Method and device for determining cost characteristics of production workshop, and storage medium
By calculating the correlation coefficient and merging process between the target cost elements of the production workshop, the problem of difficulty in in-depth analysis of the production workshop cost data in the existing technology is solved, and the accurate characteristics of cost elements are determined and analyzed is improved.
Patent Information
- Application Number
- CN202411916540.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-05-16
AI Technical Summary
The prior art is difficult to conduct in-depth analysis of the cost data of the production workshop and cannot effectively determine the cost characteristics.
By obtaining the cost data of the initial target cost factor group corresponding to the production process of the production workshop, calculate the correlation coefficient between each two target cost factors, determine the target cost factors to be merged, and merge until the preset quantity is reached, and obtain the degree of correlation of the target cost factors.
In-depth analysis of production workshop cost data is realized, the degree of correlation of target cost elements is determined, the cost data analysis capabilities are improved, and the characteristics of target cost elements are accurately determined.
Smart Images
Figure CN120013119A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of big data technology, and specifically to a method, device and storage medium for determining cost characteristics of a production workshop. Background Art
[0002] With the development of information technology, more and more factories are adopting the Internet of Things technology, deploying sensors, smart meters and other collection equipment at key nodes such as production equipment and process flow, and collecting various cost data of the production workshop in real time during the production process. However, the existing technology focuses on the cost accounting of the production workshop. For example, various cost data are summarized and counted to obtain the total cost of the production workshop, but there is a lack of methods for in-depth analysis of various cost data, and thus it is impossible to determine the cost characteristics. Therefore, how to analyze the cost data of the production workshop to determine the cost characteristics has become an urgent problem to be solved. Summary of the invention
[0003] The purpose of the embodiments of the present application is to provide a method, device, equipment and storage medium for determining the cost characteristics of a production workshop, so as to solve the problem in the prior art of how to analyze the cost data of a production workshop to determine the cost characteristics.
[0004] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining cost characteristics of a production workshop, the method comprising:
[0005] Obtain the cost data of the initial target cost element group corresponding to the production process of the production workshop;
[0006] According to the cost data of the initial target cost element group, the correlation coefficient between every two target cost elements in the initial target cost element group is determined to obtain a correlation coefficient group;
[0007] According to the correlation coefficient group, the target cost elements to be merged in the initial target cost element group are determined, wherein the correlation coefficient between the target cost elements to be merged is the maximum value in the correlation coefficient group;
[0008] Merging the target cost elements to be merged to update the initial target cost element group until the number of target cost elements in the updated target cost element group reaches the preset number;
[0009] The merging order of the target cost elements to be merged is obtained to obtain the relevance ranking of the target cost elements in the initial target cost element group.
[0010] In an embodiment of the present application, determining an initial target cost element group includes: obtaining cost data of multiple cost elements corresponding to the production processes of a production workshop; determining a target cost element based on the cost data of the multiple cost elements to obtain an initial target cost element group, wherein the target cost element is a cost element among the multiple cost elements whose importance value reaches a preset importance threshold.
[0011] In an embodiment of the present application, a target cost element is determined based on cost data of multiple cost elements, including: determining the target cost element based on the cost data of multiple cost elements based on a preset clustering algorithm; or, determining the target cost element based on the cost data of multiple cost elements based on a pre-built linear regression model; or, determining the target cost element based on the cost data of multiple cost elements based on a preset clustering algorithm and a pre-built linear regression model.
[0012] In an embodiment of the present application, the importance degree value includes a first importance degree value, the preset degree threshold includes a first preset degree threshold, and based on a preset clustering algorithm, a target cost element is determined according to the cost data of multiple cost elements, including: based on the preset clustering algorithm, the cost data of multiple cost elements are divided into multiple cost data clusters; based on the Euclidean distance algorithm and the cost data of multiple cost elements, the sum of the Euclidean distances between the cost data of each cost element and the cluster centers of the multiple cost data clusters is determined to obtain a first importance degree value for each cost element; and the cost element whose first importance degree value among multiple cost elements reaches the first preset degree threshold is determined to obtain the target cost element.
[0013] In an embodiment of the present application, the importance degree value also includes a second importance degree value, the preset degree threshold includes a second preset degree threshold, and based on a pre-constructed linear regression model, a target cost element is determined according to the cost data of multiple cost elements, including: determining the cost element regression coefficients corresponding to the multiple cost elements based on the pre-constructed linear regression model and the cost data of the multiple cost elements; determining the deviation degree values corresponding to the multiple cost elements according to the cost element regression coefficients; determining the second importance degree value corresponding to the deviation degree value based on the correspondence between the pre-constructed deviation degree value and the preset importance degree value; determining the cost element whose second importance degree value among the multiple cost elements reaches the second preset degree threshold to obtain the target cost element.
[0014] In an embodiment of the present application, the importance degree value also includes a third importance degree value, the preset degree threshold also includes a third preset degree threshold, and based on a preset clustering algorithm and a pre-built linear regression model, the target cost element is determined according to the cost data of multiple cost elements, including: determining the first importance degree value corresponding to the multiple cost elements based on the preset clustering algorithm; determining the second importance degree value corresponding to the multiple cost elements based on the pre-built linear regression model; performing weighted summation of the first importance degree value and the second importance degree value to obtain the third importance degree value corresponding to the multiple cost elements; determining the cost element whose third importance degree value among the multiple cost elements reaches the preset degree threshold to obtain the target cost element.
[0015] In an embodiment of the present application, the correlation coefficient between each two target cost elements in the initial target cost element group is determined based on the cost data of the initial target cost element group, including: based on the Pearson correlation coefficient algorithm, the correlation coefficient between each two target cost elements in the initial target cost element group is determined based on the cost data of the initial target cost element group.
[0016] A second aspect of the present application provides a device for determining the cost characteristics of a production workshop, comprising: a memory configured to store instructions; and a processor configured to call the instructions from the memory and to implement the above-mentioned method for determining the cost characteristics of a production workshop when executing the instructions.
[0017] A third aspect of the present application provides a device for determining cost characteristics of a production workshop, including: the device for determining cost characteristics of a production workshop according to the above-mentioned device.
[0018] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the above-mentioned method for determining the cost characteristics of a production workshop.
[0019] The above technical scheme obtains the cost data of the initial target cost element group corresponding to the production process of the production workshop, determines the correlation coefficient between every two target cost elements in the initial target cost element group according to the cost data of the initial target cost element group to obtain the correlation coefficient group, and then determines the target cost elements to be merged in the initial target cost element group according to the correlation coefficient group, wherein the correlation coefficient between the target cost elements to be merged is the maximum value in the correlation coefficient group, and then merges the target cost elements to be merged to update the initial target cost element group until the number of target cost elements in the updated target cost element group reaches a preset number, and obtains the merging order of the target cost elements to be merged to obtain the correlation degree ranking of the target cost elements in the initial target cost element group. In this way, an in-depth analysis of the cost data of the initial target cost element group can determine the correlation coefficient between every two target cost elements in the initial target cost element group, determine the degree of correlation between different target cost elements, determine the target cost elements to be merged, and further, determine the correlation degree ranking of the target cost elements in the initial target cost element group according to the merging order of the target cost elements to be merged, and then determine the characteristics of the target cost elements in the initial target cost element group in the production workshop, which can improve the ability to analyze the cost data of the cost element group and accurately determine the characteristics of the target cost elements.
[0020] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:
[0022] Figure 1 A flow chart of a method for determining cost characteristics of a production workshop according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION
[0023] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0024] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.
[0025] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0026] Figure 1 The following schematically shows a flow chart of a method for determining the cost characteristics of a production workshop according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for determining cost characteristics of a production workshop, and the control method is applied to a processor as an example for explanation. The method may include the following steps.
[0027] Step S101, obtaining cost data of an initial target cost element group corresponding to a production process of a production workshop.
[0028] Step S102, determining the correlation coefficient between every two target cost elements in the initial target cost element group according to the cost data of the initial target cost element group, so as to obtain a correlation coefficient group.
[0029] Step S103, determining the target cost elements to be merged in the initial target cost element group according to the correlation coefficient group, wherein the correlation coefficient between the target cost elements to be merged is the maximum value in the correlation coefficient group.
[0030] Step S104 , merging the target cost elements to be merged to update the initial target cost element group, until the number of target cost elements in the updated target cost element group reaches a preset number.
[0031] Step S105 , obtaining a merging order of the target cost elements to be merged, so as to obtain a correlation degree ranking of the target cost elements in the initial target cost element group.
[0032] It can be understood that the production workshop is a workshop that produces various types of products. The production process is the basic unit that constitutes the production process of various types of products. The initial target cost element group is a set composed of multiple initial target cost elements. The cost data is a plurality of cost data corresponding to the multiple initial target cost elements in the initial target cost element group. The correlation coefficient is a coefficient for determining the degree of correlation between every two target cost elements in the initial target cost element group, and the correlation coefficient group is a set composed of multiple correlation coefficients. The preset number is a pre-set number, and the preset number can be 1 or more. The cost elements to be merged are the two target cost elements to be merged in the initial target cost element group. The updated target cost element group is the target cost element group obtained after the initial cost element group is finally updated. The merging order is the order of merging the target cost elements to be merged. The correlation degree ranking is the ranking of the correlation degree of the target cost elements.
[0033] Specifically, the cost data of the initial target cost element group corresponding to the production process of the production workshop is obtained, the cost data of the initial target cost element group is deeply analyzed, and the correlation coefficient between each two target cost elements in the initial cost element group is determined. The correlation coefficient group (for example, a correlation coefficient matrix) corresponding to each two target cost elements in the initial cost element group can be obtained, and the two target cost elements corresponding to the largest correlation coefficient in the correlation coefficient group are determined. The two target cost elements are used as the two target cost elements to be merged to obtain a new target cost element. The cost data corresponding to the new target cost element can be the average value of the two target cost elements. The correlation coefficients corresponding to other target cost elements and the new target cost element are recalculated, thereby updating the initial target cost element group, and repeating the above updating steps until the number of target cost elements in the updated target cost element group reaches the preset number. The order of merging represents the strength of the correlation of the target cost elements. The two target cost elements that are merged first indicate that the correlation between the two target cost elements is the strongest. The merging order of the target cost elements to be merged is obtained to determine the correlation degree ranking of the target cost elements in the initial target cost element group.
[0034] The above technical scheme obtains the cost data of the initial target cost element group corresponding to the production process of the production workshop, determines the correlation coefficient between every two target cost elements in the initial target cost element group according to the cost data of the initial target cost element group to obtain the correlation coefficient group, and then determines the target cost elements to be merged in the initial target cost element group according to the correlation coefficient group, wherein the correlation coefficient between the target cost elements to be merged is the maximum value in the correlation coefficient group, and then merges the target cost elements to be merged to update the initial target cost element group until the number of target cost elements in the updated target cost element group reaches a preset number, and obtains the merging order of the target cost elements to be merged to obtain the correlation degree ranking of the target cost elements in the initial target cost element group. In this way, an in-depth analysis of the cost data of the initial target cost element group can determine the correlation coefficient between every two target cost elements in the initial target cost element group, determine the degree of correlation between different target cost elements, determine the target cost elements to be merged, and further, determine the correlation degree ranking of the target cost elements in the initial target cost element group according to the merging order of the target cost elements to be merged, and then determine the characteristics of the target cost elements in the initial target cost element group in the production workshop, which can improve the ability to analyze the cost data of the cost element group and accurately determine the characteristics of the target cost elements.
[0035] In one embodiment, determining the initial target cost element group may include: obtaining cost data of multiple cost elements corresponding to the production processes of the production workshop; determining the target cost element based on the cost data of the multiple cost elements to obtain the initial target cost element group, wherein the target cost element is a cost element among the multiple cost elements whose importance value reaches a preset importance threshold.
[0036] It can be understood that the target cost element can be a cost element whose importance value reaches a preset importance threshold among multiple cost elements corresponding to the production process, and the number of target cost elements is multiple.
[0037] Specifically, the processor can obtain the cost data of multiple cost elements corresponding to the production process of the production workshop from the pre-built database, and then according to the multiple cost data, it can determine the cost elements whose importance value reaches the preset importance threshold value among the multiple cost elements, determine the target cost elements, and thus obtain the initial target cost element group, and the target cost elements can be screened from the multiple cost elements, thereby reducing data calculation and improving the efficiency of data analysis. Before the processor obtains data from the database, multiple cost data of the production process of the production workshop can be obtained from data acquisition devices such as sensors or smart meters, or on the industrial Internet platform, and the obtained multiple cost data are classified to obtain multiple cost data corresponding to multiple cost elements. Then, the multiple cost data corresponding to the multiple cost elements are stored in the pre-built database, thereby ensuring data security and integrity, and providing a reliable data source for the big data background. At the same time, the multiple cost data corresponding to the multiple elements are pre-processed, and the steps of data pre-processing include outlier detection, missing value filling, and data smoothing, etc., to ensure that the data quality collected from the database is high, which is conducive to subsequent data analysis and data conversion (such as square root data conversion).
[0038] In one embodiment, determining the target cost element based on the cost data of multiple cost elements may include: determining the target cost element based on the cost data of multiple cost elements based on a preset clustering algorithm; or, determining the target cost element based on the cost data of multiple cost elements based on a pre-built linear regression model; or, determining the target cost element based on the cost data of multiple cost elements based on a preset clustering algorithm and a pre-built linear regression model.
[0039] It can be understood that the preset clustering algorithm is a pre-set clustering algorithm. The pre-built linear regression model is a pre-built linear regression model.
[0040] Specifically, the processor can determine the target cost element among the multiple elements based on the cost data of the multiple cost elements based on the preset clustering algorithm. The processor can also determine the target cost element among the multiple elements based on the cost data of the multiple cost elements based on the pre-built linear regression model. In addition, the processor can also determine the target cost element among the multiple cost elements based on the cost data of the multiple cost elements based on the preset clustering algorithm and the pre-built linear regression model. Thus, the cost element whose importance value reaches the preset importance threshold among the multiple cost elements is determined, that is, the target cost element. Using different processing methods (pre-set clustering algorithm, or pre-built linear regression model, or a combination of preset clustering algorithm and pre-built linear regression model), the target cost element among the multiple cost elements determined can be the same or different, which can reduce the analysis of other cost elements with relatively low importance, focus on the cost elements with relatively high importance, and improve the accuracy of the analysis of the target cost element.
[0041] In one embodiment, the importance degree value includes a first importance degree value, the preset degree threshold includes a first preset degree threshold, and based on a preset clustering algorithm, determining a target cost element based on cost data of multiple cost elements may include: based on the preset clustering algorithm, dividing the cost data of multiple cost elements into multiple cost data clusters; based on the Euclidean distance algorithm and the cost data of multiple cost elements, determining the sum of the Euclidean distances between the cost data of each cost element and the cluster centers of the multiple cost data clusters to obtain a first importance degree value for each cost element; determining a cost element among multiple cost elements whose first importance degree value reaches a first preset degree threshold to obtain a target cost element.
[0042] It can be understood that the importance degree value is the basis for judging the importance degree of the cost element, and the importance degree may include but is not limited to the first importance degree value. The preset degree threshold is a preset degree threshold, and the preset degree threshold may include but is not limited to the first preset degree threshold. The cost data cluster is a set of data that are similar to each other in certain attributes or characteristics. The cluster center is the center point of the cost data cluster, which usually represents the average value or representative value of all the data in the cluster. The cluster center does not exist directly at an actual point in the data set, but is determined by calculating the coordinate mean of each data point in the cluster in the multidimensional space.
[0043] Specifically, the cost data of multiple cost elements can be divided into multiple cost data clusters according to similar features based on the preset clustering algorithm. Then, based on the Euclidean distance algorithm and the cost data of multiple cost elements, the sum of the Euclidean distances between the cost data of each cost element and the cluster centers of multiple cost data clusters is determined to obtain the first importance value of each cost element. Further, the first importance value of each cost element is compared with the first preset degree threshold, and the cost element whose first importance value is greater than the first preset degree threshold is determined as the target cost element. In addition, the calculated sum of the Euclidean distances can be used to evaluate the standard deviation of each cost element, so as to judge the performance difference of each cost element in each cost data cluster. If the sum of the Euclidean distances is larger, it means that it has a stronger ability to distinguish between different cost data clusters.
[0044] In one embodiment, the importance degree value also includes a second importance degree value, the preset degree threshold includes a second preset degree threshold, and based on a pre-constructed linear regression model, determining the target cost element according to the cost data of multiple cost elements may include: determining the cost element regression coefficients corresponding to the multiple cost elements based on the pre-constructed linear regression model and the cost data of the multiple cost elements; determining the deviation degree values corresponding to the multiple cost elements according to the cost element regression coefficients; determining the second importance degree value corresponding to the deviation degree value based on the correspondence between the pre-constructed deviation degree value and the preset importance degree value; determining the cost element whose second importance degree value among the multiple cost elements reaches the second preset degree threshold to obtain the target cost element.
[0045] It can be understood that the importance degree value may also include a second importance degree value. The preset degree threshold may also include a second preset degree threshold. The number of deviation degree values is the number corresponding to the cost element. The correspondence between the pre-constructed deviation degree value and the preset importance degree value is the correspondence between the pre-constructed deviation degree value and the preset importance degree, and each deviation degree value corresponds to a preset importance degree value.
[0046] Specifically, based on the pre-constructed regression model, the cost data of multiple cost elements and the total cost corresponding to the cost elements are input, and the cost element regression coefficients corresponding to the multiple cost elements can be determined. Then, the deviation degree values corresponding to the multiple cost elements can be determined based on the cost element regression coefficients. In the correspondence between the pre-constructed deviation degree value and the preset importance degree value, the second importance degree value corresponding to the deviation degree value is found. If the second importance degree value (P value) of a certain cost element reaches the second preset degree threshold, then the cost element is the target cost element. In this way, relatively important cost elements can be screened out, the amount of calculation can be reduced, and the calculation efficiency can be improved.
[0047] In one embodiment, the importance level value also includes a third importance level value, the preset level threshold also includes a third preset level threshold, and based on a preset clustering algorithm and a pre-built linear regression model, determining the target cost element according to the cost data of multiple cost elements may include: determining the first importance level value corresponding to the multiple cost elements based on the preset clustering algorithm; determining the second importance level value corresponding to the multiple cost elements based on the pre-built linear regression model; performing weighted summation of the first importance level value and the second importance level value to obtain the third importance level value corresponding to the multiple cost elements; determining the cost element whose third importance level value among the multiple cost elements reaches the third preset level threshold to obtain the target cost element.
[0048] It can be understood that the importance level value may also include a third importance level value. The preset level threshold may also include a third preset level threshold.
[0049] Specifically, based on a preset clustering algorithm, determining the first importance values corresponding to the plurality of cost elements may include dividing the cost data of the plurality of cost elements into a plurality of cost data clusters based on the preset clustering algorithm; determining the sum of the Euclidean distances between the cost data of each cost element and the cluster centers of the plurality of cost data clusters based on the Euclidean distance algorithm and the cost data of the plurality of cost elements, so as to obtain the first importance value of each cost element. And based on a pre-constructed linear regression model, determining the second importance values corresponding to the plurality of cost elements may include: determining the cost element regression coefficients corresponding to the plurality of cost elements based on the pre-constructed linear regression model and the cost data of the plurality of cost elements; determining the deviation degree values corresponding to the plurality of cost elements according to the cost element regression coefficients; determining the second importance value corresponding to the deviation degree value based on the correspondence between the pre-constructed deviation degree value and the preset importance value. The processor determines the weight values corresponding to the first importance value and the second importance value respectively, and calculates the weighted sum value of the first importance value and the second importance value based on the corresponding weight values, so as to obtain the third importance value, and further determines the target cost element in the cost element whose third importance value is greater than the third preset degree threshold.
[0050] In one embodiment, determining the correlation coefficient between every two target cost elements in the initial target cost element group based on the cost data of the initial target cost element group may include: determining the correlation coefficient between every two target cost elements in the initial target cost element group based on the cost data of the initial target cost element group based on the Pearson correlation coefficient algorithm.
[0051] It can be understood that the Pearson correlation coefficient algorithm is an algorithm for determining the correlation coefficient between every two target cost elements in the initial target cost element group.
[0052] Specifically, the cost data of the initial target cost element group is input into the Pearson correlation coefficient algorithm to determine the correlation coefficient between every two target cost elements in the initial target cost element group, so that the correlation between the target cost elements in the initial target cost element group can be deeply analyzed, which helps users intuitively understand the correlation between every two target cost elements.
[0053] In a specific embodiment, the present application can combine cluster analysis and big data visualization technology to classify and cluster the cost data corresponding to each cost element in the production process of the production workshop, which can more accurately determine the correlation between the cost elements, provide more support for visual analysis, assist in management decisions, and realize accurate control and management of production costs, and provide a scientific basis for production optimization. First, the processor can obtain multiple cost data of the production process of the production workshop from data acquisition devices such as sensors or smart meters, or on the industrial Internet platform, classify the obtained multiple cost data, and obtain multiple cost data corresponding to multiple cost elements. Then, the multiple cost data corresponding to the multiple cost elements are stored in a pre-built database. At the same time, the multiple cost data corresponding to the multiple elements are pre-processed and feature engineered. The steps of data pre-processing include outlier detection, missing value filling, and data smoothing, etc. Feature engineering reduces the complexity of the data through methods such as data dimensionality reduction and feature selection, and improves the efficiency and accuracy of the clustering algorithm. This is helpful for obtaining classified and pre-processed cost data from the database, and for data analysis. Based on the cost data collected by the Industrial Internet of Things, the actual cost of product production and processing can be counted in real time, solving the problem of lagging cost accounting and accurately counting real-time costs.
[0054] Secondly, the processor may divide the cost data of the multiple cost elements into multiple cost data clusters based on a preset clustering algorithm; determine the sum of the Euclidean distances between the cost data of each cost element and the cluster centers of the multiple cost data clusters based on the Euclidean distance algorithm and the cost data of the multiple cost elements, so as to obtain a first importance value of each cost element, and the first importance value may be calculated as follows:
[0055]
[0056] Among them, D(X i ,C k ) is the cost data X corresponding to the i-th cost element i The cluster centers C of multiple cost data clusters k The sum of the Euclidean distances, m is the number of cost data clusters, X i is the cost data corresponding to the i-th cost element, C kjis the cluster center of the jth cost data cluster.
[0057] Alternatively, based on a pre-built linear regression model, the pre-built linear regression model can be as follows:
[0058] Y=β0+β1X1+β2X2+......+β n X n +σ
[0059] Among them, Y is the total cost corresponding to the cost element, X1, X2...X n is the cost data corresponding to multiple cost elements, β0, β1, β2...β n is the cost factor regression coefficient corresponding to multiple cost factors, and σ is the random error term.
[0060] Then, the processor can also determine the deviation degree values corresponding to the multiple cost elements through the following formula:
[0061]
[0062] Among them, t is the degree of deviation, β i is the cost factor regression coefficient corresponding to multiple cost factors, SE(β i ) is the standard error of the regression coefficients of multiple cost factors.
[0063] The standard error is calculated by the variance estimate of the random error term and the structure of the explanatory variable matrix. The specific method is: first calculate the residual sum of squares (RSS) of the random error term and estimate the overall error variance, then obtain the covariance matrix of the coefficient estimate through the inverse matrix of the regression matrix, and finally take the square root of the diagonal elements of the covariance matrix to obtain the standard error of each regression coefficient.
[0064] Finally, the processor can search for a second importance value (P value) corresponding to the deviation degree value in the deviation degree distribution table (including the correspondence between the pre-built deviation degree value and the preset importance value). The processor can also determine the second importance value (P value) corresponding to the deviation degree value through a programming tool. If the second importance value (P value) reaches the second preset degree threshold, it means that the cost element corresponding to the second importance value is relatively significant.
[0065] Alternatively, based on a preset clustering algorithm and a pre-built linear regression model, the third importance degree value may be determined by the following formula according to the cost data of multiple cost elements:
[0066] I=α·P+(1-α)·D
[0067] Among them, α is the weight value corresponding to the second importance value (P value), (1-α) is the weight value corresponding to the first importance value (D), and I is the third importance value.
[0068] In this way, a target cost element among multiple cost elements is determined, and target cost data whose importance reaches a preset importance value is screened out from multiple cost data.
[0069] Furthermore, the cost data of the initial target cost element group can be shown in Table 1 below:
[0070]
[0071] The production process may include but is not limited to cutting process, assembly process and painting process, and the target cost elements may include but are not limited to material cost, labor cost, energy cost, equipment maintenance cost, spare parts cost and scrap loss cost. The data in this table is the cost data of the initial target cost element group of each production process.
[0072] According to the following Pearson correlation coefficient algorithm, determine the correlation coefficient between every two target cost elements in the initial target cost element group:
[0073]
[0074] Among them, x i is the cost data of the target cost element x corresponding to the i-th production process, y i is the cost data of the target cost element y corresponding to the i-th production process, is the average value of the target cost element x corresponding to multiple production processes, It is the average value of the target cost element y corresponding to multiple production processes.
[0075] Based on the correlation coefficients determined by the above algorithm, a correlation coefficient group (such as a correlation coefficient matrix) can be constructed to intuitively display the correlation between individual cost elements and facilitate the identification of similarities and differences.
[0076] The processor may also cluster each cost element as a separate cost element based on the above correlation coefficient group, and calculate the similarity degree value (similarity measure) between two clusters by using a class average algorithm.
[0077] The similarity value between two clusters can be determined by the following cluster averaging algorithm:
[0078]
[0079] Among them, d(C i ,C j ) are two clusters Ci With C j The similarity value between them, m is the cluster C i The number of cost data corresponding to the target cost element in the cluster C j The number of cost data corresponding to the target cost element in R xy is the correlation coefficient between target cost element x and target cost element y.
[0080] Using the correlation coefficient as a similarity metric, the class average clustering method can aggregate cost elements into different clusters. The correlation coefficients of cost elements in each dimension are analyzed by combining feature engineering with clustering algorithms to solve the problem that real-time production parameters are difficult to accurately adjust. It is conducive to analyzing the key factors that actually affect cost changes and assisting cost reduction management decisions. At the same time, the corresponding visualization analysis saves the cost of manual statistics. This process helps to reveal the relationship between different cost elements and provides a basis for further cost analysis and management. In addition, the degree of similarity between each two target cost elements can be determined according to the class average algorithm to determine the target cost elements to be merged, and the target cost elements to be merged can be merged to update the initial target cost element group until the number of target cost elements in the updated target cost element group reaches the preset number, and the merging order of the target cost elements to be merged is obtained to obtain the correlation degree ranking of the target cost elements in the initial target cost element group. According to the merging order, visualization tools such as tree diagrams, distribution trend diagrams, and heat maps can be constructed. These visualization tools can display the main costs in each type of cost element and help understand the differences and similarities between different clusters. Using visualization technology to display and analyze the merging order, such as combination diagrams and parallel coordinate diagrams, can help users intuitively understand the data distribution and correlation, improve the analyzability and interpretability of the data, analyze the cost correlation and mainstream costs in the workshop process, guide the determination of product process specifications, and greatly reduce the blindness of design.
[0081] An embodiment of the present application provides a device for determining the cost characteristics of a production workshop, which may include: a memory configured to store instructions; and a processor configured to call the instructions from the memory and to implement the above-mentioned method for determining the cost characteristics of a production workshop when executing the instructions.
[0082] An embodiment of the present application also provides a device for determining cost characteristics of a production workshop, which may include: the device for determining cost characteristics of a production workshop as described above.
[0083] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned method for determining the cost characteristics of a production workshop.
[0084] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.
[0085] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0086] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0088] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0089] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0090] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0091] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0092] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A method for determining the cost characteristics of a production workshop, characterized in that: The method comprises: Obtain the cost data of the initial target cost element group corresponding to the production process of the production workshop; Determine the correlation coefficient between every two target cost elements in the initial target cost element group according to the cost data of the initial target cost element group to obtain a correlation coefficient group; Determining the target cost elements to be merged in the initial target cost element group according to the correlation coefficient group, wherein the correlation coefficient between the target cost elements to be merged is the maximum value in the correlation coefficient group; Merging the target cost elements to be merged to update the initial target cost element group until the number of target cost elements in the updated target cost element group reaches a preset number; The merging order of the target cost elements to be merged is obtained to obtain the correlation degree ranking of the target cost elements in the initial target cost element group.
2. The method according to claim 1, characterized in that The determination of the initial target cost element group includes: Obtain cost data of multiple cost elements corresponding to the production process of the production workshop; According to the cost data of the multiple cost elements, a target cost element is determined to obtain an initial target cost element group, wherein the target cost element is a cost element whose importance value reaches a preset importance threshold among the multiple cost elements.
3. The method according to claim 2, characterized in that The step of determining the target cost element according to the cost data of the plurality of cost elements comprises: Based on a preset clustering algorithm, determining the target cost element according to the cost data of the plurality of cost elements; Alternatively, based on a pre-built linear regression model, the target cost element is determined according to the cost data of the plurality of cost elements; Alternatively, based on a preset clustering algorithm and a pre-built linear regression model, the target cost element is determined according to the cost data of the multiple cost elements.
4. The method according to claim 3, characterized in that The importance degree value includes a first importance degree value, the preset degree threshold includes a first preset degree threshold, and the determining the target cost element based on the cost data of the plurality of cost elements based on a preset clustering algorithm includes: Based on a preset clustering algorithm, the cost data of the plurality of cost elements are divided into a plurality of cost data clusters; Based on the Euclidean distance algorithm and the cost data of the plurality of cost elements, determining the sum of the Euclidean distances between the cost data of each cost element and the cluster centers of the plurality of cost data clusters, so as to obtain a first importance degree value of each cost element; The cost element whose first importance value reaches the first preset degree threshold among the multiple cost elements is determined to obtain the target cost element.
5. The method according to claim 3, characterized in that: The importance degree value further includes a second importance degree value, the preset degree threshold includes a second preset degree threshold, and the determining the target cost element based on the cost data of the plurality of cost elements based on the pre-built linear regression model includes: Determine cost element regression coefficients corresponding to the multiple cost elements based on a pre-built linear regression model and the cost data of the multiple cost elements; Determining the deviation degree values corresponding to the plurality of cost elements according to the cost element regression coefficients; Based on the correspondence between the pre-constructed deviation degree value and the preset importance degree value, determining a second importance degree value corresponding to the deviation degree value; The cost element whose second importance value reaches the second preset degree threshold among the multiple cost elements is determined to obtain the target cost element.
6. The method according to claim 3, characterized in that The importance degree value further includes a third importance degree value, the preset degree threshold value further includes a third preset degree threshold value, and the determining the target cost element based on the cost data of the plurality of cost elements based on a preset clustering algorithm and a pre-built linear regression model includes: Determining first importance values corresponding to the plurality of cost elements based on a preset clustering algorithm; Determining second importance values corresponding to the plurality of cost elements based on a pre-built linear regression model; Performing a weighted summation on the first importance value and the second importance value to obtain third importance values corresponding to the multiple cost elements; The cost element whose third importance value reaches a preset degree threshold among the multiple cost elements is determined to obtain the target cost element.
7. The method according to claim 1, characterized in that: Determining the correlation coefficient between every two target cost elements in the initial target cost element group according to the cost data of the initial target cost element group includes: Based on the Pearson correlation coefficient algorithm, the correlation coefficient between every two target cost elements in the initial target cost element group is determined according to the cost data of the initial target cost element group.
8. A device for determining the cost characteristics of a production workshop, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instructions from the memory and implement the method for determining cost characteristics of a production workshop according to any one of claims 1 to 7 when executing the instructions.
9. A device for determining the cost characteristics of a production workshop, characterized in that include: The device for determining cost characteristics of a production workshop according to claim 8.
10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing a machine to execute the method for determining cost characteristics of a production workshop according to any one of claims 1 to 7.