Operation monitoring method and system for automatic punching liquid filling equipment

Abstract

The invention belongs to the technical field of industrial automation control, and particularly relates to an operation monitoring method and system for automatic stamping liquid filling equipment, and the method comprises the steps: collecting the operation process data of each filling node in real time, and mapping the operation process data into an artificial wolf in a wolf pack algorithm; calculating a flow control response efficiency coefficient based on the operation process data, and constructing flow state health potential energy in combination with elite wolf pack characteristics; the flow state health potential energy serves as a gating factor, a comprehensive optimization weight is calculated in combination with a pressure control target, and a first wolf is screened; the position updating step length is adjusted through the flow state health potential energy, and a valve opening instruction is generated with the first wolf as the guide. According to the method, oil viscosity drift interference caused by the environment is automatically counteracted through the flow state healthy potential energy, accurate recognition and soft isolation of physical fault nodes are achieved, misjudgment of a traditional algorithm and forced operation in the fault state are avoided, and the stability of whole-line lubrication and equipment safety are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology. More specifically, this invention relates to a method and system for monitoring the operation of automatic filling equipment for stamping fluid. Background Technology

[0002] The automatic filling system for stamping fluid is a core auxiliary unit in modern automobile manufacturing stamping production lines. Its main function is to precisely lubricate metal sheets through a distributed multi-nozzle network to reduce die wear and improve forming quality. In actual production, to simplify pipeline layout, each nozzle typically shares a single high-pressure oil supply pump. While this architecture is compact, it suffers from significant fluid coupling effects; that is, adjusting any valve at any filling node will cause fluctuations in the overall network pressure. To achieve precise coordination of multi-nozzle pressure, the industry widely employs swarm intelligence optimization algorithms such as wolf pack algorithms. These algorithms iteratively find the optimal combination of valve openings, attempting to maintain system pressure balance and stability under dynamic operating conditions.

[0003] However, existing wolf pack algorithms mainly use the minimization of a single pressure error as the evaluation criterion, which is prone to failure under complex physical conditions. Specifically, when a filling node experiences a partial blockage, an abnormal state of high pressure and low flow often forms in its pipeline, resulting in an artificially high pressure value with minimal error. Under the logic of traditional algorithms, this faulty node is easily misjudged as the alpha wolf in the optimal state, causing other normal nodes to blindly follow its incorrect valve adjustment strategy, leading to lubrication imbalance of the entire line and even equipment damage, seriously affecting production safety.

[0004] In addition, the viscosity drift of oil caused by changes in ambient temperature in the stamping workshop is a normal change in physical properties, but it is often misjudged as a system fault by traditional algorithms. This causes the control system to easily generate command oscillations or false alarms when operating conditions fluctuate, making it difficult to meet the requirements of high-precision and high-reliability continuous production. Summary of the Invention

[0005] To address the technical problems of existing algorithms being easily misled by faulty nodes and failing to distinguish between environmental interference and physical faults, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for monitoring the operation of an automatic filling equipment for pressurized fluid, comprising: The system collects operational data from each filling node in real time, performs spatiotemporal alignment and filtering preprocessing, and maps each filling node to an artificial wolf in the wolf pack algorithm. Based on the operational data of each filling node, it calculates the flow control response efficiency coefficient of each filling node and constructs the fluid health potential energy by combining the group cohesion characteristics of the elite wolf pack. Using the fluid health potential energy as a gating factor, it calculates the comprehensive optimization weight of each artificial wolf in combination with the pressure control target, and selects the alpha wolf based on the comprehensive optimization weight. It uses the fluid health potential energy to adjust the position update step size, and uses the position of the alpha wolf as the guiding target to generate the valve opening command for each filling node at the next moment. After safety limiting, the command is output to realize the operation monitoring of the automatic filling equipment for pressurized fluid.

[0007] This invention collects real-time operational data and maps each filling node to an artificial wolf in a wolf pack algorithm. It calculates the flow control response efficiency coefficient using fluid dynamics mechanisms and constructs a flow health potential energy by combining the group cohesion characteristics of an elite wolf pack. This allows the system to automatically offset systematic common-mode interference caused by factors such as changes in ambient temperature by utilizing the consistent change characteristics of the group, thus accurately distinguishing between environmental deviations and single-point physical faults under complex operating conditions. Furthermore, this invention incorporates the flow health potential energy as a gating factor into the calculation of the comprehensive optimization weight and the alpha wolf selection logic, forcing the artificial wolves to perform optimization under the premise of physical health. This fundamentally prevents filling nodes experiencing blockages or other faults from being mistakenly identified as alpha wolves due to false high-pressure signals generated by pipeline pressure buildup, avoiding incorrect control strategies that mislead the entire wolf pack. Furthermore, this invention utilizes the fluid dynamics and health potential energy to adjust the position update step size, enabling differentiated collaborative control. This allows healthy nodes to quickly follow the position of the leader to maintain system pressure balance, while filling nodes in abnormal states are automatically limited in their adjustment range. This achieves soft isolation protection in fault conditions, preventing equipment damage or fault propagation caused by forced large adjustments, and effectively improving the operational stability and safety of the automatic filling equipment for pressurized fluid.

[0008] Preferably, the real-time acquisition of operational data from each filling node, followed by spatiotemporal alignment and filtering preprocessing, includes: synchronously acquiring real-time pressure, instantaneous flow rate, and valve opening commands from each filling node at a preset cycle via an industrial fieldbus; performing sliding window mean filtering on the acquired raw data; and performing phase alignment compensation on the pressure and flow data based on the pipeline length to obtain the real-time pressure and instantaneous flow rate of each filling node at the current moment.

[0009] Preferably, mapping each filling node to an artificial wolf in the wolf pack algorithm further includes: defining the position of the artificial wolf as the current valve opening command of the corresponding filling node, and using the stable state data of the previous stamping stroke as the initial position of the current cycle.

[0010] Preferably, the flow control response efficiency coefficient satisfies the expression: In the formula, Indicates the first The current flow control response efficiency coefficient of each filling node; Indicates the first The instantaneous flow rate of each filling node at the current moment; Indicates the first The current valve opening command for each filling node; Indicates the first The real-time pressure of each filling node at the current moment; These are preset tiny positive numbers.

[0011] Preferably, the construction of flow health potential based on the group cohesion characteristics of elite wolf packs includes: selecting several artificial wolves with the highest average comprehensive optimization weight over a preset period as elite wolf packs, calculating the average flow control response efficiency coefficient of the elite wolf packs as the group flow attractor; and constructing flow health potential based on the group flow attractor and the current flow control response efficiency coefficient of the filling node. In the formula, Indicates the first The flow health potential of each filling node; Indicates the first The current flow control response efficiency coefficient of each filling node; Represents a community flow attractor; For response sensitivity coefficient; Represents the hyperbolic tangent function; These are preset tiny positive numbers.

[0012] This invention utilizes the hyperbolic tangent function to construct the flow state health potential energy. By comparing the differences between individuals and elite groups, the health level of filling nodes can be dynamically measured. When the filling node deviates from the group characteristics, the potential energy decays rapidly, thereby achieving keen detection and assessment of abnormal states.

[0013] Preferably, the comprehensive optimization weights satisfy the expression: In the formula, Indicates the first The comprehensive optimization weight of the artificial wolf corresponding to each filling node; Indicates the first The flow health potential of each filling node; Indicates the first The real-time pressure of each filling node at the current moment; This represents the preset target pressure for the process; exp is the natural exponential function.

[0014] This invention reconstructs the wolf pack evaluation system, requiring artificial wolves to seek optimization under the premise of physical health. This avoids false high pressure caused by pipeline pressure buildup at clogged filling nodes being misjudged as the optimal state, thereby preventing the spread of erroneous control strategies in the system.

[0015] Preferably, the step of selecting the alpha wolf based on the comprehensive optimization weight includes: sorting all artificial wolves according to the magnitude of the comprehensive optimization weight, selecting the individual with the largest comprehensive optimization weight as the alpha wolf for this period, and recording its position as... .

[0016] Preferably, the valve opening command at the next moment satisfies the expression: In the formula, Indicates the first Valve opening command at the next filling node; Indicates the first The current valve opening command for each filling node; Indicates the first The flow health potential of each filling node; This indicates the location of the alpha wolf.

[0017] This invention uses the fluid dynamic health potential energy as an inertial gating factor for position updates, enabling healthy nodes to quickly follow the lead wolf's adjustment, while abnormal nodes automatically limit the adjustment range. This achieves soft isolation of abnormal nodes, avoids forced operation under fault conditions that could damage the equipment, effectively extends equipment life and ensures production safety.

[0018] Preferably, the output after safety limiting includes: responding to a valve opening command at the next moment whose change in magnitude relative to the valve opening command at the current moment is less than a preset dead zone threshold, keeping the valve opening command at the current moment unchanged; responding to a valve opening command at the next moment whose change in magnitude relative to the valve opening command at the current moment is greater than a preset step size limit value, using the step size limit value as the maximum change amount for output.

[0019] This invention avoids frequent actuator movements caused by minute calculation jitter or noise through dead zone control, effectively reducing mechanical wear and extending valve service life. By limiting the step size, it prevents water hammer effect and pressure overshoot caused by drastic changes in valve opening, thus ensuring the physical safety of the fluid pipeline system.

[0020] Secondly, the present invention provides an operation monitoring system for an automatic filling equipment for stamping liquid, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned operation monitoring method for an automatic filling equipment for stamping liquid is implemented.

[0021] By adopting the above technical solution, a computer program is generated from the above-mentioned method for monitoring the operation of automatic filling equipment for stamping liquid, and stored in a memory so that it can be loaded and executed by a processor. A terminal device is then made based on the memory and the processor for convenient use.

[0022] The beneficial effects of this invention are as follows: By constructing a flow control response efficiency coefficient and combining it with the group cohesion characteristics of elite wolf packs to generate flow state health potential energy, this invention can automatically offset common-mode interferences such as oil viscosity drift caused by environmental temperature fluctuations by utilizing the cooperative evolution law of the group in physical characteristics. This distinguishes between systemic environmental interference and single-point physical faults, overcoming the shortcomings of traditional methods in decoupling environmental factors and physical faults. This invention deeply integrates flow state health potential energy into the calculation of comprehensive optimization weights and position update rules. By forcing artificial wolves to compete for the alpha wolf under the premise of physical health, it effectively prevents the high-pressure illusion caused by pipeline pressure buildup at blocked filling nodes from misleading the adjustment direction of the entire control system. At the same time, it uses flow state health potential energy to adaptively constrain the update step size of valve opening commands, enabling healthy filling nodes to respond sensitively to system demands, while faulty filling nodes are restricted in their adjustment range to achieve soft isolation. This avoids equipment damage caused by forced adjustment under fault conditions, improving the operational reliability and intelligent monitoring level of automatic filling equipment for pressurized fluid. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an operation monitoring method for an automatic filling equipment for smelting liquid according to the present invention; Figure 2 This is a schematic diagram illustrating the evolution trend of the flow control response efficiency coefficient with the control cycle; Figure 3 This is a schematic diagram illustrating the spatiotemporal distribution of the flow health potential energy at each filling node; Figure 4 This is a schematic diagram illustrating the coordinated convergence of valve opening commands and the effect of soft fault isolation. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention discloses an operation monitoring method for automatic filling equipment for emery fluid, referring to... Figure 1This includes steps S1-S4: S1. Real-time acquisition of operational data from each filling node, spatiotemporal alignment and filtering preprocessing, and mapping of each filling node to an artificial wolf in the wolf pack algorithm.

[0027] It should be noted that due to the complex electromagnetic interference and physical lag in fluid transmission in the stamping workshop, directly using the raw collected data for algorithm iteration will cause control command oscillations. In addition, there is a fluid coupling effect in the multi-nozzle network. Therefore, this invention first cleans and aligns the data, and maps each physical filling node to an artificial wolf in the wolf pack algorithm. It uses historical states for initialization to provide a reliable data foundation for subsequent collaborative control.

[0028] Specifically, real-time pressure, instantaneous flow rate, and valve opening commands of each filling node are synchronously collected at preset intervals via an industrial fieldbus. The collected raw data undergoes sliding window mean filtering to remove high-frequency noise, and phase alignment compensation is performed on the real-time pressure and instantaneous flow rate data based on pipeline length to obtain the real-time pressure and instantaneous flow rate of each filling node at the current moment. Each nozzle node is mapped to a wolf in a wolf pack algorithm, and the position of each wolf is defined as the valve opening command of the proportional valve of the corresponding filling node. The steady-state data of the previous pressing cycle is used as the initial position of the current cycle. The steady-state data refers to the valve opening command ultimately maintained by each filling node at the end of the previous pressing cycle or when the system reaches steady state.

[0029] S2. Based on the operational data of each filling node, calculate the flow control response efficiency coefficient of each filling node, and construct the flow health potential energy by combining the group cohesion characteristics of the elite wolf pack.

[0030] It should be noted that traditional flow resistance calculations do not consider the dynamic control effect of valve opening commands on the flow state, and rely solely on the statistical characteristics of pressure or flow resistance to determine faults, making them susceptible to interference from changes in the overall viscosity of the oil caused by changes in ambient temperature. Therefore, this invention introduces a flow control response efficiency coefficient to characterize the flow response capability under unit pressure drive, and utilizes the characteristic of artificial wolves following the alpha wolf in the wolf pack algorithm. By calculating the cohesion of each filling node with the elite wolf pack in terms of physical characteristics, a flow state health potential is constructed. Through the group cohesion characteristics, common-mode interference from the environment is automatically offset, accurately pinpointing the true physical anomaly.

[0031] Specifically, based on the flow characteristics of Bernoulli's equation, the first... Current flow control response efficiency coefficient of each filling node:

[0032] In the formula, Indicates the first The current flow control response efficiency coefficient of each filling node; Indicates the first The instantaneous flow rate of each filling node at the current moment; Indicates the first The current valve opening command for each filling node; Indicates the first The real-time pressure of each filling node at the current moment; The value is a preset small positive number to prevent the denominator from being zero. In this embodiment, The value is In other embodiments, implementers may set the appropriate parameters according to the actual implementation situation. However, in order to ensure that the flow control response efficiency coefficient can truly reflect the physical flow state and is not overwhelmed by numerical noise, Need to be less than When the valve opening command is given Increase but instantaneous flow When not increased synchronously, the flow control response efficiency coefficient will be affected. A decrease in the value indicates that there may be a blockage at the filling node; conversely, when the real-time pressure... Reduced instantaneous flow An abnormal increase will lead to a decrease in the flow control response efficiency coefficient. An increase in the value indicates that there may be a leak at the filling node.

[0033] Furthermore, the top 20 control cycles with the highest average comprehensive optimization weights were selected. Using artificial wolves as the elite pack, the mean of their flow control response efficiency coefficients is calculated as the group flow attractor. The number of individuals in the elite wolf pack is determined by the implementers based on the total number of filling nodes. In this embodiment, the total number of filling nodes is set according to a preset ratio. It is 10. In other embodiments, the ratio is set to 3, and the implementer can set it to 20% to 30% depending on the actual production line scale.

[0034] Construct the flow health potential based on the community flow attractors and the current flow control response efficiency coefficients of the filling nodes:

[0035] In the formula, Indicates the first The flow health potential of each filling node; Indicates the first The current flow control response efficiency coefficient of each filling node; Represents a community flow attractor; These are preset small positive numbers used to prevent the denominator from being zero; The sensitivity coefficient is used to adjust the tolerance of the flow health potential function to the deviation of the flow control response efficiency coefficient. In this embodiment, the value is 0.2. In other embodiments, the implementer can choose a value between 0.1 and 0.3 according to the actual implementation conditions of the oil viscosity characteristics. When the stamping fluid viscosity is low or the operating conditions cause large fluctuations in fluid turbulence, the natural dispersion of the flow control response efficiency coefficient increases. In this case, a larger value, such as 0.3, should be selected to expand the health judgment range and prevent normal fluid disturbances from being misjudged as abnormal. When the stamping fluid viscosity is high or the system operation is relatively stable, the value distribution of the flow control response efficiency coefficient is more concentrated. In this case, a smaller value, such as 0.1, should be selected to narrow the health judgment range and improve the sensitivity of identifying early minor blockage faults. Represents the hyperbolic tangent function. (When filling node) Flow control response efficiency coefficient With the community flow attractor When highly consistent, the difference items Approaching 0, making Approaching 0, thus making A value close to 1 indicates that the filling node is in a healthy state; when a filling node experiences blockage or leakage, causing its physical characteristics to deviate from those of an elite wolf pack, the difference item... Increase, making Approaching 1, leading to A rapid decay to 0 indicates that the filling node is in an abnormal state.

[0036] For example, Figure 2 The evolution trend of the flow control response efficiency coefficient with the control cycle in embodiments of the present invention is shown, such as... Figure 2 As shown, in the environmental interference range, due to the change in overall flow resistance caused by environmental factors, the collective flow attractor and the single-node curve show a synchronous drift trend, which reflects the algorithm's ability to automatically cancel common-mode interference. In the physical blockage fault range, the flow control response efficiency coefficient of the fault node deviates significantly from the collective flow attractor, showing a cliff-like drop, thus being accurately identified as an abnormal physical state.

[0037] Figure 3 This diagram illustrates the spatiotemporal distribution of the flow health potential energy at each filling node in an embodiment of the present invention, as shown below. Figure 3 As shown, before the failure and during environmental disturbances, the health potential of all filling nodes remained close to 1. When node 4 experienced a blockage failure in the 100th cycle, its corresponding health potential rapidly decreased, which intuitively demonstrates the process of the algorithm accurately locating and quantifying the state of a specific fault node by utilizing the characteristics of group cohesion.

[0038] S3. Using the fluid dynamic health potential as a gating factor, and combining it with the pressure control target, calculate the comprehensive optimization weight of each artificial wolf, and select the alpha wolf based on the comprehensive optimization weight.

[0039] It should be noted that the standard wolf pack algorithm is easily deceived by false high pressure from faulty nodes. For example, a blocked filling node may have extremely high real-time pressure and minimal pressure error due to pipeline pressure buildup. If it is selected as the alpha wolf, it will cause all valves in the line to close incorrectly. Therefore, this invention reconstructs the evaluation system of the wolf pack, which requires that the artificial wolves must perform optimization under the premise of healthy physical conditions. Only filling nodes with healthy flow potential energy and small pressure error have extremely high comprehensive optimization weight, thereby preventing faulty nodes from misleading the entire system.

[0040] Specifically, calculate the overall optimization weight for each artificial wolf:

[0041] In the formula, Indicates the first The comprehensive optimization weight of the artificial wolf corresponding to each filling node; Indicates the first The flow health potential of each filling node; Indicates the first The real-time pressure of each filling node at the current moment; This represents the preset process target pressure; exp is the natural exponential function used to transform the relative pressure error into a normalized fitness evaluation index. This invention employs a multiplicative coupling mechanism, using the flow state health potential as a gating factor for the weights. When a node fails, the flow state health potential... Approaching 0, regardless of pressure error How small, comprehensive optimization weight All will be forcibly pulled down to near 0, only when the fluid state is healthy. When the pressure error is large and the overall optimization weight is small, Only then will it reach its maximum value.

[0042] All artificial wolves are ranked according to their overall optimization weights. The individual with the highest overall optimization weight is selected as the alpha wolf for this period, and its position is recorded as _____. .

[0043] S4. Utilize the fluid dynamics and health potential energy to adjust the position update step size, and use the position of the alpha wolf as the guiding target to generate the valve opening command for the next moment of each filling node. After safety limiting, the command is output to realize the operation monitoring of the automatic filling equipment for the pressurized fluid.

[0044] Specifically, based on the siege logic of the wolf pack algorithm, the fluid dynamics health potential is introduced as the step size weight to update the th... The valve opening command for the next filling node:

[0045] In the formula, Indicates the first Valve opening command at the next filling node; Indicates the first The current valve opening command for each filling node; Indicates the first The flow health potential of each filling node; Indicates the position of the alpha wolf. Fluid health potential of each filling node The decision was made The degree to which each filling node moves closer to the alpha wolf, when When it approaches 1, the increment term Dominant, the filling process can be quickly adjusted to follow the lead of the alpha wolf; when When it approaches 0, the increment term Approaching zero, the valve opening command is locked at the current position, no longer responding to external adjustment demands. It should be noted that this invention alters the wolf pack's movement rules, using fluid dynamic health potential energy as an inertial gating factor for position updates. Filling nodes with low fluid dynamic health potential energy will have their movement restricted, achieving soft isolation of faults, while healthy filling nodes quickly follow the alpha wolf, maintaining system balance.

[0046] Furthermore, in the case of the first Valve opening command at the next filling node Before outputting to the actuator, determine Is it less than the preset dead zone threshold? If the value is less than the specified value, the valve opening command remains unchanged to avoid mechanical wear caused by frequent reciprocating movements of the actuator due to sensor noise or minor calculation fluctuations. In this embodiment, The value is set to 1% of the valve's maximum adjustable range; simultaneously, the variation range of the valve opening command is limited, if the... Valve opening command at the next filling node Relative to the current valve opening command If the change in value exceeds a preset step size limit, then that step size limit is used as the maximum change value for output to prevent water hammer or pressure overshoot from occurring in the pipeline due to excessively rapid valve adjustment. In this embodiment, the step size limit is set to 5% of the valve's maximum adjustable range; conversely, if the change in value is less than a preset step size limit, then the maximum change is output. Valve opening command at the next filling node Relative to the current valve opening command The magnitude of the change is greater than or equal to the preset dead zone threshold. If the value is less than or equal to the preset step size limit, then the valve opening command at the next moment will be... This is the output.

[0047] For example, Figure 4 This illustrates the collaborative convergence and fault soft isolation effect of valve opening commands in an embodiment of the present invention, such as... Figure 4 As shown, in the later stage of control, the system enters a high-load state, and the ideal opening command rises sharply from 0.6 to 0.78. Normal nodes closely follow the target and coordinate adjustment; while the faulty node, due to the reduction of the flow health potential energy, its valve opening command is refused to follow and locked at the current position, about 0.63, and no longer responds to large adjustment commands, thus achieving soft isolation of the fault and preventing equipment damage or fault propagation caused by forced adjustment.

[0048] This invention also discloses an operation monitoring system for an automatic filling equipment for stamping liquid, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, an operation monitoring method for an automatic filling equipment for stamping liquid according to the present invention is implemented.

[0049] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A method for monitoring the operation of an automatic filling equipment for stamping fluid, characterized in that, include: Real-time data collection of the operation process of each filling node, spatiotemporal alignment and filtering preprocessing, and mapping of each filling node to artificial wolves in the wolf pack algorithm; Based on the operational data of each filling node, the flow control response efficiency coefficient of each filling node is calculated, and the flow health potential is constructed by combining the group cohesion characteristics of the elite wolf pack. Using fluid dynamic health potential as a gating factor, combined with pressure control objectives, the comprehensive optimization weight of each artificial wolf is calculated, and the alpha wolf is selected based on the comprehensive optimization weight. The position update step size is adjusted by utilizing the fluid dynamics and health potential energy, and the position of the alpha wolf is used as the guiding target to generate the valve opening command for the next moment of each filling node. After safety limiting, the command is output to realize the operation monitoring of the automatic filling equipment for the pressurized fluid.

2. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The real-time acquisition of operational data from each filling node, followed by spatiotemporal alignment and filtering preprocessing, includes: The real-time pressure, instantaneous flow rate, and valve opening commands of each filling node are synchronously collected at a preset cycle via an industrial fieldbus. The collected raw data is subjected to sliding window mean filtering, and the pressure and flow data are phase aligned and compensated based on the pipeline length to obtain the real-time pressure and instantaneous flow rate of each filling node at the current moment.

3. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The method of mapping each filling node to an artificial wolf in the wolf pack algorithm also includes: The position of the artificial wolf is defined as the current valve opening command of the corresponding filling node, and the stable state data of the previous punching cycle is used as the initial position of the current cycle.

4. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The flow control response efficiency coefficient satisfies the expression: ； In the formula, Indicates the first The current flow control response efficiency coefficient of each filling node; Indicates the first The instantaneous flow rate of each filling node at the current moment; Indicates the first The current valve opening command for each filling node; Indicates the first The real-time pressure of each filling node at the current moment; These are preset tiny positive numbers.

5. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The construction of fluid dynamic health potential energy by combining the group cohesion characteristics of elite wolf packs includes: A number of artificial wolves with the highest average comprehensive optimization weights over a preset period are selected as the elite wolf pack. The average flow control response efficiency coefficient of the elite wolf pack is calculated as the population flow attractor. A flow health potential is constructed based on the population flow attractor and the current flow control response efficiency coefficient of the filling node. In the formula, Indicates the first The flow health potential of each filling node; Indicates the first The current flow control response efficiency coefficient of each filling node; Represents a community flow attractor; For response sensitivity coefficient; Represents the hyperbolic tangent function; These are preset tiny positive numbers.

6. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The comprehensive optimization weights satisfy the expression: ； In the formula, Indicates the first The comprehensive optimization weight of the artificial wolf corresponding to each filling node; Indicates the first The flow health potential of each filling node; Indicates the first The real-time pressure of each filling node at the current moment; This represents the preset target pressure for the process; exp is the natural exponential function.

7. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The selection of the alpha wolf based on comprehensive optimization weights includes: All artificial wolves are ranked according to their overall optimization weights. The individual with the highest overall optimization weight is selected as the alpha wolf for this period, and its position is recorded as _____. .

8. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The valve opening command at the next moment satisfies the expression: ； In the formula, Indicates the first Valve opening command at the next filling node; Indicates the first The current valve opening command for each filling node; Indicates the first The flow health potential of each filling node; This indicates the location of the alpha wolf.

9. The operation monitoring method for an automatic filling equipment for stamping fluid according to claim 1, characterized in that, The output after safety limiting includes: If the change in the valve opening command at the next moment relative to the valve opening command at the current moment is less than a preset dead zone threshold, the valve opening command at the current moment remains unchanged; if the change in the valve opening command at the next moment relative to the valve opening command at the current moment is greater than a preset step size limit, the step size limit is used as the maximum change value for output.

10. An operation monitoring system for automatic filling equipment for stamping fluid, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement an operation monitoring method for an automatic filling equipment for stamping liquid according to any one of claims 1-9.

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