Mold C value calculation method based on multi-dimensional data

Abstract

The invention relates to the technical field of automobile dies, solves the problem that the manufacturing difficulty is influenced by mostly neglected physical characteristics such as die structural strength and process complexity in the prior art, and particularly relates to a die C value calculation method based on multi-dimensional data. According to the method, a calculation strategy set comprising a general strategy, a high-order strategy and a cost control strategy is constructed, a current calculation strategy is determined, basic data of a target mold is obtained and preprocessed, a standard feature vector is constructed, and corresponding operation is executed in a standard database based on the preprocessed basic data so as to obtain a corresponding basic correction coefficient. According to the method, nonlinear fitting of the influence of the physical specification and the structural difficulty of the mold on the manufacturing cost is realized, the accurate correction coefficient can be automatically matched according to the specific attribute of the mold, and the subjective randomness of artificial experience estimation is eliminated, so that the final C value can reflect the actual consumption of the automobile mold in the manufacturing process more truly and objectively.

Description

Technical Field

[0001] This invention relates to the field of automotive mold technology, and in particular to a method for calculating the C-value of a mold based on multidimensional data. Background Technology

[0002] As a fundamental process equipment in the automotive industry, automotive molds are characterized by their unique manufacturing process, including single-piece customization, complex structure, and non-standardization. In the operation and management of mold companies, the mold's C-value is typically used as a core indicator to measure its value. It serves not only as a benchmark for commercial quotations but also as a crucial basis for subsequent production resource scheduling and time quota setting.

[0003] Existing mold valuation methods mostly employ simple multiplication or empirical estimation, relying primarily on multiplying the mold's weight (tonnage) by a fixed unit price coefficient to determine its value. This calculation method ignores the nonlinear impact of physical characteristics such as mold structural strength and process complexity on manufacturing difficulty, resulting in a single-dimensional calculation model and a lack of quantification methods for nonlinear characteristics. For example, for molds of the same tonnage, a set with extremely high structural strength requirements (such as a high-strength plate mold) and a set of ordinary casting molds have vastly different processing times and technical risks, but existing technologies often struggle to accurately correct for these differences using discretized parameter ranges (such as strength ranges). Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for calculating the C-value of molds based on multidimensional data. This method solves the technical problem that most existing technologies neglect the influence of physical characteristics such as mold structural strength and process complexity on manufacturing difficulty. It achieves nonlinear fitting of the impact of mold physical specifications and structural difficulty on manufacturing costs and can automatically match accurate correction coefficients based on the specific properties of the mold.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for calculating the C-value of a mold based on multidimensional data, the method comprising the following steps: A standard database for C-value calculation is pre-defined, a set of calculation strategies including general strategies, high-order strategies and cost control strategies is constructed, and the current calculation strategy is determined. Acquire the basic data of the target mold and preprocess it, and construct a standard feature vector. Based on the preprocessed basic data, perform corresponding operations in the standard database to obtain the corresponding basic correction coefficients. Call the predictive regression model trained based on historical mold project data, input the standard feature vector of the target mold into the predictive regression model, and obtain the comprehensive complexity weight; Based on the mold type, the basic C value is retrieved from the basic C value table. The final C value is calculated by combining the basic correction coefficient and the comprehensive complexity weight according to the preset dynamic weighted algorithm model. The final C value is converted into the total standard working hours of the project. Based on the process proportion template corresponding to the mold type, the total standard working hours are automatically discretized and allocated to each process step to generate a working hour allocation table.

[0006] Furthermore, the standard database includes a basic C-value table, a tonnage classification table, a customer coefficient table, and a strength range table. The basic data includes mold type, standard tonnage value, customer identifier, and mold structural strength value.

[0007] Furthermore, the basic correction factors include tonnage factor, customer factor, and strength factor.

[0008] Furthermore, the specific steps of performing the corresponding operation in the standard database to obtain the corresponding basic correction coefficient include the following: The corresponding tonnage coefficient is located by performing discrete interval matching in the tonnage classification table using standard tonnage values. Use the customer identifier to retrieve the corresponding customer coefficient in the customer coefficient table; The strength values ​​of the mold structure are used to determine the range in the strength interval table. By comparing the minimum and maximum strength boundaries, the corresponding strength coefficient is obtained.

[0009] Furthermore, the specific steps for calling the predictive regression model trained based on historical mold project data, inputting the standard feature vector of the target mold into the predictive regression model, and obtaining the comprehensive complexity weights include the following: A predictive regression model is generated by learning and training based on the structural feature data of historical mold projects as input features and the deviation rate between actual working hours and standard working hours as label values. Using a predictive regression model, forward inference is performed on the input standard feature vector, and combined with a preset weight matrix and bias parameters, the predicted complexity value used to characterize the structural complexity of the mold is calculated. The calculation formula is as follows: In the above formula, This represents the numerical value of the prediction complexity. Represents the regression mapping function, Represents the standard eigenvector. Indicates the internal weight parameters. Indicates the total number of feature dimensions. Indicates feature index, Represents the characteristic component values. Indicates the bias term. Represents the random error term; The predicted complexity value is boundary-checked based on the confidence interval defined by the operating environment, and the checked predicted complexity value is assigned to the comprehensive complexity weight.

[0010] Furthermore, the formula for calculating the final C value is as follows: In the above formula, This represents the final C value. Indicates the base C value. Indicates tonnage coefficient. Indicates customer coefficient. Indicates the strength coefficient. This represents the weight of the overall complexity.

[0011] Furthermore, the specific steps for converting the final C value into the total standard working hours of the project, and automatically discretizing and allocating the total standard working hours to each process step according to the process proportion template corresponding to the mold type, to generate a working hour allocation table, include the following: Based on the preset unit labor hour output parameters, the total standard labor hours for the target mold project are calculated using the following formula: In the above formula, This represents the total standard working hours of the project. This parameter represents the output value per unit of working hours. This represents the final C value; Based on the mold type of the target mold, match it in the preset time percentage database and retrieve the corresponding standard process percentage vector; Iterate through each component in the standard process proportion vector, and calculate the sub-item standard time of each process node based on the total standard working hours of the project and the process proportion coefficient corresponding to each component. The calculation formula is as follows: In the above formula, Indicates the standard working hours for each item. Indicates the percentage of processes; The standard working hours for each item are linked with the corresponding process name and responsible department attribute to generate a working hour allocation table containing the fields of process name, working hour percentage, cumulative percentage and standard working hours.

[0012] A system for calculating the C-value of a mold based on multidimensional data includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the method for calculating the C-value of a mold based on multidimensional data.

[0013] By employing the above technical solution, the present invention provides a method for calculating the C-value of a mold based on multidimensional data, which has at least the following beneficial effects: 1. This invention establishes a discrete tonnage classification table and strength interval table, realizing nonlinear fitting of the impact of mold physical specifications and structural difficulty on manufacturing costs. It can automatically match accurate correction coefficients based on the specific attributes of the mold (such as standard tonnage value and structural strength value), eliminating the subjective arbitrariness of manual experience estimation. This allows the final C value to more realistically and objectively reflect the actual consumption and technical load of automobile molds in the manufacturing process, improving the objectivity and accuracy of mold value assessment and nonlinear fitting ability.

[0014] 2. This invention introduces a complexity prediction regression model trained based on historical project data, which can establish a mapping relationship between the mold structure feature vector and the time deviation rate, and automatically output the comprehensive complexity weight as a dynamic increment to participate in the C value calculation. This mechanism effectively solves the defect of existing technologies that cannot quantify implicit complexity, and significantly improves the fault tolerance and accuracy of value assessment of high-difficulty molds.

[0015] 3. This invention achieves logical decoupling between the underlying basic database and the upper-level calculation rules by constructing a multi-version calculation strategy set containing parameter constraint logical vectors. It can enhance the business adaptability of the system by parsing the attribute identification data of the current project, and at the same time enable the same database to support multiple business models, thereby improving the scalability of the system. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the calculation method of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0018] Traditional mold valuation often relies on personal experience or simple linear summation methods, failing to fully consider the non-linear impact of physical characteristics such as mold structural strength and tonnage span on manufacturing difficulty, leading to significant deviations between quoted prices and actual costs. To accurately capture the influence of physical specifications and structural difficulty on manufacturing costs, truly reflect the comprehensive manufacturing load of the mold, and reduce reliance on manual experience, this invention proposes a mold C-value calculation method based on multi-dimensional data. The mold C-value is a numerical indicator used to quantitatively characterize the comprehensive manufacturing load and standard output value equivalent of a target mold, such as... Figure 1 As shown, the method includes the following steps: First, a standard database is pre-set for calculating the C-value of the mold, which includes a basic C-value table for storing basic value data, a tonnage classification table for mapping mold specification attributes, a customer coefficient table for characterizing customer business value, and a strength range table for defining the structural complexity of the mold. Then, a calculation strategy set is constructed, which includes at least a general strategy, a high-level strategy, and a cost control strategy. Each calculation strategy set independently defines the value range and weight logic of each coefficient. The attribute identification data of the target mold project is parsed, and the current calculation strategy is determined by indexing from the calculation strategy set based on the attribute identification data. Then, the runtime environment is initialized.

[0019] The system acquires and preprocesses basic data of the target mold, including mold type, standard tonnage value, customer identifier, and mold structural strength value. Preprocessing operations include denoising and format normalization, and constructing dimension-aligned standard feature vectors. Based on the preprocessed basic data, corresponding operations are performed in the standard database to obtain corresponding basic correction coefficients, including tonnage coefficient, customer coefficient, and strength coefficient. This ensures the quality and dimensionality consistency of the input data for subsequent calculation models. Through a discrete mapping mechanism, the system objectively reflects the stepwise impact of the mold's physical properties on its value.

[0020] Based on the preprocessed basic data, corresponding operations are performed in the standard database, specifically including the following steps: The standard tonnage value in the standard feature vector is parsed and used as a discrete index key. Hash matching is then performed in the tonnage grading mapping table of the database to directly extract the corresponding value as the tonnage coefficient. Parse the customer identifier in the standard feature vector and retrieve the original customer coefficient in the customer dimension coefficient table; at the same time, detect the parameter constraint logic vector in the current running environment. If the current strategy set defines an upper limit threshold for the customer coefficient, perform the minimum value logic operation, compare the original customer coefficient with the upper limit threshold, and take the smaller value as the final customer coefficient. Analyze the mold structure strength values ​​in the standard feature vectors, and traverse several preset continuous numerical intervals in the strength interval mapping table. The interval determination algorithm is used to identify the target interval to which the intensity value belongs, and the preset weight corresponding to the target interval is extracted as the intensity coefficient.

[0021] To address the lack of objective quantification methods for the implicit complexity of molds in existing technologies, a predictive regression model trained on historical mold project data is used. This model, trained on the structural feature data and actual time deviation data of historical mold projects, inputs the standard feature vector of the target mold into the predictive regression model to obtain a predicted comprehensive complexity weight. This weight is used to characterize the additional value fluctuation of the mold due to structural non-standardization, transforming the difficult-to-quantify complexity into an objective predicted value based on historical time deviation rates. This significantly improves the system's fault tolerance and accuracy in assessing the value of high-difficulty, non-standard molds. The specific steps include the following: Based on the structural feature data of historical mold projects as input features and the deviation rate between actual working hours and standard working hours as label values, a predictive regression model is generated through learning and training to characterize the nonlinear mapping relationship between the non-standard structure of molds and the fluctuation of additional value. Using a predictive regression model, forward inference is performed on the input standard feature vector, and combined with a preset weight matrix and bias parameters, the predicted complexity value used to characterize the structural complexity of the mold is calculated. The calculation formula is as follows: In the above formula, This represents the numerical value of the prediction complexity. Represents the regression mapping function, A standard feature vector is an ordered set of discretized or continuous feature data, which serves as the input tensor of the model. This represents the internal weight parameters, which are learned by the model through training on historical data and are used to measure the contribution of the i-th feature to the output. Indicates the total number of feature dimensions. Represents the feature index, used as a count variable to iterate through each component in the feature vector. , This represents the feature component value, specifically the numerical value of the i-th dimension in the standard feature vector. Indicates the bias term. Represents the random error term; The predicted complexity value is boundary-checked based on the confidence interval defined by the operating environment. The checked predicted complexity value is then assigned to the comprehensive complexity weight, which serves as the dynamic incremental parameter in the subsequent dynamic weighted calculation of multidimensional parameters.

[0022] To ensure the assessment results include deterministic physical costs and improve their objectivity and scientific rigor, basic C-values ​​are retrieved from the basic C-value table based on the mold type. These are then combined with basic correction coefficients and comprehensive complexity weights, and the final C-value is calculated using a pre-defined dynamic weighted algorithm model. The calculation formula is as follows: In the above formula, This represents the final C value. Indicates the base C value. Indicates tonnage coefficient. Indicates customer coefficient. Indicates the strength coefficient. This represents the weight of the overall complexity.

[0023] The final C-value is converted into the total standard working hours of the project by using the unit working hour output value parameter. Based on the process proportion template corresponding to the mold type, the total standard working hours are automatically discretized and allocated to each process step to generate a working hour allocation table. This achieves automation and refinement of working hour quotas, ensuring that the resource input of each process is strictly linked to the comprehensive value of the mold. The specific steps include the following: Based on the preset unit labor hour output parameters, the total standard labor hours for the target mold project are calculated using the following formula: In the above formula, This represents the total standard working hours of the project. This parameter represents the output value per unit of working hours. This represents the final C value; Based on the mold type of the target mold, a matching process is performed in the preset time percentage database. The corresponding standard process percentage vector is retrieved, and each component in the standard process percentage vector is traversed. Based on the total standard time of the project and the process percentage coefficient corresponding to each component, the sub-item standard time of each process node is calculated. The calculation formula is as follows: In the above formula, Indicates the standard working hours for each item. Indicates the percentage of processes; The calculated standard working hours for each item are linked with the corresponding process name and responsible department attribute to generate a structured working hour allocation table containing the fields of process name, working hour percentage, cumulative percentage and standard working hours.

[0024] The calculation method first pre-sets a standard database containing basic value data and a multi-dimensional mapping table, and constructs a set of calculation strategies including general, high-order, and cost control strategies to determine the current operating environment. Then, it acquires the basic data of the target mold and preprocesses it to construct a standard feature vector. Based on this vector, it performs a discrete mapping operation in the standard database to obtain the basic correction coefficient. At the same time, it calls a pre-trained regression model to infer the vector to predict the comprehensive complexity weight. Then, it uses a pre-set dynamic weighting algorithm model to integrate the basic C value, the basic correction coefficient, and the comprehensive complexity weight to calculate and generate the final C value. Finally, based on the unit output value parameter, the final C value is converted into the total standard working hours of the project. Combined with the standard process proportion template, the total working hours are discretized and allocated to each process link to generate a structured working hour allocation table.

[0025] The present invention also provides a system for calculating the C-value of a mold based on multidimensional data, comprising a processor and a memory, wherein the memory is used to store a computer program, and the computer program is executed by the processor to implement the method for calculating the C-value of a mold based on multidimensional data.

[0026] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application 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.

[0027] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, their descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0028] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A mold C value calculation method based on multi-dimensional data, characterized by, The method includes the following steps: A standard database for C-value calculation is pre-defined, a set of calculation strategies including general strategies, high-order strategies and cost control strategies is constructed, and the current calculation strategy is determined. Acquire the basic data of the target mold and preprocess it, and construct a standard feature vector. Based on the preprocessed basic data, perform corresponding operations in the standard database to obtain the corresponding basic correction coefficients. Call the predictive regression model trained based on historical mold project data, input the standard feature vector of the target mold into the predictive regression model, and obtain the comprehensive complexity weight; Based on the mold type, the basic C value is retrieved from the basic C value table. The final C value is calculated by combining the basic correction coefficient and the comprehensive complexity weight according to the preset dynamic weighted algorithm model. The final C value is converted into the total standard working hours of the project. Based on the process proportion template corresponding to the mold type, the total standard working hours are automatically discretized and allocated to each process step to generate a working hour allocation table.

2. The computational method of claim 1, wherein, The standard database includes a basic C-value table, a tonnage classification table, a customer coefficient table, and a strength range table. The basic data includes mold type, standard tonnage value, customer identifier, and mold structural strength value.

3. The computational method of claim 2, wherein, The basic correction factors include tonnage factor, customer factor, and strength factor.

4. The computational method of claim 3, wherein, The specific steps involved in performing the corresponding operation in the standard database to obtain the corresponding basic correction coefficient are as follows: The corresponding tonnage coefficient is located by performing discrete interval matching in the tonnage classification table using standard tonnage values. Use the customer identifier to retrieve the corresponding customer coefficient in the customer coefficient table; The strength values ​​of the mold structure are used to determine the range in the strength interval table. By comparing the minimum and maximum strength boundaries, the corresponding strength coefficient is obtained.

5. The computational method of claim 1, wherein, The specific steps involved in calling the predictive regression model trained based on historical mold project data, inputting the standard feature vector of the target mold into the predictive regression model, and obtaining the comprehensive complexity weights are as follows: A predictive regression model is generated by learning and training based on the structural feature data of historical mold projects as input features and the deviation rate between actual working hours and standard working hours as label values. Using a predictive regression model, forward inference is performed on the input standard feature vector, and combined with a preset weight matrix and bias parameters, the predicted complexity value used to characterize the structural complexity of the mold is calculated. The calculation formula is as follows: In the above formula, This represents the numerical value of the prediction complexity. Represents the regression mapping function, Represents the standard eigenvector. Indicates the internal weight parameters. Indicates the total number of feature dimensions. Indicates feature index, Represents the characteristic component values. Indicates the bias term. Represents the random error term; The predicted complexity value is boundary-checked based on the confidence interval defined by the operating environment, and the checked predicted complexity value is assigned to the comprehensive complexity weight.

6. The calculation method according to claim 1, characterized in that, The formula for calculating the final C value is as follows: In the above formula, This represents the final C value. Indicates the base C value. Indicates tonnage coefficient. Indicates customer coefficient. Indicates the strength coefficient. This represents the weight of the overall complexity.

7. The calculation method according to claim 1, characterized in that, The specific steps for converting the final C value into the total standard working hours of the project, and automatically discretizing and allocating the total standard working hours to each process step according to the process proportion template corresponding to the mold type, to generate a working hour allocation table include the following: Based on the preset unit labor hour output parameters, the total standard labor hours for the target mold project are calculated using the following formula: In the above formula, This represents the total standard working hours of the project. This parameter represents the output value per unit of working hours. This represents the final C value; Based on the mold type of the target mold, match it in the preset time percentage database and retrieve the corresponding standard process percentage vector; Iterate through each component in the standard process proportion vector, and calculate the sub-item standard time of each process node based on the total standard working hours of the project and the process proportion coefficient corresponding to each component. The calculation formula is as follows: In the above formula, Indicates the standard working hours for each item. Indicates the percentage of processes; The standard working hours for each item are linked with the corresponding process name and responsible department attribute to generate a working hour allocation table containing the fields of process name, working hour percentage, cumulative percentage and standard working hours.

8. A system for implementing the mold C-value calculation method based on multidimensional data as described in any one of claims 1-7, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the method for calculating the mold C value based on multidimensional data as described in any one of claims 1-7.

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