Untrained artificial intelligence system based on target features and control method

Abstract

This invention discloses a training-free artificial intelligence system and control method based on target features, belonging to the field of artificial intelligence and precision control technology. The system comprises a feature acquisition unit, a preset inherent feature library, a feature matching decision unit, and a targeted execution unit. These four units form a closed-loop logic, eliminating the need for training, model fitting, probability calculation, and statistical inference throughout the entire process, thus completely freeing it from the dependence of traditional artificial intelligence on massive amounts of data and computing power. This invention uses the inherent features of the target as its core, employing a deterministic logic of "real-time feature acquisition – precise matching – targeted execution" to achieve power-on operation, zero deployment cost, and cross-scenario application. It solves the technical problems of high data dependence, cumbersome deployment, high computing power consumption, uninterpretable decisions, and poor scenario adaptability in traditional artificial intelligence, and can be widely applied in general intelligent decision-making and execution fields such as industrial control, chip manufacturing, aerospace, and rail control.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence technology and precision control technology. Specifically, it relates to a training-free artificial intelligence system and control method that completely eliminates the dependence on data training, takes the inherent characteristics of the target as the core, and uses deterministic matching and targeted execution as the core logic. It can be widely used in general intelligent decision-making and execution fields such as industrial control, chip manufacturing, aerospace, track control, and precision instrument operation. Background Technology

[0002] Current mainstream artificial intelligence technologies are all based on data-driven approaches and model training. Whether it is traditional machine learning, deep learning, or large-scale model technology, they all rely on complex processes such as massive data collection, annotation, model training, and iterative optimization, which have the following inherent drawbacks: 1. Extremely high data dependence: It requires the collection and labeling of massive amounts of target data. The data collection cycle is long and costly, and there are problems such as data privacy leakage and non-standard data labeling. Especially in high-precision and high-reliability scenarios, it is difficult to achieve massive data collection and labeling. 2. Huge computational consumption: Model training and iterative optimization require a large amount of computational resources, and continuous computational support is still needed after deployment, resulting in high operating costs and making it unsuitable for scenarios with limited computational resources; 3. Extremely low deployment efficiency: From data collection to model training and optimization, and then to system deployment, the entire process is time-consuming (usually taking several days to several months), making it impossible to achieve instant deployment and immediate availability; 4. Unexplainable decision-making: Decision-making logic based on probability statistics and model fitting cannot clearly define the basis for decision-making, which is prone to misjudgment and omission, making it difficult to meet the requirements of high reliability and high precision scenarios such as industrial control, chip manufacturing, and aerospace. 5. Poor scenario adaptability: A single model is only adapted to a specific scenario. Cross-scenario migration requires retraining the model and reconstructing the system, resulting in high adaptation costs and making it impossible to achieve general deployment. 6. Insufficient system stability: During model training and iteration, overfitting and underfitting problems are prone to occur, leading to fluctuations in the system's decision accuracy, affecting execution reliability, and making it difficult to adapt to the long-term stable operation requirements of high-precision and high-reliability scenarios.

[0003] In existing technologies, some solutions attempt to reduce training dependence by simplifying models and reducing training samples, but they still do not get rid of the core of "training". Essentially, they belong to the category of lightweight training and still have problems such as computing power consumption, cumbersome deployment, and poor scene adaptability. They cannot fundamentally solve the inherent defects of traditional artificial intelligence.

[0004] To address the aforementioned technical challenges, this invention completely abandons the traditional paradigm of "data-driven + model training" and proposes a deterministic artificial intelligence system centered on "inherent features of the target," characterized by "no training, no fitting, and no probabilistic computation." Through a closed-loop logic of static feature library pre-setting, real-time precise feature matching, and targeted instruction execution, it achieves immediate operation upon power-on, zero deployment cost, and cross-scenario application, while ensuring accurate and interpretable decision-making, system stability and reliability, and adaptability to the stringent requirements of high-precision and high-reliability scenarios.

[0005] The core difference between this invention and existing technologies lies in the fact that existing technologies are all based on model training and rely on massive amounts of data and computing power; this invention is based on inherent feature matching, without any training, fitting, iterative optimization or statistical inference. Through static feature library preset and real-time feature precise matching, deterministic decision-making and targeted execution are achieved. This completely distinguishes it from all existing artificial intelligence technologies in terms of technical approach and solves the four core pain points of traditional technologies: data, computing power, deployment and interpretability. Summary of the Invention

[0006] (I) System Architecture The training-free artificial intelligence system based on target features described in this invention has a core architecture comprising a feature acquisition unit, a pre-set inherent feature library, a feature matching decision unit, and a targeted execution unit. These four units are connected sequentially to form a closed-loop logic of "acquisition-matching-decision-execution," without any training, model fitting, probability calculation, or statistical inference throughout the entire process. The specific architecture is as follows: 1 Feature Acquisition Unit The core requirements for real-time acquisition of the inherent features of target objects are high acquisition accuracy, stable signal, low data redundancy, and no training or data labeling required during the acquisition process.

[0007] The inherent characteristics of a target object are defined as the essential attributes that the target itself possesses, which can be precisely quantified and do not change with environmental factors. These include quantifiable essential attributes such as geometric structure, material properties, hardware fingerprint, spatial morphology, motion trajectory, vibration spectrum, signal characteristics, and chip target morphology, but do not involve any biological characteristics.

[0008] The core requirements for feature acquisition are: the acquisition accuracy should meet the standards of the corresponding scenario. For example, the acquisition error in industrial control scenarios is preferably no greater than 0.1%, the resolution in chip manufacturing scenarios is preferably no greater than 1nm, the positioning accuracy in aerospace scenarios is preferably no greater than 0.1nm, and the measurement error in track control scenarios is preferably no greater than 0.05%; the acquisition process should collect core data to reduce the signal processing pressure.

[0009] 1.1 Triggering methods for feature acquisition It supports active triggering (system timed triggering, the trigger period can be manually set, ranging from 10μs to 10s, adapted to the needs of the scenario) and passive triggering (external signal triggering, the trigger signal must conform to the system interface standard, high level ≥3.3V, low level ≤0.3V). The two triggering methods can be manually switched, and no system reconfiguration is required after switching.

[0010] The rules for setting the active trigger cycle are as follows: For example, the trigger cycle for industrial control scenarios is preferably no greater than 1ms, the trigger cycle for chip manufacturing scenarios is preferably no greater than 1μs, the trigger cycle for aerospace scenarios is preferably no greater than 10μs, and the trigger cycle for track control scenarios is preferably no greater than 5μs. The trigger cycle can be manually adjusted through the system interface. The adjustment takes effect immediately without restarting the system. The adjustment record is automatically archived (including the adjustment time, the cycle value before and after the adjustment, and the person making the adjustment), and the archive period is no less than 6 months.

[0011] Passive trigger signal standards: The high-level duration of the trigger signal is ≥10μs, the low-level duration is ≤1μs, and the trigger signal is free of glitches (glitches amplitude ≤0.1V). When the trigger signal is abnormal (such as insufficient high-level duration or excessive glitches), the system will automatically ignore the trigger signal, record the abnormal information (abnormality type, trigger signal parameters, and occurrence time), and retain the information for at least 3 months to facilitate subsequent troubleshooting.

[0012] 1.2 Specific parameters for feature acquisition (adapted to different scenarios) (1) Industrial control scenario: A high-precision displacement sensor is used, the measurement error is preferably no greater than 0.1%, the sampling frequency is preferably no less than 1kHz, the signal bandwidth is 10Hz-1MHz, the data acquisition format is binary encoding (8-bit byte), and the acquisition delay is preferably no greater than 1ms; (2) Chip manufacturing scenario: A nanometer-level sensor is used, with a resolution preferably not greater than 1nm, a sampling frequency preferably not less than 10kHz, a signal bandwidth of 1Hz-10MHz, a data acquisition format of hexadecimal encoding, and an acquisition delay preferably not greater than 100ns; (3) Aerospace scenario: The anti-interference sensor is adopted, the positioning accuracy is preferably no more than 0.1nm, the sampling frequency is preferably no less than 1kHz, the signal bandwidth is 10Hz-10MHz, the acquisition delay is preferably no more than 1μs, and it has the ability to resist electromagnetic interference (electromagnetic interference ≤3V / m). (4) Track control scenario: High-precision positioning sensor is used, the measurement error is preferably no more than 0.05%, the sampling frequency is preferably no less than 500Hz, the signal bandwidth is 1Hz-5MHz, the acquisition delay is preferably no more than 5μs, and it has anti-vibration capability (vibration frequency ≤50Hz, amplitude ≤0.1μm).

[0013] 1.3 Sensor Calibration Rules Calibration is performed every 3 months, following the corresponding industry standards for the specific scenario. For industrial scenarios, standard displacement block calibration is used (preferably with an error of no more than 0.01%); for chip scenarios, standard nanotemplate calibration is used (preferably with a deviation of no more than 0.1nm); for aerospace scenarios, laser interferometer calibration is used (preferably with an accuracy of no more than 0.05nm); and for track control scenarios, standard track parameter calibration is used (preferably with an error of no more than 0.02%). If the calibration fails, the sensor is replaced, and recalibrated until it passes the test.

[0014] The specific operation procedure for sensor calibration is as follows: power off the system → connect the calibration equipment → input the calibration parameters (corresponding to the standard values ​​for the scenario) → start the calibration program → collect calibration data → compare with the standard values ​​→ determine whether it is qualified; the calibration data must be recorded and archived, including the calibration time, calibration personnel, calibration parameters, and calibration results (error values), and the archiving period shall not be less than 1 year.

[0015] The handling logic for calibration failure is as follows: If the initial calibration fails, readjust the calibration parameters (adjustment range is ±5% of the standard value) and calibrate again; repeat this process a maximum of 2 times. If it still fails, replace the sensor and re-execute the calibration process after replacement. If calibration still fails after replacing the sensor, it is determined that the system interface is abnormal. Check the interface connection status (loose lines, damaged interfaces), repair and recalibrate.

[0016] 1.4 Signal Cleaning Processing During Feature Acquisition Only basic noise reduction and normalization processing are performed, without involving any feature extraction algorithms, model fitting or statistical inference; the core purpose of signal purification is to remove environmental interference (such as electromagnetic interference and vibration interference) during the acquisition process, so as to ensure that the acquired feature data truly reflects the inherent properties of the target.

[0017] Specific parameters for signal purification: The noise reduction threshold is adapted to the scenario. For example, the preferred threshold for industrial control scenarios is no greater than 0.01V, for chip manufacturing scenarios it is no greater than 0.001V, for aerospace scenarios it is no greater than 0.0001V, and for track control scenarios it is no greater than 0.005V. The normalization formula is uniformly x′=(x−xmin) / (xmax−xmin), where x is the original acquired value, and xmin and xmax are the industry standard value ranges for this feature, ensuring that the feature data acquired by different devices are consistent.

[0018] Anomaly handling for signal purification: If interference still exists in the signal after noise reduction (the interference amplitude is preferably not less than 0.1V), perform noise reduction again, repeating up to 3 times; if interference still exists after repeated processing, replace the sensor module (adapted to the anti-interference type), and perform signal purification again after replacement until the interference amplitude is lower than the set value; record the anomaly handling process (number of processing times, changes in interference amplitude, and types of modules replaced), and archive for no less than 6 months.

[0019] 1.5 Redundancy Control in Feature Acquisition The collected data retains the core parameters directly related to the inherent characteristics of the target and removes redundant parameters to ensure that the collected data is concise and efficient, reducing the signal processing pressure. The criteria for removing redundant data are as follows: clearly distinguish between core parameters and redundant parameters. Core parameters are parameters related to the inherent characteristics of the target, while the rest are redundant parameters. Removing them will not affect the feature matching accuracy.

[0020] 1.6 Interface Design and Module Replacement The interface design of the feature acquisition unit adopts a standardized interface (such as EtherCAT, RS485), with fixed interface parameters. Sensor modules in different scenarios are all compatible with this standardized interface, and can be used plug-and-play when replacing without adjusting the system interface configuration.

[0021] Sensor module replacement standard: All sensor modules adopt a standardized design with unified interface parameters (e.g., power supply voltage 12V, current ≤500mA, signal output format is binary encoding). They can be used plug and play when replaced without adjusting the system interface configuration.

[0022] 1.7 Rules for Adjusting Signal Cleanup Parameters under Extreme Environments High temperature environment (>60℃): Noise reduction threshold is reduced by 50%, normalization formula remains unchanged; High interference environment (electromagnetic interference > 3V / m): Noise reduction threshold reduced by 30%, median filter preprocessing added; High humidity environment (>85% RH): Add moisture-proof treatment, and the noise reduction threshold is increased by 20%; After parameter adjustment, feature acquisition accuracy verification is required to ensure that the error with the standard value is preferably no greater than 0.1%.

[0023] Adjustment strategy for signal purification parameters failing stability test after adjustment in extreme environments: In high-temperature environments, reduce the noise reduction threshold by 10% each time; in high-interference environments, add median filtering once each time, up to a maximum of 3 times; in high-humidity environments, reduce the noise reduction threshold by 5% each time, and can be adjusted multiple times; if it still fails after adjustment, replace with a sensor module adapted to the extreme environment (high-temperature resistant module, waterproof module, shielded module), and re-calibrate the accuracy after replacement.

[0024] Accuracy verification process after sensor module replacement in extreme environments: After replacing the sensor module, perform 3 feature acquisition tests; the acquisition accuracy must meet the requirements of the corresponding scenario, such as ≤0.1% for industrial scenarios, ≤1nm for chip scenarios, ≤0.1nm for aerospace scenarios, and ≤0.05% for track control scenarios; if the accuracy requirements are met in multiple tests, it is judged as qualified; if it fails, the sensor module is recalibrated or replaced, and the accuracy verification is performed again.

[0025] The feature acquisition unit adopts a modular design, and the sensor modules can be replaced to adapt to different scenarios. Module replacement does not require retraining or system reconstruction.

[0026] 2. Preset inherent feature library Standard inherent characteristic data for pre-storing various target objects.

[0027] The pre-defined feature library forms the core foundation, and feature data is pre-defined all at once before system deployment. For scenarios where features may mutate, standard feature templates for multiple variant subtypes of the target are pre-stored, and multi-template parallel matching is used, which still falls under the category of static configuration.

[0028] For unforeseen new features and new variant types, an offline supplementation mode is adopted to update the feature library. The specific process is as follows: manual calibration → standardization processing → template addition → library synchronization. The updated feature library still belongs to the category of static feature library and is not dynamically added, deleted or modified during operation.

[0029] 2.1 Offline Supplement Operation Standards The accuracy of manual calibration must meet the sensor parameter requirements of the corresponding scenario. For example, the calibration accuracy is preferably no greater than 0.1% in industrial scenarios, no greater than 1nm in chip manufacturing scenarios, no greater than 0.1nm in aerospace scenarios, and no greater than 0.05% in track control scenarios. The standardization processing standard strictly follows the corresponding industry standards for data format specification. The library synchronization adopts offline manual synchronization, and the synchronization permission is only granted to the system administrator. After synchronization, feature template verification is required (verifying the integrity of the template data and its consistency with standard features).

[0030] 2.2 Offline Supplementation of Exception Handling Logic If the deviation of manually calibrated data from the standard exceeds the set value (5%), it is deemed unqualified and recalibrated, which can be repeated multiple times; if it is still unqualified, it is determined that the feature cannot be standardized and will not be supplemented for the time being; if the error between the feature value and the standard value after standardization exceeds the set value (0.1%), it is deemed non-compliant and reprocessed according to the corresponding industry standard; if it is still non-compliant after repetition, it will be abandoned and supplemented. The specific contents of the record archive include: feature type, original calibration data, standardization processing method and parameters, and reasons for non-compliance (error value, deviation from the standard). The retention period is not less than 1 year, and standardization processing will be tried again after the industry standard is updated or the technology is optimized; if the template verification fails, the template will be deleted and re-added. The verification standards include data integrity and consistency with the standard feature.

[0031] 2.3 Unforeseen Feature Determination and Feature Template Validity Period Criteria for determining unforeseen features: When a feature collected in real time cannot be matched with existing standard templates and variant templates in the feature library (the matching error exceeds a preset threshold), and the change pattern of the feature can be standardized and quantified, it is determined to be an unforeseen feature, triggering an offline supplementation process.

[0032] Feature template validity period management rules: Standard feature templates are valid for 1 year, and variant templates are valid for 6 months. Manual verification is required before the expiration date. If the verification is successful, the validity period will be extended (1 year for standard templates and 6 months for variant templates). If the verification fails, the templates will be recalibrated and added according to the offline supplementation process. The verification criteria include data integrity and consistency with standard features.

[0033] 2.4 Feature Library Construction Method Standard feature data is obtained through physical measurement, theoretical calculation, and standard calibration. Each standard feature template only requires a single standard sample or theoretical value, eliminating the need for massive sample statistics. Normalization is performed according to the corresponding industry standards to ensure that feature data collected from different devices and environments can be directly compared. Each standard feature template is bound to a unique targeted execution instruction, and the binding relationship is configured once and not dynamically adjusted during operation.

[0034] The core difference between the feature library of this invention and the feature libraries of existing technologies is that the feature libraries of existing technologies rely on massive sample training to extract common features and require iterative optimization; the feature library of this invention only requires a single standard sample or theoretical value, without training or iteration, and is a static preset. Offline supplementation is only template addition and does not involve any training or fitting operations.

[0035] 3 Feature Matching Decision Unit As the decision-making core of the system, it connects the feature acquisition unit with the preset inherent feature library and only executes the deterministic logic of "feature comparison - matching judgment - instruction generation", without containing any neural network, model fitting, probability calculation, or statistical inference modules.

[0036] The core logic of feature matching is to accurately compare the real-time features collected and purified by the feature acquisition unit with the standard feature templates and variant templates in the preset inherent feature library. The matching error is used as the sole criterion for judgment, and a binary decision result (matching success / failure) is output. There is no probability output or fitting inference.

[0037] The preset threshold for matching error is adapted to the scenario: for example, the preferred threshold is no more than 0.1% for industrial control scenarios, no more than 0.01nm for chip manufacturing scenarios, no more than 0.05nm for aerospace scenarios, and no more than 0.02% for track control scenarios. The threshold can be manually adjusted and takes effect immediately after adjustment. The adjustment record is automatically archived (including adjustment time, threshold before and after adjustment, and adjustment personnel), and the archiving period is no less than 6 months.

[0038] 3.1 Feature Matching Process Real-time feature reception → Standardized verification (verifying acquisition accuracy, data format, and consistency with industry standards) → Comparison with all templates in the feature library one by one → Calculation of matching error → Comparison with preset threshold → Output of matching result (success / failure); The entire process involves no training, fitting, or probability calculation operations, and the time for a single match is preferably no more than 1ms to ensure real-time performance.

[0039] Standardized verification anomaly handling: When the standardized verification fails (acquisition accuracy is not up to standard, data format is not consistent), return to the feature acquisition unit to re-process the signal purification, repeating up to 3 times; if it still fails after repeated processing, trigger an alarm and pause matching, manually investigate the cause (sensor accuracy, signal purification parameters), and restart the matching process after the investigation is completed.

[0040] 3.2 Multi-template matching and execution logic The execution logic of multi-template parallel matching is as follows: For target features with variations, the standard template is called and compared with all variant subtype templates at the same time. If the matching error of any template is lower than the preset threshold, the matching is considered successful; if all templates fail to match (the error is higher than the threshold), the matching is considered to have failed and a fallback operation is triggered.

[0041] Execution logic for successful matching: After successful matching, the unique target execution instruction bound to the feature template is directly invoked. The instruction does not require optimization or adjustment and is directly output to the target execution unit. The instruction transmission adopts a real-time bus protocol, and the transmission delay is preferably no more than 0.1ms to ensure execution timeliness.

[0042] Fallback procedure for matching failure: After a matching failure, output a "Match Failed" command and trigger the preset fallback logic, which is as follows: re-collect features (up to 3 times) → re-perform signal purification → match again; if the matching still fails after multiple re-collections, trigger manual intervention (manually verify the collected feature data, sensor status, and integrity of the feature library template), and match again after manual verification; if the matching still fails after manual verification, it is determined to be an unforeseen feature, and trigger the offline feature library replenishment process.

[0043] 3.3 Matching Rules and Stability Guarantee Manual intervention operation procedures: Manually verify whether the accuracy of the collected features meets the requirements of the corresponding scenario, such as ≤0.1% for industrial scenarios, ≤1nm for chip scenarios, ≤0.1nm for aerospace scenarios, and ≤0.05% for track control scenarios; verify the sensor status (whether it is operating normally and whether it needs calibration); verify the integrity of the feature library template (whether there are any missing or incorrect templates); after manual verification, if it is a collection error, re-collect; if it is a sensor problem, calibrate or replace the sensor; if it is a missing template, trigger the offline supplementation process.

[0044] Feature matching priority rules: standard feature templates take precedence over variant subtype templates, and high-frequency variant templates take precedence over low-frequency variant templates; during the matching process, standard templates are compared first. If a standard template match is successful, there is no need to compare variant templates. If a standard template match fails, then variant templates are compared to ensure matching efficiency.

[0045] The matching error is calculated using the absolute error formula: |real-time feature value − standard feature value|. The calculation result is compared with the preset threshold. No complex statistical calculation is required, which ensures the calculation efficiency and accuracy. The time taken for a single calculation is preferably no more than 0.1μs.

[0046] Stability assurance of the feature matching decision unit: The unit adopts a hardware logic circuit design, without software-level iterative optimization or dynamic parameter adjustment. During operation, it only executes fixed comparison logic to ensure stable and repeatable decision results. The unit's operating status is monitored in real time, and the monitored parameters include operating temperature (0-60℃), power supply voltage (11.5-12.5V), and operating time. In case of abnormalities (temperature > 60℃, voltage deviation > 0.5V, single matching time > 1ms), an early warning is triggered, and manual troubleshooting and maintenance are required.

[0047] 4 Targeted Execution Unit It is used to receive targeted execution instructions output by the feature matching decision unit and perform precise and directional control operations. The execution results can be fed back to the feature matching decision unit in real time to form a closed-loop control (feedback is only used to determine whether the execution is in place, and is not used for training or to optimize the decision logic).

[0048] The core requirements for the targeted execution unit are: the execution accuracy and feature acquisition accuracy should be matched. For example, the execution error in industrial control scenarios is preferably no greater than 0.1%, the execution resolution in chip manufacturing scenarios is preferably no greater than 1nm, the execution accuracy in aerospace scenarios is preferably no greater than 0.1nm, and the execution error in track control scenarios is preferably no greater than 0.05%; the execution actions should have low redundancy and low latency, and the execution response time is preferably no greater than 0.1ms to ensure real-time synchronization with decision instructions.

[0049] 4.1 Execution Instruction Specification Standardized format of execution instructions: All targeted execution instructions adopt a unified binary encoding format, including instruction type (positioning, control, manipulation, early warning), execution parameters (precision requirements, movement range, execution duration), execution priority (high / medium / low), and execution confirmation signal (execution completion feedback code), ensuring that instructions are universal and can be directly invoked in different scenarios.

[0050] Priority rules for executing instructions: Emergency control instructions (such as equipment failure shutdown, accuracy over-standard correction) have the highest priority and are executed first, while other non-emergency instructions are suspended during execution; routine control instructions (such as routine positioning, routine control) have medium priority; auxiliary instructions (such as status feedback, log recording) have low priority and are executed when idle; priorities can be manually adjusted, and the adjustment takes effect immediately, and the adjustment record is automatically archived.

[0051] 4.2 Execution Feedback and Exception Handling Real-time feedback logic during execution: After the execution command is initiated, the core parameters of the execution action (such as execution accuracy, action amplitude, and response time) are collected in real time and fed back to the feature matching decision unit; the feedback frequency is consistent with the feature acquisition frequency, for example, ≥1kHz in industrial scenarios, ≥10kHz in chip scenarios, ≥1kHz in aerospace scenarios, and ≥500Hz in orbital scenarios; the feedback data is only used to determine whether the execution is in place and is not used for any training, optimization, or parameter adjustment.

[0052] The logic for handling incomplete execution is as follows: If the core parameters of the executed action deviate from the command requirements by more than the set value (0.1%), it is determined that the execution is incomplete. The command is re-executed, and this can be repeated multiple times. If it is still incomplete after repetition, a device fault warning is triggered, and the status of the actuator (such as the drive module and transmission module) is manually checked. After the check is completed, the execution is re-executed until it is completed. The relevant information of incomplete execution (command type, execution parameters, deviation value, and check results) is recorded and archived for no less than 1 year.

[0053] 4.3 Maintenance and Modular Design Maintenance rules for the actuator: The actuator shall undergo comprehensive maintenance every 6 months. The maintenance items include cleaning, lubrication, and precision calibration. After maintenance, an execution accuracy test shall be performed. It can only be used after passing the test. If the maintenance fails, the actuator module shall be replaced and retested to ensure that the execution accuracy meets the requirements of the scenario.

[0054] Replacement standard for actuator modules: All actuator modules adopt a standardized design with unified interface parameters (e.g., 12V power supply, EtherCAT signal interface), and can be used plug-and-play when replaced without adjusting the system interface configuration.

[0055] The targeted execution unit adopts a modular design, and the execution mechanism module can be replaced to adapt to different scenarios. Module replacement does not require retraining or system reconstruction.

[0056] (II) General System Design Requirements This system is applicable to various scenarios such as industrial control, chip manufacturing, aerospace, and rail control. All scenarios follow a unified core logic (no training, no fitting, no probability calculation), and are adapted only in terms of sensor parameters, feature templates, and execution accuracy. The specific adaptation rules are as follows: 1. Sensor parameter adaptation: Different scenarios use sensors with corresponding accuracy. For example, industrial control scenarios use high-precision displacement sensors (measurement error ≤0.1%), chip manufacturing scenarios use nanoscale sensors (resolution ≤1nm), aerospace scenarios use anti-interference sensors (positioning accuracy ≤0.1nm), and track control scenarios use high-precision positioning sensors (measurement error ≤0.05%). Sensor parameters can be manually adjusted, and accuracy verification is required after adjustment.

[0057] 2. Feature Template Adaptation: Standard feature templates and variant templates are preset for different scenarios. Industrial scenarios focus on equipment structure and operating status features, chip scenarios focus on chip target and manufacturing precision features, aerospace scenarios focus on equipment spatial position and signal features, and track control scenarios focus on track parameters and positioning features. Template adaptation only requires replacing the feature library file and does not require modifying the system logic.

[0058] 3. Execution accuracy adaptation: Different scenarios have different execution accuracy requirements. For example, the execution error in industrial control scenarios is ≤0.1%, the execution resolution in chip manufacturing scenarios is ≤1nm, the execution accuracy in aerospace scenarios is ≤0.1nm, and the execution error in track control scenarios is ≤0.05%. The execution accuracy can be adapted by adjusting the parameters of the actuator without reconstructing the execution unit.

[0059] The core principle of system adaptation is that all scenario adaptations adopt the method of "module replacement + parameter adjustment", without involving any training, fitting, or statistical inference, to ensure that the core logic of the system (no training, deterministic matching, targeted execution) remains unchanged, and to achieve cross-scenario application.

[0060] (III) Definition of Feature Targeting Method The feature-targeting method described in this invention refers to a precise control method that uses the quantifiable inherent features of the target as the sole basis, compares the real-time collected features with the precise error of the preset standard feature template, and uses the matching threshold as the judgment criterion to directly output a deterministic execution command. This method does not rely on probability, statistics, fitting, or training, but achieves targeted control solely through feature consistency judgment. It is the core technical means for the system to achieve training-free intelligent decision-making.

[0061] (iv) No definition of training core The term "no training" as used in this invention refers to a system that, throughout its entire lifecycle (deployment, operation, maintenance, and updates), involves no form of model training, parameter iterative optimization, feature learning, fitting inference, or statistical training. Specifically, it is defined as follows: 1. Model-free training: The system does not contain any neural networks or machine learning models, and does not perform any training-related operations such as model training, pre-training, fine-tuning, distillation, iterative optimization, etc. 2. No data fitting: No model fitting, curve fitting, or parameter fitting is performed; only basic signal cleanup (noise reduction and normalization) is done, without involving iterative optimization of the fitting algorithm. 3. No statistical inference: No probability calculations, statistical analysis, or sample inferences are performed. All decisions are based on deterministic matching and precise comparison, and only binary results are output (match successful / failed, execution in place / incomplete). 4. No dynamic learning: No dynamic learning, feature learning, or parameter learning is performed during operation. The feature library is updated offline and does not involve dynamic learning or online learning. 5. No implicit training: Any operation (such as sensor calibration, feature template supplementation, parameter adjustment) does not involve training logic, but is only for standardization and accuracy verification, and does not fall under the category of implicit training.

[0062] The core difference between this invention and existing "lightweight training" and "few-sample training" technologies is that existing technologies still focus on training, only reducing the number of samples or training steps; this invention completely abandons training logic, requiring no sample training or model building, and achieves intelligent decision-making through static feature pre-setting and deterministic matching, thus forming an essential difference from existing technologies in terms of technical approach.

[0063] (v) Control methods This invention also discloses a training-free artificial intelligence control method based on target features, implemented on the above system architecture, which involves no training, no model fitting, no probability calculation, and no statistical inference throughout the entire process. The specific steps are as follows: Step 1: System deployment, preset inherent feature library (stores standard feature templates and variant templates for corresponding scenarios), configure sensor parameters and signal purification parameters of feature acquisition unit, complete the interface connection of each unit, the system is ready to work upon power-up, without any training or optimization operations; Step 2: The feature acquisition unit collects the inherent features of the target object in real time, performs only basic signal purification preprocessing (noise reduction, normalization), does not perform complex feature extraction or data annotation, and directly outputs comparable feature data; Step 3: The feature matching decision unit will accurately compare the real-time collected feature data with the standard templates and variant templates in the preset inherent feature library one by one, calculate the matching error, compare it with the preset threshold, and output the matching result (success / failure). Step 4: When a match is successful, the feature matching decision unit calls the targeted execution instruction bound to the feature template and outputs it to the targeted execution unit; when a match fails, a fallback operation is triggered (re-collection → re-matching, which can be repeated multiple times); if it still fails, manual intervention or offline feature library supplementation process is triggered. Step 5: The targeted execution unit receives the execution command, performs precise targeted operation, and feeds back the execution parameters to the feature matching decision unit in real time to determine whether the execution is in place; if the execution is in place, a control loop is completed; if the execution is not in place, the command is re-executed (which can be repeated multiple times); if the execution is still not in place, the actuator is checked for faults. Step 6: During system operation, perform sensor calibration, feature template verification, and actuator maintenance according to the preset cycle to ensure system accuracy and stability; for unforeseen features, update the feature library according to the offline supplementation process without affecting the normal operation of the system; Step 7: When migrating across scenes, replace the sensor module, actuator module and feature library template with those suitable for the scene. No retraining or system reconstruction is required, and the system can run normally after power-on.

[0064] The core advantages of the above control method are: extremely simple process and efficient deployment, no need for massive data collection and annotation, no need for model training and iteration, and it can work as soon as it is powered on; the decision logic is transparent and traceable, all operations are based on deterministic comparison, with no probabilistic errors; low cost and high efficiency for cross-scenario migration, and adaptable to the high precision and high reliability requirements of multiple fields.

[0065] (vi) Beneficial effects This invention constructs a deterministic artificial intelligence architecture that is "train-free, fitting-free, and probabilistic computation-free," using the inherent features of the target as its core to achieve closed-loop control of "collection-matching-execution." Compared with existing technologies, it has the following outstanding technical effects: 1. Completely eliminates training dependence: There is no training, pre-training, fine-tuning, or distillation of any kind throughout the entire process. No need for massive data collection and labeling. The system can work as soon as it is powered on after deployment, significantly improving deployment efficiency and solving the core pain points of traditional artificial intelligence such as data dependence and high computing power consumption. 2. Significantly reduced computing power consumption: No model training, no fitting calculation, no statistical inference, only basic computing power is required to support feature collection and comparison. The computing power consumption is significantly reduced compared to traditional artificial intelligence, and it can be adapted to computing power-constrained scenarios (such as embedded devices, portable devices, and industrial control terminals). 3. Precise and Explainable Decision Making: Based on the inherent characteristics of the target, deterministic matching is achieved without probabilistic inference or fitting error. The decision results are traceable (the matching process, error data, and execution instructions are all recorded), making it suitable for high-reliability and high-precision scenarios such as industrial control, chip manufacturing, aerospace, and rail control. 4. Cross-scenario application and strong adaptability: Through modular design and static feature library replacement, it can achieve zero-cost migration to multiple scenarios without the need to reconstruct the system or retrain. It is compatible with multiple fields such as industry, chips, aerospace, and rail control, reducing the cost of scenario adaptation. 5. Lower deployment and maintenance costs: No need to invest in manpower and computing power for data collection, annotation, and training; system deployment time is significantly shortened; maintenance only requires routine calibration and module replacement, effectively reducing maintenance costs. 6. High system stability: There is no risk of model overfitting or underfitting, no parameter iteration fluctuations, fixed decision logic, and stable and reliable operation, which can meet the long-term stable operation requirements of high precision and high reliability scenarios. 7. Enhanced real-time performance: The preferred time for a single feature matching is no more than 1ms, the preferred instruction transmission delay is no more than 0.1ms, and the preferred execution response time is no more than 0.1ms, meeting the stringent real-time requirements of industrial control, aerospace, and rail control scenarios; 8. Data security and controllability: No need to collect massive amounts of data, only the inherent core characteristics of the target are collected, there is no risk of data privacy leakage, and the cost of massive data storage and management is avoided, which complies with data security compliance requirements. Detailed Implementation

[0066] Example 1: Precision Control Applications in Industrial Equipment When this invention is applied to precision control scenarios of industrial production line equipment (such as machine tool processing accuracy control and production line equipment posture adjustment), the core requirements are: to achieve precise matching and targeted control of equipment operating status, with a preferred control accuracy of no more than 0.1%, real-time response, no training required, and efficient deployment.

[0067] System Deployment: Feature acquisition unit configuration: A high-precision displacement sensor is used, with a measurement error preferably not greater than 0.1%, a sampling frequency preferably not less than 1kHz, a signal bandwidth of 10Hz-1MHz, a power supply voltage of 12V, and an interface using an EtherCAT bus; Signal purification parameter settings: The noise reduction threshold is preferably not greater than 0.01V, and the normalization formula is x′=(x−xmin) / (xmax−xmin) (xmin=0.1mm, xmax=100mm).

[0068] Pre-set inherent feature library configuration: Pre-store standard feature templates for normal machine tool operation posture, extreme operation posture, and fault warning posture, as well as 3 common variant templates (slight deviation posture, vibration exceeding standard posture, and abnormal load posture). Each template corresponds to the accurate measurement data of a single standard sample; the bound execution instructions include posture correction, shutdown warning, and load adjustment, and the binding relationship is configured once.

[0069] Feature matching decision unit configuration: The matching algorithm adopts the Euclidean distance calculation algorithm, the matching error threshold is preferably no greater than 0.1%, and the single matching time is preferably no greater than 1ms; the standardization verification parameter error is preferably no greater than 0.01%, and the normalization formula is consistent with that of the feature acquisition unit.

[0070] Targeted execution unit configuration: a precision servo motor is adopted, with a positioning accuracy preferably not greater than 1nm; the command transmission adopts EtherCAT bus, with a transmission delay preferably not greater than 0.1ms; the real-time feedback module acquisition frequency is preferably not less than 1kHz, and the feedback accuracy is preferably not greater than 0.1%.

[0071] System operation process: After the system is powered on, it immediately enters the working state; the feature acquisition unit collects the machine tool's running posture features in real time, and outputs them after noise reduction and normalization; the feature matching decision unit compares the real-time features with the preset template, and if the error is preferably no greater than 0.1%, it is determined that the match is successful and the corresponding instruction is called; the target execution unit performs posture correction, load adjustment or shutdown warning actions and provides real-time feedback; if the match fails, it will re-collect up to 3 times, and if it still fails, it will enter the manual verification and offline feature library supplementation process.

[0072] Performance data: In this embodiment, the single feature matching time is 0.5ms, the instruction transmission delay is 0.08ms, the control accuracy is 0.08%, and the system runs continuously for 72 hours without any lag or misjudgment. Compared with traditional training-type artificial intelligence systems, it has higher deployment efficiency, lower computing power consumption, and lower maintenance costs.

[0073] Example 2: Nanoscale alignment applications in chip manufacturing When this invention is applied to the target alignment scenario in the chip lithography process, the core requirements are: to achieve precise alignment between the chip target and the lithography lens, with the alignment accuracy preferably not greater than 1nm, real-time response, no training dependency, and adaptability to the high precision requirements of chip manufacturing.

[0074] System Deployment: Feature acquisition unit configuration: A nanoscale laser interferometer is used, with a resolution preferably not greater than 1nm, a sampling frequency preferably not less than 10kHz, a signal bandwidth of 1Hz-10MHz, an anti-electromagnetic interference capability of ≥3V / m, and a power supply voltage of 12V; the signal purification and noise reduction threshold is preferably not greater than 0.0001V, and the normalization formula is x′=(x−xmin) / (xmax−xmin) (xmin=0nm, xmax=10nm).

[0075] Preset inherent feature library configuration: Pre-stores standard alignment feature templates for chip lithography targets and two types of variant templates (micro-offset template and surface wear template), and binds alignment control and alignment failure warning instructions.

[0076] Feature matching decision unit configuration: A hash comparison algorithm is adopted, the matching error threshold is preferably no greater than 0.01%, and the single matching time is preferably no greater than 0.5ms.

[0077] Targeted execution unit configuration: a piezoelectric ceramic actuator is adopted, with a preferred positioning accuracy of no more than 0.5nm, a preferred transmission delay of no more than 0.05ms, and a preferred real-time feedback accuracy of no more than 0.1nm.

[0078] System operation process: The system starts working immediately upon power-up; the laser interferometer collects and purifies the target position features; the feature matching decision unit performs precise comparison; if the match is successful, the piezoelectric ceramic is driven to perform alignment control and correct deviations in real time; if the match fails, the target is re-acquired up to 3 times, and if it still fails, it is manually calibrated and the feature library is supplemented offline; in high electromagnetic interference environments, the noise reduction threshold and filtering strategy are automatically adjusted to ensure alignment accuracy.

[0079] Performance data: Single alignment takes 0.8ms, alignment accuracy is 0.8nm, and it runs continuously for 48 hours without any abnormalities. Compared with traditional deep learning alignment systems, it consumes less computing power and can be deployed faster, fully meeting the high-precision requirements of chip manufacturing.

[0080] Example 3: Positioning and Control Applications in Aerospace Equipment When this invention is applied to the spatial positioning and attitude control scenarios of aerospace equipment, the core requirements are: positioning accuracy preferably not greater than 0.1nm, real-time response, no training dependency, and adaptability to the extreme space environment.

[0081] System Deployment: Feature acquisition unit configuration: It adopts a radiation-resistant laser sensor, with a positioning accuracy preferably not greater than 0.1nm, a sampling frequency preferably not less than 1kHz, a signal bandwidth of 10Hz-10MHz, and an anti-electromagnetic interference capability of ≥5V / m; the signal purification and noise reduction threshold is preferably not greater than 0.0001V.

[0082] Preset inherent feature library configuration: stores standard spatial position and attitude feature templates and 3 types of radiation environment variation templates, and binds position calibration, attitude adjustment and radiation early warning commands.

[0083] Feature matching decision unit configuration: hash comparison algorithm, with an error threshold preferably not greater than 0.01% and a single matching time preferably not greater than 1ms.

[0084] Targeted execution unit configuration: radiation-resistant piezoelectric ceramic actuator, with a preferred positioning accuracy of no more than 0.5nm and a preferred transmission delay of no more than 0.08ms.

[0085] System operation process: The system starts working immediately upon power-up; sensors collect position and attitude features in real time; the matching unit compares with the preset template; if the error is > 0.01%, the matching is considered a failure, and the system is re-collected up to 3 times. If it still fails, the template is manually calibrated and supplemented; the system automatically adjusts the signal purification strategy in extreme environments to ensure positioning accuracy.

[0086] Performance data: Single positioning time is 0.6ms, positioning accuracy is 0.8nm, and it can run continuously for 72 hours with stability and reliability. Compared with traditional training systems, it has higher deployment efficiency and stronger adaptability to extreme environments.

[0087] Example 4: Track Control and Positioning Application When this invention is applied to the scenario of precise positioning and attitude control of track equipment, the core requirements are: the positioning error is preferably no more than 0.05%, real-time response, and adaptability to track vibration environment.

[0088] System Deployment: Feature acquisition unit configuration: high-precision positioning sensor, with a measurement error preferably not greater than 0.05%, a sampling frequency preferably not less than 500Hz, and vibration resistance (vibration frequency ≤ 50Hz, amplitude ≤ 0.1μm); noise reduction threshold preferably not greater than 0.005V.

[0089] Preset inherent feature library configuration: Stores standard track positioning features and 3 types of vibration variation templates, and binds positioning calibration, attitude adjustment and vibration early warning commands.

[0090] Feature matching decision unit configuration: Euclidean distance algorithm, with an error threshold preferably not greater than 0.05%, and a single matching time preferably not greater than 1ms.

[0091] Targeted execution unit configuration: vibration-resistant servo motor, with a preferred positioning accuracy of no more than 0.05% and a preferred transmission delay of no more than 0.1ms.

[0092] System operation process: The system starts working immediately upon power-up; sensors collect track position and attitude features; matching units compare and determine the match; if the match is successful, positioning calibration is performed; if it fails, the system re-collects data up to 3 times, and if it still fails, the template is manually supplemented; under vibration conditions, the system automatically optimizes signal purification parameters to ensure positioning accuracy.

[0093] Performance data: Single positioning time is 0.9ms, positioning error is 0.03%, and it can run continuously for 48 hours without any abnormalities. Compared with traditional track positioning systems, it is faster to deploy, consumes less computing power, and has lower maintenance costs.

[0094] All embodiments demonstrate that the present invention, through the core method of feature targeting + pre-set inherent feature library, can achieve precise intelligent control in multiple scenarios without training, massive data, or complex models. It is highly efficient in deployment, stable in operation, and lower in cost, and can be widely applied in the fields of industry, chips, aerospace, and rail control, fully conforming to the core solution protected by the present invention.

Claims

1. A training-free artificial intelligence system based on target features, characterized in that, The system comprises four sequentially connected units: a feature acquisition unit, a pre-defined inherent feature library, a feature matching decision unit, and a targeted execution unit. These four units form a deterministic closed-loop logic of "real-time feature acquisition → precise matching → targeted execution," without any training, model fitting, probability calculation, or statistical inference. The pre-defined inherent feature library is used to pre-store standard inherent feature templates and variant subtype templates for various target objects. Each feature template is bound to a unique targeted execution instruction. It adopts a static preset mode, and does not dynamically add, delete, or modify features during operation, only supporting offline supplementation and updates. The feature matching decision unit is the core execution feature targeting method, achieving deterministic decision-making through precise comparison of real-time features with feature templates.

2. The system according to claim 1, characterized in that, The feature acquisition unit adopts a modular and standardized design, which is suitable for multiple scenarios such as industrial control, chip manufacturing, aerospace, and track control. It can be plugged and played to replace the sensor module with the corresponding accuracy. The acquisition process only performs basic signal purification and does not involve complex feature extraction, data annotation or model training. The acquired features are the inherent attributes of the target that can be accurately quantified. The sensor accuracy is adapted to the corresponding industry standards for different scenarios.

3. The system according to claim 1, characterized in that, The construction of the pre-set inherent feature library does not require massive sample statistics. It obtains single standard sample data through physical measurement, theoretical calculation, and standard calibration, and forms standard and variant subtype templates through standardization processing, which is completed in one go. Offline supplementation and updates adopt the "manual calibration - standardization processing - template addition - library synchronization" mode, which does not involve online training or dynamic learning. The feature template contains quantifiable attributes such as target geometry and material properties and is bound to a unique execution instruction.

4. The system according to claim 1, characterized in that, The feature matching decision unit adopts a hardware logic circuit design, which only performs feature-targeted precise comparison and error judgment, without performing probability calculation or fitting inference; the matching error is preset with a fixed threshold according to the scenario, the time for a single matching is ≤1ms, and only outputs a binary result of "matching successful / failed". Before the comparison, the feature vector is normalized to ensure accuracy.

5. The system according to claim 1, characterized in that, The execution accuracy of the target execution unit matches the accuracy of the acquisition and matching. After receiving the instruction, it performs precise target operation. The execution result is fed back to the feature matching decision unit in real time to form a closed loop. The feedback data is only used to determine whether the execution is in place and is not used for training, optimization or parameter adjustment. The execution mechanism adopts a standardized modular design and can be plugged and replaced.

6. The system according to claim 1, characterized in that, The system can achieve cross-scene migration. During migration, only the sensor module of the feature acquisition unit needs to be replaced and the feature template of the preset inherent feature library needs to be updated. There is no need to retrain, reconstruct the system logic or adjust the matching decision rules. After migration, it can run normally after powering on.

7. A training-free artificial intelligence control method based on target features, applied to the system described in any one of claims 1-6, characterized in that, The entire process involves no training, no model fitting, no probability calculation, and no statistical inference. The core method is based on "feature targeting + pre-set inherent feature library". The specific steps include: system deployment and adaptation, real-time feature acquisition and purification, accurate feature comparison and decision-making, targeted execution feedback, system maintenance, and offline update of the feature library.

8. The control method according to claim 7, characterized in that, Feature acquisition is preset with a period according to the scenario. The preferred period is no more than 1ms for industrial control scenarios, no more than 1μs for chip manufacturing scenarios, no more than 10μs for aerospace scenarios, and no more than 5μs for track control scenarios. The period can be manually adjusted and takes effect immediately. Signal purification adopts the corresponding noise reduction threshold and unified normalization formula to ensure the consistency of feature data.

9. The control method according to claim 7, characterized in that, Feature comparison employs corresponding algorithms: geometric and spatial morphological features are prioritized for template matching, orbital and spatial location features are prioritized for Euclidean distance calculation, and signal and spectral features are prioritized for hash comparison. If a match fails, a fallback operation is triggered, allowing for multiple re-collection and matching. If the match still fails, manual intervention or offline supplementation of the feature database is triggered.

10. The system according to claim 1, characterized in that, The preset inherent feature library adopts high-reliability solid-state storage with a storage capacity of ≥100GB, a read / write speed of ≥1GB / s, a data retention period of no less than 10 years, and supports offline backup and recovery. The backup process does not affect the normal operation of the system and there is no data loss or deviation.