A control method for a chain - type combined tool magazine

Abstract

The present invention relates to the technical field of tool magazine control, and in particular to a control method for a chain-type combined tool magazine. The method includes: real-time monitoring of the operating state parameters of each chain in the chain-type combined tool magazine, generating a chain fault warning instruction for the operating state parameters in response to a preset fault determination condition; according to the chain fault warning instruction, controlling the power isolation of the faulty chain and activating the tool access permission of at least one spare chain, where the spare chain stores mirror tool information mapped to the tool library positions of the faulty chain; based on the spatial coordinate data of the mirror tool information, obtaining a path planning instruction for the target tool from the spare chain; executing the path planning instruction to complete the cross-chain transfer operation of the target tool, and updating the global tool distribution information based on the execution result of the cross-chain transfer operation. Through the present invention, the systematic defects in fault response and dynamic scheduling of traditional chain-type tool magazines are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of tool magazine control, and particularly to a control method for a chain - type combined tool magazine. Background Art

[0002] In a numerical control machine tool processing system, as a core module for tool storage and scheduling, a chain - type combined tool magazine realizes the cyclic storage and precise positioning of tools through a chain drive structure, and its performance directly determines the processing efficiency and equipment reliability.

[0003] In the prior art, chain - type tool magazines generally adopt a single - chain physical architecture, detecting the chain position through sensors and controlling tool access based on preset logic. Tool scheduling usually relies on sequential retrieval or fixed - priority strategies, and path planning mostly uses the shortest - path algorithm in a static environment. However, the traditional solution has the following problems: the single - chain structure lacks a backup switching mechanism in case of faults such as chain link jamming and tool damage, and the shutdown and repair of the faulty chain lead to the interruption of the overall processing flow; during the path - planning process, the dynamic obstacle interference during the operation of the chain - type tool magazine (such as the movement - trajectory conflict of adjacent chains and the occupation of positions by temporarily stored workpiece fixtures) is not fully considered, resulting in frequent triggering of collision protection or path replanning by the manipulator; at the same time, the tool - state monitoring and fault - diagnosis functions are scattered, making it difficult to timely identify systematic risks caused by uneven multi - chain loads or abnormal tool wear.

[0004] The information disclosed in this background - art section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a control method for a chain - type combined tool magazine, which can effectively solve the problems in the background art.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A control method for a chain - type combined tool magazine, the method comprising:

[0008] Real - time monitoring of the operating state parameters of each chain in the chain - type combined tool magazine, and generating a chain - fault warning instruction for the operating state parameters in response to a preset fault - determination condition;

[0009] According to the chain - fault warning instruction, controlling the power isolation of the faulty chain and activating the tool - access permission of at least one spare chain, wherein mirror - tool information mapped to the tool magazine positions of the faulty chain is stored in the spare chain;

[0010] Based on the spatial - coordinate data of the mirror - tool information, obtaining a path - planning instruction for the target tool from the spare chain;

[0011] Execute the path planning instruction to complete the cross-chain transfer operation of the target tool, and update the global tool distribution information based on the execution result of the cross-chain transfer operation.

[0012] Further, obtaining the path planning instruction for the target tool includes:

[0013] Generate an initial tool change path according to the spatial coordinate data of the mirror tool information and in combination with the conversion relationship between the tool magazine machine coordinate system and the robot base coordinate system;

[0014] Obtain the dynamic obstacle detection data of the chain-type tool magazine, and correct and optimize the initial tool change path based on the dynamic obstacle detection data. The dynamic obstacles include the link area in the motion state and the workpiece fixture coordinates temporarily stored during tool change;

[0015] Discretize the corrected initial tool change path into an executable interpolation point sequence, and generate the path planning instruction including the feed speed and acceleration curve;

[0016] Verify the logical consistency between the path planning instruction and the current spindle position. If there is an interference risk, trigger the tool re-selection process.

[0017] Further, discretizing the corrected initial tool change path into an executable interpolation point sequence includes:

[0018] Dynamically adjust the interpolation point density based on the path curvature radius. For the path segments with the path curvature radius less than the preset threshold, use a dense interpolation point distribution, and for the path segments with the path curvature radius greater than the preset threshold, use a sparse interpolation point distribution;

[0019] Generate an acceleration smoothing curve according to the motion constraint conditions of the chain-type tool magazine, and dynamically correct the interpolation points;

[0020] Verify the feasibility of the interpolation point sequence. If it is detected that the acceleration mutation between adjacent interpolation points exceeds the allowable range, perform path replanning;

[0021] Spatially synchronize and calibrate the interpolation point sequence with the machine tool spindle coordinates, and calibrate the geometric consistency between the initial tool change path and the workpiece machining coordinate system.

[0022] Further, establishing the mirror tool information includes:

[0023] Obtain the geometric parameters and real-time status data of the tools on the faulty chain, and generate a spare chain mirror relationship according to the collaborative calibration of the tool magazine machine coordinate system;

[0024] Based on the backup chain mirroring relationship and the topological structure of the chain tool magazine, dynamically calculate the tool priority weight, the tool priority weight is associated with the tool remaining life, the processing procedure matching degree and the chain link load balancing parameter;

[0025] In combination with the tool priority weight and the spindle vibration characteristics, the tool dynamic parameters in the mirror tool information are corrected in real time;

[0026] A hierarchical access control strategy is established based on the tool dynamic parameters to limit the calling authority of the tools in the standby chain.

[0027] Furthermore, the fault determination conditions include:

[0028] Establishing a dynamic fault feature library, wherein the dynamic fault feature library stores vibration modal spectra of each chain under different tool load conditions;

[0029] Collecting the vibration signal of the target chain in real time, and calculating the deviation between the vibration signal and the vibration modal spectrum corresponding to the current working condition;

[0030] When the deviation exceeds the adaptive threshold, the co-location sensor data of the adjacent chain is called for cross-checking and bidirectional verification is performed based on the check result and the blockchain historical data to generate a fault confidence level;

[0031] Whether the fault determination condition is met is comprehensively determined according to the fault confidence level, wherein a high confidence level fault triggers an emergency isolation instruction, and a medium or low level fault triggers a mirror chain pre-activation instruction.

[0032] Furthermore, the co-location sensor data of adjacent chains are called for cross-checking, including:

[0033] Determine the co-location mapping point of the tool position of the faulty chain on the adjacent chain, and synchronously collect the co-location sensing data, including displacement feature quantity and structural deformation quantity;

[0034] Dynamically generate the inter-chain data consistency judgment threshold interval based on the current processing condition parameters;

[0035] Performing difference analysis on the co-location sensing data and the fault data of the fault chain, and performing hierarchical verification when the difference value exceeds the inter-chain data consistency judgment threshold interval;

[0036] The fault confidence level is adjusted according to the hierarchical verification result, and if the difference value is within the inter-chain data consistency judgment threshold range, the online diagnosis process of the co-location mapping point is triggered.

[0037] Furthermore, two-way verification is performed based on the verification results and the blockchain historical data, including:

[0038] Generate a current fault feature identifier, wherein the fault feature identifier includes a time-frequency domain feature vector of the deviation and a spatial distribution feature of the co-located sensor data;

[0039] Initiate a verification request to the blockchain network, the verification request carrying the timestamp encrypted hash value of the fault feature identifier;

[0040] Call the blockchain smart contract to execute the two-way verification logic and generate the verification conclusion based on the blockchain node consensus result;

[0041] The fault confidence level is dynamically modified based on the verification conclusion.

[0042] Furthermore, hierarchical verification is performed, including:

[0043] The first level verification is to perform high-frequency resampling on the co-location mapping points and the fault data to extract joint features in the time-frequency domain, wherein the joint features in the time-frequency domain include short-time energy mutation rate and similarity of spectrum harmonic distribution;

[0044] The second level verification, when the time-frequency domain joint feature meets the abnormal condition, retrieve the historical operation status data of the co-location mapping point stored in the blockchain, and calculate the trajectory matching degree between the real-time data and the historical operation status data;

[0045] The third level verification is that if the trajectory matching degree is lower than the safety upper limit value, the physical self-check procedure of the tool at the same mapping point is triggered, and the physical self-check procedure includes closed-loop detection of tool clamping force and optical scanning of the tool handle cone surface.

[0046] Furthermore, the blockchain smart contract is called to execute the two-way verification logic, including:

[0047] Forward verification, traverse the historical fault feature template set, retrieve the matching degree between the current fault feature identifier and each historical template, and apply a time decay factor to reduce the weight of the outdated template;

[0048] Reverse verification: based on the current processing task and the unique identification code of the tool, retrieve the associated service history data set in the blockchain to obtain the cumulative frequency and trend correlation coefficient of abnormal events in the service history data set;

[0049] If the matching degree exceeds a first threshold and the trend correlation coefficient increases positively, it is marked as a known failure mode upgrade event;

[0050] If the matching degree is lower than the second threshold but the accumulated frequency of the abnormal event exceeds the periodic threshold, it is marked as a new potential fault event.

[0051] The technical solution of the present invention can achieve the following technical effects:

[0052] Improve the reliability, tool change efficiency and comprehensive tool utilization rate of the chain-type tool magazine, and reduce the unplanned downtime rate through multi-chain collaborative fault tolerance control, dynamic path optimization and full-life cycle collaborative management.

[0053] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific implementation manners of this application are given below. Brief Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0055] Figure 1 It is a schematic flowchart of a control method for a chain-type combined tool magazine;

[0056] Figure 2 It is a schematic flowchart of obtaining a path planning instruction;

[0057] Figure 3 It is a schematic structural diagram of establishing mirror tool information;

[0058] Figure 4 It is a schematic flowchart of setting fault determination conditions. Detailed Description of the Embodiments

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of this invention are only for the purpose of describing specific embodiments, and are not intended to limit this invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0061] Embodiment 1;

[0062] As Figure 1 shown, this application provides a control method for a chain-type combined tool magazine. The method includes:

[0063] S10: Monitor the operating state parameters of each chain in the chain - type combined tool magazine in real time. The operating state parameters generate a chain - fault warning instruction in response to a preset fault determination condition;

[0064] S20: According to the chain - fault warning instruction, control the power isolation of the faulty chain and activate the tool access permission of at least one spare chain, where the spare chain stores mirror tool information mapped to the tool library location of the faulty chain;

[0065] S30: Obtain the path - planning instruction of the target tool from the spare chain based on the spatial coordinate data of the mirror tool information;

[0066] S40: Execute the path - planning instruction to complete the cross - chain transfer operation of the target tool, and update the global tool distribution information based on the execution result of the cross - chain transfer operation.

[0067] Specifically, the chain - type combined tool magazine includes multiple independently - drivable chains. Multiple tool storage positions are provided on each chain, and each position is used to store a tool. Each chain is configured with a corresponding sensor module for collecting the operating state parameters of the chain, including but not limited to motor current, chain tension, link displacement, chain speed, temperature, and vibration information. Through distributed sensor nodes or a centralized monitoring unit, the operating state parameters of each chain in the chain - type combined tool magazine are collected in real - time. The monitoring data is sent to the central processing unit and compared and analyzed with preset fault determination conditions. If the operating state parameters exceed the normal range or match a specific abnormal mode (such as a sudden increase in current, intermittent vibration of the chain, etc.), a chain fault warning instruction is automatically generated. The warning information includes the identification of the faulty chain, the type of fault, and the emergency handling level. After the warning instruction is triggered, power isolation is executed, and the drive power of the faulty chain is immediately cut off to stop its movement, preventing the spread of chain damage or the occurrence of a cascading failure. The standby chain is activated, and one or more standby chains are selected and their access permissions are opened. The standby chain is already configured with mirror tool information corresponding one - to - one with the storage positions of the faulty chain to ensure seamless takeover of the tool task. The central processing unit generates an optimal tool access path based on the spatial coordinate data of the mirror tools, combined with the actual layout and occupancy status of the current chain. The path planning algorithm considers the shortest movement distance, avoiding interference with other operating equipment, and preferentially selecting stable chains. According to the generated path planning instruction, the robotic arm or the tool - changing actuator performs a cross - chain transfer operation, retrieves the target tool from the standby chain, and delivers it to the machine tool spindle or turret. After the cross - chain transfer operation is completed, the global tool distribution information is dynamically updated according to the actual tool - taking and tool - storing results, including the new position of each tool, the available storage position status of each chain, and the faulty chain status marked as disabled. This information serves as the basis for subsequent tool management, scheduling, and exception recovery to ensure that the system operating state is always transparently controllable. After the standby chain takes over, the repair and maintenance of the faulty chain can be arranged according to the set strategy. At the same time, when conditions permit, the mirror tool inventory of the standby chain can be dynamically replenished to maintain the redundancy backup capacity.

[0068] Through the technical solution of the present invention, the reliability, tool - changing efficiency, and comprehensive tool utilization rate of the chain - type tool magazine are improved, and the unplanned downtime rate is reduced.

[0069] Furthermore, as Figure 2 shown, obtaining the path planning instruction for the target tool includes:

[0070] Generating an initial tool - changing path based on the spatial coordinate data of the mirror tool information, combined with the conversion relationship between the tool magazine machine coordinate system and the manipulator base coordinate system;

[0071] Obtain the dynamic obstacle detection data of the chain-type tool magazine, and correct and optimize the initial tool change path based on the dynamic obstacle detection data. The dynamic obstacles include the link area in the motion state and the workpiece fixture coordinates temporarily stored during tool change;

[0072] Discretize the corrected initial tool change path into an executable interpolation point sequence, and generate a path planning instruction including the feed speed and acceleration curve;

[0073] Verify the logical consistency between the path planning instruction and the current spindle position. If there is an interference risk, trigger the tool re-selection process.

[0074] As an optimization of the above embodiments, when it is detected that tool replacement is required, the central controller extracts the spatial position parameters of the target tool in the tool magazine coordinate system according to the information of the mirrored tool in the spare chain, including three-dimensional coordinates (X, Y, Z) and orientation information (such as attitude quaternion or Euler angle form). To ensure that the robot can correctly grasp the target tool, according to the pre-stored transformation relationship between the machine tool tool magazine coordinate system and the robot base coordinate system (usually described by a set of transformation matrices, such as the homogeneous transformation matrix obtained based on calibration), the tool spatial coordinates are transformed from the tool magazine coordinate system to the robot's own reference system, and then an initial tool change path is planned and generated; the initial path generally includes: the starting point, the current position of the robot's end effector; the intermediate points, the preset approach point and grasping point; the end point, the tool change area or the spindle taper hole position; to adapt to the dynamic working environment, the built-in dynamic obstacle detection module of the chain-type tool magazine is called to collect obstacle information in real time. Dynamic obstacles mainly include the moving link areas. Even if some chains have not completely stopped, they may form obstacles due to inertia or slight vibration, as well as workpiece fixtures, replacement tools temporarily placed in the tool magazine, or tool components left during maintenance. The sensor module (such as a 3D vision system or radar) feeds back the obstacle position and movement trend information to the controller in real time. The controller automatically corrects the initial tool change path according to the current obstacle position, and uses obstacle avoidance algorithms (such as the Dynamic Window Approach DWA, RRT* planning algorithm) to re-plan a collision-free path to ensure the safety and continuity of the tool change operation; the corrected path is discretized, and the continuous path curve is divided into several interpolation point sequences. Each interpolation point is defined as pose information (position + orientation), feed speed, and acceleration / deceleration limit to meet the requirements of flexible transition and smooth control; the generated interpolation point sequence is sent to the motion controller, and the controller drives the robot to accurately execute the action through trajectory interpolation technology (such as cubic spline interpolation, trapezoidal acceleration / deceleration trajectory); before actual execution, a complete path verification is also required to detect whether there are potential interferences between the current spindle, spare chain, and robotic arm. The verification includes calculating a certain safety distance around the key points (such as setting a virtual safety shell); predicting whether the path crossing area will have physical conflicts with the spindle, chain, workpiece fixture, etc.; if it is detected that there is an interference risk (such as safety shell overlap, predicted collision, etc.), the current tool change operation is immediately interrupted and the tool re-selection process is triggered; during the re-selection process, the controller re-selects a feasible spare tool according to the mirror image information of other tools of the same type on the spare chain to ensure that the tool change task continues without affecting the overall production process.

[0075] Furthermore, discretizing the corrected initial tool change path into an executable interpolation point sequence includes:

[0076] Dynamically adjust the interpolation point density based on the path curvature radius. For path segments with a path curvature radius less than the preset threshold, a dense interpolation point distribution is adopted, and for path segments with a path curvature radius greater than the preset threshold, a sparse interpolation point distribution is adopted;

[0077] Generate an acceleration smooth curve according to the motion constraint conditions of the chain-type tool magazine and dynamically correct the interpolation points;

[0078] Verify the feasibility of the interpolation point sequence. If it is detected that the acceleration mutation between adjacent interpolation points exceeds the allowable range, path replanning is performed;

[0079] Spatially synchronize and calibrate the interpolation point sequence with the machine tool spindle coordinates to calibrate the geometric consistency between the initial tool change path and the workpiece machining coordinate system.

[0080] As an optimization of the above embodiments, the corrected initial tool change path is described by a continuous trajectory curve. First, the curvature of the entire path is analyzed, and the curvature radius at each point is calculated based on the difference method or the fitting method. If the curvature radius of a local path segment is less than a set first preset threshold (for example, the curvature radius is less than 300 mm), then this path segment is determined to be a high-curvature area, and an encryption interpolation strategy is adopted to generate a higher-density interpolation point within this segment to ensure the smoothness of the trajectory transition and the accuracy of the tool change operation. If the curvature radius of a local path segment is greater than a second preset threshold (for example, the curvature radius is greater than 800 mm), then this path segment is determined to be a low-curvature area, and a sparse interpolation strategy is adopted to appropriately reduce the number of interpolation points, improve the motion efficiency, and reduce the system load. The density of the interpolation points changes dynamically, and a linear or non-linear interpolation method based on the curvature radius can be used for automatic adjustment. After generating the preliminary interpolation point sequence, according to the kinematic constraint conditions of the chain-type tool magazine and the manipulator (such as the maximum acceleration, maximum deceleration, joint speed limit, etc.), an acceleration smoothing curve is designed for the entire path. The acceleration is automatically reduced in the high-curvature area to avoid violent motion impacts, and the acceleration efficiency is increased in the low-curvature straight path segment. The generated acceleration curve is applied to the interpolation point adjustment, that is, the time interval and pose change amount between the interpolation points are dynamically corrected to ensure that the motion of the mechanical system is continuous and controlled and complies with the dynamic constraints. After the interpolation points and acceleration correction are completed, the interpolation point sequence is verified point by point to check whether there is a sudden change in the acceleration change between adjacent interpolation points (that is, Δ acceleration exceeds the allowable maximum change rate). When the sudden change amount is large, the abnormal path segment is marked. If an abnormality is detected, a local path replanning mechanism is triggered, including recalculating the interpolation density and acceleration distribution of the abnormal segment path, and adjusting the passing points or adding transitional interpolation points if necessary to form a new continuous and smooth path segment. The replanning strategy gives priority to local optimization to avoid affecting the global trajectory execution time. To ensure the spatial accuracy during the tool change process, after the interpolation point sequence is generated, spatial synchronization calibration is performed. By docking the spindle coordinate system (usually the machine tool working coordinate system, such as G54, G55, etc.), the path point set is calibrated so that the interpolation path has geometric consistency in the machine tool workpiece coordinate system; the spatial error caused by the slight offset of the chain and the thermal deformation of the tool magazine structure is corrected. The calibration method can be fine-tuned based on sensor measurement data (such as contact sensors, vision calibration systems), or mathematical compensation can be performed based on calibration parameters. The interpolation point sequence after spatial synchronization calibration is sent as the final execution instruction to the motion controller to drive the manipulator and the tool change system to complete the accurate cross-chain transfer of the target tool.

[0081] Furthermore, as Figure 3 shown, establish mirror tool information, including:

[0082] Obtain the geometric parameters and real-time status data of the tools on the faulty chain, and generate a spare chain mirror relationship according to the collaborative calibration of the tool magazine machine tool coordinate system;

[0083] Based on the mirroring relationship of the standby chain and the topological structure of the chain-type tool magazine, dynamically calculate the tool priority weight, and the tool priority weight is associated with the remaining tool life, the matching degree of the machining process, and the link load balancing parameter;

[0084] Combined with the tool priority weight and the spindle vibration characteristics, the tool dynamic parameters in the mirror tool information are corrected in real time;

[0085] Based on the tool dynamic parameters, establish a hierarchical access control strategy to restrict the calling permissions of the tools on the standby chain.

[0086] As a preference of the above embodiments, when a certain chain in the chain - type combined tool magazine detects a fault warning, first, data collection is carried out on all the tools currently stored on the faulty chain. The collection content includes the geometric parameter information of the tools (such as tool type, diameter, length, shank size, number of blades and arrangement angle, etc.) and real - time status data (such as remaining life, wear degree, whether there is a notch, eccentricity, etc. indicators). According to the machine - tool coordinate system of the tool magazine, through laser measurement, coordinate measuring machine measurement or vision system calibration, a spatial mapping relationship is established between the faulty chain and the spare chain, that is, the corresponding relationship between the tools on the spare chain and the tools on the faulty chain in terms of geometric position and posture, so as to generate a mirror image relationship of the spare chain to ensure that the spare tool can directly replace the faulty tool to perform the tool - changing operation; after establishing the mirror image relationship, combined with the topological structure of the chain - type tool magazine, a priority weight is dynamically calculated for each mirror - image tool on the spare chain. The calculation of the weight comprehensively considers multiple factors, including the remaining life of the tool, the matching degree between the tool and the current machining process to be performed, and the balance of the link load. The longer the remaining life of the tool, the higher its priority; the higher the matching degree between the geometric characteristics of the tool and the current machining process, the higher its priority; in terms of the balance of the link load, it is preferred to select the spare tool in the link with a lighter load and more uniform distribution to avoid the risk of transmission failure or structural deformation caused by local over - load during the operation of the chain - type tool magazine. According to the comprehensive evaluation of the above - mentioned factors, a priority level is assigned to each spare tool for sorting and screening during subsequent tool calls; after the initial determination of the tool priority, the mirror - image tool information is dynamically corrected in combination with the spindle vibration characteristics. By arranging vibration sensors on the spindle, the spindle vibration data during the machining process is collected in real - time, and parameters such as vibration amplitude, vibration frequency spectrum characteristics, and vibration directionality are extracted. If it is detected that some tools cause abnormal vibration during use (such as too high acceleration peak value, abnormal frequency concentration, etc.), it is determined that there is a risk of potential wear, eccentricity or poor clamping of the tool. For such tools, their priority weights will be dynamically lowered, and the abnormal state will be marked in the mirror - image tool information, so as to avoid preferentially calling the risky spare tools in subsequent machining tasks; according to the finally corrected tool priority weights, a hierarchical access control strategy is established, and the tools in the spare chain are divided into multiple access levels. Among them, the tool with the highest priority is the first - level access object and can be directly replaced and used after a fault occurs; the tool with medium priority is the second - level access object and is suitable for non - critical machining processes or scenarios that require certain parameter compensation before use; while the tool with lower priority is classified as the third - level access object and can only be called after manual confirmation and approval when the resources of the first - and second - level tools are exhausted or in special cases.

[0087] Furthermore, as Figure 4 shown, the fault determination conditions include:

[0088] Establish a dynamic fault feature library, which stores the vibration mode spectra of each chain under different tool load conditions;

[0089] Collect the vibration signal of the target chain in real time and calculate the deviation between the vibration signal and the vibration mode spectrum corresponding to the current working condition;

[0090] When the deviation exceeds the adaptive threshold, the co-location sensor data of the adjacent chain is called for cross-checking and bidirectional verification is performed based on the verification results and the blockchain historical data to generate a fault confidence level;

[0091] Whether the fault determination conditions are met is comprehensively determined based on the fault confidence level. Faults with high confidence levels trigger emergency isolation instructions, while medium and low levels trigger mirror chain pre-activation instructions.

[0092] As a preferred embodiment of the above, based on historical operation data and experimental data, a complete set of dynamic fault feature library is established for the operation status of each chain of the chain combination tool magazine under different tool load conditions (such as light load, medium load, and heavy load). In this feature library, the standard vibration modal spectrum of each chain under the corresponding load condition is recorded, including but not limited to the main resonance frequency, amplitude distribution, energy density characteristics, and spectrum change trend and other parameters. Through the continuous optimization and updating of these standard spectra, the dynamic fault feature library can accurately reflect the variation range of the chain vibration characteristics under normal conditions, and provide a benchmark basis for subsequent fault diagnosis; during operation, the actual The vibration signal of the target chain is collected at the same time. After spectrum analysis, filtering and feature extraction, the vibration signal is compared with the vibration modal spectrum corresponding to the current load condition in the dynamic fault feature library. By comparing various characteristic indicators (such as main frequency offset, vibration energy change, abnormal increase or decrease in amplitude, etc.), the difference between the actual vibration signal and the standard spectrum is comprehensively evaluated, so as to calculate the deviation of the current chain. The larger the deviation value, the more abnormal the vibration behavior of the chain and the higher the risk of potential failure. When it is detected that the vibration deviation of the target chain exceeds the threshold set by the adaptive setting, in order to avoid misjudgment, the data collected by the co-located sensors arranged on the adjacent chains are called. The data is cross-checked, and the vibration characteristics of adjacent chains are compared to confirm whether it is a local chain abnormality rather than a misjudgment caused by systematic interference or environmental changes. On the basis of cross-check, two-way verification is performed in combination with the historical operation data managed by blockchain technology, mainly including retrieving historical chain vibration data under the same or similar working conditions for comparison, and querying the actual processing records and results of fault events under similar deviation conditions in history. Through the above cross-check and two-way verification, the authenticity and severity of the current abnormal signal can be comprehensively judged; based on the comprehensive results of cross-check and two-way verification, the corresponding fault confidence level is generated for the currently detected chain abnormality, and the fault The confidence level is evaluated comprehensively based on multiple dimensions such as the size of the abnormal deviation, the consistency of cross-checking, and the correlation of historical data, and is usually divided into three levels: high, medium, and low. At a high confidence level, the abnormal signal is clear and highly consistent with the historical fault characteristics, which directly triggers the emergency isolation command to cut off the power output of the faulty chain and prevent the fault from spreading. At a medium confidence level, the abnormal signal exists but has not yet reached a serious level. The mirror tool access rights of the backup chain are activated first, and the tool cross-chain switching is prepared at any time. At a low confidence level, the deviation exists but is small. The current chain status is continuously monitored and recorded. Isolation or switching operations are not performed immediately, but the chain is included in the key observation list.

[0093] Furthermore, the co-location sensor data of adjacent chains are called for cross-checking, including:

[0094] Determine the co-location mapping point of the tool position of the faulty chain on the adjacent chain, and synchronously collect the co-location sensor data, including displacement feature quantities and structural deformation quantities;

[0095] Dynamically generate the inter-chain data consistency judgment threshold interval based on the current processing condition parameters;

[0096] Perform difference analysis on the co-location sensor data and the fault data of the fault chain. When the difference value exceeds the threshold interval for judging the consistency of data between chains, perform hierarchical verification.

[0097] The fault confidence level is adjusted according to the hierarchical verification results. If the difference value is within the inter-chain data consistency judgment threshold range, the online diagnosis process of the co-location mapping point is triggered.

[0098] As a preference of the above embodiments, when an abnormality occurs in the detected faulty chain, first locate the specific position of the tool on the faulty chain, and determine the corresponding position of the corresponding mapped point of this tool on the adjacent chain. The corresponding mapped point refers to a point that has the same or similar function in terms of spatial position and function, usually referring to positions with similar loads, movement paths, and mechanical stresses in adjacent chains. Through a kinematic model or a three-dimensional positioning system, accurately calculate and calibrate these corresponding mapped points, and ensure that this mapped point can represent the operating state of the faulty chain; once the corresponding mapped point is determined, simultaneously start collecting sensor data at the corresponding mapped point on the adjacent chain. This data includes: displacement characteristic quantities, which reflect the movement changes of the chain and the tool during the machining process, such as link elongation, contraction, or minor deformation; structural deformation quantities, which refer to the structural deformations generated when the chain or the tool is subjected to external forces during the machining process, including curvature, deflection, torsion, etc.; through high-precision sensors (such as laser measurement, displacement sensors, or strain gauges), synchronously monitor physical quantities such as the vibration and deformation of the adjacent chain, and record these data in real time for subsequent difference analysis and verification; dynamically generate a data consistency judgment threshold interval for the current situation according to the current machining conditions (such as tool load, rotational speed, machining material characteristics, etc.). This threshold interval reflects the allowable fluctuation range of physical quantities such as vibration, displacement, and deformation between adjacent chains under normal machining conditions. The dynamic generation of the threshold interval ensures flexible adjustment of the data consistency standard under different machining states to avoid misjudgment; conduct difference analysis on the collected corresponding sensor data and the fault data of the faulty chain (i.e., abnormal vibration, displacement, deformation quantity, etc.). The process of difference analysis includes calculating the difference value between the corresponding mapped point data and the faulty chain data, and judging whether the difference value exceeds the predetermined threshold interval. If the difference value exceeds the threshold interval, it is determined that there is a large abnormality, and hierarchical verification is triggered; the hierarchical verification process is divided into multiple verification levels; primary verification, by adding more sensor data (such as sensor data at other positions of the same chain) for comparison, initially confirm the reliability of the fault data; intermediate verification, based on the correlation analysis of historical data and real-time data, compare the historical fault data under the same or similar working conditions to further determine the nature of the current fault; advanced verification, if there are still large inconsistencies in the results of the primary and intermediate verifications, automatically call an expert system or a machine learning model for comprehensive judgment, and analyze the fault probability and potential impact of the current chain; according to the results of the hierarchical verification, adjust the confidence level of the current fault, specifically including: high confidence level, if the verification results show that the data difference of the faulty chain is obvious and the fault risk is considered high, at this time the fault confidence level is high, trigger an emergency isolation instruction, and immediately take measures to cut off the power of the faulty chain and start the standby chain; medium confidence level, if the fault risk is relatively low, then set the fault confidence level to medium and start the pre-activation of the standby chain to prepare for subsequent tool change or chain switching;At a low confidence level, if the difference value is still within the allowable range and the risk of failure is considered small, emergency measures will not be taken immediately, but the chain will be listed as an online diagnostic process to continuously monitor its status to ensure timely detection of potential failures; when the fault confidence level is low, the online diagnostic process of the same-position mapping point will be triggered to continue tracking and analyzing the operating status of the chain to ensure timely response and adjustment of the processing strategy in future processing. ;

[0099] Furthermore, two-way verification is performed based on the verification results and the blockchain historical data, including:

[0100] Generate a current fault feature identifier, which includes a time-frequency domain feature vector of the deviation and a spatial distribution feature of the co-located sensor data;

[0101] Initiate a verification request to the blockchain network, which carries the timestamp encrypted hash value of the fault feature identifier;

[0102] Call the blockchain smart contract to execute the two-way verification logic and generate the verification conclusion based on the blockchain node consensus result;

[0103] Dynamically modify the fault confidence level based on the verification conclusion.

[0104] Preferably, as in the above embodiments, when an abnormality is detected in the vibration signal of the faulty chain, first, the time-frequency domain feature vector of the deviation degree is calculated through a fault diagnosis algorithm. This feature vector reflects the variation law of the vibration signal in the time domain and frequency domain, and feature extraction is performed on it to construct a time-frequency feature description of the fault signal. At the same time, according to the co-located sensing data of adjacent chains, the spatial distribution features are extracted. These features include, but are not limited to, the distribution of data such as displacement, strain, and twist in space. The comprehensive information of these time-frequency domain feature vectors and spatial distribution features forms the fault feature identifier of the faulty chain. This identifier is used for subsequent blockchain verification and can uniquely identify the characteristics and current state of the fault; after generating the fault feature identifier, it is converted into a verification request containing a timestamp encrypted hash value and a request is sent to the blockchain network. The encrypted hash value is a unique identifier generated by encrypting the fault feature identifier to ensure the security and immutability of the data. The addition of the timestamp ensures that each fault record and request for verification has a clear time mark to prevent replay attacks and provide a reference basis for subsequent comparison of historical data; in the blockchain network, by invoking a smart contract, a two-way verification logic is executed. The blockchain smart contract is an automatically executed protocol that can process according to the data stored on the chain and the verification request according to a predetermined rule; query historical fault data, retrieve the historical fault data records stored on the blockchain, especially the records similar or related to the current fault feature identifier; compare the current feature identifier, compare the currently generated fault feature identifier with the historical data to check whether similar fault features have occurred before, or the same deviation degree, spatial distribution features, etc.; perform a consistency check, through the consensus mechanism of blockchain nodes, confirm the consistency of the data, ensure the matching degree of the current request and historical data. The consensus mechanism of blockchain nodes ensures the authenticity and reliability of the data, and the smart contract ensures the automation and efficiency of the entire verification process; after the smart contract executes the two-way verification logic, a verification conclusion is generated according to the consensus result of the blockchain nodes. The verification conclusion includes: verification passed, if the current fault feature identifier matches the historical data and the data consistency is confirmed through the consensus mechanism, it is considered that the current fault state is verified; verification failed, if there are significant differences between the current fault feature and the historical data or consensus cannot be reached, it is considered that the verification of the current fault fails and there may be misjudgment or other problems; dynamically correct the fault confidence level according to the verification conclusion. If the verification conclusion shows that the fault feature identifier matches the historical data and is consensus by the blockchain network, it is considered that the existence of the fault is credible, increase the fault confidence level, and trigger corresponding isolation measures or activation of the standby chain; if the verification conclusion shows that the current fault feature cannot pass the blockchain consensus, reduce the fault confidence level, temporarily maintain the observation state or re-evaluate the fault data to avoid misoperation.

[0105] Furthermore, hierarchical verification is performed, including:

[0106] In the first level of verification, high-frequency resampling is performed on the co-located mapping points and fault data to extract the joint features in the time-frequency domain. The joint features in the time-frequency domain include the short-time energy mutation rate and the similarity of the spectrum harmonic distribution.

[0107] The second level of verification is that when the frequency domain joint feature meets the abnormal conditions, the historical operation status data of the co-location mapping point stored in the blockchain is retrieved to calculate the trajectory matching degree between the real-time data and the historical operation status data;

[0108] In the third level of verification, if the trajectory matching degree is lower than the safety upper limit, the physical self-check procedure of the tool at the same mapping point is triggered. The physical self-check procedure includes closed-loop detection of the tool clamping force and optical scanning of the tool holder cone surface.

[0109] As a preferred embodiment of the above, high-frequency resampling is performed on the co-location mapping points of the faulty chain and the adjacent chains. High-frequency resampling refers to re-collecting fine-grained data of the target chain vibration signal and the co-location mapping point signal at a frequency higher than the standard sampling frequency to capture dynamic changes. On the basis of the collected high-frequency data, joint features in the time and frequency domains are extracted, including the short-time energy mutation rate, which reflects the severity of energy changes in a short time window and is used to capture mechanical anomalies such as impact and looseness; the similarity of the spectrum harmonic distribution compares whether the harmonic distribution of the two chains on the spectrum is consistent, and identifies whether there is structural damage or transmission anomaly. A set of features will be pre-set. Abnormal detection conditions: If the above joint features show obvious abnormalities (such as high mutation rate and harmonic mismatch), the first-level verification is considered to have failed and the second-level verification is initiated. When the first-level verification detects abnormal features, the second-level verification process is initiated to retrieve the historical operating status data of the co-location mapping points recorded in the blockchain storage. These data cover the normal motion trajectory and dynamic response characteristics of the location under different loads and working conditions. The current real-time collected data is modeled to form the current operating trajectory. Through comparative analysis, the matching degree between the real-time trajectory and the historical trajectory is calculated. A high matching degree indicates that the current state is similar to the historical normal state, and the abnormal The probability is low; low matching degree means that the current state deviates from the historical normal state, and the abnormal probability is high. If the matching degree is lower than the set safety upper limit (this value is set according to the system stability requirements, such as 80% or 90% trajectory similarity), it is determined that the second-level verification has failed and enters the third-level verification; after the second-level verification shows that the trajectory is abnormal, in order to further confirm whether the fault actually exists and locate the specific problem, the physical self-test program of the tool at the same mapping point is started. The physical self-test program includes: closed-loop detection of tool clamping force. Through the built-in clamping force sensor, the clamping force of the tool in the spindle tool holder is detected in real time, and the detection result is compared with the standard clamping force range. Yes, determine whether there is insufficient clamping force, looseness or clamping mechanism failure; optical scanning of the tool holder cone surface, use a high-precision optical scanning instrument (such as a laser scanning head or a visual recognition system) to scan the cone surface of the tool holder to check whether there are abnormal conditions such as wear, damage, contamination, etc. on the tool holder cone surface. These abnormalities usually lead to loose clamping or abnormal vibration; if the physical self-inspection confirms that there is an actual mechanical problem, the fault is finally confirmed and the self-inspection results are uploaded to the blockchain system as the data basis for subsequent verification and traceability. If the physical self-inspection finds no abnormality, the fault event will be marked as a suspicious alarm and enter the manual review process or continue real-time monitoring.

[0110] Specifically, calling a blockchain smart contract to execute two-way verification logic includes:

[0111] Forward verification, traverse the historical fault feature template set, retrieve the matching degree between the current fault feature identifier and each historical template, and apply a time decay factor to reduce the weight of the outdated template;

[0112] Reverse verification: Based on the current machining task and the unique identification code of the tool, retrieve the associated service history dataset in the blockchain, and obtain the cumulative frequency of abnormal events and the trend correlation coefficient in the service history dataset;

[0113] If the matching degree exceeds the first threshold and the trend correlation coefficient shows a positive growth, it is marked as an upgraded event of a known failure mode;

[0114] If the matching degree is lower than the second threshold but the cumulative frequency of abnormal events exceeds the cycle threshold, it is marked as a new potential failure event.

[0115] Preferably, as in the above embodiments, according to the currently detected fault feature identifier, traverse and retrieve in the historical fault feature template set maintained in the blockchain. Each historical fault template records the feature descriptions of the corresponding typical faults that actually occurred in the past, including time-frequency features, vibration patterns, spatial distribution characteristics, etc. Calculate the matching degree between the current fault feature identifier and each historical template. The matching degree measures the consistency between the current anomaly and the past fault patterns, usually based on feature vector similarity or pattern distance. To prevent old data from interfering with the current judgment, apply a time decay factor to each historical template. As the storage time of historical data increases, the weight of the corresponding template gradually decreases. The role of the time decay factor is to encourage more reference to the fault patterns that occurred recently, rather than the old patterns that have not occurred for a long time. After completing the forward matching, record the several results with the highest matching degree between the current fault and the historical templates, and mark their timestamps and associated processing conditions. Based on the current processing task and the unique identification code of the detected abnormal tool, retrieve the complete service history data set of this tool on the blockchain. The service history data set contains all the operation records of this tool since it was put into use, including the working condition parameters, abnormal alarm events, maintenance records, etc. of each processing task. On this basis, record the cumulative frequency of abnormal events, which refers to the cumulative number of times of abnormalities that occurred in the history of this tool; the trend correlation coefficient, which refers to the trend of increase or decrease in the abnormal frequency over time. Through analysis, it can be judged whether the current abnormality of this tool belongs to the trend of cumulative deterioration of abnormalities or an isolated occasional event. According to the comprehensive results of the forward matching degree and the reverse trend analysis, perform a logical judgment. If the matching degree between the current fault feature identifier and the historical template exceeds the first threshold (for example, 90%), and the trend correlation coefficient of the abnormal events of the corresponding tool shows a positive increase (that is, the abnormalities are becoming more frequent or severe), it is determined as an evolution or upgrade event under the known fault mode, and the maintenance plan can be pre-warned and the fault mode can be classified and managed. If the matching degree between the current fault and the historical template is lower than the second threshold (for example, 50%, indicating a large difference), but the cumulative frequency of abnormal events of this tool in the recent period exceeds the set threshold (for example, the number of abnormalities exceeds 3 times within a week), it is determined as a new potential fault, and this fault feature identifier is incorporated into the new entry in the blockchain as an important basis for subsequent learning and abnormal mode expansion. All verification conclusions are executed and recorded through the blockchain smart contract to ensure the immutability and traceability of the data.

[0116] Although the present application has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the present application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined by the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A control method for a chain - type combined tool magazine, characterized in that, The method comprises: Real-time monitoring of the operating state parameters of each chain in the chain-type combined tool magazine, wherein the operating state parameters respond to preset fault judgment conditions and generate chain fault alarm instructions; According to the chain fault alarm instruction, control the power isolation of the faulty chain and activate the tool access rights of at least one spare chain, wherein the spare chain stores the mirror tool information mapped to the tool storage location of the faulty chain; Based on the spatial coordinate data of the mirrored tool information, obtaining a path planning instruction of a target tool from the backup chain; Executing the path planning instruction to complete the cross-chain transfer operation of the target tool, and updating the global tool distribution information based on the execution result of the cross-chain transfer operation; Obtain the path planning instructions for the target tool, including: Generate an initial tool exchange path based on the spatial coordinate data of the mirror tool information and the conversion relationship between the tool magazine machine tool coordinate system and the manipulator base coordinate system; Acquire dynamic obstacle detection data of the chain-type combined tool magazine, and modify and optimize the initial tool exchange path based on the dynamic obstacle detection data, wherein the dynamic obstacle includes a chain link area in motion and a workpiece fixture coordinate temporarily stored during tool exchange; Discretizing the modified initial tool exchange path into an executable interpolation point sequence to generate the path planning instruction including feed speed and acceleration curve; Verify the logical consistency between the path planning instructions and the current spindle position, and trigger the tool reselection process if there is an interference risk; Establishing the mirror tool information includes: Acquire the geometric parameters and real-time status data of the tool of the faulty chain, and generate the mirror image relationship of the spare chain according to the coordinated calibration of the tool magazine machine tool coordinate system; Based on the mirror relationship of the spare chain and the topological structure of the chain-type combined tool magazine, the tool priority weight is dynamically calculated, and the tool priority weight is associated with the remaining life of the tool, the matching degree of the processing procedure and the chain link load balancing parameter; In combination with the tool priority weight and the spindle vibration characteristics, the tool dynamic parameters in the mirror tool information are corrected in real time; Establishing a hierarchical access control strategy based on the tool dynamic parameters to limit the calling authority of the tools in the standby chain; The fault determination conditions include: Establishing a dynamic fault feature library, wherein the dynamic fault feature library stores vibration modal spectra of each chain under different tool load conditions; Collecting the vibration signal of the target chain in real time, and calculating the deviation between the vibration signal and the vibration modal spectrum corresponding to the current working condition; When the deviation exceeds the adaptive threshold, the co-location sensor data of the adjacent chain is called for cross-checking and bidirectional verification is performed based on the check result and the blockchain historical data to generate a fault confidence level; Whether the fault determination condition is met is comprehensively determined according to the fault confidence level, wherein a high confidence level fault triggers an emergency isolation instruction, and a medium or low level fault triggers a mirror chain pre-activation instruction.

2. The control method of the chain-type combined tool magazine according to claim 1, wherein, Discretizing the modified initial tool exchange path into an executable interpolation point sequence, including: Dynamically adjust the interpolation point density based on the path curvature radius, wherein the path segment with a path curvature radius smaller than a preset threshold adopts an encrypted interpolation point distribution, and the path segment with a path curvature radius larger than the preset threshold adopts a sparse interpolation point distribution; Generating a smooth acceleration curve according to the motion constraint conditions of the chain-type combined tool magazine, and dynamically correcting the interpolation point; Verify the feasibility of the interpolation point sequence, and if it is detected that the acceleration mutation between adjacent interpolation points exceeds the allowable range, replan the path; The interpolation point sequence and the machine tool spindle coordinates are spatially synchronized and calibrated to calibrate the geometric consistency of the initial tool exchange path and the workpiece processing coordinate system.

3. The control method of the chain - type combined tool magazine according to claim 1, wherein, Call the same-position sensor data of adjacent chains for cross-checking, including: Determine the co-location mapping point of the tool position of the faulty chain on the adjacent chain, and synchronously collect the co-location sensing data, including displacement feature quantity and structural deformation quantity; Dynamically generate the inter-chain data consistency judgment threshold interval based on the current processing condition parameters; Performing difference analysis on the co-location sensing data and the fault data of the fault chain, and performing hierarchical verification when the difference value exceeds the inter-chain data consistency judgment threshold interval; The fault confidence level is adjusted according to the hierarchical verification result, and if the difference value is within the inter-chain data consistency judgment threshold range, the online diagnosis process of the co-location mapping point is triggered.

4. The control method of the chain - type combined tool magazine according to claim 1, characterized in that, Two-way verification is performed based on the verification results and the historical data of the blockchain, including: Generate a current fault feature identifier, wherein the fault feature identifier includes a time-frequency domain feature vector of the deviation and a spatial distribution feature of the co-located sensor data; Initiate a verification request to the blockchain network, the verification request carrying the timestamp encrypted hash value of the fault feature identifier; Call the blockchain smart contract to execute the two-way verification logic and generate the verification conclusion based on the blockchain node consensus result; The fault confidence level is dynamically modified based on the verification conclusion.

5. The control method of the chain-type combined tool magazine according to claim 3, characterized in that Perform hierarchical verification, including: The first level verification is to perform high-frequency resampling on the co-location mapping points and the fault data to extract joint features in the time-frequency domain, wherein the joint features in the time-frequency domain include short-time energy mutation rate and similarity of spectrum harmonic distribution; The second level verification, when the time-frequency domain joint feature meets the abnormal condition, retrieve the historical operation status data of the co-location mapping point stored in the blockchain, and calculate the trajectory matching degree between the real-time data and the historical operation status data; The third level verification is that if the trajectory matching degree is lower than the safety upper limit value, the physical self-check procedure of the tool at the same mapping point is triggered, and the physical self-check procedure includes closed-loop detection of tool clamping force and optical scanning of the tool handle cone surface.

6. The control method of the chain - type combined tool magazine according to claim 4, wherein, Call the blockchain smart contract to execute two-way verification logic, including: Forward verification, traverse the historical fault feature template set, retrieve the matching degree between the current fault feature identifier and each historical template, and apply a time decay factor to reduce the weight of the outdated template; Reverse verification: based on the current processing task and the unique identification code of the tool, retrieve the associated service history data set in the blockchain to obtain the cumulative frequency and trend correlation coefficient of abnormal events in the service history data set; If the matching degree exceeds the first threshold and the trend correlation coefficient shows a positive growth, it is marked as a known fault mode upgrade event; If the matching degree is lower than the second threshold but the cumulative frequency of the abnormal events exceeds the cycle threshold, it is marked as a new potential fault event.

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