Intelligent collaborative office method and system for power enterprises based on artificial intelligence

Abstract

The present invention discloses an artificial intelligence-based intelligent collaborative office method and system for electric power enterprises, relating to the field of electric power information technology. This artificial intelligence-based intelligent collaborative office method and system for electric power enterprises receives request data from user terminals through a deep learning model, identifies the type of business demand, urgency, and task complexity, generates a business priority index, and performs classification and priority assignment. Based on the business priority, the system matches associated tasks and resource requirements through a model, retrieves relevant historical data to optimize resource allocation paths, dynamically tracks task progress, execution status, and resource consumption, identifies bottlenecks in the execution process, and generates optimization instructions. Finally, it dynamically adjusts model parameters and resource allocation rules through an adaptive learning algorithm, forming a closed-loop feedback mechanism. This method effectively integrates information flow and resource allocation, improves the flexibility and responsiveness of collaborative office work, and adapts to the development needs of smart grids and smart cities.

Description

Technical Field

[0001] The present invention relates to the field of power information technology, and specifically to an intelligent collaborative office method and system for power enterprises based on artificial intelligence. Background Art

[0002] With the growth of global energy demand and the increasing digitalization of power systems, power companies are facing pressure to maintain efficient operations in an increasingly complex environment. The power industry requires more flexible and intelligent collaborative office models to cope with the increasing data and management demands. Traditional manual management and decentralized information processing are no longer sufficient for efficient collaboration across departments and regions. By introducing artificial intelligence, power companies can gain support in demand forecasting, resource scheduling, and emergency response, enabling information integration, workflow optimization, and improved management efficiency to adapt to the overall development trend of smart grids and smart cities.

[0003] For example, a collaborative office method and system with artificial intelligence learning capabilities, with announcement number CN116993307B, receives collaborative office requests from user terminals, extracts business demand data from the collaborative office requests; obtains demand feature information from the business demand data, generates corresponding communication data based on the demand feature information, and transmits the communication data to the data processing service; obtains data records of artificial intelligence learning, and generates a data resource pool based on the data records, calls the data processing service to receive communication data, matches the communication data with the data resource pool, and configures target collaborative work service data corresponding to the communication data; manages the target collaborative work service data to convert it into a business data packet, uses the business data packet to match the corresponding target collaborative application, calls and pushes the target collaborative application; can achieve more effective collaborative office that meets actual needs, and greatly improves enterprise office efficiency and user experience.

[0004] Existing collaborative office methods in power companies are primarily based on traditional information systems and manual collaboration methods, lacking intelligent support and exhibiting significant limitations. For example, while collaborative office methods and systems with artificial intelligence learning capabilities can achieve AI-based collaborative office processing, in practice, they still suffer from insufficient task response accuracy, poor alignment between resource allocation and actual task requirements, delayed execution optimization, and poor model adjustment adaptability. These issues make it difficult to dynamically optimize resource allocation paths based on task urgency and complexity, and unable to achieve closed-loop adaptive adjustment of parameters and rules during task execution. This results in low efficiency in critical task processing and limited overall office intelligence. First, relying on manual operations and single data rules makes it difficult to handle complex and multidimensional data requirements, resulting in inefficient information sharing and processing. Second, existing methods are often based on pre-set processes, lack dynamic response capabilities, and are unable to flexibly adapt to changing task requirements during actual operations. Furthermore, existing collaborative office methods lack intelligent capabilities in data analysis and task allocation, lacking intelligent algorithm support. This results in low efficiency and accuracy in resource allocation and emergency response, failing to fully meet the deep-seated needs of power companies for intelligent office work. Summary of the Invention

[0005] In response to the deficiencies of the existing technology, the present invention provides an intelligent collaborative office method and system for power enterprises based on artificial intelligence, which solves the problems of the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent collaborative office method for power enterprises based on artificial intelligence, comprising the following steps: S1. Identifying the business demand type, urgency and task complexity from the request data received from the user terminal according to the deep learning model, analyzing the business demand type, urgency and task complexity, generating a business priority index, and classifying and assigning priorities to the requests according to the business priority index; S2. Based on the business priority index, matching the associated tasks and resource requirements of the request through the deep learning model, retrieving historical data and knowledge base information related to the business demand type, urgency and task complexity, and analyzing and optimizing the required resource configuration path; S3. Dynamically tracking the task progress, execution status and resource consumption according to the deep learning model, identifying bottlenecks in the task execution process, and generating optimization instructions; S4. Based on the optimization instructions during the task execution process, dynamically adjusting the parameters and resource configuration rules of the deep learning model through an adaptive learning algorithm.

[0007] Furthermore, the business demand type, urgency and task complexity are analyzed to generate the business priority index. The specific process is as follows: user request data is parsed according to the deep learning model to extract the business demand type, urgency and task complexity features; the extracted features are standardized, weights are assigned to different features through a weighted algorithm, and comprehensive calculations are performed to generate the business priority index.

[0008] Furthermore, the specific process of classifying requests and assigning priorities according to the business priority index is as follows: setting a classification threshold according to the business priority index to divide the request level; comparing the request data with the classification threshold to determine the priority category of the request; generating a priority identifier according to the priority category, and assigning the request to the corresponding processing queue.

[0009] Furthermore, based on the business priority index, the specific process of matching the associated tasks and resource requirements of the request through the deep learning model is as follows: input the business priority index into the deep learning model to identify the associated tasks and required resources of the request; retrieve resource information related to the business requirement type, urgency and task complexity from historical data and knowledge base; perform resource demand analysis based on the retrieval results and request characteristics, and match the optimal resource configuration path.

[0010] Furthermore, the specific process of analyzing and optimizing the required resource configuration path is as follows: establishing an optimization model for the resource configuration path based on the requested business priority index; obtaining resource configuration data, including manpower, power equipment, and response time; and evaluating the performance of the resource configuration path through a multi-objective optimization algorithm to adjust the resource configuration path.

[0011] Furthermore, task progress, execution status, and resource consumption are dynamically tracked based on the deep learning model, and the identification logic for bottlenecks in the task execution process is as follows: task progress, execution status, and resource consumption data are monitored based on the deep learning model to extract key features; performance benchmarks are set, and the current status is compared with the expected target to identify deviations in the execution process; threshold judgments are used to determine the links that do not meet the set standards; and the identified bottlenecks are prioritized.

[0012] Furthermore, the specific process of generating optimization instructions is as follows: based on the identified bottlenecks, analyze the factors affecting task execution, including resource allocation and execution time; convert the optimization plan into specific operation instructions, including resource reconfiguration, task scheduling and priority adjustment; set the execution time frame, and feed the generated optimization instructions back to the deep learning model to update the model parameters.

[0013] Furthermore, the specific process of dynamically adjusting the parameters and resource allocation rules of the deep learning model through the adaptive learning algorithm is as follows: obtaining the task execution data after executing the optimization instructions, covering the execution effect, resource usage and completion time; analyzing the differences between the task execution data and the optimization goals, and identifying the key parameters affecting the model performance; calculating the gradients of the key parameters and adjusting the deep learning model parameters accordingly; updating the resource allocation rules, and feeding the adjusted parameters back to the deep learning model to form a closed-loop feedback mechanism.

[0014] The intelligent collaborative office system for power enterprises based on artificial intelligence includes the following modules: a business demand identification module, a resource matching optimization module, a bottleneck identification module, and an adaptive adjustment module; the business demand identification module is used to identify the business demand type, urgency and task complexity from the request data received from the user terminal according to the deep learning model, analyze the business demand type, urgency and task complexity, generate a business priority index, and classify and prioritize the requests according to the business priority index; the resource matching optimization module is used to match the associated tasks and resource requirements of the request through the deep learning model based on the business priority index, retrieve historical data and knowledge base information related to the business demand type, urgency and task complexity, and analyze and optimize the required resource configuration path; the bottleneck identification module is used to dynamically track the task progress, execution status and resource consumption according to the deep learning model, identify bottlenecks in the task execution process, and generate optimization instructions; the adaptive adjustment module is used to dynamically adjust the parameters and resource configuration rules of the deep learning model through an adaptive learning algorithm based on the optimization instructions in the task execution process.

[0015] The present invention has the following beneficial effects:

[0016] (1) This AI-based intelligent collaborative office method for power companies uses a deep learning model to analyze the request data of user terminals, effectively classifying business needs, assessing their urgency and complexity, and thus generating a business priority index. This precise identification helps companies quickly respond to customer needs, optimize task allocation, and ensure that urgent and complex tasks are prioritized, thereby improving customer satisfaction and the quality of corporate services. Based on the generated business priority index, the deep learning model can accurately match the associated tasks and resource requirements of the request. This process intelligently analyzes and optimizes the resource allocation path by retrieving historical data and knowledge base information, reducing the redundant use of manpower and equipment, ensuring that resources are reasonably allocated at critical moments, improving work efficiency, and reducing the risk of delays caused by resource shortages.

[0017] (2) The intelligent collaborative office system for power enterprises based on artificial intelligence uses a deep learning model to monitor the progress, execution status and resource consumption of tasks in real time, and promptly identifies bottlenecks in the execution process through data analysis. This dynamic monitoring can help managers quickly understand the progress of projects and implement targeted adjustment measures to ensure that tasks can be smoothly carried out and reduce project delays and cost overruns. After identifying bottlenecks, the system can generate optimization instructions and dynamically adjust the parameters of the deep learning model using adaptive learning algorithms. This adjustment ensures that the model can continuously adapt to new business needs and environmental changes, improve the rationality of resource allocation rules, form a feedback loop, further optimize system performance and response speed, and improve the overall operational efficiency and flexibility of power enterprises.

[0018] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the intelligent collaborative office method for power enterprises based on artificial intelligence of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of the intelligent collaborative office system for power enterprises based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present application solve the problems of low efficiency of traditional collaborative office methods in processing complex data requirements, dynamic response and resource allocation through an intelligent collaborative office method and system for power enterprises based on artificial intelligence.

[0022] The overall approach to the problems in the embodiments of this application is as follows:

[0023] Based on the deep learning model, the service requirement type, urgency and task complexity are identified from the request data received from the user terminal, the service requirement type, urgency and task complexity are analyzed, and a service priority index is generated. The requests are classified and prioritized according to the service priority index.

[0024] Based on the business priority index, a deep learning model is used to match the associated tasks and resource requirements of the request, retrieve historical data and knowledge base information related to the business requirement type, urgency and task complexity, and analyze and optimize the required resource configuration path.

[0025] Dynamically track task progress, execution status, and resource consumption based on deep learning models, identify bottlenecks in task execution, and generate optimization instructions.

[0026] Based on the optimization instructions during task execution, the parameters and resource configuration rules of the deep learning model are dynamically adjusted through adaptive learning algorithms.

[0027] See also Figure 1 , an embodiment of the present invention provides a technical solution: an intelligent collaborative office method for power enterprises based on artificial intelligence, comprising the following steps: S1. identifying the business demand type, urgency and task complexity from the request data received from the user terminal according to the deep learning model, analyzing the business demand type, urgency and task complexity, generating a business priority index, and classifying and assigning priorities to the requests according to the business priority index; S2. based on the business priority index, matching the associated tasks and resource requirements of the request through the deep learning model, retrieving historical data and knowledge base information related to the business demand type, urgency and task complexity, and analyzing and optimizing the required resource configuration path; S3. dynamically tracking the task progress, execution status and resource consumption according to the deep learning model, identifying bottlenecks in the task execution process, and generating optimization instructions; S4. based on the optimization instructions in the task execution process, dynamically adjusting the parameters and resource configuration rules of the deep learning model through an adaptive learning algorithm.

[0028] In this implementation, step S1 uses a deep learning model to analyze request data submitted by user terminals to identify and categorize business needs. Based on the urgency and complexity of the needs, a business priority index is generated to prioritize subsequent processing. A deep learning model is a machine learning model that mimics the structure of the human brain's neural network and is trained on large amounts of data to extract features and patterns. S2. In this stage, based on the generated business priority index, the deep learning model matches user requests with relevant tasks and resource requirements, retrieves historical data and knowledge base information, and analyzes and optimizes resource allocation paths. Historical data and knowledge base refer to the company's historical operational data and accumulated knowledge, which provide a basis for current decision-making. S3. In this step, the deep learning model monitors the progress, status, and resource consumption of task execution, identifies bottlenecks during execution, and generates corresponding optimization instructions. S4. Based on the identified bottlenecks and generated optimization instructions, an adaptive learning algorithm is used to dynamically adjust the deep learning model's parameters and resource allocation rules to continuously optimize work efficiency. A deep learning model is a machine learning model based on a multi-layer neural network that can automatically extract features and learn complex patterns from large amounts of data. It is commonly used in fields such as image recognition and natural language processing. Business priority index: a quantitative indicator used to evaluate and prioritize different business needs, usually considering factors such as urgency, resource requirements and potential impact. Resource allocation path: refers to the specific plan for the allocation and use of resources (such as manpower, material resources, etc.) during task execution. Optimization instructions: specific operational recommendations generated based on data analysis, aimed at improving the efficiency and effectiveness of task execution. Bottleneck analysis: Identify and evaluate the links that limit performance or efficiency in the production or service process, and improve the overall process efficiency by resolving bottleneck problems. Adaptive learning algorithm: refers to a type of machine learning algorithm that can self-adjust in the process of continuously acquiring new data to improve the model's predictive accuracy and adaptability.

[0029] Specifically, the business demand type, urgency and task complexity are analyzed to generate the business priority index. The specific process is as follows: user request data is parsed according to the deep learning model to extract the business demand type, urgency and task complexity features; the extracted features are standardized, weighted algorithms are used to assign weights to different features, and comprehensive operations are performed to generate the business priority index.

[0030] In this implementation, data analysis: using deep learning models to analyze user request data and extract the following features: business requirement type (T), urgency (E), task complexity (C); feature standardization: standardize the extracted features to ensure that each feature is compared under the same dimension. The standardization formula is: ;in, is the original eigenvalue, is the mean of the feature, is the standard deviation of the feature. The weighted algorithm is used to assign weights to different features and generate the business priority index. After setting the weights, the business priority index ( ) is calculated as: ; : Business priority index, which indicates the priority of the task or request. The higher the value, the higher the priority. : The standardized business requirement type, urgency and task complexity feature values ​​correspond to the extracted original features respectively. : weight coefficients corresponding to business requirement type, urgency and task complexity, indicating the importance of each feature in the priority calculation. Their values ​​range from 0 to 1 and must meet . Normalization factor, used to adjust the proportion of each part in the formula so that the final priority index is within a reasonable range, usually : Resource availability, which indicates the quantity or quality of resources currently available for the task. The value range is 0 to 1, where 0 indicates no available resources and 1 indicates sufficient resources. : Historical completion efficiency, which indicates the efficiency of executing similar tasks in the past. It is usually the ratio of completion time to expected time. A value greater than 1 indicates that the execution efficiency is lower than expected, and a value less than 1 indicates that it is better than expected. Adjustment factor: This factor is used to adjust the impact of resource availability and historical efficiency on priority. It is set dynamically based on actual needs. : A constant correction term used to compensate for priority deviations caused by model limitations or external factors. It can be set according to specific application scenarios to ensure the rationality and accuracy of the business priority index.

[0031] Specifically, the specific process of classifying requests and assigning priorities according to the business priority index is as follows: setting a classification threshold based on the business priority index to divide the request level; comparing the request data with the classification threshold to determine the priority category of the request; generating a priority identifier based on the priority category, and assigning the request to the corresponding processing queue.

[0032] In this implementation, the process consists of three main steps: setting classification thresholds, comparing request data with the classification thresholds, generating priority identifiers, and assigning requests to processing queues. Step 1: Setting classification thresholds. Based on the distribution of the service priority index, a classification threshold is set to prioritize requests. This is done based on historical data or real-time feedback. Fixed threshold methods use experience or industry standards to set fixed priority thresholds. For example, a high priority threshold is P ≥ 0.75, a medium priority threshold is 0.50 ≤ P < 0.75, and a low priority threshold is P < 0.50. Request data is compared with the classification thresholds, and the calculated service priority index (P) is compared with the set classification thresholds to determine the priority category of each request. The specific comparison process is as follows: Each request's priority index is compared with the set thresholds in turn: If P ≥ 0.75, the request is classified as high priority. If 0.50 ≤ P < 0.75, the request is classified as medium priority. If P < 0.50, the request is classified as low priority. This comparison accurately determines the urgency of request processing, facilitating subsequent task allocation. Priority identifiers are generated and requests are assigned to processing queues. Based on the determined priority category, a corresponding priority identifier is generated for each request and assigned to the corresponding processing queue: high-priority requests are identified as "High"; medium-priority requests are identified as "Medium"; and low-priority requests are identified as "Low." High-priority requests are immediately placed in the "High Priority Processing Queue" for priority processing. Medium-priority requests are placed in the "Medium Priority Processing Queue" and processed sequentially. Low-priority requests are placed in the "Low Priority Processing Queue" and processed when resources are available.

[0033] Specifically, based on the business priority index, the specific process of matching the associated tasks and resource requirements of the request through the deep learning model is as follows: input the business priority index into the deep learning model to identify the associated tasks and required resources of the request; retrieve resource information related to the business requirement type, urgency and task complexity from historical data and knowledge base; perform resource demand analysis based on the retrieval results and request characteristics, and match the optimal resource configuration path.

[0034] In this implementation, the process is divided into four main steps: inputting a service priority index, identifying the request's associated tasks and required resources, retrieving resource information, and performing resource demand analysis and matching the optimal resource allocation path. Step 1: Inputting the service priority index into the deep learning model. The calculated service priority index (P) is passed as input to the deep learning model (CNN model). The model processes the input data through forward propagation and outputs the tasks and resource requirements associated with the request. The input data structure may include the following information: Request feature vector: Contains characteristic data such as the service request type, urgency (e.g., a value from 1 to 5, with 1 representing low urgency and 5 representing high urgency), and task complexity (e.g., simple, medium, complex). Step 2: Identifying the request's associated tasks and required resources. The model processes the input data to identify the specific tasks and corresponding resources required for the request. Specifically, this includes: Task identification: The model determines the specific tasks associated with the request based on patterns learned from historical data. For example, for a power outage handling request, the model might identify "fault detection" and "on-site repair" as associated tasks. Resource requirement identification: Based on the identified tasks, the model outputs the specific resource types and quantities required. For example, a repair task is identified as requiring two technicians, one piece of maintenance equipment, and appropriate safety equipment. Step 3: Retrieve Resource Information: After identifying the requested task and resource requirements, relevant resource information is retrieved from the company's historical database and knowledge base. The specific process includes: Historical Data Analysis: By analyzing the processing records of similar requests (for example, using historical records to determine the average time and resource allocation required to complete a fault detection task), the system determines which resources are most effective in similar situations. Knowledge Base Query: Extracting best practices and resource allocation recommendations based on the task type, urgency, and complexity from the knowledge base. For example, for complex fault handling tasks, the knowledge base may contain a list of recommended resource allocations for specific equipment failures. Step 4: Analyze Resource Requirements and Match the Optimal Resource Allocation Path: Based on the request characteristics, associated tasks, and retrieved resource information, a resource requirement analysis is performed and the optimal resource allocation path is matched. The specific process is as follows: Requirements Analysis: Using the results of historical data analysis, the system assesses the specific resource requirements of the request and determines the number of resources and their corresponding allocation required to complete the task. For example, if historical data indicates that a "fault detection" task typically takes three hours and requires one experienced technician, the system takes this information into consideration.

[0035] Path Matching: Using an optimization algorithm (linear programming), the system compares task and resource requirements to determine the optimal resource allocation path. The system then generates an optimal resource scheduling list based on real-time available resources. For example, if two technicians are available, the system prioritizes the experienced technician for fault diagnosis and assigns the appropriate equipment.

[0036] Specifically, the specific process of analyzing and optimizing the required resource configuration path is as follows: based on the requested business priority index, an optimization model of the resource configuration path is established; resource configuration data, including manpower, power equipment, and response time, is obtained; and the performance of the resource configuration path is evaluated through a multi-objective optimization algorithm, and the resource configuration path is adjusted.

[0037] In this implementation plan, the process mainly includes four steps: establishing an optimization model for resource allocation path, obtaining resource allocation data, performance evaluation, and adjusting resource allocation path. Step 1: Establishing an optimization model for resource allocation path based on the requested business priority index: Based on the requested business priority index ( ), build a mathematical optimization model. This model can be in the form of linear programming or integer programming, and its goal is to minimize the cost of resource usage while maximizing the efficiency of task execution. The basic structure of the model is as follows: ;in: is the total cost; It means that the goal is to minimize the total resource usage cost C; is the resource usage cost of the i-th task; The amount of resources required for the i-th task; subject to means satisfying the following constraints, which refers to the amount of resources required for each task i. Must be no less than the demand ; The contribution coefficient of the i-th task to the j-th resource; For the task Demand; is the allocation quantity of the jth resource; m is the total number of tasks; and n is the total number of resources. Objective Function: Set an appropriate objective function based on business needs and resource availability. For example, minimize response time and total resource consumption. Step 2: Data Collection: Extract relevant resource allocation data from internal enterprise systems and external databases, including the following: Human Resources: Obtain information on the number of available technicians, their skill levels, and work schedules. Power Equipment: Collect information on equipment availability, processing capacity (such as load and response time), and maintenance status. Response Time: Analyze the average response time of specific tasks based on historical data. Data Standardization: To ensure compatibility of different data types, standardize the acquired data for subsequent analysis. Z-score standardization is used as the standardization method. Step 3: Use a multi-objective optimization algorithm to evaluate the performance of the resource allocation path to ensure the optimal balance between multiple objectives. Multi-objective optimization algorithms aim to simultaneously optimize multiple objectives, minimizing cost, maximizing efficiency, and reducing response time. This algorithm allows for evaluating the pros and cons of different resource configurations and generates a set of feasible solutions. Step 4: Path Adjustment: Adjust the resource allocation path based on the output of the multi-objective optimization algorithm. This may include: Resource reallocation: Adjusting the resource allocation required for each task based on optimization results. For example, reassigning a technician from a lower-priority task to a higher-priority request. Time adjustment: Optimizing the execution order of tasks to reduce overall response time. For example, prioritizing high-urgency tasks. Iterative optimization: Based on actual execution feedback and performance evaluation results, continuously iterate and optimize resource allocation paths to continuously improve efficiency and responsiveness.

[0038] Specifically, task progress, execution status, and resource consumption are dynamically tracked based on the deep learning model, and the identification logic for bottlenecks in the task execution process is as follows: task progress, execution status, and resource consumption data are monitored based on the deep learning model to extract key features; performance benchmarks are set, the current status is compared with the expected target, and deviations in the execution process are identified; threshold judgments are used to determine the links that do not meet the set standards; and the identified bottlenecks are prioritized.

[0039] In this implementation, the specific logic for dynamically tracking task progress, execution status, and resource consumption in the intelligent collaborative office system of a power enterprise is as follows: The system uses a deep learning model to monitor task progress, execution status, and resource consumption in real time. The monitored data sources include: Task progress: This records the percentage of task completion and the achievement of milestones at each stage. Execution status: This records the current status of the task (e.g., in progress, paused, completed, delayed). Resource consumption: This includes the use of personnel and equipment, as well as resources such as time and electricity consumed. Key features are extracted to understand the actual execution of tasks for subsequent analysis. Next, the system establishes performance benchmarks to compare the current status with expected targets. These performance benchmarks may include: Expected progress targets: This specifies the task progress requirement, such as the completion percentage a task should reach by a specific point in time. Expected status requirements: This specifies the task execution status, such as the expected progress of the task. Resource usage expectations: This specifies the maximum amount of resources required to complete the task, including time and equipment usage constraints. By comparing actual monitoring data with these expected targets, the system can identify deviations in task execution. Threshold determination and bottleneck identification: The system sets thresholds to determine which links are not meeting the set standards. For example: Schedule behind: If the current progress is significantly lower than the expected target, the task is marked as a schedule bottleneck. Status issue: If the execution status of the task fails to proceed according to the scheduled time node, it is marked as a status bottleneck. Resource overrun: If the actual resource consumption exceeds the expected upper limit, the system marks the link as a resource bottleneck. The identification of these bottlenecks helps to take timely measures to make adjustments. Bottleneck priority sorting. Finally, the system prioritizes the identified bottlenecks based on the following factors: Severity: Assess the degree of task deviation. Links with larger deviations in progress and resource consumption have higher priority. Scope of impact: Analyze the impact of bottlenecks on the overall project progress, and give priority to links with greater impact. Difficulty of resolution: Consider the resources and time required to fix the bottleneck. The easier the bottleneck is to resolve, the higher the priority.

[0040] Specifically, the process of generating optimization instructions is as follows: based on the identified bottlenecks, analyze the factors affecting task execution, including resource allocation and execution time; convert the optimization plan into specific operation instructions, including resource reconfiguration, task scheduling and priority adjustment; set the execution time frame, and feed the generated optimization instructions back to the deep learning model to update the model parameters.

[0041] In this implementation, in the intelligent collaborative office system, the specific process of generating optimization instructions is as follows:

[0042] Bottleneck Identification and Influencing Factor Analysis: After identifying bottlenecks in task execution, the system first conducts an in-depth analysis of these bottlenecks, primarily encompassing the following two aspects: Resource Allocation: This examines the current resource allocation, including human resources, equipment usage, and budget allocation, to assess whether it is reasonable and how it can be optimized. For example, if a task's execution is overly reliant on a single resource, the system will consider increasing or reallocating that resource to alleviate the bottleneck. Execution Time: This analyzes task time management, including comparing actual execution times with expected times. By monitoring this data, the system can identify specific areas of delay and the causes (such as resource shortages and task complexity). Optimization Plans Translated into Actionable Instructions: Once the influencing factors have been analyzed, the system then translates the optimization plan into actionable instructions, including but not limited to the following: Resource Reallocation: Based on the bottleneck situation, the system may instruct the transfer of certain resources from lower-priority tasks to higher-priority tasks. For example, if a project is progressing slowly and has a high level of urgency, the system may recommend additional manpower or equipment support. Task Scheduling: This optimizes the scheduling of tasks, improving overall efficiency by adjusting the execution order and start times of each task. The system may recommend postponing certain non-urgent tasks to focus resources on current critical tasks. Priority adjustment: Based on the business priority index and the results of bottleneck analysis, the system will adjust the priority of tasks to ensure that the most important tasks receive resources and support first. Setting the execution time frame: After generating the optimization instructions, the system will set an execution time frame for each operation instruction to ensure that various optimization measures can be implemented in a timely manner. The time frame includes: Specific start time: Indicates when the optimization instructions should be implemented. Expected completion time: Estimates the implementation time of each optimization measure to monitor progress. Optimization instruction feedback and model update: Finally, the generated optimization instructions are fed back to the deep learning model, and the model parameters are updated. This process includes: Encoding and input of optimization instructions: Converting the optimization plan into a format that can be understood by the model and inputting it into the deep learning model for subsequent data training and optimization. Model parameter update: Based on the new feedback information, adjust the model parameters to ensure that the model can better adapt to the real-time changing task execution environment and resource allocation requirements.

[0043] Specifically, the specific process of dynamically adjusting the parameters and resource allocation rules of the deep learning model through the adaptive learning algorithm is as follows: obtaining the task execution data after executing the optimization instructions, covering the execution effect, resource usage and completion time; analyzing the differences between the task execution data and the optimization goals, and identifying the key parameters that affect the model performance; calculating the gradients of the key parameters and adjusting the deep learning model parameters accordingly; updating the resource allocation rules, and feeding the adjusted parameters back to the deep learning model to form a closed-loop feedback mechanism.

[0044] In this implementation, the specific process for dynamically adjusting the parameters and resource allocation rules of the deep learning model using an adaptive learning algorithm in the intelligent collaborative office system is as follows: Acquiring task execution data: First, the system collects task execution data after executing optimization instructions. This data primarily includes: Execution performance: The quality and efficiency of task completion, including whether the task was completed within the expected timeframe and whether the task results met the set standards. Resource utilization: The actual utilization of various resources (such as manpower and equipment), including resource input, frequency of use, and effectiveness in task execution. Completion timeliness: The time required to complete a task is compared with the preset completion timeframe to assess whether the task was delivered on time. Analyzing discrepancies between task execution data and optimization targets: By analyzing the collected data, the system can identify discrepancies between task execution data and the predetermined optimization targets. This step primarily involves: Discrepancy identification: Comparing actual execution performance, resource utilization, and completion timeliness with the optimization targets to identify areas where expectations were not met. For example, if the actual completion time of a task far exceeds expectations, this indicates insufficient resource allocation or improper scheduling. Key parameter identification: Based on discrepancy analysis, the system identifies key parameters that impact model performance. These parameters may include task complexity, resource allocation ratios, and the capabilities of the execution team. Calculating Key Parameter Gradients: After identifying key parameters, the system performs gradient calculations. The specific steps are as follows: Gradient Calculation: Using an optimization algorithm (stochastic gradient descent), the system calculates the gradients of key parameters relative to the model's loss function. This process helps the system understand how to adjust parameters to better optimize task execution under the current configuration. Parameter Update Direction: Based on the calculated gradients, the system determines the direction and magnitude of model parameter adjustments to reduce the gap between model output and actual execution results. Adjusting Deep Learning Model Parameters: After calculating the gradients of key parameters, the system adjusts the parameters of the deep learning model accordingly. The specific process is as follows: Parameter Adjustment: Based on the gradient information, the model parameters are updated according to the set learning rate. The learning rate determines the magnitude of each adjustment. A reasonable learning rate can effectively improve the model's convergence speed and stability. Model Retraining: After parameter adjustments, the model may require retraining to adapt to the new parameter configuration, thereby improving prediction accuracy and execution efficiency. Updating Resource Allocation Rules: After adjusting the parameters of the deep learning model, the system next updates the resource allocation rules to ensure the effective implementation of the optimization measures. This step includes: Resource Allocation Rule Evaluation: Based on the newly adjusted model parameters, existing resource allocation rules are re-evaluated to analyze which rules need to be modified to improve overall performance. Rule Update: Resource allocation rules are updated to integrate the adjusted parameters with the new configuration requirements to ensure more efficient resource utilization in subsequent task execution. Feedback to the deep learning model to form a closed-loop feedback mechanism: All adjustments and updates are fed back to the deep learning model, forming a closed-loop feedback mechanism to ensure continuous learning and optimization of the system.Specifically, the system includes: A feedback mechanism: This feeds execution data, model parameters, and resource allocation rules into the deep learning model, enabling it to learn from historical data and improve its intelligence. Continuous optimization: This feedback mechanism enables the system to dynamically adjust itself to the ever-changing task execution environment and management requirements, improving the operational efficiency of power companies.

[0045] See also Figure 2 The intelligent collaborative office system for power enterprises based on artificial intelligence includes the following modules: business demand identification module, resource matching optimization module, bottleneck identification module, and adaptive adjustment module; the business demand identification module is used to identify the business demand type, urgency and task complexity from the request data received from the user terminal according to the deep learning model, analyze the business demand type, urgency and task complexity, generate a business priority index, classify the requests and assign priorities according to the business priority index; the resource matching optimization module is used to match the associated tasks and resource requirements of the request through the deep learning model based on the business priority index, retrieve historical data and knowledge base information related to the business demand type, urgency and task complexity, and analyze and optimize the required resource configuration path; the bottleneck identification module is used to dynamically track the task progress, execution status and resource consumption according to the deep learning model, identify bottlenecks in the task execution process, and generate optimization instructions; the adaptive adjustment module is used to dynamically adjust the parameters and resource configuration rules of the deep learning model through the adaptive learning algorithm based on the optimization instructions in the task execution process.

[0046] In this implementation, the business requirement identification module is primarily responsible for receiving request data from user terminals and parsing and analyzing the data using deep learning models. Specific functions include: identifying business requirement types: determining the specific business area involved in the user request for targeted processing; assessing urgency: analyzing the urgency of the request to prioritize high-priority business requirements; analyzing task complexity: assessing the complexity of the request to help the system allocate appropriate resources and tasks; generating a business priority index: combining the above analysis results to generate a comprehensive business priority index, which is used to classify and prioritize requests, ensuring that high-priority tasks are processed first. The resource matching optimization module uses a deep learning model based on the business priority index to perform resource allocation and task matching. Specific functions include: associating tasks with resource requirements: identifying historical tasks and resource requirements related to the request based on its characteristics; retrieving historical data and knowledge base information: extracting historical data related to the current request from the system's knowledge base to provide reference for resource allocation; and analyzing and optimizing resource allocation paths: optimizing the allocation path of required resources through data analysis to achieve optimal resource utilization and task execution results. The Bottleneck Identification Module is responsible for dynamically monitoring key indicators during task execution and identifying potential bottlenecks. Specific functions include: Dynamically Tracking Task Progress and Execution Status: This module monitors task execution in real time to ensure timely detection of issues. Resource Consumption Analysis: This module analyzes resource consumption during task execution to identify areas with insufficient or inappropriate resource allocation. Optimization Instruction Generation: After identifying bottlenecks, this module generates targeted optimization instructions to guide subsequent resource adjustments and task rescheduling. Adaptive Adjustment Module: This module dynamically adjusts deep learning models and resource allocation rules based on optimization instructions during task execution. Specific functions include: Dynamically Adjusting Model Parameters: This module utilizes adaptive learning algorithms to update deep learning model parameters in real time to improve the model's prediction accuracy and task processing capabilities. Updating Resource Allocation Rules: This module promptly adjusts resource allocation rules based on new task execution data and optimization instructions to ensure the system can flexibly respond to changing task requirements. Forming a closed-loop feedback mechanism: Through continuous feedback and adjustment, the system achieves self-learning and optimization, improving overall operational efficiency.

[0047] In summary, this application has at least the following effects:

[0048] This AI-based intelligent collaborative office method and system for power enterprises uses a deep learning model to receive request data from user terminals, identify the type of business demand, urgency, and task complexity, generate a business priority index, and perform classification and priority assignment. Based on business priorities, the model matches associated tasks and resource requirements, retrieves relevant historical data to optimize resource allocation paths, dynamically tracks task progress, execution status, and resource consumption, identifies bottlenecks in the execution process, and generates optimization instructions. Finally, an adaptive learning algorithm dynamically adjusts model parameters and resource allocation rules, forming a closed-loop feedback mechanism. This effectively integrates information flow and resource allocation, improves the flexibility and responsiveness of collaborative office work, and adapts to the development needs of smart grids and smart cities.

[0049] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0053] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0054] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent collaborative office method for power enterprises based on artificial intelligence, characterized by: The following steps are involved: S1. Identify the business requirement type, urgency, and task complexity from the request data received from the user terminal using a deep learning model, analyze the business requirement type, urgency, and task complexity, generate a business priority index, and classify and prioritize the requests based on the business priority index. S2. Based on the business priority index, a deep learning model is used to match the requested tasks and resource requirements. Historical data and knowledge base information related to the business requirement type, urgency, and task complexity are retrieved to analyze and optimize the required resource allocation path. S3. Dynamically track task progress, execution status, and resource consumption based on deep learning models, identify bottlenecks in task execution, and generate optimization instructions. S4. Based on the optimization instructions during task execution, the parameters and resource configuration rules of the deep learning model are dynamically adjusted through adaptive learning algorithms.

2. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 1 is characterized by: The specific process of analyzing business requirement type, urgency, and task complexity to generate a business priority index is as follows: Analyze user request data based on deep learning models to extract business demand type, urgency, and task complexity characteristics; The extracted features are standardized, weights are assigned to different features through a weighted algorithm, and comprehensive calculations are performed to generate a business priority index.

3. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 2 is characterized by: The specific process of classifying and prioritizing requests based on the business priority index is as follows: Set classification thresholds based on the business priority index and divide the request levels; Compare request data with classification thresholds to determine the priority category of the request; Generates a priority ID based on the priority category and assigns the request to the corresponding processing queue.

4. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 3 is characterized by: The specific process of matching the associated tasks and resource requirements of a request using a deep learning model based on the business priority index is as follows: Input the business priority index into the deep learning model to identify the associated tasks and required resources of the request; Retrieve resource information related to business requirement type, urgency, and task complexity from historical data and knowledge base; Based on the search results and request characteristics, resource demand analysis is performed to match the optimal resource configuration path.

5. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 4 is characterized by: The specific process of analyzing and optimizing the required resource configuration path is as follows: Establish an optimization model for resource allocation paths based on the requested business priority index; Obtain resource configuration data, including manpower, power equipment, and response time; The resource allocation path is evaluated and adjusted through a multi-objective optimization algorithm.

6. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 5 is characterized by: The deep learning model is used to dynamically track task progress, execution status, and resource consumption, and identify bottlenecks during task execution. The following logic is used: Monitor task progress, execution status, and resource consumption data based on deep learning models and extract key features; Set performance benchmarks, compare current status with expected goals, and identify deviations in execution; Through threshold judgment, determine the links that do not meet the set standards; Prioritize the identified bottlenecks.

7. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 6 is characterized by: The specific process of generating optimization instructions is as follows: Based on the identified bottlenecks, analyze the factors affecting task execution, including resource allocation and execution time; Convert optimization plans into specific operational instructions, including resource reallocation, task scheduling, and priority adjustment; Set the execution time frame and feed the generated optimization instructions back to the deep learning model to update the model parameters.

8. The intelligent collaborative office method for electric power enterprises based on artificial intelligence according to claim 7 is characterized by: The specific process of dynamically adjusting the parameters and resource configuration rules of the deep learning model through the adaptive learning algorithm is as follows: Obtain task execution data after executing optimization instructions, covering execution results, resource usage, and completion time; Analyze the differences between task execution data and optimization targets to identify key parameters that affect model performance; Calculate the gradients of key parameters and adjust deep learning model parameters accordingly; Update resource configuration rules and feed the adjusted parameters back to the deep learning model to form a closed-loop feedback mechanism.

9. An intelligent collaborative office system for electric power enterprises based on artificial intelligence, applying the intelligent collaborative office method for electric power enterprises based on artificial intelligence according to any one of claims 1 to 8, characterized in that: Includes the following modules: Business demand identification module, resource matching optimization module, bottleneck identification module, and adaptive adjustment module; The business demand identification module is used to identify the business demand type, urgency and task complexity from the request data received from the user terminal based on the deep learning model, analyze the business demand type, urgency and task complexity, generate a business priority index, and classify and prioritize the requests based on the business priority index; The resource matching optimization module is used to match the associated tasks and resource requirements of the request based on the business priority index through a deep learning model, retrieve historical data and knowledge base information related to the business requirement type, urgency and task complexity, and analyze and optimize the required resource configuration path; The bottleneck identification module is used to dynamically track task progress, execution status and resource consumption based on the deep learning model, identify bottlenecks in the task execution process, and generate optimization instructions; The adaptive adjustment module is used to dynamically adjust the parameters and resource configuration rules of the deep learning model through an adaptive learning algorithm based on the optimization instructions during the task execution process.

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