Data analysis method and system for material processing cost in engineering management

Abstract

The present invention relates to the technical field of data processing, and provides a method and system for analyzing the cost of material processing for project management, including: obtaining a number of value sets; obtaining the first steady-state feature of two value sets according to the difference in the data values corresponding to different data points in the two value sets; obtaining the second steady-state feature of the value set according to the difference in the data values of the data points in the value set; obtaining the steady-state factor of the two value sets according to the first steady-state feature and the second steady-state feature; obtaining similar classes according to the steady-state factors of the two value sets; obtaining the correlation of the value sets not in the similar classes for the value sets not in the similar classes; obtaining relevant classes based on the correlation; compressing the steady-state factors in the similar classes and relevant classes to obtain compressed data, and completing data analysis according to the compressed data. The present invention ensures the processing effect under run-length compression while highlighting the characteristic relationships in each index data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for analyzing the cost of material processing for project management. Background Art

[0002] The cost data of building material processing refers to the cost data generated by the processing and treatment of various building materials in the construction industry. These data usually include the materials, labor, equipment energy, and other related cost data required from raw materials to the processing process. The analysis and estimation of such data are very important, as they provide information about the resources and workload required during the material processing, helping project managers, architects, and other stakeholders with cost control and decision-making. And since these data can involve different types of building materials, such as wood, metal, concrete, stone, etc. The processing process may include steps such as cutting, sawing, milling, welding, spraying, polishing, and assembly. Each step consumes a certain amount of time and resources, so cost calculation and management are needed. In addition, the collection and analysis of the cost data of building material processing can help evaluate the efficiency and cost-effectiveness of different processing methods, optimize the production process, and provide a reference for the budget preparation of construction projects. These data can also be used to compare the costs of different suppliers or contractors and support contract negotiation and cost control decisions. However, due to the large number of data segments involved in building materials, the amount of data to be analyzed and stored is huge; usually, data compression is used for processing when analyzing and storing such massive data, and the commonly used run-length encoding compression method is highly dependent on the data characteristics within the dataset to be compressed; that is, it only has an ideal compression effect for datasets with a high degree of similarity and redundancy, but has a poor compression effect for datasets with a low degree of similarity and large differences, and even risks data expansion.

[0003] Due to the fact that the traditional run-length encoding compression method is overly dependent on the data characteristics within the dataset, and because of the strong diversity and large differences in magnitude of data indicators in the scenario of building material processing cost, the run-length compression effect of each indicator on the processing cost data is not stable directly; at the same time, it is not convenient to analyze and evaluate the data. Summary of the Invention

[0004] The present invention provides a method for analyzing the cost of material processing for project management to solve the problem of unstable run-length compression effect. The specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for analyzing the cost of material processing for project management, the method comprising the following steps:

[0006] Obtain a number of value sets;

[0007] Obtain the first steady state feature of any two value sets according to the differences in the data values corresponding to different data points in the two value sets; obtain the second steady state feature of each value set by taking the difference between the data value of each data point in each value set and the data values of the remaining data points; obtain the steady state factor of any two value sets according to the first steady state feature of any two value sets and the second steady state features of the two value sets respectively.

[0008] Obtain similarity classes according to the steady state factors among all value sets; calculate the correlation coefficients for the value sets not in the similarity classes, and obtain the correlation between any two value sets not in the similarity classes according to the correlation coefficients and entropy of the value sets; obtain correlation classes according to the correlations among all value sets not in the similarity classes.

[0009] Compress the steady state factors in the similarity classes and correlation classes to obtain compressed data and store it.

[0010] Preferably, the method for obtaining several value sets is as follows:

[0011] Statistically analyze the processing process of each building material, and count each expense item involved in each building material as the expense set of the building material. The expense set includes all expense items of all building materials. If the expense items of one building material do not include all expense items, mark the expense items not included in the building material as 0, and normalize the expense items in each expense set to obtain the value set of each building material.

[0012] Preferably, the method for obtaining the first steady state feature of any two value sets according to the differences in the data values corresponding to different data points in the two value sets is as follows:

[0013] Denote any one value set as the current compression set, and denote any other set except the current compression set as the matching set. Obtain the maximum data value and the minimum data value in the current compression set and the matching set, and obtain the first steady state feature of the current compression set and the matching set according to the difference between each data value in the current compression set and the matching set and the ratio of the minimum value to the maximum value in the current compression set and the ratio of the minimum value to the maximum value in the matching set.

[0014] Preferably, the method for obtaining the first steady state feature of the current compression set and the matching set according to the difference between each data value in the current compression set and the matching set and the ratio of the minimum value to the maximum value in the current compression set and the ratio of the minimum value to the maximum value in the matching set is as follows:

[0015] Let the data values of the data points at each corresponding position in the current compression set and the matching set be subtracted, and take the absolute value of the difference. Accumulate the absolute values of the differences to obtain the first parameter.

[0016] Take the reciprocal of the difference between the ratio of the minimum data value to the maximum data value of the current compressed set and the ratio of the minimum data value to the maximum data value of the matching set, and denote the absolute value of the reciprocal as the second parameter;

[0017] Obtain the first steady-state feature of the current compressed set and the matching set by weighted summation of the first parameter and the second parameter.

[0018] Preferably, the method for obtaining the second steady-state feature of each value set by taking the difference between the data value of each data point in each value set and the data values of the remaining data points is as follows:

[0019]

[0020] In the formula, represents the data value of the i-th data point of the value set S, represents the data value of the j-th data point of the value set S, and n represents the number of data points in the value set, represents the exponential function with the natural constant as the base, represents the second steady-state feature of the value set S.

[0021] Preferably, the method for obtaining the steady-state factor of any two value sets according to the first steady-state feature of any two value sets and the second steady-state features of the two value sets respectively is as follows:

[0022] Obtain the first weight value by weighting the first steady-state feature of the current compressed set and the matching set with the second steady-state feature of the current compressed set, obtain the second weight value by weighting the first steady-state feature of the current compressed set and the matching set with the second steady-state feature of the matching set, and take the maximum value of the first weight value and the second weight value as the steady-state factor of the current compressed set and the matching set.

[0023] Preferably, the method for obtaining the similarity class according to the steady-state factors between all value sets is as follows:

[0024] For the current compressed set, obtain the steady-state factors of the current compressed set and all the remaining value sets. Place the value sets with steady-state factors greater than the preset threshold and the current compressed set in one class. Calculate the steady-state factors between each value set and all the remaining value sets in the class, and place the value sets with steady-state factors greater than the preset threshold into the class. After all value sets have been calculated, in the class, save the value sets with a preset number of steady-state factors greater than the preset threshold in the class, and denote the saved class as the similarity class.

[0025] Preferably, the method for calculating the correlation coefficient of the value sets not in the similarity class and obtaining the correlation between any two value sets not in the similarity class according to the correlation coefficient and entropy of the value sets is as follows:

[0026] Denote any set not in the similar class as the first set, and any value set not in the similar class except the first set as the second set. Calculate the Pearson correlation coefficient and the Spearman correlation coefficient between the first set and the second set, and take the larger of the Pearson correlation coefficient and the Spearman correlation coefficient as the correlation coefficient between the first set and the second set;

[0027] Calculate the difference between the entropy values of the first set and the second set to obtain the differential entropy between the first set and the second set;

[0028] Weight the correlation coefficient and the differential entropy between the first set and the second set to obtain the correlation between the first set and the second set.

[0029] Preferably, the method for compressing and storing the steady-state factors in the similar class and the related class is as follows:

[0030] For the value sets in the similar class, directly perform run-length encoding compression processing. For the value sets in the related class, retain one value set, and use the correlation coefficients between the remaining value sets in the related class and the value set as the compression values. Take the retained value set and the compression values as the compression results of the related class.

[0031] In a second aspect, an embodiment of the present invention further provides a material processing cost data analysis system for project management, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0032] The beneficial effects of the present invention are as follows: The present invention proposes a method and a system for analyzing material processing cost data for project management; by analyzing and calculating the redundancy similarity of each material processing index data, obtaining a highly correlated index data set and performing classified personalized compression processing; enabling the data sets with correlation or similarity to achieve integrated synchronous compression processing, ensuring the processing effect under run-length compression while facilitating highlighting the characteristic relationships in each index data, and contributing to the final cost control decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1A schematic flowchart of a method for analyzing the cost of material processing in project management provided by an embodiment of the present invention. Detailed implementation manners

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Please refer to Figure 1 , which shows a method for analyzing the cost of material processing in project management provided by an embodiment of the present invention. The method includes the following steps:

[0037] Step S001, obtain a number of value sets.

[0038] The cost data of building material processing includes multiple sectors and data indicators: such as material costs; which cover concrete, bricks, steel bars, wood, glass, paint, etc. The costs of labor, equipment, and other resources involved in processing building materials. This includes processing processes such as cutting, grinding, spraying, painting, laying, etc.; as well as subsequent transportation cost indicators, installation cost indicators, technical service cost indicators, etc. Multiple indicator data are designed for each different building material, that is, an indicator data set is obtained for one building material. If a certain material does not include the involved indicators, then set this item to 0. For example, there is no cost for cutting paint materials, so this item is 0 in the corresponding indicator data set of paint.

[0039] Generally, the cost of each building material has the highest and lowest cost values according to its different types. In order to reduce the influence of the difference in magnitude between data and facilitate better data compression storage and feature analysis; normalize the data values in the indicator data set corresponding to each material. The formula is as follows:

[0040]

[0041] In the formula, represents the data value of the i-th data point in building material A, represents the smallest data value in building material A, represents the largest data value in building material A, represents the normalized data value of the i-th data point in building material A.

[0042] Replace the data values in the indicator data set corresponding to each building material with the normalized values to obtain the value set of each building material.

[0043] So far, the value sets of each building material have been obtained.

[0044] Step S002: Obtain the first steady-state feature of any two value sets based on the differences in the data values corresponding to different data points in the two value sets; obtain the second steady-state feature of each value set by taking the difference between the data value of each data point in each value set and the data values of the remaining data points; obtain the steady-state factor of any two value sets based on the first steady-state feature of the two value sets and the second steady-state features of the two value sets respectively.

[0045] Since different value sets may have the same distribution characteristics, similar or highly correlated material index data sets are obtained from diverse material cost data indicators for classification and compression.

[0046] For run-length encoding compression, if the similarity and redundancy of the data values within the value set are relatively high, it will have a positive impact on the final compression effect. However, for a large number of material cost indicators, there are certain differences in the internal feature distributions of each indicator data information, that is, the similarity and redundancy cannot be guaranteed. Therefore, this step analyzes the internal features of each value set. Obtain multiple groups of indicator data with relatively similar data similarity degrees and distribution rules (i.e., similar steady-state factors), classify and integrate them, thereby obtaining the steady-state factors of two value sets for the steady-state factor evaluation model, and classify, integrate, and compress the data set with a higher steady-state factor.

[0047] Specifically, obtain the maximum data value and the minimum data value in each value set. Denote any one value set as the current compression set. Obtain the first steady-state feature of the current compression set and the value set based on the differences between the data values of each data point in the current compression set and each value set and the amplitude difference between the two sets. The formula is as follows:

[0048]

[0049] In the formula, represents the data value of the i-th data point in the current compression set S, represents the data value of the i-th data point in the value set B, represents the minimum data value of the current compression set S, represents the minimum data value of the value set B, represents the maximum data value of the current compression set S, represents the maximum data value of the value set B, represents the exponential function with the natural constant as the base, n represents the number of data points in the value set, and is the empirical weight, in this embodiment is 0.6, is 0.4, represents the first steady-state feature of the current compression set S and the value set B.

[0050] Among them, for the first term, the smaller the mean of the absolute values of the differences of the internal data values of the current index data, the stronger the first steady-state feature of the current two sets. Under the action of the inverse proportional normalization function , that is, the closer the final calculation result is to 1, the stronger the first steady-state feature. For the second term, the fraction represents the proportion of the internal data value difference. Then, the smaller the difference between the internal data difference proportions of any pair of current data sets, the stronger the first steady-state feature; similarly, under the action of the inverse proportional normalization function , the second term is closer to 1.

[0051] For the current compression set, the difference between the data value of each data point in it and the data values of the remaining data points obtains the second steady-state feature of the current compression set. The formula is as follows:

[0052]

[0053] In the formula, represents the data value of the i-th data point of the current compression set S, represents the data value of the j-th data point of the current compression set S, n represents the number of data points in the value set, represents the exponential function with the natural constant as the base, represents the second steady-state feature of the current compression set S.

[0054] Among them, the absolute value represents the difference between the current data value and the value in its index data set; the second steady-state feature describes the average difference between each value in the current compression set and the mean value; the smaller this value is, the stronger the second steady-state feature. Under the action of the inverse proportional normalization function , the result value is closer to 1.

[0055] According to the second steady-state feature of the current compression set and the first steady-state feature of the current compression set and the remaining value sets, obtain the steady-state factor of the current compression set and the remaining value sets. The formula is as follows:

[0056]

[0057] In the formula, represents the first steady-state feature of the current compression set S and the value set B, represents the second steady-state feature of the current compression set S, represents the second steady-state feature of the value set B, represents the maximum value function, , are weight values. In this embodiment, is 0.6, is 0.4, represents the steady-state factor of the current compression set S and the value set B.

[0058] Thus, the steady-state factors of any two value sets are obtained.

[0059] Step S003: Obtain similar classes according to the steady-state factors between all value sets; calculate the correlation coefficients for the value sets not in the similar classes and combine the entropy of the value sets to obtain the correlation between any two value sets not in the similar classes; obtain the correlation classes according to the correlation between all value sets not in the similar classes.

[0060] The steady-state factors of two value sets are obtained in the above manner. Set a steady-state threshold. If the steady-state factors of two value sets are greater than the steady-state threshold, it means the two value sets are similar; if the steady-state factors of two value sets are less than the steady-state threshold, it means the two value sets are not similar. In this embodiment, the steady-state threshold is 0.7.

[0061] Calculate the steady-state factors for any one value set and the remaining value sets. Mark the value sets with steady-state factors greater than the steady-state threshold as similar and put them into a class. Calculate the steady-state factors for each value set in the class and the remaining value sets, and also put the value sets with steady-state factors greater than the steady-state threshold into the class until the steady-state factors of all value sets are calculated with the remaining value sets. In the class, for each value set, if the steady-state factors calculated with 5 or more value sets are greater than the steady-state threshold, then keep it in the class; otherwise, exclude it from the class. Thus, the similar classes are obtained.

[0062] The value sets in the similar classes have a high degree of similarity and stability. There will be a large amount of data redundancy in theory when performing run-length encoding compression. For the value sets not in the similar classes, the internal data features are theoretically that the data values are quite different and the mean value is quite different from the mean value of the other class index data sets. Although there is no similarity, different value sets may have a correlation. Therefore, calculate the correlation for the value sets not in the similar classes, obtain the correlation coefficient between two value sets and combine the entropy difference between the two value sets to obtain the correlation between the two value sets. The formula is as follows:

[0063]

[0064]

[0065]

[0066] In the formula, represents the value set S, represents the value set B, represents the Pearson correlation coefficient between the value set S and the value set B, represents the Spearman correlation coefficient between the value set S and the value set B, represents selecting the maximum value of the two correlation coefficients, represents the correlation coefficient between the value set S and the value set B. represents the entropy value of all data values in the value set S, represents the entropy value of all data values in the value set B, represents the exponential function with the natural constant as the base, represents the differential entropy between the value set S and the value set B. and respectively represent the weight values. Since the correlation coefficient has a greater impact on the correlation, in this embodiment .

[0067] Among them, since the correlation coefficients all tend to 1 indicating a strong positive correlation, tend to -1 indicating a strong negative correlation, and tend to 0 indicating a low correlation, their absolute values are respectively taken; so that they all satisfy that tending to 1 indicates a strong correlation. Since meeting one of the strong correlations is sufficient, the maximum value of the correlation coefficients is taken to represent the correlation coefficient between the current two sets of index data. The entropy values of the two value sets reflect the degree of chaos and dispersion of the internal data. If the difference value is smaller, it indicates that the distribution characteristics of the internal data of the current two sets of index data are relatively similar, reflecting a strong correlation between them. Therefore, the correlation between the two value sets is obtained based on these two.

[0068] Set the correlation threshold. In this embodiment, the correlation threshold is 0.7. If the correlation between two value sets is greater than the correlation threshold, it means that the two value sets are related. For all value sets that are not in the similar class, find the value sets with correlation. If there are two or more related value sets, they form a related class.

[0069] So far, the similar class and several related classes have been obtained.

[0070] Step S004, compress the steady-state factors in the similar class and related classes to obtain compressed data, and complete the data analysis according to the compressed data and the value sets that are not in the similar class and related classes.

[0071] For the value sets in the similar classes, run-length encoding is directly used for compression processing. For the value sets in the related classes, one of the value sets is retained, and the correlation coefficients between the remaining value sets in the related classes and the retained value set are used as compression values. The retained value set and all the compression values are the compression results of the related classes. Decompression is completed based on the retained value set and the set of compression values. For the value sets that are not in the related classes and similar classes, no compression is performed. This reduces the amount of data information for processing costs and saves storage space. The compressed data is transmitted to the data analysis module, and the compressed data is decompressed in the data analysis module to obtain the original data. Thus, this embodiment completes the efficient compression storage of data by analyzing the data of the processing costs of building materials.

[0072] Based on the same inventive concept as the above method embodiment, the present invention provides a data analysis system for the processing cost of materials in engineering management. The system includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a data analysis method for the processing cost of materials in engineering management.

[0073] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A material processing cost data analysis method for engineering management, characterized in that: The method comprises the following steps: The processing process of each building material is counted, and each cost involved in each building material is counted as the cost set of the building material, where the cost set includes the cost items of all building materials. If the cost item of one building material does not include all the cost items, the cost item not included in the building material is marked as 0, and the cost items in each cost set are normalized to obtain the value set of each building material; Record any value set as the current compression set, record any set other than the current compression set as the matching set, and obtain the first steady-state characteristics of the current compression set and the matching set according to the difference between each data value in the current compression set and the matching set and the ratio of the minimum and maximum values ​​of the current compression set to the minimum and maximum values ​​of the matching set; obtain the second steady-state characteristic of each value set by subtracting the data value of each data point in each value set from the data values ​​of the remaining data points; obtain the steady-state factors of any two value sets according to the first steady-state characteristics of any two value sets and the second steady-state characteristics of the two value sets respectively; Obtain similar classes based on the steady-state factors between all value sets; calculate the correlation coefficient of the value sets that are not in the similar class, and obtain the correlation between any two value sets that are not in the similar class based on the correlation coefficient and entropy of the value sets; obtain the related class based on the correlation between all value sets that are not in the similar class; Compress the steady-state factors in the similar class and the related class to obtain compressed data and store them; the storage method is: for the value set in the similar class, run-length encoding is directly used for compression processing, and for the value set in the related class, one of the value sets is retained, and the correlation coefficients of the remaining value sets in the related class and the value set are used as compression values, and the retained value set and the compressed value are used as the compression result of the related class; The method for obtaining the correlation between the two value sets is: Any set that is not in the similar class is recorded as the first set, and any value set that is not in the similar class except the first set is recorded as the second set. The Pearson correlation coefficient and the Spearman correlation coefficient are calculated for the first set and the second set, and the largest one between the Pearson correlation coefficient and the Spearman correlation coefficient is taken as the correlation coefficient between the first set and the second set; the difference between the entropy values ​​of the first set and the second set is calculated to obtain the differential entropy of the first set and the second set; the correlation coefficient and the differential entropy of the first set and the second set are weighted to obtain the correlation between the first set and the second set.

2. A material processing cost data analysis method for engineering management according to claim 1, characterized in that: The method for obtaining the first steady-state characteristics of the current compression set and the matching set according to the difference between each data value in the current compression set and the matching set and the ratio of the minimum value to the maximum value of the current compression set to the minimum value to the maximum value of the matching set is: Subtract the data value of the data point at each corresponding position of the current compression set from that of the matching set, calculate the absolute value of the difference, and accumulate the absolute values ​​of the difference to obtain the first parameter; The reciprocal of the difference between the ratio of the minimum data value to the maximum data value of the current compression set and the ratio of the minimum data value to the maximum data value of the matching set is taken, and the absolute value of the reciprocal is recorded as the second parameter; The first parameter and the second parameter are weightedly summed to obtain the first steady-state characteristics of the current compression set and the matching set.

3. The material processing cost data analysis method for engineering management according to claim 1 is characterized in that: The method for obtaining the second steady-state feature of each value set by subtracting the data value of each data point in each value set from the data values ​​of the remaining data points is: In the formula, represents the data value of the i-th data point in the value set S, represents the data value of the jth data point in the value set S, n represents the number of data points in the value set, represents an exponential function with a natural constant as base, Represents the second steady-state characteristics of the value set S.

4. The material processing cost data analysis method for engineering management according to claim 1 is characterized in that: The method for obtaining the steady-state factors of any two value sets according to the first steady-state characteristics of any two value sets and the second steady-state characteristics of the two value sets respectively is: The first steady-state feature of the current compression set and the matching set is weighted with the second steady-state feature of the current compression set to obtain a first weight, the first steady-state feature of the current compression set and the matching set is weighted with the second steady-state feature of the matching set to obtain a second weight, and the maximum value of the first weight and the second weight is used as the steady-state factor of the current compression set and the matching set.

5. The material processing cost data analysis method for engineering management according to claim 1 is characterized in that: The method for obtaining similarity classes based on the steady-state factors between all value sets is: For the current compression set, obtain the steady-state factors of the current compression set and all other value sets, put the value sets with steady-state factors greater than the preset threshold and the current compression set in a class, calculate the steady-state factors of each value set and all other value sets in the class, put the value sets with steady-state factors greater than the preset threshold into the class, until all value sets are calculated, and then save the value sets with a preset number of steady-state factors greater than the preset threshold in the class, and record the saved classes as similar classes.

6. A material processing cost data analysis system for engineering management, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

Images