A support intelligent manufacturing self-controllable continuous interval logic high-precision control method

Abstract

This invention discloses a high-precision continuous interval logic control method supporting independent and controllable intelligent manufacturing, belonging to the fields of intelligent manufacturing industrial control, precision machining, domestically produced industrial chips, and AI intelligent control technology. Based on the mathematically fundamental continuous interval theory and STE industrial control-specific coding, this invention integrates domestically produced industrial control chips and precision control models to construct a full-process industrial control system, setting high-specification performance thresholds such as nanometer-level control precision, zero error accumulation, and low latency. By establishing a continuous interval mathematical model, calibrating fundamental parameters, replacing discrete control with continuous logic, issuing STE-coded instructions, and implementing real-time error correction, the method eliminates the accumulation of discrete control errors, achieving high-precision control in high-end precision manufacturing that is entirely domestically produced and free from reliance on foreign technologies. It is adaptable to various precision manufacturing scenarios, contributing to the independent upgrading of intelligent manufacturing and filling gaps in related domestic underlying control technologies.

Description

Technical Field

[0001] This invention pertains to intelligent manufacturing, industrial automatic control, precision machining control, domestic industrial chip design, and AI. In the field of intelligent industrial control technology, specifically, it involves a continuous interval logic high-resolution system that supports autonomous and controllable intelligent manufacturing. Precision control methods and systems are particularly suitable for high-end precision parts processing and precise control of industrial robots. Intelligent workshop full-process management and control, and the operation of industrial equipment under extreme working conditions, all place demands on control precision, stability, and autonomous controllability. Intelligent manufacturing scenarios with stringent requirements. Background Technology

[0002] Currently, my country's intelligent manufacturing industry faces the dual core challenges of high-precision control and independent controllability. Existing industrial control... The technology has insurmountable technical shortcomings: Firstly, mainstream industrial control uses discrete logic control algorithms, which only... It can achieve point-to-point control, but it lacks continuous interval control logic, resulting in significant error accumulation during the control process. The control precision can only reach the micrometer level, which cannot meet the nanometer-level control requirements of high-end precision manufacturing; secondly, the core control... The control algorithms, industrial control frameworks, and dedicated chips all rely on foreign technologies, posing risks of technology and data breaches. The risks of malicious tampering, the extremely poor self-sufficiency and controllability of domestically produced products, and the inability to guarantee the security of the national intelligent manufacturing industry; thirdly. The control process lacks dynamic intrinsic calibration and closed-loop real-time correction mechanisms, resulting in insufficient control stability and a high failure rate under extreme conditions (high temperature, high pressure, high load); fourthly, the control commands lack domestically produced proprietary coding, leading to inconsistent command transmission. The input is easily interfered with, the execution accuracy is insufficient, and it is impossible to achieve precise and controllable operation throughout the entire process; fifth, it lacks AI intelligent assistance. The lack of adaptive control parameters, coupled with poor versatility and adaptability, severely restricts the development of high-end technologies in my country. The intelligent manufacturing industry is developing in a high-quality and autonomous manner.

[0003] To address the aforementioned technical challenges, this invention starts from the underlying logic of industrial control and constructs a domestically produced continuous interval control system. The system, combining dedicated domestically produced chips with AI precision control models, achieves zero error accumulation and nanometer-level high precision. The fully autonomous and controllable intelligent manufacturing control completely solves the shortcomings of existing technologies. Summary of the Invention

[0004] 1. Core Definition 1. Continuous Interval Control Logic: Based on mathematically derived proportions, a continuously controllable interval for equipment operation and processing is constructed. It replaces traditional discrete point control, achieving smooth, jump-free control throughout the entire process. 2. Intrinsic Parameter Calibration: Using a continuous interval benchmark as the core, dynamic calibration of real-time equipment operating parameters is performed to eliminate biases. Poor data ensures accurate control benchmarks. 3. STE Industrial Control Coding: Domestically developed proprietary industrial control instruction coding, free from foreign patent barriers, ensuring instruction security. Secure transmission and precise execution 4. Error Cumulative Rate: The cumulative increase in error during the control process. This invention achieves a 0% error cumulative rate, meaning no deviation. enlarge 2. Key quantization thresholds (can be directly accessed by the programmer) 1. Control accuracy: ≤±5nm (precision machining), ≤±0.01mm (industrial production line), accuracy improvement ≥92%. 2. Control error accumulation rate: 0%, completely eliminating discrete control error accumulation. 3. Continuous interval response delay: ≤1ms, instruction execution delay ≤2ms 4. Extreme operating condition stability: ≥99.99%, suitable for operating conditions of -40℃~120℃, high vibration, and high load. 5. Intrinsic parameter calibration accuracy: ≤±0.1%, calibration cycle ≤1ms 6. STE control code width: 128 bits, instruction transmission bit error rate ≤ 10^-15 7. AI control model inference latency: ≤0.8ms, adaptive accuracy under operating conditions ≥99%

[0005] 1. Mathematical Modeling Formula for Continuous Intervals Icontrol =[Imin ,Imax ]Fcont (x)=ω. f(x)+(1−ω). ∫Imin Imax f(t)dt where: Icontrol For the target control continuous interval, Imin / Imax For the interval The lower limit, ω is the interval weighting coefficient (dynamically adjusted from 0.5 to 0.9), and Fcont(x) is the continuous interval control function. The number of steps is constant throughout, eliminating error accumulation.

[0006] 2. Origin parameter calibration formula: Pcalib = Preal . Ireal Itarget Among them: Pcalib for Post-calibration control parameters, Preal To collect parameters in real time, Itarget For the target interval value, Ireal For the actual interval value, the parameter deviation after calibration is ≤ ±0.1%. 3. Continuous interval logic operation formula: Ccmd = Kp .ΔI+Ki . ∫ΔIdt+Kd . dtdΔI An improved continuous interval PID algorithm with no discrete sampling error; ΔI is the interval deviation value. Kp / Ki / Kd It is a domestically produced adaptive coefficient that dynamically matches the operating conditions.

[0007] 4. STE Industrial Control Instruction Coding Rules STEInd =Hash(Ccmd )⊕KIndustrial ⊕IDDevice ⊕CRCControl 1. Hash(Ccmd): Hash value of a continuous range of control instructions. 2. KIndustrial 128-bit domestically produced industrial control proprietary key, generated via hardware TRNG. 3. IDDevice Unique identifier for intelligent manufacturing equipment, enabling precise command matching. 4. CRCControl Control command verification codes to prevent command transmission errors. 5. ⊕: Low-level XOR encoding, instructions are irreversible, only domestically produced chips can decode and execute it.

[0008] Chip model: GZ-Industrial-RISC-V, 1GHz clock speed, industrial-grade computing power ≥2.5TOPS, wide temperature range Adaptable to temperatures ranging from -40℃ to 125℃, vibration-resistant and interference-resistant, specifically designed for continuous range control in intelligent manufacturing. The hardware is domestically produced, with no foreign components.

[0009] asm Continuous interval modeling instructions INTERVAL_SET rD, rMin, rMax ; Sets the upper and lower limits of the control interval, executed in a single cycle. Source parameter calibration command PARAM_CALIB rD, rReal, rTarg ; Real-time parameter dynamic calibration, executed in a single cycle. ; CONT_CALC rD, rDelta instruction for continuous interval logic operations; performs continuous interval control operations, executed in two cycles. STE control instruction encoding instructions IND_STE_ENC rD, rCmd ; Control instructions encoded in STE format, executed in two cycles. Domestic command issuance and execution instructions CMD_EXEC rD, rDevice ; Encoded command is sent to the device for execution, single-cycle execution. Real-time operating condition monitoring commands WORK_MON rD, rStatus ; Device status and range deviation monitoring, executed in a single cycle. Closed-loop error correction command ERR_CORR rD, rErr ; Real-time error reverse correction, executed in a single cycle. The chip integrates a hardware encryption module, a true random number generator, and an industrial-grade anti-interference unit, supporting encrypted instruction transmission. It features data transmission protection, tamper-proof design, and eavesdropping prevention, and is compatible with all domestically produced intelligent manufacturing equipment.

[0010] 1. Model Architecture: Lightweight CNN + LSTM industrial-grade hybrid architecture with only 8.2M parameters and INT8 quantization. It runs in real time on the client side and has no dependency on foreign frameworks.

[0011] 2. Core Model Functions: Intelligent modeling of continuous intervals, adaptive parameter adjustment under working conditions, error prediction, and extreme working conditions. The compensation and control effects have been optimized.

[0012] 3. Inference Process: Operating condition data acquisition → Continuous interval feature extraction → AI interval modeling → Parameter calibration → Continuous logic... Logical operation → Instruction generation → Execution monitoring → Error correction.

[0013] 4. Core model metrics: Inference latency ≤ 0.8ms, operating condition recognition accuracy ≥ 99%, error prediction accuracy ≥ 98.5%. The response time for dynamic parameter adjustment is ≤1ms.

[0014] 5. Loss function: Interval control deviation loss + accuracy loss + stability loss + time delay loss, quadruple loss. The beam ensures optimal control accuracy and stability.

[0015] Step 1: Mathematical Modeling of Continuous Intervals in Manufacturing Operations 1. Collect core parameters of intelligent manufacturing equipment processing and operation to determine the target control continuous interval. Icontrol = [Imin, Imax] 2. Based on the continuous interval modeling formula, a low-level continuous control function Fcont(x) is constructed to replace the traditional discrete control. Control Logic 3. Set interval weighting coefficients, delineate safety control sub-intervals and precision control sub-intervals, and establish control logic benchmarks. 4. The entire modeling process uses no foreign algorithms; it is entirely domestically developed and adapted to the specific operating conditions of the equipment.

[0016] 1. Real-time acquisition of equipment operating parameters such as speed, position, temperature, pressure, and load. 2. According to the original parameter calibration formula, compare the real-time parameters with the interval benchmark and eliminate invalid parameters with excessive deviations. 3. Dynamically calibrate control parameters to match the parameters with a continuous interval reference, with a calibration deviation ≤ ±0.1%. 4. Calibration cycle of 1ms, real-time calibration throughout, eliminating control errors caused by parameter deviations.

[0017] 1. An improved continuous interval PID algorithm is adopted to perform continuous logic operations and generate high-precision control commands. 2. The calculation process involves no discrete sampling and no point jumps, completely eliminating error accumulation, with an error accumulation rate of 0%. 3. The AI ​​model adaptively adjusts the control coefficients to match real-time operating conditions and optimize the accuracy of control commands. 4. The generated control commands meet nanometer-level precision requirements, adapting to the needs of high-end precision manufacturing.

[0018] 1. Encode and encrypt control commands using 128-bit encoding according to STE industrial control coding rules. 2. Using the domestically produced GZ-Industrial-RISC-V chip, encoded instructions are sent to intelligent manufacturing equipment. 3. The device-side chip decodes commands to accurately execute processing, operation, and control actions. 4. Instruction execution latency ≤2ms, no instruction interference, no execution deviation, fully domestically produced with no foreign intervention.

[0019] 1. Real-time monitoring of equipment operating status, control effectiveness, and range deviation values. 2. Feed the deviation data back to the control core, and correct the control parameters and interval according to the closed-loop error correction formula. coefficient 3. A closed-loop correction is completed every 1ms, continuously maintaining control accuracy ≤ ±5nm. 4. An automatic compensation mechanism is activated under extreme operating conditions to ensure control stability ≥99.99%. 5. Generate control logs, encrypt and retain them for easy operation, maintenance, and quality traceability.

[0020] 1. Continuous Interval Modeling Unit: Completes the acquisition of operating condition data, mathematical modeling, interval division, and establishes control benchmarks. 2. Parameter Calibration Unit: Real-time parameter acquisition, intrinsic calibration, and deviation elimination ensure parameter accuracy. 3. Continuous Logic Unit: Equipped with a domestically produced chip, it performs continuous range operations and generates control instructions. 4. STE Encoding and Execution Unit: Instruction encoding, encrypted transmission, and device-side decoding and execution. 5. AI Closed-Loop Control Unit: Operating condition monitoring, error prediction, real-time correction, and stability optimization. VI. Beneficial Effects 1. High Precision with No Error Accumulation: Control accuracy reaches within ±5nm, an improvement of over 92% compared to discrete control. With an error accumulation rate of 0%, it completely solves the problem of error accumulation in precision manufacturing and meets the nanoscale control requirements of high-end manufacturing.

[0021] 2. Fully Independent and Controllable: Control logic, algorithms, chips, and coding are 100% domestically produced, with no foreign technology or chips. Eliminate reliance on components and frameworks, avoid technological and security risks, and safeguard the autonomy and security of the intelligent manufacturing industry.

[0022] 3. High stability under extreme conditions: Wide temperature range, vibration resistant, and interference resistant; control stability ≥99.99% under extreme conditions. With an extremely low failure rate, it is suitable for various harsh industrial manufacturing scenarios.

[0023] 4. Low latency and high response: Interval response latency ≤ 1ms, instruction execution latency ≤ 2ms, strong real-time performance of AI inference, and control... The manufacturing process is smooth and without jams, improving production efficiency and product qualification rate.

[0024] 5. High adaptability to all scenarios: Adaptable to high-end precision machining, industrial robots, smart workshops, equipment operating in extreme conditions, etc. Intelligent manufacturing in various scenarios can be quickly compatible with existing domestic industrial equipment and has extremely high versatility. Detailed Implementation

[0025] 1. Processing scenario: Processing of high-end precision aerospace components, requiring processing accuracy ≤ ±5nm, with no error accumulation.

[0026] 2. Implementation process: (1) Continuous interval modeling: Set the continuous interval for machining position, tool movement, and speed control. Precise definition of upper and lower limits of the interval (2) Parameter calibration: Real-time acquisition of tool position, spindle speed, and machining pressure Parameters, original calibration deviation ≤ 0.1% (3) Continuous operation: The continuous interval algorithm is used to generate processing instructions, without Discrete jump, error accumulation rate 0% (4) Instruction execution: STE encoded instructions are issued by domestic chips, machine The machine tool executes precisely with a delay of 1.5ms (5) Closed-loop correction: Real-time monitoring of machining accuracy, correction every 1ms. Positive control parameters, maintaining accuracy of ±4nm throughout the entire process. 3. Implementation Results: Processing accuracy reached ±4nm, with no error accumulation, 100% product qualification rate, and no overseas involvement throughout the entire process. Control technology enables independent and controllable high-end precision machining.

[0027] 1. Application Scenario: Robot assembly on automotive intelligent manufacturing production lines, requiring assembly positioning accuracy ≤ ±0.01mm. The writing is smooth and without deviation.

[0028] 2. Implementation process: (1) Modeling: Set the continuous interval of the robot's motion trajectory, and perform discrete control without point position (2) Calibration Accuracy: Real-time calibration of joint angle and motion speed parameters, deviation ≤ 0.1% (3) Calculation: Continuous logic generates motion (4) Execution: The domestic chip issues instructions, and the robot assembles precisely. (5) Control: Real-time correction of trajectory deviations, 99.99% stability. 3. Implementation Results: Assembly accuracy ±0.008mm, fast action response, no assembly errors, and a 40% increase in production line efficiency. The entire process is controlled domestically.

[0029] 1. Application Scenario: Control of high-temperature, high-vibration industrial smelting equipment, requiring control stability ≥99.99% and no loss of control. risk.

[0030] 2. Implementation process: (1) Modeling: Construct the continuous control range of equipment operating temperature, speed, and pressure. (2) Calibration: Dynamic calibration of parameters under extreme conditions, elimination of interference data (3) Calculation: AI model adaptive adjustment control system Number, adaptable to extreme working conditions (4) Execution: Wide temperature range domestic chip executes instructions, strong anti-interference ability (5) Repair Positive: Real-time compensation for operating condition deviations to maintain stable operation. 3. Implementation results: Equipment stability 99.995%, no loss of control, no downtime, control accuracy unaffected by operating conditions, ensuring... To ensure continuous and stable industrial production.

Claims

1. A high-precision continuous interval logic control method supporting autonomous and controllable intelligent manufacturing, characterized in that, Includes the following steps: Based on the fundamental continuous interval theory of mathematics, a continuous interval mathematical model of the manufacturing process is completed, and a non-jumping continuous control function is constructed. The real-time parameters of equipment operation are dynamically calibrated to ensure that the parameter matching deviation with the interval benchmark is ≤±0.1%. A high-precision control command is generated by replacing the traditional discrete control with an improved continuous interval logic algorithm, achieving an error accumulation rate of 0%. The command is encrypted using domestically produced STE industrial control coding and executed through domestically produced industrial chips. The control effect is monitored in real time and the parameters are corrected in reverse to form a closed-loop high-precision regulation. The entire process is set with a control accuracy of ≤±5nm, an interval response delay of ≤1ms, and an extreme condition stability of ≥99.99%. The control logic, chips, and coding are all domestically produced, with no reliance on foreign technology.

2. The method according to claim 1, characterized in that, The continuous interval mathematical modeling adopts the formula Icontrol=[Imin ,Imax ] and Fcont (x)=ω. f(x)+(1−ω). ∫Imin Imaxf(t)dt, where ω is a dynamic interval weight coefficient of 0.5-0.9, to construct a continuous control logic without discrete jumps throughout the entire process.

3. The method according to claim 1, characterized in that, The original calibration of control parameters uses the formula Pcalib = Preal . Ireal Itarget, with a calibration cycle of ≤1ms. After calibration, the parameter deviation is ≤±0.1%, completely eliminating control errors caused by parameter deviation.

4. The method according to claim 1, characterized in that, The continuous interval logic operation adopts the improved PID formula Ccmd = Kp . ΔI+Ki . ∫ΔIdt+Kd . dtdΔI, which has no discrete sampling error and dynamically and adaptively adjusts the coefficients of Kp / Ki / Kd.

5. The method according to claim 1, characterized in that, The STE industrial control instruction encoding rule is STEInd=Hash(Ccmd)⊕KIndustrial⊕IDDevice⊕CRCControl, using a 128-bit domestically produced proprietary key encoding, with an instruction transmission error rate ≤10^-15.

6. The method according to claim 1, characterized in that, Equipped with the GZ-Industrial-RISC-V domestic industrial control chip, the instruction set includes dedicated instructions such as INTERVAL_SET, PARAM_CALIB, CONT_CALC, IND_STE_ENC, CMD_EXEC, WORK_MON, and ERR_CORR. The chip's main frequency is ≥1GHz, and the instruction execution latency is ≤2ms.

7. The method according to claim 1, characterized in that, The IntervalControl-AI precision control model enables adaptive regulation based on operating conditions, with model inference latency ≤0.8ms, operating condition recognition accuracy ≥99%, automatic activation of compensation mechanism under extreme operating conditions, and stability ≥99.99%.

8. The method according to claim 1, characterized in that, Compared with traditional discrete control, the control accuracy is improved by ≥92%, with an error accumulation rate of 0%, making it suitable for intelligent manufacturing control in all scenarios, including high-end precision machining, industrial robots, smart workshops, and industrial equipment under extreme working conditions.

9. A high-precision continuous interval logic control system supporting autonomous and controllable intelligent manufacturing, characterized in that, It includes a continuous interval modeling unit, a parameter calibration unit, a continuous logic operation unit, a STE encoding execution unit, and an AI closed-loop control unit. The units are electrically connected and work together to realize the control method described in any one of claims 1-8.

10. An industrial control terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the continuous interval logic high-precision control method for supporting intelligent manufacturing with autonomous controllability as described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the continuous interval logic high-precision control method for supporting intelligent manufacturing with autonomous controllability as described in any one of claims 1 to 8.