Printing color matching check optimization method and system based on work order semantic constraint

Abstract

The invention relates to the crossing field of printing technology and electronic information processing, in particular to a printing color matching check optimization method and system based on work order semantic constraints. Comprising the steps that printing work order semantic information is received, and N-dimensional core semantic features are extracted and normalized; configuring a correlation coefficient and assigning a basic weight coefficient, and calculating a semantic feature comprehensive quantization value through a semantic feature quantization core formula; after collecting and normalizing computing power resource values, calculating a computing power load distribution value through a computing power load dynamic distribution formula and issuing an instruction; collecting an actual computing power occupancy value, calculating a time sequence regulation and control value through a checking time sequence dynamic regulation and control formula, obtaining a target checking period, executing a checking action, and feeding back a result for iterative updating; the problems that in the prior art, semantics and checking are disjointed, and calculation power and time sequence regulation and control are rigid are solved, collaborative checking of semantics, calculation power and time sequences is achieved, and the color matching checking adaptability and stability are improved.

Description

Technical Field

[0001] This invention relates to the intersection of printing technology and electronic information processing, specifically to a printing color matching verification and optimization method and system based on work order semantic constraints. Background Technology

[0002] Existing printing color matching verification technologies only operate on a single dimension of color value data, failing to deeply bind work order semantic information with color matching verification parameters. This results in key information such as substrate type and printing process within the work order semantics not effectively constraining the verification process. Traditional verification methods employ fixed computing power allocation and uniform verification sequence, ignoring the impact of varying work order semantic complexity on computing power requirements. Furthermore, they lack a dynamic linkage mechanism between semantic features and the verification process, leading to insufficient computing power for high-semantic-complexity work orders and redundant computing power for low-complexity work orders. Additionally, the verification sequence mismatches with actual semantic requirements and computing power status, causing implicit color matching deviations or resource waste. Existing technologies cannot resolve the core bottleneck of the disconnect between semantic constraints and the verification process.

[0003] Based on the above problems, there is an urgent need for a printing color matching verification scheme that can achieve coordinated control of semantics, computing power, and timing. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a printing color matching verification and optimization method based on work order semantic constraints, comprising the following steps: S1. Receive the semantic information of the printing work order, extract the N-dimensional core semantic features corresponding to the substrate type, printing process, batch size, and surface characteristics of the substrate, normalize the N-dimensional core semantic features to obtain the quantized values ​​of each dimension feature, and normalize the original values ​​corresponding to the printing batch to obtain the batch size value. S2. Configure the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, semantic feature weight attenuation coefficient, and batch scale load coefficient, assign basic weight coefficients to the N-dimensional core semantic features respectively, and calculate the comprehensive quantization value of semantic features through the semantic feature quantization core formula. S3. Collect the total computing power resource value and the baseline computing power resource value of the color matching verification system and complete the normalization. Configure the semantic-computing power coupling coefficient, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient. Calculate the computing power load allocation value through the computing power load dynamic allocation formula and issue computing power allocation instructions to the system. S4. Real-time acquisition of the actual computing power occupancy of the system, configuration of computing power-timing coupling coefficient, timing deviation compensation coefficient, semantic-timing attenuation coefficient, calculation of timing control value through verification timing dynamic control formula, obtaining the target verification cycle by multiplying the timing control value by the benchmark verification cycle, and executing printing color matching verification action; S5. Collect the color matching verification results, feed the color matching verification results back to the semantic feature quantification stage, update each coefficient, and repeat steps S2 to S4.

[0005] In a further preferred embodiment, the sum of the basic weight coefficients in S2 is 1, and the values ​​of the feature quantization values ​​and batch magnitude values ​​of each dimension are all greater than 0 and less than or equal to 1.

[0006] In a further preferred embodiment, the semantic adaptation coefficient of substrate color absorption in S2 is adjusted according to the color absorption characteristics of the substrate type, the process coupling coding coefficient represents the degree of correlation between different printing processes, the semantic feature weight attenuation coefficient is updated according to the color matching verification process, and the batch size load coefficient is adjusted according to the batch size value.

[0007] In a further preferred embodiment, the timing control value in S4 is a dimensionless parameter, and the benchmark calibration cycle is a fixed cycle that meets the basic color matching calibration requirements.

[0008] Further optimized, the parameters involved in the core formula for semantic feature quantization include the comprehensive quantization value of semantic features, the semantic adaptation coefficient of substrate color absorption, the process coupling coding coefficient, the basic weight coefficient, the quantization value of each dimension feature, the semantic feature weight attenuation coefficient, the batch size load coefficient, the batch size value, and the number of core semantic feature dimensions. All parameters are dimensionless parameters, and the comprehensive quantization value of semantic features is obtained through the multiplication and addition operations of each parameter.

[0009] In a further preferred embodiment, the parameters involved in the dynamic allocation formula for computing power load include computing power load allocation value, semantic-computing power coupling coefficient, semantic feature comprehensive quantization value, total computing power resource value, baseline computing power resource value, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient. All parameters are dimensionless parameters, and the computing power load allocation value is obtained by combining the semantic feature comprehensive quantization value with the computing power resource ratio.

[0010] In a further preferred embodiment, the parameters involved in the verification of the dynamic timing control formula include timing control value, computing power-timing coupling coefficient, actual computing power occupancy value, computing power load allocation value, semantic feature comprehensive quantization value, timing deviation compensation coefficient, and semantic-timing attenuation coefficient. All parameters are dimensionless parameters, and the timing control value is obtained by calculating the computing power ratio and the reciprocal of the semantic feature comprehensive quantization value.

[0011] In a further preferred embodiment, the updated coefficients in S5 include the substrate color absorption semantic adaptation coefficient, the process coupling coding coefficient, and the basic weight coefficient. The updated coefficients are substituted into the semantic feature quantization core formula to calculate the iterative semantic feature comprehensive quantization value.

[0012] A printing color matching verification and optimization system based on work order semantic constraints includes a work order semantic parsing module, a semantic feature quantization module, a computing power load allocation module, a verification timing control module, and a color matching verification execution module. The work order semantic parsing module receives the semantic information of printing work orders, extracts N-dimensional core semantic features, normalizes the N-dimensional core semantic features and printing batch size, and outputs the quantized values ​​of each dimension's features and the batch size. The semantic feature quantization module receives the quantized values ​​of each dimension's features and the batch size, configures the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, semantic feature weight attenuation coefficient, batch size load coefficient, and basic weight coefficient, calculates the comprehensive quantized value of the semantic features using the semantic feature quantization core formula, and outputs it. The computing power load allocation module is used for... The system integrates and normalizes the total computing power resource value and the baseline computing power resource value, configures the semantic-computing power coupling coefficient, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient, calculates the computing power load allocation value through the computing power load dynamic allocation formula, issues computing power allocation instructions, and collects the actual computing power occupancy value; the timing control module receives the semantic feature comprehensive quantization value, computing power load allocation value, and actual computing power occupancy value, configures the computing power-timing coupling coefficient, timing deviation compensation coefficient, and semantic-timing attenuation coefficient, calculates the timing control value through the timing control dynamic control formula, and obtains the target verification cycle by multiplying the timing control value by the baseline verification cycle; the color matching verification execution module executes the printing color matching verification action according to the target verification cycle, collects the color matching verification results, and feeds them back to the semantic feature quantization module.

[0013] In a further preferred embodiment, the semantic feature quantization module is used to receive the color matching verification results fed back by the color matching verification execution module, update the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient and basic weight coefficient, substitute the updated coefficients into the semantic feature quantization core formula, and output the iterative semantic feature comprehensive quantization value to the computing power load allocation module.

[0014] Technical effect The core inventive technology of this invention lies in the closed-loop linkage between semantic feature quantification and verification results, semantic-driven dynamic computing power allocation, and dual-control timing regulation of computing power and semantics, precisely solving the core problem of the disconnect between semantics and the verification process in the background technology. Through in-depth mining and quantification of work order semantic features, it achieves precise adaptation between verification parameters and semantic requirements; dynamic computing power allocation matches work order requirements with different semantic complexities; and a closed-loop iterative mechanism continuously optimizes verification accuracy, avoiding implicit biases and resource waste, and ensuring the adaptability and stability of color matching verification. Attached Figure Description

[0015] Figure 1 This is a flowchart of the printing color matching verification and optimization method based on work order semantic constraints in this application. Figure 2This is a connection diagram of the printing color matching verification and optimization system based on work order semantic constraints in this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Traditional technical solutions have the following technical problems: existing printing color matching verification technology does not deeply bind the semantic information of the work order with the verification process, and adopts fixed computing power allocation and unified timing, which leads to the disconnect between semantic requirements, computing power status and verification process, resulting in implicit color matching deviations or resource waste.

[0018] Based on this, please refer to Figures 1-2 This embodiment provides a printing color matching verification and optimization method based on work order semantic constraints, including the following steps: S1. Receive the semantic information of the printing work order, extract the N-dimensional core semantic features corresponding to the substrate type, printing process, batch size, and surface characteristics of the substrate, normalize the N-dimensional core semantic features to obtain the quantized values ​​of each dimension feature, and normalize the original values ​​corresponding to the printing batch to obtain the batch size value. S2. Configure the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, semantic feature weight attenuation coefficient, and batch scale load coefficient, assign basic weight coefficients to the N-dimensional core semantic features respectively, and calculate the comprehensive quantization value of semantic features through the semantic feature quantization core formula. S3. Collect the total computing power resource value and the baseline computing power resource value of the color matching verification system and complete the normalization. Configure the semantic-computing power coupling coefficient, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient. Calculate the computing power load allocation value through the computing power load dynamic allocation formula and issue computing power allocation instructions to the system. S4. Real-time acquisition of the actual computing power occupancy of the system, configuration of computing power-timing coupling coefficient, timing deviation compensation coefficient, semantic-timing attenuation coefficient, calculation of timing control value through verification timing dynamic control formula, obtaining the target verification cycle by multiplying the timing control value by the benchmark verification cycle, and executing printing color matching verification action; S5. Collect the color matching verification results, feed them back to the semantic feature quantization stage, update the coefficients, and repeat steps S2 to S4. This scheme solves the problem of the disconnect between semantics and verification through the collaborative design of semantic quantization, dynamic computing power allocation, time-series control, and closed-loop iteration, and achieves coordinated control of the three.

[0019] It is worth mentioning that the N-dimensional core semantic features explicitly cover four core dimensions: substrate type, printing process, batch size, and substrate surface characteristics. None of these can be omitted. Substrate type directly relates to color adsorption effect, printing process determines color overlay rules, batch size affects verification efficiency requirements, and substrate surface characteristics relate to the texture of the color presentation. These four elements together constitute the key semantic factors influencing color verification. The core purpose of normalization is to eliminate the dimensional differences between different semantic features, ensuring that the quantified values ​​of different dimensions such as substrate type and batch size are within the same comparable range, avoiding distortion of quantification results due to differences in the numerical range of the features themselves. Basic weight coefficients must be assigned according to the actual impact of each feature on color verification, ensuring that the quantification results align with the actual needs of the printing scenario. For example, for substrates sensitive to color absorption, the basic weight coefficient for the corresponding substrate type feature must be higher than that for other features. The core formula for semantic feature quantification is the core link connecting semantic information and subsequent control steps, constructing a mapping relationship from multi-dimensional semantic features to a unified quantification index. Computational power allocation and timing control are based on this quantification result, ensuring that control actions are precisely matched with semantic requirements. The closed-loop iteration process drives dynamic updates of each coefficient by feeding back the verification results, breaking the rigidity of traditional open-loop verification. The product of the timing adjustment value and the baseline verification cycle essentially performs dynamic calibration of the basic verification cycle based on semantics and computing power status, ensuring both basic verification requirements and on-demand adjustment. The entire method deeply integrates work order semantic information into the entire verification process through coordinated steps, enabling computing power and timing resources to dynamically adjust according to semantic needs. Simultaneously, it continuously optimizes parameters through closed-loop iteration, fundamentally solving the core problem of the disconnect between semantics and verification. The technical effects achieved by the above embodiments include: realizing semantic-computing-timing collaborative verification, adapting to different work order semantic requirements, avoiding resource waste and implicit deviations, and ensuring verification stability.

[0020] Traditional technical solutions have the following technical problems: existing methods do not clearly define the value constraints of parameters related to semantic feature quantization, resulting in poor consistency of quantization results and affecting the accuracy of subsequent computing power allocation and timing control.

[0021] Based on this, the sum of the basic weight coefficients in S2 is 1, and the values ​​of the quantized features and batch size values ​​for each dimension are all greater than 0 and less than or equal to 1. This scheme ensures the standardization and comparability of semantic feature quantization results by clearly defining parameter value constraints.

[0022] It's worth noting that the total base weight coefficient is set to 1. Essentially, this employs a normalized weight allocation logic, ensuring that the sum of the weight percentages of the core semantic features across all dimensions is consistent. This prevents any single feature from excessively dominating the quantization result due to excessive weight, while also ensuring that the weight percentages of each feature are clear and traceable. For example, the weights for substrate type (0.3), printing process (0.25), batch size (0.2), and substrate surface characteristics (0.25) are all equal to 1, highlighting the impact of key features while also considering the synergistic effects of each dimension. The quantization values ​​of each dimension and the batch size are limited to a range greater than 0 and less than or equal to 1. The core purpose is to eliminate the interference of extreme values ​​on the quantization calculation. For instance, this prevents an excessively large quantization value of one feature from masking the actual impact of other features. It also provides a standardized input range for the multiplication and addition operations of the core formula for subsequent semantic feature quantization, ensuring that the semantic quantization results of different work orders are within the same range. This guarantees the consistency and comparability of parameters in subsequent stages such as computing power allocation and timing control. The value constraints are not arbitrarily set, but rather combined with the computational logic of semantic feature quantization to ensure that all parameters can be effectively integrated in the formula calculation, preventing distortion of calculation results due to differences in value ranges. Simultaneously, it provides a unified standard for parameter assignment in different scenarios, enabling different operators to obtain consistent quantization results when implementing this method, thus improving the repeatability and versatility of the method. The technical effects achieved by the above embodiments include: standardizing parameter values, improving the reliability of semantic quantization results, and providing accurate input for subsequent computing power and timing control.

[0023] Traditional technical solutions have the following technical problems: the adjustment of semantic correlation coefficients in existing methods lacks clear basis, resulting in a mismatch between the coefficients and the semantic requirements of work orders, which affects the accuracy of semantic quantification.

[0024] Based on this, the semantic adaptation coefficient for substrate color absorption in S2 is adjusted according to the color absorption characteristics of the substrate type, the process coupling coding coefficient represents the degree of correlation between different printing processes, the semantic feature weight attenuation coefficient is updated according to the color matching verification process, and the batch size load coefficient is adjusted according to the batch size value. This scheme sets clear adjustment criteria for each coefficient, achieving precise adaptation between the coefficient and semantic requirements.

[0025] It's worth noting that the core of adjusting the semantic adaptation coefficient for substrate absorbency is to align with the differences in absorbency of different substrates. For example, paper substrates have strong absorbency, resulting in a higher coefficient value than plastic substrates. By quantifying the influence of substrate absorbency characteristics on color matching through coefficient differences, the semantic quantification results can accurately reflect the actual role of the substrate in color matching verification. The process coupling coding coefficient is specifically used to quantify the correlation between different printing processes. For example, overprinting and hot stamping processes have a synergistic effect, resulting in a higher coefficient value. Conversely, independent processes have low correlation, leading to a lower coefficient value. This coefficient incorporates the impact of multi-process collaboration on color matching into the quantification system, compensating for the shortcomings of traditional methods that ignore process correlation. The semantic feature weight decay coefficient is updated according to the color matching verification process. Its core is to adapt to the dynamic changes in the influence of semantic information during the verification process. For example, in the early stages of verification, semantic information has a strong guiding role in color matching, resulting in a higher coefficient value. As verification progresses, the reference value of actual color matching data increases, the influence of semantic information weakens, and the coefficient value gradually decreases. This allows the quantification results to dynamically adjust with the verification process, aligning with the changing needs of actual verification scenarios. The batch size load coefficient is directly linked to the batch size value and is adjusted accordingly. Larger batch sizes require higher verification efficiency, and this coefficient is adjusted to reflect the impact of batch size requirements on verification resources, providing a precise basis for subsequent computing power allocation. Each coefficient is adjusted based on specific semantic features or verification processes, avoiding subjectivity and arbitrariness in coefficient configuration. This ensures that the value of each coefficient is highly adapted to actual semantic needs and verification scenarios, enabling the comprehensive quantization value of semantic features to fully and accurately reflect the combined impact of various semantic factors. The technical effects achieved by the above embodiments include: achieving precise coefficient adjustment and improving the fit between semantic quantization results and actual needs.

[0026] Traditional technical solutions have the following technical problems: existing methods do not clearly define the dimensional attributes of time series control values ​​and the definition of the benchmark verification period, which leads to ambiguity in the time series calculation logic and affects the accuracy of the target verification period.

[0027] Based on this, the timing control value in S4 is a dimensionless parameter, and the baseline calibration cycle is a fixed cycle that meets the basic color matching calibration requirements. This scheme clarifies the attributes and definitions of timing-related parameters and standardizes the timing calculation logic.

[0028] It's worth noting that the timing adjustment value is set as a dimensionless parameter. The core purpose is to adapt it to the multiplication logic with the baseline verification period. The baseline verification period has a time dimension, and the unitless timing adjustment value ensures that the product of the two retains the time dimension, avoiding dimensional conflicts that could invalidate the calculation results. Simultaneously, it allows the timing adjustment value to focus on representing the adjustment ratio, unaffected by specific time units. For example, an adjustment value of 0.8 indicates shortening the baseline period to 80%, and an adjustment value of 1.2 indicates extending it to 120%. Regardless of whether the baseline period is in seconds or minutes, the adjustment ratio remains consistent. The core definition of the baseline verification period is to meet basic color matching verification requirements. That is, it is the minimum necessary verification period set based on conventional printing scenarios, standard semantic complexity, and sufficient computing power. This ensures the basic accuracy and completeness of color matching verification, providing a stable benchmark for timing adjustment. Setting a fixed benchmark verification cycle avoids confusion in the calculation of the target verification cycle due to fluctuations in the base cycle. Simultaneously, it allows the core of timing control to focus on dynamically calibrating the base cycle based on semantics and computing power status. This ensures that basic verification requirements are not lacking while enabling on-demand adaptation through adjustment values. The dimensionless attribute and the fixed benchmark cycle together construct a clear timing calculation logic, making the calculation process of the target verification cycle traceable and repeatable. This avoids timing control deviations caused by ambiguous parameter definitions and ensures that timing adjustments accurately match semantic requirements and computing power status. The technical effects achieved by the above embodiments include: standardizing timing calculation logic and improving the accuracy and reliability of the target verification cycle.

[0029] Traditional technical solutions have the following technical problems: existing methods lack standardized computational logic for semantic feature quantification, and cannot transform multi-dimensional semantic features into unified quantitative indicators, which affects the realization of subsequent collaborative regulation.

[0030] Based on this, the core formula for semantic feature quantization involves parameters including the comprehensive quantized value of semantic features, the semantic adaptation coefficient of substrate color absorption, the process coupling coding coefficient, the basic weight coefficient, the quantized values ​​of each dimension of features, the semantic feature weight attenuation coefficient, the batch size load coefficient, the batch size value, and the number of core semantic feature dimensions. All parameters are dimensionless, and the comprehensive quantized value of semantic features is obtained through multiplication and addition operations of each parameter. This scheme constructs a standardized semantic quantization formula to achieve unified quantization of multi-dimensional semantic features.

[0031] It is worth mentioning that the core formula for semantic feature quantization is as follows: ; Its theoretical design is based on the influence of semantic factors on color matching in printing color science and the quantification method of information theory characteristics. Its core innovation lies in transforming multi-dimensional and dispersed semantic information into a single, quantifiable core indicator through systematic parameter design and computational logic, providing a unified basis for subsequent coordinated control. From the perspective of parameter design… As a comprehensive quantitative value of semantic features, it is the core output of the entire formula operation. It is used to centrally represent the comprehensive influence of all semantic factors on color matching verification. All parameters are set as dimensionless parameters, which fully comply with the principle of homogeneity of dimensions, ensuring that the multiplication and addition operation logic is compliant and avoiding the distortion of results caused by dimension conflicts. This is the semantic adaptation coefficient for substrate color absorption, specifically used to quantify the impact of substrate color absorption characteristics on color matching. The process coupling coding coefficient is used to characterize the correlation between multiple printing processes. The two are multiplied together, which is essentially to realize the coupling quantification of the two core semantic factors of substrate and process, accurately reflecting the comprehensive effect of their synergy on color matching verification, which is different from the limitations of traditional methods that consider the two separately. This is a weighted summation of multi-dimensional core semantic features, where The basic weight coefficients for the nth-dimensional core semantic feature. For the feature quantization value of the corresponding dimension, The summation term represents the number of dimensions of the core semantic features. By assigning weights, it quantifies the degree of influence of each semantic feature, ensuring that the dominant role of key features and the auxiliary role of secondary features are both reflected. For example, the weight coefficients of substrate type and printing process are higher than the batch size, making the quantification results more in line with actual color matching requirements. This is the semantic feature weight attenuation coefficient, used to characterize the dynamic changes in the influence of semantic information during the verification process. This is a batch size load factor used to quantify the impact of batch size on verification requirements. The product of these two factors is then multiplied by the batch size value. Multiplying these terms again creates a coupled quantification term for batch and time-series decay factors, compensating for the shortcomings of traditional methods that ignore batch requirements and dynamic changes in the verification process. The entire formula employs multiplication-addition logic, organically integrating three modules: substrate process coupling term, multi-feature weighted summation term, and batch decay coupling term. Through the synergistic effect of various parameters, it achieves comprehensive and accurate quantification of multi-dimensional semantic features, ensuring that the influence of each semantic factor is effectively incorporated and forming a unified indicator through systematic calculation. The formula implementation requires no complex equipment; it only requires assigning parameters based on the extracted semantic features and obtaining the result through conventional mathematical operations. Furthermore, the parameter assignments have clear basis, ensuring the repeatability of the calculation process and the verifiability of the results. Its core innovation lies in breaking the traditional model of scattered consideration of semantic features and constructing a standardized quantification logic of multi-factor coupling and dynamic adaptation. This allows semantic information to be transformed into a quantification indicator that can be directly used for subsequent computing power and time-series control, laying a core foundation for the coordinated control of these three aspects. The technical effects achieved by the above embodiment include: realizing standardized quantification of semantic features and providing a unified core indicator for subsequent coordinated control.

[0032] Traditional technical solutions have the following technical problems: the existing methods lack the logic of associating computing power allocation with semantic features, and cannot dynamically adapt computing power according to semantic complexity, resulting in waste or insufficiency of computing power resources.

[0033] Based on this, the parameters involved in the computing power load allocation formula include the computing power load allocation value, the semantic-computing power coupling coefficient, the semantic feature comprehensive quantization value, the total computing power resource value, the baseline computing power resource value, the computing power redundancy compensation coefficient, and the semantic complexity computing power correction coefficient. All parameters are dimensionless. The computing power load allocation value is obtained by combining the semantic feature comprehensive quantization value with the computing power resource ratio. This scheme constructs a semantic-driven computing power allocation formula to achieve dynamic adaptation between computing power and semantic complexity.

[0034] It is worth mentioning that the specific formula for computing power load allocation is as follows: ; Its theoretical design is based on the principles of computational power allocation in algorithm engineering. Its core innovation lies in establishing a direct link between semantic features and computational power allocation, enabling dynamic adjustment of computational resources according to semantic complexity, while simultaneously considering system resource status and redundancy requirements, thus solving the rigidity problem of traditional fixed computational power allocation. From the perspective of parameters and operational logic, The value allocated to the computing load is the core indicator output by the formula. It is directly used to guide the allocation and scheduling of computing resources in the system. All parameters are dimensionless to ensure that the operation logic is compliant and the results are comparable. This is the semantic-computational coupling coefficient. Its core function is to quantify the correlation strength between semantic features and computational power requirements. The greater the impact of semantic complexity on computational power requirements, the higher the value of this coefficient, enabling computational power allocation to accurately follow the comprehensive quantification value of semantic features. Adjustments are made to adapt to changes and realize semantically driven core logic. For example, high semantic complexity work orders correspond to high coupling coefficients, ensuring sufficient computing power supply. This is a relative ratio of computing resources, where This represents the total computing power resource value of the system. As the baseline computing power resource value, this ratio represents the redundancy ratio of the system's available computing power relative to the baseline computing power. It ensures that the computing power allocation is in line with the actual resource status of the system and avoids exceeding the system's computing power limit or falling below the baseline requirement. For example, when the available computing power is sufficient, the ratio is higher and the allocation can be appropriately increased. When the computing power is tight, the ratio is lower and the basic computing power requirement is prioritized. This is a computing power redundancy compensation coefficient, used to cope with sudden computing power demands and avoid verification interruptions due to computing power fluctuations. This is a semantic complexity computational power correction coefficient, used to calibrate the deviation in computational power requirements under different semantic complexities. The product of these two factors is then summed with... Multiplication creates a non-linear correction term for semantic complexity. The core reason for using square root operations is that the relationship between semantic complexity and computing power requirements is not a simple linear one. Computing power requirements grow rapidly in low-complexity ranges and slow down in high-complexity ranges. Square root operations can accurately adapt to this non-linear relationship, avoiding the problem of excessive computing power at low complexity and insufficient computing power at high complexity caused by a simple linear relationship. The formula implementation process consists of two steps: first, calculating the relative ratio based on the system's computing power resources; then, combining the semantic features with the comprehensive quantization value and various coefficients, and obtaining the final computing power allocation value through multiplication and addition operations. The calculation process is concise and efficient, and can respond in real time to changes in semantics and resource status. All parameter assignments have clear justifications. , , Calibrate according to the actual application scenario and Based on system hardware configuration settings, the allocation results are ensured to closely match actual needs. Its core innovation lies in constructing a three-dimensional computing power allocation logic of "semantic drive + resource adaptation + nonlinear correction." This logic uses semantic features as the core basis while also considering system resource status and redundancy requirements. Simultaneously, it adapts the complex relationship between semantics and computing power through nonlinear operations, enabling computing power allocation to accurately match semantic needs while avoiding resource waste, achieving a balance between computing power utilization efficiency and verification requirements. The technical effects achieved by the above embodiments include: achieving precise adaptation between computing power and semantic complexity, and optimizing the efficiency of computing power resource allocation.

[0035] Traditional technical solutions have the following technical problems: the timing control of existing methods cannot simultaneously adapt to semantic features and computing power status, resulting in a disconnect between timing and actual needs, affecting the verification effect and resource utilization efficiency.

[0036] Based on this, the parameters involved in the verification of the dynamic timing control formula include the timing control value, the computing power-timing coupling coefficient, the actual computing power occupancy value, the computing power load allocation value, the semantic feature comprehensive quantization value, the timing deviation compensation coefficient, and the semantic-timing decay coefficient. All parameters are dimensionless. The timing control value is obtained by calculating the computing power ratio and the reciprocal of the semantic feature comprehensive quantization value. This scheme constructs a timing control formula with dual computing power and semantic control, achieving precise adaptation between timing and both factors.

[0037] It is worth mentioning that the specific formula for dynamic adjustment of the verification time series is as follows: ; Its theoretical design is based on the feedback regulation principle of dynamic system control theory. Its core innovation lies in breaking the limitations of single-factor timing control, constructing a dual-factor collaborative control logic of computing power and semantics, and simultaneously achieving precise timing calibration through nonlinear correction and deviation compensation, thus solving the problem of timing discrepancies with actual needs. From the perspective of parameters and operational logic, The timing control value serves as the core output of the formula, used to calibrate the benchmark period to obtain the target period. All parameters are dimensionless, ensuring compliance of the product operation with the benchmark period. This is the computing power-timing coupling coefficient, which is specifically used to quantify the correlation between computing power status and timing control. The more sufficient the computing power, the more the value of this coefficient can be adjusted so that the timing can be dynamically optimized according to the computing power status. For example, when the computing power is sufficient, the timing can be shortened to improve the verification efficiency, and when the computing power is tight, the timing can be extended to ensure the integrity of the verification. This is the ratio of actual computing power to allocated computing power, where This represents the actual computing power usage. This is the computing power load allocation value. This ratio item provides real-time feedback on the matching degree between the computing power allocation and the actual usage. A ratio greater than 1 indicates that the computing power is over-allocated, and a ratio less than 1 indicates that the computing power is insufficient. This ratio enables the timing control to respond to the actual computing power fluctuations and avoids deviations caused by control based on theoretical allocation values. This is the reciprocal of the comprehensive quantized value of semantic features. The core implementation shows an inverse correlation between semantic complexity and temporal sequence; the higher the semantic complexity, the better. The larger the value, The smaller the value, the smaller the corresponding timing control value and the shorter the target cycle, ensuring that work orders with high semantic complexity receive a faster verification response. Conversely, the cycle is appropriately extended to optimize resource utilization. This is the timing deviation compensation coefficient. The semantic-temporal decay coefficient is multiplied by the following: Multiplication creates a nonlinear compensation term for computing power deviation. The purpose of squaring is to amplify the corrective effect of computing power deviation on timing, preventing frequent timing fluctuations caused by small deviations. Simultaneously, coefficient calibration ensures that the deviation correction magnitude is reasonable and does not affect the verification stability. The formula implementation is based on the real-time collected actual computing power occupancy value and the previously calculated... , The value is obtained through step-by-step calculations after substituting the parameters. The computation process can be executed in real time, adapting to dynamically changing computing power and semantic states. Parameter assignments are calibrated according to the scenario. , , The optimal range was determined through multiple experiments to ensure a balance between the two-factor control logic, responding to semantic requirements while adapting to computing power conditions. Its core innovation lies in constructing a time-series logic of collaborative two-factor control and nonlinear deviation compensation. This allows the time sequence to not only dynamically adjust with semantic complexity but also adapt to computing power fluctuations in real time. Simultaneously, frequent fluctuations are suppressed through squared terms, achieving a triple balance between verification timeliness, resource utilization, and stability. The technical effects achieved by the above embodiments include: achieving dual adaptation of time sequence, computing power, and semantics, and improving verification timeliness and resource utilization efficiency.

[0038] Traditional technical solutions have the following technical problems: existing methods lack an iterative update mechanism for semantic coefficients, which makes it impossible for coefficients to adapt to dynamic changes during the verification process, affecting the accuracy of continuous verification.

[0039] Based on this, the updated coefficients in S5 include the substrate color absorption semantic adaptation coefficient, the process coupling coding coefficient, and the basic weight coefficient. The updated coefficients are then substituted into the semantic feature quantization core formula to calculate the iterative semantic feature comprehensive quantization value. This scheme constructs a coefficient iterative update mechanism to achieve dynamic adaptation between the coefficients and the verification process.

[0040] It is worth mentioning that the core logic of this mechanism is to use the color matching verification results as the core basis for coefficient updates, breaking the limitations of traditional fixed coefficients. This allows semantic correlation coefficients to be dynamically optimized along with the actual verification results, forming a closed-loop iterative quantification system. The three types of updated coefficients are all key parameters of the core formula for semantic feature quantification. The substrate color absorption semantic adaptation coefficient directly affects the quantification accuracy of substrate factors; the process coupling coding coefficient relates to the characterization effect of process synergy; and the basic weight coefficient determines the influence ratio of each semantic feature. These three factors together determine the accuracy of the semantic quantification results. Targeted updates to these three types of coefficients can achieve precise optimization of quantification accuracy. The update process is not a blind adjustment, but rather an analysis of the reasons for deviations based on the verification results, with targeted calibration of coefficients. For example, if the verification finds that the color matching deviation stems from insufficient consideration of the substrate color absorption characteristics, the substrate color absorption semantic adaptation coefficient is appropriately adjusted. If the deviation stems from inaccurate quantification of process synergy, the process coupling coding coefficient is calibrated. If the actual influence of a certain semantic feature does not match its weight ratio, the basic weight coefficient is adjusted. The updated coefficients are directly substituted into the core formula for semantic feature quantization, ensuring that the quantization results are optimized synchronously with the coefficient updates. This allows the next round of semantic quantization to avoid the deviations of the previous round. Simultaneously, the optimized comprehensive quantized value of semantic features is passed to the computing power load allocation and timing control stages, driving subsequent control actions to optimize synchronously. This iterative mechanism requires no additional complex operations; it only needs to adjust the coefficients based on the existing verification results. It can be integrated into the regular verification process to achieve continuous optimization. Furthermore, each update has a clear basis for deviation, ensuring that the adjustment logic is traceable and verifiable, avoiding subjectivity in coefficient updates. Through this mechanism, semantic correlation coefficients can gradually adapt to the semantic characteristics of different work orders and actual verification scenarios, continuously improving the fit between semantic quantization results and actual color matching requirements, providing a more accurate basis for subsequent collaborative control. The technical effects achieved by the above embodiments include: realizing dynamic coefficient iteration and continuously improving the fit between semantic quantization and verification results.

[0041] Traditional technical solutions have the following technical problems: existing systems lack a modular architecture that is compatible with semantic-computing-temporal coordinated control, which makes it impossible to achieve efficient linkage between various links and affects the implementation of the verification solution.

[0042] Based on this, this embodiment provides a printing color matching verification and optimization system based on work order semantic constraints, including a work order semantic parsing module, a semantic feature quantization module, a computing power load allocation module, a verification timing control module, and a color matching verification execution module. The work order semantic parsing module receives the semantic information of printing work orders, extracts N-dimensional core semantic features, normalizes the N-dimensional core semantic features and printing batch size, and outputs the quantized values ​​of each dimension's features and the batch size value. The semantic feature quantization module receives the quantized values ​​of each dimension's features and the batch size value, configures the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, semantic feature weight attenuation coefficient, batch size load coefficient, and basic weight coefficient, calculates the comprehensive quantized value of the semantic features using the semantic feature quantization core formula, and outputs it. The computing power load allocation module... The first module collects and normalizes the total system computing power resource value and the baseline computing power resource value, configures the semantic-computing power coupling coefficient, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient, calculates the computing power load allocation value through the computing power load dynamic allocation formula, issues computing power allocation instructions, and collects the actual computing power occupancy value. The second module receives the semantic feature comprehensive quantization value, computing power load allocation value, and actual computing power occupancy value, configures the computing power-time coupling coefficient, time series deviation compensation coefficient, and semantic-time series attenuation coefficient, calculates the time series control value through the verification time series dynamic control formula, and obtains the target verification period by multiplying the time series control value by the baseline verification period. The third module executes the printing color matching verification action according to the target verification period, collects the color matching verification results, and feeds them back to the semantic feature quantization module. This solution constructs an adaptive and collaborative control module architecture to achieve efficient linkage between various links.

[0043] It is worth mentioning that the work order semantic parsing module, as the data entry point, has the core function of extracting and standardizing semantic information. The received work order semantic information covers raw data such as substrate, process, batch size, and surface characteristics of the printing substrate. Through feature extraction, N-dimensional core semantic features are selected, and then normalization is used to eliminate dimensional differences, outputting standardized quantized values ​​for each dimension of features and batch size. This provides unified data input for subsequent modules, avoiding processing deviations caused by messy raw data. The semantic feature quantization module, as the core processing unit, receives the data output from the parsing module, configures various semantic correlation coefficients according to set rules, and then completes the unified quantization of multi-dimensional semantic features through the core formula of semantic feature quantization. The output comprehensive quantized value of semantic features is the core link connecting semantic parsing and subsequent control modules, directly determining the accuracy of computing power allocation and timing control. The computing power load allocation module focuses on the dynamic scheduling of computing resources. It collects and normalizes the total system computing power and baseline computing power, combines the comprehensive quantification value of semantic features with the configured coefficients, and calculates the computing power allocation value adapted to semantic requirements through the computing power load dynamic allocation formula. After issuing instructions, it also collects the actual computing power occupancy value in real time, providing computing power status data for timing control, and achieving accurate adaptation of computing power allocation with semantics and resource status. The timing control verification module, as a timing calibration unit, integrates three types of core data: comprehensive quantification value of semantic features, computing power allocation value, and actual computing power occupancy value. After configuring relevant coefficients, it calculates the timing control value through the timing control verification dynamic control formula, and then combines it with the baseline verification cycle to obtain the target cycle, realizing timing control driven by both semantics and computing power, ensuring that the verification rhythm adapts to actual needs. The color matching verification execution module, as the execution and feedback unit, strictly executes verification actions according to the target verification cycle, while accurately collecting verification results and promptly feeding them back to the semantic feature quantification module, providing data support for coefficient iterative updates and building a closed-loop optimization link. The data flow between modules is efficient and smooth. The parsing module outputs data to the quantization module, the quantization module outputs data to the computing power and timing module, the timing module outputs the target period to the execution module, and the execution module feeds back the results to the quantization module, forming a complete data closed loop. The module architecture design is fully adapted to the collaborative control logic. The function of each module corresponds to the key steps of the method, ensuring that each step of the method has corresponding module support. At the same time, the functions between modules are complementary, avoiding functional overlap or omissions. This allows core logics such as semantic integration, computing power control, timing calibration, and closed-loop iteration to be efficiently implemented through module linkage. The technical effects achieved by the above embodiments include: realizing efficient linkage between various links, ensuring the implementation of the collaborative control scheme, and improving the overall verification performance of the system.

[0044] Traditional technical solutions have the following technical problems: the semantic feature quantization module in the existing system lacks coefficient iterative update function, which makes it impossible to continuously optimize the quantization results and affects the long-term verification accuracy of the system.

[0045] Based on this, according to the above system, the semantic feature quantization module receives the color matching verification results from the color matching verification execution module, updates the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, and basic weight coefficient, substitutes the updated coefficients into the semantic feature quantization core formula, and outputs the iteratively obtained comprehensive quantized value of semantic features to the computing power load allocation module. This scheme endows the semantic feature quantization module with the function of iteratively updating coefficients, realizing continuous optimization of quantization results.

[0046] It's worth noting that the semantic feature quantization module, as the core carrier of coefficient updates, doesn't simply adjust parameters through iterative updates. Instead, it's a precise optimization process based on the verification results. This function upgrades the module from a single quantization calculation unit to a core unit that combines quantization and optimization. The module first receives the verification results from the color matching verification execution module, analyzes the results, identifies the correlation between color matching deviations and semantic quantization, and determines whether the deviation stems from unreasonable values ​​in the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, or basic weight coefficient. For example, if the deviation is concentrated in the substrate-related color matching stage, the focus is on adjusting the substrate color absorption semantic adaptation coefficient; if the deviation originates from the multi-process collaborative color matching stage, the process coupling coding coefficient is calibrated; if the deviation stems from an imbalance in the influence of a certain semantic feature, the basic weight coefficient is adjusted. The adjustment process strictly follows the pre-set coefficient adjustment criteria to ensure that the updated coefficients are still accurately adapted to the semantic requirements and verification scenario, without arbitrary adjustments. The updated coefficients are directly substituted into the core formula of semantic feature quantization to recalculate the comprehensive quantization value of semantic features. This iteratively quantized value is more accurate than the previous one, avoiding the deviations present in the previous round of quantization. Simultaneously, the module outputs this quantized value to the computing power load allocation module, enabling the module to recalculate the computing power allocation value based on the optimized quantized value. This, in turn, drives the synchronous optimization of the timing calibration of the verification timing control module, forming a chain optimization effect between modules and continuously improving the verification accuracy of the entire system. This function requires no additional hardware modules; it can be achieved solely by optimizing the software logic of the semantic feature quantization module. It can be seamlessly integrated into the existing system architecture. Furthermore, each coefficient update has a clear verification result as a basis, ensuring that the update process is traceable and verifiable. This allows the semantic quantization accuracy to gradually improve with the number of verifications during long-term system operation, adapting to the semantic characteristics and complex scenarios of different work orders and avoiding the long-term verification accuracy decay problem caused by fixed coefficients. The technical effects achieved by the above embodiment include: continuous optimization of quantization results, improving the long-term verification accuracy and stability of the system.

[0047] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A printing color matching verification and optimization method based on work order semantic constraints, characterized in that, Includes the following steps: S1. Receive the semantic information of the printing work order, extract the N-dimensional core semantic features corresponding to the substrate type, printing process, batch size, and surface characteristics of the substrate, normalize the N-dimensional core semantic features to obtain the quantized values ​​of each dimension feature, and normalize the original values ​​corresponding to the printing batch to obtain the batch size value. S2. Configure the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, semantic feature weight attenuation coefficient, and batch scale load coefficient, assign basic weight coefficients to the N-dimensional core semantic features respectively, and calculate the comprehensive quantization value of semantic features through the semantic feature quantization core formula. S3. Collect the total computing power resource value and the baseline computing power resource value of the color matching verification system and complete the normalization. Configure the semantic-computing power coupling coefficient, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient. Calculate the computing power load allocation value through the computing power load dynamic allocation formula and issue computing power allocation instructions to the system. S4. Real-time acquisition of the actual computing power occupancy of the system, configuration of computing power-timing coupling coefficient, timing deviation compensation coefficient, semantic-timing attenuation coefficient, calculation of timing control value through verification timing dynamic control formula, obtaining the target verification cycle by multiplying the timing control value by the benchmark verification cycle, and executing printing color matching verification action; S5. Collect the color matching verification results, feed the color matching verification results back to the semantic feature quantification stage, update each coefficient, and repeat steps S2 to S4.

2. The printing color matching verification and optimization method based on work order semantic constraints according to claim 1, characterized in that, In S2, the sum of the basic weight coefficients is 1, and the values ​​of the feature quantization values ​​and batch magnitude values ​​of each dimension are all greater than 0 and less than or equal to 1.

3. The printing color matching verification and optimization method based on work order semantic constraints according to claim 1, characterized in that, In S2, the semantic adaptation coefficient of substrate color absorption is adjusted according to the color absorption characteristics of the substrate type, the process coupling coding coefficient represents the degree of correlation between different printing processes, the semantic feature weight attenuation coefficient is updated according to the color matching verification process, and the batch size load coefficient is adjusted according to the batch size value.

4. The printing color matching verification and optimization method based on work order semantic constraints according to claim 1, characterized in that, In S4, the timing control value is a dimensionless parameter, and the benchmark calibration cycle is a fixed cycle that meets the basic color matching calibration requirements.

5. The printing color matching verification and optimization method based on work order semantic constraints according to claim 1, characterized in that, The core formula for semantic feature quantization involves parameters including the comprehensive quantization value of semantic features, the semantic adaptation coefficient of substrate color absorption, the process coupling coding coefficient, the basic weight coefficient, the quantization value of each dimension feature, the semantic feature weight attenuation coefficient, the batch size load coefficient, the batch size value, and the number of core semantic feature dimensions. All parameters are dimensionless, and the comprehensive quantization value of semantic features is obtained through multiplication and addition operations of each parameter.

6. The printing color matching verification and optimization method based on work order semantic constraints according to claim 5, characterized in that, The parameters involved in the dynamic allocation formula of computing power load include computing power load allocation value, semantic-computing power coupling coefficient, semantic feature comprehensive quantization value, total computing power resource value, baseline computing power resource value, computing power redundancy compensation coefficient, and semantic complexity computing power correction coefficient. All parameters are dimensionless parameters. The computing power load allocation value is obtained by combining the semantic feature comprehensive quantization value and the computing power resource ratio.

7. The printing color matching verification and optimization method based on work order semantic constraints according to claim 6, characterized in that, The parameters involved in the verification of the dynamic control formula for timing include timing control value, computing power-timing coupling coefficient, actual computing power occupancy value, computing power load allocation value, semantic feature comprehensive quantization value, timing deviation compensation coefficient, and semantic-timing attenuation coefficient. All parameters are dimensionless. The timing control value is obtained by calculating the computing power ratio and the reciprocal of the semantic feature comprehensive quantization value.

8. The printing color matching verification and optimization method based on work order semantic constraints according to claim 1, characterized in that, The updated coefficients in S5 include the substrate color absorption semantic adaptation coefficient, the process coupling coding coefficient, and the basic weight coefficient. The updated coefficients are substituted into the semantic feature quantization core formula to calculate the iterative semantic feature comprehensive quantization value.

9. A printing color matching verification and optimization system based on work order semantic constraints, characterized in that, It includes a work order semantic parsing module, a semantic feature quantization module, a computing power load allocation module, a verification timing control module, and a color matching verification execution module. The work order semantic parsing module receives the semantic information of printing work orders, extracts N-dimensional core semantic features, normalizes the N-dimensional core semantic features and printing batch, and outputs the quantized values ​​of each dimension feature and the batch size value. The semantic feature quantization module receives the quantized values ​​of each dimension feature and the batch size value, configures the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, semantic feature weight attenuation coefficient, batch size load coefficient, and basic weight coefficient, calculates the comprehensive quantized value of semantic features through the semantic feature quantization core formula, and outputs it. The computing power load allocation module is used to collect and normalize the total computing power resource value of the system and the baseline computing power resource value, configure the semantic-computing power coupling coefficient, computing power redundancy compensation coefficient, semantic complexity computing power correction coefficient, calculate the computing power load allocation value through the computing power load dynamic allocation formula, issue computing power allocation instructions and collect the actual computing power occupancy value. The timing control module is used to receive the comprehensive quantization value of semantic features, the computing power load allocation value and the actual computing power occupancy value, configure the computing power-timing coupling coefficient, the timing deviation compensation coefficient and the semantic-timing attenuation coefficient, calculate the timing control value through the timing control formula, and obtain the target verification period by multiplying the timing control value and the benchmark verification period. The color matching verification execution module is used to perform printing color matching verification actions according to the target verification cycle, collect the color matching verification results and feed them back to the semantic feature quantization module.

10. The printing color matching verification and optimization system based on work order semantic constraints according to claim 9, characterized in that, The semantic feature quantization module receives the color matching verification results from the color matching verification execution module, updates the substrate color absorption semantic adaptation coefficient, process coupling coding coefficient, and basic weight coefficient, substitutes the updated coefficients into the semantic feature quantization core formula, and outputs the iterative semantic feature comprehensive quantization value to the computing power load allocation module.

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