A Multi-Process Intelligent Collaborative Scheduling System and Method Based on Real-Time Status Awareness

Abstract

This invention relates to the field of industrial production scheduling technology, and discloses a multi-process intelligent collaborative scheduling system and method based on real-time status perception. The system includes a process status perception unit, a logical potential energy mapping unit, a logical damping operator generation unit, and a scheduling execution control unit. By acquiring the stacking density, execution rate, and equipment health coefficient of the process logic nodes, a process logical potential energy field is constructed and the node potential energy value is determined. Based on the potential energy gradient between nodes, a logical damping compensation amount is generated. Accordingly, the effective execution time slot of the resource flow carrier is adjusted, generating a resource allocation sequence with a smooth displacement-time distribution. This compensation amount is used to implement nonlinear modulation of the execution amplitude. This invention smooths flow fluctuations through logical damping characteristics, offsets system oscillations caused by transport travel time delays, ensures the continuity of production rhythm, and reduces equipment wear.

Description

Technical Field

[0001] This invention relates to a multi-process intelligent collaborative scheduling system based on real-time status awareness, belonging to the field of industrial production scheduling technology. Background Technology

[0002] Current multi-process collaborative scheduling systems collect material location data and equipment operating status through sensors deployed at each workstation on the production line, and adjust the execution frequency of each process according to preset material balance thresholds, thereby maintaining material balance in each link of the production line.

[0003] In actual industrial production, the physical displacement and mechanical inertia of the drive mechanism during material conveying cause a time lag between the issuance of scheduling commands and the physical execution results. Traditional feedback control mechanisms only issue control commands after material accumulation has occurred. This delayed control logic leads to frequent start-stop cycles of the drive unit, resulting in oscillations in the overall production cycle and structural wear of mechanical transmission components, limiting the overall efficiency of the production line. System instability caused by hardware delays cannot be solved simply by optimizing sensor accuracy or shortening the acquisition cycle. Existing state perception and information processing technologies at the software control level are not yet fully integrated with the underlying system. The deep coupling of layered industrial mechanisms, for example, Chinese invention patent with authorization announcement number CN119205945B discloses a method and device for processing wire network information based on real-time state perception images, which maps wire network equipment information to image pixels and uses logic templates to perform graphic calculations to identify abnormal states. This technology faces dynamic fluctuations in discrete manufacturing, is based on static threshold comparison logic, and lacks real-time mining of parameters reflecting the degradation mechanism of equipment health, such as the variance distribution of drive current. Faced with system oscillations caused by physical travel time delays, it cannot build an energy dissipation mechanism at the logic level to predict and smooth flow shocks, and the scheduling strategy lags behind the physical entity's operating state.

[0004] Therefore, the technical problem to be solved by this invention is how to utilize the existing frequency converter feedback current and programmable logic controller register clock data in the production line to absorb the fluctuation energy of material flow at the logic level, so as to eliminate system oscillations caused by physical time delay and inertia accumulation, and improve the overall equipment efficiency of discrete manufacturing production lines. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A multi-process intelligent collaborative scheduling system based on real-time state perception, comprising a process state perception unit, a logical potential energy mapping unit, a logical damping operator generation unit, and a scheduling execution control unit:

[0006] The process status sensing unit is used to acquire material bulk density data, process execution rate data, and equipment health coefficient determined based on the variance distribution of equipment current load data of the controlled process logic node.

[0007] The logic potential energy mapping unit, connected to the process status sensing unit, is used to construct a process logic potential energy field based on material bulk density data, process execution rate data, and equipment health coefficient through pure Chinese logic rules. The logic rules are defined as follows: the potential energy value of the controlled process logic node in the process logic potential energy field is equal to the product of the material bulk density data, process execution rate data, and equipment health coefficient of the controlled process logic node.

[0008] The logic damping operator generation unit, connected to the logic potential energy mapping unit, is used to calculate the potential energy gradient between two adjacent controlled process logic nodes and generate the logic damping compensation amount based on the potential energy gradient.

[0009] The scheduling execution control unit is connected to the logic damping operator generation unit. It is used to adjust the effective execution time slot of the resource flow carrier according to the logic damping compensation amount, generate a resource allocation sequence that conforms to the smooth distribution of displacement time, and use the logic damping compensation amount to implement nonlinear modulation on the execution amplitude of the resource allocation sequence. Through dynamic compensation of the effective execution time slot, a logic damping characteristic that smooths the fluctuation of material flow in the resource flow carrier is formed to offset the system oscillation caused by the time delay of physical conveying.

[0010] Preferably, when generating the logic damping compensation amount, the logic damping operator generation unit introduces time-domain feedback constraints: obtaining the rate of change of the potential energy gradient between the current time and the previous time; then multiplying the rate of change by a preset fluctuation weight to obtain a dynamic adjustment term; finally, summing the dynamic adjustment term with a preset base value to determine the logic damping compensation amount; the scheduling execution control unit corrects the step value of the drive execution unit in real time based on the logic damping compensation amount, so that the rate change trajectory of the resource flow carrier approaches the preset smooth function curve within the range defined by the fluctuation weight, ensuring the continuous evolution of the scheduling logic in the time domain.

[0011] Preferably, the logic potential energy mapping unit is also used to identify logic bottleneck states: when the potential energy gradient between two adjacent controlled process logic nodes exceeds a preset congestion threshold of 15% to 25%, the logic potential energy mapping unit marks the upstream controlled process logic node as a lagging node and outputs a resource reallocation request for the lagging node to the scheduling execution control unit, so as to eliminate the backlog at the logic level by adjusting the resource allocation weight.

[0012] Preferably, when adjusting the effective execution time slot, the scheduling execution control unit establishes a causal relationship between the material transmission rate and the physical journey to achieve time delay compensation: the physical time delay constant is calculated based on the logical length of the resource circulation carrier and the nominal material transmission rate, and the physical time delay constant is introduced into the generation logic of the resource allocation sequence, so that the issuance time of the collaborative scheduling instruction is ahead of the predicted time of the material arriving at the logical node of the controlled process, and the lead time of the issuance time is not less than 50ms.

[0013] Preferably, the process status sensing unit acquires the operating frequency and current load data of the controlled process logic node in real time through the industrial fieldbus; in the process of determining the equipment health coefficient, when the variance of the current load data in the preset time window exceeds the preset stable interval boundary, the process status sensing unit monotonically reduces the set value of the equipment health coefficient according to a preset ratio, thereby reducing the weight of the controlled process logic node in the process logic potential energy field.

[0014] Preferably, the logic damping operator generation unit also includes a fluctuation energy absorption module: when the potential energy value of the upstream controlled process logic node increases instantaneously within 100ms, exceeding the preset mutation threshold, the fluctuation energy absorption module increases the logic damping compensation amount, and performs logic filtering on the flow impact by extending the effective execution time slot of the resource flow carrier, preventing local jitter from evolving into full-line production oscillation.

[0015] Preferably, the scheduling execution control unit further includes a feedback correction subunit: the feedback correction subunit acquires the actual state data after the execution of the coordinated scheduling instruction, calculates the execution error between the actual state data and the displacement-time smooth distribution, and dynamically corrects the product weights used to calculate the potential energy value in the logical potential energy mapping unit based on the execution error, forming a closed-loop optimization circuit.

[0016] Preferably, the scheduling execution control unit follows a spatiotemporal coupling rule when generating the resource allocation sequence. The spatiotemporal coupling rule is expressed as follows: ,in, In order to effectively execute time slots, The preset sensitivity factor, For the first The potential energy value of each logic node of the controlled process. This represents the potential energy value of the logic node of the adjacent downstream controlled process. As a time-domain correction constant determined based on the logic damping compensation amount, the system also includes an abnormal state adaptive module: the abnormal state adaptive module is used to monitor the micro-stop signals at the execution end of each process; when the micro-stop signal is received, the abnormal state adaptive module resets the corresponding equipment health coefficient to the minimum value, forcibly triggers the logic damping operator generation unit to recalculate the global logic potential energy distribution, and smoothly reduces the output weight of the upstream node.

[0017] Preferably, the scheduling execution control unit converts the resource allocation sequence into a drive execution signal sequence. The scheduling execution control unit is equipped with a periodic shift register clock, which is used to drive the output of the execution signal sequence to ensure that the execution of the coordinated scheduling instructions at the physical layer has a certain timing accuracy.

[0018] A multi-process intelligent collaborative scheduling method based on real-time state awareness includes the following steps:

[0019] Acquire material bulk density data, process execution rate data, and equipment health coefficient of the logic nodes of the controlled process;

[0020] Based on material bulk density data, process execution rate data, and equipment health coefficient, a process logic potential energy field is constructed using logical rules.

[0021] Calculate the potential energy gradient between two adjacent controlled process logic nodes, and generate the logic damping compensation amount based on the potential energy gradient.

[0022] The effective execution time slot of the resource flow carrier is adjusted according to the logical damping compensation amount to generate a resource allocation sequence that conforms to the smooth distribution of displacement time. The execution amplitude of the resource allocation sequence is nonlinearly modulated using the logical damping compensation amount.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] 1. In multi-process intelligent collaborative scheduling, the scheduling instructions are modulated by the logic damping operator, and the traditional step speed adjustment instructions are converted into a resource allocation sequence that conforms to the smooth distribution of displacement and time. This effectively eliminates the frequent start and stop of the execution unit caused by random fluctuations in material flow. While ensuring the continuity of production rhythm, it reduces the structural fatigue of mechanical transmission components and reduces the wear rate of equipment under complex working conditions.

[0025] 2. This invention constructs the logical potential energy function of each process node, and calculates the potential energy gradient between adjacent processes in real time by comprehensively mapping the material distribution density and equipment health coefficient. Before physical congestion actually forms, the material flow is pre-smoothed by adjusting the effective execution time slot of the conveyor track, so that the originally discrete production connection presents a fluid-like continuous operation characteristic, eliminating the invalid waiting time caused by material accumulation or idling between processes.

[0026] 3. This invention is based on the coupled logic of the spatiotemporal state field. It uses a logic damping mechanism to absorb the fluctuation energy of upstream processes, ensuring that the issuance of scheduling instructions matches the time delay characteristics of the physical transport journey. This prevents small disturbances at local workstations from superimposing in the time domain and evolving into full-line production oscillations, thus improving the system's adaptive adjustment capability to random micro-stop interference in multi-product mixed-line production environments. This invention uses the feedback execution error to dynamically correct the weight coefficients in the logic potential function, realizing closed-loop iterative optimization of the scheduling logic. Under the premise of maintaining the existing industrial control hardware configuration, through in-depth mechanism analysis of the inverter drive current and motor output frequency, it completes the optimized allocation of production resources among various processes, improving the overall equipment efficiency of the entire production line. Attached Figure Description

[0027] Figure 1 This is a flowchart of the multi-process collaborative scheduling control of the logical potential field and damping compensation of the present invention.

[0028] Figure 2 This is a functional interaction architecture diagram of the multi-process collaborative scheduling system with a closed-loop perception feedback mechanism according to the present invention. Detailed Implementation

[0029] The following embodiments are provided to illustrate the present invention in detail and are intended to explain the content of the present invention, but should not be construed as limiting the scope of protection of the present invention.

[0030] This invention provides a multi-process intelligent collaborative scheduling system and method based on real-time status awareness. It comprises a process status awareness unit, a logic potential energy mapping unit, a logic damping operator generation unit, and a scheduling execution control unit. Each unit communicates via industrial Ethernet, forming a closed-loop control link from physical status acquisition to drive execution. To address the process connection imbalance problem caused by data silos in traditional scheduling, the process status awareness unit is deployed at each control node of the production line, reading the output frequency of the field frequency converter in real time via an industrial bus. With drive current In the specific operating procedures, the system sets the sampling period. The time is 100ms, and the material bulk density of the controlled process logic node is acquired simultaneously. The digital acquisition of material bulk density d is based on a sliding window counting logic of pulses from station sensors: the process status sensing unit opens a 500-millisecond shift buffer within the controller, and counts the total number of material arrival pulses in the buffer every 10 milliseconds; this count is divided by a preset maximum count value of 50 pulses under fully loaded conveyor belt conditions, thereby calculating the material bulk density d in real time. Its value ranges from 0.0 when the material is idle to 1.0 when it is fully congested. To ensure the authenticity of the data at the physical level, the process status sensing unit collects the drive current... Perform variance calculation when the drive current If the variance value fluctuates beyond the preset stability interval boundary within 10 consecutive sampling periods, the system determines that the equipment has mechanical wear or abnormal load, and proportionally reduces the equipment health coefficient. The specific quantification logic of the device health coefficient H is implemented through the following deterministic steps: The system retrieves the initial operating variance benchmark of the device from the storage register of the programmable logic controller and sets it to the square of 0.08 amperes; it obtains the sample variance of the drive current in the current 10 sampling periods. When the measured sample variance is the square of 0.16 amperes, it performs a mapping operation of dividing the benchmark variance by the sample variance, that is, it obtains 0.5 as the output value of the device health coefficient H by multiplying 0.08 by 0.16; when the sample variance is less than or equal to the benchmark variance, the device health coefficient H is forcibly locked to the maximum value of 1.0, thereby ensuring that the calculation result is always within the range of 0.0 to 1.0, eliminating the risk of negative values ​​caused by variance fluctuations.

[0031] Given the nonlinear characteristics of process capabilities in industrial production, the logic potential energy mapping unit receives the aforementioned sensing data and quantifies the production load of each node by constructing a process logic potential energy field. The logic rule executed by this unit is limited to: the potential energy value of the controlled process logic node in the process logic potential energy field. Equal to material bulk density data Process execution rate data and equipment health coefficient The product of the three is The system calculates the potential energy gradient between two adjacent logic nodes of the controlled process. To identify bottleneck conditions, among which This represents the potential energy value of the current node. Let be the potential energy value of the downstream adjacent node, and if the gradient of this potential energy is... If the system remains outside the 15% to 25% congestion threshold range, the logical potential energy mapping unit marks lagging nodes and generates resource reallocation requests. This is obtained through a 60-second steady-state operation test during the initial power-on phase. The process status sensing unit continuously collects 600 samples and calculates the average potential energy under the nominal state of no-load operation and an execution rate of 50 Hz on the production line. This average potential energy is set as the 100% reference denominator. When the ratio of the absolute value of the potential energy difference between two adjacent nodes to this reference denominator exceeds 0.25, the logic determines that there is a risk of physical accumulation at that location. By converting physical occupancy into logical potential energy, this invention can predict potential bottleneck trends, thereby completing logical-level pre-reallocation of resources before physical congestion actually occurs. For system oscillations caused by physical transport travel delays, the logical damping operator generation unit calculates the potential energy gradient. The rate of change is used to generate the logic damping compensation. The specific operating procedures include: obtaining the potential energy gradient between the current time and the previous time, calculating the difference between the two to determine the rate of change of the potential energy gradient; multiplying it by the preset fluctuation weight and summing it with the base value to determine the logic damping compensation amount. It simulates the energy absorption mechanism of a physical damper, and by absorbing the fluctuation energy of material flow at the logic level, it eliminates the stability risks caused by physical time delay and random disturbances.

[0032] The scheduling execution control unit is based on the logic damping compensation amount. Adjusting the effective execution time slots of resource circulation carriers Its execution logic follows the spatiotemporal field coupling rule, that is... ,in, For sensitivity factor, For logic damping compensation amount Determined time-domain correction constant, time-domain correction constant The logic damping compensation amount C satisfies a dynamic incremental mapping relationship, and its calibration process is as follows: The scheduling execution control unit obtains the logic damping compensation amount C output by the logic damping operator generation unit in real time, multiplies it by a phase lead gain coefficient of 1.2, and sums it with the 250-millisecond physical response time constant of the material conveying track. The final value generated is the time-domain correction constant. This logic ensures that when a 10-millisecond logic damping compensation C occurs on the production line, the issuance of the coordinated scheduling command will automatically receive an additional 12-millisecond lead time. This is the physical response cycle required for the drive mechanism to switch from static to nominal speed. The unit receives the logic damping compensation. The amplitude of the drive unit is then nonlinearly modulated, following the relationship between the peak drive current and the logic damping compensation. The inverse mapping relationship between them is used to smooth the acceleration distribution of the physical journey by limiting the instantaneous power output when the potential energy gradient fluctuates drastically. The execution instruction of this nonlinear modulation is obtained by querying the 5-level discrete amplitude mapping table preset in the controller memory. Its logical steps are as follows: when the logical damping compensation amount C is in the range of 0 ms to 20 ms, the output amplitude coefficient is locked at 1.0; when the logical damping compensation amount C is in the range of 20 ms to 50 ms, the output amplitude coefficient is stepped down by 0.1 for every 10 ms increase; when the logical damping compensation amount C exceeds 80 ms, the output amplitude coefficient is maintained at the lower limit threshold of 0.6. By directly multiplying the output amplitude coefficient by the pulse duty cycle of the drive motor, the dynamic limit suppression of electromagnetic torque is achieved. The system converts the traditional step speed adjustment instruction into a resource allocation sequence that conforms to the smooth distribution of displacement time, and uses the periodic shift register clock to drive the signal output. In order to offset the fixed time delay caused by physical displacement, the time of issuance of the scheduling instruction is at least ahead of the predicted time of material arrival. When this procedure is followed, the output power of the motor on the conveyor track will gradually adjust, making the material flow of the entire production line exhibit fluid-like continuity.

[0033] The scheduling execution control unit implements nonlinear modulation based on a preset amplitude mapping function and logic damping compensation. As the input variable, the corresponding output amplitude coefficient is obtained by querying the discrete gain mapping table of the controller, when the logic damping compensation amount... When the instantaneous rate of change exceeds the preset stable threshold range, the output amplitude coefficient shrinks from 1.0 to 0.6 according to an exponential decay law. By multiplying this coefficient by the original drive pulse duty cycle, the electromagnetic torque output at the motor end is dynamically limited and suppressed when the potential energy gradient fluctuates violently. This keeps the starting acceleration of the material conveying mechanism below the fatigue limit of the mechanical transmission components, suppressing system oscillations caused by physical time delays; the proportional coefficient is determined. With sensitivity factor During value determination, the system is calibrated based on step response test. Under no-load conditions on the production line, a material sequence with fixed frequency intervals is sent to the conveyor track. The actual arrival time of the material at the process logic node is read via the industrial bus. The difference between the actual arrival time and the theoretical time of the instruction issued by the scheduling execution control unit is calculated to obtain the physical response time constant. , the proportionality coefficient The initial value is set to 1.0, and the search is iterated in increments of 0.05 within the range of 0.5 to 1.5 to select the material that maximizes the bulk density of all materials in the entire process. The minimum standard deviation is used as the proportionality coefficient. Determined time-domain correction constant This invention enables the coordinated scheduling command to be issued ahead of the physical switching cycle of the drive mechanism from static to nominal rate. Furthermore, the invention achieves closed-loop optimization of the scheduling logic through a feedback correction subunit. After the coordinated scheduling command is executed, this subunit acquires the actual state data fed back by each drive unit and calculates the execution error. If the deviation between the actual material distribution and the preset smooth distribution continues to exceed [a certain threshold], [further action is taken]. The system then dynamically adjusts the weight coefficients in the logic potential energy function, and the feedback correction subunit executes an adaptive calibration procedure for the weights of the logic potential energy field distribution, updating the potential energy value according to the error gradient descent rule. The allocation ratio of material bulk density data, process execution rate data, and equipment health coefficient in the calculation formula enables the generation logic of scheduling instructions to converge with the physical execution capability on site. Through this adaptive adjustment mechanism, the scheduling strategy can iterate according to the long-term evolution of equipment operating status, ensuring the long-term stability of the system in mixed-line production environment.

[0034] Example 1: In a specific automated high-speed filling production line environment, the system faces instantaneous flow rate changes caused by material jamming in the upstream bottle unscrambler, and frequent start-stop cycles in the downstream case-packing process due to the physical travel lag of the conveyor track, resulting in more than 12 current surges in the drive motor within 60 seconds. To cope with this situation, the process status sensing unit continuously collects the drive current of the frequency converter. It calculates the variance distribution over 10 consecutive sampling periods and simultaneously reads the material bulk density data recorded by the programmable logic controller register clock. With execution rate data This determines the device health coefficient of the node. The logic potential energy mapping unit uses the above parameters to calculate the node potential energy value. Its quantification rules follow ,in, This represents the nodal potential energy value. For material bulk density data, This is the process execution rate data. The device health coefficient; when the potential energy gradient between adjacent nodes... When a step increase occurs due to localized material jamming and exceeds the preset 20% critical threshold, the logic damping operator generation unit does not issue a stop command. Instead, it generates a logic damping compensation amount based on the rate of change of the potential energy gradient. The scheduling execution control unit adjusts the effective execution slots of the resource circulation carrier. Its computational logic follows ,in, In order to effectively execute time slots, For sensitivity factor, For potential energy gradient, For logic damping compensation amount A defined time-domain correction constant.

[0035] During this process, the logic damping compensation amount At the logical level, it absorbs the fluctuating energy generated by the inertia of material displacement, transforming the originally step-like frequency adjustment into a resource allocation sequence with a smooth displacement-time distribution, thus making the output power of the conveyor track motor exhibit a smooth flow. The curve changes, and the system eventually returns to steady-state operation within 500ms after the bottle unscrambler resumes operation; when the production line faces health coefficient issues due to equipment bearing wear... When the potential energy level monotonically decreases to 0.85 under objective operating conditions, the logic potential energy mapping unit automatically reduces the potential energy level of that node, thus reducing the potential energy gradient. The computational baseline shifts towards a reality where execution capabilities are limited; the system collects the drive current through the process status sensing unit. The variance distribution is used to correct the potential energy field distribution of the process logic in real time, thus coupling the generation logic of scheduling instructions with the attenuation state of physical entities, preventing the scheduling center from issuing instructions that exceed the actual capacity of the equipment; the scheduling execution control unit dynamically adjusts the sensitivity factor by periodically obtaining feedback to correct the execution error calculated by the subunit. While ensuring the continuity of material flow, it keeps the operating load of damaged processes within a safe threshold. This collaborative mechanism based on real-time state perception and potential energy compensation utilizes the existing controller register resources within the system to eliminate the stability risks caused by physical delays and random disturbances, enabling the production system to maintain a stable operating trajectory under fluctuating operating conditions.

[0036] Example 2: To verify the operational stability and bottleneck resolution capabilities in a multi-process industrial production environment, the test platform was built on an automated packaging line containing four discrete processing units. An industrial-grade current sensor with a sampling rate of 1kHz was used to acquire the driving current. The fluctuation characteristics are analyzed, and the material position pulses recorded by the programmable logic controller register clock are read synchronously to determine the material bulk density data. Sampling period during the experiment The settings are determined by balancing the timeliness of data capture with the load pressure of processor interrupt response, when the production line has its maximum execution rate. When the signal bandwidth is widened due to the increase, the sampling period The value of is reduced accordingly. Under the typical process speed in this experiment, the sampling period is... The time was set to 100ms. To simulate the electromagnetic background of a real industrial site, Gaussian white noise with a signal-to-noise ratio of 20dB and power frequency harmonic interference with a frequency of 50Hz were superimposed on the signal source of the test group. During the test operation phase, the system faced gradient pressure test with a sudden surge in material flow. The table below records the key performance data of the sample group of the present invention and two control groups under different working conditions. Control group A adopts a feedback adjustment method based on a fixed physical threshold, and control group B adopts a collaborative mechanism after removing the logic damping operator generation unit. The original input data, intermediate feature values ​​and final output results of the sample group of the present invention, control group A and control group B are shown in Table 1.

[0037] Table 1: Data from the Multidimensional Controlled Trial

[0038]

[0039] Based on the test data in Table 1, when the sample group of this invention faces a flow gradient increase of 5.0% to 25.0%, the logic damping compensation calculated by the logic damping operator generation unit is [data missing]. It is positively correlated with the flow deviation, which makes the effective execution time slots generated by the scheduling execution control unit more effective. The distribution is smooth, and the motor oscillation frequency decreases from 4.25Hz in control group A to below 0.22Hz, indicating that the logic damping compensation... It absorbs the fluctuation energy generated by the physical travel time delay; when the proportion of sudden flow changes increases to 35.0% in the out-of-range region, due to the potential energy gradient The physical limit constraints of the system's mechanical structure were triggered, and the effective execution time slot was reached. The reduction in capacity reached saturation, causing the motor oscillation frequency to rise back to 2.85Hz; regarding the decrease in operating capacity caused by equipment wear, when the equipment health coefficient... When the process status sensing unit determines that the value has dropped to 0.8, the system corrects the distribution of the logical potential energy field to adjust the calculated potential energy value. The corresponding contraction ensures that effective execution slots can still be maintained even under interference from a 20.0% sudden change in traffic. Maintaining a time of 124.6ms ensured that the overall equipment efficiency of the production line was at 91.2%, avoiding the risk of overload shutdown caused by the control group's failure to account for hardware losses.

[0040] Example 3: This example combines Figures 1 to 2 This section describes a multi-process intelligent collaborative scheduling system and method based on real-time status awareness, such as... Figure 1As shown, the process begins with the input of logic node data for the controlled process. This data stream includes material bulk density, process execution rate, and equipment current load information. These basic parameters are transmitted to the process status sensing unit, which performs processing steps to acquire material bulk density data, acquire process execution rate data, and determine equipment health coefficients. The resulting sensing data is unidirectionally transmitted to the logic potential energy mapping unit. The logic potential energy mapping unit applies pure Chinese logic rules, constructs the process logic potential energy field, and calculates node potential energy values. Its output potential energy gradient parameters are directly used as input to the logic damping operator generation unit, driving the unit to perform calculations of potential energy gradients, generate logic damping compensation amounts, and introduce time-domain feedback constraints. The generated logic damping compensation amount is finally transmitted to the scheduling execution control unit. Based on this amount, the unit adjusts the effective execution time slot, implements nonlinear modulation, and offsets physical transport time delay oscillations, thereby outputting a resource allocation sequence that conforms to a smooth displacement-time distribution through nonlinear modulation commands.

[0041] like Figure 2 As shown, the process sensor is responsible for providing data, which is directly linked to the process perception data use case that includes material density and execution rate. This use case extends to the function of calculating the equipment health coefficient. The core execution multi-process collaborative scheduling business not only responds to the operations of the production scheduler, but also includes the specific logic of constructing the process logic potential energy field, generating logic damping compensation, and identifying and redistributing logic bottlenecks when the potential energy gradient exceeds the limit. At the same time, this core business also integrates the abnormal state adaptive processing function triggered by micro-stop signals. At the execution output end, a smoothing sequence is issued to the field execution mechanism through the implementation of execution amplitude nonlinear modulation to smooth flow fluctuations. It also includes an optimization loop based on the execution error closed-loop correction weight coefficient.

[0042] Example 4: In a discrete material conveying scenario involving drive motors with varying degrees of wear, the mechanical performance degradation of the drive mechanism leads to control step size deviation, causing the process logic potential field to fail to accurately map the actual load of the physical entity, resulting in inaccurate cycle time between processes. To address this situation, the process status sensing unit executes the equipment health coefficient. The calibration procedure involves acquiring the drive current over 10 consecutive sampling cycles via an industrial bus. The instantaneous values ​​and their sample variances are calculated. The sample variance The device initial operating variance benchmark stored in the programmable logic controller register Perform ratio calculations and follow a monotonically decreasing function. Determine the equipment health coefficient The current quantization value, where, For equipment health coefficient, The sample variance of the current load data, in units of , The preset baseline variance, in units of After this procedure is implemented, when the measured current variance is... And the baseline variance is At that time, the system will determine the device health coefficient. Revised to This value serves as the weight input to the logical potential energy mapping unit, making the calculated potential energy value... It reflects in real time the decline in process execution capability caused by increased mechanical friction.

[0043] To address system oscillations caused by sudden changes in flow, the logic damping operator generation unit performs logic damping compensation. The time-domain discretization algorithm reads the current sampling time. Compared with the previous sampling time The potential energy gradient data are used to calculate the rate of change of the potential energy gradient using the difference method. And according to the mapping relationship Generate logic damping compensation amount The instantaneous instruction increment, where, This is the logic damping compensation amount. The potential energy gradient rate of change is expressed in units of 1000 kJ / m². , The preset fluctuation weight, To compensate for the base value; simultaneously, the scheduling execution control unit adjusts the physical response time constant of the material conveying track. Sensitivity factor Perform correlation calibration and set the sensitivity factor. With response time constant Satisfying the inverse proportional constraint relationship ensures effective execution of time slots. The adjustment rate is within the linear response range of the mechanical drive unit.

[0044] Example 5: In a field deployment scenario including heterogeneous frequency converter drive units, the system executes the initial operating variance benchmark of the equipment. According to the measurement procedure, when the conveyor track is in an unloaded state, the frequency converter is driven to run continuously at the rated frequency of 50Hz for 60 seconds, and the process status sensing unit is based on the sampling period. Collect 600 drive currents at a frequency of 100ms Numerical samples were obtained, and the noise baseline of the drive unit under standard operating conditions was determined by calculating the variance of the sample set. This baseline value was recorded in the storage register of the programmable logic controller as the initial operating variance baseline of the device. The number of citations, of which the initial operating variance benchmark of the equipment. The unit is For different batches of mechanical transmission components, the impact of manufacturing tolerances on equipment health coefficients was obtained through no-load testing. The accuracy correction benchmark is used to determine the drive current during operation. Sample variance relative to the initial operating variance benchmark of the equipment To achieve the mapping, when the initial operating variance benchmark of the equipment is measured... for And the actual operating sample variance for At that time, the system will determine the device health coefficient. The value is adjusted to 0.5 and used as the weight input to the logic potential energy mapping unit, so that the calculated potential energy value... It reflects the decline in the process execution capability in real time.

[0045] When the system encounters a dynamic access condition where the material conveying distance changes, the scheduling execution control unit initiates a time-domain correction constant. The parameter calibration procedure involves issuing a step speed command to the drive unit and recording the time interval between the feedback pulse reaching the preset displacement point to measure the physical response time constant of the conveyor track. It is 250ms, based on the physical response time constant. With effective execution slot The spatiotemporal alignment requirements, according to Calculate the time-domain correction constant, where the time-domain correction constant is... The unit is , The preset proportional coefficient, physical response time constant The unit is The system observes the potential energy gradient by changing the input flow rate amplitude. The dynamic response process is analyzed, and a value that keeps the motor oscillation frequency within a set threshold range is selected as the sensitivity factor. and in the physical response time constant The sensitivity factor decreases inversely when the value increases. The value ensures that the timing of the scheduling instruction is ahead of the predicted arrival time of the material by an offset that aligns with the hysteresis of the mechanical execution system, so that the entire process maintains a resource allocation sequence with a smooth distribution of displacement time after load changes.

[0046] Example 6: In a multi-process scheduling system deployment scenario for precision electronic component assembly, the conveyor track experiences execution delay fluctuations due to mechanical assembly tolerances. To determine the sampling period of the process status sensing unit... The system executes an offline calibration procedure based on signal spectral characteristics, acquires the operating current sequence of the drive unit at the highest process execution rate through the industrial bus, and extracts the main frequency component of the signal using a fast Fourier transform algorithm. and the sampling period Set to no greater than the main frequency component One-tenth of the reciprocal, and not less than the value of the inverter communication cycle, of which, The sampling period is expressed in units of 10 ... , Main frequency component, unit: When the main frequency component of the motor current fluctuation is measured... When the frequency is equal to 1.5Hz and the inverter communication period is 20ms, the system will sample the period. The time was set to 60ms to ensure that the feature vector acquired by the sensing unit covers the dynamic changes in material flow.

[0047] To address the quantitative requirement of congestion thresholds in the production bottleneck determination logic, the logic potential energy mapping unit initiates a field boundary optimization procedure. This involves simulating an increase in upstream flow from 80% to 150% of the rated value under controlled conditions, and recording the potential energy gradient. The system identifies the critical potential energy gradient value when the overall equipment efficiency of the production line decreases by 5%, based on the correlation curve with the overall equipment efficiency of the production line. And set the lower limit of the congestion threshold as a critical value. 0.75 times, with the upper limit set as a critical value. The threshold value is 1.25 times the threshold value to determine the trigger boundary of the logic potential field; the scheduling execution control unit then applies a sensitivity factor based on this threshold. The online iterative logic calculates the deviation between the actual material distribution and the preset smooth distribution at the end of each scheduling cycle using a feedback correction subunit. When the deviation value When the increase occurs for three consecutive cycles, the system follows... The formula determines the corrected sensitivity factor, where, This is the corrected sensitivity factor. The sensitivity factor before correction. The preset correction factor. The deviation value is the resource allocation sequence that enables the transmission mechanism to maintain a smooth displacement-time distribution under random disturbances.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-process intelligent collaborative scheduling system based on real-time status awareness, characterized in that, It includes a process status sensing unit, a logic potential energy mapping unit, a logic damping operator generation unit, and a scheduling execution control unit: The process status sensing unit is used to acquire material bulk density data, process execution rate data, and equipment health coefficient determined based on the variance distribution of equipment current load data of the controlled process logic node. The logic potential energy mapping unit, connected to the process status sensing unit, is used to construct a process logic potential energy field based on material bulk density data, process execution rate data, and equipment health coefficient through pure Chinese logic rules. The logic rules are defined as follows: the potential energy value of the controlled process logic node in the process logic potential energy field is equal to the product of the material bulk density data, process execution rate data, and equipment health coefficient of the controlled process logic node. The logic damping operator generation unit, connected to the logic potential energy mapping unit, is used to calculate the potential energy gradient between two adjacent controlled process logic nodes and generate the logic damping compensation amount based on the potential energy gradient. The scheduling execution control unit is connected to the logic damping operator generation unit. It is used to adjust the effective execution time slot of the resource flow carrier according to the logic damping compensation amount, generate a resource allocation sequence that conforms to the smooth distribution of displacement time, and use the logic damping compensation amount to implement nonlinear modulation on the execution amplitude of the resource allocation sequence. Through dynamic compensation of the effective execution time slot, a logic damping characteristic that smooths the fluctuation of material flow in the resource flow carrier is formed to offset the system oscillation caused by the time delay of physical conveying.

2. The multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, When generating the logic damping compensation amount, the logic damping operator generation unit introduces a time-domain feedback constraint: obtain the rate of change of the potential energy gradient between the current time and the previous time; then multiply the rate of change by the preset fluctuation weight to obtain the dynamic adjustment term; finally, sum the dynamic adjustment term with the preset base value to determine the logic damping compensation amount. The scheduling execution control unit corrects the step value of the drive execution unit in real time based on the logic damping compensation amount, so that the rate change trajectory of the resource circulation carrier approaches the preset smooth function curve within the range defined by the fluctuation weight, ensuring the continuous evolution of the scheduling logic in the time domain.

3. The multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, The logic potential energy mapping unit is also used to identify logic bottleneck states: when the potential energy gradient between two adjacent controlled process logic nodes exceeds the preset congestion threshold of 15% to 25%, the logic potential energy mapping unit marks the upstream controlled process logic node as a lagging node and outputs a resource reallocation request for the lagging node to the scheduling execution control unit, so as to eliminate the backlog at the logic level by adjusting the resource allocation weight.

4. The multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, When adjusting the effective execution time slot, the scheduling execution control unit establishes a causal relationship between the material transmission rate and the physical journey to achieve time delay compensation: the physical time delay constant is calculated based on the logical length of the resource circulation carrier and the nominal material transmission rate, and this physical time delay constant is introduced into the generation logic of the resource allocation sequence, so that the issuance time of the collaborative scheduling instruction is ahead of the predicted time of the material arriving at the logical node of the controlled process, and the lead time of the issuance time is not less than 50ms.

5. A multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, The process status sensing unit acquires the operating frequency and current load data of the controlled process logic node in real time through the industrial fieldbus. In the process of determining the equipment health coefficient, when the variance of the current load data in the preset time window exceeds the preset stable interval boundary, the process status sensing unit monotonically reduces the set value of the equipment health coefficient according to the preset ratio, thereby reducing the weight of the controlled process logic node in the process logic potential energy field.

6. The multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, The logic damping operator generation unit also includes a fluctuation energy absorption module: when the potential energy value of the upstream controlled process logic node increases instantaneously within 100ms, exceeding the preset mutation threshold, the fluctuation energy absorption module increases the logic damping compensation amount. By extending the effective execution time slot of the resource flow carrier, it performs logic filtering on the flow impact to prevent local jitter from evolving into full-line production oscillation.

7. A multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, The scheduling execution control unit also includes a feedback correction subunit: the feedback correction subunit obtains the actual state data after the execution of the coordinated scheduling instruction, calculates the execution error between the actual state data and the displacement-time smooth distribution, and dynamically corrects the product weights used to calculate the potential energy value in the logical potential energy mapping unit based on the execution error, forming a closed-loop optimization circuit.

8. A multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, When generating resource allocation sequences, the scheduling execution control unit follows a spatiotemporal coupling rule, which is expressed as follows: ,in, In order to effectively execute time slots, The preset sensitivity factor, For the first The potential energy value of each logic node of the controlled process. This represents the potential energy value of the logic node of the adjacent downstream controlled process. As a time-domain correction constant determined based on the logic damping compensation amount, the system also includes an abnormal state adaptive module: the abnormal state adaptive module is used to monitor the micro-stop signals at the execution end of each process; when the micro-stop signal is received, the abnormal state adaptive module resets the corresponding equipment health coefficient to the minimum value, forcibly triggers the logic damping operator generation unit to recalculate the global logic potential energy distribution, and smoothly reduces the output weight of the upstream node.

9. A multi-process intelligent collaborative scheduling system based on real-time status awareness according to claim 1, characterized in that, The scheduling execution control unit converts the resource allocation sequence into a drive execution signal sequence. The scheduling execution control unit has a periodic shift register clock, which is used to drive the output of the execution signal sequence to ensure that the execution of the coordinated scheduling instructions at the physical layer has a certain timing accuracy.

10. A multi-process intelligent collaborative scheduling method based on real-time state awareness, characterized in that, This method is executed using a multi-process intelligent collaborative scheduling system based on real-time state awareness as described in claim 1, and includes the following steps: Acquire material bulk density data, process execution rate data, and equipment health coefficient of the logic nodes of the controlled process; Based on material bulk density data, process execution rate data, and equipment health coefficient, a process logic potential energy field is constructed using logical rules. Calculate the potential energy gradient between two adjacent controlled process logic nodes, and generate the logic damping compensation amount based on the potential energy gradient. The effective execution time slot of the resource flow carrier is adjusted according to the logical damping compensation amount to generate a resource allocation sequence that conforms to the smooth distribution of displacement time. The execution amplitude of the resource allocation sequence is nonlinearly modulated using the logical damping compensation amount.

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