Enterprise internal affairs management system based on cloud-based intelligent collaborative processing

Abstract

The present invention relates to an enterprise internal affairs management system based on cloud-based intelligent collaborative processing, which collects business information data samples processed in large-scale historical periods on the cloud; divides the business information data samples into several clusters according to the business processing situation of the business information, and uses the response time and processing time to characterize the importance of the business, and uses the K-means clustering method to determine the importance level of the business types contained in each cluster; determines the correlation between different business types by using the dependency relationship between different business types; and determines the processing priority of the cloud-based pending business by using the importance level of different business types and the correlation between them. The present invention solves the problem that the prior art is difficult to coordinate processing according to the importance of the business and the correlation between the businesses, resulting in low management efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud computing and relates to an enterprise internal affairs management system based on cloud-based intelligent collaborative processing. Background Art

[0002] Internal management of an enterprise involves many aspects, such as finance, human resources, project management, customer relations, etc., while traditional management methods often rely on manual operations and decentralized systems, which are inefficient and prone to errors. With the development of cloud computing technology, enterprises are increasingly migrating their businesses and data to the cloud. Cloud computing provides efficient resource management, elastic expansion capabilities and lower costs, allowing enterprises to respond more flexibly to changes in business needs.

[0003] However, most existing technologies deploy corporate internal affairs to the cloud for processing in an irregular manner, and are rarely able to coordinate processing based on the importance of the business and the correlation between businesses, resulting in low management efficiency. Summary of the invention

[0004] The present invention provides an enterprise internal affairs management system based on cloud-based intelligent collaborative processing, which aims to use large-scale business information from historical periods to establish a big data model to quantify the importance of different businesses and the correlation between businesses, thereby formulating relevant business processing rules to solve the problem of low management efficiency.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The present application provides an enterprise internal affairs management system based on cloud intelligent collaborative processing, including a business upload module, a business analysis module and a business processing module, wherein the business upload module, the business analysis module and the business processing module are communicatively connected, wherein:

[0007] The business uploading module is used to upload the pending businesses in batches to the cloud;

[0008] The business analysis module is used to obtain business information of the business to be processed, input the business information into a preset business analysis model, and output the importance level of each business to be processed and the correlation between the businesses to be processed; the business information includes the business type and business processing status;

[0009] The preset business analysis model includes the following construction steps:

[0010] S1. Collect business information data samples that have been processed in large-scale historical periods on the cloud;

[0011] S2. According to the business processing situation of the business information, the K-means clustering method is used to divide the data samples into several clusters, and the importance level of the business types contained in each cluster is determined;

[0012] S3. Determine the correlation between different business types by using the dependency relationship between different business types;

[0013] The business processing module is used to sort the processing priorities of the pending businesses according to the importance levels of the pending businesses and the relevance between the pending businesses, and process the pending businesses in sequence.

[0014] Furthermore, the business types include financial management, human resource management, project management, customer relationship management, operations management, information technology management, strategic management, administrative management, corporate culture building, and public relations and brand management.

[0015] Furthermore, the business processing status includes response time and processing time.

[0016] Furthermore, the K-means clustering method comprises the following steps:

[0017] S21, normalization processing: normalizing the business processing data variables;

[0018] S22, initializing cluster centers: randomly selecting several data objects in the data space as initial cluster centers;

[0019] S23, initializing clusters: calculating the Euclidean distance between all data objects and the initial cluster center, and dividing each data object into the initial cluster center with the smallest Euclidean distance to form an initial cluster;

[0020] S24, updating the cluster center: calculating the average value of the data objects of each initial cluster, and taking the average value as the new cluster center;

[0021] S25. Determine the final cluster: repeatedly calculate the Euclidean distance between all data objects and the new cluster center, divide each data object into the new cluster center with the smallest Euclidean distance to form a new cluster; calculate the average value of each new cluster as the new cluster center again until the new cluster center no longer changes, determine the final cluster, and determine the business types included in each final cluster and their corresponding importance levels.

[0022] Furthermore, in step S21, the normalization process is calculated using the following formula:

[0023]

[0024] Where Yi is the normalized data sample of the ith business processing situation; K i is the data sample of the i-th business processing situation; K max Indicates the maximum value of all business processing data samples; K min Indicates the minimum value of all business processing data samples.

[0025] Furthermore, in step S23, the Euclidean distance is calculated by the following formula:

[0026]

[0027] In the formula, d(x,C i ) represents the Euclidean distance function; x is the normalized data object; C i represents the i-th cluster center; n is the number of samples in the data set; x j Represents the data object of the jth variable in the data set; C ij represents the i-th cluster center of the j-th variable.

[0028] Further, in step S25, the business types included in each final cluster and their corresponding importance levels are determined by comparing the numerical values ​​of the business processing conditions of each final cluster; sorting the final clusters according to the numerical values ​​from small to large; the smaller the numerical value, the higher the importance level of the business type included in the final cluster.

[0029] Furthermore, in step S3, the dependencies between the different business types include time dependency, resource dependency and process rules.

[0030] Furthermore, the processing priorities of the pending services are sorted, specifically including the following steps:

[0031] Sort the processing priorities from high to low according to the importance of the business to be processed. The higher the importance, the higher the processing priority.

[0032] According to the correlation between the pending businesses, two or more associated pending businesses are ranked together at the highest processing priority.

[0033] Furthermore, the business processing module also visualizes the business process and displays the dependency relationship and processing priority between the businesses to be processed.

[0034] Beneficial effects of the present invention:

[0035] By collecting business information data samples processed in large historical periods on the cloud; according to the business processing of the business information, the business processing situation uses response time and processing time to characterize the importance of the business, and uses the K-means clustering method to divide the business information data samples into several clusters, and determine the importance level of the business types contained in each cluster; using the dependency between different business types, determine the correlation between different business types; using the importance level of different business types and the correlation between them, determine the processing priority of the cloud-to-be-processed business. The present invention solves the problem that the prior art is difficult to coordinate processing according to the importance of the business and the correlation between the businesses, resulting in low management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0037] Figure 1 This is a structural diagram of an enterprise internal affairs management system based on cloud-based intelligent collaborative processing in the present invention. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0039] See also Figure 1 The present application provides an enterprise internal affairs management system based on cloud intelligent collaborative processing, including a business upload module, a business analysis module and a business processing module, wherein the business upload module, the business analysis module and the business processing module are communicatively connected, wherein:

[0040] The business uploading module is used to upload the pending businesses in batches to the cloud;

[0041] The business analysis module is used to obtain business information of the business to be processed, input the business information into a preset business analysis model, and output the importance level of each business to be processed and the correlation between the businesses to be processed; the business information includes the business type and business processing status;

[0042] In this example, the internal affairs of an enterprise involve a wide range of daily operations and management activities to ensure the effective operation and long-term development of the enterprise. The main types of internal business include:

[0043] 1) Financial management:

[0044] Accounting: recording and reporting financial transactions and preparing financial statements;

[0045] Budget management: prepare and monitor corporate budgets and control costs;

[0046] Fund management: managing cash flow, making financing and investment decisions;

[0047] Tax administration: handling tax returns and tax planning.

[0048] 2) Human Resource Management:

[0049] Recruitment and selection: Develop recruitment plans, conduct interviews and selections;

[0050] Training and Development: Providing employee training and career development opportunities;

[0051] Performance management: evaluate employee performance and develop incentive mechanisms;

[0052] Compensation and Benefits Management: Design and manage employee compensation, benefits and bonus systems.

[0053] 3) Project Management:

[0054] Project planning: Develop project plans and determine project goals and milestones;

[0055] Resource scheduling: Allocate and manage project resources, including human, material and financial resources;

[0056] Progress control: monitor project progress and ensure on-time completion;

[0057] Risk Management: Identify and address project risks.

[0058] 4) Customer Relationship Management:

[0059] Customer service: provide pre-sales, sales and after-sales services to solve customer problems;

[0060] Customer feedback: Collect and analyze customer feedback to improve products and services;

[0061] Sales management: develop and implement sales strategies and manage customer relationships;

[0062] Market research: Understand customer needs and market trends, and adjust business strategies.

[0063] 5) Operation Management:

[0064] Production management: plan and control the production process to ensure product quality and production efficiency;

[0065] Supply Chain Management: managing procurement, inventory, logistics, and supplier relationships;

[0066] Quality management: implement quality control and quality assurance measures to ensure the quality of products and services; Equipment maintenance: ensure the normal operation of production equipment and facilities.

[0067] 6) Information Technology Management:

[0068] System maintenance: Maintain enterprise information systems and network infrastructure;

[0069] Data management: manage and protect enterprise data to ensure data security;

[0070] Software Development: Develop and maintain enterprise application software and systems;

[0071] Technical Support: Provide technical support and troubleshooting services.

[0072] 7) Strategic Management:

[0073] Strategic planning: formulate the company's long-term development strategy and goals;

[0074] Market analysis: Conduct market research and competition analysis to support strategic decision-making;

[0075] Business development: exploring and developing new markets and business opportunities;

[0076] Risk Management: Identify and respond to strategic enterprise risks.

[0077] 8) Administration:

[0078] Document management: manage corporate documents and records to ensure information flow;

[0079] Office facilities: managing office space and equipment, and providing logistical support;

[0080] Safety management: ensuring internal enterprise security and employee safety;

[0081] Legal affairs: Handle corporate legal affairs to ensure legality and compliance.

[0082] 9) Corporate culture construction:

[0083] Cultural communication: promote corporate values ​​and culture and enhance employees’ sense of identity;

[0084] Employee activities: Organize various employee activities to enhance team cohesion;

[0085] Communication mechanism: Establish effective internal communication channels to promote information exchange.

[0086] 10) Public relations and brand management:

[0087] Brand promotion: enhance corporate brand awareness and image;

[0088] Media relations: Establish good relations with the media and release corporate news and information;

[0089] Social responsibility: fulfill corporate social responsibility and carry out public welfare activities.

[0090] Therefore, further, the business types include financial management, human resource management, project management, customer relationship management, operation management, information technology management, strategic management, administrative management, corporate culture building, and public relations and brand management.

[0091] However, due to the large number of business types uploaded to the cloud, operators often need to determine the priority order in the process of collaborative business processing, and processing based on subjective business priority order often leads to the neglect of important businesses due to inconsistent rules. To this end, this embodiment analyzes the business processing situation in historical periods to determine the importance of different businesses and make good arrangements for processing priorities.

[0092] Furthermore, the business processing status includes response time and processing time.

[0093] In this embodiment, response time and processing time are two key indicators for evaluating the importance of business processing. Response time refers to the time it takes for a business to be uploaded to the cloud and actually start processing. A shorter response time means a higher priority for business processing, which shows the company's sensitivity to the urgency and priority of the business. This indicator reflects the ability of a system or team to respond quickly after receiving business needs, and is usually applicable to high-value customers or critical businesses, which often require priority attention to avoid long customer waiting times or business delays. Optimizing response time can improve the company's service level and enhance customer experience, thereby improving customer satisfaction and business competitiveness.

[0094] On the other hand, processing time refers to the time required from the start of business processing to completion. The length of processing time directly affects the processing efficiency of the business. A shorter processing time indicates that the business processing process is efficient and can quickly solve problems or complete tasks. Efficient processing time is usually associated with the company's workflow optimization, effective resource utilization, and strong execution capabilities. Shortening the processing time can not only improve the overall efficiency of the business, but also reduce resource usage and improve the company's operational capabilities. This is also crucial to improving customer satisfaction and enhancing business competitiveness.

[0095] In summary, response time and processing time together determine the priority and importance of the business. Short response time indicates the urgency and priority of business processing, while short processing time reflects the efficiency and execution capability of business processing. By effectively managing and optimizing these two indicators, enterprises can improve the overall efficiency of business processing, reduce customer waiting time, enhance customer satisfaction, and improve the operational efficiency of the enterprise. Ultimately, these improvements will help enhance the competitiveness of enterprises in the market and ensure the smooth operation of business processes.

[0096] The preset business analysis model includes the following construction steps:

[0097] S1. Collect business information data samples that have been processed in large-scale historical periods on the cloud;

[0098] S2. According to the business processing situation of the business information, the K-means clustering method is used to divide the data samples into several clusters, and the importance level of the business types contained in each cluster is determined;

[0099] In this embodiment, K-means clustering is a widely used unsupervised learning algorithm for dividing data samples into several non-overlapping clusters, with the purpose of maximizing the similarity of data points within the same cluster and maximizing the difference of data points between different clusters. The core of the algorithm is to minimize the total distance from the data points in each cluster to the cluster center, thereby achieving effective data grouping and pattern recognition. Its workflow includes: first selecting the number of clusters, then randomly initializing the cluster centers, then assigning the data points to the cluster represented by the nearest cluster center, then updating the position of the cluster center (usually the mean of all data points in the cluster), and finally repeating the assignment and update steps until the position of the cluster center is stable. The K-means clustering method plays a vital role in the analysis of large-scale historical business processing data. It helps us identify and understand different types of business processing situations by dividing data samples into several clusters. Specifically, K-means clustering helps to simplify data analysis. By dividing data into several clusters, it is easier to identify the pattern of business processing. For example, some clusters may show the characteristics of short processing time and high success rate, which may represent a situation with high business processing efficiency. Other clusters may show longer processing times or higher failure rates, which may indicate problems in the business process. Such grouping not only simplifies the complexity of the data, but also allows us to analyze and compare more intuitively. K-means clustering can also be used to optimize resource allocation and business processes. By analyzing the characteristics of different clusters, we can understand which business types or processing situations are the most critical. For high-priority clusters, such as those with high processing efficiency or those that are critical to business goals, more resources and attention can be allocated on a priority basis. For low-priority clusters, in-depth analysis can be performed to identify bottlenecks, thereby improving related business processes and improving overall efficiency.

[0100] Furthermore, the K-means clustering method comprises the following steps:

[0101] S21, normalization processing: normalizing the business processing data variables;

[0102] In this embodiment, the purpose of normalizing the business processing data variables is to unify the data ranges between different features so that they have the same scale. This can avoid the problem of weight imbalance between different features due to different data ranges, and ensure that each feature can be properly reflected in the clustering process. Through normalization, the dimensional influence between data can be eliminated, making the data easier to compare and analyze, thereby improving the accuracy and efficiency of clustering. In addition, normalization can also make the clustering algorithm more stable and converge faster, which is conducive to obtaining more accurate and reliable clustering results. Therefore, before performing cluster analysis on business processing data variables, the data is usually normalized to achieve better clustering effect.

[0103] S22, initializing cluster centers: randomly selecting several data objects in the data space as initial cluster centers;

[0104] In this embodiment, when initializing the cluster centers, a common practice is to randomly select several data objects in the data space as the initial cluster centers. The reason for this is that by randomly selecting the initial cluster centers, it can be ensured that the algorithm will not be affected by the position of a specific initial cluster center, thereby avoiding falling into a local optimal solution. By randomly selecting data objects in the data space as the initial cluster centers, the algorithm can have a certain degree of randomness, which helps to explore different aspects of the data, and can increase the robustness and reliability of the algorithm, and improve the quality of the final clustering results. Randomly selecting data objects as initial cluster centers is a common and effective initialization method.

[0105] S23, initializing clusters: calculating the Euclidean distance between all data objects and the initial cluster center, and dividing each data object into the initial cluster center with the smallest Euclidean distance to form an initial cluster;

[0106] S24, updating the cluster center: calculating the average value of the data objects of each initial cluster, and taking the average value as the new cluster center;

[0107] S25. Determine the final cluster: repeatedly calculate the Euclidean distance between all data objects and the new cluster center, divide each data object into the new cluster center with the smallest Euclidean distance to form a new cluster; calculate the average value of each new cluster as the new cluster center again until the new cluster center no longer changes, determine the final cluster, and determine the business types included in each final cluster and their corresponding importance levels.

[0108] Furthermore, in step S21, the normalization process is calculated using the following formula:

[0109]

[0110] Where Y i is the normalized data sample of the ith business processing situation; K i is the data sample of the i-th business processing situation; K max Indicates the maximum value of all business processing data samples; K min Indicates the minimum value of all business processing data samples.

[0111] Furthermore, in step S23, the Euclidean distance is calculated by the following formula:

[0112]

[0113] In the formula, d(x,C i ) represents the Euclidean distance function; x is the normalized data object; C i represents the i-th cluster center; n is the number of samples in the data set; x j Represents the data object of the jth variable in the data set; C ij represents the i-th cluster center of the j-th variable.

[0114] Further, in step S25, the business types included in each final cluster and their corresponding importance levels are determined by comparing the numerical values ​​of the business processing conditions of each final cluster; sorting the final clusters according to the numerical values ​​from small to large; the smaller the numerical value, the higher the importance level of the business type included in the final cluster.

[0115] S3. Determine the correlation between different business types by using the dependency relationship between different business types;

[0116] In this embodiment, relevance refers to the logical dependency between different business types, that is, after completing a business, it is necessary to immediately process the next business related to it to ensure the smooth progress of the entire process. In order to determine the priority, the relevance between the businesses can be analyzed according to the following association rules:

[0117] 1) Time dependency: If the completion of one business requires the result of another business as a prerequisite, then the first business needs to be processed first. For example, order confirmation can only be completed after order payment, so order payment has a higher priority.

[0118] 2) Resource dependency: If two businesses require shared resources, such as equipment, personnel, or information, then the business with closer resource dependency needs to be processed first.

[0119] 3) Process rules: The enterprise may have established a clear business processing process, which stipulates the order and priority of different businesses. Processing according to process rules can ensure the standardization and efficiency of the business.

[0120] Furthermore, in step S3, the dependencies between the different business types include time dependency, resource dependency and process rules.

[0121] The business processing module is used to sort the processing priorities of the pending businesses according to the importance levels of the pending businesses and the relevance between the pending businesses, and process the pending businesses in sequence.

[0122] Furthermore, the processing priorities of the pending services are sorted, specifically including the following steps:

[0123] Sort the processing priorities from high to low according to the importance of the business to be processed. The higher the importance, the higher the processing priority.

[0124] According to the correlation between the pending businesses, two or more associated pending businesses are ranked together at the highest processing priority.

[0125] In this embodiment, the association between different business types is identified so that the associated businesses can be processed together, thereby improving the processing efficiency of the businesses. Therefore, when determining the importance levels of different business types, even if some business types with low importance levels have an association with business types with higher importance levels, their processing priority should be increased to ensure that the business processing is organized, thereby improving management efficiency.

[0126] Furthermore, the business processing module also visualizes the business process and displays the dependency relationship and processing priority between the businesses to be processed.

[0127] Beneficial effects of the present invention:

[0128] By collecting business information data samples processed in large historical periods on the cloud; according to the business processing of the business information, the business processing situation uses response time and processing time to characterize the importance of the business, and uses the K-means clustering method to divide the business information data samples into several clusters, and determine the importance level of the business types contained in each cluster; using the dependency between different business types, determine the correlation between different business types; using the importance level of different business types and the correlation between them, determine the processing priority of the cloud-to-be-processed business. The present invention solves the problem that the prior art is difficult to coordinate processing according to the importance of the business and the correlation between the businesses, resulting in low management efficiency.

[0129] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An enterprise internal affairs management system based on cloud-based intelligent collaborative processing, characterized by: It includes a business upload module, a business analysis module and a business processing module, and the business upload module, the business analysis module and the business processing module are communicatively connected, wherein: The business uploading module is used to upload the pending businesses in batches to the cloud; The business analysis module is used to obtain business information of the business to be processed, input the business information into a preset business analysis model, and output the importance level of each business to be processed and the correlation between the businesses to be processed; the business information includes the business type and business processing status; the business processing status includes the response time and processing time; The preset business analysis model includes the following construction steps: S1. Collect business information data samples that have been processed in large-scale historical periods on the cloud; S2. According to the business processing situation of the business information, the K-means clustering method is used to divide the data samples into several clusters, and the importance level of the business types contained in each cluster is determined; S3. Determine the correlation between different business types by using the dependency relationship between different business types; The business processing module is used to sort the processing priorities of the pending businesses according to the importance levels of the pending businesses and the relevance between the pending businesses, and process the pending businesses in sequence; the dependency relationships between the different business types include time dependency, resource dependency and process rules; The ordering of the processing priorities of the pending services specifically includes the following steps: Sort the processing priorities from high to low according to the importance of the business to be processed. The higher the importance, the higher the processing priority. According to the correlation between the pending businesses, two or more associated pending businesses are ranked together at the highest processing priority.

2. According to claim 1, the enterprise internal affairs management system based on cloud intelligent collaborative processing is characterized by: The business types include financial management, human resources management, project management, customer relationship management, operations management, information technology management, strategic management, administrative management, corporate culture building, and public relations and brand management.

3. The enterprise internal affairs management system based on cloud intelligent collaborative processing according to claim 1 is characterized by: The K-means clustering method comprises the following steps: S21, normalization processing: normalizing the business processing data variables; S22, initializing cluster centers: randomly selecting several data objects in the data space as initial cluster centers; S23, initializing clusters: calculating the Euclidean distance between all data objects and the initial cluster center, and dividing each data object into the initial cluster center with the smallest Euclidean distance to form an initial cluster; S24, updating the cluster center: calculating the average value of the data objects of each initial cluster, and taking the average value as the new cluster center; S25. Determine the final cluster: repeatedly calculate the Euclidean distance between all data objects and the new cluster center, divide each data object into the new cluster center with the smallest Euclidean distance to form a new cluster; calculate the average value of each new cluster as the new cluster center again until the new cluster center no longer changes, determine the final cluster, and determine the business types included in each final cluster and their corresponding importance levels.

4. The enterprise internal affairs management system based on cloud intelligent collaborative processing according to claim 3 is characterized by: In step S21, the normalization process is calculated using the following formula: ， In the formula, Y i After normalization, i A sample of business processing data; K i For the i A sample of business processing data; K max Indicates the maximum value of all business processing data samples; K min Indicates the minimum value of all business processing data samples.

5. The enterprise internal affairs management system based on cloud intelligent collaborative processing according to claim 3 is characterized by: In step S23, the Euclidean distance is calculated using the following formula: ， In the formula, d ( x, C i ) represents the Euclidean distance function; x is the normalized data object; C i Indicates i Cluster centers; n is the number of samples in the data set; x j Indicates the data set j A data object with a variable; C ij Indicates j The variable i Cluster centers.

6. The enterprise internal affairs management system based on cloud intelligent collaborative processing according to claim 3 is characterized by: In step S25, the business types included in each final cluster and their corresponding importance levels are determined by comparing the numerical values ​​of the business processing conditions of each final cluster; sorting the final clusters according to the numerical values ​​from small to large; the smaller the numerical value, the higher the importance level of the business types included in the final cluster.

7. The enterprise internal affairs management system based on cloud intelligent collaborative processing according to claim 1, characterized in that: The business processing module also visualizes the business process and displays the dependency relationship and processing priority between the businesses to be processed.

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