Distributed collaborative office data processing method and system

Abstract

The invention relates to the technical field of collaborative data processing, in particular to a distributed collaborative office data processing method and system, and the method comprises the following steps: receiving task data, calculating a priority index of each task, determining a processing sequence through an emergency degree and a critical weight, and generating a task priority sequence. According to the method, the resource demand of each task is evaluated in real time and compared with the current state of the computing node, dynamic optimization of resource configuration is achieved, the tasks obtain computing resources according to the actual demand, task distribution and execution efficiency are optimized, data of each task are encrypted, safety is improved, privacy of data exchange is ensured, and a prediction model is established; the task execution transparency is improved, team members are allowed to adjust a work plan according to a prediction result, the flexibility of project management is improved, the clustering analysis of user behaviors deepens the understanding of a team cooperation mode, resource allocation and task allocation are optimized, and the team cooperation efficiency and satisfaction are improved.

Description

Technical Field

[0001] This invention relates to the field of collaborative data processing technology, and in particular to a distributed collaborative office data processing method and system. Background Technology

[0002] The field of collaborative data processing technology involves methods and systems for managing and processing data in multi-user environments. The core of this technology lies in achieving real-time sharing and updating of data in distributed systems to support seamless collaboration among multiple users or devices. Its main technical challenges include data synchronization, access control, data integrity protection, and network latency optimization. In practical applications, collaborative data processing not only supports document sharing and real-time editing, but also includes various forms of collaboration such as collaborative design, software development, and online meetings. An effective collaborative data processing system can significantly improve team work efficiency, reduce information silos, and promote instant information exchange and decision-making.

[0003] Distributed collaborative office data processing is a technological approach designed to support users in different geographical locations to collaboratively complete office tasks via a network. It utilizes distributed computing technology to store and process data, ensuring consistency and accessibility across all nodes. Its main applications include collaborative document editing, project management, instant messaging, and resource sharing, greatly facilitating remote work, virtual team collaboration, and cross-time zone work models. Through this method, team members can view, edit, and discuss the same document or project in real time, thereby improving work efficiency and project management flexibility.

[0004] Existing distributed collaborative office data processing methods have shortcomings in handling data synchronization, access control, and network latency optimization. Although they can support basic document sharing and real-time editing, they lack sufficient adaptability and safeguards in terms of resource allocation flexibility and data security. For example, in traditional collaborative data processing, task resource allocation is static, and once allocated, it is difficult to dynamically adjust according to the actual workload. This leads to inefficient resource use and computing bottlenecks. Data security measures rely on uniform security protocols, failing to provide customized security processing for various tasks, increasing security risks in complex or sensitive task processing, limiting the collaboration efficiency of teams working remotely and in multiple time zones, and also increasing the complexity of project management and potential operational risks. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a distributed collaborative office data processing method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a distributed collaborative office data processing method, comprising the following steps: S1: Receive task data, calculate the priority index of each task, determine the processing order based on urgency and criticality weight, and generate a task priority sequence; S2: Based on the task priority sequence, evaluate the resource requirements of each task, monitor the CPU usage, memory usage and network bandwidth status of the computing nodes, adjust the resource configuration of each task, and generate a resource allocation table. S3: Perform security processing on the tasks in the resource allocation table, encrypt the task data with a public key, and encrypt a random key for each operation to obtain an encrypted task dataset; S4: Using the encrypted task dataset, analyze historical task data, including completion time and resource usage, predict the resource requirements and expected completion time of each task in the future time period, and generate task prediction results; S5: Extract data from the task prediction results, analyze task types and user behavior, perform cluster analysis, identify user work habits and collaboration patterns, and obtain user behavior analysis results; S6: Based on the user behavior analysis results, dynamically adjust the task allocation strategy, reconfigure computing resources, continuously monitor the performance of computing nodes, perform task load balancing, and obtain the data processing task scheduling effect.

[0007] As a further aspect of the present invention, the task priority sequence includes task number, urgency ranking, and priority score; the resource allocation table includes task resource matching degree, node resource utilization rate, and scheduling priority; the encrypted task dataset includes encrypted data items, key index, and encryption standard; the task prediction results include resource consumption prediction, expected task cycle, and performance evaluation indicators; the user behavior analysis results include behavior classification, collaboration frequency, and behavior impact score; and the data processing task scheduling effect includes performance optimization records, load adjustment results, and efficiency improvement index.

[0008] As a further aspect of the present invention, the steps of receiving task data, calculating the priority index of each task, determining the processing order based on urgency and criticality weights, and generating a task priority sequence are as follows: S101: Receive task data, parse the data and identify the attributes of each task, including task ID, urgency, expected resources required, and expected completion time. Then, classify and store the attributes to obtain a task feature table. S102: Extract urgency and criticality data from the task feature table, apply predetermined weights to the data, calculate the urgency and criticality scores for each task, summarize the calculated values ​​to form a priority index, and obtain a weighted task table. S103: Using the priority scores in the weighted task table, perform task sorting operations, compare the priority index of each task numerically, reorganize the task list and sort it from high to low priority, determine the optimal order for processing tasks, and obtain the task priority sequence.

[0009] As a further aspect of the present invention, the steps of evaluating the resource requirements of each task based on the task priority sequence, monitoring the CPU usage, memory usage, and network bandwidth status of computing nodes, adjusting the resource configuration of each task, and generating a resource allocation table are as follows: S201: Based on the task priority sequence, periodically capture the CPU utilization, memory usage and network bandwidth data of each computing node through the monitoring interface, record the data to the time series database, perform historical data comparison and trend analysis, and generate resource status records; S202: Using the resource status record, analyze the CPU, memory and bandwidth requirements of each task, manage resources hierarchically according to task priority and expected execution time, and establish a task resource requirement table; S203: Based on the task resource requirement table, allocate resources, adjust CPU allocation, reallocate memory resources, optimize network bandwidth configuration, meet the resource requirements of differentiated tasks, and construct a resource allocation table.

[0010] As a further aspect of the present invention, the steps of performing security processing on the tasks in the resource allocation table, encrypting task data with a public key, and encrypting a random key for each operation to obtain an encrypted task dataset are as follows: S301: Based on the resource allocation table, deploy a key management service and encrypt each data packet during data transmission and storage. By securely transmitting and storing data in the network, generate a task public key set. S302: Obtain the public key of each task from the task public key set, encrypt each data operation with a random key, securely distribute the random key using a key exchange mechanism, and establish a random key set; S303: Use the keys in the random key set to encrypt the data of each task, manage and update the encrypted data, and construct an encrypted task dataset.

[0011] As a further aspect of the present invention, the steps of analyzing historical task data, including completion time and resource usage, using the encrypted task dataset to predict the resource requirements and expected completion time of each task in the future time period, and generating task prediction results are as follows: S401: Based on the encrypted task dataset, perform decryption processing on the dataset, extract the historical records of each task, including the completion time and resource usage of the task, format the extracted data, and organize it into a unified data format, including standardized time format and resource quantity unit, to form a historical data index. S402: Based on the historical data index, the task data is summarized and classified, and the peak and valley values ​​of resource usage for task types are extracted by data sorting. The average resource consumption and completion time are calculated, and a resource usage pattern diagram is generated. S403: Using the resource usage pattern diagram, apply time series analysis technology to predict the resource requirements and expected completion time of the task in the future time period. Based on the comparative analysis of historical data patterns and the current task status, perform trend projection of the data, construct a probability model for task execution, and obtain the task prediction result.

[0012] As a further aspect of the present invention, the formula for the time series analysis technique is as follows: ; in, For time series at time points The value, For constant terms, The coefficients of the autoregressive model are... This is the demand from the previous day. The influence coefficient of demand. Current temperature The effect coefficient of temperature, This is the sales data for the previous time period. The impact coefficient of sales data. This is the noise term.

[0013] As a further aspect of the present invention, the steps of extracting data from the task prediction results, analyzing task types and user behaviors, performing cluster analysis, identifying user work habits and collaboration patterns, and obtaining user behavior analysis results are as follows: S501: Based on the task prediction results, the key indicators in the task prediction results are captured through the data access layer, including task type identifier, user response time and task interaction frequency, to generate a task and behavior dataset. S502: Extract user interaction data from the task and behavior dataset, perform preliminary sorting and classification according to data type, identify differentiated task processing patterns, including individual task completion and team collaboration tasks, and establish a user interaction pattern set. S503: Based on the user interaction pattern set, perform data clustering, and group user behaviors by analyzing the frequency and type of interaction patterns to obtain user behavior analysis results.

[0014] As a further aspect of the present invention, the steps of dynamically adjusting the task allocation strategy, reconfiguring computing resources, continuously monitoring the performance of computing nodes, performing task load balancing, and obtaining the data processing task scheduling effect based on the user behavior analysis results are as follows: S601: Based on the user behavior analysis results, collect user behavior data, identify the current task processing capacity and load of computing nodes, dynamically adjust the task allocation strategy based on the performance data of each node and the execution history of user tasks, and perform load balancing and resource optimization to generate an adjusted task allocation table. S602: Based on the adjusted task allocation table, perform resource reconfiguration, including adjusting the allocation of memory and processors to computing nodes, optimizing resource configuration by real-time monitoring of node resource usage and task execution status, and matching current task requirements to obtain an optimized resource configuration model; S603: Utilize the optimized resource allocation model to continuously monitor the performance of computing nodes, including real-time tracking and recording of each node's CPU and memory usage, response time, and task processing speed. Adjust task allocation strategies and resource configurations based on the monitoring results to generate data processing task scheduling effects.

[0015] A distributed collaborative office data processing system, the distributed collaborative office data processing system being used to execute the above-mentioned distributed collaborative office data processing method, the system comprising: The data receiving module receives task data through a network interface. The data items include task ID, urgency level, estimated resources, and completion time. It calculates the weight of each task, scores them based on urgency and importance, and generates a task priority sequence. The resource allocation module evaluates the resource requirements of tasks in the task priority sequence, monitors the CPU, memory and network status of computing nodes in real time, reallocates tasks to matching nodes based on resource availability, and optimizes resource allocation to obtain optimized resource mapping. The data encryption module encrypts the data for each task in the optimized resource mapping, using a public key for encryption and a random key for each task, thus constructing a secure encrypted data set. The performance prediction module uses the encrypted data security set to evaluate the historical completion time and resource usage of the task, predict the resource requirements and completion time in the future time period, and generate performance prediction indicators. The behavior analysis and scheduling module analyzes the performance prediction indicators and identifies task types and user behavior patterns. It analyzes user data and adjusts task scheduling to optimize resource usage and performance, and obtains the data processing task scheduling effect.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, dynamic optimization of resource allocation is achieved by assessing the resource requirements of each task and comparing them in real time with the current state of computing nodes. Resource matching and adjustment allow each task to acquire the necessary computing resources according to its actual needs, thereby optimizing task distribution and execution efficiency. Encrypting the data of each task improves data security and ensures that data exchange in a distributed environment does not leak sensitive information. The establishment of predictive models makes task execution more transparent, allowing team members to adjust work plans based on prediction results, improving the flexibility and responsiveness of project management. Cluster analysis of user behavior further deepens the understanding of team collaboration patterns, making resource allocation and task assignment more in line with actual work habits and needs, thereby effectively improving overall collaboration efficiency and team job satisfaction. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flowchart of S1 of the present invention; Figure 3 This is a detailed flowchart of the S2 process of the present invention; Figure 4 This is a detailed flowchart of the S3 process of the present invention; Figure 5 This is a detailed flowchart of the S4 process of the present invention; Figure 6 This is a detailed flowchart of S5 of the present invention; Figure 7 This is a detailed flowchart of S6 of the present invention; Figure 8 This is a system flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Example 1 Please see Figure 1 This invention provides a technical solution, a distributed collaborative office data processing method, comprising the following steps: S1: Receive task data, including task ID, urgency, estimated resources required, and estimated completion time. Calculate the priority index of each task by weighting urgency and criticality, determine the processing order, and obtain the task priority sequence. S2: Based on the task priority sequence, evaluate the resource requirements of each task, while monitoring the current CPU usage, memory usage and network bandwidth status of the computing nodes, adjust the resource configuration of each task according to the resource matching situation, optimize task distribution, and generate a resource allocation table. S3: Perform security processing on the tasks in the resource allocation table, encrypt the data of each task with a public key, encrypt a random key for each operation, and obtain an encrypted task dataset; S4: Utilize the encrypted task dataset to analyze historical task data, including completion time and resource usage, predict the resource requirements and estimated completion time for each task in the future time period, establish a probabilistic model for task execution by comparing historical data and the current state, and generate task prediction results; S5: Extract data from task prediction results, analyze task types and user behavior, perform cluster analysis based on user interaction data, identify user work habits and collaboration patterns, and obtain user behavior analysis results; S6: Based on user behavior analysis results, dynamically adjust task allocation strategies, reconfigure computing resources, continuously monitor computing node performance and perform task load balancing, optimize operating efficiency, and obtain data processing task scheduling effects.

[0021] The task priority sequence includes task number, urgency ranking, and priority score; the resource allocation table includes task resource matching degree, node resource utilization rate, and scheduling priority; the encrypted task dataset includes encrypted data items, key index, and encryption standard; the task prediction results include resource consumption prediction, expected task cycle, and performance evaluation indicators; the user behavior analysis results include behavior classification, collaboration frequency, and behavior impact score; and the data processing task scheduling effect includes performance optimization records, load adjustment results, and efficiency improvement index.

[0022] Specifically, such as Figure 2 As shown, the specific steps for receiving task data, calculating the priority index of each task, determining the processing order based on urgency and criticality weights, and generating a task priority sequence are as follows: S101: Receive task data, parse the data and identify the attributes of each task, including task ID, urgency, expected resources required, and expected completion time. Then classify and store the attributes to obtain the task feature table. The execution flow is as follows: S101: Upon receiving task data, a data parser is first initialized. This parser is configured with various data extraction rules to specifically extract key information such as task ID, urgency, estimated resources required, and estimated completion time from the JSON structure. During parsing, type tags and validation rules are set for each task attribute to ensure data integrity and accuracy. After parsing, a data processing flow quantifies the urgency and estimated resources for each task. The urgency is standardized according to a preset level (e.g., 1 to 5), and the estimated resources are normalized based on the average resource requirement according to the task type. The normalized data is then categorized by task type, and a hash function is used to generate task feature vectors. These vectors are stored in an efficient database to form a task feature table for subsequent querying and analysis, using the formula: ; in, The storage vector representing the task. Represents the task ID. This function is used to convert task IDs into a format more suitable for storage and retrieval. The function is used to standardize input data, ensuring that all data points are compared on the same order of magnitude. and These are the weight coefficients of the attributes.

[0023] S102: Extract urgency and criticality data from the task feature table, apply predetermined weights to the data, calculate the urgency and criticality score for each task, summarize the calculated values ​​to form a priority index, and obtain the weighted task table. The execution flow is as follows. S102: Extract urgency and criticality data from the task feature table. First, through a query interface, extract relevant data based on the urgency and resource requirement fields in the task feature vector. Then, according to pre-defined weight parameters, such as urgency weight and resource requirement weight (which reflect the influence of different task attributes on task urgency and criticality), calculate the urgency and criticality scores for each task using a weighted algorithm. Weights are applied to urgency and resource requirements, and these weighted values ​​are summed to form a comprehensive priority index. This index will be used to evaluate task priority for subsequent task sorting and scheduling, using the formula: ; in, Representing urgency and criticality, and These represent the urgency level and standardized resource requirement values ​​after processing according to preset conversion rules, respectively. and These are the corresponding weight parameters.

[0024] S103: Using the priority scores in the weighted task table, perform task sorting operations. By comparing the priority index of each task numerically, and reorganizing the task list to sort from high to low priority, determine the optimal order for processing tasks. The execution flow of the task priority sequence is as follows. S103: Utilize the priority scores in the weighted task table to deploy a ranking algorithm. This algorithm ranks tasks based on their priority index. The ranking process considers not only single-dimensional numerical comparisons but also the combined impact of task urgency and criticality, as well as dependencies between tasks. The algorithm prioritizes tasks with high urgency and criticality, while adjusting the ranking of tasks with low urgency based on their criticality and resource consumption. This approach ensures the priority execution of critical tasks and optimizes overall resource utilization efficiency. The ranking results are output as a task priority sequence for project managers to schedule and allocate resources, using the formula: ; in, Representative task Priority ranking and Representing tasks respectively and Priority index, This is the total number of tasks.

[0025] Specifically, such as Figure 3As shown, the specific steps for evaluating the resource requirements of each task based on the task priority sequence, monitoring the CPU usage, memory usage, and network bandwidth status of computing nodes, adjusting the resource configuration of each task, and generating a resource allocation table are as follows: S201: Based on the task priority sequence, the CPU utilization, memory usage and network bandwidth data of each computing node are periodically captured through the monitoring interface, the data is recorded to the time series database, historical data comparison and trend analysis are performed, and the resource status record is generated. The execution process is as follows: S201: Based on task priority sequences, a monitoring module is configured to periodically capture CPU utilization, memory usage, and network bandwidth data for each computing node through a monitoring interface. This process involves each node's monitoring agent periodically sending its status data to the central monitoring system. The collected data is then serialized and stored in a time-series database. This database optimizes query efficiency and storage density for time-series data, performs historical data comparison and trend analysis, and identifies resource usage patterns and trends for each computing node through statistical analysis of past data. The analysis results are compiled into resource status records, detailing the resource usage status of each node at different points in time, providing a basis for subsequent resource management and task scheduling. The formula used is:

[0026] in, Representing a point in time Resource status, , ,and They represent time CPU utilization, memory usage, and network bandwidth usage, with weighting coefficients. , and This is used to adjust the influence weights of different resource types, thereby accurately reflecting the overall status of resource usage.

[0027] S202: Using resource status records, analyze the CPU, memory, and bandwidth requirements of each task, and manage resources hierarchically according to task priority and expected execution time. The execution flow for establishing the task resource requirement table is as follows; S202: Resource status recording is employed. First, data analysis tools are used to quantitatively analyze the CPU, memory, and bandwidth requirements of each task, including statistically analyzing the peak and average resource consumption during each task's execution. Based on this data, combined with task priority and estimated execution time, resources are managed in a hierarchical manner. During management, tasks are divided into several levels, each corresponding to a certain range of resource requirements, thus making resource allocation more precise and efficient. The information is compiled into a task resource requirement table, which details the resource categories and specific requirements for each task, facilitating resource allocation and adjustment. The formula used is: ; in, Representative task Resource demand level, and Representing tasks respectively Priority and expected execution time, weighting coefficient and Used to balance the impact of task priority and execution time on resource requirement assessment.

[0028] S203: Based on the task resource requirement table, allocate resources, adjust CPU allocation, reallocate memory resources, optimize network bandwidth configuration, meet the resource requirements of differentiated tasks, and construct the resource allocation table. The execution flow is as follows. S203: Based on the task resource requirements table, perform detailed resource allocation, including adjusting CPU allocation, allocating more CPU cycles to high-priority tasks according to their resource level and priority; reallocating memory resources to ensure sufficient memory space for memory-intensive tasks; and optimizing network bandwidth configuration, especially for tasks requiring large amounts of data transfer. These adjustments effectively meet the differentiated resource needs of different tasks. All resource configuration information is ultimately compiled into a resource allocation table, which records the resource allocation for each task in detail and supports dynamic resource adjustments during runtime, using the following formula: ; in, Representative task Resource allocation situation, Represents the level of resource demand. Represents specific resource types (CPU, memory, bandwidth), weighting coefficients and Used to adjust the contribution of resource demand levels and resource types to the final allocation decision.

[0029] Specifically, such as Figure 4As shown, the specific steps for performing security processing on tasks in the resource allocation table, encrypting task data with a public key, and encrypting with a random key for each operation to obtain an encrypted task dataset are as follows: S301: Based on the resource allocation table, deploy the key management service and encrypt each data packet during data transmission and storage. The execution flow for generating the task public key set by securely transmitting and storing data in the network is as follows; S301: Based on the resource allocation table, deploy a key management service. This service is responsible for generating, storing, and managing encryption keys. The deployment process includes configuring the security settings of the key server to ensure that key generation and storage are performed in a secure environment. This service uses high-security standard algorithms to generate public and private key pairs for each task. Each data packet is encrypted during transmission and storage, using the corresponding public key to ensure data security. Advanced symmetric encryption technology is used to ensure secure data transmission and storage over the network. All generated public keys are organized in a task public key set. Each task's public key is associated with the task ID through a database index for quick retrieval and use when needed, using the formula: ; in, Representative task Public key encryption of data, It is the key used for data encryption. It is a public-key encryption function. It is a task The formula demonstrates how to use the task's public key to encrypt the data encryption key, ensuring its secure transmission over the network.

[0030] S302: Obtain the public key for each task from the task public key set, encrypt each data operation with a random key, and securely distribute the random key using a key exchange mechanism. The execution flow for establishing a random key set is as follows; S302: After obtaining the public key for each task from the task public key set, a unique random key is generated for each data operation. This random key is created using a secure pseudo-random number generator to ensure its randomness and unpredictability. Using a key exchange mechanism, such as the Diffie-Hellman key exchange protocol, the random keys are securely distributed to the tasks requiring data operations. The random keys do not pass through any insecure channels, ensuring security during distribution. All random keys are collected and stored in a random key set to provide the necessary keys for subsequent data encryption operations, using the formula: ; in, Representative task A securely distributed random key. It is the generated random key. It is a Diffie-Hellman key exchange encryption function. It is a task The formula shows how to use the public key to securely encrypt a random key for secure distribution.

[0031] S303: The execution flow for encrypting the data of each task using keys from the random key set, managing and updating the encrypted data, and constructing the encrypted task dataset is as follows; S303: Data for each task is encrypted using a key from a random key set. During each data operation, the key for the corresponding task is retrieved from the random key set and used to encrypt the data. The encryption process employs an efficient symmetric encryption algorithm to ensure the security and integrity of the data during processing and transmission. The encrypted data is managed and updated uniformly, forming an encrypted task dataset. This dataset includes encrypted data from all tasks, categorized and indexed by task, facilitating efficient data retrieval and decryption operations when needed. The formula is: ; in, Representative task Encrypted data, It is a task raw data It is a symmetric encryption function. This is the mission The formula illustrates the process of encrypting task data using a randomly assigned key.

[0032] Specifically, such as Figure 5 As shown, the specific steps for generating task prediction results by using an encrypted task dataset to analyze historical task data, including completion time and resource usage, to predict the resource requirements and estimated completion time for each task in the future are as follows: S401: Based on the encrypted task dataset, the dataset is decrypted, the historical records of each task are extracted, including the completion time and resource usage of the task, the extracted data is formatted and organized into a unified data format, including standardized time format and resource quantity unit, and the execution flow of forming historical data index is as follows; S401: Based on the encrypted task dataset, data decryption is first performed, involving decrypting each task's data using its corresponding random key to ensure data integrity and readability. The decrypted data includes the completion time and resource usage for each task. This data is extracted and formatted. During formatting, completion times are uniformly converted to a standard time format (e.g., ISO 8601), and resource usage is converted to a uniform unit of measurement (e.g., GB for memory, GHz for CPU), thus ensuring data consistency and standardization. The standardized data is then organized into a historical data index. This index facilitates subsequent data retrieval and analysis while ensuring data usability and accessibility, using the formula: ; in, Representative task Historical data, It is encrypted task data. It is a symmetric decryption function. It is a task The formula illustrates the process of decrypting encrypted data using a random key.

[0033] S402: Based on historical data indexing, the task data is summarized and classified. The peak and valley values ​​of resource usage for task types are extracted by data sorting. The average resource consumption and completion time are calculated, and the execution flow of generating a resource usage pattern diagram is as follows. S402: Based on historical data indexing, task data is summarized and classified. This process includes using efficient data processing algorithms to sort and classify historical records, particularly identifying peak and trough resource usage for different task types. The average resource consumption and completion time for each task type are calculated to identify typical resource usage patterns and efficiency. This information is used to generate a resource usage pattern graph, which clearly shows the resource consumption trends of different task types over different time periods. This provides crucial decision support information for resource management and task scheduling. The formula used is: ; in, This represents average resource consumption. This refers to the resource usage of a single task. This represents the total number of tasks. The formula is used to calculate the average resource consumption for different task types.

[0034] S403: Using a resource usage pattern diagram and applying time series analysis techniques, the resource requirements and expected completion time of the task in the future time period are predicted. Based on the comparative analysis of historical data patterns and the current task status, the data trend is projected, and a probability model for task execution is constructed. The execution flow of the task prediction results is as follows. S403: Utilizing resource usage pattern diagrams, time series analysis techniques are applied to predict the resource requirements and estimated completion times of tasks in future time periods. The prediction process is based on a comprehensive comparative analysis of historical data patterns and the current task status. Statistical analysis methods, such as moving averages or exponential smoothing, are used to project data trends to predict future changes in resource requirements and task completion times. The predicted data is used to construct a probabilistic model for task execution. This model assesses the likelihood and resource requirements of different task executions, generating task prediction results that provide a scientific basis for resource allocation and task scheduling.

[0035] The formulas for time series analysis techniques are as follows: ; in, For time series at time points The value, For constant terms, The coefficients of the autoregressive model are... This is the demand from the previous day. The influence coefficient of demand. Current temperature The effect coefficient of temperature, This is the sales data for the previous time period. The impact coefficient of sales data. This is the noise term.

[0036] The execution process is as follows: The autoregressive component, based on historical data, calculates the contribution of each historical time point to the current forecast, while also incorporating the demand from the previous day. and its influence coefficient These two factors are combined to assess the impact of changes in demand on future forecasts, taking current temperature into account. and its influence coefficient Analyze the impact of temperature changes on task performance, and incorporate sales data from the previous time period. and its influence coefficient Consider how sales trends affect resource demand, plus noise. This represents random disturbance, completing the construction of the overall prediction model. Each newly introduced parameter... The coefficient values ​​will be determined through linear regression analysis of historical data. By minimizing the prediction error, the coefficients will be adjusted to improve prediction accuracy. The improved model will then be used to predict the completion time and resource requirements of future tasks, thereby obtaining more accurate task prediction results.

[0037] Specifically, such as Figure 6 As shown, the specific steps for extracting data from task prediction results, analyzing task types and user behavior, performing cluster analysis, identifying user work habits and collaboration patterns, and obtaining user behavior analysis results are as follows: S501: Based on the task prediction results, the execution flow of generating a task and behavior dataset is as follows: The key indicators in the task prediction results are captured through the data access layer, including task type identifier, user response time and task interaction frequency. S501: Based on task prediction results, a data access layer is first configured to capture key metrics, including task type identifiers, user response time, and task interaction frequency. The data access layer interacts with the database through an efficient query interface to ensure the accuracy and timeliness of data capture. It performs data cleaning and verification processes to eliminate any abnormal or incomplete data records. The cleaned data is used to generate a task and behavior dataset, which records in detail the dynamics of user interaction for each task type, including response time and interaction frequency. This provides a data foundation for further user behavior analysis and task optimization, using the following formula: ; in, Represents the index item in the task and behavior dataset. It is a task type identifier. It is the user response time. It refers to the frequency of task interactions and the weighting coefficient. , ,and Adjust the influence of different indicators in the index items.

[0038] S502: Extract user interaction data from the task and behavior dataset, perform preliminary sorting and classification according to data type, identify differentiated task processing patterns, including individual task completion and team collaboration tasks, and establish the execution flow of the user interaction pattern set as follows; S502: Extract user interaction data from task and behavior datasets. This involves using data analysis tools to sort and classify the datasets, grouping the data into different groups based on interaction frequency and response time, such as individual task completion groups and team collaboration task groups. By identifying data patterns within groups, different user interaction patterns can be clearly distinguished. These patterns are further analyzed and recorded to form a user interaction pattern set. This set details the behavioral characteristics and interaction frequency of users in different task types, providing a basis for designing more effective user interaction strategies. The formula used is: ; in, The pattern representing the concentration of user interaction patterns , It is a data item. It is a binary indicator that represents a data item. Does it belong to a pattern? The formula is used to count the data frequency of each interaction mode based on the data classification results.

[0039] S503: Based on the user interaction pattern set, perform data clustering, and group user behaviors by analyzing the frequency and type of interaction patterns to obtain the user behavior analysis results. The execution flow is as follows: S503: Based on the user interaction pattern set, perform data clustering analysis. The process uses sophisticated machine learning algorithms, such as K-means or hierarchical clustering, to group user behavior data. The analysis considers the frequency and type of interaction patterns, as well as user response time and task interaction context. Through the clustering results, user groups with similar behavioral characteristics can be identified, thereby generating user behavior analysis results. These results help understand user behavioral preferences in different tasks, supporting the development of more personalized and effective user interaction strategies. The formula used is: ; in, Representative clustering The center point, It belongs to clustering Data points, It is clustering The set of all data points in the set. It is clustering The number of data points in the cluster is used by a formula to calculate the centroid of each cluster, which represents the average value of all user behavior data within that cluster.

[0040] Specifically, such as Figure 7As shown, the specific steps for dynamically adjusting task allocation strategies, reconfiguring computing resources, continuously monitoring computing node performance, performing task load balancing, and obtaining the data processing task scheduling effect based on user behavior analysis results are as follows: S601: Based on user behavior analysis results, collect user behavior data, identify the current task processing capacity and load of computing nodes, dynamically adjust the task allocation strategy based on the performance data of each node and the execution history of user tasks, and perform load balancing and resource optimization to generate the adjusted task allocation table. The execution process is as follows. S601: Based on user behavior analysis results, collect user behavior data, including task interaction frequency and response time, to identify the current task processing capacity and load of each computing node, including analyzing the node's CPU utilization, memory usage, and the number of tasks being processed. Based on each node's performance data and the execution history of user tasks, dynamically adjust the task allocation strategy using machine learning algorithms. This includes using predictive models to predict the node's load in future time periods and adjusting task allocation based on the prediction results to achieve load balancing and optimized resource utilization. An adjusted task allocation table is generated, which records in detail the new task allocation for each computing node and the expected resource utilization efficiency, using the formula: ; in, Represents computing nodes The amount of tasks allocated, It is a node In the mission The predicted load on the platform, It is a task The priority weights are used in a formula to dynamically adjust task allocation based on the node's predicted load and task priority.

[0041] S602: Based on the adjusted task allocation table, the execution flow of the optimized resource allocation model is as follows: the resource allocation is optimized by real-time monitoring of the node's resource usage and task execution status, and the optimized resource allocation model is matched with the current task requirements. S602: Based on the adjusted task allocation table, resource reconfiguration is performed, including adjusting memory and processor allocation according to the task load and resource utilization efficiency of the computing nodes. By monitoring node resource usage and task execution status in real time, data analysis tools are used to optimize resource configuration, ensuring that the resource configuration of each node best matches the current task requirements. The resource configuration adjustment is based on a comprehensive consideration of node performance, task requirements, and historical data analysis, resulting in an optimized resource configuration model. This model can dynamically adapt to different task and load changes, improving resource utilization efficiency. The formula used is: ; in, Represents computing nodes Resource allocation model, and These represent the amount of processor and memory respectively. and It is a weighting coefficient for resource allocation, used to balance the proportion of processor and memory in resource allocation.

[0042] S603: Utilizing an optimized resource allocation model, continuously monitor the performance of computing nodes, including real-time tracking and recording of each node's CPU and memory usage, response time, and task processing speed. Based on the monitoring results, adjust task allocation strategies and resource configurations to generate the execution flow of data processing task scheduling effects as follows; S603: Utilizing an optimized resource allocation model, it continuously monitors the performance of computing nodes, including real-time tracking and recording of each node's CPU and memory usage, response time, and task processing speed. Monitoring data is updated in real-time through high-performance data collection, ensuring accuracy and timeliness. Based on the collected performance data, using a pre-defined performance evaluation model and resource adjustment algorithm, it adjusts task allocation strategies and resource configurations according to the monitoring results. These adjustments aim to optimize computing resource utilization, reduce response time, and improve task processing speed. The generated data processing task scheduling effect report will show the performance differences and resource utilization efficiency improvements of each node before and after the adjustment, using the formula: ; in, Representative node The data processing task scheduling effect ,and These represent CPU usage, memory usage, and response time, respectively, with weighting coefficients. ,and Used to adjust the influence of different performance indicators in the evaluation of scheduling effectiveness.

[0043] like Figure 8 As shown, a distributed collaborative office data processing system includes: The data receiving module receives task data through a network interface. The data items include task ID, urgency level, estimated resources, and completion time. It calculates the weight of each task, scores them based on urgency and importance, and generates a task priority sequence. The resource allocation module evaluates the resource requirements of tasks in the task priority sequence, monitors the CPU, memory and network status of computing nodes in real time, reallocates tasks to matching nodes based on resource availability, and optimizes resource allocation to obtain optimized resource mapping. The data encryption module encrypts the data for each task in the resource mapping optimization, using a public key for encryption and a random key for each task to build a secure encrypted data set. The performance prediction module uses encrypted data security sets to evaluate the historical completion time and resource usage of tasks, predict resource requirements and completion time in the future, and builds a probability model for task execution by comparing historical data and the current state to generate performance prediction metrics. The behavior analysis and scheduling module analyzes performance prediction metrics and identifies task types and user behavior patterns. It analyzes user data and adjusts task scheduling to optimize resource usage and performance, and obtains the results of data processing task scheduling.

[0044] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A distributed collaborative office data processing method, characterized in that, Includes the following steps: Receive task data, calculate the priority index of each task, determine the processing order based on urgency and criticality weight, and generate a task priority sequence; Based on the task priority sequence, assess the resource requirements of each task, monitor the CPU usage, memory usage, and network bandwidth status of computing nodes, adjust the resource configuration of each task, and generate a resource allocation table. The tasks in the resource allocation table are subjected to security processing: the task data is encrypted with a public key, and a random key is used to encrypt the data for each operation, resulting in an encrypted task dataset. Using the encrypted task dataset, analyze historical task data, including completion time and resource usage, predict the resource requirements and estimated completion time for each task in the future time period, and generate task prediction results; Data is extracted from the task prediction results, task types and user behaviors are analyzed, cluster analysis is performed, user work habits and collaboration patterns are identified, and user behavior analysis results are obtained. Based on the user behavior analysis results, the task allocation strategy is dynamically adjusted, computing resources are reconfigured, the performance of computing nodes is continuously monitored, task load balancing is performed, and the data processing task scheduling effect is obtained.

2. The distributed collaborative office data processing method according to claim 1, characterized in that, The task priority sequence includes task number, urgency ranking, and priority score; the resource allocation table includes task resource matching degree, node resource utilization rate, and scheduling priority; the encrypted task dataset includes encrypted data items, key index, and encryption standard; the task prediction results include resource consumption prediction, expected task cycle, and performance evaluation indicators; the user behavior analysis results include behavior classification, collaboration frequency, and behavior impact score; and the data processing task scheduling effect includes performance optimization records, load adjustment results, and efficiency improvement index.

3. The distributed collaborative office data processing method according to claim 1, characterized in that, The specific steps for receiving task data, calculating the priority index of each task, determining the processing order based on urgency and criticality weights, and generating a task priority sequence are as follows: Receive task data, parse the data and identify the attributes of each task, including task ID, urgency, expected resources required, and expected completion time. Then classify and store the attributes to obtain a task feature table. Data on urgency and criticality are extracted from the task feature table. The data are then numerically weighted using predetermined weights to calculate the urgency and criticality scores for each task. The calculated values ​​are then summarized to form a priority index, resulting in a weighted task table. Using the priority scores in the weighted task table, a task sorting operation is performed. By comparing the priority index of each task numerically, the task list is reorganized and sorted from high to low priority to determine the optimal order for processing tasks, thus obtaining a task priority sequence.

4. The distributed collaborative office data processing method according to claim 1, characterized in that, Based on the task priority sequence, the specific steps for evaluating the resource requirements of each task, monitoring the CPU usage, memory usage, and network bandwidth status of computing nodes, adjusting the resource configuration of each task, and generating a resource allocation table are as follows: Based on the task priority sequence, the CPU utilization, memory usage and network bandwidth data of each computing node are periodically captured through the monitoring interface, the data is recorded into the time series database, historical data comparison and trend analysis are performed, and resource status records are generated. Using the resource status records, analyze the CPU, memory, and bandwidth requirements of each task, and manage resources hierarchically according to task priority and expected execution time to establish a task resource requirement table; Based on the task resource requirement table, resources are allocated, CPU allocation is adjusted, memory resources are reallocated, and network bandwidth configuration is optimized to meet the resource requirements of differentiated tasks, and a resource allocation table is constructed.

5. The distributed collaborative office data processing method according to claim 1, characterized in that, The specific steps for performing security processing on the tasks in the resource allocation table, encrypting task data with a public key, and encrypting with a random key for each operation to obtain an encrypted task dataset are as follows: Based on the resource allocation table, a key management service is deployed, and each data packet during data transmission and storage is encrypted. By securely transmitting and storing data in the network, a task public key set is generated. Obtain the public key of each task from the task public key set, encrypt each data operation with a random key, securely distribute the random key using a key exchange mechanism, and establish a random key set; The data for each task is encrypted using keys from the random key set. The encrypted data is then managed and updated to construct an encrypted task dataset.

6. The distributed collaborative office data processing method according to claim 1, characterized in that, The specific steps for analyzing historical task data, including completion time and resource usage, using the encrypted task dataset to predict the resource requirements and estimated completion time for each task in the future time period, and generating task prediction results are as follows: Based on the encrypted task dataset, the dataset is decrypted, and the historical records of each task are extracted, including the completion time and resource usage. The extracted data is formatted and organized into a unified data format, including standardized time format and resource quantity unit, to form a historical data index. Based on the historical data index, the task data is summarized and classified. The peak and valley values ​​of resource usage for task types are extracted by sorting the data. The average resource consumption and completion time are calculated, and a resource usage pattern diagram is generated. Using the resource usage pattern diagram, time series analysis technology is applied to predict the resource requirements and expected completion time of the task in the future time period. Based on the comparative analysis of historical data patterns and the current task status, data trend projection is performed to construct a probability model for task execution and obtain the task prediction results.

7. The distributed collaborative office data processing method according to claim 6, characterized in that, The formula for the time series analysis technique is as follows: in, For time series at time points The value, For constant terms, The coefficients of the autoregressive model are... This is the demand from the previous day. The influence coefficient of demand. Current temperature The effect coefficient of temperature, This is the sales data for the previous time period. The impact coefficient of sales data. This is the noise term.

8. The distributed collaborative office data processing method according to claim 1, characterized in that, The specific steps for extracting data from the task prediction results, analyzing task types and user behavior, performing cluster analysis, identifying user work habits and collaboration patterns, and obtaining user behavior analysis results are as follows: Based on the task prediction results, key indicators in the task prediction results are captured through the data access layer, including task type identifier, user response time and task interaction frequency, to generate a task and behavior dataset. User interaction data is extracted from the task and behavior dataset, and preliminary sorting and classification are performed according to data type to identify differentiated task processing patterns, including individual task completion and team collaboration tasks, and to establish a set of user interaction patterns. Based on the user interaction pattern set, data clustering is performed. By analyzing the frequency and type of interaction patterns, user behaviors are grouped to obtain user behavior analysis results.

9. The distributed collaborative office data processing method according to claim 1, characterized in that, Based on the user behavior analysis results, the specific steps for dynamically adjusting the task allocation strategy, reconfiguring computing resources, continuously monitoring the performance of computing nodes, performing task load balancing, and obtaining the data processing task scheduling effect are as follows: Based on the user behavior analysis results, user behavior data is collected, the current task processing capacity and load of computing nodes are identified, and the task allocation strategy is dynamically adjusted based on the performance data of each node and the execution history of user tasks. Load balancing and resource optimization are performed to generate an adjusted task allocation table. Based on the adjusted task allocation table, the resources are reconfigured, including adjusting the allocation of memory and processors to computing nodes. The resource configuration is optimized by monitoring the resource usage and task execution status of the nodes in real time and matching the current task requirements to obtain an optimized resource configuration model. Using the optimized resource allocation model, the performance of computing nodes is continuously monitored, including real-time tracking and recording of each node's CPU and memory usage, response time, and task processing speed. Based on the monitoring results, task allocation strategies and resource configurations are adjusted to generate data processing task scheduling effects.

10. A distributed collaborative office data processing system, characterized in that, The distributed collaborative office data processing method according to any one of claims 1-9, wherein the system comprises: The data receiving module receives task data through a network interface. The data items include task ID, urgency level, estimated resources, and completion time. It calculates the weight of each task, scores them based on urgency and importance, and generates a task priority sequence. The resource allocation module evaluates the resource requirements of tasks in the task priority sequence, monitors the CPU, memory and network status of computing nodes in real time, reallocates tasks to matching nodes based on resource availability, and optimizes resource allocation to obtain optimized resource mapping. The data encryption module encrypts the data for each task in the optimized resource mapping, using a public key for encryption and a random key for each task, thus constructing a secure encrypted data set. The performance prediction module uses the encrypted data security set to evaluate the historical completion time and resource usage of the task, predict the resource requirements and completion time in the future time period, and generate performance prediction indicators. The behavior analysis and scheduling module analyzes the performance prediction indicators and identifies task types and user behavior patterns. It analyzes user data and adjusts task scheduling to optimize resource usage and performance, and obtains the data processing task scheduling effect.

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