Industrial multi-project multi-level index calculation method, system and equipment and storage medium

Abstract

The invention relates to the technical field of business data processing, and discloses an industrial multi-project multi-level index calculation method, system and device and a storage medium, and the method comprises the steps: collecting multi-dimensional data from different industrial projects and levels, and carrying out the preprocessing; establishing an analysis model by using the preprocessed data, and analyzing the dependency relationship among the data indexes; based on the analyzed dependency relationship, forming an initial hierarchical structure, and automatically adjusting the hierarchical structure according to data and business requirements; and optimizing calculation path selection and resource allocation. The hierarchical structure is formed and automatically adjusted by dynamically analyzing the dependency relationship between the data, so that the system can flexibly meet complex industrial data processing requirements. The calculation path selection and the resource allocation are optimized, and the data processing efficiency and the decision accuracy are improved. And the intelligence and practicability of industrial multi-project multi-level index calculation are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of business data processing, and specifically to an industrial multi-project and multi-level indicator calculation method, system, equipment and storage medium. Background Art

[0002] In modern industrial environments, efficient data processing and decision support systems play a vital role in improving production efficiency and optimizing resource management. With the improvement of industrial automation and intelligence, more and more companies are beginning to rely on advanced data analysis methods to process and analyze complex data sets from different projects and levels. In this context, industrial multi-project and multi-level indicator calculation technologies have developed rapidly, which enable companies to extract valuable information from large-scale data sets to support more accurate decision making. Traditional data processing methods usually rely on batch processing mode and adopt static data analysis frameworks, which often show the shortcomings of slow processing speed and poor adaptability when processing dynamically changing industrial data.

[0003] The main shortcoming of existing technologies is that their static data processing mode is difficult to adapt to rapidly changing production conditions and market demands. Especially in a multi-project and multi-level environment, traditional methods are often unable to effectively identify and process complex dependencies between data, resulting in data analysis results that cannot fully reflect the actual business situation. In addition, existing technologies lack flexibility and automation in data preprocessing, indicator calculation, and resource allocation optimization, making the data processing process time-consuming and inefficient, and making it difficult to achieve data-driven dynamic decision support. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are the problems of insufficient data dependency analysis, inability to dynamically adjust the hierarchical structure, and difficulty in achieving intelligent optimization of calculation path selection and resource allocation in the process of industrial multi-project and multi-level data processing.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for calculating industrial multi-project and multi-level indicators, comprising: Collect multi-dimensional data from different industrial projects and levels and perform pre-processing; Using the preprocessed data, an analysis model is established to analyze the dependencies between various data indicators; Based on the analyzed dependencies, an initial hierarchical structure is formed, and the hierarchical structure is automatically adjusted according to data and business requirements; Based on the adjusted hierarchical structure, the computing path selection and resource allocation are optimized.

[0007] As a preferred solution of the industrial multi-project multi-level indicator calculation method described in the present invention, wherein: the multi-dimensional data includes production efficiency data, quality control data, energy and resource consumption data, equipment health status data, maintenance prediction and health management data, supply chain optimization data, environmental compliance and sustainability data, safety monitoring data, cost-benefit analysis data, and real-time operation data; The preprocessing includes: cleaning the multidimensional data, processing missing values, outliers and deleting duplicate data; converting the multidimensional data and integrating multi-source data; labeling and classifying the multidimensional data and performing data consistency checks.

[0008] As a preferred solution of the industrial multi-project and multi-level indicator calculation method described in the present invention, the establishment of the analysis model includes extracting features from the pre-processed multidimensional data, using the supervised learning model of random forest to learn known dependencies, and combining the unsupervised learning method of association rule mining to discover potential dependencies.

[0009] As a preferred solution of the industrial multi-project multi-level indicator calculation method of the present invention, wherein: the forming of the initial hierarchical structure includes extracting the dependency relationship between the indicators from the analysis model; The dependency relationship includes the strength of the dependency relationship and the directionality of the dependency relationship; The strength of the dependency relationship includes a correlation coefficient and mutual information; The directionality of the dependency relationship includes the direction of causality; The dependency data is structured to generate a dependency matrix, which is expressed as follows: , in, represents the dependency matrix, Indicator For indicators The dependence strength of each It is a composite value that combines the dependence strength and direction, which can be further expressed as: , in, Indicator and indicators The correlation coefficient between Indicator and indicators The mutual information between Indicator For indicators directionality of causal relationships.

[0010] As a preferred solution of the method for calculating the industrial multi-project and multi-level indicators of the present invention, the forming of the initial hierarchical structure includes clustering the indicators using agglomerative hierarchical clustering based on the dependency matrix, aggregating the indicators with strong dependency relationships in the same level to form the initial hierarchical structure; The dependency matrix is ​​set as an adjacency matrix of a weighted graph, wherein nodes represent indicators and edge weights represent dependency relationships between indicators, and a degree matrix of the weighted graph is constructed; The formula of the degree matrix is ​​expressed as: , in, The degree matrix Line The elements of the column, Indicates the total number of indicators. Indicator For indicators The strength of dependence, represents the adjustment parameter; represents the adjustment parameter; The Laplace matrix of the weighted graph is defined as the difference between the degree matrix and the dependency matrix. The Laplace matrix is ​​normalized to obtain a symmetric normalized Laplace matrix, which is expressed as follows: , in, represents the symmetric normalized Laplacian matrix; represents the degree matrix, Degree matrix The inverse square root matrix of ; represents the Laplacian matrix; Calculating the eigenvalues ​​and eigenvectors of the normalized Laplacian matrix includes, in descending order, The eigenvectors of the eigenvalues ​​form the characteristic matrix, which is expressed as follows: , in, represents the final feature matrix, It means finding the value that minimizes the objective function. represents the trace operation of a matrix, Representation Matrix The transposed matrix of represents the symmetric normalized Laplacian matrix, represents the process characteristic matrix, represents the orthogonality constraint, represents the regularization parameter, represents the number of eigenvectors in the feature matrix; The process characteristic matrix feature vectors, Representation vector The gradient of Representation vector The squared norm of the gradient of ; Treat each row of the feature matrix as a new data point and use the K-means clustering algorithm to divide these data points into clusters; The result of K-means clustering is hierarchical division, where each cluster represents a level, generating an initial hierarchical structure; Each level contains groups of indicators assigned by the clustering algorithm, ensuring strong dependencies between these indicators; The hierarchical structure is represented as a hierarchical matrix, and the formula is expressed as: , in, Indicates the first The indicator belongs to The membership degree of each cluster; Indicates The indicators in the final feature matrix The eigenvectors in ; Indicates The center vector of each cluster, Represents the feature vector and The square of the Euclidean distance between Represents the feature vector and The square of the Euclidean distance between Represents the square of the bandwidth parameter of the Gaussian kernel function; Indicates The cluster center vectors.

[0011] As a preferred solution of the industrial multi-project multi-level indicator calculation method described in the present invention, the automatic adjustment of the hierarchical structure includes setting a dynamic threshold of the dependency strength. , only if the dependence strength exceeds Only pairs of indicators that are equal will be considered as the same level; The formula for the intensity-dependent dynamic threshold is: , in, A dynamic threshold adjustment function representing the strength of the dependency, represents the threshold of the dependency strength, Indicators For indicators The intensity of dependence; represents the smoothed logistic regression function, represents the smoothing parameter, Represents the inverse operation of the logistic regression function; The value threshold of is 0 to 1; Cross-validation and A / B testing are used to verify the hierarchical structure to ensure its rationality and robustness. The formula is expressed as: , in, represents the cross validation loss function, It means averaging the results of cross validation. Indicates the number of cross-validation folds; represents the trace operation of a matrix, Indicates The compromise feature matrix, Indicates The transpose of the compromised feature matrix, represents the symmetric normalized Laplacian matrix, represents the regularization parameter, Represents the feature vector The squared norm of the gradient of Represents the feature vector The gradient of A dynamic threshold adjustment function representing the strength of dependency; Indicates the cross validation fold; represents the index of the feature vector; The value threshold of is from 0 to positive infinity. The smaller the value, the better the fitting effect of the hierarchical structure. Categorize the verification results at each level. When the values ​​of are all lower than the preset global threshold, the hierarchical structure is determined to have passed the verification and enters the next step of adjustment; When the verification result of a certain layer exceeds the global threshold, the hierarchical structure is determined to be an item that needs to be adjusted and is readjusted; when When the values ​​of are all globally close to the threshold, but the dependence strength is greater than the preset upper limit, it is determined that the layer is too dependent on the overall structure, resulting in a single point failure of the system; After completing the above-mentioned multi-level adjustments, the entire hierarchical structure is globally optimized, and the secondary verification mechanism is triggered to re-verify the hierarchical structure using cross-validation and A / B testing.

[0012] As a preferred solution of the industrial multi-project multi-level indicator calculation method of the present invention, wherein: the optimization of calculation path selection and resource allocation includes constructing an optimization model using a multi-objective optimization algorithm to optimize the calculation path selection and resource allocation; The formula of the optimization model is expressed as: , in, represents the result of optimizing computation path selection and resource allocation, represents the decision variable for computing path selection, Indicates The weight of the target, Represents the decision variables To minimize, Indicates A function related to a target, Indicates The cost of resources, Indicates The amount of resources allocated, represents an exponential decay term, represents an optimization parameter, represents the constraint function, represents the gamma function; represents the starting point of optimization, represents the end point of optimization, represents the adjustment parameter; The value threshold of is a positive real number. The larger the value, the lower the efficiency in resource allocation and path selection.

[0013] An industrial multi-project and multi-level indicator calculation system, wherein: Data collection and preprocessing module, which collects multi-dimensional data from different industrial projects and levels and performs preprocessing operations; The analysis model building module uses the pre-processed multi-dimensional data to analyze the dependencies between various data indicators; The hierarchical structure formation and adjustment module forms the initial hierarchical structure based on the dependencies in the analysis model and automatically adjusts the hierarchical structure according to data and business requirements; Multi-objective optimization and resource allocation module, which uses multi-objective optimization algorithm to optimize calculation path selection and resource allocation; The hierarchical verification and optimization module verifies the formed hierarchical structure and uses cross-validation and A / B testing to ensure its rationality and robustness.

[0014] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0016] Beneficial effects of the present invention: The industrial multi-project multi-level indicator calculation method provided by the present invention forms and automatically adjusts the hierarchical structure by dynamically analyzing the dependencies between data, so that the system can flexibly respond to complex industrial data processing needs. Combined with the multi-objective optimization algorithm, the calculation path selection and resource allocation are optimized, and the efficiency of data processing and the accuracy of decision-making are improved. In addition, the present invention effectively solves the problems of rigid hierarchical structure and inefficient resource allocation in traditional methods, and significantly improves the intelligence and practicality of industrial multi-project multi-level indicator calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 An overall flow chart of an industrial multi-project and multi-level indicator calculation method provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides an industrial multi-project multi-level indicator calculation method, comprising: S1: Collect multi-dimensional data from different industrial projects and levels and perform pre-processing.

[0021] Multi-dimensional data includes production efficiency data, quality control data, energy and resource consumption data, equipment health status data, maintenance prediction and health management data, supply chain optimization data, environmental compliance and sustainability data, safety monitoring data, cost-benefit analysis data, and real-time operation data.

[0022] The preprocessing includes: cleaning the multidimensional data, processing missing values, outliers and deleting duplicate data; converting the multidimensional data and integrating multi-source data; labeling and classifying the multidimensional data and performing data consistency checks.

[0023] Furthermore, by collecting and preprocessing multi-dimensional data from different industrial projects and levels, the integrity and consistency of the data are ensured, avoiding the interference of data noise, missing values ​​and outliers on subsequent analysis. The preprocessed data is of higher quality, laying a solid foundation for subsequent analysis and model building.

[0024] Furthermore, through a systematic data processing process, the reliability and accuracy of data analysis can be improved, thereby ensuring the reliability of basic data in the entire industrial indicator calculation process, which is crucial for achieving accurate decision-making and resource optimization.

[0025] S2: Use the preprocessed data to establish an analysis model and analyze the dependencies between various data indicators.

[0026] Building an analysis model includes extracting features from preprocessed multidimensional data, using a random forest model to learn known dependencies, and combining unsupervised learning methods for association rule mining to discover potential dependencies.

[0027] Furthermore, advanced machine learning technology can be used to achieve deep mining of industrial data, ensure comprehensive analysis of multi-dimensional data, and dynamically track its changes, providing a scientific basis for forming and adjusting hierarchical structures.

[0028] S3: Based on the analyzed dependencies, an initial hierarchical structure is formed, and the hierarchical structure is automatically adjusted according to data and business requirements.

[0029] Forming the initial hierarchical structure includes extracting the dependencies between indicators from the analysis model.

[0030] Dependency includes the strength of the dependency and the directionality of the dependency.

[0031] The strength of the dependency relationship includes the correlation coefficient and mutual information.

[0032] The directionality of dependency includes the direction of causality.

[0033] The dependency data is structured to generate a dependency matrix, which is expressed as follows: , in, represents the dependency matrix, Indicator For indicators The dependence strength of each It is a composite value that combines the dependence strength and direction, which can be further expressed as: , in, Indicator and indicators The correlation coefficient between Indicator and indicators The mutual information between Indicator For indicators directionality of causal relationships.

[0034] Based on the dependency matrix, the indicators are clustered using agglomerative hierarchical clustering, and the indicators with strong dependencies are aggregated in the same level to form an initial hierarchical structure.

[0035] The dependency matrix is ​​set as an adjacency matrix of a weighted graph, in which nodes represent indicators and edge weights represent dependency relationships between indicators, and a degree matrix of the weighted graph is constructed.

[0036] The formula of the degree matrix is ​​expressed as: , in, The degree matrix Line The elements of the column, Indicates the total number of indicators. Indicator For indicators The strength of dependence, Indicates the adjustment parameter. Indicates the adjustment parameter.

[0037] The Laplace matrix of the weighted graph is defined as the difference between the degree matrix and the dependency matrix. The Laplace matrix is ​​normalized to obtain a symmetric normalized Laplace matrix, which is expressed as follows: , in, represents the symmetric normalized Laplacian matrix; represents the degree matrix, Degree matrix The inverse square root matrix of ; represents the Laplacian matrix.

[0038] Calculating the eigenvalues ​​and eigenvectors of the normalized Laplacian matrix includes, in descending order, The eigenvectors of the eigenvalues ​​form the characteristic matrix, which is expressed as follows: , in, The final feature matrix represented by It means finding the value that minimizes the objective function. represents the trace operation of a matrix, Representation Matrix The transposed matrix of represents the symmetric normalized Laplacian matrix, represents the process characteristic matrix, represents the orthogonality constraint, represents the regularization parameter, represents the number of eigenvectors in the feature matrix; The process characteristic matrix feature vectors, Representation vector The gradient of Representation vector The squared norm of the gradient of .

[0039] Treat each row of the feature matrix as a new data point and use the K-means clustering algorithm to divide these data points into Clusters.

[0040] The result of K-means clustering is hierarchical division, where each cluster represents a level, generating an initial hierarchical structure.

[0041] Each level contains groups of indicators assigned by the clustering algorithm, ensuring strong dependencies between these indicators.

[0042] The hierarchical structure is represented as a hierarchical matrix, and the formula is expressed as: , in, Indicates the first The indicator belongs to The membership degree of each cluster; Indicates The indicators in the final feature matrix The eigenvectors in ; Indicates The center vector of each cluster, Represents the feature vector and The square of the Euclidean distance between Represents the feature vector and The square of the Euclidean distance between Represents the square of the bandwidth parameter of the Gaussian kernel function; Indicates The cluster center vectors.

[0043] Automatically adjust the hierarchy including setting dynamic thresholds for dependency strength , only if the dependence strength exceeds The indicator pairs are considered to be at the same level.

[0044] The formula for the intensity-dependent dynamic threshold is: , in, A dynamic threshold adjustment function representing the strength of the dependency, represents the threshold of the dependency strength, Indicators For indicators The intensity of dependence; represents the smoothed logistic regression function, represents the smoothing parameter, Represents the inverse operation of the logistic regression function.

[0045] The value threshold is between 0 and 1.

[0046] Cross-validation and A / B testing are used to verify the hierarchical structure to ensure its rationality and robustness. The formula is expressed as: , in, represents the cross validation loss function, It means averaging the results of cross validation. Indicates the number of cross-validation folds; represents the trace operation of a matrix, Indicates The compromise feature matrix, Indicates The transpose of the compromised feature matrix, represents the symmetric normalized Laplacian matrix, represents the regularization parameter, Represents the feature vector The squared norm of the gradient of Represents the feature vector The gradient of A dynamic threshold adjustment function representing the strength of dependency; Indicates the cross validation fold; Represents the index of the feature vector.

[0047] The value threshold of is from 0 to positive infinity. The smaller the value, the better the fitting effect of the hierarchical structure.

[0048] Categorize the verification results at each level. When the values ​​of are all lower than the preset global threshold, the hierarchical structure is determined to have passed the verification and proceed to the next step of adjustment.

[0049] When the verification result of a certain layer exceeds the global threshold, the hierarchical structure is determined to be an item that needs to be adjusted and is readjusted.

[0050] when When the values ​​of are globally close to the threshold, but the dependence strength is greater than the preset upper limit, it is determined that the layer is too dependent on the overall structure, resulting in a single point failure of the system.

[0051] After completing the above-mentioned multi-level adjustments, the entire hierarchical structure is globally optimized, and the secondary verification mechanism is triggered to re-verify the hierarchical structure using cross-validation and A / B testing.

[0052] Furthermore, by forming an initial hierarchical structure and automatically adjusting the hierarchical structure according to business needs, the flexibility and adaptability of the system are guaranteed. This process can dynamically respond to changes in data, optimize the hierarchical structure, and make decisions more accurate and efficient.

[0053] Furthermore, through a dynamic adjustment mechanism, the limitations of the traditional static hierarchical structure can be broken, the system's adaptability in complex industrial environments can be enhanced, and it can be ensured that the hierarchical structure can reflect changes in actual business needs and data relationships in real time.

[0054] S4: Based on the adjusted hierarchical structure, the computing path selection and resource allocation are optimized.

[0055] Optimizing the calculation path selection and resource allocation includes building an optimization model using a multi-objective optimization algorithm to optimize the calculation path selection and resource allocation.

[0056] The formula of the optimization model is expressed as: , in, represents the result of optimizing computation path selection and resource allocation, represents the decision variable for computing path selection, Indicates The weight of the target, Represents the decision variables To minimize, Indicates A function related to a target, Indicates The cost of resources, Indicates The amount of resources allocated, represents an exponential decay term, represents an optimization parameter, represents the constraint function, represents the gamma function; represents the starting point of optimization, represents the end point of optimization, Indicates the adjustment parameter.

[0057] The value threshold of is a positive real number. The larger the value, the lower the efficiency in resource allocation and path selection.

[0058] Furthermore, by optimizing the calculation path selection and resource allocation, the overall efficiency of the system is improved, ensuring maximum utilization under limited resources. The multi-objective optimization algorithm can balance multiple objectives to achieve the global optimal effect.

[0059] Furthermore, improving the intelligence of resource management and path selection can avoid resource waste and irrational path selection, thereby maximizing the efficiency of the industrial system and improving the competitiveness and production efficiency of enterprises.

[0060] On the other hand, this embodiment also provides an industrial multi-project multi-level indicator calculation system, which includes: The data collection and preprocessing module collects multi-dimensional data from different industrial projects and levels and performs preprocessing operations.

[0061] The analysis model building module uses the preprocessed multidimensional data to analyze the dependencies between various data indicators.

[0062] The hierarchical structure formation and adjustment module forms an initial hierarchical structure based on the dependencies in the analysis model and automatically adjusts the hierarchical structure according to data and business requirements.

[0063] The multi-objective optimization and resource allocation module uses a multi-objective optimization algorithm to optimize calculation path selection and resource allocation.

[0064] The hierarchical verification and optimization module verifies the formed hierarchical structure and uses cross-validation and A / B testing to ensure its rationality and robustness.

[0065] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0067] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0068] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0069] In Example 2, three different projects (Project A, Project B, Project C) of an industrial enterprise were selected for the experiment, which covered different production links and management levels. In order to fully verify the advantages of the present invention, multi-dimensional data were collected from each project, including production efficiency data (unit: %), equipment health status data (unit: %), resource consumption data (unit: tons), quality control data (unit: times / 1000 pieces), maintenance cost data (unit: yuan), and energy consumption data (unit: kWh). The data collection period was 30 days, and preliminary preprocessing was performed, including data cleaning, missing value filling, outlier processing, and data standardization to ensure the quality and consistency of the data.

[0070] After data preprocessing, the processed data was used to build an analysis model. The model used the random forest algorithm and association rule mining method to analyze the dependencies between various data indicators. After the model was built, the initial hierarchical structure was formed through the dependency matrix. Due to the large differences in production environments and requirements of different projects, the hierarchical structure was automatically adjusted according to actual business needs after it was formed to ensure the best match between the data dependencies and business logic of each project.

[0071] Subsequently, based on the adjusted hierarchical structure, the calculation path selection and resource allocation were optimized. A multi-objective optimization algorithm was used to set multiple optimization goals including resource utilization, production efficiency, and maintenance cost. Through the dynamic adjustment of the algorithm, the optimal path selection and resource allocation under different hierarchical structures were achieved. During the experiment, three rounds of adjustments were made for each project. After each adjustment, the rationality of the hierarchical structure was re-verified, and finally an optimized resource allocation plan was generated. The experimental data is shown in Table 1.

[0072] Table 1 Experimental data table Parameter name Project A - Initial Value Project A - Adjusted Project B - Initial Value Project B - Adjusted Project C - Initial Value Project C-Adjusted Production efficiency (%) 85 92 78 88 82 90 Equipment health status (%) 70 80 65 78 72 85 Resource consumption (tons) 154 132 173.3 145 162.3 141.1 Quality control (times / 1000 pieces) 15 10 20 12 18 11 Maintenance cost (yuan) 50000 45000 55000 48000 52000 47000 Energy consumption (kWh) 20310 18056 22159 19547.8 21334.6 19459.3

[0073] Through the comparative analysis of the above table data, it can be clearly seen that the implementation of the present invention has a significant optimization effect on the data of each dimension of Project A, Project B, and Project C. First, in terms of production efficiency, the efficiency of each project has been improved, among which the production efficiency of Project A has increased from 85% to 92%, Project B has increased from 78% to 88%, and Project C has increased from 82% to 90%. This significant improvement is mainly due to the fact that the dynamically adjusted hierarchical structure can better reflect the actual business needs of each project, thereby optimizing the production path and resource allocation.

[0074] Secondly, in terms of equipment health, the health indicators of equipment in each project have also been significantly improved, indicating that through the optimized resource allocation, equipment maintenance has been more effectively managed and equipment failures have been reduced. In particular, in Project B, the equipment health status increased from 65% to 78%. This change shows that the adjusted hierarchical structure can better balance the allocation of maintenance resources and improve the operational reliability of equipment.

[0075] In terms of resource consumption and energy consumption, the resource consumption and energy consumption of each project have decreased. The resource consumption of Project A dropped from 154 tons to 132 tons, and the energy consumption dropped from 20310 kWh to 18056 kWh; Project B and Project C also showed similar optimization effects. This shows that the multi-objective optimization algorithm plays a significant role in resource allocation, and the optimized calculation path can minimize resource waste and improve resource utilization.

[0076] In addition, quality control and maintenance costs have also improved significantly. For example, the quality control issues of Project A have been reduced from 15 times / 1,000 pieces to 10 times / 1,000 pieces, and the maintenance cost has been reduced from 50,000 yuan to 45,000 yuan. This further proves that by adjusting the hierarchical structure, the system can more accurately identify and solve the weak links in quality control and reduce maintenance costs through more efficient resource allocation.

[0077] The present invention shows significant advantages in processing complex industrial multi-project multi-level data by adopting a multi-objective optimization algorithm, especially in terms of improving production efficiency, optimizing resource utilization and enhancing system robustness. Compared with the traditional single-objective optimization algorithm, the multi-objective optimization algorithm can more effectively coordinate the balance between different objectives, so that the system can achieve overall performance improvement in multiple dimensions. In specific applications, the algorithm can respond to changes in business data in real time, achieve optimal resource allocation by dynamically adjusting the hierarchical structure and optimizing path selection, and maintain efficient operation of the system when dealing with emergencies. In addition, the system has the ability of continuous learning and self-optimization, and can continuously adjust the optimization strategy according to historical data to ensure continuous efficiency after long-term operation. Through visualization tools, users can view the system status in real time and adjust the optimization target according to actual needs, further enhancing the user-friendliness and practicality of the system.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for calculating industrial multi-project and multi-level indicators, characterized in that: include: Collect multi-dimensional data from different industrial projects and levels and perform pre-processing; Using the preprocessed data, an analysis model is established to analyze the dependencies between various data indicators; Based on the analyzed dependencies, an initial hierarchical structure is formed, and the hierarchical structure is automatically adjusted according to data and business requirements; Based on the adjusted hierarchical structure, the computing path selection and resource allocation are optimized.

2. The method for calculating industrial multi-project and multi-level indicators as claimed in claim 1, characterized in that: The multi-dimensional data includes production efficiency data, quality control data, energy and resource consumption data, equipment health status data, maintenance prediction and health management data, supply chain optimization data, environmental compliance and sustainability data, safety monitoring data, cost-benefit analysis data, and real-time operation data; The preprocessing includes: cleaning the multidimensional data, processing missing values, outliers and deleting duplicate data; converting the multidimensional data and integrating multi-source data; labeling and classifying the multidimensional data and performing data consistency checks.

3. The method for calculating industrial multi-project and multi-level indicators as claimed in claim 1, characterized in that: The establishment of the analysis model includes extracting features from the pre-processed multidimensional data, using a random forest model to learn known dependencies, and combining an unsupervised learning method of association rule mining to discover potential dependencies.

4. The method for calculating industrial multi-project and multi-level indicators as claimed in any one of claims 1, 2 and 3, characterized in that: The initial hierarchical structure is formed. Including, extracting the dependency relationship between indicators from the analysis model; The dependency relationship includes the strength of the dependency relationship and the directionality of the dependency relationship; The strength of the dependency relationship includes a correlation coefficient and mutual information; The directionality of the dependency relationship includes the direction of causality; The dependency data is structured to generate a dependency matrix, which is expressed as follows: ， in, represents the dependency matrix, Indicator For indicators The dependence strength of each It is a composite value that combines the dependence strength and direction, which can be further expressed as: ， in, Indicators and indicators The correlation coefficient between Indicators and indicators The mutual information between Indicators For indicators directionality of causal relationships.

5. The method for calculating industrial multi-project and multi-level indicators as claimed in claim 4, characterized in that: The forming of the initial hierarchical structure includes clustering the indicators using agglomerative hierarchical clustering based on the dependency matrix, aggregating the indicators with strong dependency relationships in the same level to form the initial hierarchical structure; The dependency matrix is ​​set as an adjacency matrix of a weighted graph, wherein nodes represent indicators and edge weights represent dependency relationships between indicators, and a degree matrix of the weighted graph is constructed; The formula of the degree matrix is ​​expressed as: ， in, The degree matrix Line The elements of the column, Indicates the total number of indicators. Indicators For indicators The strength of dependence, represents the adjustment parameter; represents the adjustment parameter; The Laplace matrix of the weighted graph is defined as the difference between the degree matrix and the dependency matrix. The Laplace matrix is ​​normalized to obtain a symmetric normalized Laplace matrix, which is expressed as follows: ， in, represents the symmetric normalized Laplacian matrix; represents the degree matrix, Degree matrix The inverse square root matrix of ; represents the Laplacian matrix; Calculating the eigenvalues ​​and eigenvectors of the normalized Laplacian matrix includes, in descending order, The eigenvectors of the eigenvalues ​​form the characteristic matrix, which is expressed as follows: ， in, The final feature matrix represented by It means finding the value that minimizes the objective function. represents the trace operation of a matrix, Representation Matrix The transposed matrix of represents the symmetric normalized Laplacian matrix, represents the process characteristic matrix, represents the orthogonality constraint, represents the regularization parameter, represents the number of eigenvectors in the feature matrix; The process characteristic matrix feature vectors, Representation vector The gradient of Representation vector The squared norm of the gradient of ; Treat each row of the feature matrix as a new data point and use the K-means clustering algorithm to divide these data points into clusters; The result of K-means clustering is hierarchical division, where each cluster represents a level, generating an initial hierarchical structure; Each level contains groups of indicators assigned by the clustering algorithm, ensuring strong dependencies between these indicators; The hierarchical structure is represented as a hierarchical matrix, and the formula is expressed as: ， in, Indicates the first The indicator belongs to The membership degree of each cluster; Indicates The indicators in the final feature matrix The eigenvectors in ; Indicates The center vector of each cluster, Represents the feature vector and The square of the Euclidean distance between Represents the feature vector and The square of the Euclidean distance between Represents the square of the bandwidth parameter of the Gaussian kernel function; Indicates The cluster center vectors.

6. The method for calculating industrial multi-project and multi-level indicators as described in any one of claims 1, 2, 3, and 5, characterized in that: The automatic adjustment of the hierarchy structure includes setting a dynamic threshold of dependency strength. , only if the dependence strength exceeds Only pairs of indicators that are equal will be considered as the same level; The formula for the intensity-dependent dynamic threshold is: ， in, A dynamic threshold adjustment function representing the strength of the dependency, represents the threshold of the dependency strength, Indicator For indicators The intensity of dependence; represents the smoothed logistic regression function, represents the smoothing parameter, Represents the inverse operation of the logistic regression function; The value threshold of is 0 to 1; Cross-validation and A / B testing are used to verify the hierarchical structure to ensure its rationality and robustness. The formula is expressed as: ， in, represents the cross validation loss function, Indicates averaging the results of cross validation. Indicates the number of cross-validation folds; represents the trace operation of a matrix, Indicates The compromise feature matrix, Indicates The transpose of the compromised feature matrix, represents the symmetric normalized Laplacian matrix, represents the regularization parameter, Represents the feature vector The squared norm of the gradient of Represents the feature vector The gradient of A dynamic threshold adjustment function representing the strength of dependency; Indicates the cross validation fold; represents the index of the feature vector; The value threshold of is from 0 to positive infinity. The smaller the value, the better the fitting effect of the hierarchical structure. Categorize the verification results at each level. When the values ​​of are all lower than the preset global threshold, the hierarchical structure is determined to have passed the verification and enters the next step of adjustment; When the verification result of a certain layer exceeds the global threshold, the hierarchical structure is determined to be an item that needs to be adjusted and is readjusted; when When the values ​​of are all globally close to the threshold, but the dependence strength is greater than the preset upper limit, it is determined that the layer is too dependent on the overall structure, resulting in a single point failure of the system; After completing the above-mentioned multi-level adjustments, the entire hierarchical structure is globally optimized, and the secondary verification mechanism is triggered to re-verify the hierarchical structure using cross-validation and A / B testing.

7. The method for calculating industrial multi-project and multi-level indicators as claimed in claim 6, characterized in that: The optimization of computing path selection and resource allocation includes: using a multi-objective optimization algorithm to build an optimization model to optimize computing path selection and resource allocation; The formula of the optimization model is expressed as: ， in, represents the result of optimizing computation path selection and resource allocation, represents the decision variable for computing path selection, Indicates The weight of the target, Represents the decision variables To minimize, Indicates A function related to a target, Indicates The cost of resources, Indicates The amount of resources allocated, represents an exponential decay term, represents an optimization parameter, represents the constraint function, represents the gamma function; represents the starting point of optimization, represents the end point of optimization, represents the adjustment parameter; The value threshold of is a positive real number. The larger the value, the lower the efficiency in resource allocation and path selection.

8. An industrial multi-project and multi-level indicator calculation system, characterized by: Data collection and preprocessing module, which collects multi-dimensional data from different industrial projects and levels and performs preprocessing operations; The analysis model building module uses the pre-processed multi-dimensional data to analyze the dependencies between various data indicators; The hierarchical structure formation and adjustment module forms the initial hierarchical structure based on the dependencies in the analysis model and automatically adjusts the hierarchical structure according to data and business requirements; Multi-objective optimization and resource allocation module, which uses multi-objective optimization algorithms to optimize computational path selection and resource allocation; and, The hierarchical verification and optimization module verifies the formed hierarchical structure and uses cross-validation and A / B testing to ensure its rationality and robustness.

9. A computer device comprising: Memory and processor; The memory stores a computer program, which is characterized in that when the processor executes the computer program, it implements the steps of the method for calculating industrial multi-project and multi-level indicators.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for calculating industrial multi-project and multi-level indicators are implemented.

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