Enterprise collaborative office management method and system

Abstract

The invention relates to an enterprise collaborative office management method and system, and relates to the technical field of enterprise management, and the method comprises the steps: obtaining resource use data in enterprise office; analyzing the resource use data through a threshold judgment model, and when the value of the resource use data exceeds a preset threshold, executing a resource allocation algorithm to minimize the total resource allocation deviation and load imbalance; in response to completion of execution of the resource allocation algorithm, sending an adjustment instruction to the cloud resource management platform, and adding resource allocation to the server of the enterprise; obtaining and analyzing workflow data of an enterprise, and identifying project delay factors to obtain a corresponding analysis result; according to the analysis result, executing a workflow adjustment algorithm to minimize task delay and task load imbalance; and analyzing the event mode in the working data flow, and executing an event response algorithm to optimize the event mode so as to maximize the event response efficiency and the resource utilization rate. The office management effect of an enterprise system can be improved.

Description

Technical Field

[0001] This invention relates to the field of enterprise management technology, and in particular to an enterprise collaborative office management method, system, electronic device, and non-transitory computer-readable storage medium. Background Technology

[0002] Today, collaborative office management technology is particularly crucial, supporting communication, resource sharing, and decision-making within remote teams. Existing collaborative office systems primarily rely on basic communication tools and document management systems. While these systems optimize the use of sharing platforms and data analysis tools to some extent, traditional methods struggle to efficiently handle cross-departmental and cross-regional collaborative work as businesses grow and projects become more complex.

[0003] However, existing collaborative office systems have many shortcomings. Information silos, slow processing speeds, and difficulties in real-time updates and integration of different data sources remain prominent issues. Traditional systems lack flexibility in resource management, failing to adjust resource allocation based on real-time data, potentially leading to resource shortages during critical project phases, impacting project progress and quality. Furthermore, manually assigning tasks easily results in redundancy or resource waste, further affecting operational efficiency and cost control. As companies grow and projects become more complex, the problems of untimely information updates and difficulties in data source integration become increasingly apparent, severely hindering cross-departmental and cross-regional collaborative work efficiency. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method, system, electronic device, and non-transitory computer-readable storage medium for enterprise collaborative office management that can improve the effectiveness of enterprise collaborative office management.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides a method for managing collaborative office work in enterprises, the method comprising: Obtain data on resource usage in the enterprise office; The resource usage data is analyzed by a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, a resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. In response to the completion of the resource allocation algorithm, an adjustment instruction is sent to the cloud resource management platform to increase resource allocation to the enterprise's servers; Acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results; Based on the analysis results, a workflow adjustment algorithm is executed to minimize task latency and task load imbalance. Analyze the event patterns in the work data stream, and execute an event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0006] Optionally, the step of analyzing the resource usage data through a threshold judgment model, and executing a resource allocation algorithm when the value of the resource usage data exceeds a preset threshold to minimize the total resource allocation deviation and load imbalance, includes: Obtain resources from multiple categories, and the average value for each category; Obtain the CPU utilization, memory usage, and network bandwidth for each server; The resource allocation algorithm is constructed and executed based on each type of resource allocated to each of the servers, the CPU utilization, memory usage, and network bandwidth of each of the servers.

[0007] Optionally, the resource allocation algorithm is expressed as: ; in, This represents the CPU utilization of the i-th server. This is the memory usage of the i-th server. R represents the network bandwidth of the i-th server, and R represents the resource allocation matrix. This represents the j-th type of resource allocated to the i-th server. , , These are the first, second, and third weights for CPU, memory, and network bandwidth, respectively. It is the fourth weight in load balancing. It is the target load of the i-th server.

[0008] Optionally, the step of executing a workflow adjustment algorithm based on the analysis results to minimize task latency and task load imbalance includes: Based on the analysis results, the completion time and weight of each task are obtained; Obtain resources allocated to each team member; Obtain the target load for each of the tasks; The workflow adjustment algorithm is constructed and executed based on the completion time, weight, and target load of each task, as well as the resources allocated to each team member.

[0009] Optionally, the workflow adjustment algorithm is expressed as: ; in, It is the completion time of the kth task. It is the weight of the k-th task. It is the j-th type of resource allocated to the i-th team member. It is the fifth weight in load balancing. It is the target load of the kth task.

[0010] Optionally, the analysis of event patterns in the working data stream and the execution of an event response algorithm to optimize the event patterns maximize event response efficiency and resource utilization: The availability of each type of resource is determined based on its degree of availability in the system. Determine the efficiency coefficient of each type of resource based on its utilization efficiency in the system. Obtain the allocation quantity and average allocation quantity for each type of resource; The event response algorithm is constructed and executed based on the availability, efficiency coefficient, allocation quantity, and average allocation quantity of each type of resource.

[0011] Optionally, the event response algorithm is expressed as: ; in, The average allocation quantity of the i-th type of resource. It is the efficiency coefficient of the i-th type of resource. It is the allocation quantity of the i-th type of resource. It is the availability of the i-th type of resource. This is the sixth parameter for resource utilization.

[0012] Optionally, the constraints of the resource allocation algorithm are: ; ; The constraints of the workflow adjustment algorithm are as follows: ; ; ; The constraints of the event response algorithm are as follows: ; ; .

[0013] Optionally, the resource usage data includes at least CPU utilization, memory usage, and network bandwidth, and the workflow data includes at least task completion time and team members' work records.

[0014] The present invention also provides an enterprise collaborative office management system, the system comprising: The data acquisition module is used to acquire resource usage data in the enterprise office; the resource usage data includes at least CPU utilization, memory usage, and network bandwidth. The resource allocation module is used to analyze the resource usage data through a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, the resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. The resource addition module is used to send an adjustment instruction to the cloud resource management platform in response to the completion of the resource allocation algorithm, thereby increasing the resource allocation to the enterprise's servers. The data analysis module is used to acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results. The data adjustment module is used to execute a workflow adjustment algorithm based on the analysis results to minimize task delays and task load imbalances. The event optimization module is used to analyze the event patterns in the working data stream and execute the event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0015] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the enterprise collaborative office management method described above.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements an enterprise collaborative office management method as described above.

[0017] The beneficial effects of this invention are: This invention ensures that resource allocation always matches current needs by monitoring business demands and resource availability in real time. This dynamic management approach provides necessary computing resources promptly when demands increase, avoiding work delays caused by resource shortages. Furthermore, the intelligent agent can identify key project phases and prioritize the allocation of high-performance resources, thereby accelerating the processing speed of critical tasks and improving overall project efficiency. Machine learning-enhanced workflow automation predicts and adjusts work allocation by analyzing project milestones and workload data, effectively avoiding the imbalances and inefficiencies caused by manual allocation. This technology makes task allocation more scientific and reasonable, greatly increasing the likelihood of projects being completed on schedule. Real-time data integration and intelligent agent responsiveness allow for cross-system data integration, making decision-making more comprehensive and timely. This rapid response capability of the intelligent agent enables enterprises to flexibly respond to emergencies and maintain operational continuity and stability. Attached Figure Description

[0018] Figure 1 A scenario diagram illustrating an enterprise collaborative office management method provided by this invention; Figure 2 A flowchart of an enterprise collaborative office management method provided by the present invention; Figure 3 A schematic diagram of the structure of an enterprise collaborative office management system provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0022] Please see Figure 1 , Figure 1 This is a scenario diagram illustrating an enterprise collaborative office management method provided by the present invention. (Example) Figure 1As shown, the terminal and server are connected via a network, such as a wired or wireless network. The terminal can include, but is not limited to, portable devices such as mobile phones and tablets with various network platform applications installed, as well as fixed terminals such as computers, kiosks, and advertising machines. The server provides users with various business services, including service push servers and user recommendation servers.

[0023] It should be noted that, Figure 1 The scenario diagram illustrating a collaborative office management method for enterprises is merely an example. The terminals, servers, and application scenarios described in this embodiment of the invention are intended to more clearly illustrate the technical solutions of this embodiment and do not constitute a limitation on the technical solutions provided by this embodiment. As those skilled in the art will recognize, with the evolution of systems and the emergence of new business scenarios, the technical solutions provided by this embodiment of the invention are equally applicable to similar technical problems.

[0024] The terminal can be used for: Obtain data on resource usage in the enterprise office; The resource usage data is analyzed by a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, a resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. In response to the completion of the resource allocation algorithm, an adjustment instruction is sent to the cloud resource management platform to increase resource allocation to the enterprise's servers; Acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results; Based on the analysis results, a workflow adjustment algorithm is executed to minimize task latency and task load imbalance. Analyze the event patterns in the work data stream, and execute an event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0025] Please see Figure 2 The present invention provides a flowchart of an enterprise collaborative office management method, including the following steps: Step 201: Obtain resource usage data in the enterprise office.

[0026] In some embodiments, resource usage data may include at least CPU utilization, memory usage, and network bandwidth. CPU utilization refers to the percentage of CPU usage on a server or compute node, typically expressed as a percentage, and is obtained in real-time through system monitoring tools and APIs.

[0027] Memory usage refers to the amount of memory currently used by a server or computing node, usually expressed in bytes or as a percentage. Real-time memory usage data can be obtained through memory monitoring tools and APIs.

[0028] Network bandwidth refers to the network data transmission rate of a server or computing node, usually expressed in bytes per second. Real-time network bandwidth usage data can be obtained through network monitoring tools and APIs.

[0029] In some embodiments, system monitoring tools (such as Nagios, Zabbix, Prometheus, etc.) can be used to collect resource usage data in real time. Data can be obtained from monitoring systems via API interfaces, which typically provide data in JSON or XML format. Log analysis tools (such as the ELK Stack) can be used to collect and analyze system logs to extract resource usage information. Analyze resource usage records in system logs, which are typically stored in structured or semi-structured formats. Cloud service provider resource management tools (such as AWS CloudWatch, Azure Monitor, Google CloudOperations) can be used to monitor cloud resource usage. Real-time resource usage data can be obtained through API interfaces provided by cloud service providers.

[0030] Step 202: Analyze the resource usage data using a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, execute a resource allocation algorithm to minimize the total resource allocation deviation and load imbalance.

[0031] In some embodiments, step 202 may include: Obtain resources from multiple categories, and the average value for each category; Obtain the CPU utilization, memory usage, and network bandwidth for each server; The resource allocation algorithm is constructed and executed based on each type of resource allocated to each of the servers, the CPU utilization, memory usage, and network bandwidth of each of the servers.

[0032] In some embodiments, the resource allocation algorithm can be expressed as: ; in, This represents the CPU utilization of the i-th server. This is the memory usage of the i-th server. R represents the network bandwidth of the i-th server, and R represents the resource allocation matrix. This represents the j-th type of resource allocated to the i-th server. , , These are the first, second, and third weights for CPU, memory, and network bandwidth, respectively. It is the fourth weight in load balancing. It is the target load of the i-th server.

[0033] In the specific implementation, The goal is to minimize the allocation bias of each server on each resource. It is the j-th type of resource actually allocated to the i-th server, and This represents the average value of the j-th type of resource. This part expresses the desire for resource allocation on each server to be as close as possible to the average resource allocation, avoiding some servers having too much or too little resource, thus helping to achieve a balanced allocation of resources and improving the stability and efficiency of the overall system.

[0034] The goal of this section is to minimize the deviation between the overall load on each server and the target load. These are the CPU utilization, memory usage, and network bandwidth of the i-th server, respectively. These are the corresponding weighting coefficients, representing the importance of different resources in the total load calculation. It is the target load of the i-th server. It is the fourth weight, indicating the importance of load balancing.

[0035] This section expresses the desire for the overall load (considering CPU, memory, and bandwidth) of each server to be as close as possible to its target load, thereby achieving load balancing. By balancing the load on servers, overloading a single server can be avoided, thus improving system stability and performance.

[0036] This formula combines the goals of resource allocation and load balancing. By minimizing the sum of these two components, it ensures that resources are evenly distributed among servers, while simultaneously balancing the load on each server as much as possible. By monitoring server load in real time, this formula dynamically adjusts resource allocation to avoid resource waste and performance bottlenecks. and The value can be flexibly adjusted to prioritize resource allocation and load balancing based on actual needs and system characteristics.

[0037] Data such as CPU utilization, memory usage, and network bandwidth are collected from various servers and cloud resource management systems via API. When the CPU utilization of a server exceeds a preset threshold (e.g., 80%), a resource allocation algorithm is activated. Based on a formula, the optimal resource allocation scheme is calculated, and the system automatically sends adjustment instructions to the cloud resource management platform to increase or decrease resource allocation, such as automatically scaling the number of virtual machines horizontally. The results and raw data of each resource allocation are recorded for subsequent analysis and optimization.

[0038] Through the above process, this invention achieves dynamic optimization of resource allocation and load balancing, improves system stability and operating efficiency, and meets the collaborative office needs of enterprises in large-scale and complex projects.

[0039] In some embodiments, the constraints of the resource allocation algorithm are as follows: ; ; The first constraint ensures that the total resources allocated to each server do not exceed its actual capacity, preventing server overload. By setting appropriate weighting coefficients, the proportion of different types of resources in the overall load capacity can be balanced.

[0040] The second constraint ensures that resource allocation is practically feasible and that there is no negative resource allocation, which is consistent with the logic of resource allocation in reality.

[0041] By applying the above constraints, system stability and operational efficiency can be ensured while optimizing resource allocation and load balancing. This is crucial for achieving resource management goals in enterprise collaborative office management methods.

[0042] Step 203: In response to the completion of the resource allocation algorithm, an adjustment instruction is sent to the cloud resource management platform to increase resource allocation to the enterprise's servers.

[0043] In some embodiments, the resource allocation algorithm is triggered when resource overload or other conditions requiring optimization are detected. For example, a server's CPU utilization exceeds 80% and memory usage exceeds 70%. The resource allocation algorithm can use resource usage data (including CPU utilization, memory usage, and network bandwidth) obtained from a real-time monitoring system as input.

[0044] In some embodiments, specific resource adjustment instructions can be generated based on the calculation results of the resource allocation algorithm. These instructions include increasing or reallocating the server's CPU, memory, and network bandwidth resources. Adjustment instructions are typically generated in JSON or XML format for easy system parsing and execution.

[0045] In some embodiments, the generated adjustment instructions can be sent to the cloud resource management platform by calling its API interface. Common cloud service providers (such as AWS, Azure, and Google Cloud) provide rich API interfaces to support automated resource management. The adjustment instructions contain specific resource adjustment operations, such as increasing the number of virtual machines, expanding storage space, and adjusting network bandwidth.

[0046] In some embodiments, upon receiving an adjustment instruction, the cloud resource management platform automatically executes resource expansion operations. For example, it increases the number of virtual machine instances to meet computing demands. It adjusts load balancing strategies to ensure the new resources are effectively utilized and improve system performance. The intelligent agent continues to monitor resource allocation, verifying whether the adjustment instruction has taken effect and whether resources are being used appropriately. Monitoring systems and log analysis tools are used to verify the system performance and stability after resource expansion.

[0047] Step 204: Obtain and analyze the enterprise's workflow data, identify project delay factors, and obtain corresponding analysis results.

[0048] In some embodiments, workflow data can be obtained from a project management system used by the enterprise (such as JIRA, Trello, Asana, etc.). This data typically includes task creation time, completion time, responsible person, task status, etc.

[0049] Relevant records for projects and tasks can be extracted from internal enterprise databases. These databases may store detailed task information, resource usage, and progress data. System log files can be analyzed to extract task execution and completion timestamps, as well as related resource usage records.

[0050] In some embodiments, data types may include task ID, name, description, priority, start time, end time, responsible person, dependencies, etc. Task notes, comments, problem reports, etc., require text analysis and processing.

[0051] In some implementations, duplicate, invalid, and erroneous data can be removed to ensure data accuracy and consistency. Data from different sources is integrated, formatted uniformly, and formed into a comprehensive workflow dataset.

[0052] In some embodiments, the start time, end time, estimated completion time, and actual completion time of a task can be extracted. Resource information required for the task, including personnel, equipment, and software tools, can be extracted. Dependencies between tasks can be identified, and a task dependency graph can be constructed.

[0053] In some embodiments, statistical metrics such as average completion time, standard deviation, and delay rate of a task can be calculated. Visualization tools such as Gantt charts, network diagrams, and heatmaps can be used to display the progress and dependencies of a task.

[0054] In some embodiments, suitable machine learning algorithms (such as random forests, decision trees, support vector machines, etc.) can be selected to identify factors contributing to project delays. Historical project data can be used to train machine learning models that learn the relationship between task features and delays. Feature importance analysis can be used to identify the factors with the greatest impact on project delays, such as the complexity of a specific task, insufficient resources, and delays in dependent tasks.

[0055] In some embodiments, a pre-trained machine learning model can be used to predict in real time whether an ongoing task is likely to be delayed. A risk assessment is performed on each task, providing the probability of delay and key risk factors.

[0056] In some embodiments, a project delay factor analysis report can be generated, detailing the main reasons for project delays and the corresponding tasks. A task risk assessment report can also be generated, listing high-risk tasks and their probability of delay and influencing factors.

[0057] In some embodiments, resource adjustment suggestions can be provided based on the analysis results, such as increasing personnel, extending working hours, and adjusting task priorities. Based on the analysis results of delay factors, task scheduling can be optimized, task order and dependencies adjusted, and the risk of delays reduced.

[0058] In some embodiments, the analysis results can be integrated into the enterprise's decision support system to help management make more informed project management decisions. A continuous monitoring mechanism should be established to regularly update the analysis models and data, ensuring real-time response to project changes.

[0059] In some embodiments, task data can be extracted from the JIRA project management system, including task creation time, completion time, and responsible person. This structured data includes task ID, name, start time, end time, and responsible person.

[0060] In some embodiments, invalid data can be removed to ensure data accuracy. Temporal and resource characteristics of the tasks are extracted to construct a task dependency graph.

[0061] In some embodiments, a random forest algorithm can be used to train a model to identify the relationship between task characteristics and delays. The importance of model features is analyzed to identify key delay factors. A delay factor analysis report can be generated, listing the main reasons for delays and providing suggestions for resource adjustments and task scheduling optimization.

[0062] This invention effectively identifies key factors causing project delays by acquiring and analyzing enterprise workflow data. It utilizes machine learning models for prediction and risk assessment, generating detailed analysis reports and optimization suggestions to help enterprises optimize resource management and task scheduling, thereby increasing the likelihood of projects being completed on schedule.

[0063] Step 205: Based on the analysis results, execute the workflow adjustment algorithm to minimize task latency and task load imbalance.

[0064] In some embodiments, step 205 may include: Based on the analysis results, the completion time and weight of each task are obtained; Obtain resources allocated to each team member; Obtain the target load for each of the tasks; The workflow adjustment algorithm is constructed and executed based on the completion time, weight, and target load of each task, as well as the resources allocated to each team member.

[0065] In some embodiments, the workflow adjustment algorithm can be expressed as: ; in, It is the completion time of the kth task. It is the weight of the k-th task. It is the j-th type of resource allocated to the i-th team member. It is the fifth weight in load balancing. It is the target load of the kth task.

[0066] In the specific implementation, The goal of this section is to minimize the completion time of each task. Compared with average completion time The deviation between them, weight Ensure that time deviations in the completion of important tasks are given priority.

[0067] By minimizing this deviation, the uniformity of task completion time can be improved, ensuring that the completion time of critical tasks (high-weight tasks) is as close to the average as possible, thereby avoiding excessive delays for some tasks.

[0068] This part, It is the sum of all resources allocated to the i-th team member. It is the target load of the k-th task. These are the weighting parameters for load balancing. The goal of this part is to minimize the deviation between the actual resource load and the target load of each task. By minimizing this deviation, load balancing of resources can be achieved, ensuring that the resources allocated to each task match its needs, thereby improving resource utilization efficiency and task completion quality.

[0069] By considering deviations in task completion times and assigning different weights to different tasks, we can ensure that critical tasks are completed on time, improving overall project time management. Load balancing ensures that the amount of resources allocated to each task matches its actual needs, avoiding resource over- or under-allocation, thereby improving project execution efficiency and quality.

[0070] In practical applications, this formula can be used in the following scenarios: Based on the task completion time and resource requirements, resource allocation is dynamically adjusted to ensure that critical tasks receive sufficient resource support while avoiding resource waste.

[0071] Real-time monitoring of task progress and resource usage; optimization of resource allocation using formulas to ensure good load balance across tasks during execution. Based on task importance (weight). By rationally prioritizing tasks and allocating resources, the efficiency and effectiveness of overall project management can be improved.

[0072] In summary, this invention provides an efficient and dynamic enterprise collaborative office management method by minimizing task completion time deviation and resource load balancing deviation, which helps to improve project execution efficiency and resource utilization.

[0073] In some embodiments, the constraints of the workflow adjustment algorithm are as follows: ; ; ; In the specific implementation, This represents the completion time of each task k. It must be less than or equal to its deadline. This constraint ensures that all tasks are completed on time and do not exceed the scheduled deadline. By ensuring tasks are completed on time, project delays can be avoided, improving the reliability of project management and customer satisfaction.

[0074] This means that the total resources allocated to the i-th team member must be greater than or equal to the resource requirement of the k-th task. .

[0075] It is the weight of the k-th task, representing the importance or priority of the task. This represents the basic resource requirement for the k-th task. This constraint ensures that the total resources allocated to each team member at least meet the basic resource requirements of the tasks they are responsible for. By ensuring that each task is allocated sufficient resources, the quality and efficiency of task completion can be improved, and task delays or failures due to insufficient resources can be avoided.

[0076] This represents the j-th type of resource allocated to the j-th team member. The value must be non-negative. This constraint ensures the practical feasibility of resource allocation and prevents negative resource allocation amounts. By guaranteeing non-negative resource allocation, the feasibility and rationality of the resource allocation plan in actual operation can be ensured.

[0077] Each task must be completed before its deadline to ensure the project progresses as planned. The total resources allocated to each team member must meet the basic requirements of their assigned task to ensure sufficient resources for task completion. Resource allocations must be non-negative to ensure the allocation plan is practically feasible.

[0078] In practical applications, these constraints can be used for: Ensure that the completion time of each task does not exceed the scheduled deadline. To avoid project delays, resources should be allocated rationally based on task priority and resource requirements to ensure sufficient resources for each task, thereby improving the quality and efficiency of task completion. The feasibility of the resource allocation plan should be ensured to avoid negative resource allocation and to guarantee its rationality in actual operation.

[0079] These constraints enable the rational allocation of resources and the effective scheduling of tasks, thereby improving the overall efficiency of enterprise collaborative office management and the reliability of project management.

[0080] Step 206: Analyze the event patterns in the working data stream, and execute the event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0081] In some embodiments, step 206 may include: The availability of each type of resource is determined based on its degree of availability in the system. Determine the efficiency coefficient of each type of resource based on its utilization efficiency in the system. Obtain the allocation quantity and average allocation quantity for each type of resource; The event response algorithm is constructed and executed based on the availability, efficiency coefficient, allocation quantity, and average allocation quantity of each type of resource.

[0082] In some embodiments, the event response algorithm is represented as: ; in, The average allocation quantity of the i-th type of resource. It is the efficiency coefficient of the i-th type of resource. It is the allocation quantity of the i-th type of resource. It is the availability of the i-th type of resource. This is the sixth parameter for resource utilization.

[0083] In the specific implementation, It is the quantity of resources Regarding the contribution to efficiency, using a logarithmic function can smooth out the impact of resource quantity, avoiding the excessive influence of extreme resource quantities on the results. The goal of this section is to maximize the sum of the products of availability and efficiency for each type of resource. By using a logarithmic function to handle the quantity of resources, we can ensure that diminishing marginal returns occur as the quantity of resources increases. By maximizing resource efficiency, we can improve the overall resource utilization of the system, ensuring that the availability and efficiency of each type of resource are optimally utilized.

[0084] middle, This represents the ratio of the number of resources allocated to the i-th type to its average number of resources allocated. It represents the square of the ratio, emphasizing the balance of resource allocation. It is the weighting parameter for load balancing, which adjusts the importance of load balancing in the overall objective.

[0085] The goal of this section is to maximize the balance of resource allocation. By squaring the resource allocation ratio, the distribution of resources can be better balanced. Maximizing load balancing ensures a more even distribution of resources, preventing overuse or underuse of certain resources and improving system stability and availability. By considering resource availability and efficiency and using a logarithmic function to handle resource quantities, the efficiency of resource usage can be ensured, improving overall system performance. Load balancing ensures even resource allocation, preventing overuse or underuse of resources and improving system stability and resource utilization efficiency.

[0086] In practical applications, this formula can be used in the following scenarios: Based on resource availability and efficiency, dynamically adjust resource allocation to ensure maximum utilization of each resource. Monitor resource usage in real time and optimize resource allocation using formulas to ensure balanced distribution of resources throughout the system and improve system stability. Based on the optimization results of resource efficiency and load balancing, provide decision support to management to formulate more scientific and reasonable resource management strategies.

[0087] Through this optimization formula, the present invention can improve resource utilization efficiency while ensuring the balance of resource allocation, thereby achieving optimal overall system performance and improving the overall efficiency and effectiveness of enterprise collaborative office management.

[0088] In some embodiments, event patterns refer to the real-time detection and processing of various events and their associated patterns within the system. By identifying event patterns, the system can quickly respond to specific business needs and changes, achieving efficient resource management and task allocation.

[0089] For example, the event triggering conditions for resource overload detection can be specific indicators or signals monitored by the system. For example, CPU utilization exceeding 80%, a surge in demand for a specific product, or task delays.

[0090] The system can collect real-time data from various business modules and resource management systems. This data typically includes, but is not limited to, CPU utilization, memory usage, network bandwidth, sales data, and inventory data.

[0091] These rules define how to combine and interpret event triggering conditions. For example, a load balancing operation is triggered when both CPU utilization and memory usage simultaneously exceed a predetermined threshold. These are operations that the system automatically performs after detecting an event pattern. Examples include adjusting resource allocation, adjusting task priorities, and generating alarm notifications.

[0092] The event triggering conditions for resource overload detection can be: CPU utilization > 80% and memory utilization > 70%. The real-time monitoring system obtains server CPU and memory usage data via API. If the CPU and memory utilization of a server simultaneously exceed predetermined thresholds, a resource allocation operation is triggered. The resource allocation algorithm is activated, increasing resource allocation (e.g., horizontally scaling the number of virtual machines). System logs are generated, recording the resource allocation results and raw data.

[0093] In some embodiments, the constraints of the event response algorithm are as follows: ; ; ; This refers to the number of resources allocated to each resource i. It must be less than or equal to its average distribution quantity. This ensures that the actual allocated resources do not exceed their average allocation, preventing resource overuse. Limiting resource allocation within their average range avoids overuse of certain resources, preventing resource overload and ensuring system stability and reliability. Maintaining fairness and balance in resource allocation ensures that all resources are used rationally, avoiding resource bottlenecks.

[0094] This refers to the efficiency coefficient of each resource i. It must be greater than or equal to its minimum efficiency threshold. This constraint ensures that the utilization efficiency of each resource will not fall below a predetermined minimum standard. By ensuring that resource efficiency is above the minimum threshold, resources maintain high efficiency throughout their use, avoiding inefficient resource utilization and improving overall system performance. Guaranteeing the high efficiency of critical resources enhances the system's speed and capability in responding to emergencies.

[0095] This refers to the number of resources allocated to each resource i. The constraint that resources must be non-negative ensures the practical feasibility of resource allocation and prevents negative resource allocations. It also ensures the rationality of the resource allocation scheme in actual operation, avoiding negative resource allocations. By ensuring all resource allocations are non-negative, it maintains the stability and normal operation of the system.

[0096] These constraints ensure the rationality, feasibility, and efficiency of resource allocation and use in practical applications: ensuring that the allocation of each resource is within a reasonable range, preventing resource overload or under-allocation. By monitoring and limiting resource allocation, the balance and fairness of resource use are maintained.

[0097] Ensure that the utilization efficiency of each resource exceeds the minimum standard to avoid inefficient resource use. Set a minimum efficiency threshold to ensure that resources maintain high efficiency throughout their use. Ensure the practical feasibility of the resource allocation plan to avoid negative resource allocation. Set resource allocation values ​​to be non-negative to ensure the rationality of the plan in actual operation.

[0098] Through these constraints, the event response algorithm in this solution can achieve rational allocation and efficient use of resources, improving the overall performance and reliability of the system. These conditions work together to ensure that the system can maintain stable and efficient operation in a dynamically changing environment, thereby better responding to various emergencies and business demands.

[0099] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an enterprise collaborative office management system provided by the present invention.

[0100] like Figure 3 As shown in the figure, an enterprise collaborative office management system proposed in this embodiment of the invention includes: Data acquisition module 301 is used to acquire resource usage data in the enterprise office; the resource usage data includes at least CPU utilization, memory usage and network bandwidth; The resource allocation module 302 is used to analyze the resource usage data through a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, the resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. The resource addition module 303 is used to send an adjustment instruction to the cloud resource management platform in response to the completion of the resource allocation algorithm, and to increase resource allocation to the enterprise's servers. Data analysis module 304 is used to acquire and analyze the enterprise's workflow data, identify project delay factors, and obtain corresponding analysis results; The data adjustment module 305 is used to execute a workflow adjustment algorithm based on the analysis results to minimize task delays and task load imbalances. The event optimization module 306 is used to analyze the event patterns in the working data stream and execute the event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0101] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps: Obtain data on resource usage in the enterprise office; The resource usage data is analyzed by a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, a resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. In response to the completion of the resource allocation algorithm, an adjustment instruction is sent to the cloud resource management platform to increase resource allocation to the enterprise's servers; Acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results; Based on the analysis results, a workflow adjustment algorithm is executed to minimize task latency and task load imbalance. Analyze the event patterns in the work data stream, and execute an event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0102] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 5As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it performs the following steps: Obtain data on resource usage in the enterprise office; The resource usage data is analyzed by a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, a resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. In response to the completion of the resource allocation algorithm, an adjustment instruction is sent to the cloud resource management platform to increase resource allocation to the enterprise's servers; Acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results; Based on the analysis results, a workflow adjustment algorithm is executed to minimize task latency and task load imbalance. Analyze the event patterns in the work data stream, and execute an event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

[0103] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0109] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for managing collaborative office work in an enterprise, characterized in that, The method includes: Obtain data on resource usage in the enterprise office; The resource usage data is analyzed by a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, a resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. In response to the completion of the resource allocation algorithm, an adjustment instruction is sent to the cloud resource management platform to increase resource allocation to the enterprise's servers; Acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results; Based on the analysis results, a workflow adjustment algorithm is executed to minimize task latency and task load imbalance. Analyze the event patterns in the work data stream, and execute an event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

2. The enterprise collaborative office management method according to claim 1, characterized in that, The step of analyzing the resource usage data using a threshold judgment model, and executing a resource allocation algorithm when the value of the resource usage data exceeds a preset threshold, to minimize the total resource allocation deviation and load imbalance, includes: Obtain resources from multiple categories, and the average value for each category; Obtain the CPU utilization, memory usage, and network bandwidth for each server; The resource allocation algorithm is constructed and executed based on each type of resource allocated to each of the servers, the CPU utilization, memory usage, and network bandwidth of each of the servers.

3. The enterprise collaborative office management method according to claim 2, characterized in that, The resource allocation algorithm is expressed as follows: ； in, This represents the CPU utilization of the i-th server. This is the memory usage of the i-th server. R represents the network bandwidth of the i-th server, and R represents the resource allocation matrix. This represents the j-th type of resource allocated to the i-th server. , , These are the first, second, and third weights for CPU, memory, and network bandwidth, respectively. It is the fourth weight in load balancing. It is the target load of the i-th server.

4. The enterprise collaborative office management method according to claim 3, characterized in that, Based on the analysis results, the workflow adjustment algorithm is executed to minimize task latency and task load imbalance, including: Based on the analysis results, the completion time and weight of each task are obtained; Obtain resources allocated to each team member; Obtain the target load for each of the tasks; The workflow adjustment algorithm is constructed and executed based on the completion time, weight, and target load of each task, as well as the resources allocated to each team member.

5. The enterprise collaborative office management method according to claim 4, characterized in that, The workflow adjustment algorithm is expressed as follows: ； in, It is the completion time of the kth task. It is the weight of the k-th task. It is the j-th type of resource allocated to the i-th team member. It is the fifth weight in load balancing. It is the target load of the kth task.

6. The enterprise collaborative office management method according to claim 5, characterized in that, The process involves analyzing event patterns in the workflow data stream and executing an event response algorithm to optimize these patterns, thereby maximizing event response efficiency and resource utilization. The availability of each type of resource is determined based on its degree of availability in the system. Determine the efficiency coefficient of each type of resource based on its utilization efficiency in the system. Obtain the allocation quantity and average allocation quantity for each type of resource; The event response algorithm is constructed and executed based on the availability, efficiency coefficient, allocation quantity, and average allocation quantity of each type of resource.

7. The enterprise collaborative office management method according to claim 6, characterized in that, The event response algorithm is expressed as follows: ； in, The average allocation quantity of the i-th type of resource. It is the efficiency coefficient of the i-th type of resource. It is the allocation quantity of the i-th type of resource. It is the availability of the i-th type of resource. This is the sixth parameter for resource utilization.

8. The enterprise collaborative office management method according to claim 7, characterized in that, The constraints of the resource allocation algorithm are: ； ； The constraints of the workflow adjustment algorithm are as follows: ； ； ； The constraints of the event response algorithm are as follows: ； ； 。 9. The enterprise collaborative office management method according to claim 8, characterized in that, The resource usage data includes at least CPU utilization, memory usage, and network bandwidth, and the workflow data includes at least task completion time and team members' work records.

10. A collaborative office management system for enterprises, characterized in that, The system includes: The data acquisition module is used to acquire resource usage data in the enterprise office; the resource usage data includes at least CPU utilization, memory usage, and network bandwidth. The resource allocation module is used to analyze the resource usage data through a threshold judgment model. When the value of the resource usage data exceeds a preset threshold, the resource allocation algorithm is executed to minimize the total resource allocation deviation and load imbalance. The resource addition module is used to send an adjustment instruction to the cloud resource management platform in response to the completion of the resource allocation algorithm, thereby increasing the resource allocation to the enterprise's servers. The data analysis module is used to acquire and analyze the enterprise's workflow data, identify factors causing project delays, and obtain corresponding analysis results. The data adjustment module is used to execute a workflow adjustment algorithm based on the analysis results to minimize task delays and task load imbalances. The event optimization module is used to analyze the event patterns in the working data stream and execute the event response algorithm to optimize the event patterns in order to maximize event response efficiency and resource utilization.

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