Multi-device big data content optimization processing method

Abstract

The invention relates to the technical field of laser processing, in particular to a multi-device big data content optimization processing method, and the method comprises the steps: carrying out the initialization processing of a marking device through a step S1, obtaining the marking device and the current marking content through a step S2, carrying out the content separation of the current marking content through a step S3, and carrying out the optimization processing of the current marking content through a step S4. According to the method, laser machining is carried out according to different machining methods through the step S4, and according to the step S5, the state of index updating times is judged, and cold word statistics is carried out on all content of a control card storage area. According to the invention, the current marking content is subjected to content separation to obtain the minimum identification unit, and the minimum identification unit is optimized, so that repeated calculation during laser processing of equipment is reduced, the data downloading speed is increased, and the laser processing efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of laser processing, in particular to a multi-device large data content optimization processing method. BACKGROUND

[0002] As an energy-saving and environment-friendly identification scheme, laser marking has been applied in various industries. In order to save costs, one control card is usually used to control multiple devices in the prior art. However, if large data content is processed, a large amount of time is required for the computer to calculate and download the processing data to the control card, which will affect the calculation and download of processing data to other devices. Moreover, the processing content of many devices is often repetitive, and repeated calculation and download are also a waste of performance. Therefore, there is a need for a scheme for saving processing data to a laser control card and processing repetitive large data content by setting different offsets.

[0003] A Chinese patent application file with application number CN116441764A discloses a laser marking method, system, device, equipment and storage medium, which is applied to a laser marking system including a laser and a refracting mirror. The method includes: when a container to be marked is placed at a specified position, the scale line and scale content of the container to be marked are obtained, and the specified position is determined according to the size of the container and the position of the refracting mirror; the pulse parameters of the laser are adjusted according to the scale content and the container material of the container, so that the pulse parameters of the laser emitted through the refracting mirror act on the container to complete the marking of the scale line. It can be seen that this scheme still has the problem that the processing data of multiple devices is not optimized for use, which leads to repeated calculation of the devices during laser processing, more data download, slow data download speed, and waiting between different devices caused by long data processing time, resulting in low laser processing efficiency of multiple devices and single devices. SUMMARY

[0004] Therefore, the present application provides a multi-device large data content optimization processing method to overcome the problem that the processing data of multiple devices is not optimized for use in the prior art, which leads to repeated calculation of the devices during laser processing, more data download, slow data download speed, and waiting between different devices caused by long data processing time, resulting in low laser processing efficiency of multiple devices and single devices.

[0005] To achieve the above-mentioned purpose, the present application provides a multi-device large data content optimization processing method, which includes: Step S1, initializing multiple devices to obtain devices in a normal working state; Step S2, obtaining a marking device and current marking content in the devices in the normal working state; Step S3: Separate the current marking content to obtain the smallest identification unit, and compare the processing traces of the smallest identification unit to obtain the smallest identification unit processing method; Step S4: Laser processing is performed using a marking device according to the minimum identification unit processing method; Step S5: Obtain the number of index updates, optimize the content separation process based on the number of index updates, detect the capacity of the control card storage area in multiple devices to obtain the remaining capacity, perform cold keyword statistics based on the remaining capacity to obtain nested cold keywords, and further optimize the separation optimization process based on the nested cold keywords.

[0006] Further, in step S1, when initializing multiple devices according to the initialization processing method, the initialization processing method includes: Step A01: Inspect multiple devices to obtain normal multiple devices; Step A02: Load the engraving template for normal multi-device operation to obtain the template initialization state device; Step A03: Configure the IO signal group for a single device in the template initialization state device to obtain the configured initialization state device; Step A04: Configure the processing scheme for the device in the initial state after setup to obtain the device in normal working state.

[0007] Furthermore, in step S2, the marking device and the current marking content in the normally operating device are acquired to obtain the marking device and the current marking content.

[0008] Further, in step S3, when performing content separation on the currently marked content according to the content separation method, the content separation method includes: Step B01: Perform type identification on the current marked content to obtain type identification results, which include text type and vector graphic type; Step B02: Perform text splitting on the text type to obtain the smallest text unit; Step B03: Perform vector graphic splitting on the vector graphic type to obtain the smallest vector graphic unit; Step B04: Output the smallest text unit and the smallest vector graphic unit as the smallest recognition unit.

[0009] Further, in step S3, when comparing the processing traces of the smallest identification unit, the smallest identification unit is compared with the preset smallest identification unit in the memory-stored information, and the smallest identification unit processing method is output based on the comparison result, wherein: When the minimum identification unit is inconsistent with the preset minimum identification unit in the memory-stored information, the processing method will be output as the minimum identification unit processing method. When the minimum identification unit matches the preset minimum identification unit stored in memory, the processing scheme corresponding to the preset minimum identification unit is compared with the current processing scheme. Based on the comparison result, the processing method for the minimum identification unit is output, where: If the processing scheme corresponding to the preset minimum recognition unit is inconsistent with the current processing scheme, the processing method will be output as the minimum recognition unit processing method; If the processing scheme corresponding to the preset minimum identification unit is consistent with the current processing scheme, the deviation processing method will be output as the minimum identification unit processing method.

[0010] Further, in step S4, when laser processing is performed according to the processing method, the processing method includes: Step C01: The control card in the multi-device performs laser processing on the smallest identification unit according to the current processing scheme to obtain the laser-processed content; Step C02: Set the storage index for the smallest identification unit; Step C03: Store the current processing scheme and the content after laser processing as the associated content of the storage index in the control card storage area; Step C04, repeat steps S3 to S4 until all the smallest recognition units have completed laser processing.

[0011] Further, in step S4, when laser processing is performed according to the deviation processing method, the deviation processing method includes: Step D01: Input the position of the smallest identification unit and the position of the processed smallest identification unit into the position deviation judgment model to obtain the position deviation value Ys output by the position deviation judgment model; Step D02: Send the storage index and position deviation value Ys corresponding to the processed smallest identification unit to the control card in the multi-device; Step D03: The control card in the multi-device system adjusts the current processing scheme according to the position deviation value Ys to obtain the adjusted current processing scheme, and outputs the adjusted current processing scheme as the current processing scheme. Step D04: The control card in the multi-device system performs laser processing on the smallest identification unit according to the current processing scheme; Step D05: Repeat steps S3 to S4 until all the smallest recognition units have completed laser processing.

[0012] Further, in step S5, when obtaining the index update count Np, the index update count Np is compared with the preset index update count Np0. Based on the comparison result, the state of the index update count is determined, and based on the determination result, the content separation process is optimized, wherein: When Np≤Np0, the index update count is determined to be infrequent, and no separation optimization is performed on the content separation process. When Np > Np0, the index update count is determined to be frequent, and the content separation process is optimized: the laser-processed content is sent to the cloud, the cloud inputs the laser-processed content into the combined recognition model, obtains the combined smallest recognition unit output by the combined recognition model, and replaces the smallest recognition unit with the combined smallest recognition unit, and performs laser processing on the combined smallest recognition unit according to the current processing scheme.

[0013] Furthermore, in step S5, when performing capacity detection on the control card storage area, the remaining capacity ES is calculated based on the total storage area capacity LS and the available storage area capacity KS, and ES is set to LS-KS to obtain the remaining capacity ES. In step S5, when performing cold word statistics based on the remaining capacity, the remaining capacity ES is compared with the preset remaining capacity ES0. The status of the remaining capacity is determined based on the comparison result, and cold word statistics are performed on all content in the control card storage area based on the determination result, wherein: When ES≥ES0, the remaining capacity is considered sufficient, and cold word statistics are not performed. When ES < ES0, the remaining capacity is determined to be insufficient, and cold word statistics are performed: all contents of the control card storage area are sent to the cloud, and cold word statistics are performed through the cloud to obtain nested cold words.

[0014] Furthermore, in step S5, when supplementing and optimizing the separation optimization process based on nested cold words, the smallest recognition unit after combination containing nested cold words is taken as the smallest recognition unit of the cold word group, and the smallest recognition unit of the cold word group is added to the training set of the combination recognition model to retrain the combination recognition model.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the method initializes the marking equipment in step S1 to ensure its normal operation during laser processing; the method also acquires the marking equipment and the current marking content in step S2 to optimize the multi-device laser processing process based on the marking equipment and the current marking content; the method further separates the current marking content in step S3 to obtain the smallest identification unit and retrieves whether the smallest identification unit has been laser-processed under the current processing scheme from the information stored in memory, so as to select a reasonable processing method accordingly; and the method further employs a processing method to laser-process the smallest identification unit when the smallest identification unit is inconsistent with the preset smallest identification unit in the information stored in memory in step S4. This method facilitates the storage of laser processing information for the smallest identification unit for easy retrieval. When the processing scheme corresponding to the preset smallest identification unit is consistent with the current processing scheme, laser processing is performed using a deviation processing method. This allows for real-time correction of the smallest identification unit position based on the already processed smallest identification unit position, improving the efficiency of data optimization and the accuracy of laser processing. The method also uses step S5 to determine the status of the index update count and optimize the content separation process to give the smallest identification unit a connection meaning, reducing the number of index updates. Simultaneously, it performs cold word statistics on all content in the control card storage area in a timely manner to identify nested cold words and supplement and optimize based on the nested cold words, thereby reducing redundant calculations for the smallest identification unit, improving data processing efficiency, and shortening data processing time. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the multi-device big data content optimization processing method of this embodiment; Figure 2 This is a flowchart illustrating the initialization process of this embodiment; Figure 3 This is a flowchart illustrating the content separation method in this embodiment; Figure 4 This is a flowchart illustrating the processing method in this embodiment; Figure 5 This is a flowchart illustrating the deviation processing method in this embodiment. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper," "lower," "left," "right," "inner," and "outer," etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is merely for ease of description and does not indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Please see Figures 1 to 5 As shown, this is a flowchart illustrating the multi-device big data content optimization processing method of this embodiment. The method includes: Step S1: Initialize multiple devices to obtain devices in normal working state; Step S2: Obtain the marking equipment and the current marking content in the equipment under normal working condition; Step S3: Separate the current marking content to obtain the smallest identification unit, and compare the processing traces of the smallest identification unit to obtain the smallest identification unit processing method; Step S4: Laser processing is performed using a marking device according to the minimum identification unit processing method; Step S5: Obtain the number of index updates, optimize the content separation process based on the number of index updates, detect the capacity of the control card storage area in multiple devices to obtain the remaining capacity, perform cold keyword statistics based on the remaining capacity to obtain nested cold keywords, and further optimize the separation optimization process based on the nested cold keywords.

[0022] Specifically, the multi-device big data content optimization processing method is applied to multi-device big data laser processing terminals, such as automotive parts marking equipment and mobile phone parts marking equipment. The method separates the current marking content to obtain the smallest recognition unit, and then optimizes this smallest recognition unit, thereby reducing repetitive calculations during laser processing, increasing data transmission speed, and improving laser processing efficiency. Specifically, step S1 initializes the marking equipment to ensure its normal operation during laser processing. Step S2 acquires information about the marking equipment and the current marking content to optimize the multi-device laser processing process. Step S3 further separates the current marking content to obtain the smallest recognition unit and retrieves information from memory indicating whether the smallest recognition unit has undergone laser processing under the current processing scheme, allowing for the selection of a suitable processing method. The method further includes step S4, where, when the minimum identification unit differs from the preset minimum identification unit stored in memory, a processing method is used to laser-process the minimum identification unit. This allows the laser processing status of the minimum identification unit to be stored for easy retrieval. When the processing scheme corresponding to the preset minimum identification unit is consistent with the current processing scheme, a deviation processing method is used for laser processing. This allows for real-time correction of the minimum identification unit position based on the processed minimum identification unit position, improving the efficiency of data optimization and the accuracy of laser processing. Step S5 also involves judging the status of the index update count and optimizing the content separation process to give the minimum identification unit a connection meaning, reducing the number of index updates. Simultaneously, cold word statistics are performed on all content in the control card storage area to identify nested cold words and supplement and optimize based on the nested cold words. This reduces redundant calculations for the minimum identification unit, thereby improving data processing efficiency and shortening data processing time.

[0023] Specifically, in step S1, when initializing multiple devices according to the initialization processing method, the initialization processing method includes: Step A01: Inspect multiple devices to obtain normal multiple devices; Step A02: Load the engraving template for normal multi-device operation to obtain the template initialization state device; Step A03: Configure the IO signal group for a single device in the template initialization state device to obtain the configured initialization state device; Step A04: Configure the processing scheme for the device in the initial state after setup to obtain the device in normal working state.

[0024] Specifically, the "multiple devices" refer to multiple collaborative devices for laser processing of the target product. This embodiment does not limit the specific number of devices; those skilled in the art can freely choose according to actual needs. The target product refers to a product laser-processed using marking equipment based on the current marking content. This embodiment does not limit the specific method of checking the marking equipment; those skilled in the art can freely choose according to actual needs. For example, multiple devices can be checked through the equipment system alarm list. When alarm information appears in the equipment system alarm list, the faulty devices are repaired according to the alarm information until there are no alarm information in the equipment system alarm list. The "engraving template loading" refers to the process of loading the engraving template into the normal multiple devices. The engraving template refers to the pre-designed fixed content and the content that needs to be changed. During the laser processing, only the changed content needs to be filled in to quickly generate a complete and uniform marking file. The fixed content refers to the unchanging part of the marking content, as in the title... The "object" and "framework" refer to the parts of the marking content that need to be changed, such as blanks. The "IO signal group setting" refers to the process of setting the IO signal grouping method for a single device in the template initialization state device. The IO signal refers to the input / output signal, which is a general term for electrical signals used to exchange information between the device controller and the device actuator. This embodiment does not limit the IO signal grouping method. Those skilled in the art can freely choose according to actual needs, such as grouping the IO signals of the same device in the same group. The "processing scheme setting" refers to the process of setting the content and processing scheme to be processed by the initialization state device after setting. The content to be processed refers to the specific marking information that needs to be left on the surface of the target product. The processing scheme refers to the equipment parameters required to present the content to be processed on the product surface, such as the filling density and the minimum identification unit position. This embodiment does not limit the specific methods of loading the marking template, setting the IO signal group, and setting the processing scheme, such as setting them through the parameter setting panel.

[0025] Specifically, in step S1, the marking equipment is initialized to ensure its normal operation during the laser processing.

[0026] Specifically, in step S2, the marking device and the current marking content in the normally operating device are acquired to obtain the marking device and the current marking content.

[0027] Specifically, the marking equipment refers to the equipment currently performing laser processing among multiple devices, and the current marking content refers to the specific processing content of the target product being laser-processed based on big data. This embodiment does not limit the specific method of obtaining the marking equipment and the current marking content. Those skilled in the art can freely choose according to actual needs, such as obtaining it through an MES system. MES refers to a central system that manages, tracks, and controls the laser processing process of multiple devices. Its full name is Manufacturing Execution System.

[0028] Specifically, in step S2, the marking equipment and the current marking content are acquired so that the laser processing process of multiple devices can be optimized based on the marking equipment and the current marking content, thereby improving the efficiency of laser processing.

[0029] Specifically, in step S3, when performing content separation on the currently marked content according to the content separation method, the content separation method includes: Step B01: Perform type identification on the current marked content to obtain type identification results, which include text type and vector graphic type; Step B02: Perform text splitting on the text type to obtain the smallest text unit; Step B03: Perform vector graphic splitting on the vector graphic type to obtain the smallest vector graphic unit; Step B04: Output the smallest text unit and the smallest vector graphic unit as the smallest recognition unit.

[0030] Specifically, type identification refers to the process of identifying the type of the currently marked content. This embodiment does not limit the specific method of type identification, and those skilled in the art can freely choose according to actual needs, such as identifying by the source and data format of the currently marked content. The text type refers to the current marked content of the text type obtained by type identification of the current marked content. The vector graphic type refers to the current marked content of the vector graphic type obtained by type identification of the current marked content. Text splitting refers to the process of splitting the text type to obtain the smallest text unit. The smallest text unit refers to the smallest laser processing unit obtained after splitting the text type. This embodiment does not limit the specific method of text splitting, and those skilled in the art can freely choose according to actual needs, such as splitting the text type by individual characters: splitting "product" into "production" and "product". Vector graphic splitting refers to the process of splitting the vector graphic type into the smallest vector graphic unit. The smallest vector graphic unit refers to the smallest laser processing unit obtained after splitting the vector graphic type. This embodiment does not limit the specific method of vector graphic splitting, and those skilled in the art can freely choose according to actual needs, such as separating according to file name to obtain the smallest vector graphic unit.

[0031] Specifically, in step S3, the current marked content is separated to obtain the smallest identification unit, which facilitates information storage and content recognition, thereby improving the efficiency of big data content optimization processing.

[0032] Specifically, in step S3, when comparing the processing traces of the smallest identification unit, the smallest identification unit is compared with the preset smallest identification unit in the memory-stored information. Based on the comparison result, the smallest identification unit processing method is output, wherein: When the minimum identification unit is inconsistent with the preset minimum identification unit in the memory-stored information, the processing method will be output as the minimum identification unit processing method. When the minimum identification unit matches the preset minimum identification unit stored in memory, the processing scheme corresponding to the preset minimum identification unit is compared with the current processing scheme. Based on the comparison result, the processing method for the minimum identification unit is output, where: If the processing scheme corresponding to the preset minimum recognition unit is inconsistent with the current processing scheme, the processing method will be output as the minimum recognition unit processing method; If the processing scheme corresponding to the preset minimum identification unit is consistent with the current processing scheme, the deviation processing method will be output as the minimum identification unit processing method.

[0033] Specifically, the memory-stored information refers to an information repository in the control card storage area that uses a preset minimum identification unit as an index and processing schemes as associated content corresponding to the index. The preset minimum identification unit refers to a preset index for searching processing schemes. The minimum identification unit being inconsistent with the preset minimum identification unit in the memory-stored information means that the content of the minimum identification unit is different from that of the preset minimum identification unit in the memory-stored information. The minimum identification unit being consistent with the preset minimum identification unit in the memory-stored information means that the content of the minimum identification unit is exactly the same as that of the preset minimum identification unit in the memory-stored information. The current processing scheme refers to the processing scheme of the device performing marking at the current moment. The processing scheme corresponding to the preset minimum identification unit being inconsistent with the current processing scheme means that at least one aspect of the processing scheme corresponding to the preset minimum identification unit is different from that of the current processing scheme. The processing scheme corresponding to the preset minimum identification unit being consistent with the current processing scheme means that the content of the processing scheme corresponding to the preset minimum identification unit is exactly the same as that of the current processing scheme. The current processing scheme refers to the processing scheme for laser processing at the current moment.

[0034] Specifically, in step S3, by comparing the processing traces, it is found whether the smallest identification unit in the memory-stored information has been laser-processed under the current processing scheme, so as to select a reasonable processing method and improve data processing efficiency.

[0035] Specifically, in step S4, when laser processing is performed according to the processing method, the processing method includes: Step C01: The control card in the multi-device performs laser processing on the smallest identification unit according to the current processing scheme to obtain the laser-processed content; Step C02: Set the storage index for the smallest identification unit; Step C03: Store the current processing scheme and the content after laser processing as the associated content of the storage index in the control card storage area; Step C04, repeat steps S3 to S4 until all the smallest recognition units have completed laser processing.

[0036] Specifically, the storage index refers to a preset index for searching the smallest identification unit in the control card storage area. This embodiment does not limit the setting method of the storage index. Those skilled in the art can freely choose according to actual needs, such as numbering the smallest identification unit, such as number 1, and using the number as the storage index. The control card storage area refers to the component in multiple devices that stores the storage index and its associated content.

[0037] Specifically, in step S4, when the minimum identification unit is inconsistent with the preset minimum identification unit in the memory-stored information, the minimum identification unit is laser-processed using a processing method, and the storage index of the minimum identification unit is reset so as to store the laser processing status of the minimum identification unit for easy retrieval, thereby reducing repetitive calculations during laser processing and improving laser processing efficiency.

[0038] Specifically, in step S4, when laser processing is performed according to the deviation processing method, the deviation processing method includes: Step D01: Input the position of the smallest identification unit and the position of the processed smallest identification unit into the position deviation judgment model to obtain the position deviation value Ys output by the position deviation judgment model; Step D02: Send the storage index and position deviation value Ys corresponding to the processed smallest identification unit to the control card in the multi-device; Step D03: The control card in the multi-device system adjusts the current processing scheme according to the position deviation value Ys to obtain the adjusted current processing scheme, and outputs the adjusted current processing scheme as the current processing scheme. Step D04: The control card in the multi-device system performs laser processing on the smallest identification unit according to the current processing scheme; Step D05: Repeat steps S3 to S4 until all the smallest recognition units have completed laser processing.

[0039] Specifically, the minimum identification unit position refers to the specific stamping position of the preset minimum identification unit in the current processing scheme; the processed minimum identification unit position refers to the historical minimum identification unit position in the control card storage area; and the position deviation determination model is a recurrent neural network model that takes the minimum identification unit position and the processed minimum identification unit position as input data and the position deviation value as output data. This embodiment does not limit the specific construction method of the position deviation determination model; those skilled in the art can freely choose according to actual needs, such as using the historical minimum identification unit position and the processed minimum identification unit position as a historical position dataset. The position deviation values ​​corresponding to the historical position dataset are used as the training set to train the recurrent neural network model to obtain the position deviation judgment model. The position deviation value refers to the specific position deviation value between the position of the smallest identification unit obtained according to the position deviation judgment model and the position of the smallest identification unit that has been processed. The offset adjustment refers to the process by which the control card corrects the position of the smallest identification unit in the current processing scheme according to the position deviation value Ys(x1,y1). For example, if the position of the smallest identification unit in the current processing scheme is set to (x,y), then the position of the smallest identification unit after offset adjustment is (x+x1,y+y1). The control card refers to the execution component for laser processing in multiple devices.

[0040] Specifically, in step S4, when the processing scheme corresponding to the preset minimum identification unit is consistent with the current processing scheme, laser processing is performed by the deviation processing method so as to make real-time correction of the position of the minimum identification unit according to the position of the processed minimum identification unit, thereby improving the efficiency of data optimization and the accuracy of laser processing.

[0041] Specifically, in step S5, when obtaining the index update count Np, the index update count Np is compared with the preset index update count Np0. Based on the comparison result, the status of the index update count is determined, and the content separation process is optimized based on the determination result. When Np≤Np0, the index update count is determined to be infrequent, and no separation optimization is performed on the content separation process. When Np > Np0, the index update count is determined to be frequent, and the content separation process is optimized: the laser-processed content is sent to the cloud, the cloud inputs the laser-processed content into the combined recognition model, obtains the combined smallest recognition unit output by the combined recognition model, and replaces the smallest recognition unit with the combined smallest recognition unit, and performs laser processing on the combined smallest recognition unit according to the current processing scheme.

[0042] Specifically, the index update count refers to the total number of times the storage index is set for the smallest identification unit in the processing method. This embodiment does not limit the specific method for obtaining the index update count; those skilled in the art can freely choose according to actual needs, such as obtaining the index update count through device system logs. The preset index update count refers to a preset value for judging the state of the index update count. This embodiment does not limit the specific value setting of the preset index update count Np0; those skilled in the art can freely choose according to actual needs, such as setting Np0=15 times in this embodiment. The state of the index update count refers to the frequency of the index update count judged based on the index update count and the preset index update count. The state of the index update count includes frequent and infrequent. The cloud refers to the minimum identification unit. This embodiment describes an internet computing method for combined identification of individual units. Combined identification refers to the process of analyzing the spatial relationships, arrangement patterns, and combination structures between the smallest identification units to obtain the meaning of their connections. For example, if the content after laser processing of the smallest identification unit only forms a closed boundary, the system identifies the connection meaning of the smallest identification unit as "outline". The combined identification model refers to a recurrent neural network model that uses the content after laser processing as input data and the combined smallest identification units as output data. This embodiment does not limit the specific construction method of the combined identification model; those skilled in the art can freely choose according to actual needs. For example, the recurrent neural network model can be trained using historical laser processing content and its corresponding combined smallest identification units as a training set to obtain the combined identification model.

[0043] Specifically, in step S5, by judging the state of the number of index updates, when the state of the number of index updates is frequent, the content separation process is optimized to give the smallest identification unit a connection meaning, reduce the number of index updates, so as to reduce the repeated calculation of the smallest identification unit in the future, thereby improving the efficiency of data processing and shortening the data processing time.

[0044] Specifically, in step S5, when performing capacity detection on the control card storage area, the remaining capacity ES is calculated based on the total storage area capacity LS and the available storage area capacity KS. The remaining capacity ES is then set to ES = LS - KS. In step S5, when performing cold word statistics based on the remaining capacity, the remaining capacity ES is compared with the preset remaining capacity ES0. The status of the remaining capacity is determined based on the comparison result, and cold word statistics are performed on all content in the control card storage area based on the determination result, wherein: When ES≥ES0, the remaining capacity is considered sufficient, and cold word statistics are not performed. When ES < ES0, the remaining capacity is determined to be insufficient, and cold word statistics are performed: all contents of the control card storage area are sent to the cloud, and cold word statistics are performed through the cloud to obtain nested cold words.

[0045] Specifically, the total storage capacity refers to the total storage capacity of the control card storage area, and the available storage capacity refers to the currently usable storage capacity of the control card storage area. This embodiment does not limit the specific methods for obtaining the total storage capacity and the available storage capacity. Those skilled in the art can freely choose according to actual needs, such as viewing the storage management page through the tagging software interface to obtain the total storage capacity and the available storage capacity. The preset remaining capacity refers to a preset value for judging the status of the remaining capacity. This embodiment does not limit the specific value setting of the preset remaining capacity ES0. Those skilled in the art can freely choose according to actual needs. For example, in this embodiment, ES0 is set to 60% of the total storage capacity. The status of the remaining capacity refers to the sufficiency of the remaining capacity judged based on the remaining capacity and the preset remaining capacity. The status of the remaining capacity includes sufficient and insufficient. The cold word statistics refer to the data processing process of identifying and extracting words and phrases with low frequency of occurrence but important value from all the content of the control card storage area.

[0046] Specifically, in step S5, by judging the status of the remaining capacity, when the remaining capacity is insufficient, cold word statistics are performed on all contents of the control card storage area in a timely manner to identify nested cold words, so as to facilitate subsequent updates and optimizations based on nested cold words and improve the efficiency of data processing.

[0047] Specifically, in step S5, when supplementing and optimizing the separation optimization process based on nested cold words, the smallest recognition unit after the combination containing nested cold words is taken as the smallest recognition unit of the cold word group, and the smallest recognition unit of the cold word group is added to the training set of the combination recognition model to retrain the combination recognition model.

[0048] Specifically, in step S5, by supplementing and optimizing the separation optimization process, nested cold words are added to the combination recognition model to retain key data, improve data accuracy, reduce the storage pressure of the smallest recognition unit after combination, and improve data processing efficiency.

[0049] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing and processing large amounts of data across multiple devices, characterized in that: The method includes: Step S1: Initialize multiple devices to obtain devices in normal working state; Step S2: Obtain the marking equipment and the current marking content in the equipment under normal working condition; Step S3: Separate the current marking content to obtain the smallest identification unit, and compare the processing traces of the smallest identification unit to obtain the smallest identification unit processing method; Step S4: Laser processing is performed using a marking device according to the minimum identification unit processing method; Step S5: Obtain the number of index updates, optimize the content separation process based on the number of index updates, detect the capacity of the control card storage area in multiple devices to obtain the remaining capacity, perform cold keyword statistics based on the remaining capacity to obtain nested cold keywords, and further optimize the separation optimization process based on the nested cold keywords.

2. The multi-device big data content optimization processing method according to claim 1, characterized in that, In step S1, when initializing multiple devices according to the initialization processing method, the initialization processing method includes: Step A01: Inspect multiple devices to obtain normal multiple devices; Step A02: Load the engraving template for normal multi-device operation to obtain the template initialization state device; Step A03: Configure the IO signal group for a single device in the template initialization state device to obtain the configured initialization state device; Step A04: Configure the processing scheme for the device in the initial state after setup to obtain the device in normal working state.

3. The multi-device big data content optimization processing method according to claim 2, characterized in that, In step S2, the marking device and the current marking content in the normally operating equipment are acquired to obtain the marking device and the current marking content.

4. The multi-device big data content optimization processing method according to claim 3, characterized in that, In step S3, when performing content separation on the currently marked content according to the content separation method, the content separation method includes: Step B01: Perform type identification on the current marked content to obtain type identification results, which include text type and vector graphic type; Step B02: Perform text splitting on the text type to obtain the smallest text unit; Step B03: Perform vector graphic splitting on the vector graphic type to obtain the smallest vector graphic unit; Step B04: Output the smallest text unit and the smallest vector graphic unit as the smallest recognition unit.

5. The multi-device big data content optimization processing method according to claim 4, characterized in that, In step S3, when comparing the processing traces of the smallest identification unit, the smallest identification unit is compared with the preset smallest identification unit in the memory-stored information. Based on the comparison result, the smallest identification unit processing method is output, wherein: When the minimum identification unit is inconsistent with the preset minimum identification unit in the memory-stored information, the processing method will be output as the minimum identification unit processing method. When the minimum identification unit matches the preset minimum identification unit stored in memory, the processing scheme corresponding to the preset minimum identification unit is compared with the current processing scheme. Based on the comparison result, the processing method for the minimum identification unit is output, where: If the processing scheme corresponding to the preset minimum recognition unit is inconsistent with the current processing scheme, the processing method will be output as the minimum recognition unit processing method; If the processing scheme corresponding to the preset minimum identification unit is consistent with the current processing scheme, the deviation processing method will be output as the minimum identification unit processing method.

6. The multi-device big data content optimization processing method according to claim 5, characterized in that, In step S4, when laser processing is performed according to the processing method, the processing method includes: Step C01: The control card in the multi-device performs laser processing on the smallest identification unit according to the current processing scheme to obtain the laser-processed content; Step C02: Set the storage index for the smallest identification unit; Step C03: Store the current processing scheme and the content after laser processing as the associated content of the storage index in the control card storage area; Step C04, repeat steps S3 to S4 until all the smallest recognition units have completed laser processing.

7. The multi-device big data content optimization processing method according to claim 6, characterized in that, In step S4, when laser processing is performed according to the deviation processing method, the deviation processing method includes: Step D01: Input the position of the smallest identification unit and the position of the processed smallest identification unit into the position deviation judgment model to obtain the position deviation value Ys output by the position deviation judgment model; Step D02: Send the storage index and position deviation value Ys corresponding to the processed smallest identification unit to the control card in the multi-device; Step D03: The control card in the multi-device system adjusts the current processing scheme according to the position deviation value Ys to obtain the adjusted current processing scheme, and outputs the adjusted current processing scheme as the current processing scheme. Step D04: The control card in the multi-device system performs laser processing on the smallest identification unit according to the current processing scheme; Step D05: Repeat steps S3 to S4 until all the smallest recognition units have completed laser processing.

8. The multi-device big data content optimization processing method according to claim 7, characterized in that, In step S5, when obtaining the index update count Np, the index update count Np is compared with the preset index update count Np0. Based on the comparison result, the status of the index update count is determined, and the content separation process is optimized based on the determination result. Specifically: When Np≤Np0, the index update count is determined to be infrequent, and no separation optimization is performed on the content separation process. When Np > Np0, the index update count is determined to be frequent, and the content separation process is optimized: the laser-processed content is sent to the cloud, the cloud inputs the laser-processed content into the combined recognition model, obtains the combined smallest recognition unit output by the combined recognition model, and replaces the smallest recognition unit with the combined smallest recognition unit, and performs laser processing on the combined smallest recognition unit according to the current processing scheme.

9. The multi-device big data content optimization processing method according to claim 8, characterized in that, In step S5, when performing capacity detection on the control card storage area, the remaining capacity ES is calculated based on the total storage area capacity LS and the available storage area capacity KS. The remaining capacity ES is then set to ES = LS - KS. In step S5, when performing cold word statistics based on the remaining capacity, the remaining capacity ES is compared with the preset remaining capacity ES0. The status of the remaining capacity is determined based on the comparison result, and cold word statistics are performed on all content in the control card storage area based on the determination result, wherein: When ES≥ES0, the remaining capacity is considered sufficient, and cold word statistics are not performed. When ES < ES0, the remaining capacity is determined to be insufficient, and cold word statistics are performed: all contents of the control card storage area are sent to the cloud, and cold word statistics are performed through the cloud to obtain nested cold words.

10. The multi-device big data content optimization processing method according to claim 8, characterized in that, In step S5, when supplementing and optimizing the separation optimization process based on nested cold words, the smallest recognition unit after combination containing nested cold words is taken as the smallest recognition unit of the cold word group, and the smallest recognition unit of the cold word group is added to the training set of the combination recognition model to retrain the combination recognition model.

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