Data-aware hose valve resource supply collaborative balancing and task scheduling management system

Abstract

The application relates to the technical field of industrial fluid control and production resource scheduling, and discloses a rubber pipe valve resource supply collaborative balance and task scheduling management system based on data sensing, which comprises an operating state monitoring unit, a time window calculation unit and a collaborative scheduling control unit. The system collects air cavity pressure data in the action process of the rubber pipe valve in real time, extracts pneumatic response delay parameters and maps the pneumatic response delay parameters into lining wear states, and then calculates a predicted failure time node of the rubber pipe valve. In combination with the earliest arrival time of the spare part supply chain, whether there is a supply-demand time gap is judged. If there is, by comparing a production side regulation cost index for prolonging the service life of the equipment with a supply side regulation cost index for advancing the arrival of the spare parts, a production beat adjustment instruction or a supply chain urgent scheduling instruction is generated. Through the establishment of a two-way sensing and collaborative arbitration mechanism of equipment working conditions and the supply chain data, the application effectively solves the problem of unscheduled shutdown caused by the lag of spare part supply.

Description

Technical Field

[0001] This invention relates to the field of industrial fluid control and production resource scheduling technology, and more specifically, to a data-aware hose valve resource supply collaborative balancing and task scheduling management system. Background Technology

[0002] Currently, in continuous industrial production scenarios such as lithium battery electrode slurry preparation, fine chemical transportation, and mine tailings treatment, hose valves, as core shut-off and regulating actuators in fluid pipelines, have their inner liner components subjected to high-frequency opening and closing actions and the erosion of highly abrasive media for extended periods. Currently, the industry typically follows a static management approach based on statistical experience for the maintenance of such critical and vulnerable components. Production maintenance departments set preventative replacement cycles based on equipment design cycles, while material supply departments set safety levels for inventory based on historical average consumption rates. To compensate for the lack of real-time capability in static statistical management, existing technologies... The technology proposes an online monitoring solution for the equipment itself. For example, Chinese invention patent CN117329323A discloses a pneumatic clamp valve hose wear detection alarm device. By constructing a layered hose structure with a surface layer, an intermediate layer and a warning layer, and cooperating with a pressure sensor and a pressure compensation circuit, the device maintains the cut-off capability by physically replenishing air and pressurizing after the hose wears. When the wear reaches the warning layer, it triggers a terminal alarm. The device triggers a replenishment process through periodic inventory checks. This management mode can maintain basic production continuity with low management costs in an ideal environment with constant operating conditions and single media properties.

[0003] However, in the increasingly complex and dynamic production environment of modern industry, the physicochemical properties of fluid media often fluctuate with the adjustment of raw material batches or process formulations, resulting in a highly nonlinear and random physical wear rate of hose valve liner. Especially when conveying shear-thickening slurries or high-solids suspensions, the actual remaining life window of the liner may shrink sharply in a short period of time due to sudden changes in operating conditions. At this time, the rigid response cycle of the physical supply chain, including physical processes such as order processing, logistics transportation and incoming inspection, is often much longer than the remaining safe operating time before the sudden failure of the equipment. This temporal mismatch between the instantaneity of the evolution of operating conditions and the lag of physical supply constitutes a fundamental physical barrier faced by existing technologies in ensuring production continuity.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a control system that can perceive the evolution of equipment physical conditions in real time and achieve precise coordination and dynamic balance between the rigid constraints of production task scheduling and the elastic boundaries of supply chain logistics response. Summary of the Invention

[0005] This invention provides a data-aware hose valve resource supply collaborative balancing and task scheduling management system, comprising:

[0006] The operation status monitoring unit is connected to the pneumatic control circuit of the hose valve. It is used to collect the air chamber pressure data of the hose valve during the cut-off action at a preset sampling frequency, calculate the pneumatic response delay parameter based on the falling edge characteristics of the air chamber pressure data, and map the pneumatic response delay parameter into physical health status data that characterizes the wear degree of the inner liner.

[0007] The time window calculation unit is connected in communication with the operation status monitoring unit. It is used to calculate the expected failure time of the hose valve based on physical health status data and simultaneously obtain the expected earliest arrival time of the spare parts supply chain system.

[0008] The collaborative scheduling control unit, connected to the time window calculation unit, is used to execute supply and demand time balance logic. Specifically, the collaborative scheduling control unit is configured to perform the following calculation steps when a time gap is detected, where the expected failure time is earlier than the earliest expected arrival time: calculating the production-side control cost index required to extend the operating time of the hose valves by the time gap based on the current production line's unit-time capacity and output value data; and calculating the supply-side control cost index required to advance the earliest expected arrival time by the time gap based on logistics expedited rate data. The collaborative scheduling control unit further compares the production-side control cost index with the supply-side control cost index, and when it determines that the production-side control cost index is less than the supply-side control cost index, it generates and outputs a production cycle adjustment instruction containing load reduction parameters. This production cycle adjustment instruction drives the production line to perform load reduction operations to cover the time gap.

[0009] Preferably, the operating status monitoring unit includes: a high-frequency pressure sensor, installed at the inflation / deflation port of the pneumatic control loop, used to capture the transient pressure change curve in the air chamber when the hose valve performs a cut-off action; and a feature analysis module, connected to the high-frequency pressure sensor, used to extract the time difference from the moment the control signal is issued to the moment when the air chamber pressure reaches a preset cutoff threshold in the transient pressure change curve, and define the time difference as a pneumatic response delay parameter; wherein, the feature analysis module is configured to trigger the update of physical health status data only when the drift of the calculated pneumatic response delay parameter relative to the initial reference value exceeds a preset safety tolerance.

[0010] Preferably, the time window calculation unit includes: a life prediction model, which stores data on the nonlinear decay relationship between the life of the hose valve and the load intensity, used to calculate the expected failure time node by combining the current production load and physical health status data; and a logistics data interface module, used to retrieve spare parts inventory status and logistics transportation plan data, calculate the transportation time under different logistics methods, and determine the earliest expected arrival time node based on the transportation time with the smallest value.

[0011] Preferably, the collaborative scheduling control unit follows the following mathematical formula when calculating the production-side regulation cost index: ,in, As a production-side regulation cost index, This refers to the current production line's output value per unit time. This represents the normalized production load factor for the current production line. To determine the normalized target load factor required to extend the operating time gap of the hose valve, This represents the value of the time gap.

[0012] Preferably, the collaborative scheduling control unit is also used to call the life-load sensitivity model of the hose valve when determining the normalized target load coefficient, and through reverse iterative calculation, determine the maximum production load value allowed to cover the time gap while keeping the hose valve from physical failure, and set the maximum production load value as the normalized target load coefficient.

[0013] Preferably, the calculation logic of the supply-side regulation cost index executed by the collaborative scheduling control unit includes: a scheme screening step, used to screen all expedited logistics schemes that can eliminate time gaps from a preset logistics scheme library; a cost difference step, used to calculate the additional logistics cost of each expedited logistics scheme relative to the benchmark logistics scheme; and an index determination step, used to define the minimum value of the additional logistics cost as the supply-side regulation cost index.

[0014] Preferably, the system further includes: a fluid impact monitoring sensor, installed on the fluid pipeline where the hose valve is located, for real-time monitoring of the instantaneous impact pressure of the fluid medium; wherein, when calculating the aerodynamic response delay parameter, the operating status monitoring unit introduces the instantaneous impact pressure as a correction factor to eliminate the interference of fluid pressure fluctuations on the deformation characteristics of the inner liner, ensuring that the physical health status data only characterizes the material fatigue characteristics of the inner liner itself.

[0015] Preferably, the collaborative scheduling control unit is also used to perform the following operations: when it is determined that the production-side control cost index is greater than or equal to the supply-side control cost index, it generates an emergency supply chain scheduling instruction; the emergency supply chain scheduling instruction is used to trigger the spare parts supply chain system to lock and execute a specific emergency logistics plan, which is a plan that minimizes the supply-side control cost index.

[0016] Preferably, the production cycle adjustment command includes: a pumping frequency control parameter, used to reduce the operating frequency of the delivery pump connected in series with the hose valve; or an intermittent ratio adjustment parameter, used to increase the pause interval between two adjacent delivery actions while keeping the single delivery volume unchanged; the production cycle adjustment command aims to passively extend the operating time by reducing the unit time operation frequency of the hose valve or reducing the fluid impact load of a single action.

[0017] Preferably, the system further includes: a closed-loop feedback correction unit, used to continuously track the actual rate of change of the aerodynamic response delay parameter during the execution of the production cycle adjustment command; wherein, if the actual rate of change is higher than the expected life extension trajectory, the closed-loop feedback correction unit triggers the collaborative scheduling control unit to recalculate the production-side control cost index and dynamically increase the load reduction magnitude in the production cycle adjustment command.

[0018] The embodiments of the present invention have at least the following beneficial effects:

[0019] 1. In the coordinated balancing of hose valve resource supply, by configuring strategy sensitivity verification logic in the task scheduling coordination unit, the open-loop control risk of traditional management systems when facing complex fluid conditions is solved. Existing technologies usually assume that reducing the frequency of action will inevitably extend the equipment life. However, in actual working conditions such as handling shear-thickening fluids or in the late stage of fatigue crack propagation, the physical object's response to the deceleration command often exhibits nonlinear or even negative correlation characteristics. By calculating the response sensitivity coefficient of the rate of change gradient of pneumatic hysteresis time relative to the adjustment range of production cycle in real time, a physical effectiveness verification loop for the management strategy itself is constructed. This mechanism can immediately identify the failure state where the hose valve wear rate does not decrease as expected with the cycle adjustment and trigger an emergency resource adjustment command.

[0020] 2. Construct a two-way comparison logic that includes the deformation cost modulus on the production side and the deformation cost modulus on the supply side. This breaks through the limitation of existing technologies that treat supply chain delivery cycles and equipment remaining lifespan as rigid constraints. The system maps the converted value of production line capacity loss and the additional acceleration cost value of spare parts logistics to the same calculation dimension. By comparing the magnitude of the two values, it determines whether to execute a reverse adjustment of production cycle or trigger a high-priority logistics switch. This transforms the flexibility of the time dimension into a game of economic costs, enabling the system to automatically lock in the solution with the lowest marginal cost between the two paths of trading capacity for time and trading funds for time. This not only avoids paying a disproportionate capacity cost for a single spare parts shortage, but also prevents blindly paying high logistics expedited fees in low-value downtime scenarios, achieving a dynamic balance between ensuring production continuity and controlling operating costs.

[0021] 3. To address the frequent downtime caused by independent maintenance of multiple devices in continuous production scenarios, this embodiment of the invention introduces a multi-node task merging logic. While locking the dominant maintenance event, the system scans the status of other non-emergency hose valves in the same section and calculates the residual value loss characteristic value caused by their early replacement and the start-up cost characteristic value of the independent shutdown operation of the production line. By comparing these two characteristic values, the system attracts subordinate nodes with start-up costs higher than residual value loss to the dominant maintenance window for joint processing. This logic creatively uses the remaining lifespan of low-value consumables as leverage to save on high production line start-up and shutdown costs, eliminates the fragmented loss of production efficiency caused by discrete maintenance needs, and improves the long-cycle operating efficiency of the entire line. Attached Figure Description

[0022] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the invention are illustrated by way of example and not limitation, wherein:

[0023] Figure 1 This is a flowchart of the resource supply coordination and task scheduling management system for hose valves of the present invention.

[0024] Figure 2 This is a block diagram illustrating the overall architecture and data interaction principle of the hose valve resource supply collaborative balancing and task scheduling management system of the present invention. Detailed Implementation

[0025] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments in conjunction with the accompanying drawings. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0026] A data-aware hose valve resource supply collaborative balancing and task scheduling management system includes:

[0027] The operation status monitoring unit is connected to the pneumatic control circuit of the hose valve. It is used to collect the air chamber pressure data of the hose valve during the cut-off action at a preset sampling frequency, calculate the pneumatic response delay parameter based on the falling edge characteristics of the air chamber pressure data, and map the pneumatic response delay parameter into physical health status data that characterizes the wear degree of the inner liner.

[0028] The time window calculation unit is connected in communication with the operation status monitoring unit. It is used to calculate the expected failure time of the hose valve based on physical health status data and simultaneously obtain the expected earliest arrival time of the spare parts supply chain system.

[0029] The collaborative scheduling control unit, connected to the time window calculation unit, is used to execute supply and demand time balance logic. Specifically, the collaborative scheduling control unit is configured to perform the following calculation steps when a time gap is detected, where the expected failure time is earlier than the earliest expected arrival time: calculating the production-side control cost index required to extend the operating time of the hose valves by the time gap based on the current production line's unit-time capacity and output value data; and calculating the supply-side control cost index required to advance the earliest expected arrival time by the time gap based on logistics expedited rate data. The collaborative scheduling control unit further compares the production-side control cost index with the supply-side control cost index, and when it determines that the production-side control cost index is less than the supply-side control cost index, it generates and outputs a production cycle adjustment instruction containing load reduction parameters. This production cycle adjustment instruction drives the production line to perform load reduction operations to cover the time gap.

[0030] Preferably, the operating status monitoring unit includes: a high-frequency pressure sensor, installed at the inflation / deflation port of the pneumatic control loop, used to capture the transient pressure change curve in the air chamber when the hose valve performs a cut-off action; and a feature analysis module, connected to the high-frequency pressure sensor, used to extract the time difference from the moment the control signal is issued to the moment when the air chamber pressure reaches a preset cutoff threshold in the transient pressure change curve, and define the time difference as a pneumatic response delay parameter; wherein, the feature analysis module is configured to trigger the update of physical health status data only when the drift of the calculated pneumatic response delay parameter relative to the initial reference value exceeds a preset safety tolerance.

[0031] Preferably, the time window calculation unit includes: a life prediction model, which stores data on the nonlinear decay relationship between the life of the hose valve and the load intensity, used to calculate the expected failure time node by combining the current production load and physical health status data; and a logistics data interface module, used to retrieve spare parts inventory status and logistics transportation plan data, calculate the transportation time under different logistics methods, and determine the earliest expected arrival time node based on the transportation time with the smallest value.

[0032] Preferably, the collaborative scheduling control unit follows the following mathematical formula when calculating the production-side regulation cost index: ,in, As a production-side regulation cost index, This refers to the current production line's output value per unit time. This represents the normalized production load factor for the current production line. To determine the normalized target load factor required to extend the operating time gap of the hose valve, This represents the value of the time gap.

[0033] Preferably, the collaborative scheduling control unit is also used to call the life-load sensitivity model of the hose valve when determining the normalized target load coefficient, and through reverse iterative calculation, determine the maximum production load value allowed to cover the time gap while keeping the hose valve from physical failure, and set the maximum production load value as the normalized target load coefficient.

[0034] Preferably, the calculation logic of the supply-side regulation cost index executed by the collaborative scheduling control unit includes: a scheme screening step, used to screen all expedited logistics schemes that can eliminate time gaps from a preset logistics scheme library; a cost difference step, used to calculate the additional logistics cost of each expedited logistics scheme relative to the benchmark logistics scheme; and an index determination step, used to define the minimum value of the additional logistics cost as the supply-side regulation cost index.

[0035] Preferably, the system further includes: a fluid impact monitoring sensor, installed on the fluid pipeline where the hose valve is located, for real-time monitoring of the instantaneous impact pressure of the fluid medium; wherein, when calculating the aerodynamic response delay parameter, the operating status monitoring unit introduces the instantaneous impact pressure as a correction factor to eliminate the interference of fluid pressure fluctuations on the deformation characteristics of the inner liner, ensuring that the physical health status data only characterizes the material fatigue characteristics of the inner liner itself.

[0036] Preferably, the collaborative scheduling control unit is also used to perform the following operations: when it is determined that the production-side control cost index is greater than or equal to the supply-side control cost index, it generates an emergency supply chain scheduling instruction; the emergency supply chain scheduling instruction is used to trigger the spare parts supply chain system to lock and execute a specific emergency logistics plan, which is a plan that minimizes the supply-side control cost index.

[0037] Preferably, the production cycle adjustment command includes: a pumping frequency control parameter, used to reduce the operating frequency of the delivery pump connected in series with the hose valve; or an intermittent ratio adjustment parameter, used to increase the pause interval between two adjacent delivery actions while keeping the single delivery volume unchanged; the production cycle adjustment command aims to passively extend the operating time by reducing the unit time operation frequency of the hose valve or reducing the fluid impact load of a single action.

[0038] Preferably, the system further includes: a closed-loop feedback correction unit, used to continuously track the actual rate of change of the aerodynamic response delay parameter during the execution of the production cycle adjustment command; wherein, if the actual rate of change is higher than the expected life extension trajectory, the closed-loop feedback correction unit triggers the collaborative scheduling control unit to recalculate the production-side control cost index and dynamically increase the load reduction magnitude in the production cycle adjustment command.

[0039] Example 1: In a continuous production scenario for lithium-ion battery cathode material preparation, this invention utilizes a data-aware hose valve resource supply collaborative balancing and task scheduling management system. This system is deployed to control a critical slurry delivery pipeline equipped with a pneumatic hose valve as the core cut-off actuator. In this production environment, the fluid medium is a ternary precursor slurry with high solids content and shear thickening properties. Its physical wear on the hose valve's inner liner exhibits highly nonlinear and random characteristics. When the system is in real-time operation, the operation status monitoring unit continuously collects air chamber pressure data during the hose valve's cut-off action at a sampling frequency of 1 kHz via a high-frequency pressure sensor connected to the pneumatic control loop. The system extracts the time difference between the air chamber pressure from the moment the control signal is issued and the moment it drops to the preset cut-off threshold, defining this as the air chamber pressure. In a specific operating condition, due to fluctuations in the particle size of raw material batches, the system detected an accelerating upward trend in the drift of the pneumatic response delay parameter relative to the initial baseline value, exceeding the preset safety tolerance. The time window calculation unit, based on the current drift rate and the stored lifespan prediction model, calculated the expected failure time of the hose valve to be 48 hours from the current moment. Simultaneously, the time window calculation unit synchronously acquired the status data of the spare parts supply chain system through the logistics data interface module. The data indicated that the current spare parts inventory for this specification of inner liner was zero, and the earliest expected arrival time based on the standard logistics transportation plan was 72 hours from the current moment. At this point, the system identified a supply-demand time gap of 24 hours between the expected failure time and the earliest expected arrival time, denoted as... .

[0040] Faced with this objective situation of supply and demand mismatch, the collaborative scheduling control unit immediately triggers the supply and demand time balancing logic to resolve the unplanned downtime that may result from the physical supply chain lagging behind the evolution of equipment operating conditions. The collaborative scheduling control unit calculates the bidirectional control costs required to cover this time gap, and the system obtains the unit time capacity and output value data of the current production line. The figure is 10,000 CNY / h, and the normalized production load factor of the current production line. To physically extend the operating time of the hose valve by 24 hours to cover the aforementioned time gap, the system invokes the hose valve's life-load sensitivity model and initiates a step-approximation search subroutine: The controller initializes the normalized target load coefficient in the temporary register to 1.0, setting the single decrement step size to 0.01; in each calculation cycle, the controller substitutes the current normalized target load coefficient into the life prediction formula to calculate the theoretical remaining life. If the calculated theoretical remaining life is less than the required total coverage time, the current remaining life is added to the 24-hour gap, and the normalized target load coefficient is subtracted by 0.01 before entering the next cycle. The cycle stops when the calculated theoretical remaining life is first greater than or equal to the required total coverage time. The normalized target load coefficient is then determined through reverse iterative calculation. It needs to be reduced to 0.8. Based on the above data, the collaborative scheduling control unit uses the formula... Perform the operation; where, As a production-side regulation cost index, This refers to production capacity and output value data per unit time. This is the normalized production load factor; The normalized target load factor; The system calculates the production-side regulation cost index to account for the supply-demand time gap. It is 48000.

[0041] Simultaneously, the collaborative scheduling control unit executes the calculation logic of the supply-side regulation cost index. The system selects an expedited logistics plan from the preset logistics plan library that can eliminate the 24-hour time gap. This plan is an air freight express delivery plan. The system calculates the additional logistics cost of this expedited logistics plan compared to the benchmark land transportation logistics plan, and derives the supply-side regulation cost index. It is 12000; of which, As the supply-side regulation cost index, the coordinated scheduling and control unit compares the calculated production-side regulation cost index with the supply-side regulation cost index to determine... The value 48000 is greater than The value is 12000. Based on this comparison result, the system generates an expedited supply chain dispatch instruction. This instruction directly identifies the air freight delivery plan that minimizes the supply-side control cost index and triggers the spare parts supply chain system to execute this specific plan. In another scenario, if the calculated production-side control cost index is less than the supply-side control cost index, for example, during off-peak order periods... At lower speeds, the system generates a production cycle adjustment command, which includes pumping frequency control parameters. The calculation logic of these parameters follows a linear mapping principle: the system reads the rated operating frequency of the delivery pump, such as 50Hz, and multiplies it directly by the calculated normalized target load factor of 0.8 to obtain a target operating frequency of 40Hz. The controller then rewrites the inverter's output frequency register value to 40Hz, driving the delivery pump connected in series with the hose valve to reduce its operating frequency to the level corresponding to the normalized target load factor of 0.8. By reducing the fluid impact load per unit time, the system physically reduces the wear rate of the inner liner, delaying the expected failure time of the hose valve until after the arrival of spare parts, thus eliminating the risk of unplanned downtime without incurring high logistics costs.

[0042] Example 2: This experiment established a closed-loop fluid transport test platform incorporating high-frequency disturbances and nonlinear wear characteristics to simulate the slurry transport section in the production of lithium-ion battery cathode materials. The core test object was a pneumatic hose valve with a nominal diameter of DN50. The fluid medium was a silicon carbide suspension with a solid content of 60% to simulate high wear conditions. The test environment actively introduced Gaussian white noise with a signal-to-noise ratio of 20dB and power frequency interference at a frequency of 50Hz to reproduce the real interference of the electromagnetic environment on the acquisition of air chamber pressure data in the industrial field. The experiment set up three independent parallel test groups: control group A, control group B, and the sample group of this invention. Control group A adopted a preventive maintenance strategy based on a fixed number of actions, with a replacement cycle of 3000 times. Control group B adopted a supply chain response strategy based on a fixed safety stock, setting a standard replenishment process to be triggered when the stock is less than 2 units, with a fixed replenishment cycle of 72 hours. The sample group of this invention was fully loaded and executed with the operating status monitoring, time window calculation, and collaborative scheduling control logic of this invention.

[0043] After the experiment started, the three test groups simultaneously performed continuous opening and closing cycles. During the 1200th cycle, a high concentration of large-particle abrasive was instantaneously injected into the fluid medium to simulate the nonlinear accelerated wear condition caused by raw material batch fluctuations. At this time, noise spikes were superimposed on the raw air chamber pressure data collected by the operating status monitoring unit. The sample group of this invention used a built-in low-pass filtering algorithm to process the raw signal and extract the pneumatic response delay parameter after cleaning. The data showed that this parameter suddenly increased from the initial 120ms to 280ms, and the rate of change gradient showed an exponential upward trend. Based on this physical fact, the time window calculation unit calculated using the life prediction model that the remaining effective life of the hose valve was only 36 hours, denoted as... Simultaneously, the system retrieved supply chain status data, showing no spare parts inventory. For control group A, since the number of actions did not reach the 3000-action threshold, the system did not trigger any maintenance actions. For control group B, although a replenishment request was triggered, according to the standard logistics plan, the earliest expected arrival time for the spare parts was 72 hours later, denoted as [missing information]. At this time, there is an objective supply and demand time gap. It lasts for 36 hours.

[0044] Faced with this operating condition, the present invention activates the bidirectional spatiotemporal deformation matching logic, the system calculates the production-side control cost index, and sets the current unit time output value. The current normalized production load factor is 5000 CNY / h. Given a value of 1.0, based on the hose valve life-load nonlinear sensitivity model, to physically extend the remaining life from 36 hours to 72 hours to cover the gap, i.e., double the life, the target load factor is normalized. The cost of non-linear reduction to 0.6 is required, and based on this, the system calculates the production-side regulation cost index. Given 5000 × (1.0 - 0.6) × 36 = 72000, the system calculates the supply-side regulation cost index and searches the logistics solution database. It finds an air freight service solution that can deliver within 24 hours, with an additional cost of 15000 CNY compared to standard logistics. The value is 15000. The system executes the minimum resistance path decision and determines... Less than Therefore, the prototype of this invention did not implement a deceleration strategy, but instead directly generated an expedited supply chain dispatch order, triggering dedicated air freight delivery. The test results showed that: Control group A experienced a hose valve inner liner rupture in the 38th hour, leading to slurry leakage and an unplanned production line shutdown until the standard spare part arrived in the 72nd hour, resulting in a cumulative downtime loss of 170,000 CNY; Control group B, although aware of the risk before the 38th hour, was also constrained by the rigid logistics cycle and experienced a 34-hour downtime wait; while the prototype of this invention received the expedited spare part in the 24th hour and completed the online replacement, maintaining full-load production at 1.0 throughout the process, incurring only 15,000 CNY in logistics costs, avoiding the risk of unplanned downtime. Further gradient stress testing showed that when the unit time capacity output value was set... When reduced to 800 CNY / h (simulating low-value order periods), the production-side control cost index When the value drops to 11520, the system logic automatically flips and determines... Less than This generates a production cycle adjustment instruction, reducing the production load to 0.6. Actual test data shows that under low load operation, the actual failure time of the hose valve in the sample group of this invention is delayed to 75 hours, covering the standard replenishment cycle of 72 hours.

[0045] Example 3: This example details the system initialization parameter calibration and core model construction procedures performed by the data-aware hose valve resource supply collaborative balancing and task scheduling management system before its formal online operation. The aim is to provide deterministic physical benchmarks and computational model parameters for the real-time decision-making logic in the aforementioned examples. In the initial stage of system deployment, technicians perform an inner liner wear-pneumatic fingerprint mapping calibration process to establish a quantitative correlation function between physical wear and pneumatic response delay parameters. This calibration process selects a group of no less than 5 brand-new hose valve samples of the same specification (e.g., DN50) and material (e.g., EPDM rubber) and connects them to a test bench consistent with the actual production line's air circuit architecture. The test bench is set to continuously excite the hose valves with a rated operating frequency (e.g., 0.5Hz) and a standard air source pressure (e.g., 0.6MPa).

[0046] During the calibration process, the operation status monitoring unit continuously collects data on the falling edge of the air chamber pressure and calculates the aerodynamic response delay parameter corresponding to each action. Whenever the cumulative number of actions reaches a preset step size, such as every 5000 times, a sample is randomly selected for destructive physical sectioning, and the remaining wall thickness at the weakest point of the inner liner is measured using a laser thickness gauge with an accuracy of not less than 0.01 mm. And compare the physical measurement value with the value recorded at that time. The mean forms a set of mapping data pairs Repeat the above steps until the sample fails, obtaining a full lifecycle data sequence covering the entire lifecycle from brand new to completely failed. Based on this data sequence, the system uses the least squares method to perform nonlinear regression analysis and constructs a wear state mapping function in the following form: ,in , and All coefficients are constants determined through regression analysis. This function is stored in the memory of the operating status monitoring unit and serves as the sole calculation basis for converting real-time aerodynamic data into physical health status data during subsequent online operation, thereby eliminating the uncertainty of judging wear status based on experience.

[0047] Following this, the system executes a lifetime-load sensitivity model construction process to determine the key parameters used in the collaborative scheduling control unit for calculating the production-side control cost index. This process employs a gradient load accelerated aging test method, dividing the remaining samples into three groups. These groups are subjected to continuous operation tests until failure under normalized production load coefficients L of 0.8 (low load), 1.0 (rated load), and 1.2 (overload), respectively. Here, the normalized production load coefficient L = 1.0 is defined as the operating state of the hose valve at its rated pumping frequency (e.g., 45Hz) and rated fluid pressure (e.g., 0.4MPa). The system records the average failure lifetime of each group of samples under different loads. Based on measured data, the system verifies and establishes a lifetime-load sensitivity model that conforms to an inverse power-law distribution, and its mathematical expression follows the formula. ;in, The rated life is the reference load, and β is the life degradation sensitivity index. This is achieved by analyzing three groups of... The data undergoes logarithmic linear regression, and the system calculates a specific sensitivity index β. For example, when processing highly abrasive silicon carbide slurry, β≈2.5 is measured. This specific β value is injected into the algorithm library of the collaborative scheduling control unit. When the system needs to calculate the load adjustment required to extend the lifespan by a specific time in subsequent actual operation, it no longer relies on linear extrapolation, but performs accurate back-calculation based on this measured and calibrated nonlinear model, ensuring the production-side control cost index. The calculation results conform to the actual attenuation law of the physical object, thus ensuring the scientific nature and engineering effectiveness of the two-way cost comparison decision.

[0048] Example 4: When the data-aware hose valve resource supply collaborative balancing and task scheduling management system is formally deployed on any new physical production line or encounters changes in raw material properties, such as fluctuations in slurry solid content exceeding ±5%, the on-site operating condition baseline calibration procedure is executed, and the system is required to enter self-learning calibration mode. In this mode, the system drives the production line to operate with a step-increasing flow load, while continuously collecting pneumatic response data for no less than 100 complete opening and closing cycles. This data is used to dynamically construct a pneumatic response delay benchmark distribution model under the current operating conditions, and automatically calculates the dynamic threshold boundary for determining the wear state of the inner liner. During this calibration phase, the system simultaneously executes fluid pressure- Delay compensation coefficient determination: Under three gradients of fluid static pressure in the hose valve, namely 0 MPa, 0.2 MPa, and 0.4 MPa, the pneumatic response delay time was recorded respectively. The compensation coefficient was calculated by linear fitting, which showed that for every 0.1 MPa increase in fluid pressure, the average pneumatic delay time increased by 5 milliseconds. In subsequent real-time monitoring, the system reads the real-time pressure value of the fluid impact monitoring sensor and subtracts the corresponding delay component caused by fluid resistance from the original pneumatic response delay parameter according to the compensation coefficient. This ensures that the system can identify and filter out systematic errors introduced by environmental factors such as air source pressure fluctuations and pipeline resistance differences, thereby ensuring the accuracy of subsequent wear condition assessment.

[0049] Furthermore, to address the potential dispersion of material fatigue characteristics in different batches of inner bushings, this embodiment introduces an adaptive sensitivity coefficient correction mechanism. In the initial stage of system operation, the deviation between the physical residual wall thickness of the inner bushing and the system's predicted value is monitored and recorded in real time for each actual maintenance. When the cumulative deviation exceeds a preset tolerance, such as ±3%, the system automatically triggers a correction algorithm to fine-tune and update the sensitivity index β in the life prediction model. Through this closed-loop correction based on measured feedback, the system can continuously approximate the decay law of the real physical object, effectively eliminating the prediction drift risk caused by material batch differences, and ensuring that collaborative scheduling decisions are always based on high-confidence equipment health status data.

[0050] Example 5: In the engineering configuration phase of this system, to establish the accurate trigger boundary for the collaborative scheduling control unit to execute the supply-demand balance logic and prevent control command oscillations, it is necessary to execute an operating threshold calibration and decision stability configuration procedure based on statistical process control principles. This involves retrieving historical data sequences of pneumatic response delay parameters from the production line over at least one complete maintenance cycle. After removing outliers caused by planned downtime or known faults, the arithmetic mean μ and standard deviation σ of the sequence are calculated. Based on the statistical properties of the normal distribution, the system uses the formula... Calculate and lock the abnormal trigger threshold of the aerodynamic response delay parameter. This quantified value serves as the physical anchor point for the preset safety tolerance in the aforementioned embodiments, ensuring that the supply and demand balance calculation logic is activated only when the equipment status deviates in a statistically significant way; subsequently, to address the production-side control cost index... Supply-side regulation cost index To address the high-frequency switching problem of scheduling strategies that may be induced in the numerical critical region, a decision lag loop mechanism is introduced. The system sets a dimensionless decision dead zone coefficient γ, whose value is iteratively optimized by backtracking the oscillation frequency and switching cost in historical scheduling data, and is ultimately determined to be 0.05. In real-time operation, when... Only when the current cost difference is deemed worthwhile will the system generate a new scheduling instruction; otherwise, the current control strategy will remain unchanged. Through this standardized parameter calibration and logic solidification process, the system transforms fuzzy decision boundaries into definite mathematical constraints, ensuring the statistical and engineering stability of scheduling instructions under dynamic operating conditions.

[0051] To eliminate the influence of high-frequency electromagnetic interference or air source pressure fluctuations on the modulation of the air chamber pressure signal, the feature analysis module performs a signal cleaning procedure before extracting the air chamber pressure drop edge features. It uses a sliding time window of width M to perform real-time mean filtering on the original pressure data stream. The timing logic for the pneumatic response delay parameter is triggered only when the first derivative of the filtered pressure curve continuously crosses a preset action recognition threshold. Specifically, the timer's start trigger condition is set to the moment when the control voltage signal of the pneumatic control circuit solenoid valve jumps from 24 volts to 0 volts; the timer's stop trigger condition is set to the moment when the real-time pressure value fed back by the air chamber pressure sensor first falls below 0.05 MPa. Simultaneously, to prevent misjudgment due to signal jitter, the system requires the real-time slope (i.e., the first derivative) of the pressure drop edge to remain less than -0.5 MPa per second, ensuring that the parameters involved in the life prediction calculation strictly correspond to the mechanical movement stroke of the inner liner rather than random signal noise fluctuations.

[0052] During the initial deployment or replacement of the inner liner, due to the lack of historical data to fit the sensitivity index β, the collaborative scheduling control unit executes a cold start procedure. This involves anchoring the initial β value to 1.0 and setting the minimum allowable production load clamping value to 50% of the rated load to avoid low-flow-rate slurry deposition. After acquiring real physical life data each time, the system calls the recursive least squares method to calculate the deviation gradient between the predicted and actual failure times, and dynamically corrects the β value step by step, so that the calculation model gradually converges to approximate the nonlinear decay law of the actual working conditions. In response to sudden abnormalities in the aerodynamic response delay parameter, the operating status monitoring unit has a built-in independent physical logic circuit breaker procedure. When the gradient of the rate of change of the aerodynamic response delay parameter exceeds the preset physical tear threshold in adjacent N action cycles, it is determined to be a sudden physical failure of the inner liner rather than normal fatigue wear. The collaborative scheduling control unit forcibly suspends the supply and demand time balance calculation logic and outputs the highest priority emergency shutdown command to prevent the expansion of fluid leakage accidents caused by the execution of life extension strategies under physical failure conditions.

[0053] The above description is only a few preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, technical solutions formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A data-aware hose valve resource supply collaborative balancing and task scheduling management system, characterized in that, include: The operation status monitoring unit is connected to the pneumatic control circuit of the hose valve. It is used to collect the air chamber pressure data of the hose valve during the cut-off action at a preset sampling frequency, calculate the pneumatic response delay parameter based on the falling edge characteristics of the air chamber pressure data, and map the pneumatic response delay parameter into physical health status data that characterizes the wear degree of the inner liner. The time window calculation unit is connected in communication with the operation status monitoring unit. It is used to calculate the expected failure time of the hose valve based on physical health status data and simultaneously obtain the expected earliest arrival time of the spare parts supply chain system. The collaborative scheduling control unit, connected to the time window calculation unit, is used to execute supply and demand time balance logic. The collaborative scheduling control unit is configured to perform the following calculation steps when a time gap is detected, where the expected failure time is earlier than the earliest expected arrival time: calculating the production-side control cost index required to extend the operating time of the hose valves by the time gap based on the current production line's unit-time capacity and output value data; and calculating the supply-side control cost index required to advance the earliest expected arrival time by the time gap based on logistics expedited rate data. The collaborative scheduling control unit is configured to compare the production-side control cost index with the supply-side control cost index, and when it determines that the production-side control cost index is less than the supply-side control cost index, generate and output a production cycle adjustment command containing load reduction parameters. This production cycle adjustment command is used to drive the production line to perform load reduction operations to cover the time gap. Furthermore, the collaborative scheduling control unit follows the following mathematical formula when calculating the production-side control cost index: ,in, As a production-side regulation cost index, This refers to the current production line's output value per unit time. This represents the normalized production load factor for the current production line. To determine the normalized target load factor required to extend the operating time gap of the hose valve, This represents the value of the time gap; The calculation logic of the supply-side regulation cost index executed by the collaborative scheduling control unit includes: a scheme screening step, used to screen all expedited logistics schemes that can eliminate time gaps from a preset logistics scheme library; a cost differential step, used to calculate the additional logistics costs added by each expedited logistics scheme relative to the baseline logistics scheme; and an index determination step, used to define the minimum value of the additional logistics costs as the supply-side regulation cost index. The collaborative scheduling control unit is configured to call the life-load sensitivity model of the hose valve when determining the normalized target load coefficient, and through reverse iterative calculation, determine the highest production load value allowed to cover the time gap while keeping the hose valve from physical failure, and set this highest production load value as the normalized target load coefficient.

2. The data-aware hose valve resource supply collaborative balancing and task scheduling management system according to claim 1, characterized in that, The operational status monitoring unit includes: a high-frequency pressure sensor, installed at the inflation / deflation port of the pneumatic control loop, used to capture the transient pressure change curve in the air chamber when the hose valve performs a cut-off action; and a feature analysis module, connected to the high-frequency pressure sensor, used to extract the time difference from the moment the control signal is issued to the moment the air chamber pressure reaches a preset cutoff threshold in the transient pressure change curve, and define the time difference as a pneumatic response delay parameter; wherein, the feature analysis module is configured to trigger an update of the physical health status data only when the drift of the calculated pneumatic response delay parameter relative to the initial reference value exceeds a preset safety tolerance.

3. The data-aware hose valve resource supply collaborative balancing and task scheduling management system according to claim 1, characterized in that, The time window calculation unit includes: a life prediction model, which stores data on the nonlinear decay relationship between the life of the hose valve and the load intensity, used to calculate the expected failure time node by combining the current production load and physical health status data; and a logistics data interface module, used to retrieve spare parts inventory status and logistics transportation plan data, calculate the transportation time under different logistics methods, and determine the earliest expected arrival time node based on the transportation time with the smallest value.

4. The data-aware hose valve resource supply collaborative balancing and task scheduling management system according to claim 1, characterized in that, The system also includes a fluid impact monitoring sensor, which is installed on the fluid pipeline where the hose valve is located, to monitor the instantaneous impact pressure of the fluid medium in real time; wherein, when the operation status monitoring unit calculates the aerodynamic response delay parameter, it introduces the instantaneous impact pressure as a correction factor to eliminate the interference of fluid pressure fluctuations on the deformation characteristics of the inner liner.

5. A data-aware hose valve resource supply collaborative balancing and task scheduling management system according to claim 1, characterized in that, The collaborative scheduling control unit is configured to generate an expedited supply chain scheduling instruction when it is determined that the production-side control cost index is greater than or equal to the supply-side control cost index. The expedited supply chain scheduling instruction is used to trigger the spare parts supply chain system to lock and execute a specific expedited logistics plan, which is a plan that minimizes the supply-side control cost index.

6. The data-aware hose valve resource supply collaborative balancing and task scheduling management system according to claim 1, characterized in that, Production cycle adjustment instructions include: pumping frequency control parameters, used to reduce the operating frequency of the conveying pump connected in series with the hose valve; or intermittent ratio adjustment parameters, used to increase the pause interval between two adjacent conveying actions while keeping the single conveying volume constant.

7. A data-aware hose valve resource supply collaborative balancing and task scheduling management system according to claim 1, characterized in that, The system also includes a closed-loop feedback correction unit, which continuously tracks the actual rate of change of aerodynamic response delay parameters during the execution of production cycle adjustment commands. If the actual rate of change is higher than the expected life extension trajectory, the closed-loop feedback correction unit triggers the collaborative scheduling control unit to recalculate the production-side control cost index and dynamically increase the load reduction magnitude in the production cycle adjustment command.

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