Enterprise management optimization method and system based on big data

Abstract

The invention relates to the technical field of enterprise resource management, in particular to an enterprise management optimization method and system based on big data, and the method comprises the following steps: capturing data of employee skills, a project execution process and customer interaction according to enterprise resource planning in an enterprise, analyzing the captured data, and carrying out the multi-dimensional information classification processing; and constructing an enterprise resource comprehensive data set. According to the method, the flow and utilization efficiency of the resources is improved by establishing the graphic database to integrate the enterprise resources and converting the data elements into the network nodes and the connecting edges, and the resource flow and utilization efficiency is improved by identifying and analyzing the association strength of the nodes and the weights of the edges in the resource network. Through detailed flow path analysis, an enterprise can formulate a scientific resource allocation strategy according to market requirements and current resource situations, resource allocation and use are optimized after dynamic resource allocation is implemented, the strategy is adjusted in time by comparing and analyzing resource efficiency and cost effectiveness before and after optimization, and the market response speed and competitiveness of the enterprise are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise resource management, and in particular to an enterprise management optimization method and system based on big data. Background Art

[0002] The field of enterprise resource management (ERP) technology involves the use of integrated software applications to manage a company's internal and external resources, including finance, human resources, logistics, production, and services. ERP systems provide a platform for data flow and information sharing, enabling efficient collaboration across departments, optimizing business processes, reducing redundant operations, and improving decision-making efficiency. These systems support core business operations by integrating information across all key business processes, enhancing data analysis and reporting capabilities, and ensuring information consistency and traceability.

[0003] Big data enterprise management optimization methods. This approach aims to enhance the decision-making support capabilities of enterprise resource management systems by applying big data technologies and analytical tools. Applying big data can help companies capture more detailed business data, enabling more accurate demand forecasting, resource allocation, and risk management. By implementing this optimization approach, companies can better adapt to market changes, improve operational efficiency, and enhance competitiveness. This technology primarily enhances the quality of corporate decision-making and the speed of market response, and is a key component of digital transformation and intelligent management.

[0004] Existing enterprise resource management technologies are deficient in resource mobility analysis and real-time resource allocation strategies. Existing systems often lack in-depth insight into resource flows, resulting in resource allocation that fails to respond promptly to market changes, hindering enterprise flexibility and efficiency. The lack of detailed analysis of resource flow patterns can hinder rapid resource adjustments when market demand fluctuates dramatically, leading to missed market opportunities and unnecessary operating costs. Furthermore, while traditional ERP systems ensure information consistency and traceability, their responsiveness and adaptability are insufficient in rapidly changing market environments, hindering the full potential of data-driven decision-making. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose an enterprise management optimization method and system based on big data.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing enterprise management based on big data, comprising the following steps:

[0007] S1: Based on the enterprise resource planning within the enterprise, capture data on employee skills, project execution processes, and customer interactions, analyze the captured data, and perform multi-dimensional information classification processing to build a comprehensive enterprise resource data set;

[0008] S2: Based on the comprehensive enterprise resource dataset, a graph database is established, and data elements are processed and converted into network nodes and connecting edges. Employee information, project data, and skill matching of all nodes are identified, the flow and utilization efficiency of enterprise resources are analyzed, and an enterprise resource network framework is constructed.

[0009] S3: Based on the enterprise resource network framework, analyze the node association strength and edge weight, identify the flow pattern of key resources and skills within the enterprise, and extract the core resource flow path;

[0010] S4: Based on the core resource flow path, combined with market demand data and internal resource status, formulate resource allocation strategies, establish and implement resource allocation models, record and adjust resource allocation in real time, and generate dynamic resource allocation plans;

[0011] S5: Implement the dynamic resource allocation plan, optimize the allocation and use of resources, compare and analyze the performance changes and cost-effectiveness before and after the resource allocation optimization, provide improvement indicators and suggestions, and generate a resource optimization performance evaluation report.

[0012] As a further solution of the present invention, the enterprise resource comprehensive data set includes employee capability files, project execution records and customer interaction history, the enterprise resource network framework includes employee node information, project node information and skill matching connections, the core resource flow path includes key talent trends, important skill distribution and priority project connections, the resource allocation model includes deployment logic rules, market demand analysis and resource status monitoring, and the resource optimization efficiency evaluation report includes efficiency change index, cost-benefit comparison and optimization suggestion list.

[0013] As a further solution of the present invention, based on the enterprise resource planning within the enterprise, data on employee skills, project execution processes, and customer interactions are captured, the captured data is analyzed, and multi-dimensional information classification is performed to construct a comprehensive enterprise resource data set. The specific steps are:

[0014] S101: Collect employee skill data, project information, and customer interaction records within the enterprise, identify and classify the data, organize it according to a predetermined format, and generate a preliminary data set;

[0015] S102: performing data cleaning on the preliminary data set, identifying and eliminating erroneous and incomplete data records, formatting and encoding the identified data, and extracting key fields from the classified data to generate a preprocessed data set;

[0016] S103: Based on the pre-processed data set, core data elements are extracted, and employee skills, project execution, and customer interaction information are merged to obtain a comprehensive enterprise resource data set.

[0017] As a further solution of the present invention, based on the enterprise resource comprehensive data set, a graph database is established, and data elements are processed and converted into network nodes and connecting edges. Employee information, project data, and skill matching of all nodes are identified, and the flow and utilization efficiency of enterprise resources are analyzed. The specific steps for constructing an enterprise resource network framework are as follows:

[0018] S201: Using the enterprise resource comprehensive data set, constructing nodes and edges, configuring employee, project, and skill data elements as graph nodes, converting data content into graph edges, and generating an initialized graph database;

[0019] S202: Based on the initialized graph database, label each node with the employee's position, skill level, project status and category, and edge type, perform attribute assignment processing, and generate an identification classification data set;

[0020] S203: Utilizing the identified classification data set, analyzing the flow path of enterprise resources through graphics, calculating utilization efficiency, displaying the dynamic circulation and usage of resources within the enterprise, and constructing an enterprise resource network path.

[0021] As a further solution of the present invention, the utilization efficiency calculation formula is specifically:

[0022]

[0023] Among them, W represents utilization efficiency, r i Represents resource flow, u i represents resource utilization, c i represents the resource carrying capacity, p i Path efficiency adjustment factor.

[0024] As a further solution of the present invention, based on the enterprise resource network framework, the association strength of nodes and the weight of edges are analyzed to identify the flow pattern of key resources and skills within the enterprise. The specific steps of extracting the core resource flow path are as follows:

[0025] S301: Based on the enterprise resource network framework, by counting the number of connections of all nodes and the frequency of use of edges, analyzing the type and strength of interactions between nodes, assigning a weight to each connection edge, and calculating the association strength index for each node to form a node association and edge weight index table;

[0026] S302: Based on the node association and edge weight index table, the association strength index is compared with a preset threshold, key nodes and edges with high connectivity and high weight are identified, and the interaction patterns and frequencies of key nodes and edges within the enterprise are analyzed to obtain a key resource flow network;

[0027] S303: Using the key resource flow network, identify key interaction paths, highlight the core operations and key skill transfer in the paths, extract and mark the advantageous resource flow channels in the enterprise, and build a core resource flow framework.

[0028] As a further solution of the present invention, based on the core resource flow path, combined with market demand data and internal resource status, a resource allocation strategy is formulated, a resource allocation model is established and implemented, and resource allocation is recorded and adjusted in real time. The specific steps for generating a dynamic resource allocation plan are as follows:

[0029] S401: Based on the core resource flow path and market demand data, the core resource flow path is analyzed, resource demand differences and flow priorities are identified through data comparison, resource supply and demand are matched, and a resource and demand matching list is generated;

[0030] S402: Based on the resource and demand matching list, adjust and mobilize resource flows, set up monitoring points, track the implementation of resource allocation, and generate a preliminary resource allocation plan;

[0031] S403: Implement the preliminary resource allocation plan, dynamically adjust resource flow and usage, improve resource allocation according to market changes and resource consumption, and generate a dynamic resource allocation plan.

[0032] As a further solution of the present invention, the specific steps of implementing the dynamic resource allocation solution, optimizing resource allocation and usage, comparing and analyzing the performance changes and cost-effectiveness before and after resource allocation optimization, and providing improvement indicators and suggestions to generate a resource optimization performance evaluation report are as follows:

[0033] S501: Based on the dynamic resource allocation plan, adjust the key resource allocation amount of the high-demand department, record the time, department, and resource type of each resource allocation, and output detailed resource allocation records;

[0034] S502: Analyze resource usage before and after resource configuration using the detailed resource allocation record, calculate cost-effectiveness changes, and evaluate the actual impact of resource optimization to generate performance change comparison results;

[0035] S503: Based on the performance change comparison result, the effectiveness of resource optimization is revealed, the resource allocation speed is updated, the resource configuration is optimized, and a resource optimization performance evaluation report is obtained.

[0036] As a further solution of the present invention, the cost-effectiveness calculation formula is specifically:

[0037]

[0038] Among them, ΔE represents cost-effectiveness, U b and U a Represent the utilization rate before and after resource allocation, C b and C a Represents the total cost before and after optimization, and V represents the cost change.

[0039] Enterprise management optimization system based on big data, including:

[0040] The data classification and integration module captures data on employee skills, project execution processes, and customer interactions based on the company's internal enterprise resource planning, and performs multi-dimensional information classification and processing to build a comprehensive enterprise resource data set;

[0041] The resource network construction module establishes a graph database based on the enterprise resource comprehensive data set, processes data elements into network nodes and connection edges, analyzes the flow and utilization efficiency of enterprise resources, and constructs an enterprise resource network framework;

[0042] The critical path analysis module analyzes the node association strength and edge weight based on the enterprise resource network framework, identifies the flow pattern of key resources and skills within the enterprise, and extracts the core resource flow path;

[0043] The resource allocation strategy module formulates resource allocation strategies based on the core resource flow path, combined with market demand data and internal resource status, and establishes and implements resource allocation models;

[0044] The dynamic configuration recording module records and adjusts resource allocation in real time based on the resource configuration model to generate a dynamic resource configuration plan;

[0045] The optimization efficiency evaluation module implements the dynamic resource allocation plan, compares and analyzes the efficiency changes and cost-effectiveness before and after resource allocation optimization, provides improvement indicators and suggestions, and generates a resource optimization efficiency evaluation report.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, a method of integrating enterprise resources by establishing a graphic database and converting data elements into network nodes and connecting edges is used to improve resource flow and utilization efficiency. By identifying and analyzing the correlation strength of nodes and the weight of edges in the resource network, detailed flow path analysis enables enterprises to formulate scientific resource allocation strategies based on market demand and resource status. After implementing dynamic resource configuration, resource allocation and utilization are optimized. By comparing and analyzing the resource efficiency and cost-effectiveness before and after optimization, strategies can be adjusted in a timely manner, significantly improving the market response speed and competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 Schematic diagram of the steps of the present invention;

[0050] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0051] Figure 3 This is a flow chart of the steps of S2 of the present invention;

[0052] Figure 4 This is a flow chart of the steps of S3 of the present invention;

[0053] Figure 5 This is a flow chart of the steps of S4 of the present invention;

[0054] Figure 6 This is a flow chart of the steps of S5 of the present invention;

[0055] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0058] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] See also Figure 1 , a big data-based enterprise management optimization method, comprising the following steps:

[0062] S1: Based on the enterprise resource planning within the enterprise, capture data on employee skills, project execution processes, and customer interactions, analyze the captured data, and perform multi-dimensional information classification processing to build a comprehensive enterprise resource data set;

[0063] S2: Based on the comprehensive enterprise resource data set, a graph database is established. The data elements are converted into network nodes and connecting edges. The employee information, project data, and skill matching of all nodes are identified. The flow and utilization efficiency of enterprise resources are analyzed to build an enterprise resource network framework.

[0064] S3: Based on the enterprise resource network framework, analyze the node association strength and edge weight, identify the flow pattern of key resources and skills within the enterprise, and extract the core resource flow path;

[0065] S4: Based on the core resource flow path, combined with market demand data and internal resource status, formulate resource allocation strategies, establish and implement resource allocation models, record and adjust resource allocation in real time, and generate dynamic resource allocation plans;

[0066] S5: Implement dynamic resource allocation plans, optimize resource allocation and usage, compare and analyze performance changes and cost-effectiveness before and after resource allocation optimization, provide improvement indicators and suggestions, and generate resource optimization performance evaluation reports;

[0067] The comprehensive enterprise resource data set includes employee capability profiles, project execution records, and customer interaction history. The enterprise resource network framework includes employee node information, project node information, and skill matching connections. The core resource flow path includes key talent trends, important skill distribution, and priority project connections. The resource allocation model includes allocation logic rules, market demand analysis, and resource status monitoring. The resource optimization efficiency evaluation report includes an efficiency change index, cost-benefit comparison, and an optimization suggestion list.

[0068] See also Figure 2 , the specific steps of S1 are:

[0069] S101: Collect employee skill data, project information, and customer interaction records within the enterprise, identify and classify the data, organize it according to a predetermined format, and generate a preliminary data set;

[0070] Ensure that every piece of data is correctly identified and classified. The identification and classification of data involves the level division of employee skills, the stage marking of projects, and the identification of types of customer interactions. Once the data is identified and classified, it will be organized according to the predetermined format. The data organization process includes formatting the data to ensure the consistency of the data structure, so as to facilitate subsequent data processing and analysis. The organized data set becomes a preliminary data set, laying the foundation for the next step of data cleaning and processing.

[0071] S102: Clean the preliminary data set, identify and remove erroneous and incomplete data records, unify the format and encode the identified data, and extract key fields from the classified data to generate a preprocessed data set;

[0072] Identify and eliminate erroneous and incomplete data records according to the formula

[0073]

[0074] Calculate the quality of the dataset.

[0075] Where E represents the data quality assessment result, V j Represents a single data item, Represents the mean value of the data item, and m represents the total number of data items. This formula is based on the variance calculation formula and is used to evaluate the consistency and integrity of data in a data set. The smaller the variance, the higher the data quality. For example, a data set contains 5 data items, and the values ​​of each data item are 10, 10, 9, 11, and 10 respectively. The mean value is The mean is 10, and the squares of the differences obtained by subtracting the mean from the value of each data item are 0, 0, 1, 1, and 0 respectively. The sum of these values ​​is 2, and when divided by m-1 (i.e. 4), the variance obtained is 0.5, indicating that the data has high consistency.

[0076] S103: Based on the preprocessed data set, extract core data elements and merge employee skills, project execution, and customer interaction information to obtain a comprehensive enterprise resource data set;

[0077] Statistical analysis methods are used to extract features from the data. These features include key skills of employees, key stages of projects, and key behaviors in customer interactions. These features are intelligently merged and algorithms are used to match and integrate information from different data sources to ensure data continuity and relevance. The resulting comprehensive enterprise resource data set will directly support the company's decision-making and strategy deployment, providing comprehensive data support for the company, enabling it to better understand its own resource allocation and business operation status.

[0078] See also Figure 3 , the specific steps of S2 are:

[0079] S201: Using the enterprise resource comprehensive data set, construct nodes and edges, and configure employee, project, and skill data elements as graph nodes, converting data content into graph edges to generate an initialized graph database;

[0080] The specific technical details of mapping data elements to graph nodes, such as how to select the data type of the node and how to ensure the integrity and accuracy of the node data, and the steps of converting data content into graph edges, including determining the type of edge, such as associated edge or direct edge, and setting the edge weight. These technical operations must be performed strictly in accordance with the logical relationship of the data and actual application requirements. The generated initialized graph database provides the basic data architecture for subsequent operations, which provides a strong data support for the in-depth analysis and management of enterprise resources.

[0081] S202: Based on the initialized graph database, label each node with the employee's position, skill level, project status and category, and edge type, perform attribute assignment processing, and generate an identification classification dataset;

[0082] This involves labeling the detailed attributes of each node, such as the employee's position, skill level, project status and category, and edge type. This requires detailed analysis and identification of the specific characteristics of each node and edge. The accuracy of attribute assignment directly affects the functionality and effectiveness of the graph database. Through this step, the generated identification classification dataset can clearly display the configuration of enterprise resources and the relationship between various resource elements, which is of great significance for further data application and resource management.

[0083] S203: Using the identification classification data set, the flow path of enterprise resources is analyzed through graphics, the utilization efficiency is calculated, the dynamic circulation and usage of resources within the enterprise are displayed, and the enterprise resource network path is constructed;

[0084]

[0085] Source carrying capacity, p i Path efficiency adjustment factor.

[0086] Parameter r i It represents the flow of a certain resource in tons, and its value is collected in real time by the resource flow monitoring system. i = 200 tons, which represents the total flow of the resource within a certain period of time.

[0087] Parameter u i Indicates the utilization rate of resources on a specific node. This value is collected by the resource usage monitoring system and is expressed as a percentage. For example, if u i = 0.75 or 75%, indicating that 75% of the resources are consumed by the current node.

[0088] Parameter c i Indicates the maximum carrying capacity of resources on the circulation path, in tons, and its value is determined by the resource management system. For example, c i =500 tons, indicating the maximum amount of resources that the path can carry.

[0089] Parameter p i It represents the flow efficiency adjustment coefficient of the path, which is obtained by path complexity analysis. This value reflects the path complexity or obstacles. For example, if p i =0.8, it means that the path condition causes a 20% reduction in efficiency.

[0090] Enter the numerical value to calculate the formula:

[0091] The calculation process is as follows:

[0092] Calculate the product of resource flow and utilization rate, 200 × 0.75 = 150.

[0093] Calculate the quotient of the path capacity and the efficiency adjustment factor, 500 ÷ 0.8 = 625.

[0094]

[0095] This indicates that the efficiency of resource usage per unit exceeds the efficiency of the path capacity per unit, revealing potential for optimization and improvement. This value is valuable for evaluating and improving the dynamic flow and use of resources within an enterprise.

[0096] See also Figure 4 , the specific steps of S3 are:

[0097] S301: Based on the enterprise resource network framework, by counting the number of connections of all nodes and the frequency of use of edges, the type and strength of interactions between nodes are analyzed, a weight is assigned to each connection edge, and an association strength index is calculated for each node to form a node association and edge weight index table;

[0098] It involves the use of data analysis tools and techniques to quantify the type and intensity of interactions between nodes, including calculating the in-degree and out-degree of each node, as well as the traffic and frequency of each edge. Through these statistical data, weights can be assigned to each connecting edge. This process requires precise data collection and processing technology to ensure the accuracy and fairness of weight distribution. At the same time, the calculation of the association strength index of each node requires a comprehensive consideration of the node's connectivity and the edge weight. The resulting node association and edge weight index table provides the enterprise with a clear view of the relative importance of different nodes and edges in the enterprise resource network.

[0099] S302: Based on the node association and edge weight index table, the association strength index is compared with the preset threshold, key nodes and edges with high connectivity and high weight are identified, and the interaction patterns and frequencies of key nodes and edges within the enterprise are analyzed to obtain a key resource flow network;

[0100] Compare the correlation strength index with the preset threshold and use the formula

[0101]

[0102] Calculate the identification criteria of key nodes.

[0103] In the formula, S represents the recognition threshold of key nodes, y l represents the weight of node l, z l represents the association strength index of node 1, and q represents the total number of nodes. This formula uses a weighted average to determine which nodes have association strengths exceeding a preset threshold, becoming key nodes. For example, if there are five nodes with weights of 2, 3, 1, 5, and 4, and association strengths of 0.5, 0.8, 0.3, 0.9, and 0.7, respectively, the products of the weights and association strengths are first calculated, yielding 1, 2.4, 0.3, 4.5, and 2.8. The sum of these products is 11, which is divided by the total number of nodes, 5, to obtain an average of 2.2, which serves as the threshold for identifying key nodes.

[0104] S303: Use the key resource flow network to identify key interaction paths, highlight the core operations and key skills transfer in the paths, extract and mark the advantageous resource flow channels in the enterprise, and build a core resource flow framework;

[0105] Detailed analysis of the delivery methods of core operations and key skills, such as determining the main channels and key nodes of resource flow through data flow analysis. Through this analysis, the advantageous resource flow channels in the enterprise can be marked and extracted. The constructed core resource flow framework not only reflects the dynamic utilization of enterprise resources, but also reveals how advantageous resources flow efficiently within the enterprise. This is of great significance for optimizing resource allocation and improving operational efficiency. Through these operations, enterprises can better manage and utilize their key resources to promote business growth and enhance competitiveness.

[0106] See also Figure 5 , the specific steps of S4 are:

[0107] S401: Based on the core resource flow path and market demand data, analyze the core resource flow path, identify resource demand differences and flow priorities through data comparison, match resource supply and demand, and generate a resource and demand matching list;

[0108] Data analysis technology is used to identify differences in resource demand and flow priorities. This includes comparing resource demand in different time periods and regions, and analyzing the correlation and matching between resource supply and demand. This process ensures the effective matching of resource supply and demand status. Through these comparisons and analyses, the generated resource and demand matching list will list in detail the specific matching points between the supply of various resources and market demand. This matching list provides enterprises with a clear resource allocation guide, helping enterprises adjust their resource allocation strategies to better meet market demand.

[0109] S402: Based on the resource and demand matching list, adjust and mobilize resource flows, set up monitoring points, track the implementation of resource allocation, and generate a preliminary resource allocation plan;

[0110] This includes a detailed analysis of the current state of resource flows and the instructions in the demand matching list in order to optimize resource allocation, while setting up monitoring points. These monitoring points are designed to track the implementation of resource allocation in real time to ensure the efficiency and effectiveness of resource allocation. Through the data collected by these monitoring points, the enterprise can generate a preliminary resource allocation plan, which will reflect the initial effect of resource allocation and provide basic data for adjustment.

[0111] S403: Implement the preliminary resource allocation plan, dynamically adjust resource flow and usage, improve resource allocation based on market changes and resource consumption, and generate a dynamic resource allocation plan;

[0112] Dynamically adjust according to market changes and resource consumption, according to the formula

[0113]

[0114] Compute the adaptability index of resource configuration.

[0115] In the formula, B represents the resource allocation adaptability index, g m represents the demand for the mth resource, h m Represents the supply of the mth resource, and t represents the number of resource types. This formula calculates the degree of match between the demand and supply of various resources using a weighted average method, where the weights are determined by the supply of the resources. For example, if there are three resources with supply of 100, 150, and 200 units, and demand of 90, 140, and 210 units, respectively, the calculation process is as follows: First, the product of the demand and supply of each resource is calculated, resulting in 9,000, 21,000, and 42,000. The sum of these products is 72,000, which is then divided by the total supply of the resource, 450, to obtain an adaptability index of 160. This index reflects the overall degree of adaptability of resource allocation to the market.

[0116] See also Figure 6 , the specific steps of S5 are:

[0117] S501: Based on the dynamic resource allocation plan, adjust the key resource allocation amount of the high-demand department, record the time, department, and resource type of each resource allocation, and output detailed resource allocation records;

[0118] This includes accurately recording the time, department, and resource type of each resource allocation. These records provide enterprises with detailed data to support the formulation of resource management decisions. Through these detailed resource allocation records, enterprises can ensure that resources are optimally allocated to the departments that need them most, thereby improving overall operational efficiency. The output of detailed resource allocation records not only helps management monitor the status of resource allocation, but also provides a basis for future resource allocation strategies to ensure optimal resource utilization.

[0119] S502: Analyze resource usage before and after resource allocation using detailed resource allocation records, calculate cost-effectiveness changes, and evaluate the actual impact of resource optimization to generate performance change comparison results;

[0120]

[0121] C a Represents the total cost before and after optimization, and V represents the cost change.

[0122] Parameter U b and U a They are the utilization rates before and after resource allocation, which are obtained through real-time monitoring of the resource management system. Assume that U b =50% (0.50) and U a =70% (0.70), these values ​​reflect the changes in utilization before and after resource optimization measures.

[0123] Parameter C b and C a is the total cost before and after optimization, obtained through financial system records. Let C b = 100,000 units of currency and C a = 80,000 units of currency, representing the cost savings from resource optimization.

[0124] Parameter V is the cost change, calculated as V = C b -C a =100,000-80,000=20,000 units of currency, representing the net cost savings after the optimization measures are implemented.

[0125]

[0126] The absolute value of the difference in usage rate is calculated, |0.70-0.50|=0.20.

[0127] Calculate the absolute value of the cost difference and raise it to the power of 2 / 3, |100,000-20,000|=80,000,80,000 2 / 3 ≈40,000.

[0128] Final calculation

[0129] The results show that the efficiency is improved by about 1.118 times after resource optimization, which means that the resource utilization efficiency per unit cost is improved by 11.8%, reflecting the actual impact of resource allocation optimization measures and providing important decision-making support information.

[0130] S503: Based on the performance change comparison results, the effectiveness of resource optimization is revealed, the resource allocation speed is updated, the resource configuration is optimized, and a resource optimization performance evaluation report is obtained;

[0131] This includes updating resource allocation speed and optimizing resource configuration measures. Through these adjustments, a resource optimization efficiency evaluation report is obtained. This report records in detail the comparison before and after resource optimization, including resource utilization, cost-effectiveness changes and the improvement of resource allocation speed. Through these data, enterprises can clearly understand the specific results of resource optimization, provide data support for further resource management decisions, and ensure that the enterprise's resource allocation remains optimized under changing market conditions.

[0132] See also Figure 7 , an enterprise management optimization system based on big data, including:

[0133] The data classification and integration module captures data on employee skills, project execution processes, and customer interactions based on the company's internal enterprise resource planning, and performs multi-dimensional information classification and processing to build a comprehensive enterprise resource data set;

[0134] The resource network construction module builds a graph database based on the comprehensive enterprise resource data set, processes data elements and converts them into network nodes and connecting edges, analyzes the flow and utilization efficiency of enterprise resources, and constructs the enterprise resource network framework;

[0135] The critical path analysis module analyzes the node association strength and edge weight based on the enterprise resource network framework, identifies the flow pattern of key resources and skills within the enterprise, and extracts the core resource flow path;

[0136] The resource allocation strategy module formulates resource allocation strategies based on the core resource flow path, combined with market demand data and internal resource status, and establishes and implements resource allocation models;

[0137] The dynamic configuration recording module records and adjusts resource allocation in real time based on the resource configuration model and generates a dynamic resource configuration plan;

[0138] The optimization efficiency evaluation module implements dynamic resource allocation plans, compares and analyzes the efficiency changes and cost-effectiveness before and after resource allocation optimization, provides improvement indicators and suggestions, and generates resource optimization efficiency evaluation reports.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for optimizing enterprise management based on big data, characterized in that: The following steps are involved: Based on the enterprise resource planning within the enterprise, data on employee skills, project execution processes, and customer interactions are captured, analyzed, and multi-dimensionally categorized to build a comprehensive enterprise resource data set. Based on the comprehensive enterprise resource data set, a graph database is established, and data elements are processed and converted into network nodes and connecting edges. Employee information, project data, and skill matching of all nodes are identified, the flow and utilization efficiency of enterprise resources are analyzed, and an enterprise resource network framework is constructed. Based on the enterprise resource network framework, the association strength of nodes and the weight of edges are analyzed to identify the flow pattern of key resources and skills within the enterprise and extract the core resource flow path; Based on the core resource flow path, combined with market demand data and internal resource status, formulate resource allocation strategies, establish and implement resource allocation models, record and adjust resource allocation in real time, and generate dynamic resource allocation plans; Implement the dynamic resource allocation plan, optimize the allocation and use of resources, compare and analyze the performance changes and cost-effectiveness before and after resource allocation optimization, provide improvement indicators and suggestions, and generate a resource optimization performance evaluation report.

2. The enterprise management optimization method based on big data according to claim 1, characterized in that: The enterprise resource comprehensive data set includes employee capability files, project execution records and customer interaction history; the enterprise resource network framework includes employee node information, project node information and skill matching connections; the core resource flow path includes key talent trends, important skill distribution and priority project connections; the resource allocation model includes deployment logic rules, market demand analysis and resource status monitoring; the resource optimization efficiency evaluation report includes efficiency change index, cost-benefit comparison and optimization suggestion list.

3. The enterprise management optimization method based on big data according to claim 1, characterized in that: Based on the enterprise resource planning within the enterprise, data on employee skills, project execution processes, and customer interactions is captured. The captured data is analyzed and categorized into multiple dimensions to construct a comprehensive enterprise resource data set. The specific steps are as follows: Collect employee skill data, project information, and customer interaction records from across the enterprise in a unified manner, identify and categorize the data, organize it in a predetermined format, and generate a preliminary data set. Performing data cleaning on the preliminary data set, identifying and eliminating erroneous and incomplete data records, formatting and encoding the identified data, and extracting key fields from the classified data to generate a preprocessed data set; Based on the preprocessed data set, core data elements are extracted, and employee skills, project execution, and customer interaction information are merged to obtain a comprehensive enterprise resource data set.

4. The enterprise management optimization method based on big data according to claim 1, characterized in that: Based on the comprehensive enterprise resource dataset, a graph database is established, and data elements are processed and converted into network nodes and connecting edges. Employee information, project data, and skill matching of all nodes are identified, and the flow and utilization efficiency of enterprise resources are analyzed. The specific steps for building an enterprise resource network framework are as follows: Using the enterprise resource comprehensive data set, constructing nodes and edges, configuring employee, project, and skill data elements as graph nodes, converting data content into graph edges, and generating an initialized graph database; Based on the initialized graph database, the employee's position, skill level, project status and category, and edge type of each node are labeled, attribute assignment processing is performed, and an identification classification data set is generated; By using the identified classification data set, the flow path of enterprise resources is analyzed through graphics, the utilization efficiency is calculated, the dynamic circulation and use of resources within the enterprise are displayed, and the enterprise resource network path is constructed.

5. The enterprise management optimization method based on big data according to claim 1, characterized in that: The specific steps are: the utilization efficiency calculation formula is specifically: Among them, W represents utilization efficiency, r i Represents resource flow, u i represents resource utilization, c i represents the resource carrying capacity, p i Path efficiency adjustment factor.

6. The enterprise management optimization method based on big data according to claim 1, characterized in that: Based on the enterprise resource network framework, we analyze the node association strength and edge weight, identify the flow pattern of key resources and skills within the enterprise, and extract the core resource flow path in the following specific steps: Based on the enterprise resource network framework, by counting the number of connections of all nodes and the frequency of use of edges, the type and intensity of interactions between nodes are analyzed, a weight is assigned to each connection edge, and an association strength index is calculated for each node to form a node association and edge weight index table; Based on the node association and edge weight index table, the association strength index is compared with the preset threshold, key nodes and edges with high connectivity and high weight are identified, and the interaction patterns and frequencies of key nodes and edges within the enterprise are analyzed to obtain a key resource flow network; Using the key resource flow network, we identify key interaction paths, highlight the core operations and key skill transfer in the paths, extract and mark the advantageous resource flow channels in the enterprise, and build a core resource flow framework.

7. The enterprise management optimization method based on big data according to claim 1, characterized in that: Based on the core resource flow path, combined with market demand data and internal resource status, a resource allocation strategy is formulated, a resource allocation model is established and implemented, and resource allocation is recorded and adjusted in real time. The specific steps for generating a dynamic resource allocation plan are as follows: Based on the core resource flow paths and market demand data, analyze the core resource flow paths, identify resource demand differences and flow priorities through data comparison, match resource supply and demand conditions, and generate a resource and demand matching list; Based on the resource and demand matching list, adjust and mobilize resource flows, set up monitoring points, track the implementation of resource allocation, and generate a preliminary resource allocation plan; Implement the preliminary resource allocation plan, dynamically adjust resource flow and usage, improve resource allocation based on market changes and resource consumption, and generate a dynamic resource allocation plan.

8. The enterprise management optimization method based on big data according to claim 1, characterized in that: The specific steps for implementing the dynamic resource allocation plan, optimizing resource allocation and usage, comparing and analyzing the performance changes and cost-effectiveness before and after resource allocation optimization, and providing improvement indicators and suggestions to generate a resource optimization performance evaluation report are as follows: Based on the dynamic resource allocation plan, adjust the key resource allocation amount of high-demand departments, record the time, department, and resource type of each resource allocation, and output detailed resource allocation records; Utilizing the detailed resource allocation records, analyzing resource utilization rates before and after resource configuration, calculating cost-effectiveness changes, and evaluating the actual impact of resource optimization to generate performance change comparison results; Based on the performance change comparison results, the effectiveness of resource optimization is revealed, the resource allocation speed is updated, the resource configuration is optimized, and a resource optimization performance evaluation report is obtained.

9. The enterprise management optimization method based on big data according to claim 1, characterized in that: The specific steps are: the cost-effectiveness calculation formula is specifically: Among them, ΔE represents cost-effectiveness, U b and U a Represent the utilization rate before and after resource allocation, C b and C a Represents the total cost before and after optimization, and V represents the cost change.

10. An enterprise management optimization system based on big data, characterized in that: According to the enterprise management optimization method based on big data according to any one of claims 1 to 9, the system comprises: The data classification and integration module captures data on employee skills, project execution processes, and customer interactions based on the company's internal enterprise resource planning, and performs multi-dimensional information classification and processing to build a comprehensive enterprise resource data set; The resource network construction module establishes a graph database based on the enterprise resource comprehensive data set, processes data elements into network nodes and connection edges, analyzes the flow and utilization efficiency of enterprise resources, and constructs an enterprise resource network framework; The critical path analysis module analyzes the node association strength and edge weight based on the enterprise resource network framework, identifies the flow pattern of key resources and skills within the enterprise, and extracts the core resource flow path; The resource allocation strategy module formulates resource allocation strategies based on the core resource flow path, combined with market demand data and internal resource status, and establishes and implements resource allocation models; The dynamic configuration recording module records and adjusts resource allocation in real time based on the resource configuration model to generate a dynamic resource configuration plan; The optimization efficiency evaluation module implements the dynamic resource allocation plan, compares and analyzes the efficiency changes and cost-effectiveness before and after resource allocation optimization, provides improvement indicators and suggestions, and generates a resource optimization efficiency evaluation report.

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