Industrial manufacturing tool digital intelligent recording method and calculating instrument

Abstract

The invention relates to the field of industrial automation, in particular to an industrial manufacturing tool digital intelligent recording method and a calculating instrument, and the method comprises the steps: collecting production manufacturing tool information and production operation data; matching a production process blueprint according to the production tool information; extracting current process features based on the production operation data and the production process blueprint, and obtaining production operation parameters based on the production operation data; determining a current processing procedure according to the current procedure characteristics; querying a standard process sequence from the production process blueprint to obtain a current process standard parameter corresponding to the current processing process; comparing the production operation parameter with a current process standard parameter to obtain a process deviation value; and when the process deviation value exceeds a preset allowable deviation threshold value, reporting a production abnormity warning, and recording the process deviation value to a preset digital file. The manufacturing method has the effect of improving the production stability of the manufacturing tool.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial automation, in particular to an industrial tool digital intelligent recording method and a computing instrument. BACKGROUND

[0002] Industrial tool digital intelligent recording refers to collecting, storing, analyzing and managing the information related to the tool used in industrial production through digital technology throughout the cycle, automatically, and finally realizing the intelligent management mode of tool state traceability and production risk early warning.

[0003] At present, the production and operation of industrial tools mainly rely on manual recording of key parameters by operators according to experience. In the production process, the operator needs to manually record the actual running data by manually judging the current process according to the process file, so as to complete the monitoring and recording of the production process.

[0004] However, the manual recording method is prone to data recording errors, delayed updates, information islands, and difficult real-time tracing of tool state, which reduces the production stability of the tool and needs to be improved. SUMMARY

[0005] In order to improve the production stability of the tool, the present application provides an industrial tool digital intelligent recording method and a computing instrument.

[0006] In the first aspect, the present application provides an industrial tool digital intelligent recording method, which adopts the following technical scheme: An industrial tool digital intelligent recording method, comprising: collecting production tool information and production running data; matching the production process blueprint according to the production tool information; extracting the current process characteristics based on the production running data and the production process blueprint, and obtaining the production running parameters based on the production running data; determining the current machining process according to the current process characteristics; querying the standard process sequence from the production process blueprint to obtain the current process standard parameters corresponding to the current machining process; comparing the production running parameters with the current process standard parameters to obtain the process deviation value; when the process deviation value exceeds the preset allowable deviation threshold, reporting a production abnormal warning, and recording the process deviation value in the preset digital archive.

[0007] By adopting the above technical solution, production tooling information and production operation data are first collected, and the corresponding production process blueprint is accurately matched based on the tooling information. Then, the production operation data and the process blueprint are compared to extract the characteristics of the current process to determine the current processing step, while retrieving the standard parameters of the process. The process deviation value is calculated by comparing the real-time production operation parameters with the standard parameters. When the deviation exceeds the allowable threshold, a production anomaly warning is immediately reported to facilitate timely intervention and adjustment and prevent the expansion of defects. At the same time, all deviation data is automatically recorded to a digital archive, realizing full-cycle traceability of the tooling production process. This eliminates the errors and lags of manual recording and improves the stability and controllability of tooling production as a whole.

[0008] Optionally, it may also include specific steps for extracting the features of the current process: Collect real-time production videos and production sensor data of the fixture; The real-time production video is input into a preset visual recognition model to extract tool shape features and operation trajectory. Multimodal feature vectors are generated by combining tool shape characteristics, operation trajectory, and production sensor data; Based on the production process blueprint, process feature templates are obtained; The multimodal feature vector is matched with the process feature template to obtain the current process feature.

[0009] By adopting the above technical solution, production tooling information and production operation data are first collected, and the corresponding production process blueprint is accurately matched based on the tooling information. Then, the characteristics of the current process are extracted by combining the production operation data and the process blueprint. While determining the current processing step, the standard parameters of the current step are retrieved from the standard process sequence. The process deviation value is obtained by comparing the real-time production operation parameters with the current process standard parameters. If the deviation value exceeds the allowable deviation threshold, a production anomaly warning is immediately reported, and the process deviation value is recorded in a preset digital file. This achieves full-cycle traceability of the tooling production process, thereby improving the overall stability and controllability of tooling production.

[0010] Optional, also includes: If the current processing step is consistent with the preset completion step, collect the regional image information of the preset finished product completion area; When the regional image information contains preset finished product features of the fixture, the finished product features of the fixture are scanned and identified from the regional image information to obtain the current product shape quality; If the current product's appearance quality is not lower than the preset non-conforming quality standard, a finished product counting signal is triggered, thereby automatically updating the finished product quantity statistics. The production batch number is derived based on the production fixture information, and the current equipment status parameters and production processing time are collected. The system combines finished product quantity statistics, production batch number, production processing time, and equipment status parameters to generate a complete production record, which is then stored in a digital archive.

[0011] By adopting the above technical solution, upon completion of all processes, images of the finished product area are captured, and the characteristics of the finished fixture are identified to obtain its shape quality. If the quality is qualified, counting is automatically triggered and the finished product quantity statistics are updated. Combined with the production batch number, processing time, and equipment status parameters, a complete production record is generated and stored in a digital archive. This automates finished product statistics and recording, avoids errors from manual counting and recording, and provides complete records covering all production elements, further enhancing the traceability of fixture production and improving the completeness and accuracy of production management.

[0012] Optional, also includes: Collect tooling order information; Based on the fixture order information, obtain the required quantity of fixtures and the current fixture molds; The current quantity of manufacturing tools can be determined based on the finished product quantity statistics; If the current quantity of fixtures matches the required quantity, collect subsequent order information; Determine the mold requirements for subsequent tooling based on subsequent order information; If the mold required for subsequent tooling is different from the current mold, the preset clamping device will clamp the current mold to the preset mold placement area.

[0013] By adopting the above technical solution, the required quantity of fixtures and the current mold are obtained by first collecting fixture order information; the current fixture quantity is determined based on the finished product quantity; when the quantity matches the requirement, subsequent order information is collected and the subsequent required mold is determined. If the subsequent mold is different from the current one, the clamping device is controlled to move the current mold to a preset placement area. This avoids the tediousness and errors of manual judgment and mold handling, ensuring that subsequent production can quickly switch to the corresponding mold, thus improving the continuity of fixture production and order response efficiency.

[0014] Optionally, it also includes the step of the clamping device clamping the current fixture mold into the mold placement area: Collect the mold weight value; The standard weight value is derived based on the current tooling and mold; When the weight of the mold exceeds the standard weight value, the control clamping device switches the clamping route to the preset cleaning route and collects the current position of the mold; When the mold falls into the preset cleaning area, the mold is cleaned using the preset mold cleaning method, and the weight value of the mold after cleaning is collected. When the weight of the mold after cleaning exceeds the standard weight, the control clamping device will transport the current mold to the preset abnormal cleaning area and report the prompt. When the weight of the mold after cleaning does not exceed the standard weight value, the control clamping device will transport the current mold to the mold placement area.

[0015] By adopting the above technical solution, when the clamping device transports the mold, it first collects the mold's weight and compares it with the standard weight. If the mold is overweight, it switches to the cleaning route, transports the mold to the cleaning area for cleaning, and weighs it again. If the mold is still overweight after cleaning, it is transported to the abnormal cleaning area and a notification is sent. If the weight is normal, it is then transported to the mold placement area. This achieves automated detection and handling of mold overweight issues, preventing molds from carrying residues that could affect subsequent production.

[0016] Optionally, the mold cleaning method includes: When the current position of the mold falls into the preset cleaning area, the mold opening parameters are determined based on the current mold. The clamping device is controlled to stop moving, and the current mold is opened according to the mold opening parameters, and then the internal image information is acquired. When the internal image information contains preset features of attached foreign objects, the internal image information, the features of attached foreign objects, and the preset features of the inner wall of the mold are combined to generate a foreign object removal route. Based on the foreign object removal route, a preset removal device is controlled to remove the attached foreign object; When the foreign object removal is completed or the internal image information does not contain the preset attached foreign object characteristics, the preset cleaning device is controlled to clean according to the preset cleaning process, and after the cleaning process is completed, the clamping device is controlled to transport the current tooling mold to the mold placement area.

[0017] By adopting the above technical solution, after the mold enters the cleaning area, the mold opening parameters are determined according to the current mold and the mold is opened, and the internal image is collected; if there are attached foreign objects in the image, the hook removal route is generated by combining the image and mold features, and the control device removes the foreign objects; after that, regardless of whether there are foreign objects, the cleaning device is used to clean according to the preset process, and finally it is transported to the placement area.

[0018] This approach automates and enhances the precision of mold cleaning. By using image recognition to target and remove foreign objects, combined with a standardized cleaning process, it ensures the mold remains clean, preventing residues from affecting the quality of subsequent tooling production and improving the thoroughness and efficiency of mold cleaning.

[0019] Optionally, mold stabilization methods during foreign object removal may also be included: Collect the volume of the attached foreign object; Based on the current mold, the volume of attached foreign matter, and the route for removing the foreign matter, a predicted value for mold wobbling is generated. Based on the predicted value of mold wobbling, clamping parameters are generated and adjusted accordingly. Collect mold displacement data and mold vibration frequency during the hooking process; Based on mold displacement data, mold vibration frequency, and clamping parameter adjustment parameters, clamping parameter correction parameters are obtained; During the hook removal process, the clamping device is controlled to adjust the clamping parameters by correcting the clamping parameters, thereby stabilizing the mold.

[0020] By adopting the above technical solution, during foreign object removal, the volume of the attached foreign object is first collected, and the mold wobbling is predicted based on the mold, volume, and removal path to generate clamping parameter adjustment parameters. Then, the mold displacement and vibration frequency during the removal process are collected to generate clamping parameter correction parameters, and the clamping device is adjusted in real time to stabilize the mold. This dynamic clamping adjustment, combining prediction and real-time monitoring, avoids incomplete foreign object removal or mold damage caused by mold wobbling during removal, improving the accuracy and safety of foreign object removal and ensuring the mold cleaning effect and subsequent stability.

[0021] Optional, the method of transporting the products may also be included: Acquire current signals from the mold; When the current mold signal is the preset mold opening signal, the bottom lifting and fixing position is determined based on the production fixture information; The pre-set tooling transport device is controlled to clamp the produced tooling into the pre-set burr treatment area. At the same time, during the transport process, the pre-set lifting and fixing device is controlled to move with the tooling transport device and lift and fix the bottom lifting and fixing position of the tooling. Acquire moving image information; When the moving image information contains preset obstacle features, the obstacle size is obtained based on the moving image information and the obstacle features; The safe position of the device is obtained by combining the size of the obstacle, the bottom lifting and fixing position, and the preset size of the lifting and fixing device; Based on moving image information, obstacle features, and the safe location of the device, a detour route for the device is generated; The device movement parameters are generated based on the device's bypass route and the preset fixture movement speed. The control fixture transport device continues to clamp and move the fixture, while the lifting and fixing device is controlled to move along the device's bypass route according to the device's movement parameters, thereby avoiding obstacles.

[0022] By adopting the above technical solution, after the mold is opened, the bottom of the fixture is first lifted and fixed. When the transport device clamps the fixture to the burr treatment area, the lifting and fixing device simultaneously follows and fixes the bottom of the fixture. During movement, images are collected to identify obstacles. Combining the obstacle size and the lifting device size, a safe position is determined, and a detour route and movement parameters are generated to control the transport device and the lifting device to work together to avoid obstacles. This achieves stable fixing of the fixture during transportation and automatic obstacle avoidance, preventing the fixture from shaking or colliding and being damaged during transportation. It improves the safety, stability, and efficiency of product transportation and ensures the smooth progress of subsequent processing stages.

[0023] Optionally, edge removal methods are also included: Acquire images of the rough edge area in the rough edge processing region; When the rough edge area image contains preset fixture features, the location of the rough edge distribution is identified based on the rough edge area image and the fixture features. Iron powder injection parameters are generated based on the location of the burr distribution. The preset heating device heats the iron powder to a preset activation temperature, and then the preset spraying device sprays the heated iron powder onto the burr distribution position according to the iron powder spraying parameters.

[0024] By adopting the above technical solution, images of the burr treatment area are first acquired to identify the fixture features and burr distribution location, thereby generating iron powder spraying parameters. Then, the heating device is controlled to heat the iron powder to the activation temperature, and the spraying device sprays it onto the burr location according to the parameters. This method achieves precise burr location through image recognition, combined with directional spraying of heated iron powder, realizing automated burr removal. This improves the accuracy and efficiency of burr treatment, avoids potential fixture damage from manual handling, and ensures the quality of the finished fixture.

[0025] Secondly, this application provides an industrial tool digital intelligent recording and computing instrument, which adopts the following technical solution: An industrial tool digital intelligent recording computing instrument includes a memory and a processor. The memory stores a method for digital intelligent recording of industrial tools that can be loaded and executed by the processor.

[0026] In summary, this application includes at least one of the following beneficial technical effects: 1. By first collecting production tooling information and production operation data, and then accurately matching the corresponding production process blueprint based on the tooling information, the system compares the production operation data with the process blueprint to extract the characteristics of the current process to determine the current processing step, while retrieving the standard parameters for that step. The system compares the real-time production operation parameters with the standard parameters to calculate the process deviation value. When the deviation exceeds the allowable threshold, a production anomaly warning is immediately reported to facilitate timely intervention and adjustment and prevent the expansion of defects. At the same time, all deviation data is automatically recorded to a digital archive, realizing full-cycle traceability of the tooling production process. This eliminates the errors and lags of manual recording and improves the overall stability and controllability of tooling production. 2. During foreign object removal, the system first collects the volume of the attached foreign object, then predicts mold wobbling based on the mold, volume, and removal path, generating clamping parameter adjustment parameters. Next, it collects mold displacement and vibration frequency during removal to generate clamping parameter correction parameters, adjusting the clamping device in real time to stabilize the mold. This dynamic clamping adjustment, combining prediction and real-time monitoring, avoids incomplete foreign object removal or mold damage caused by mold wobbling during removal, improving the accuracy and safety of foreign object removal and ensuring mold cleaning effectiveness and subsequent stability. 3. After mold opening, the bottom of the fixture is first positioned for lifting and fixing. When the transport device clamps the fixture to the burr treatment area, the lifting and fixing device simultaneously follows and fixes the bottom of the fixture. During movement, images are captured to identify obstacles. Based on the obstacle size and the lifting device size, a safe position is determined, and a detour route and movement parameters are generated. The transport device and lifting device are then controlled to work together to avoid obstacles. This achieves stable fixing of the fixture during transport and automatic obstacle avoidance, preventing fixture shaking or collision damage during transport, improving the safety, stability, and efficiency of product transport, and ensuring the smooth progress of subsequent processing stages. Attached Figure Description

[0027] Figure 1 This is a flowchart of a digital intelligent recording method for industrial fixtures; Figure 2 This is a simplified diagram illustrating the removal of foreign objects from the current tooling and mold within the cleaning area.

[0028] The parts referred to by the numbers in the above attached figures are as follows: 1. Clamping device; 2. Current fixture mold; 3. Cleaning area; 4. Attached foreign matter; 5. Hook removal device. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0030] Reference Figure 1 This application discloses a digital intelligent recording method for industrial fixtures, comprising the following steps: S10: Collect information on production fixtures and production operation data.

[0031] Production tooling information refers to the basic attribute data of the tooling that needs to be used in production, including tooling number, model specifications, and design parameters.

[0032] Production fixture information is obtained by scanning the RFID tag of the fixture. The method of obtaining production fixture information by scanning the RFID tag of the fixture is common knowledge in this field and will not be elaborated here.

[0033] Production operation data refers to real-time monitoring data generated during the production process of the fixture, including sensor readings and equipment operating status.

[0034] Production operation data is obtained in real time through various sensors installed on the mold. The specific sensors installed are set in advance by those skilled in the art according to requirements, and will not be described in detail here.

[0035] S11: Match the production process blueprint based on the production tooling information.

[0036] A production process blueprint is a pre-defined standard process configuration file for a specific product, which includes the sequence of processes and standard process parameters.

[0037] By understanding the information of the production fixtures, the fixture number is obtained. Then, the production process blueprint corresponding to the fixture number is matched from the preset cloud process library. The cloud process library stores the mapping relationship between different fixture numbers and production process blueprints. The cloud process library is manually configured and established by those skilled in the art based on the fixture design drawings, processing requirements and historical process data, which will not be elaborated here.

[0038] S12: Extract the characteristics of the current process based on production operation data and production process blueprint, and obtain production operation parameters based on production operation data.

[0039] Current process features refer to feature vectors extracted from production operation data that can characterize the current processing status.

[0040] The current process characteristics are obtained through real-time analysis and feature extraction of production operation data. The specific extraction steps will be explained in detail in subsequent sections S20 to S24, and will not be repeated here.

[0041] Production operation parameters refer to the specific parameter values ​​quantified from production operation data.

[0042] Production operation parameters are key process parameters obtained directly from production operation data, including but not limited to temperature, pressure, and speed values. Specifically, standardized production operation parameters are obtained by calibrating and converting units on the raw data collected by sensors. The calibration methods and unit conversion rules used in the parameter analysis process are common knowledge in the field and will not be elaborated upon here.

[0043] S13: Determine the current processing step based on the characteristics of the current process.

[0044] The current processing step refers to the specific processing stage that the tool is currently performing.

[0045] The current processing step is obtained by querying a preset process identification lookup table. This lookup table records the current processing steps corresponding to different current process characteristics. The process identification lookup table is established by those skilled in the art by collecting a large amount of historical processing data and analyzing the correspondence between different process characteristics and processing steps. The content of the lookup table is configured according to actual production needs, which will not be elaborated here.

[0046] S14: Query the standard process sequence from the production process blueprint to obtain the standard parameters of the current process corresponding to the current processing process.

[0047] The current process standard parameters refer to the standard process parameter values ​​corresponding to the current processing process, including process specification indicators such as standard temperature range, standard pressure value, and standard processing speed.

[0048] The standard parameters for the current process are obtained by consulting a preset standard parameter lookup table. This table records the standard parameters for different current processing steps. The standard parameter lookup table is manually configured and established by those skilled in the art based on the standard process sequence in the production process blueprint and by analyzing the process requirements of each step; this will not be elaborated upon here.

[0049] S15: Obtain the process deviation value by comparing the production operation parameters with the current process standard parameters.

[0050] Process deviation value refers to the quantitative index of the difference between production operation parameters and current process standard parameters, which is used to reflect the degree of deviation between the actual processing process and the standard process requirements.

[0051] The process deviation value is obtained by calculating the absolute difference between the production operation parameters and the current process standard parameters.

[0052] S16: When the process deviation value exceeds the preset allowable deviation threshold, report a production abnormality warning and record the process deviation value to the preset digital file.

[0053] The permissible deviation threshold refers to the maximum allowable deviation value of a process parameter. The permissible deviation threshold is set in advance by those skilled in the art and will not be elaborated here.

[0054] Digital archives refer to structured storage systems used to store data throughout the entire lifecycle of toolmaking.

[0055] Digital archives are built on cloud databases and receive and store various process data and event records through data interfaces.

[0056] It also includes the specific steps for extracting the features of the current process: S20: Collect real-time production video and production sensor data of the fixture.

[0057] Real-time production video refers to image data of the tooling process continuously collected by industrial cameras, including tool operation screens and visual information on working status.

[0058] Production sensor data refers to physical quantity data collected by sensors installed on the fixture, including process parameters such as pressure, temperature, and displacement. The specific sensors used are pre-selected by those skilled in the art based on requirements, and will not be elaborated upon here.

[0059] S21: Input the real-time production video into the preset visual recognition model to extract tool shape features and operation trajectory.

[0060] A visual recognition model refers to a trained deep learning network used to identify tool types and actions from video data. Visual recognition models are pre-defined by those skilled in the art and will not be elaborated upon here.

[0061] Tool morphological features refer to the geometric features of a tool extracted from a video frame, including its outline shape, size ratio, and spatial orientation.

[0062] Operation trajectory refers to the movement path characteristics of a tool during operation.

[0063] The tool's shape and operation trajectory are obtained by processing real-time production video into a visual recognition model.

[0064] Specifically, the object detection network in the visual recognition model locates the tool's position, and the feature extraction network obtains the tool's morphological features. Simultaneously, a trajectory tracking algorithm analyzes the tool's motion trajectory in the video sequence to generate operation action trajectory data. The specific implementation methods of the object detection network, feature extraction network, and trajectory tracking algorithm are well-known technologies in the field and will not be elaborated upon here.

[0065] S22: Combine tool shape features, operation trajectory and production sensor data to generate multimodal feature vectors.

[0066] Multimodal feature vectors refer to a unified feature representation formed by fusing feature data from different sources, including a joint representation of visual features and sensor features.

[0067] Multimodal feature vectors are generated by standardizing the tool's morphological features, operational trajectory, and sensor data, using feature concatenation. The feature dimension settings are determined by those skilled in the art based on actual needs and will not be elaborated upon here. The feature concatenation method is common knowledge in the field and will not be described in detail here.

[0068] S23: Obtain process feature templates based on production process blueprints.

[0069] A process feature template refers to standard process feature data extracted from the production process blueprint, which includes the expected standard feature range for each process.

[0070] The process feature templates are obtained by querying a preset feature template lookup table, which records the standard process feature data corresponding to different processes. The feature template lookup table is manually configured and established by those skilled in the art based on the process specifications in the production process blueprint, by analyzing the standard tool shapes, standard motion trajectories, and standard sensor data ranges of each process; this will not be elaborated upon here.

[0071] S24: Match the multimodal feature vector with the process feature template to obtain the current process feature.

[0072] By calculating the cosine similarity between the multimodal feature vector and the feature templates of each process, the feature template with the highest similarity is selected as the matching result, and its feature value is used as the current process feature. The similarity calculation threshold is set by those skilled in the art based on the actual situation, and will not be elaborated here.

[0073] Also includes: S30: If the current processing step is consistent with the preset completion step, collect the regional image information of the preset finished product completion area.

[0074] The final processing step refers to the last processing step in the production process, marking the end of the tooling process.

[0075] The finished product area refers to a designated area on the production line specifically used to place finished processed products.

[0076] The completion process and the finished product area are predetermined by those skilled in the art, and will not be elaborated here.

[0077] Regional image information refers to images of finished product areas captured by industrial cameras.

[0078] S31: When the regional image information contains preset finished product features of the fixture, the finished product features of the fixture are scanned and identified from the regional image information to obtain the current product shape quality.

[0079] Finished fixture characteristics refer to the visual features that a qualified finished fixture should possess. The finished product area is predetermined by those skilled in the art and will not be elaborated upon here.

[0080] Current product appearance quality refers to the evaluation result of the current product's appearance quality.

[0081] The current product appearance quality is determined by inputting regional image information into a pre-defined product recognition model. This model extracts the finished product features from the image using a convolutional neural network, compares them with standard features, and outputs a current product appearance quality score. The training and use of the product recognition model are well-known techniques in the field and will not be elaborated upon here.

[0082] S32: If the current product's appearance quality is not lower than the preset non-conforming quality standard, trigger the finished product counting signal to automatically update the finished product quantity statistics.

[0083] The nonconforming quality standard refers to the lowest quality threshold for judging a product as nonconforming. The nonconforming quality standard is set in advance by those skilled in the art and will not be elaborated here.

[0084] The finished product count signal is an electronic signal that triggers an update of the finished product quantity. The finished product count signal is transmitted through a preset signal transceiver.

[0085] The current product's appearance quality is compared with the non-conforming quality standard. When the quality score is not lower than the standard, it indicates that the fixture is qualified, and a finished product count signal is generated, automatically incrementing the finished product quantity count by 1. The comparison logic and counting mechanism are set by those skilled in the art according to quality requirements and will not be elaborated here.

[0086] S33: Based on the production fixture information, derive the production batch number and collect the current equipment status parameters and production processing time.

[0087] The production batch number refers to the code of the current production batch of the fixture.

[0088] The production batch number can be retrieved from the production fixture information, which contains the production batch number.

[0089] Production processing time refers to the timestamp of completing the processing of the current product. Production processing time can be obtained from the system clock.

[0090] Current equipment status parameters refer to the current operating status data of the fixture-making equipment.

[0091] Current equipment status parameters are acquired in real time through sensor arrays installed on the fixture equipment and the equipment control system. Sensor selection, installation location, and data acquisition frequency are determined by those skilled in the art based on equipment characteristics and monitoring requirements, and will not be elaborated upon here. The acquired status parameters, after signal conditioning and analog-to-digital conversion, are transmitted to the central processing system via the equipment data bus.

[0092] S34: Combine finished product quantity statistics, production batch number, production processing time, and equipment status parameters to generate a complete production record, and store the complete production record in a digital archive.

[0093] Complete production records refer to structured data records that contain complete production information.

[0094] By integrating finished product quantity statistics, production batch numbers, production processing time, and equipment status parameters, a complete production record in a standard format can be generated, which is then stored in a digital archive system via a database interface. The data integration and storage methods are well-known technologies in the field and will not be elaborated upon here.

[0095] Also includes: S40: Collect tooling order information.

[0096] Fixture order information refers to the relevant data of the fixtures customized by the customer, including order number, product specifications, delivery date, quantity requirements, etc.

[0097] The fixture order information is pre-entered by those skilled in the art and will not be elaborated upon here.

[0098] S41: Based on the fixture order information, obtain the fixture requirement quantity and the current fixture mold 2.

[0099] The required quantity of fixtures refers to the total number of fixtures required to be produced in the order. This required quantity can be obtained by reviewing the fixture order information. The fixture order information contains the required quantity of fixtures.

[0100] The current tooling mold 2 refers to the mold used to produce the tooling for this tooling order information.

[0101] The current fixture mold 2 is obtained by querying a preset mold lookup table, which records the current fixture mold 2 corresponding to different fixture order information. The mold lookup table is manually configured and established by those skilled in the art based on the production plan and mold configuration requirements by analyzing the correspondence between order product specifications and mold models, and will not be elaborated here.

[0102] S42: The current quantity of fixtures is known based on the finished product quantity statistics.

[0103] The current quantity of fixtures refers to the quantity of fixtures that have been produced and passed inspection.

[0104] By understanding the finished product quantity statistics, you can find out the current number of qualified tooling units produced, i.e., the current tooling quantity.

[0105] S43: If the current quantity of fixtures matches the required quantity of fixtures, collect subsequent order information.

[0106] Subsequent order information refers to the data of the next batch of tooling orders to be produced.

[0107] The highest-priority orders awaiting production are retrieved from the order management system as subsequent order information. The order management system contains all subsequent orders that have not yet been produced. The order priority determination rules are set by those skilled in the art based on the production plan and will not be elaborated upon here.

[0108] If the current quantity of fixtures matches the required quantity, it means that the fixtures for the current batch have been produced. It is necessary to collect subsequent order information for subsequent steps.

[0109] S44: Determine the mold requirements for subsequent tooling based on subsequent order information.

[0110] Subsequent tooling requirements refer to the mold models required for subsequent order production.

[0111] The mold requirements for subsequent fixture manufacturing are obtained by consulting a pre-set order mold lookup table. This table records the mold requirements for different subsequent orders. The order mold lookup table is manually configured and established by those skilled in the art based on the product process requirements by analyzing the correspondence between the product specifications and the required mold models. This will not be elaborated upon here.

[0112] S45: If the mold required for subsequent tooling is different from the current tooling mold 2, control the preset clamping device 1 to clamp the current tooling mold 2 to the preset mold placement area.

[0113] Clamping device 1 refers to an automated mechanical device used for handling molds.

[0114] The mold placement area refers to a designated area specifically used for storing molds. The mold placement area is predetermined by those skilled in the art and will not be elaborated upon here.

[0115] When a mismatch in mold models is detected, a mold replacement instruction needs to be generated to control the clamping device 1 to pick up the current mold 2 and transport it to the mold placement area for subsequent mold replacement.

[0116] It also includes the steps of clamping the current die 2 into the die placement area by the clamping device 1: S50: Collect mold weight value.

[0117] The mold weight value refers to the actual weight measurement of the current mold 2.

[0118] The weight of the mold is collected in real time by a weight sensor installed on the clamping device 1.

[0119] S51: Determine the standard weight value based on the current fixture mold 2.

[0120] The standard weight value refers to the standard weight of the current tooling mold 2 in a clean state.

[0121] The standard weight value is obtained by consulting a pre-set mold weight reference table, which records the standard weight values ​​corresponding to different mold models. The mold weight reference table is manually configured and established by those skilled in the art based on mold design drawings and technical parameters, and will not be elaborated here.

[0122] S52: When the weight of the mold exceeds the standard weight value, the clamping device 1 is controlled to switch the clamping route to the preset cleaning route and collect the current position of the mold.

[0123] The cleanup route refers to the predetermined transportation path to cleanup area 3. The cleanup route is predetermined by those skilled in the art and will not be described in detail here.

[0124] The current position of the mold refers to the real-time position coordinates of the mold in the workshop coordinate system.

[0125] The current position of the mold is obtained by a positioning sensor installed on the clamping device 1.

[0126] When the detected mold weight exceeds the standard weight value, it indicates that there are residual foreign objects inside the mold. It is necessary to control the clamping device 1 to switch the clamping route to the cleaning route and then collect the current position of the mold for subsequent steps.

[0127] S53: When the current position of the mold falls into the preset cleaning area 3, the mold is cleaned using the preset mold cleaning method, and the weight value of the mold after cleaning is collected.

[0128] Cleaning area 3 refers to the working area specifically used for mold cleaning. Cleaning area 3 is predetermined by those skilled in the art and will not be described in detail here.

[0129] Mold cleaning methods refer to methods for removing deposits from inside the mold. Specific mold cleaning methods will be explained in detail in subsequent sections S60 to S64, and will not be repeated here.

[0130] The weight value of the mold after cleaning refers to the weight measurement of the mold after cleaning.

[0131] The weight of the mold after cleaning is obtained by a weight sensor.

[0132] When the mold falls into cleaning area 3, it needs to be cleaned using the mold cleaning method, and the weight value of the mold after cleaning should be collected for subsequent steps.

[0133] S54: When the weight value of the mold after cleaning exceeds the standard weight value, the control clamping device 1 will transport the current mold 2 to the preset abnormal cleaning area and report the prompt.

[0134] The "cleaning anomaly area" refers to a dedicated, isolated area for storing molds that have been cleaned to prevent damage. This area is pre-defined by those skilled in the art and will not be elaborated upon here.

[0135] If the weight of the mold after cleaning still exceeds the standard weight value, the clamping device 1 needs to be controlled to transport the mold to the abnormal cleaning area, and at the same time, an abnormal prompt message is generated and reported to the monitoring system.

[0136] S55: When the weight value of the mold after cleaning does not exceed the standard weight value, control the clamping device 1 to transport the current mold 2 to the mold placement area.

[0137] When the weight of the mold after cleaning meets the standard, the clamping device 1 controls the mold to be transported to the mold placement area according to the original route.

[0138] Mold cleaning methods include: S60: When the current position of the mold falls into the preset cleaning area 3, the mold opening parameters are determined based on the current mold 2.

[0139] Mold opening parameters refer to the control parameters required when performing mold opening operations.

[0140] The mold opening parameters are obtained by consulting a preset mold opening parameter lookup table, which records the mold opening parameters corresponding to different current molds 2. The mold opening parameter lookup table is manually configured and established by those skilled in the art based on the mold structure characteristics by analyzing the required parameters such as mold opening distance, mold opening speed, and mold opening angle for different mold models, and will not be elaborated here.

[0141] When the mold falls into cleaning area 3, the mold opening parameters need to be checked first for subsequent steps.

[0142] S61: Control the clamping device 1 to stop moving, open the current mold 2 with the mold opening parameters, and then collect internal image information.

[0143] Internal image information refers to images of the internal surface of the mold. Internal image information is obtained by capturing images using a camera.

[0144] Once the mold opening parameters are obtained, the clamping device 1 needs to be stopped from moving, and the mold 2 of the current tooling needs to be opened according to the mold opening parameters. Then, internal image information needs to be collected for subsequent steps.

[0145] S62: When the internal image information contains preset attached foreign object features, combine the internal image information, attached foreign object features, and preset mold inner wall features to generate a foreign object removal route.

[0146] The characteristics of foreign matter attachment refer to the visual features of foreign matter remaining inside the mold.

[0147] The characteristics of the inner wall of a mold refer to the structural features of the inner surface of the mold.

[0148] The characteristics of the attached foreign matter and the inner wall of the mold are predetermined by those skilled in the art and will not be elaborated here.

[0149] The foreign object removal route refers to the movement path of the hook-removal device 5 when removing foreign objects.

[0150] By identifying the spatial location of the attached foreign object within the mold and combining this with the three-dimensional structural data of the mold's inner wall features, a path planning algorithm is used to calculate the optimal hooking path. The specific implementations of the image analysis method and the path planning algorithm are well-known techniques in the field and will not be elaborated upon here.

[0151] S63: Based on the foreign object removal route, the preset removal device 5 is controlled to remove the attached foreign object 4.

[0152] The hook removal device 5 refers to a special tool used to remove foreign objects from inside the mold.

[0153] The control hook removal device 5 moves along the foreign object removal route, thereby removing the attached foreign object 4 by mechanical hooking and dropping it into the water pool of the cleaning area 3.

[0154] S64: When the foreign object removal is completed or the internal image information does not contain the preset attached foreign object features, control the preset cleaning device to clean according to the preset cleaning process, and when the cleaning process is completed, control the clamping device 1 to transport the current tooling mold 2 to the mold placement area.

[0155] A cleaning device is an automated equipment used for cleaning the surface of a mold. It can perform operations such as blowing, rinsing, and drying inside the mold.

[0156] The cleaning process refers to the standard operating procedure for cleaning the mold. In this embodiment, the cleaning process is blowing, rinsing, and drying.

[0157] When the removal of foreign objects is completed or the internal image information does not contain any features of attached foreign objects, the cleaning device needs to be controlled to perform the cleaning process. After the cleaning process is completed, the clamping device 1 is controlled to transport the current tooling mold 2 to the mold placement area.

[0158] It also includes mold stabilization methods during foreign object removal: S70: Collect the volume of attached foreign matter.

[0159] The volume of an attached foreign object refers to the size of the volume occupied by the identified foreign object in three-dimensional space.

[0160] The volume of the attached foreign object is calculated by extracting the three-dimensional contour information of the foreign object from the internal image information and combining it with preset calibration parameters.

[0161] Specifically, point cloud data of the foreign object can be acquired using a binocular vision system or structured light 3D scanning technology, and then the volume of the foreign object can be calculated using a voxelization algorithm. The specific algorithms for 3D reconstruction and volume calculation are well-known technologies in this field and will not be elaborated here.

[0162] S71: Combine the current mold 2, the volume of the attached foreign object, and the route for removing the foreign object to generate a predicted value for mold shaking.

[0163] The predicted value of mold wobbling refers to a quantitative indicator that predicts the degree of wobbling that the mold may produce during the hooking process.

[0164] By inputting the structural parameters of the current mold 2, the volume of the attached foreign object, and the foreign object removal route into a preset mechanical simulation model, the degree of mold sway is predicted and a predicted value is generated by calculating the forces during the removal process. The specific parameters of the mechanical simulation model are set by those skilled in the art based on the mold characteristics, and will not be elaborated here.

[0165] S72: Adjust the clamping parameters based on the predicted values ​​of mold wobbling.

[0166] The clamping parameter adjustment parameters refer to the control parameters that need to be adjusted for the clamping device 1 to suppress mold wobbling.

[0167] The clamping parameter adjustment parameters are obtained by consulting a preset clamping adjustment reference table, which records the clamping parameter adjustment parameters corresponding to the predicted values ​​of different mold wobbling conditions. The clamping adjustment reference table is manually configured and established by those skilled in the art based on the characteristics and stability requirements of the clamping device 1 by analyzing the clamping force adjustment required for different wobbling conditions, and will not be elaborated here.

[0168] S73: Collect mold displacement data and mold vibration frequency during the hooking process.

[0169] Mold displacement data refers to the amount of displacement generated by the mold during the removal process. Mold displacement data is acquired in real time through displacement sensors.

[0170] The mold vibration frequency refers to the vibration frequency characteristics of the mold during the hook-and-unhook process. The mold vibration frequency is obtained by collecting data through a vibration sensor.

[0171] S74: Based on mold displacement data, mold vibration frequency, and clamping parameter adjustment parameters, the clamping parameter correction parameters are obtained.

[0172] Clamping parameter correction parameters refer to the final parameters that are further corrected based on real-time monitoring data.

[0173] The clamping parameter correction parameters are calculated by inputting mold displacement data, mold vibration frequency, and clamping parameter adjustment parameters into the adaptive control algorithm.

[0174] Specifically, a closed-loop control system incorporating displacement and vibration feedback is established, and a PID control algorithm is used to correct the clamping parameters in real time, generating the final corrected clamping parameters. The specific parameters of the control algorithm are determined by those skilled in the art based on the characteristics of the clamping device 1, and will not be elaborated here.

[0175] S75: During the hook removal process, the clamping device 1 is controlled to adjust the clamping parameters by adjusting the clamping parameters to stabilize the mold.

[0176] During the hooking process, the clamping device 1 is controlled to adjust the clamping parameters by correcting the clamping parameters to counteract the shaking generated during the hooking process and ensure the stability of the mold.

[0177] It also includes methods for transporting products: S80: Acquire current signals from the mold.

[0178] The current mold signal refers to signal data reflecting the real-time working status of the mold. The current mold signal is acquired through a signal transceiver.

[0179] S81: When the current mold signal is the preset mold opening signal, the bottom lifting and fixing position is determined based on the production fixture information.

[0180] A mold opening signal is a specific signal indicating that the mold opening operation has been completed. Mold opening signals are preset by those skilled in the art and will not be elaborated upon here.

[0181] The bottom lifting and fixing position refers to the specific position where the bottom of the tooling is lifted and fixed during the transportation of the tooling to reduce the shaking of the tooling.

[0182] The bottom lifting and fixing position is obtained by consulting a preset lifting position lookup table, which records the bottom lifting and fixing positions corresponding to different production fixture information. The lifting position lookup table is manually configured and established by those skilled in the art based on the structural characteristics and stability requirements of the fixtures by analyzing the center of gravity distribution and support requirements of different fixture models, and will not be elaborated here.

[0183] When the current signal of the mold is the mold opening signal, it means that the mold has been opened, which means that the tooling has been completed. The bottom lifting and fixing position needs to be determined first for subsequent steps.

[0184] S82: Control the preset fixture transport device to clamp the produced fixture to the preset burr treatment area. At the same time, during the transport process, control the preset lifting and fixing device to move with the fixture transport device and lift and fix the bottom lifting and fixing position of the fixture.

[0185] A tool transport device is a device used to clamp and transport tooling.

[0186] The burr removal area refers to a work area specifically used for removing burrs from tooling. The burr removal area is predetermined by those skilled in the art and will not be elaborated upon here.

[0187] A lifting and fixing device is a device used to lift and fix the bottom of a tooling during transportation to reduce swaying.

[0188] The control fixture transport device clamps the produced fixture to the burr treatment area. At the same time, during the transport process, the control lifting and fixing device moves with the fixture transport device and lifts and fixes the bottom lifting and fixing position of the fixture to reduce shaking during the fixture transport process.

[0189] S83: Acquire moving image information.

[0190] Moving image information refers to real-time environmental images captured during transportation. This information is obtained in real-time by industrial cameras mounted on a lifting and fixing device.

[0191] S84: When the moving image information contains preset obstacle features, obtain the obstacle size based on the moving image information and obstacle features.

[0192] Obstacle features refer to the visual characteristic parameters of various obstacles. Obstacle features are pre-defined by those skilled in the art and will not be elaborated upon here.

[0193] Obstacle dimensions refer to the external dimensions of an obstacle.

[0194] Image measurement techniques can be used to calculate and identify the dimensions of obstacles in moving image information, i.e., the obstacle dimensions. The identification algorithms and measurement methods are well-known in the field and will not be elaborated upon here.

[0195] When moving image information contains obstacle features, the size of the obstacle must be identified first for subsequent steps.

[0196] S85: Combine the obstacle size, the bottom lifting and fixing position, and the preset lifting and fixing device size to obtain the safe position of the device.

[0197] The dimensions of the lifting and fixing device refer to the external dimensional parameters of the lifting and fixing device. The dimensions of the lifting and fixing device are predetermined by those skilled in the art and will not be elaborated here.

[0198] The safe position of a device refers to the coordinates of a safe position that can avoid obstacles.

[0199] By understanding the obstacle's dimensions, the obstacle boundary is determined. A three-dimensional spatial model is then established based on the bottom lifting and fixing position and the dimensions of the lifting and fixing device. A collision detection algorithm is used to calculate the safe position coordinates where the device will not interfere with the obstacle; this is the safe position of the device. The specific algorithms for spatial modeling and collision detection are well-known in the field and will not be elaborated upon here.

[0200] S86: Generate a detour route for the device based on moving image information, obstacle features, and the device's safe location.

[0201] The device detour route refers to the optimized travel route to avoid obstacles.

[0202] The device's detour route is generated in real time using a path planning algorithm. First, an environmental map is constructed based on moving image information to identify the location and contours of obstacles. Starting from the device's current location and targeting its safe location, the optimal obstacle avoidance path is calculated using the A* algorithm. The specific implementation of the path planning algorithm is well-known in the field and will not be elaborated upon here.

[0203] S87: Generate device movement parameters based on the device detour route and the preset fixture movement speed.

[0204] The tooling moving speed refers to the moving speed of the tooling transport device. The tooling moving speed is preset by those skilled in the art and will not be elaborated here.

[0205] Device movement parameters refer to the specific parameters that control the movement of the jacking and fixing device.

[0206] The device's movement parameters are calculated using a kinematic model. First, based on the path curvature and length of the device's bypass route, and combined with the fixture's movement speed, the movement speed, acceleration, and trajectory parameters of the lifting and fixing device are calculated using kinematic equations; these are the device's movement parameters. The specific methods for establishing the kinematic model and the parameter calculation process are well-known techniques in the field and will not be elaborated upon here.

[0207] S88: Control the fixture transport device to continue clamping and moving the fixture, while controlling the lifting and fixing device to move along the device bypass route according to the device movement parameters, so as to avoid obstacles.

[0208] The control fixture transport device continues to clamp the fixture and move it toward the burr treatment area. At the same time, the control lifting and fixing device moves along the device bypass route according to the device movement parameters to avoid obstacles. After avoiding obstacles, the device can continue to lift and fix the fixture.

[0209] It also includes methods for removing rough edges: S90: Acquire an image of the rough edge area in the rough edge processing region.

[0210] A rough edge area image refers to a visual image captured by a camera that includes the rough edge area of ​​the fixture.

[0211] S91: When the rough edge area image contains preset fixture features, the location of the rough edge distribution is identified based on the rough edge area image and the fixture features.

[0212] The fixture features refer to the external shape characteristics of the finished product. The fixture features are predetermined by those skilled in the art and will not be elaborated here.

[0213] The location of burrs refers to the location of burrs present on the edge of the tool.

[0214] By inputting the image of the rough edge area into a preset fixture recognition model, the fixture features are identified, and then the location of the rough edge distribution is determined through edge detection algorithms and differential image analysis. The specific implementation of the recognition model and image processing algorithm is well-known in the field and will not be elaborated here.

[0215] When the image of the rough edge area contains tooling features, the location of the rough edge distribution needs to be identified first for subsequent steps.

[0216] S92: Generate iron powder spraying parameters based on the location of the burr distribution.

[0217] Iron powder injection parameters refer to the various process parameters that control iron powder injection, including injection volume, injection pressure, injection angle, injection duration, and injection coverage.

[0218] The iron powder spraying parameters are obtained by consulting a preset spraying parameter reference table, which records the iron powder spraying parameters corresponding to different burr distribution locations. The spraying parameter reference table is manually configured and established by those skilled in the art based on the requirements of the burr treatment process by analyzing the required combinations of spraying parameters for different burr distribution locations, and will not be elaborated here.

[0219] S93: Control the preset heating device to heat the iron powder to the preset activation temperature, and then spray the heated iron powder onto the burr distribution position through the preset spraying device according to the iron powder spraying parameters.

[0220] A heating device is a specialized piece of equipment used to heat iron powder.

[0221] Activation temperature refers to the temperature value at which iron powder reaches its optimal reactivity. The activation temperature is set in advance by those skilled in the art and will not be elaborated here.

[0222] A spraying device is a specialized piece of equipment used to spray heated iron powder.

[0223] The heating device is controlled to heat the iron powder to the activation temperature, and then the spraying device is controlled to precisely spray the iron powder onto the burr distribution location according to the iron powder spraying parameters, thereby achieving the thermochemical removal of the burrs.

[0224] Based on the same inventive concept, embodiments of the present invention provide an industrial fixture digital intelligent recording computing instrument, including a memory and a processor, wherein the memory stores a method for industrial fixture digital intelligent recording that can be loaded and executed by the processor.

[0225] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0226] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An industrial tool digital smart record method, characterized by, The method comprises the following steps: Collecting production tool information and production operation data; Matching production process blueprint based on production tool information; Extracting current process features based on production operation data and production process blueprint, and obtaining production operation parameters based on production operation data; Determining the current machining process according to the current process features; Querying the standard process sequence from the production process blueprint to obtain the current machining process corresponding to the current process standard parameters; Comparing the production operation parameters with the current process standard parameters to obtain the process deviation value; When the process deviation value exceeds the preset allowable deviation threshold, report the production abnormal warning, and record the process deviation value in the preset digital archive.

2. The method of claim 1, wherein, The specific steps of extracting the current process features are as follows: Collecting real-time production video and production sensor data of the tool; Inputting the real-time production video into the preset visual recognition model to extract tool shape features and operation action trajectory; Combining tool shape features, operation action trajectory and production sensor data to generate a multi-modal feature vector; Obtaining process feature templates based on production process blueprint; Matching and calculating the multi-modal feature vector with the process feature template to obtain the current process features.

3. The method of claim 1, wherein, Further comprising: If the current machining process is consistent with the preset finished process, collecting area image information of the preset finished product completion area; When the area image information contains the preset tool product features, scanning and identifying the tool product features from the area image information to obtain the current product shape quality; If the current product shape quality is not lower than the preset unqualified quality standard, trigger the finished product counting signal to automatically update the finished product quantity statistics; Based on the production tool information, the production batch number is obtained, and the current equipment state parameters and production processing time are collected; Combining the finished product quantity statistics, production batch number, production processing time and equipment state parameters to generate a complete production record, and storing the complete production record in the digital archive.

4. The method of claim 3, wherein, Further comprising: Collecting tool order information; Based on the tool order information, the tool demand quantity and the current tool mold (2) are obtained; According to the finished product quantity statistics, the current tool quantity is known; If the current tool quantity is consistent with the tool demand quantity, collect the subsequent order information; Based on the subsequent order information, determine the subsequent tool demand mold; If the subsequent tool demand mold is inconsistent with the current tool mold (2), control the preset clamping device (1) to clamp the current tool mold (2) to the preset mold placement area.

5. The method of claim 4, wherein, Further comprising the steps when the clamping device (1) clamps the current tool mold (2) to the mold placement area: Collecting the mold weight value; Based on the current tool mold (2), the standard weight value is obtained; When the mold weight value exceeds the standard weight value, control the clamping device (1) to switch the clamping route to the preset cleaning route, and collect the current mold position; When the current mold position falls into the preset cleaning area (3), clean the mold by the preset mold cleaning method, and collect the mold weight value after cleaning; When the mold weight value after cleaning exceeds the standard weight value, control the clamping device (1) to transport the current tool mold (2) to the preset cleaning abnormal area, and report the prompt; When the weight value of the mold after cleaning does not exceed the standard weight value, the clamping device (1) is controlled to transport the current mold (2) to the mold placement area.

6. The method of claim 5, wherein, The mold cleaning method comprises: When the current position of the mold falls into the preset cleaning area (3), the opening parameters of the current mold (2) are determined based on the current mold; The clamping device (1) is controlled to stop moving, and the current mold (2) is opened with the opening parameters, and internal image information is collected again; When the internal image information contains a preset foreign matter feature, the foreign matter hooking route is generated based on the internal image information, the foreign matter feature and the preset mold inner wall feature; The preset hooking device (5) is controlled to remove the attached foreign matter (4) based on the foreign matter hooking route; When the foreign matter removal is completed or the internal image information does not contain the preset foreign matter feature, the preset cleaning device is controlled to perform cleaning according to the preset cleaning process, and after the cleaning process is completed, the clamping device (1) is controlled to transport the current mold (2) to the mold placement area.

7. The method of claim 6, wherein, It also includes a mold stabilizing method during foreign matter hooking: The volume of the attached foreign matter is collected; The mold shaking situation prediction value is generated based on the current mold (2), the volume of the attached foreign matter and the foreign matter hooking route; The clamping parameter adjustment parameter is generated according to the mold shaking situation prediction value; The mold displacement data and the mold vibration frequency during the hooking process are collected; The clamping parameter correction parameter is obtained based on the mold displacement data, the mold vibration frequency and the clamping parameter adjustment parameter; During the hooking process, the clamping device (1) is controlled to adjust the clamping according to the clamping parameter correction parameter, so as to stabilize the mold.

8. The method of claim 1, wherein: It also includes a product transportation method: The current mold signal is collected; When the current mold signal is a preset mold opening signal, the bottom jacking fixed position is determined based on the production mold information; The preset mold transportation device is controlled to clamp the produced mold to the preset burr processing area, and in the transportation process, the preset jacking fixing device is controlled to move with the mold transportation device and jacks up the bottom fixed position of the mold; The moving image information is collected; When the moving image information contains a preset obstacle feature, the obstacle size is obtained based on the moving image information and the obstacle feature; The device safety position is obtained based on the obstacle size, the bottom jacking fixed position and the preset jacking device size; The device detour route is generated based on the moving image information, the obstacle feature and the device safety position; The device moving parameter is generated based on the device detour route and the preset mold moving speed; The mold transportation device is controlled to continue clamping the mold to move, and the jacking device is controlled to move along the device detour route according to the device moving parameter, so as to avoid the obstacle.

9. The method of claim 8, wherein, It also includes a burr removal method: The burr area image of the burr processing area is collected; When the burr area image contains a preset mold feature, the burr distribution position is identified based on the burr area image and the mold feature; The iron powder spraying parameter is generated based on the burr distribution position; The preset heating device is controlled to heat the iron powder to a preset activation temperature, and the heated iron powder is sprayed to the burr distribution position according to the iron powder spraying parameter through the preset spraying device.

10. An industrial tool digital intelligent recording computer, characterized by, The industrial tool digital smart record method as claimed in any one of claims 1 to 9, comprising a memory and a processor, the memory having stored thereon a program capable of being loaded and executed by the processor.

Images