Multi-terminal cooperative intelligent control system based on internet of things

Abstract

This invention relates to the field of Internet of Things (IoT) operating system technology and discloses a multi-terminal collaborative intelligent control system based on IoT, including multiple terminals, a motion execution module, and an edge control module. The edge control module reads the pulse count value of the motion execution module through memory mapping to determine the local displacement and extracts the logical timing code broadcast by adjacent terminals as an external reference. The edge control module compares the local displacement with the external reference to obtain the synchronization deviation. When the synchronization deviation exceeds a threshold, the edge control module generates a high-priority interrupt signal to modify the register instruction and corrects the weight based on the transmission timestamp difference to offset nondeterministic communication delay. This invention utilizes the collaborative determination of physical parameters and logical virtual parameters to construct a hardware-level security barrier, eliminate the interference of network jitter on motion timing, and ensure that the control beat is aligned with the execution beat.

Description

Technical Field

[0001] This invention relates to a multi-terminal collaborative intelligent control system based on the Internet of Things (IoT), belonging to the field of IoT operating system technology. Background Technology

[0002] Current multi-terminal collaborative control typically adopts a centralized scheduling architecture, where the central controller periodically collects the operating status of each terminal node and constructs a global logical view, thereby uniformly planning the motion path of each actuator. This control method relies on real-time synchronization between the central node and subordinate nodes in terms of logical structure, and it has good system stability in low-dynamic environments with fixed control logic.

[0003] With the widespread application of IoT communication links in the physical production layer, the non-deterministic characteristics of communication channels, such as bandwidth fluctuations, queuing jitter, and network latency, constrain the real-time response of control systems. The terminal status data collected by the central controller exhibits significant asynchronicity in the time dimension. When multiple terminals collaborate at high frequency within a limited physical space, the control commands generated by the central controller often lag behind the actual physical location of the terminals, causing a misalignment between the control cycle and the physical action cycle. This command delay distortion caused by outdated data can easily lead to physical collisions of actuators or trigger system control logic lock-up under critical interference conditions. Conventional approaches include deploying time-sensitive networks to reduce physical layer communication latency, or using predictive algorithms at the central controller to estimate terminal status. In addition to hardware topology improvements, eliminating the impact of communication jitter on operation timing from the control method level is a key focus in the field of dynamic trajectory. For example, Chinese invention patent application CN111208784A discloses a motion control system and its synchronization method, which obtains the frame synchronization signal generated by the local clock and generates a synchronization pulse by combining it with a preset delay time compensation value to synchronize I / O devices with network traffic. This scheme is based on static compensation of preset parameters and relies on the determinism of the underlying real-time Ethernet clock signal. When the Internet of Things environment has random network fluctuations or bandwidth limitations, it lacks real-time feedback from physical entities and cannot dynamically trigger intervention based on the actual displacement of the terminal and the logical timing slippage. It is difficult to solve the cumulative error of nondeterministic communication loss, and it lacks the terminal's local self-determination capability when there are conflicts in collaborative decision-making.

[0004] Therefore, how to construct a decentralized intelligent scheduling mechanism that binds the flow of control logic with the urgency of physical actions to achieve terminal collaboration in a non-deterministic Internet of Things environment has become the technical problem to be solved by this invention. 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-terminal collaborative intelligent control system based on the Internet of Things, comprising: Multiple terminals, each connected to a motion execution module; The edge control module is connected to each terminal, and the edge control module communicates with the motion execution module through a bus protocol; The edge control module includes a pulse reading unit, a reference extraction unit, a synchronization determination unit, an interrupt management unit, and a delay compensation unit. The pulse reading unit reads the pulse count value of the system to which the motion execution module belongs through memory mapping to determine the local feedback displacement. The reference extraction unit extracts the logical timing code broadcast by the neighboring terminal in the most recent communication cycle from the circular buffer of the local communication port and determines it as the external synchronization reference quantity; The synchronization determination unit calculates the synchronization deviation, which characterizes the degree of alignment between physical displacement and logical timing, by comparing the local feedback displacement with the external synchronization reference. When the synchronization deviation exceeds the preset threshold, the interrupt management unit modifies the control instructions of the drive register by generating a high-priority interrupt signal in order to adjust the motion state of the motion execution module. The delay compensation unit obtains the timestamp difference of data packets during network transmission and corrects the participation weight of the synchronization deviation based on the linear positive correlation between the timestamp difference and the weight, so as to offset the nondeterministic communication delay generated by the network layer.

[0006] Preferably, the edge control module further includes a static storage unit, which stores preset virtual constraint stiffness parameters. The edge control module is used to call the virtual constraint stiffness parameters as a logical arbitration benchmark when the dynamic remaining time parameters of multiple terminals tend to be consistent, and introduce a repulsive force field correction amount in the calculation of the synchronization deviation based on the logical arbitration benchmark, thereby completing the preventive intervention for potential physical collisions between terminals at the logical layer.

[0007] Preferably, the edge control module also includes a damping adjustment unit, which monitors the dwell time of the master control on the terminal and dynamically adjusts the calculation weight of the virtual constraint stiffness parameter according to the dwell time, so as to prevent a single terminal from monopolizing the control resources of a local area for a long time.

[0008] Preferably, when calculating the synchronization deviation, the edge control module completes the following processing steps: Step S101: Calculate the motion trajectory vector of the local entity based on the pulse count value; Step S102: Solve the logical motion envelope of the adjacent terminal based on the external synchronization reference value; Step S103: Perform spatiotemporal correlation mapping on the motion trajectory vector and the logical motion envelope to output the synchronization deviation.

[0009] Preferably, each terminal interacts with the other in real time via a time-sensitive network; the edge control module is used to extract the global clock synchronization pulse from the scheduling cycle of the time-sensitive network as the reference time origin for determining the synchronization deviation.

[0010] Preferably, the pulse count value is read at a frequency higher than the external synchronization reference value is acquired at a frequency higher than the frequency of the pulse count value; the edge control module is used to perform linear interpolation on two adjacent external synchronization reference values ​​to fit a virtual external reference path that is synchronized with the reading frequency.

[0011] Preferably, the edge control module is also used to calculate the characteristic value of the rate of change of the deviation based on the trend of the synchronization deviation. : ,in, The characteristic value of the rate of change of deviation. This represents the change in synchronization deviation between two adjacent communication cycles. This refers to the communication cycle duration, which ranges from 10ms to 100ms. This is the preset system damping coefficient.

[0012] Preferably, the edge control module is used to determine the characteristic value of the deviation change rate. Real-time adjustment of the feed rate of the drive pulse within the motion execution module; at the characteristic value of the deviation change rate. When the preset alarm threshold is exceeded, the edge control module is used to broadcast an emergency avoidance status word to the other terminals.

[0013] Preferably, a distributed deterministic state machine unit is deployed within the edge control module; the distributed deterministic state machine unit is used to complete state transitions based on synchronization deviations and to intercept expired motion control messages at the local logic layer.

[0014] Preferably, the system also includes a global scheduling module; the global scheduling module is used to receive the synchronization deviation distribution characteristics reported by each terminal and statistically analyzed by the edge control module, and to reallocate the communication priority weights of each terminal in the Internet of Things accordingly.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In multi-terminal collaborative intelligent control, by extracting the remaining execution time step of the current process of each terminal and the physical anti-collision stiffness coefficient of the equipment, a distributed collaborative mechanism based on competitive state weights is constructed. This decomposes the pressure of traditional centralized global scheduling into the local state self-determination logic of terminal nodes. Under this mechanism, the transfer of control saves the round-trip delay of data aggregation to the central control node and waiting for instructions to be issued. This enables the triggering time of control instructions to be strongly bound to the actual execution time of physical equipment at the underlying level. This spontaneous scheduling method driven by the urgency of physical execution can prevent instruction delay distortion caused by network transmission queuing when the system is dealing with high-frequency concurrent actions, thereby ensuring the time sequence determinism in the multi-terminal collaboration process.

[0016] 2. By leveraging the synergistic effect of the composite competitive weight calculation logic and the damping attenuation mechanism, the decision-making conflict and control deadlock problems in a decentralized environment can be resolved without increasing the additional communication load. When the dynamic remaining time parameters of multiple terminals tend to be consistent, the statically stored physical anti-collision stiffness coefficient provides a deterministic arbitration benchmark, while the damping attenuation mechanism dynamically adjusts the weight by monitoring the master control dwell time, preventing a single terminal from monopolizing the control resources of a local area for a long time. This dynamic and static combined feedback adjustment path with clock constraints ensures the logical convergence and task flow activity of the entire collaborative system under extreme conditions.

[0017] 3. By deploying a delay compensation module within the edge control unit, the timestamp difference generated during network transmission is directly introduced into the weight correction stage, achieving dynamic cancellation of communication dead time. This design enables the system to absorb network physical jitter in situ, transforming the originally uncontrollable nondeterministic loss of the communication layer into an observable control logic correction quantity. When facing channel fluctuations or bandwidth limitations in the Internet of Things environment, this mechanism can maintain the stability of weight determination, ensuring that the avoidance command issuance between terminals is still within a safe time threshold, thereby enhancing the robustness of the system in a highly dynamic competitive environment. Attached Figure Description

[0018] Figure 1 This is a diagram of the multi-terminal collaborative intelligent control system architecture for edge control according to the present invention. Figure 2 This is a flowchart illustrating the steps for calculating the synchronization deviation in the spatiotemporal mapping of this invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] A multi-terminal collaborative intelligent control system based on the Internet of Things, comprising: Multiple terminals, each connected to a motion execution module; The edge control module is connected to each terminal, and the edge control module communicates with the motion execution module through a bus protocol; The edge control module includes a pulse reading unit, a reference extraction unit, a synchronization determination unit, an interrupt management unit, and a delay compensation unit. The pulse reading unit reads the pulse count value of the system to which the motion execution module belongs through memory mapping to determine the local feedback displacement. The reference extraction unit extracts the logical timing code broadcast by the neighboring terminal in the most recent communication cycle from the circular buffer of the local communication port and determines it as the external synchronization reference quantity; The synchronization determination unit calculates the synchronization deviation, which characterizes the degree of alignment between physical displacement and logical timing, by comparing the local feedback displacement with the external synchronization reference. When the synchronization deviation exceeds the preset threshold, the interrupt management unit modifies the control instructions of the drive register by generating a high-priority interrupt signal in order to adjust the motion state of the motion execution module. The delay compensation unit obtains the timestamp difference of data packets during network transmission and corrects the participation weight of the synchronization deviation based on the linear positive correlation between the timestamp difference and the weight, so as to offset the nondeterministic communication delay generated by the network layer.

[0022] Preferably, the edge control module further includes a static storage unit, which stores preset virtual constraint stiffness parameters. The edge control module is used to call the virtual constraint stiffness parameters as a logical arbitration benchmark when the dynamic remaining time parameters of multiple terminals tend to be consistent, and introduce a repulsive force field correction amount in the calculation of the synchronization deviation based on the logical arbitration benchmark, thereby completing the preventive intervention for potential physical collisions between terminals at the logical layer.

[0023] Preferably, the edge control module also includes a damping adjustment unit, which monitors the dwell time of the master control on the terminal and dynamically adjusts the calculation weight of the virtual constraint stiffness parameter according to the dwell time, so as to prevent a single terminal from monopolizing the control resources of a local area for a long time.

[0024] Preferably, when calculating the synchronization deviation, the edge control module completes the following processing steps: Step S101: Calculate the motion trajectory vector of the local entity based on the pulse count value; Step S102: Solve the logical motion envelope of the adjacent terminal based on the external synchronization reference value; Step S103: Perform spatiotemporal correlation mapping on the motion trajectory vector and the logical motion envelope to output the synchronization deviation.

[0025] Preferably, each terminal interacts with the other in real time via a time-sensitive network; the edge control module is used to extract the global clock synchronization pulse from the scheduling cycle of the time-sensitive network as the reference time origin for determining the synchronization deviation.

[0026] Preferably, the pulse count value is read at a frequency higher than the external synchronization reference value is acquired at a frequency higher than the frequency of the pulse count value; the edge control module is used to perform linear interpolation on two adjacent external synchronization reference values ​​to fit a virtual external reference path that is synchronized with the reading frequency.

[0027] Preferably, the edge control module is also used to calculate the characteristic value of the rate of change of the deviation based on the trend of the synchronization deviation. : ,in, The characteristic value of the rate of change of deviation. This represents the change in synchronization deviation between two adjacent communication cycles. This refers to the communication cycle duration, which ranges from 10ms to 100ms. This is the preset system damping coefficient.

[0028] Preferably, the edge control module is used to determine the characteristic value of the deviation change rate. Real-time adjustment of the feed rate of the drive pulse within the motion execution module; at the characteristic value of the deviation change rate. When the preset alarm threshold is exceeded, the edge control module is used to broadcast an emergency avoidance status word to the other terminals.

[0029] Preferably, a distributed deterministic state machine unit is deployed within the edge control module; the distributed deterministic state machine unit is used to complete state transitions based on synchronization deviations and to intercept expired motion control messages at the local logic layer.

[0030] Preferably, the system also includes a global scheduling module; the global scheduling module is used to receive the synchronization deviation distribution characteristics reported by each terminal and statistically analyzed by the edge control module, and to reallocate the communication priority weights of each terminal in the Internet of Things accordingly.

[0031] Example 1: In a multi-device synchronous operation environment of an industrial discrete manufacturing production line with timing dependencies and spatial interference risks, four six-axis robotic arms perform high-frequency collaborative operations within a circular area with a radius of 2m. The physical motion alignment accuracy of each terminal working mechanism needs to reach the microsecond level. However, the IoT communication link within the factory is limited by the physical layer queuing mechanism, resulting in non-deterministic network jitter of 10ms to 50ms. This causes the trajectory commands issued by the traditional centralized scheduling architecture to become outdated slices of the global physical view they rely on by the time they reach the terminal actuator. This low-level mismatch between the timing of control commands and the continuity of physical actions can easily trigger physical interference or control deadlock of the robotic arm working parts. In the actual deployment of the above-mentioned working conditions, the technical solution of this invention deploys edge control units in each collaborative terminal, enabling each terminal node to have local state self-determination capabilities. The edge control unit reads the remaining operation time step parameters of the current process fed back by the servo driver through memory mapping. And retrieve the device's physical impact stiffness coefficient from local memory. According to the formula The composite competitive weights are calculated. ;in, Represents the composite competitive state weights. Represents the first preset normalized mapping coefficients. Indicates the remaining time step of the current process. This represents the second preset normalized mapping coefficient. This represents the physical collision stiffness coefficient of the equipment. When performing the above mathematical accumulation operation to output the composite competitive weight, the first preset normalized mapping coefficient is injected with a time compensation dimension that cancels out the time step. At the same time, the second preset normalized mapping coefficient is matched with an equivalent flexibility dimension that is the mathematical inverse of mechanical stiffness. After being multiplied in as scaling factors, the time quantities and forces from different physical domains are not only scaled to the dimensionless scalar category of the [0,1] interval, but also the algebraic superposition conflict caused by the physical difference between the time dimension frequency and the spatial dimension intensity is smoothed out. When the edge control unit identifies the local composite competitive weight through the underlying multicast protocol... When the value is at its maximum within the current multicast group, the first edge control unit writes a high-level status flag to its local logic operation register to lock the master control, thereby bypassing the centralized polling instruction and directly outputting synchronization avoidance control instructions to other subordinate edge control units within the multicast group. This spontaneous scheduling mechanism driven by the urgency of physical operations aligns the control flow rhythm with the actual operation rhythm of the physical equipment.

[0032] To address the communication dead time generated by the edge control unit during multicast interaction weighting, the internally deployed latency compensation module extracts the timestamp difference between message transmission and reception. And when calculating the initial weights, the communication dead time and the remaining job time step are included. Perform difference correction; after completing the above difference correction, the delay compensation module further generates participation weights for the synchronization deviation based on the timestamp difference. The relation is ;in, Indicates the participation weight. This represents the pre-calibrated positive delay sensitivity coefficient of the system, which is a participating weight. As a linear scaling multiplier, it is directly multiplied into the end of the subsequently calculated original synchronization deviation formula. This allows the obtained synchronization deviation result to be artificially amplified proportionally when the underlying network communication latency increases. This, in turn, triggers a safety avoidance judgment with a higher displacement prediction margin, thus absorbing the judgment error caused by network jitter. Simultaneously, to prevent a single terminal from monopolizing local area control resources for an extended period due to long-cycle tasks, the damping adjustment unit monitors the master control lock-in time. When the dwell time exceeds the preset maximum master control dwell clock, the composite competitive weights are adjusted. Multiply by the previously set attenuation coefficient Its value ranges from 0.1 to 0.3. This damping mechanism deprives timed-out terminals of absolute control, ensuring the activity of the system's global control flow and the traversability of tasks.

[0033] When network fluctuations cause a slippage between logical instructions and physical entity states that exceeds the safety margin, the edge logic determination unit reads the local displacement parameters. External reference parameters According to the formula Calculate the synchronization deviation ;in, This indicates the synchronization deviation. Indicates the local displacement parameter. Indicates external reference parameters. This represents the fixed mapping coefficient between the preset unit pulse and the logic sequence. Before applying the above formula to extract the pure quantization deviation value, the edge control unit generates a three-dimensional motion trajectory vector of the local entity in Cartesian space based on the pulse count value through positive kinematic transformation. At the same time, it restores the predicted logical motion envelope of the adjacent terminal based on the external reference parameter and its sequence number identifier. When completing the spatiotemporal correlation mapping, the system performs a one-dimensional spatial projection operation on the above three-dimensional motion trajectory vector and logical motion envelope onto the high-risk interference axis of the current action. The effective scalar after the projection dimensionality reduction is used as the equivalent input value of the local displacement parameter and the external reference parameter participating in the above absolute value formula, thereby converting the multi-dimensional spatial boundary interference judgment into the underlying scalar algebraic slip operation. When the synchronization deviation is... When the error exceeds the preset elastic tolerance threshold, the interrupt management unit generates a high-priority interrupt request, intercepts all regular instructions issued by the IoT protocol stack, and writes a position holding instruction directly to the drive register, causing the local physical operating mechanism to enter a resting and waiting state until the recalculated synchronization deviation in subsequent cycles falls back to within the safety threshold. This architecture, which uses local underlying determinism to offset global network nondeterminism, achieves timing safety and physical collision avoidance of the control system in industrial environments without increasing hardware costs.

[0034] Example 2: In an industrial IoT test environment simulating the assembly of high-precision electronic components, the test data originated from a physical simulation platform integrating six work terminals and a time-sensitive network protocol backbone, possessing a communication bandwidth of no less than 10Gbps and a clock synchronization accuracy better than 1μs. To simulate a real industrial electromagnetic environment, Gaussian white noise with a root mean square value of 20dB and power frequency electromagnetic interference at a frequency of 50Hz were injected into the communication link using a signal generator. Simultaneously, a nonlinear network jitter of 10ms to 100ms was generated at the bottom layer of the protocol stack using a delay generator to verify the ability of the edge self-determination mechanism based on physical parameters to suppress control command slippage. The test sampling period was set. The decision-making logic is constrained by the physical bandwidth of the mechanical actuator and the computational load of the processor. To balance data real-time performance and processor power consumption, the sampling period... The sampling theorem must be satisfied, and a three-fold safety margin must be reserved. In this example, with a mechanical bandwidth of 100Hz, the sampling period is... The timeframe was set to 1ms; the experiment was divided into an experimental group using the scheme of this invention, a control group using a centralized scheduling architecture, and a feature-deficient control group with the delay compensation module removed; among which Indicates the sampling period; during the test group operation, the edge logic determination unit monitors the local displacement parameters. External reference parameters When the injected network jitter is 30ms, the measured original synchronization deviation is 15.6μm. At this time, the delay compensation module extracts the timestamp difference. And correct the synchronization deviation. This stabilizes the effective deviation output written to the driver register within the range of 1.2μm to 1.5μm, while the synchronization deviation of the control group under the same jitter conditions shows a linear increasing trend and reaches 124.8μm, resulting in physical execution interference errors. When the network jitter is 50ms, the synchronization deviation of the feature-deficient control group jumps to 42.7μm, indicating that the lack of time characteristic compensation for communication dead zones will cause local feedback to be unable to eliminate the cumulative error caused by link nondeterminism.

[0035] Regarding the attenuation coefficient Perform boundary gradient tests when the decay coefficient When the lower limit is set to 0.1, the damping adjustment unit's intervention on the master control causes long-cycle tasks to trigger weight decay when they reach 85% completion, ensuring the real-time access of subsequent tasks. When the decay coefficient... When increased to the upper limit of 0.3, the master control dwell time is extended, and the task throughput under high-frequency competition conditions with multiple devices remains above 92% of the baseline value; if the attenuation coefficient is increased... Adjusting the overrange to 0.05 resulted in frequent loss of control by the terminal, causing the task success rate to drop to 64%. When increased to 0.5, a single device locking the shared space caused other terminals to experience logical wait times of up to 850ms. This data demonstrates that the value range of 0.1 to 0.3 is the optimal window for balancing system flexibility and operational continuity. This represents the attenuation coefficient. Experimental results confirm the effectiveness of using pulse feedback from physical actuators as a logic synchronization criterion, as demonstrated by the calculation formula. The determined synchronization deviation reflects the real position of the physical entity relative to the logical sequence. As network jitter surged from 20ms to 80ms, the system's task success rate fluctuated from 99.5% to 98.2%, significantly higher than the control group's success rate of less than 40% under similar conditions. This method anchors the arbitration of control to the remaining execution time step of the physical task. Physical impact stiffness coefficient of equipment This method aligns the flow rhythm of the control logic with the actual operating rhythm of the physical equipment in complex interference environments.

[0036] Example 3: In an IoT control system for semiconductor wafer handling requiring micron-level repeatability, each work terminal is connected to a servo driver via a fieldbus. Due to the non-deterministic concurrent access generated by heterogeneous IoT nodes, the system bus experiences random instruction suspensions under specific operating conditions. This leads to a displacement slip between the logic control sequence and the physical pulse count of the work mechanism. If the slip exceeds the physical clearance space of the wafer fixture, it results in physical collisions during the handling process. To determine the key parameters required for system operation, a standardized calibration procedure is implemented during the system initialization phase. The work mechanism is placed at the zero reference point, and a single-step pulse instruction is written to the drive register through the controller to record the unit physical distance moved by the work mechanism. Required pulse count value According to the formula Determine the mapping coefficients between the unit pulse and the logic sequence. ;in, This represents the mapping coefficient, with the dimension being pulses per meter. Indicates the pulse count value. Represents unit physical distance; measures the amplitude of mechanical oscillations of the working mechanism in a stationary state using a laser interferometer. The elastic tolerance threshold is determined as This provides the logic decision unit with a decision boundary after excluding environmental noise. The edge logic decision unit allocates a shared memory region in the operating system kernel mode through the low-level driver. This region is mapped to the pulse feedback register address of the servo driver at the hardware level. In each sampling cycle, the processor directly reads the current job pulse number from this memory offset address through an atomic locking operation to determine the local displacement parameter. It also retrieves external reference parameters containing logical job sequence numbers from the communication port cache, which are sent by adjacent terminals via multicast protocol.

[0037] To eliminate dimensional conflicts during heterogeneous parametric logic comparison, a scaling constant is introduced as a mapping coefficient to convert dimensionless sequence numbers into equivalent physical displacements. This ensures that the external reference parameters have coordinate attributes that completely correspond to the local entity's pulse feedback. If the synchronization deviation... If the elastic tolerance threshold is exceeded for three consecutive cycles, the interrupt management unit triggers a non-maskable interrupt request. This interrupt request has a higher response priority than the task scheduling of the IoT protocol stack. By directly writing a deceleration stop code of value 0x0006 into the driver's control word register, the mechanical operating mechanism is forced into a safety locked state. The damping adjustment unit dynamically reconstructs the attenuation logic during the master control residency process. When the master control resides in the current terminal for a certain period of time... When the threshold is exceeded, the system adjusts the composite competitive state weights in a step-by-step manner. To address the difference in physical response scale between the nanosecond-level computation cycle of the bridging microprocessor and the millisecond-level avoidance action of the mechanical mechanism, the edge control module is equipped with an independent hardware timer counter unit to generate decaying trigger beats with a fixed period of 10 milliseconds, and its decay coefficient... according to The logic decreases progressively with each clock cycle until... The lower limit of protection is 0.1; among which, Indicates the duration of control residency. Represents the composite competitive state weights. Indicates the first The attenuation coefficient per cycle, This represents the decay coefficient of the previous cycle; this tiered weight stripping mechanism ensures that the competitive logic of multiple devices operating in a shared domain does not fall into a long-term deadlock due to the accumulation of single-point instructions, and enables the global task flow efficiency of the system to achieve closed-loop alignment with the dynamic response capability of the physical operation unit.

[0038] Example 4: When the system is deployed in a multi-terminal collaborative working environment containing heterogeneous physical hardware, the first preset normalized mapping coefficients stored inside the edge control unit... With the second preset normalized mapping coefficient The stress testing process was used to determine the steps, including issuing a 10Hz step position command to the servo driver, reading and recording the corresponding physical response delay time through memory mapping, while maintaining the remaining operation time step of the current process. Under the condition of a constant value, the physical collision stiffness coefficient of the working terminal under different equipment is measured using a torque sensor. The peak collision response force is determined, and the composite competitive state weight is calculated based on the physical laws governing the relationship between response delay time and peak collision response force. The mapping parameters of the growth trend are established based on the principle of polynomial fitting, and an offline parameter calibration procedure is established. The least squares algorithm is used to complete the two-dimensional function curve fitting of the discrete test data of the acquired response delay time and the peak value of the collision response force. The two-dimensional function curve fitting operation explicitly adopts a univariate linear polynomial regression model. The system regards the recorded physical response delay time as the set of independent variables on the horizontal axis and the collected peak value of the collision response force as the set of dependent variables on the vertical axis. Based on this, a basic linear equation describing the orthogonal relationship between force and delay is established. The microprocessor extracts the constant term of the converged fitted curve and assigns it to the first preset normalized mapping coefficient. Extract the coefficients of the first-order term of the fitted curve and assign them to the second preset normalized mapping coefficients. By establishing mathematical analytical relations, the discrete physical resistance response is transformed into a continuous calculation factor, and the calculated first preset normalized mapping coefficients are used. With the second preset normalized mapping coefficient Write to non-volatile memory to enable actuators of different specifications to operate under the same composite race condition weights. This generates an aligned avoidance response action.

[0039] When the system is deployed on a distributed industrial IoT infrastructure with a working span of 50m, the edge control module performs reference displacement alignment calibration. With the working terminal in a stationary locked state, the edge logic judgment unit continuously collects local displacement parameters for 1000 cycles. External reference parameters The background interference of the IoT protocol stack under static operating conditions is quantified by calculating the discrete variance of the sampled data. According to the formula Determine the elasticity tolerance threshold ;in, Indicates the elasticity tolerance threshold. This indicates a statically stored, safe operator with a value of 5. This indicates background interference; background interference was measured during the calibration process. The elastic tolerance threshold is calculated to be 0.4 μm. The value is 2.0μm, thereby eliminating the interference of signal noise floor on logic decision-making.

[0040] Example 5: In a distributed flexible assembly unit with dynamic random task flow input, the collaborative terminal connects to the multi-dimensional operating mechanism through the system bus interface. During the system initialization phase, hardware resource virtualization mapping is implemented. The edge control module maps the physical address of the servo driver's feedback counter register to the virtual address space of the user-mode process through the operating system kernel's memory management unit, and records the starting address of the mapping as... This enables the edge logic decision unit to acquire local displacement parameters with an access cycle of less than 500ns. At the component level, in-situ alignment of physical location data and logical decision instructions is achieved; to enable the response of control arbitration logic in the real-time kernel, the system implements weight and priority mapping calibration according to the formula. Composite competitive weights Convert to kernel-mode task scheduling priority ,in, Indicates the kernel-mode task scheduling priority. Represents the composite competitive state weights. This represents the scaling factor and has a value of 100. Simultaneously, to determine the boundary of the maximum master control dwell clock, the system conducts load stress testing during the pre-deployment debugging phase. This involves recording the distribution of task deadlock probabilities generated by the system under different task concurrency frequencies, and when the rate of change of deadlock probability reaches an inflection point, using the cumulative dwell clock cycles corresponding to that moment as a benchmark threshold written into the damping adjustment unit. This ensures that the attenuation coefficient can complete the adjustment of the composite competitive state weights before the physical operating components enter the space interference exclusion zone. To mitigate the impact of network congestion, the interrupt management unit employs a double-buffered queue structure to cache IoT protocol stack instructions during obstacle avoidance. When the logical judgment triggers the highest priority interrupt request, the interrupt handler directly rewrites the mask bit in the control register to lock the current state of the operating mechanism. This structural design, which embeds logical judgment into the kernel scheduling layer, shortens the local obstacle avoidance response time of the collaborative terminal compared to the periodic polling method when the network congestion rate reaches 75%, maintaining the deterministic offset of the physical entity's work cycle time relative to the logical work sequence.

[0041] In the physical network port driver layer deployment of distributed collaborative terminals, the reference extraction unit loads external synchronization reference quantities into the circular buffer of the local communication port through direct memory access technology. This circular buffer is divided into... Each storage unit has a fixed step size and maps to a data frame containing logical timing codes. To eliminate read conflicts, the reference extraction unit maintains a header pointer to the latest complete message and uses a lock-free queue algorithm to update the external reference parameters in real time. ,in, Indicates the number of storage units. This represents external reference parameters. This extraction method, based on the offset of the underlying register, eliminates the processing latency caused by user-space encapsulation in the network protocol stack, keeping the extraction latency of external reference data within 10μs. During the race condition adjudication process in multi-device co-domain operations, the edge control module calls the virtual constraint stiffness parameters stored in the static storage unit. And based on local displacement parameters External reference parameters of adjacent terminals Calculate synchronization deviation When the motion envelopes of the two terminals tend to overlap and the dynamic remaining time parameters tend to be consistent, the system uses the formula... Calculate the correction amount for the repulsive force field ,in, This represents the correction amount for the repulsive force field. Represents the virtual constraint stiffness parameter. This represents the preset safety clearance constant. Indicates the local displacement parameter. Indicates the synchronization deviation; corrects the repulsive force field. Superimposed on the composite competitive weights In the computational loop, local avoidance priority is increased, thereby generating position compensation through nonlinear growth of the weight space before physical interference occurs.

[0042] 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.

[0043] 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-terminal collaborative intelligent control system based on the Internet of Things, characterized in that, include: Multiple terminals, each connected to a motion execution module; The edge control module is connected to each terminal, and the edge control module communicates with the motion execution module through a bus protocol; The edge control module includes a pulse reading unit, a reference extraction unit, a synchronization determination unit, an interrupt management unit, and a delay compensation unit. The pulse reading unit reads the pulse count value of the system to which the motion execution module belongs through memory mapping to determine the local feedback displacement. The reference extraction unit extracts the logical timing code broadcast by the neighboring terminal in the most recent communication cycle from the circular buffer of the local communication port and determines it as the external synchronization reference quantity; The synchronization determination unit calculates the synchronization deviation, which characterizes the degree of alignment between physical displacement and logical timing, by comparing the local feedback displacement with the external synchronization reference. When the synchronization deviation exceeds the preset threshold, the interrupt management unit modifies the control instructions of the drive register by generating a high-priority interrupt signal in order to adjust the motion state of the motion execution module. The delay compensation unit obtains the timestamp difference of data packets during network transmission and corrects the participation weight of the synchronization deviation based on the linear positive correlation between the timestamp difference and the weight, so as to offset the nondeterministic communication delay generated by the network layer.

2. The multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: The edge control module also includes a static storage unit, which stores preset virtual constraint stiffness parameters. The edge control module is used to call the virtual constraint stiffness parameter as the logical arbitration benchmark when the dynamic remaining time parameters of multiple terminals tend to be consistent. Based on the logical arbitration benchmark, a repulsive force field correction is introduced in the calculation of the synchronization deviation, thereby completing the preventive intervention for potential physical collisions between terminals at the logical layer.

3. The multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 2, characterized in that: The edge control module also includes a damping adjustment unit, which monitors the dwell time of the master control on the terminal and dynamically adjusts the calculation weight of the virtual constraint stiffness parameter according to the dwell time to prevent a single terminal from monopolizing the control resources of a local area for a long time.

4. The multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: When calculating the synchronization deviation, the edge control module completes the following processing steps: Step S101: Calculate the motion trajectory vector of the local entity based on the pulse count value; Step S102: Solve the logical motion envelope of the adjacent terminal based on the external synchronization reference value; Step S103: Perform spatiotemporal correlation mapping on the motion trajectory vector and the logical motion envelope to output the synchronization deviation.

5. The multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: The terminals interact with each other in real time via a time-sensitive network; the edge control module is used to extract the global clock synchronization pulse from the scheduling cycle of the time-sensitive network as the reference time origin for determining the synchronization deviation.

6. The multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: The pulse count value is read at a frequency higher than the external synchronization reference value is acquired. The edge control module is used to perform linear interpolation on two adjacent external synchronization reference values ​​to fit a virtual external reference path that is synchronized with the reading frequency.

7. The multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: The edge control module is also used to calculate the characteristic value of the rate of change of the deviation based on the trend of the synchronization deviation. : ,in, The characteristic value of the rate of change of deviation. This represents the change in synchronization deviation between two adjacent communication cycles. This refers to the communication cycle duration, which ranges from 10ms to 100ms. This is the preset system damping coefficient.

8. A multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 7, characterized in that: The edge control module is used to determine the characteristic value of the deviation change rate. Adjust the feed rate of the drive pulse within the motion execution module in real time; In the characteristic value of the rate of change of deviation When the preset alarm threshold is exceeded, the edge control module is used to broadcast an emergency avoidance status word to the other terminals.

9. A multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: The edge control module is equipped with a distributed deterministic state machine unit; the distributed deterministic state machine unit is used to complete state transitions based on the synchronization deviation and to intercept expired motion control messages at the local logic layer.

10. A multi-terminal collaborative intelligent control system based on the Internet of Things according to claim 1, characterized in that: The system also includes a global scheduling module; the global scheduling module is used to receive the synchronization deviation distribution characteristics reported by each terminal and statistically analyzed by the edge control module, and to reallocate the communication priority weights of each terminal in the Internet of Things accordingly.

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