Automatic framework inserting device capable of achieving automatic feeding

Abstract

The invention discloses an automatic framework inserting device capable of automatically feeding, and relates to the field of motor production, the automatic framework inserting device comprises a framework inserting machine main body, a bus controller, an analysis unit and sensing execution modules, the surface of the framework inserting machine main body is provided with a plurality of sensing execution modules, one end of the framework inserting machine main body is provided with the bus controller, and the other end of the framework inserting machine main body is provided with the analysis unit. The bus controller is connected with an analysis unit through an electrical medium, and the framework inserting machine main body is used for conveying and inserting a plurality of motor frameworks in sequence; the bus controller is used for being in butt joint with a PLC bus and editing and sending operation instructions of the function modules; the risk is recognized in advance through the prediction model, the feeding track and vibration data are monitored in real time, large-scale production line data are read and processed in real time through data analysis and trend prediction, feeding path digital marks are rapidly formed, and it is ensured that the sensing point location layout always meets the feeding precise monitoring requirement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor production, in particular to an automatic skeleton inserting device capable of automatic feeding. BACKGROUND

[0002] With the rapid development of manufacturing industry, especially under the promotion of intelligent manufacturing, automated production line and industry, enterprises have increasingly high requirements for production efficiency, product quality and production flexibility. Modern manufacturing industry needs efficient supply chain management to ensure timely supply of raw materials and products. An automatic feeding system can improve the processing speed and accuracy of materials, reduce idle time, and thus improve the overall efficiency of the production line. During the assembly process of the motor, the stator of the motor and the skeleton are assembled by automatic feeding and installation equipment. The purpose is to help enterprises improve production efficiency, optimize resource utilization, realize intelligent and digital transformation, and meet the diversified and personalized needs of modern manufacturing industry.

[0003] The existing automatic feeding system often adopts a post-monitoring method for abnormal events such as feeding blockage and excessive vibration during the feeding process, which cannot respond in time and may affect production efficiency and product quality, affecting the assembly effect of the stator and the rotor. The feeding path and sensor layout in most current systems are set once, and lack adaptability. Once the production line or production conditions change, the layout often needs to be adjusted again, causing a lot of time and resource waste. There is a lack of in-depth analysis and utilization of operation data, and it cannot be self-adjusted and optimized according to historical and real-time data, resulting in reduced efficiency in long-term operation. SUMMARY

[0004] (I) The technical problem solved: In view of the above-mentioned shortcomings of the prior art, the present application provides an automatic skeleton inserting device capable of automatic feeding, which can effectively solve the problems of the prior art.

[0005] (II) Technical solution: In order to achieve the above purpose, the present application is implemented by the following technical solution, The present application discloses an automatic skeleton inserting device capable of automatic feeding, comprising an inserting skeleton machine main body, a bus controller, an analysis unit and a sensing execution module. The surface of the inserting skeleton machine main body is provided with a plurality of sensing execution modules. One end of the inserting skeleton machine main body is provided with a bus controller. The bus controller is connected with an analysis unit through an electrical medium. Wherein: The inserting skeleton machine main body is used for sequentially conveying and inserting a plurality of motor skeletons; The bus controller is used for interfacing with a PLC bus, editing and sending function module running instructions; The lower part of the analysis unit is deployed with a sub-module, wherein: the sensing execution module is deployed by the acquisition unit to provide deployment suggestions, the current feeding trajectory data and the feeding vibration data are acquired, the feeding process is preformed by the feeding preformance module according to the current feeding trajectory and vibration data, the prediction model is used to simulate the feeding deviation in the future period under the preset speed target, and the strategy adjustment is provided by the adjustment unit in combination; The sensing execution module is used for deploying a plurality of sensing collectors according to the deployment suggestions provided by the acquisition unit.

[0006] Further, the sub-module deployed under the analysis unit further comprises: The permission acquisition module is used for acquiring the data reading permission of each functional component and functional module, and providing security verification; The path marking module is used for real-time scanning of each region of the main body of the bone framework machine, obtaining the conveying path data in each feeding region, marking the positive and negative abnormal information in the feeding path, and forming a digital map; The point planning module is used for defining key nodes and planning the deployment position of the sensing collector according to the digital map generated by the path marking module, and dynamically changing the key nodes according to the dynamic change of the production line; The judgment module is used for comparing the data collected by the acquisition unit with the preset target parameters in real time, identifying the deviation existing in the feeding trajectory and vibration data by using a data analysis algorithm, triggering an alarm if the detected deviation exceeds the critical value of the preset target, and feeding the abnormal data to the acquisition unit and the bus controller.

[0007] Further, the permission acquisition module is interactively connected with the path marking module, the point planning module and the acquisition unit through a wireless network, the acquisition unit is interactively connected with the sensing execution module and the judgment module through an electrical medium, the feeding preformance module is interactively connected with the judgment module and the adjustment unit through a wireless network, and the adjustment unit is connected with the point planning module through an electrical medium.

[0008] Further, the acquisition unit is deployed with a sub-module, and the sub-module comprises a data acquisition module, a target definition module and a configuration module, which are interactively connected through a wireless network, wherein: The data acquisition module is used for acquiring the displacement, speed and angle information of the current feeding, and the feeding vibration frequency and energy distribution data, and performing standardization processing; The target definition module is used for receiving the pre-set production task target of the main body of the bone framework machine, including the feeding speed, the feeding trajectory accuracy and the vibration control threshold, and storing it as a target reference parameter, supporting automatic optimization of the target parameter in combination with the historical data of the equipment and the real-time feedback of the adjustment unit.

[0009] Further, the configuration module is configured to extract the standardized collection data provided by the data collection module and the target reference parameters provided by the target definition module, continuously submit to the judgment module at a preset period, and accept the feedback data adjusted by the adjustment unit strategy.

[0010] Further, the workflow of the point planning module is: Step 1: Based on the digital map generated by the path marking module, the key nodes in the feeding path are identified through path topology analysis algorithm; Step 2: According to the distribution density and risk level of the key nodes, the deployment position of the sensing collector is planned by using coverage optimization algorithm; Step 3: Real-time monitoring of production line dynamic change parameters is realized through edge computing device, the parameters include feeding speed adjustment signal, mechanical arm path update data and vibration suppression device start-stop state, when the trigger condition is monitored, the key node dynamic change is triggered; Step 4: According to the distribution of the changed key nodes, the sensor layout re-planning process is started.

[0011] Further, the key nodes in step 1 include physical structure nodes and dynamic feature nodes, the physical structure nodes are the bend area with turning radius less than the preset threshold in the conveying path, the branch point of multi-path intersection and the mechanical interface connection position; the dynamic feature nodes are the excitation area with vibration amplitude exceeding the safety threshold and the abnormal frequent area with feeding trajectory deviation frequency higher than the set value.

[0012] Further, the trigger conditions in step 3 include: the feeding speed change rate exceeds ± 15% and lasts for more than 3 production cycles; new branch path is added or removed in the digital map; the vibration energy distribution pattern of the excitation area changes suddenly.

[0013] Further, the adjustment unit is deployed with a sub-module, the sub-module includes a strategy definition module, a verification module and an evaluation adjustment module, the strategy definition module and the verification module are connected through wireless network, the verification module and the evaluation adjustment module are connected through wireless network, wherein: The strategy definition module is configured to analyze the influence of the associated conveying and plug-in equipment in the plug-in skeleton machine body according to the prediction data, calculate the adjustment influence coefficient of each associated equipment based on the deviation data, generate the adjustment strategy of speed and feeding trajectory according to the preset target and current prediction data, and simultaneously generate the operation setting adjustment scheme of each associated equipment; The verification module is configured to trigger again through the adjustment unit to collect feeding data in the next period after the strategy is issued, judge the effect of the pre-adjustment strategy by obtaining the feedback result data of alarm state and data deviation; An evaluation adjustment module is configured to combine historical data and real-time collected data to quantitatively analyze the feeding path, vibration data and adjustment influence coefficient, evaluate the contribution of each data in the prediction calculation, and update the marking planning reference of the feeding path and the sensor collection point based on the analysis result of the contribution.

[0014] Further, in the judgment stage, if the feedback result data in the subsequent period does not exceed the preset threshold, it is considered that the current adjustment strategy is effective, and the next stage of monitoring is entered; otherwise, the feedback result data is sent to the strategy definition module for further correction.

[0015] (Three) beneficial effects: compared with the known prior art, the technical scheme provided by the present application has the following beneficial effects, 1. The path marking module is used to read and process large-scale production line data in real time, quickly form digital marking of the feeding path, and use a self-optimizing and dynamically adjusting layout algorithm in the point planning module to ensure that the sensor point layout always meets the accurate monitoring demand of the feeding path, dynamically evaluates and optimizes the sensor layout and the feeding path, adapts to the production rhythm change and temporary adjustment demand, realizes multi-index collaborative management, and improves the intelligent regulation and control level of the feeding process.

[0016] 2. The risk is identified in advance through the prediction model, the feeding trajectory and vibration data are monitored in real time, the data analysis and trend prediction are used to issue an alarm before the problem occurs, active intervention is realized, potential production interruption is avoided, potential abnormalities are predicted through pre-rehearsal, the change from passive monitoring to active prediction is realized, sufficient time and data support are provided for automatic adjustment, intelligent feedback is constructed based on real-time prediction data and equipment operation parameters, and the self-adjustment of the fully automatic feeding process is realized, and the adjustment strategy of multi-device linkage is generated, thereby enhancing the overall coordination of the system.

[0017] 3. The evaluation adjustment module can quantify the contribution of various data based on the analysis of historical data and real-time collected data, and generate intelligent marking planning and adjustment strategy, so that the device not only improves the current work efficiency, but also provides a basis for future production optimization. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0019] Figure 1 The application scenario diagram of the automatic skeleton inserting device of the embodiment of the present application; Figure 2This is another application scenario diagram of the automatic skeleton insertion device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the framework of the analysis unit and the sensing execution module in an embodiment of the present invention; Figure 4 This is a schematic diagram of the acquisition unit in an embodiment of the present invention; Figure 5 This is a schematic diagram of the framework of the adjustment unit according to an embodiment of the present invention; Figure 6 In this invention Figure 1 A magnified schematic diagram of the local structure at point A; Figure 7 In this invention Figure 1 A magnified schematic diagram of the local structure at point B; Figure 8 In this invention Figure 1 A magnified schematic diagram of the structure at point C.

[0020] The labels in the diagram represent: 1. Main body of the frame insertion machine; 2. Bus controller; 3. Analysis unit; 31. Permission acquisition module; 32. Path marking module; 33. Point planning module; 34. Acquisition unit; 41. Data acquisition module; 42. Target definition module; 43. Configuration module; 35. Judgment module; 36. Material feeding simulation module; 37. Adjustment unit; 71. Strategy definition module; 72. Verification module; 73. Evaluation and adjustment module; 4. Sensor execution module. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] The present invention will be further described below with reference to embodiments.

[0023] ① Example 1: An automatic feeding and inserting skeleton device according to this example, such as Figures 1-8 As shown, the assembly includes a main body 1 for inserting a skeleton, a bus controller 2, an analysis unit 3, and a sensor execution module 4. Several sensor execution modules 4 are mounted on the surface of the main body 1. The bus controller 2 is mounted at one end of the main body 1, and the bus controller 2 is electrically connected to the analysis unit 3. Wherein: The main body 1 of the frame insertion machine is used to perform the sequential conveying and insertion of several motor frames; Bus controller 2 is used to connect PLC bus, edit and send function module operation instruction.

[0024] The analysis unit 3 is deployed with sub-modules, wherein: The permission acquisition module 31 is used to acquire the data reading permission of each functional component and functional module, and provide security verification.

[0025] The path marking module 32 is used to scan each area of the skeleton machine body 1 in real time, obtain the conveying path data in each feeding area, the data including the mechanical arm driving path, the feeding track and the material distribution, mark the positive and negative abnormal information in the feeding path, and form a digital map.

[0026] The point planning module 33 is used to define the key nodes according to the digital map generated by the path marking module 32, and plan the deployment position of the sensing collector, and dynamically change the key nodes according to the dynamic change of the production line.

[0027] The acquisition unit 34 is used to provide deployment suggestions of the sensing execution module 4, and acquire the current feeding track data and feeding vibration data.

[0028] The acquisition unit 34 is deployed with sub-modules, and the sub-modules include a data acquisition module 41, a target definition module 42 and a configuration module 43, and the data acquisition module 41, the target definition module 42 and the configuration module 43 are connected through a wireless network, wherein: The data acquisition module 41 is used to acquire the displacement, speed and angle information of the current feeding, and the feeding vibration frequency and energy distribution data, and perform standardization processing.

[0029] The target definition module 42 is used to receive the pre-set production task target of the skeleton machine body 1, including the feeding speed, the feeding track accuracy and the vibration control threshold, and store it as a target reference parameter, and combine the historical data of the equipment and the real-time feedback of the adjustment unit 37 to support automatic optimization of the target parameter.

[0030] The configuration module 43 is used to extract the standardized acquisition data provided by the data acquisition module 41 and the target reference parameter provided by the target definition module 42, continuously submit to the judgment module 35 according to the pre-set period, and accept the feedback data of the strategy adjustment of the adjustment unit 37.

[0031] The judgment module 35 is used to compare the acquisition data of the acquisition unit 34 with the pre-set target parameter in real time, identify the deviation existing in the feeding track and the vibration data by using a data analysis algorithm, if the detected deviation exceeds the critical value of the pre-set target, an alarm is triggered immediately, and the abnormal data is fed back to the acquisition unit 34 and the bus controller 2.

[0032] The feeding pre-performance module 36 is used for constructing a prediction model through a neural network algorithm, inputting the current feeding trajectory and vibration data into the prediction model to perform feeding process pre-performance, simulating the feeding deviation in the future period under the preset speed target, and presenting the feeding trajectory, vibration trend and deviation prediction in real time.

[0033] The sensing execution module 4 is used for deploying a plurality of sensing collectors according to the deployment suggestion provided by the acquisition unit 3.

[0034] The permission acquisition module 31 is connected with the path marking module 32, the point position planning module 33 and the acquisition unit 34 through a wireless network, the acquisition unit 34 is connected with the sensing execution module 4 and the judgment module 35 through an electrical medium, the feeding pre-performance module 36 is connected with the judgment module 35 and the adjustment unit 37 through a wireless network, and the adjustment unit 37 is connected with the point position planning module 33 through an electrical medium.

[0035] The scene is as shown in Figure 1 , Figure 6 , Figure 7 and Figure 8 , the motor stator f and the motor skeleton g are respectively conveyed through a plurality of conveying belts e to be assembled, the motor stator and the motor rotor are assembled through the main body 1 of the skeleton inserting machine to obtain the motor assembly h.

[0036] Compared with the prior art, the judgment module 35 is used for comparing the collected data with the preset target in real time, which not only can identify the deviation in the feeding trajectory and the vibration, but also can trigger an alarm in time when the deviation exceeds the preset value, respond to abnormal conditions in time, reduce the loss caused by faults, and dynamically optimize the feeding path and the sensor deployment position of the key node based on the real-time data and the digital map through the path marking module 32 and the point position planning module 33, thereby improving the response speed of the device, making timely adjustment according to the actual changes of the production line, and greatly enhancing the flexibility and adaptability of the system.

[0037] The target definition module 42 supports automatic optimization of target reference parameters, combines historical data and real-time feedback, and can continuously adjust the feeding speed and the vibration control threshold, thereby providing the device with the ability of continuous improvement, ensuring that the production is always in the optimal state, and the feeding pre-performance module 36 can perform pre-performance on the feeding process through the neural network algorithm to simulate the future feeding deviation, so that the device can prospectively identify potential problems and take remedial measures in advance, thereby further ensuring the continuity and stability of the production.

[0038] ②Embodiment 2: In other aspects, the embodiment also provides another optimization mechanism based on embodiment 1, which is a planning strategy of a sensor point position, as shown in Figure 3 , the following processes are included: Step 1: Based on the digital map generated by the path marking module 32, the key nodes in the feeding path are identified by a path topology analysis algorithm; the key nodes include physical structure nodes and dynamic feature nodes, the physical structure nodes are the bend area with a turning radius less than a preset threshold in the conveying path, the branch point of multi-path intersection and the mechanical interface connection site; the dynamic feature nodes are the excitation region with a historical feeding vibration amplitude exceeding a safety threshold and the abnormal frequent region with a feeding trajectory deviation frequency higher than a set value.

[0039] Step 2: According to the distribution density and risk level of the key nodes, a coverage optimization algorithm is used to plan the deployment position of the sensor collector; at the physical structure nodes, redundant sensor units are deployed, which contain at least two heterogeneous sensors for collecting displacement data and vibration spectrum data, at the dynamic feature nodes, high-precision vibration sensors are deployed, and the sampling frequency is dynamically adjusted based on the node risk level, wherein the sampling frequency of high-risk nodes is not less than 1 kHz, and the basic sensor units are deployed at the straight-line feeding section between adjacent key nodes according to the equal interval principle, and the interval length is adaptively calculated according to the current feeding speed.

[0040] Step 3: Real-time monitoring of production line dynamic change parameters is realized through edge computing devices, the parameters include feeding speed adjustment signal, mechanical arm path update data and vibration suppression device start-stop state, when the trigger condition is monitored, the key node dynamic change is triggered.

[0041] The trigger conditions include: the feeding speed change rate exceeds ±15% and lasts for more than 3 production cycles; a branch path is added or removed in the digital map; the vibration energy distribution pattern of the excitation region changes abruptly. The key nodes are divided into physical structure nodes and dynamic feature nodes, breaking through the traditional deployment method relying only on mechanical structure, realizing intelligent node identification driven by data, implementing differentiated sensor deployment strategies for different node types, optimizing hardware resource utilization while ensuring monitoring accuracy, setting accurate speed change rate, path update rules and vibration pattern mutation criteria, establishing a quantifiable layout adjustment trigger mechanism, introducing historical experience reuse and extreme condition simulation in layout re-planning, greatly improving the effectiveness and robustness of the deployment scheme.

[0042] Step 4: According to the distribution of the changed key nodes, start the sensor layout re-planning process.

[0043] ③Example 3: This example provides an adjustment unit 37, as shown in Figure 5 The adjustment unit 37 is deployed with sub-modules, the sub-modules include a strategy definition module 71, a verification module 72 and an evaluation adjustment module 73, the strategy definition module 71 and the verification module 72 are connected through a wireless network, the verification module 72 and the evaluation adjustment module 73 are connected through a wireless network, wherein: The strategy definition module 71 is used for influencing analysis on the associated conveying and inserting equipment in the bone framework machine main body 1 according to the prediction data, calculating the adjustment influence coefficient of each associated equipment based on the deviation data, generating the adjustment strategy of the speed and the feeding track according to the preset target and the current prediction data, and synchronously generating the operation setting adjustment scheme of each associated equipment.

[0044] The verification module 72 is used for triggering in the next period after the strategy is issued, collecting the feeding data again through the adjustment unit 37, judging the effect of the pre-adjustment strategy through the feedback result data of the alarm state and the data deviation, and if the feedback result data in the subsequent period does not exceed the preset threshold, considering that the current adjustment strategy is effective, and entering the next stage of monitoring, otherwise, feeding the feedback result data to the strategy definition module 71 for further correction.

[0045] The evaluation adjustment module 73 is used for quantitative analysis on the feeding path, the vibration data and the adjustment influence coefficient in combination with the historical data and the real-time collected data, evaluating the contribution degree of each data in the prediction calculation, and updating the marking planning reference of the feeding path and the sensor collection point based on the contribution degree analysis result.

[0046] Compared with the prior art, the embodiment realizes intelligent influence analysis on the associated conveying and inserting equipment and dynamic optimization of the adjustment strategy, can evaluate the strategy effect based on the real-time feedback data, and corrects itself when the target is not achieved, enhances the self-adaptation ability and precision of the device, compared with the single and static adjustment mode in the prior art, significantly improves the flexibility and stability of the feeding process, and thus improves the overall production efficiency and quality.

[0047] In summary, the bone framework machine main body 1 is used for sequentially conveying and inserting a plurality of motor skeletons, the bus controller 2 is used for editing and sending operation instructions, the permission acquisition module 31 is used for acquiring the control permission of each functional module and component, the path marking module 32 is used for reading the conveying path of each region of the current production line of the bone framework machine main body 1 which has a feeding relationship, the point planning module 33 is used for planning and deploying the sensing collector of the sensing execution module 4 on the current conveying path, the target definition module 42 is used for acquiring the preset task target, the data acquisition module 41 is used for acquiring the current feeding track and feeding vibration data, the configuration module 43 is used for submitting the acquired content to the judgment module 35, the judgment module 35 is used for identifying the acquired feeding track and vibration data, and if there is feeding deviation data exceeding the current preset target, an alarm is given.

[0048] The feeding preview module 36 receives the current feeding track and feeding vibration data or feeding deviation data, analyzes the feeding deviation data caused by the future feeding track and vibration based on the preset speed target.

[0049] Through the strategy definition module 71, based on the prediction data, the adjustment influence coefficient of the bias data of the associated equipment is analyzed, based on the adjustment influence coefficient, the adjustment strategy based on the preset target on the speed is generated, and the adjustment strategy of the running setting of the associated equipment is synchronously generated, the alarm state of the judgment module 35 of the subsequent period is provided through the verification module 72, and if the alarm state does not exceed the preset threshold value, it is considered that the strategy is effective, the contribution degree of the calculation data used based on the prediction data is evaluated through the evaluation adjustment module 73, and based on the contribution degrees of various data, the label planning reference of the collected feeding path and the collection point is generated.

[0050] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An automatic feeding and inserting skeleton device, characterized in that, The assembly includes a main body (1) for inserting a skeleton, a bus controller (2), an analysis unit (3), and a sensing and execution module (4). Several sensing and execution modules (4) are mounted on the surface of the main body (1). A bus controller (2) is mounted at one end of the main body (1). The bus controller (2) is connected to the analysis unit (3) via an electrical medium. The main body of the frame insertion machine (1) is used to perform the sequential conveying and insertion of several motor frames; Bus controller (2) is used to interface with the PLC bus to edit and send function module operation instructions; The analysis unit (3) has sub-modules deployed below it, including: the acquisition unit (34) provides deployment suggestions for the sensing execution module, acquires current material feeding trajectory data and material feeding vibration data, and the material feeding simulation module (36) uses a prediction model to simulate the material feeding process based on the current material feeding trajectory and vibration data, simulates the material feeding deviation in the future cycle under the preset speed target, and provides strategy adjustment in conjunction with the adjustment unit (37); The sensing execution module (4) is used to deploy several sensor collectors according to the deployment suggestions provided by the acquisition unit.

2. The automatic feeding and inserting skeleton device according to claim 1, characterized in that, The sub-modules deployed at the lower level of the analysis unit (3) also include: The permission acquisition module (31) is used to acquire data reading permissions for each functional component and functional module, and to provide security verification. The path marking module (32) is used to scan each area of ​​the main body (1) of the insert frame machine in real time, obtain the conveying path data in each feeding area, mark the positive and negative abnormal information in the feeding path, and form a digital map. The point planning module (33) is used to define key nodes based on the digital map generated by the path marking module (32), and plan the deployment location of the sensor acquisition device, and dynamically change the key nodes according to the dynamic changes of the production line. The judgment module (35) is used to compare the data collected by the acquisition unit (34) with the preset target parameters in real time, and use the data analysis algorithm to identify the deviation in the feeding trajectory and vibration data. If the deviation exceeds the critical value of the preset target, an alarm is triggered immediately, and the abnormal data is fed back to the acquisition unit (34) and the bus controller (2).

3. The automatic feeding and inserting skeleton device according to claim 2, characterized in that, The permission acquisition module (31) is interconnected with the path marking module (32), the location planning module (33), and the acquisition unit (34) via a wireless network. The acquisition unit (34) is interconnected with the sensing execution module (4) and the judgment module (35) via an electrical medium. The material feeding simulation module (36) is interconnected with the judgment module (35) and the adjustment unit (37) via a wireless network. The adjustment unit (37) is connected to the location planning module (33) via an electrical medium.

4. The automatic feeding and inserting skeleton device according to claim 1, characterized in that, The acquisition unit (34) has sub-modules deployed below it, including: a data acquisition module (41), a target definition module (42), and a configuration module (43). The data acquisition module (41), the target definition module (42), and the configuration module (43) are interconnected via a wireless network. The data acquisition module (41) is used to collect the displacement, speed and angle information of the current material supply, as well as the vibration frequency and energy distribution data of the material supply, and to perform standardized processing. The target definition module (42) is used to receive the pre-set production task target of the main body (1) of the insert frame machine, including the feeding speed, feeding trajectory accuracy and vibration control threshold, and store it as the target reference parameter. Combined with the equipment historical data and the real-time feedback from the adjustment unit (37), it supports automatic optimization of the target parameters.

5. The automatic feeding and inserting skeleton device according to claim 4, characterized in that, The configuration module (43) is used to extract the standardized acquisition data provided by the data acquisition module (41) and the target benchmark parameters provided by the target definition module (42), continuously submit them to the judgment module (35) according to a preset cycle, and receive feedback data on strategy adjustment from the adjustment unit (37).

6. The automatic feeding and inserting skeleton device according to claim 2, characterized in that, The workflow of the location planning module (33) is as follows: Step 1: Based on the digital map generated by the path marking module (32), identify key nodes in the material supply path through the path topology analysis algorithm; Step 2: Based on the distribution density and risk level of key nodes, use a coverage optimization algorithm to plan the deployment locations of sensor data acquisition units; Step 3: Monitor the dynamic changes of production line parameters in real time through edge computing devices. These parameters include material feeding speed adjustment signals, robotic arm path update data, and vibration suppression device start / stop status. When a trigger condition is detected, trigger dynamic changes to key nodes are initiated. Step 4: Based on the changed distribution of key nodes, initiate the sensor layout replanning process.

7. The automatic feeding and inserting skeleton device according to claim 6, characterized in that, The key nodes in step 1 include physical structure nodes and dynamic feature nodes. The physical structure nodes are the curves in the conveying path with a turning radius less than a preset threshold, the bifurcation points where multiple paths intersect, and the mechanical interface connection points. The dynamic feature nodes are the excitation areas where the historical material feeding vibration amplitude exceeds the safety threshold and the abnormally frequent areas where the material feeding trajectory deviation frequency is higher than the set value.

8. The automatic feeding and inserting skeleton device according to claim 6, characterized in that, The triggering conditions in step 3 include: the rate of change of the feeding speed exceeds ±15% and lasts for more than 3 production cycles; a new or removed branch path is added to the digital map; and a sudden change occurs in the vibration energy distribution pattern of the excitation area.

9. The automatic feeding and inserting skeleton device according to claim 1, characterized in that, The adjustment unit (37) has sub-modules deployed below it. The sub-modules include a policy definition module (71), a verification module (72), and an evaluation adjustment module (73). The policy definition module (71) and the verification module (72) are interconnected via a wireless network. The verification module (72) and the evaluation adjustment module (73) are interconnected via a wireless network. The strategy definition module (71) is used to perform an impact analysis on the associated conveying and inserting equipment in the main body (1) of the insert frame machine based on the predicted data, calculate the adjustment impact coefficient of each associated equipment based on the deviation data, generate the speed and feeding trajectory adjustment strategy according to the preset target and the current predicted data, and simultaneously generate the operation setting adjustment scheme of each associated equipment. The verification module (72) is used to trigger the next cycle after the strategy is issued, and collect the material supply data again through the adjustment unit (37). By obtaining the feedback results of alarm status and data deviation, the effect of the pre-adjustment strategy is judged. The evaluation and adjustment module (73) is used to combine historical data and real-time acquired data to perform quantitative analysis on the feeding path, vibration data and adjustment influence coefficient, evaluate the contribution of each data in the prediction calculation, and update the marking planning reference of the feeding path and sensor acquisition points based on the contribution analysis results.

10. An automatic feeding and inserting skeleton device according to claim 9, characterized in that, In the judgment phase, if the feedback result data in the subsequent cycle does not exceed the preset threshold, the verification module (72) considers the current adjustment strategy to be effective and enters the next stage of monitoring; otherwise, the feedback result data is sent to the strategy definition module (71) for further correction.

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