is used to generate a continuous energy field migration trajectory based on the target flow channel switching command and the current fluid energy field distribution matrix. Virtual impedance adaptation and transient shaping unit, used to calculate virtual impedance adaptation vector based on energy field migration trajectory; A drive control unit is used to drive the drive module according to the virtual impedance adaptation vector.

[0026] The drive module includes a main drive motor, which is connected to the valve core and used to drive and control the rotation of the valve core. The main drive motor is also connected to the drive control unit.

[0027] Each of the flow channel interfaces is also provided with a fine-tuning flow control valve, and the drive motor of the fine-tuning flow control valve is connected to the drive control unit.

[0028] like Figure 1 As shown, a control method for a multi-port valve system with intelligent cooperative switching flow channels is applied to the aforementioned multi-port valve system with intelligent cooperative switching flow channels, comprising: Step 1: Collect the inlet pressure and instantaneous flow rate values ​​of each flow channel interface in real time using a sensor array; Step 2: Calculate the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate using the state sensing and energy field construction unit, and construct the current fluid energy field distribution matrix; Step 3: Receive the target flow channel switching command through the dynamic path planning and energy shaping unit, parse the command into the target fluid energy field distribution matrix, and generate the energy field migration trajectory by solving the optimization problem; Step 4: Based on the virtual impedance adaptation and transient shaping unit, establish the Jacobian relationship and solve for the virtual impedance adaptation vector according to the energy field migration trajectory and the current system state; Step 5: The drive control unit generates a control instruction set based on the virtual impedance adaptation vector, and synchronously drives the main drive motor and the slave drive motor to complete the flow channel switching.

[0029] In step 1, the inlet pressure and instantaneous flow rate of each flow channel interface are collected in real time using a sensor array, specifically as follows: This step involves real-time acquisition of raw physical quantities using a sensor array. After preprocessing, fault diagnosis, and reconstruction, the real-time hydraulic impedance of each flow channel is calculated, and finally, a fluid energy field distribution matrix describing the current energy distribution state of the system is constructed. This step is further divided into specific steps, including: Step 101: Data Acquisition and Preprocessing With a fixed sampling frequency The output signals of all pressure and flow sensors are acquired synchronously. For the first... Each flow channel interface, recorded The original pressure sample value at time 10:00 The original flow sampling value is To suppress high-frequency electromagnetic noise and fluid pulsation interference, a second-order Butterworth low-pass filter was applied to the original signal, with the cutoff frequency set to [value missing]. The filtered values ​​are used as inputs for subsequent calculations, and are denoted as follows: and .

[0030] Step 102: Sensor Fault Diagnosis and Data Reconstruction To ensure the reliability of input data, an adaptive fault detection and data reconstruction mechanism based on analytical redundancy is introduced. This mechanism utilizes the inherent physical constraints of the system (flow continuity) and the statistical correlation between sensors to diagnose faulty sensors online and reconstruct their effective values.

[0031] First, based on the current valve core position (The connectivity of each flow channel within the valve body is determined by real-time feedback from the encoder of the main drive motor.) Define a connectivity matrix to represent the total number of flow channel interfaces. Its elements Values Indicates flow channel With flow channel There is a direct passage in the current valve core position, with a value of [value]. This indicates a disconnection. This matrix is ​​obtained in real-time from a pre-existing valve core position-connectivity mapping table in the controller.

[0032] Based on the principle of flow continuity, for incompressible fluids, the total flow rate into the valve body at any given moment should equal the total flow rate out of the valve body. Let the inflow direction be positive and the outflow direction be negative, then the sign of the direction... The pressure at the interface and the average pressure inside the valve Comparison determines: ; Among them, the average pressure inside the valve This can be approximated as a weighted average of the pressure across all interfaces, with the weights determined by the absolute value of the instantaneous traffic of each interface: ; In the formula, The number of interfaces; Constructing continuous residuals : ; Under normal circumstances, It should be close to zero. Set an adaptive threshold. This threshold is related to the statistical fluctuations in the current total traffic: ; in, For empirical coefficients, take... .like If so, it is determined that there is a sensor malfunction.

[0033] To isolate faulty sensors, a process of elimination is used. Assume sequentially... One sensor failed, utilizing the remaining Flow rate and direction symbol of each sensor Estimate the first by using the continuity equation Theoretical value of each flow : ; Then calculate the hypothesis. Reconstruction residuals under fault conditions .make smallest This is the most likely faulty sensor number. .

[0034] Once the faulty sensor is identified Its measured value will not be used in subsequent calculations. , and using reconstructed values Alternative. For pressure sensors, the pressure-flow relationship can be used for reconstruction. In a known faulty flow path... Under the connectivity relationship, if there is a normal flow channel directly connected to it. (Right now ), and the path impedance is known. Then, the known faulty flow path can be estimated based on the simplified Bernoulli equation. : ; in, For symbolic functions, This represents the flow rate of the normal flow path. If multiple adjacent normal flow paths exist, the weighted average of the estimated values ​​is taken as the final reconstructed value. If reconstruction cannot be achieved through adjacent flow paths, the last valid value before the failure is temporarily retained, and an alarm is issued.

[0035] In step 2, the state sensing and energy field construction unit calculates the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate, and constructs the current fluid energy field distribution matrix, specifically as follows: Step 201: Real-time hydraulic impedance calculation For each flow channel In fact, hydraulic resistance Defined as the ratio of transient pressure to flow rate at this interface: ; In the formula, For the first Each flow channel in The inlet pressure value at any given moment. For the first Each flow channel in The instantaneous flow rate at any given moment, expressed in Pascals per second per cubic meter. ; when The absolute value is less than the preset minimum flow threshold. When, to avoid division by zero, The value assigned is a maximum value. This characterizes the near-closed state of the flow channel.

[0036] Step 202: Construction of the energy field distribution matrix Energy field distribution matrix It is A matrix whose off-diagonal elements Indicates in At any moment, from the flow channel Flowing towards the channel Transient fluid power; diagonal element Indicates flow channel Its own net power storage rate (i.e., the difference between the instantaneous water power and the sum of the outflow power at this interface) is used to ensure energy conservation. The matrix construction process is as follows: First, determine the connection between any two flow channels. and Path impedance This impedance consists of two parts: the fundamental impedance of the internal passage of the valve core. and through the flow channel and flow channel Additional impedance introduced by the fine-tuning choke valve at the location and .in, The current valve core rotation angle fed back by the encoder of the main drive motor. For the first The opening degree of a fine-tuning choke valve (feedback from the encoder of the drive motor). Basic impedance. Obtained through interpolation using a pre-calibrated three-dimensional angle-impedance mapping table; additional impedance. It is a monotonic function of the fine-tuning valve opening, which is also fitted into a polynomial form through calibration experiments and stored in the controller. The formula for calculating the path impedance is: ; in These are the elements of the aforementioned connected matrix.

[0037] Then, calculate the flow channel. With flow channel Pressure difference between .when At that time, the power is from Flow direction The transmission power is given by the following formula: ; when At that time, the actual power cannot be transmitted in reverse, so take This definition has the dimension of power ( The physical meaning is clear: under a given pressure difference and path impedance, the fluid mechanical energy transferred from the high-pressure end to the low-pressure end per unit time.

[0038] Finally, to ensure energy balance, we define diagonal elements. For flow channel The net power is the instantaneous input / output power of the interface minus the sum of all outgoing power: ; in For flow channel The instantaneous water power at a given point (positive values ​​indicate power flowing into the system, negative values ​​indicate power flowing out). Therefore, the entire energy field distribution matrix... It provides a complete description of the injection, transmission, and dissipation of fluid energy within the system.

[0039] To facilitate trajectory planning and optimization in subsequent steps, matrices are often used. Convert to energy field vector For example, extracting all off-diagonal elements (arranged in a fixed order) to form a Dimensional vector.

[0040] In step 3, the target flow channel switching command is received through the dynamic path planning and energy shaping unit. The command is parsed into the target fluid energy field distribution matrix, and the energy field migration trajectory is generated by solving the optimization problem. Specifically: Step 301: Receiving and parsing the target flow channel switching command The target flow channel switching command originates from the upper-level controller of the system (e.g., a distributed control system DCS, a programmable logic controller PLC, or an operator station). This command can be sent to the collaborative controller of this invention in digital form via industrial communication protocols (such as Modbus, PROFIBUS, EtherNet / IP).

[0041] The format of the instruction can adopt a compact and explicit data structure, such as a... A binary number, where each bit represents the desired state (on / off) of the corresponding flow channel interface, or more precisely, a... Target Connectivity Matrix The encoding is as follows. In this invention, for ease of parsing, a target connectivity table is used. This table is pre-stored in the non-volatile memory of the cooperative controller and contains several records, each corresponding to a predefined switching mode. Each record contains the following fields: Pattern Number ; Target Connectivity Matrix Its elements A value of 1 indicates that the flow channel is in the target state. With flow channel The matrix should be connected; a value of 0 indicates that the matrix is ​​not connected. Note that this matrix should be symmetric and satisfy the requirements of actual physical realizability (i.e., the valve core must have a certain angle to achieve the connection). Desired power allocation weight ,one A dimension vector is used to specify the desired power distribution ratio on each path when there are multiple potentially conductive channel pairs. For example, in a split-flow scenario, the power distribution ratio between channel 1 and channel 2 and between channel 1 and channel 3 can be set to 0.6:0.4.

[0042] When the upper-level controller issues a switching command, its content can be "Switch to mode". Upon receiving the instruction, the collaborative controller first verifies its validity, including checking the mode number. Check whether it is within the pre-stored range and whether the current system state allows the switch to be performed (for example, some channels may be disabled due to a fault). After the verification is successful, proceed to the parsing stage.

[0043] Command parsed as target energy field vector : The core of instruction parsing is to transform abstract connectivity relationships and power allocation weights, combined with the current boundary conditions of the system (mainly the pressure source or load characteristics of each flow channel), into a concrete, physically realizable target energy field vector. .

[0044] First, based on the target connectivity matrix This determines which flow pairs should have energy transfer under the target conditions. Only... and Only specific channel pairs can transfer energy. For these channel pairs, the target power transfer is... The following constraints must be met: Energy conservation: for each flow channel The net power (i.e., the sum of all outflowing power minus the sum of all inflowing power) must equal the boundary power at the interface of that flow channel. The boundary power is determined by the characteristics of the piping network to which the flow channel is connected. For simplicity, it is assumed that during the switching process, the pressure in some channels (such as the inlet) is essentially constant (pressure source), and the flow demand in some channels (such as the outlet) is essentially constant (flow load). Based on this, the nodal analysis method in circuit simulation can be used to solve for the power of each branch under the target state.

[0045] Constructing the equivalent circuit of the fluid network: pressure Equivalent to voltage, flow rate Equivalent to current, hydraulic resistance Equivalent to a resistor. Each flow channel interface is considered a node, with an active component (constant voltage source or constant current source) or load connected at the node. The path impedance between nodes... The target power is determined by the target connectivity matrix, but its specific value is unknown because the fine-tuning valve opening in the target state is not yet determined. However, the relative impedance relationship can be estimated using the expected power distribution weights in the target state, thereby solving for the target power.

[0046] A more practical method is to calculate the target power directly based on the target connectivity and power allocation weights, combined with the currently measured boundary pressure (or expected pressure), without relying on specific impedance values. For example, if channel 1 is a constant pressure source and the pressure is... Flow channel 2 is a constant flow load, with a required flow rate of... Furthermore, the target connectivity requires that channel 1 and channel 2 be conductive. Therefore, under steady-state conditions, the power transferred from channel 1 to channel 2 should be... (Ignore losses along the way). If there are multiple loads, distribute them according to their weights.

[0047] set up For pressure source flow channel collection, This is the set of flow channels for flow load. For each pressure source flow channel... Its pressure From real-time measurement values Approximate substitution (assuming a rapid switching process and minimal change in boundary pressure); for each load channel Its demand flow This can be obtained from historical data or higher-level settings. Therefore, for the target connectivity matrix, from the source... to load The target power of the branch is: ; in It is a pre-existing mode The power allocation weight in the data represents the weight from the source. to load The power percentage; the denominator is all the power percentages related to the source. Sum the connected loads to ensure that the total output power equals Multiply by source The total output flow should be equal to the sum of all load demands (to ensure flow continuity).

[0048] Other types of boundary conditions (such as constant pressure loads, constant current sources, etc.) can be handled similarly. In this invention, for the sake of simplicity, it is assumed that the system has a defined pressure source and flow load, and that the required flow of all loads is known or can be estimated from the current flow (e.g., keeping the total load flow unchanged before and after the switch).

[0049] Nonnegativity and Realizability: All It must be a non-negative real number, and for The target power of the flow channel pair is forcibly set to 0.

[0050] In addition, diagonal elements Determined automatically by the law of conservation of energy: ; Where the boundary power For the pressure source flow channel is (Outflow is positive), for the load channel is (Inflow is negative). The total output flow of the source channel should equal the sum of the load demands of all connected components. Therefore, the entire target energy field vector... By all and Composition. In practical implementations, typically only off-diagonal elements are considered, as they directly characterize energy transfer between channels. Diagonal elements serve as check or spares.

[0051] Step 302: Optimized generation of energy field migration trajectory Obtain the current energy field vector and target energy field vector Then, the core task of step 3 is to plan a route from... arrive Continuous time trajectory ,in This ensures that the entire migration process meets specific performance metrics.

[0052] 1. Trajectory parameterization To facilitate solving the optimization problem, continuous trajectories are... Discretize in time. Divide the time interval... Evenly divided into Step size, step size Discrete time points are The trajectory is then composed of a series of vectors. It means that among them ,and Changes between adjacent time points . The value of needs to be determined by balancing computational accuracy and real-time performance; in this embodiment, is taken as . , It is typically on the order of 10ms (depending on the system's dynamic response time).

[0053] 2. Optimize the objective function The quality of a migration trajectory is determined by the following objective function. The measurement and optimization process is to find what makes... Minimized trajectory sequence .

[0054] ; The following is a detailed explanation of each term in the objective function: First item Smoothness constraint of energy field changes.

[0055] in, It represents the Euclidean norm of a vector (i.e., the square root of the sum of squares). This represents the square of the Euclidean norm of the vector. This term penalizes abrupt changes in the energy field, forcing the trajectory to be as smooth as possible, thus avoiding shocks. This is the smoothing term.

[0056] Second item Power dissipation penalty during energy transfer.

[0057] Changes in the energy field are inevitably accompanied by fluid flow, and flow generates frictional dissipation. This term is used to estimate the energy field from the state. arrive The mechanical energy loss during the process due to flow. Assume the dissipated power is proportional to the square of the flow rate, and the flow rate is related to the rate of change of the energy field. A dissipation function can be constructed, for example: ; in It is a vector The middle corresponds to the flow channel pair The amount, It is a vector The middle corresponds to the flow channel pair The amount; It is the equivalent current resistance coefficient of the path, which can be approximated by the current path impedance. Instead (because the higher the impedance, the greater the flow rate for the same power, and the greater the dissipation). For simplification, we can take... This is a dissipation term; the guiding trajectory should select a path with the lowest possible flow resistance to transfer energy.

[0058] Third item Penalty for increased entropy in the energy field.

[0059] Drawing on thermodynamic concepts, we define the entropy of an energy field. As a measure of the disorder of energy distribution, entropy can be defined as follows for a discrete energy field: ; in This refers to the normalized transmission power (absolute value, considering direction). Higher entropy indicates a more dispersed and disordered energy distribution. The switching process aims for a smooth transition of energy from the current distribution to the target distribution, avoiding excessively disordered intermediate states; that is, the change in entropy should be as small or monotonic as possible. Therefore, a penalty for entropy can be added to the objective function, for example... It is itself, or the rate of change of entropy. This invention employs... As an intermediate state, the entropy value is used in optimization to guide the trajectory through intermediate states with lower entropy values. This term is the entropy term.

[0060] Fourth item Endpoint constraint.

[0061] This is a relaxed endpoint constraint that ensures the final state. Get as close as possible to the target state Theoretically, it should be strictly equal, but introducing a relaxation term can avoid the optimization problem being unsolvable, while allowing for small errors that can be corrected by subsequent feedback control. Take a larger value, for example To ensure the accuracy of the endpoint.

[0062] Weighting coefficients are used to balance the importance of each item. They are pre-tuned through simulation or experimentation. In this invention, we take... .

[0063] 3. Constraints During the optimization process, the trajectory A series of physical and system constraints must be met: Energy conservation constraint: for each moment and each flow channel Its net power must be equal to the boundary power at the flow channel interface, that is, it must satisfy the node power balance equation: ; in yes Time flow channel The boundary net injection power. The method for determining the boundary conditions is as follows: Boundary conditions may change during the switching process. However, for simplicity, it is assumed that the pressure of the pressure source and the demand flow of the load remain constant during a short switching time. It can be taken as a constant, that is, the boundary power at the current moment (calculated from the measured value). or More precisely, it can be It can also be used as an optimization variable, but this would increase complexity. In this invention, the current measurement value is used and assumed to be constant.

[0064] Feasible region constraint: for each energy component It must be within physically realizable limits. For example, for a certain flow channel pair Its transmission power cannot exceed the upper limit determined by the current maximum allowable voltage drop and minimum impedance. However, in practice, this upper limit is difficult to quantify precisely. A conservative constraint is... (By definition, there is only positive power flow when the pressure difference is positive, and zero when it is negative), and ,in It is a sufficiently large positive number (e.g., 10 times the system's rated power) to ensure that the search space is bounded.

[0065] Connectivity constraint: For channel pairs that are not connected in the target state, their power should gradually approach zero throughout the migration process. However, to avoid introducing hard constraints that lead to no solution, this can be achieved indirectly through the endpoint term and entropy term in the objective function.

[0066] 4. Optimize the solution method The optimization problem described above is a typical quadratic programming problem (the objective function is a quadratic form, and the constraints are linear equality and inequality), which can be solved using numerical optimization algorithms. Because and Not big ( This can be solved in real time within the collaborative controller. The specific method is as follows: The decision variable is defined as a hypervector composed of the energy field vectors at all times. ( and Determined by the current measured value and the target value respectively, they are not considered variables, but and The bias is handled through the relaxation term in the objective function, so in reality... It's also a variable, but we usually consider it... This is also included in the optimization, but with an added endpoint penalty. More rigorously, all... ( Both are treated as variables, but Fixed as ,and It's not fixed; it's determined by optimization, with an endpoint penalty added. Thus... The dimension is .because and The problem is finite; it can be solved once within each control cycle (e.g., 100ms) to obtain the entire trajectory. Then, the first point is taken as the next execution target, achieving rolling optimization (model predictive control). For simplicity, it can be assumed that after the switching command is issued, the entire trajectory is solved quickly offline or online in one go, and then executed in an open-loop manner over time.

[0067] In controllers, the effective set method or interior point method can be used to solve this quadratic programming problem. For embedded implementations, the problem can be pre-converted into a standard form and solved quickly by calling mature numerical libraries (such as C code generated by CVXGEN).

[0068] After obtaining the optimal trajectory, it can be fitted as a continuous function or passed directly to the next step as a discrete sequence. The expected rate of change of the energy field needs to be known, so the derivative of each discrete point can be calculated.

[0069] In step 4, based on the virtual impedance adaptation and transient shaping unit, the Jacobian relationship is established and the virtual impedance adaptation vector is solved according to the energy field migration trajectory and the current system state. Specifically: According to the desired direction of energy field evolution Based on the dependence of the energy field changes of the current real system on the changes in physical impedance, the additional virtual impedance that needs to be applied to each actuator (the main drive motor and each slave drive motor) is solved. This virtual impedance is achieved through active control of the actuator, with the aim of dynamically reconstructing the fluid network impedance characteristics of the system, thereby guiding the real energy field. Along the planned trajectory The evolution ultimately achieves a smooth flow channel switching, and the specific steps are as follows: Step 401: Obtain the current system status In each control cycle, step 4 obtains the following real-time data from step 1: Current true energy field vector ; Current true physical impedance matrix ; Current true physical impedance change rate matrix (This can be obtained by calculating the difference from the impedance value stored at the previous moment).

[0070] At the same time, obtain the current time from step 3. Corresponding expected rate of change of energy field Since the trajectory in step 3 is based on discrete time points... Given, for any It can be obtained through linear interpolation. .

[0071] Step 402: Dynamic Relationship between Energy Field Change and Impedance Change The system's true energy field With real physical impedance There exists a definite functional relationship between them, denoted as ,in For boundary conditions (such as source pressure, load demand), the following assumptions are made in this invention. It remains constant during the switching process. Therefore, the rate of change of the energy field... It can be determined by the rate of change of impedance. Through the Jacobian matrix Linear approximation: ; in: yes dimensional vector, , which represents the rate of change of power transferred between all directed flow pairs; yes dimensional vector, This refers to the number of controllable actuators in the system, including one main valve core and... A fine-tuning flow control valve, therefore This vector is determined by the rate of change of the main valve core angle. and the rate of change of opening of each fine-tuning choke valve Composition. Note the impedance matrix. Each element in These are all nonlinear functions of the actuator positions, therefore This is the fundamental reason driving these changes; yes The Jacobian matrix, whose elements , indicating the first The energy field component affects the first The sensitivity of an actuator to changes in position (and consequently, to changes in the impedance of the corresponding path).

[0072] Step 403: Jacobian Matrix Analytical calculation Jacobian matrix This describes the local linear response of the energy field to changes in the positions of each actuator at the current operating point. It can be calculated analytically as follows: Establish energy field components With the impedance of each path Relationship: According to the definition of an energy field Only when The time is not zero, and .and It is also influenced by the impedance distribution of the entire network and is an implicit function. For simplicity, we can use a small-signal model near the current operating point, assuming... Its sensitivity to impedance changes is much smaller than right Direct dependence, i.e. ignoring network coupling, is approximated as... Mainly affected by its own path impedance The impact of this. More precise processing requires solving for the sensitivity of the entire network, but this increases computational complexity. In this embodiment, the following simplified Jacobi calculation method is used: First, calculate the impedance of each path. The derivative with respect to each actuator position. Since From the fundamental impedance and additional impedance The components are known, and the functional forms of the basic impedance and the additional impedance are known (through calibration fitting), therefore they can be analytically differentiated: ; ; ; For other unrelated actuators, the derivative is zero.

[0073] Then, calculate the energy field components. For path impedance The partial derivatives (ignoring dependencies on other pathways): ; This formula utilizes The relationship, and assume Not following Changes (local linearization).

[0074] Combining the derivatives above, we can obtain the result using the chain rule. Elements: ; ; ; For other actuators Unrelated situations, .

[0075] This yields the Jacobian matrix. The analytical expression for this matrix is ​​given. This matrix is ​​time-varying because it depends on the current energy field. and impedance , and the derivative at the current actuator position.

[0076] Step 404: Solving the inverse problem: Virtual impedance adaptation vector

[0077] We hope the rate of change of the real energy field Equal to the expected rate of change However, changes in real-world systems are caused by the movement of real actuators. The resulting, that is Meanwhile, the actuator's motion is driven by the drive control unit based on virtual impedance commands. We introduce the concept of virtual impedance: in addition to its own physical characteristics, the actuator is controlled to have an additional virtual impedance, so that under the action of external forces (fluid pressure difference), the actuator's response exhibits specific damping and stiffness. In effect, virtual impedance is equivalent to adding an increment to the original physical impedance. This causes a change in the rate of change of impedance.

[0078] A more direct approach is to compensate for the error between the current actual energy field change and the expected change. Let the current actual energy field change rate be... We want to apply a virtual impedance change. This causes the total equivalent impedance to change. The resulting change in the energy field is equal to the expected value: ; Therefore, the required virtual impedance change rate This can be obtained by solving the following system of linear equations: ; in, It is the actual rate of change of the energy field calculated based on the current actual actuator motion (which can be obtained from step 1). (Obtained through Jacobian mapping). Let This is the error vector of the rate of change of the energy field.

[0079] because It is The matrix, usually (The number of energy field components exceeds the number of actuators), which is an overdetermined system of equations, generally lacking an exact solution. Therefore, we seek a least-squares solution, i.e., minimizing... The solution is: ; in yes The Moore-Penrose pseudoinverse can be calculated using singular value decomposition (SVD): ,in , It is an orthogonal matrix. To convert singular moments The matrix obtained by taking the reciprocal of the non-zero singular values ​​and then transposing it.

[0080] The virtual impedance change rate was obtained Next, we need to convert it into an actual virtual impedance adaptation vector. The vector is A 3D vector, where each element represents the additional virtual impedance value (such as damping coefficient, stiffness coefficient, etc.) that the corresponding actuator should exhibit. The virtual impedance value is the integral of the rate of change of virtual impedance over time. However, in discrete control, we can directly provide the target virtual impedance value for each control cycle to drive the actuator's impedance controller. A simple method is to let... ,in It is a diagonal gain matrix used to convert the rate of change into impedance. To control the cycle. However, a more direct way is to... As a correction for the actuator position change rate, but in the virtual impedance framework, we usually output the target impedance value directly.

[0081] In impedance control, the dynamic behavior of the actuator is designed as follows: ; in , These are the inertia, damping, and stiffness matrices, respectively. The generalized force generated by external fluid pressure. Virtual impedance matching means that we adjust according to external commands. and Parameters, etc. It is directly defined as the desired additional damping matrix, i.e., the additional damping coefficient that each actuator should provide. Damping directly affects speed, and thus the rate of change of impedance. Therefore, let: ; in It is A diagonal matrix whose diagonal elements These are pre-tuned coefficients used to convert the virtual rate of change into a damping value, in units of... (For linear motion) or (For rotational motion). Each element in Indicates the first The additional rate of position change (in rad / s or m / s) that each actuator should produce, multiplied by The required additional damping force / torque is obtained, which will cause the actuator to produce a corresponding speed change, thereby achieving the desired virtual impedance effect.

[0082] Step 405: Virtual Impedance Fit Vector Output Calculated It is dimensional vector, The first component of the vector The virtual additional damping corresponding to the main drive motor, and the remaining components to Corresponding to the first to The virtual additional damping of each fine-tuning choke valve is applied. These values ​​are sent in real time to the drive control unit in step 5 as target parameters for the actuator impedance controller. Based on these damping values ​​and the current actual motion state of the actuator, the drive control unit generates corresponding current / voltage commands using a force / position hybrid control algorithm. This causes the actuator to exhibit the required mechanical impedance characteristics, thereby dynamically reconstructing the impedance of the fluid network and guiding the real energy field. Gradually approaching the planned trajectory .

[0083] In step 5, the drive control unit generates a control command set based on the virtual impedance adaptation vector, synchronously driving the main drive motor and the slave drive motor to complete the flow channel switching, specifically as follows: The virtual impedance adaptation vector output in step 4 As the desired dynamic characteristic parameter, precise current / voltage commands are generated through the underlying servo controllers of each actuator, causing the actuators to physically exhibit this virtual impedance. This dynamically changes the actual impedance of the fluid network, ultimately revealing the true energy field of the system. Able to track the trajectory planned in step 3 This process is executed in a closed loop within each control cycle, and its specific steps are as follows: Step 501: Actuator System Modeling At the actuator level, each drive motor (including the main drive motor and the slave drive motor) can be considered as a torque / force source driven by current. Taking a permanent magnet synchronous motor as an example, its electromagnetic torque... With q-axis current Proportional: ; in: The electromagnetic torque of the motor is expressed in Newton-meters (N·m). ; This is the torque constant of the motor, measured in Newton-meters per ampere. ; This is a q-axis current command, in amperes. .

[0084] The motor is connected to the valve core or fine-tuning choke valve through a reduction mechanism (such as a gearbox), and the equivalent torque on the output shaft... The relationship with motor torque is as follows ,in For the reduction ratio, For transmission efficiency. To simplify, the reduction mechanism and load characteristics can be combined to establish a dynamic equation from motor current to actuator angular acceleration: ; in: The equivalent moment of inertia referred to the motor shaft is expressed in units of 1. ; This is the equivalent viscous damping coefficient, in units of... ; The load torque acting on the actuator is determined by the fluid pressure difference. The position is determined by the fluid forces acting on the valve core / valve plate. Nonlinear function of pressure difference; These are the angular acceleration and angular velocity of the actuator, respectively. This is the equivalent torque constant.

[0085] For fine-tuning choke valves, the motion is usually linear, and the dynamic equations are similar, except that the angle variable is replaced with linear displacement, the torque is replaced with force, and the moment of inertia is replaced with mass.

[0086] Step 502: Impedance Control Law Design To make the actuator exhibit the desired virtual damping An impedance control strategy is employed. The goal of impedance control is to ensure that the dynamic behavior of the actuator satisfies the following desired impedance model: ; in: These are the desired inertia, damping, and stiffness coefficients, which are the design parameters of the impedance controller. For actual location, For actual speed, This is the actual acceleration; The desired position, velocity, and acceleration trajectory are given by the upper-level planning (but in this invention, step 3 plans the energy field trajectory, not the actuator position trajectory). The desired actuator position trajectory is actually implicit, determined by both the energy field trajectory and impedance matching. Therefore, we adopt a force-controlled impedance implementation method, without explicitly specifying the trajectory. Instead, it adjusts the control force directly based on virtual damping.

[0087] A more suitable impedance control method for this invention is: directly based on the desired additional damping. This is used to correct the driving force / torque output of the motor. Based on basic position or speed servo systems, a damping force term proportional to the speed is added, i.e.: ; in: The final torque command (corresponding to the current command) sent to the motor driver ); This is the feedforward torque, used to compensate for the known load torque. And inertial force, which can be determined based on the current desired acceleration. calculate: ,in This is an estimated value of the load torque (which can be estimated using a differential pressure sensor and valve characteristic model). This is a feedback control term used to eliminate position / velocity tracking errors; it is typically implemented using PID control. The virtual damping value given in step 4, This represents the actual speed of the current actuator.

[0088] The physical meaning of this control law: when the actuator moves at a speed... During movement, the control algorithm will actively apply a force opposite to (or the same as) the direction of movement, depending on the direction. An additional torque (positive or negative) is generated, the magnitude of which is proportional to the velocity and the virtual damping. If If , then it is positive damping, hindering motion and making the system more stable; if This results in negative damping, actively propelling motion and can be used to accelerate changes in the energy field. By adjusting... It can dynamically change the equivalent damping characteristics of the actuator, thereby affecting the impedance of the entire fluid network.

[0089] Step 503: Design of Feedback Control Items Feedback control items Used to eliminate the deviation between the actual position and the desired position of the actuator. Achieved using speed control mode: desired speed. Position error-based PD control can be used, with position commands... This can be obtained from the steady-state valve core position corresponding to the target energy field and the fine-tuning valve opening (i.e., the valve core position corresponding to the target connectivity matrix and the fine-tuning valve opening corresponding to the target power distribution). During the switching process, it can be set... Linear interpolation to target value, velocity feedforward term Let it be a constant. Therefore: ; in For feedback gain.

[0090] In summary, the final torque command for each actuator is: ; For the driven motor, since the fine-tuning choke valve is usually small, feedforward load compensation can be omitted, and the control law can be simplified as follows: ; in This is the actual opening. The desired opening is obtained by interpolation of the steady-state opening corresponding to the target energy field. This is the expected rate of change of opening (which can be set as a constant or given by the planning).

[0091] Step 504: Current command generation and execution Receive torque command Then, based on the motor torque constant Calculate the q-axis current command: ; For permanent magnet synchronous motors, d-axis current control is also required, and a PWM signal is generated through vector control or direct torque control to drive the inverter, ultimately enabling the motor to output the desired torque. These low-level controls are implemented by the motor driver hardware.

[0092] Step 505: Coordination and Synchronization of Multiple Actuators Step 5 manages both the main drive motor and all slave drive motors simultaneously. In each control cycle, Step 5 obtains the complete virtual damping vector from Step 4. Based on the current state (position, speed) and desired position (given by trajectory planning) of each motor, the torque / current commands are calculated in parallel and sent synchronously to each motor driver via fieldbus or parallel I / O, ensuring that all actuators start executing new commands at the same time and achieve time coordination.

[0093] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.

Claims

1. A multi-bore valve system with intelligent collaborative switching of flow channels, characterized in that, include: The system includes a collaborative controller, multiple valve bodies, valve cores, sensor arrays, and a drive module. Each valve body has multiple flow channel interfaces, and each flow channel interface is connected to the flow channel interface of the next valve body through a pipeline, forming a flow channel network. The valve core is rotatably installed inside each valve body to connect different flow channel interfaces. Each flow channel interface has a sensor array at its inlet to acquire the inlet pressure and instantaneous flow rate of that flow channel interface. The valve core is connected to the drive module, and the sensor array and drive module are connected to the collaborative controller. The cooperative controller includes: a state perception and energy field construction unit, a dynamic path planning and energy shaping unit, a virtual impedance adaptation and transient shaping unit, and a drive control unit. The cooperative controller executes the following method: The state sensing and energy field construction unit calculates the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate, and constructs the current fluid energy field distribution matrix. The target flow channel switching command is received by the dynamic path planning and energy shaping unit, the command is parsed into the target fluid energy field distribution matrix, and the energy field migration trajectory is generated by solving the optimization problem. Based on the virtual impedance adaptation and transient shaping unit, the Jacobian relationship is established and the virtual impedance adaptation vector is solved according to the energy field migration trajectory and the current system state. The drive control unit generates a set of control instructions based on the virtual impedance adaptation vector, and synchronously drives the main drive motor and the slave drive motor to complete the flow channel switching.

2. The intelligent collaborative switching flow channel multi-bore valve system according to claim 1, characterized in that, The drive module includes a main drive motor, which is connected to the valve core and used to drive and control the rotation of the valve core. The main drive motor is also connected to the drive control unit.

3. The intelligent collaborative switching flow channel multi-bore valve system according to claim 1, characterized in that, Each of the flow channel interfaces is also provided with a fine-tuning flow control valve, and the drive motor of the fine-tuning flow control valve is connected to the drive control unit.

4. A control method for a multi-port valve system with intelligent cooperative switching flow channels, applied to the multi-port valve system with intelligent cooperative switching flow channels as described in any one of claims 1-3, characterized in that, include: Step 1: Collect the inlet pressure and instantaneous flow rate values ​​of each flow channel interface in real time using a sensor array; Step 2: Calculate the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate using the state sensing and energy field construction unit, and construct the current fluid energy field distribution matrix; Step 3: Receive the target flow channel switching command through the dynamic path planning and energy shaping unit, parse the command into the target fluid energy field distribution matrix, and generate the energy field migration trajectory by solving the optimization problem; Step 4: Based on the virtual impedance adaptation and transient shaping unit, establish the Jacobian relationship and solve for the virtual impedance adaptation vector according to the energy field migration trajectory and the current system state; Step 5: The drive control unit generates a control instruction set based on the virtual impedance adaptation vector, and synchronously drives the main drive motor and the slave drive motor to complete the flow channel switching.

5. The control method for a multi-bore valve system with intelligent collaborative switching flow channels according to claim 4, characterized in that, In step 1, after collecting the inlet pressure and instantaneous flow rate values ​​of each flow channel interface, the sensor array is subjected to fault diagnosis and data reconstruction based on the adaptive fault detection and data reconstruction mechanism with analytical redundancy.

6. The control method for a multi-bore valve system with intelligent cooperative switching flow channels according to claim 5, characterized in that, In step 2, the state sensing and energy field construction unit calculates the real-time hydraulic impedance based on the inlet pressure and instantaneous flow rate, and constructs the current fluid energy field distribution matrix, specifically as follows: For each flow channel Its real-time hydraulic impedance Defined as the ratio of transient pressure to flow rate at the flow channel interface, it is: ； In the formula, For the first Each flow channel in The inlet pressure value at any given moment. For the first Each flow channel in The instantaneous flow rate at any given moment; Construct the current fluid energy field distribution matrix, where the off-diagonal elements of the current fluid energy field distribution matrix are... Indicates in At any moment, from the flow channel Flowing towards the channel Transient fluid power, diagonal element Indicates flow channel Its own net power storage rate.

7. The control method for a multi-port valve system with intelligent cooperative switching flow channels according to claim 6, characterized in that, In step 3, the target flow channel switching command is received through the dynamic path planning and energy shaping unit. The command is parsed into the target fluid energy field distribution matrix, and the energy field migration trajectory is generated by solving the optimization problem. Specifically: The dynamic path planning and energy shaping unit receives the target flow channel switching command from the upper controller, analyzes it, and obtains the target fluid energy field distribution matrix; Obtain the target energy field vector from the target fluid energy field distribution matrix, and obtain the current energy field vector from the current fluid energy field distribution matrix; Construct an objective function based on the target energy field vector and the current energy field vector. ,for: ； In the formula, For a moment The energy field vector form; For smoothness constraints; This is a power dissipation penalty term; This is a penalty term for the increase in entropy of the energy field; The preset weighting coefficients, Let the target energy field vector be the final state. The target energy field vector represents the target state. Solving the objective function using numerical optimization algorithms Thus, the energy field migration trajectory is obtained.

8. The control method for a multi-port valve system with intelligent collaborative switching flow channels according to claim 7, characterized in that, In step 4, based on the virtual impedance adaptation and transient shaping unit, the Jacobian relationship is established and the virtual impedance adaptation vector is solved according to the energy field migration trajectory and the current system state. Specifically: Obtain the current real energy field vector, the real physical impedance matrix, and its rate of change matrix; obtain the expected energy field rate of change at the current moment from the energy field migration trajectory. Establish the mapping relationship between the actuator position vector and the impedance matrix; Calculate the Jacobian matrix based on the mapping relationship; The current actual energy field change rate is calculated using the Jacobian matrix based on the current actual actuator position change rate vector. Subtracting the expected rate of change of the energy field from the actual rate of change of the energy field yields the error vector of the rate of change of the energy field. The error vector of the energy field change rate is attributed to the change rate of the virtual actuator position that needs to be added, and a system of linear equations is established. The virtual actuator position change rate is converted into the desired additional damping value to obtain the virtual impedance adaptation vector.

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