Model printing pigment matching method and system based on time sequence analysis

Abstract

The invention discloses a time sequence analysis-based model printing pigment matching method and system. The method comprises the following steps of: obtaining a plurality of pigment use records of a to-be-sliced model in a historical time period; on the basis of task parameters corresponding to each pigment use record, preprocessing all the pigment use records to obtain a pigment record sequence; predicting a prediction matching pigment set of the to-be-sliced model according to the pigment record sequence based on an LSTM neural network; and according to the predicted matching pigment set, determining a pigment matching operation of the to-be-sliced model. It can be seen that accurate pigment matching management based on historical data and time sequence prediction can be achieved, the accuracy and matching efficiency of pigment use of the to-be-sliced model are improved, and the printing defect risk caused by improper pigment matching is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for matching model printing inks based on time-series analysis. Background Technology

[0002] With the widespread application of 3D printing technology in multi-color, high-precision manufacturing, enterprises and users are increasingly focusing on improving printing efficiency and quality by optimizing pigment matching prediction in slicing software. Existing technologies typically determine and adjust the pigment configuration of the model based on current task parameters and color plans to support the printing process. However, existing solutions lack time-series analysis of the model's historical pigment data and dynamic prediction of task color usage, making it difficult to accurately determine the matching pigment set and optimize replacement operations. Commonly used static pigment management strategies are unsuitable for complex multi-color printing scenarios, resulting in insufficient accuracy in the slicing software's prediction of model pigment usage. This can easily lead to printing defects or inefficiencies due to improper matching, limiting the performance and product quality of 3D model printing. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a model printing ink matching method and system based on time series analysis, which can realize accurate ink matching management based on historical data and time series prediction, improve the accuracy and matching efficiency of ink use in the model to be sliced, and reduce the risk of printing defects caused by improper ink matching.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a model printing pigment matching method based on time-series analysis, the method comprising: Obtain multiple pigment usage records for the model to be sliced ​​over a historical time period; Based on the task parameters corresponding to each pigment usage record, all pigment usage records are preprocessed to obtain a pigment record sequence; Based on the LSTM neural network, the predicted matching set of the colorant is predicted according to the colorant recording sequence; Based on the predicted matching pigment set, the pigment matching operation of the model to be sliced ​​is determined.

[0005] As an optional implementation, in the first aspect of the present invention, the colorant usage record includes the color type used at the corresponding historical time point, the color type used before the previous historical time point, and the color type used after the next historical time point.

[0006] As an optional implementation, in the first aspect of the present invention, the task parameters include at least one of the following: printing time point, printing data volume, printing model parameters, printing location, printing time, and printing device parameters.

[0007] As an optional implementation, in the first aspect of the present invention, the step of preprocessing all the pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence includes: Based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all the pigment usage records; Sort all the reference pigment records from earliest to latest according to the historical time point corresponding to each reference pigment record to obtain the pigment record sequence.

[0008] As an optional implementation, in the first aspect of the invention, the step of selecting multiple reference pigment records from all pigment usage records based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced ​​includes: For each pigment usage record, calculate the parameter similarity between the task parameters corresponding to the pigment usage record and the task parameters of the current printing task of the model to be sliced; Calculate the color matching degree corresponding to the color usage record based on the color plan of the current printing task; Calculate the product of the parameter similarity and the color matching degree to obtain the record priority corresponding to the color material usage record; From all the pigment usage records, records with a priority greater than a preset priority threshold are selected to obtain multiple reference pigment records.

[0009] As an optional implementation, in the first aspect of the present invention, calculating the color matching degree corresponding to the pigment usage record based on the color plan of the current printing task includes: Calculate the percentage of the first usage frequency of the color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the second usage frequency of the previous color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the third usage frequency of the next color type corresponding to the color usage record in the color plan of the current print job; Calculate the weighted sum of the first usage frequency ratio, the second usage frequency ratio, and the third usage frequency ratio to obtain the color matching degree corresponding to the color usage record.

[0010] As an optional implementation, in the first aspect of the present invention, the step of predicting the predicted matching pigment set of the model to be sliced ​​based on the pigment recording sequence using an LSTM neural network includes: The pigment recording sequence is input into a trained LSTM neural network to obtain multiple possible predicted pigments and corresponding prediction probabilities for the model to be sliced ​​at the next time point; the LSTM neural network is trained using a training dataset that includes multiple training pigment usage time sequences. All predicted pigments with a predicted probability greater than a probability threshold are selected to obtain the predicted matching pigment set for the model to be sliced.

[0011] As an optional implementation, in the first aspect of the present invention, the step of determining the pigment matching operation of the model to be sliced ​​based on the predicted matching pigment set includes: Determine the color scheme for the current printing task of the model to be sliced ​​at the next time point; Calculate the set similarity between the pigment usage combination and the predicted matching pigment set; Determine whether the similarity of the sets is greater than a preset similarity threshold; If not, generate a colorant error alarm; If so, calculate the intersection of the pigment usage combination and the predicted matching pigment set to obtain the expected pigment of the model to be sliced ​​at the next time point, and generate a pigment matching operation for changing the output mode of the pigment bin to the expected pigment.

[0012] A second aspect of this invention discloses a model printing ink matching system based on time-series analysis, the system comprising: The acquisition module is used to acquire multiple pigment usage records of the model to be sliced ​​over a historical time period; The preprocessing module is used to preprocess all the pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence; The prediction module is used to predict the set of predicted matching pigments for the model to be sliced ​​based on the pigment recording sequence using an LSTM neural network. The determination module is used to determine the color matching operation of the model to be sliced ​​based on the predicted matching color set.

[0013] As an optional implementation, in the second aspect of the present invention, the colorant usage record includes the color type used at the corresponding historical time point, the color type used before the previous historical time point, and the color type used after the next historical time point.

[0014] As an optional implementation, in a second aspect of the present invention, the task parameters include at least one of the following: printing time point, printing data volume, printing model parameters, printing location, printing time, and printing device parameters.

[0015] As an optional implementation, in a second aspect of the invention, the preprocessing module preprocesses all the pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence, including: Based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all the pigment usage records; Sort all the reference pigment records from earliest to latest according to the historical time point corresponding to each reference pigment record to obtain the pigment record sequence.

[0016] As an optional implementation, in a second aspect of the invention, the specific method by which the preprocessing module selects multiple reference pigment records from all pigment usage records based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced ​​includes: For each pigment usage record, calculate the parameter similarity between the task parameters corresponding to the pigment usage record and the task parameters of the current printing task of the model to be sliced; Calculate the color matching degree corresponding to the color usage record based on the color plan of the current printing task; Calculate the product of the parameter similarity and the color matching degree to obtain the record priority corresponding to the color material usage record; From all the pigment usage records, records with a priority greater than a preset priority threshold are selected to obtain multiple reference pigment records.

[0017] As an optional implementation, in a second aspect of the invention, the preprocessing module calculates the specific method by which it determines the color matching degree corresponding to the pigment usage record based on the color plan of the current printing task, including: Calculate the percentage of the first usage frequency of the color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the second usage frequency of the previous color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the third usage frequency of the next color type corresponding to the color usage record in the color plan of the current print job; Calculate the weighted sum of the first usage frequency ratio, the second usage frequency ratio, and the third usage frequency ratio to obtain the color matching degree corresponding to the color usage record.

[0018] As an optional implementation, in a second aspect of the invention, the prediction module, based on an LSTM neural network, predicts the predicted matching pigment set of the model to be sliced ​​according to the pigment recording sequence in a specific manner, including: The pigment recording sequence is input into a trained LSTM neural network to obtain multiple possible predicted pigments and corresponding prediction probabilities for the model to be sliced ​​at the next time point; the LSTM neural network is trained using a training dataset that includes multiple training pigment usage time sequences. All predicted pigments with a predicted probability greater than a probability threshold are selected to obtain the predicted matching pigment set for the model to be sliced.

[0019] As an optional implementation, in a second aspect of the invention, the determining module determines the specific method of the pigment matching operation for the model to be sliced ​​based on the predicted matching pigment set, including: Determine the color scheme for the current printing task of the model to be sliced ​​at the next time point; Calculate the set similarity between the pigment usage combination and the predicted matching pigment set; Determine whether the similarity of the sets is greater than a preset similarity threshold; If not, generate a colorant error alarm; If so, calculate the intersection of the pigment usage combination and the predicted matching pigment set to obtain the expected pigment of the model to be sliced ​​at the next time point, and generate a pigment matching operation for changing the output mode of the pigment bin to the expected pigment.

[0020] A third aspect of this invention discloses another model printing ink matching system based on time-series analysis, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the model printing ink matching method based on time-series analysis disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the time-series analysis-based model printing pigment matching method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention obtains the pigment usage records of the model to be sliced ​​over a historical period and generates a pigment record sequence based on task parameters. It then uses an LSTM neural network to predict and match the pigment set and combines it with the current printing task's color plan to determine the pigment matching operation. This enables accurate pigment matching management based on historical data and time-series prediction, improving the accuracy and matching efficiency of pigment usage in the model to be sliced ​​and reducing the risk of printing defects caused by improper pigment matching. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a model printing ink matching method based on time-series analysis disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a model printing ink matching system based on time-series analysis disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another model printing ink matching system based on time-series analysis disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a model printing ink matching method and system based on time-series analysis. It obtains ink usage records for a historical time period of the model to be sliced ​​and generates an ink record sequence based on task parameters. Then, it uses an LSTM neural network to predict the matching ink set and combines it with the current printing task's color plan to determine the ink matching operation. This enables accurate ink matching management based on historical data and time-series prediction, improving the accuracy and matching efficiency of ink usage for the model to be sliced ​​and reducing the risk of printing defects caused by improper ink matching. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a model printing ink matching method based on time-series analysis disclosed in an embodiment of the present invention. Figure 1 The described time-series analysis-based model printing ink matching method can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 1 As shown, the model printing pigment matching method based on time-series analysis can include the following operations: 101. Obtain multiple pigment usage records for the model to be sliced ​​over a historical time period.

[0032] 102. Based on the task parameters corresponding to each pigment usage record, preprocess all pigment usage records to obtain a pigment record sequence. 103. Based on the LSTM neural network, predict the set of matching pigments for the model to be sliced ​​according to the pigment recording sequence. 104. Based on the predicted matching pigment set, determine the pigment matching operation for the model to be sliced.

[0033] As can be seen, the above-described embodiments of the invention obtain the pigment usage records of the model to be sliced ​​over a historical period and generate a pigment record sequence based on task parameters. They then use an LSTM neural network to predict and match the pigment set and combine it with the current printing task's color plan to determine the pigment matching operation. This enables accurate pigment matching management based on historical data and time-series prediction, improves the accuracy and matching efficiency of pigment usage in the model to be sliced, and reduces the risk of printing defects caused by improper pigment matching.

[0034] As an optional embodiment, the colorant usage record in the above steps includes the color type used at the corresponding historical time point, the color type used before the previous historical time point, and the color type used after the next historical time point.

[0035] As can be seen, the above optional embodiments limit the content of the pigment usage record to comprehensively characterize the features of historical pigment usage records, assist in achieving accurate pigment matching management based on historical data and time-series prediction, improve the accuracy and matching efficiency of pigment usage in the model to be sliced, and reduce the risk of printing defects caused by improper pigment matching.

[0036] As an optional embodiment, the task parameters in the above steps include at least one of the following: printing time point, printing data volume, printing model parameters, printing location, printing time, and printing device parameters.

[0037] As can be seen, the above optional embodiments limit the content of task parameters to comprehensively characterize the features of printing tasks, assist in achieving accurate pigment matching management based on historical data and time-series prediction, improve the accuracy and matching efficiency of pigment use in the model to be sliced, and reduce the risk of printing defects caused by improper pigment matching.

[0038] As an optional embodiment, the step described above, which involves preprocessing all pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence, includes: Based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all pigment usage records. All reference pigment records are sorted from earliest to latest based on the historical time point corresponding to each reference pigment record to obtain the pigment record sequence.

[0039] As can be seen, through the above optional embodiments, by filtering reference pigment records based on task parameters and the current printing task and generating a pigment record sequence by sorting by time, the relevance and serialization accuracy of pigment records are improved through task-related filtering and time-series processing on the basis of accurate pigment matching management, providing a more reliable data foundation for subsequent pigment prediction and reducing the risk of prediction errors caused by improper record selection.

[0040] As an optional embodiment, in the above steps, based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all pigment usage records, including: For each pigment usage record, calculate the parameter similarity between the task parameters corresponding to the pigment usage record and the task parameters of the current printing task of the model to be sliced; Calculate the color matching degree of the color usage record corresponding to the color plan of the current printing task; Calculate the product of parameter similarity and color matching degree to obtain the record priority corresponding to the use record of the colorant; From all pigment usage records, records with a priority greater than a preset priority threshold are selected to obtain multiple reference pigment records.

[0041] As can be seen, through the above optional embodiments, the product of the similarity and color matching degree between the pigment usage record and the current printing task parameters is calculated as the record priority. Reference pigment records with priority exceeding the threshold are filtered out. Thus, on the basis of accurate pigment matching management, the accuracy and applicability of reference pigment record filtering are improved by multi-dimensional parameter matching, providing high-quality data for pigment record sequence generation and reducing the risk of prediction deviation caused by record irrelevance.

[0042] As an optional embodiment, the step above, calculating the color matching degree corresponding to the pigment usage record based on the color plan of the current printing task, includes: Calculate the percentage of the first use of the color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the second usage of the previous color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the third use of the next color type corresponding to the color usage record in the color plan of the current print job; Calculate the weighted sum of the percentage of first, second, and third uses to obtain the color matching degree corresponding to the color usage record.

[0043] As can be seen, through the above optional embodiments, the color matching degree is obtained by calculating the weighted sum of the color type of the pigment usage record and the proportion of the number of times the previous and previous color types were used in the current printing task color plan. Based on accurate pigment matching management, the accuracy of pigment matching is improved through multi-level color association analysis, which provides a more accurate basis for the selection of reference pigment records and reduces the risk of printing quality due to pigment selection mismatch.

[0044] As an optional embodiment, the step above, predicting the predicted matching pigment set for the model to be sliced ​​based on the pigment recording sequence using an LSTM neural network, includes: The pigment recording sequence is input into a trained LSTM neural network to obtain multiple possible predicted pigments and corresponding predicted probabilities for the output model to be sliced ​​at the next time point; optionally, the LSTM neural network is trained using a training dataset that includes multiple training pigment usage time sequences. All predicted pigments with a predicted probability greater than the probability threshold are selected to obtain the set of predicted matching pigments for the model to be sliced.

[0045] As can be seen, through the above optional embodiments, by inputting the pigment recording sequence into a trained LSTM neural network to predict the possible pigments and probabilities at the next time point, and filtering pigments with probabilities exceeding the threshold to form a set of predicted matching pigments, the accuracy and reliability of pigment prediction are improved through time series modeling and probability screening on the basis of precise pigment matching management. This provides precise guidance for pigment matching operations and reduces the risk of pigment waste caused by inaccurate prediction.

[0046] As an optional embodiment, the step above, determining the color matching operation of the model to be sliced ​​based on the predicted matching color set, includes: Determine the color scheme for the current printing task of the model to be sliced ​​and the color combination to be used at the next time point; Calculate the set similarity between sets of pigments using combinations and predict matching pigment sets; Determine whether the similarity of the sets is greater than a preset similarity threshold; If not, generate a colorant error alarm; If so, the intersection of the combined and predicted matching color sets is calculated to obtain the expected color of the model to be sliced ​​at the next time point, and a color matching operation is generated to replace the color bin with the expected color.

[0047] As can be seen, through the above optional embodiments, by calculating the set similarity between the current printing task's color plan and the predicted matching color set, a color error alarm is generated or the expected color is determined based on the intersection and a replacement operation is generated. Thus, on the basis of accurate color matching management, the accuracy and security of color matching decisions are further optimized through similarity verification and intersection analysis, improving the running efficiency of the model to be sliced ​​and reducing the risk of printing interruption caused by color matching errors.

[0048] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a model printing pigment matching system based on time-series analysis disclosed in an embodiment of the present invention. Figure 2The described time-series analysis-based model printing ink matching system can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 2 As shown, the model printing ink matching system based on time-series analysis may include: The acquisition module 201 is used to acquire multiple pigment usage records of the model to be sliced ​​over a historical time period.

[0049] The preprocessing module 202 is used to preprocess all pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence. The prediction module 203 is used to predict the set of matching pigments for the model to be sliced ​​based on the pigment record sequence using an LSTM neural network. The determination module 204 is used to determine the color matching operation of the model to be sliced ​​based on the predicted matching color set.

[0050] As can be seen, the above-described embodiments of the invention obtain the pigment usage records of the model to be sliced ​​over a historical period and generate a pigment record sequence based on task parameters. They then use an LSTM neural network to predict and match the pigment set and combine it with the current printing task's color plan to determine the pigment matching operation. This enables accurate pigment matching management based on historical data and time-series prediction, improves the accuracy and matching efficiency of pigment usage in the model to be sliced, and reduces the risk of printing defects caused by improper pigment matching.

[0051] As an optional embodiment, the colorant usage record includes the color type used at the corresponding historical time point, the color type used before the previous historical time point, and the color type used after the next historical time point.

[0052] As can be seen, the above optional embodiments limit the content of the pigment usage record to comprehensively characterize the features of historical pigment usage records, assist in achieving accurate pigment matching management based on historical data and time-series prediction, improve the accuracy and matching efficiency of pigment usage in the model to be sliced, and reduce the risk of printing defects caused by improper pigment matching.

[0053] As an optional embodiment, the task parameters include at least one of the following: print time point, print data volume, print model parameters, print location, print time, and print device parameters.

[0054] As can be seen, the above optional embodiments limit the content of task parameters to comprehensively characterize the features of printing tasks, assist in achieving accurate pigment matching management based on historical data and time-series prediction, improve the accuracy and matching efficiency of pigment use in the model to be sliced, and reduce the risk of printing defects caused by improper pigment matching.

[0055] As an optional embodiment, the preprocessing module preprocesses all pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence, including: Based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all pigment usage records. All reference pigment records are sorted from earliest to latest based on the historical time point corresponding to each reference pigment record to obtain the pigment record sequence.

[0056] As can be seen, through the above optional embodiments, by filtering reference pigment records based on task parameters and the current printing task and generating a pigment record sequence by sorting by time, the relevance and serialization accuracy of pigment records are improved through task-related filtering and time-series processing on the basis of accurate pigment matching management, providing a more reliable data foundation for subsequent pigment prediction and reducing the risk of prediction errors caused by improper record selection.

[0057] As an optional embodiment, the preprocessing module selects multiple reference pigment records from all pigment usage records based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, including: For each pigment usage record, calculate the parameter similarity between the task parameters corresponding to the pigment usage record and the task parameters of the current printing task of the model to be sliced; Calculate the color matching degree of the color usage record corresponding to the color plan of the current printing task; Calculate the product of parameter similarity and color matching degree to obtain the record priority corresponding to the use record of the colorant; From all pigment usage records, records with a priority greater than a preset priority threshold are selected to obtain multiple reference pigment records.

[0058] As can be seen, through the above optional embodiments, the product of the similarity and color matching degree between the pigment usage record and the current printing task parameters is calculated as the record priority. Reference pigment records with priority exceeding the threshold are filtered out. Thus, on the basis of accurate pigment matching management, the accuracy and applicability of reference pigment record filtering are improved by multi-dimensional parameter matching, providing high-quality data for pigment record sequence generation and reducing the risk of prediction deviation caused by record irrelevance.

[0059] As an optional embodiment, the preprocessing module calculates the specific method of color matching corresponding to the pigment usage record based on the color plan of the current printing task, including: Calculate the percentage of the first use of the color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the second usage of the previous color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the third use of the next color type corresponding to the color usage record in the color plan of the current print job; Calculate the weighted sum of the percentage of first, second, and third uses to obtain the color matching degree corresponding to the color usage record.

[0060] As can be seen, through the above optional embodiments, the color matching degree is obtained by calculating the weighted sum of the color type of the pigment usage record and the proportion of the number of times the previous and previous color types were used in the current printing task color plan. Based on accurate pigment matching management, the accuracy of pigment matching is improved through multi-level color association analysis, which provides a more accurate basis for the selection of reference pigment records and reduces the risk of printing quality due to pigment selection mismatch.

[0061] As an optional embodiment, the prediction module, based on an LSTM neural network, predicts the specific method by which it predicts the matching set of pigments for the model to be sliced, according to the pigment recording sequence, including: The pigment recording sequence is input into a trained LSTM neural network to obtain multiple possible predicted pigments and corresponding predicted probabilities for the output model to be sliced ​​at the next time point; optionally, the LSTM neural network is trained using a training dataset that includes multiple training pigment usage time sequences. All predicted pigments with a predicted probability greater than the probability threshold are selected to obtain the set of predicted matching pigments for the model to be sliced.

[0062] As can be seen, through the above optional embodiments, by inputting the pigment recording sequence into a trained LSTM neural network to predict the possible pigments and probabilities at the next time point, and filtering pigments with probabilities exceeding the threshold to form a set of predicted matching pigments, the accuracy and reliability of pigment prediction are improved through time series modeling and probability screening on the basis of precise pigment matching management. This provides precise guidance for pigment matching operations and reduces the risk of pigment waste caused by inaccurate prediction.

[0063] As an optional embodiment, the determining module determines the specific method of the pigment matching operation for the model to be sliced ​​based on the predicted matching pigment set, including: Determine the color scheme for the current printing task of the model to be sliced ​​and the color combination to be used at the next time point; Calculate the set similarity between sets of pigments using combinations and predict matching pigment sets; Determine whether the similarity of the sets is greater than a preset similarity threshold; If not, generate a colorant error alarm; If so, the intersection of the combined and predicted matching color sets is calculated to obtain the expected color of the model to be sliced ​​at the next time point, and a color matching operation is generated to replace the color bin with the expected color.

[0064] As can be seen, through the above optional embodiments, by calculating the set similarity between the current printing task's color plan and the predicted matching color set, a color error alarm is generated or the expected color is determined based on the intersection and a replacement operation is generated. Thus, on the basis of accurate color matching management, the accuracy and security of color matching decisions are further optimized through similarity verification and intersection analysis, improving the running efficiency of the model to be sliced ​​and reducing the risk of printing interruption caused by color matching errors.

[0065] Example 3 Please see Figure 3 , Figure 3 This is another model printing ink matching system based on time-series analysis disclosed in the embodiments of the present invention. Figure 3 The described time-series analysis-based model printing ink matching system is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the model printing ink matching system based on time-series analysis may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the model printing pigment matching method based on time analysis described in Embodiment 1.

[0066] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the model printing ink matching method based on time-series analysis described in Embodiment 1.

[0067] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the time-series analysis-based model printing ink matching method described in Embodiment 1.

[0068] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0070] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0071] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0077] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0080] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0081] Finally, it should be noted that the model printing ink matching method and system based on time-series analysis disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model printing ink matching method based on time-series analysis, characterized in that, The method includes: Obtain multiple pigment usage records for the model to be sliced ​​over a historical time period; Based on the task parameters corresponding to each pigment usage record, all pigment usage records are preprocessed to obtain a pigment record sequence; Based on the LSTM neural network, the predicted matching set of the colorant is predicted according to the colorant recording sequence; Based on the predicted matching pigment set, the pigment matching operation of the model to be sliced ​​is determined.

2. The model printing ink matching method based on time-series analysis according to claim 1, characterized in that, The colorant usage record includes the color type used at the corresponding historical time point, the color type used before the previous historical time point, and the color type used after the next historical time point.

3. The model printing ink matching method based on time-series analysis according to claim 1, characterized in that, The task parameters include at least one of the following: printing time point, printing data volume, printing model parameters, printing location, printing time, and printing equipment parameters.

4. The model printing ink matching method based on time-series analysis according to claim 2, characterized in that, The step of preprocessing all the pigment usage records to obtain a pigment record sequence based on the task parameters corresponding to each pigment usage record includes: Based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all the pigment usage records; Sort all the reference pigment records from earliest to latest according to the historical time point corresponding to each reference pigment record to obtain the pigment record sequence.

5. The model printing ink matching method based on time-series analysis according to claim 4, characterized in that, Based on the task parameters corresponding to each pigment usage record and the task parameters of the current printing task of the model to be sliced, multiple reference pigment records are selected from all pigment usage records, including: For each pigment usage record, calculate the parameter similarity between the task parameters corresponding to the pigment usage record and the task parameters of the current printing task of the model to be sliced; Calculate the color matching degree corresponding to the color usage record based on the color plan of the current printing task; Calculate the product of the parameter similarity and the color matching degree to obtain the record priority corresponding to the color material usage record; From all the pigment usage records, records with a priority greater than a preset priority threshold are selected to obtain multiple reference pigment records.

6. The model printing ink matching method based on time-series analysis according to claim 5, characterized in that, The step of calculating the color matching degree corresponding to the color usage record based on the color plan of the current printing task includes: Calculate the percentage of the first usage frequency of the color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the second usage frequency of the previous color type corresponding to the color usage record in the color plan of the current print job; Calculate the percentage of the third usage frequency of the next color type corresponding to the color usage record in the color plan of the current print job; Calculate the weighted sum of the first usage frequency ratio, the second usage frequency ratio, and the third usage frequency ratio to obtain the color matching degree corresponding to the color usage record.

7. The model printing ink matching method based on time-series analysis according to claim 1, characterized in that, The step of predicting the predicted matching pigment set for the model to be sliced ​​based on the LSTM neural network and the pigment recording sequence includes: The pigment recording sequence is input into a trained LSTM neural network to obtain multiple possible predicted pigments and corresponding predicted probabilities for the model to be sliced ​​at the next time point; the LSTM neural network is trained using a training dataset that includes multiple training pigment usage time sequences. All predicted pigments with a predicted probability greater than a probability threshold are selected to obtain the predicted matching pigment set for the model to be sliced.

8. The model printing ink matching method based on time-series analysis according to claim 1, characterized in that, The step of determining the color matching operation of the model to be sliced ​​based on the predicted matching color set includes: Determine the color scheme for the current printing task of the model to be sliced ​​at the next time point; Calculate the set similarity between the pigment usage combination and the predicted matching pigment set; Determine whether the similarity of the sets is greater than a preset similarity threshold; If not, generate a colorant error alarm; If so, calculate the intersection of the pigment usage combination and the predicted matching pigment set to obtain the expected pigment of the model to be sliced ​​at the next time point, and generate a pigment matching operation for changing the output mode of the pigment bin to the expected pigment.

9. A model printing ink matching system based on time-series analysis, characterized in that, The system includes: The acquisition module is used to acquire multiple pigment usage records of the model to be sliced ​​over a historical time period; The preprocessing module is used to preprocess all the pigment usage records based on the task parameters corresponding to each pigment usage record to obtain a pigment record sequence; The prediction module is used to predict the set of predicted matching pigments for the model to be sliced ​​based on the pigment recording sequence using an LSTM neural network. The determination module is used to determine the color matching operation of the model to be sliced ​​based on the predicted matching color set.

10. A model printing ink matching system based on time-series analysis, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the model printing ink matching method based on time-series analysis as described in any one of claims 1-8.

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