Method for improving organization energy efficiency

Abstract

The invention relates to the technical field of organization management and informatization, and discloses a method for improving organization energy efficiency, comprising the following steps: S1, introducing a thought for improving organization efficiency, fusing the thought into each link of enterprise management, and analyzing each process link to mine efficiency improvement points for constructing a standard process framework of organization operation, the process framework is constructed through an analytic hierarchy process, and the framework comprises a target layer, a module layer and a task layer; and S2, performing flow optimization and organization resource configuration analysis based on the efficiency improvement point to form an executable organization optimization scheme, performing flow classification and cutting through a K-means clustering algorithm in the flow optimization, and defining a node execution role, a task time window and a dependency relationship in a flow chart generated in the cutting process. According to the invention, through process modeling, intelligent scheduling and knowledge sharing mechanisms, the effects of efficient coordination of organized tasks, dynamic and adjustable execution and continuous experience precipitation are realized.

Description

Technical Field

[0001] The present invention relates to the field of organizational management and information technology, and in particular to a method for improving organizational energy efficiency. Background Art

[0002] Amidst the increasing complexity of business operations and team collaboration, systematically improving organizational execution effectiveness has evolved from a matter of management philosophy to a competition of technical capabilities. Many companies are realizing that relying solely on institutional constraints or manual experience is no longer sufficient to support efficient and sustainable business growth. Frequent employee turnover, uneven task distribution, and poor process integration are continually reducing organizational efficiency.

[0003] Some companies have begun experimenting with optimizing task execution processes through information platforms or management systems. Some tools enable hierarchical management and visualization of project tasks, making task flow more intuitive and responsibilities more clearly defined. Some organizations have also established shared document systems or public knowledge bases to centralize internal experience, systems, and process documentation, thereby reducing duplication of effort. These technologies have played a positive role in improving organizational transparency and preventing information loss, helping companies establish a foundational framework for digital management.

[0004] However, judging from their implementation results, these existing technologies still have some shortcomings. First, task execution mostly relies on manual coordination, and the system lacks intelligent allocation capabilities, resulting in common phenomena such as duplicate assignments and disconnected progress. Second, although the process is visual, the path planning is still static and cannot be dynamically adjusted when encountering resource conflicts. In addition, the scheduling system lacks real-time feedback capabilities. Once the process deviates from the plan, it is easy to be interrupted or even reworked, lacking the necessary error correction mechanism. In addition, although many companies have knowledge bases, the content is often disorganized, lacking an update and review mechanism, the credibility of the information is low, and there is little knowledge that can be truly used. Finally, employees still have to rely on experience and ask people when doing things, making it difficult to accumulate experience and even more difficult to reuse it. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method for improving organizational energy efficiency, which solves the problems in the existing technology that task execution relies on manual labor, processes are rigid, knowledge is difficult to reuse, and organizational efficiency is difficult to improve automatically.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for improving tissue energy efficiency, comprising the following steps: S1. Introduce the concept of improving organizational effectiveness and integrate it into all aspects of enterprise management. Analyze each process link to identify areas for improving effectiveness and build a standard process framework for organizational operations. The process framework is constructed using the Analytic Hierarchy Process (AHP) and includes a goal layer, a module layer, and a task layer. S2. Based on the efficiency improvement points, process optimization and organizational resource allocation analysis are performed to form an executable organizational optimization plan. The process optimization is performed through process classification and tailoring using the K-means clustering algorithm. The flowchart generated by the tailoring process defines node execution roles, task time windows, and dependencies; S3. Through the information module, the idea of improving organizational efficiency is solidified into each node of the process to achieve automated process execution, role collaboration and knowledge reuse. The information module supports convolutional neural networks to identify task types and uses genetic algorithms to optimize task field configurations.

[0007] Preferably, the process of building organizational process standardization includes: Building a three-level planning structure based on the analytic hierarchy process, the three-level structure includes the target layer, the module layer and the task layer; Set the name, type, planned working hours, input, output and knowledge fields in each process task node; The task fields are structured and edited in a visual editing interface using a decision tree algorithm and saved to a system database. The editing process uses an ID3 algorithm to divide field information. The decision tree algorithm further supports automatic optimization and selection of subsequent nodes.

[0008] Preferably, the standard process framework for building an organization's operations includes: Set up cross-departmental collaboration nodes in the three-level plan template to clarify the input and output flow logic; Use visual editing controls to implement structured editing of task fields; The plan template supports node review flow settings and version control mechanisms.

[0009] Preferably, the process optimization and organizational resource allocation analysis includes: The dependency relationship is verified by using a depth-first search algorithm to check path connectivity, and the task execution path is optimized in combination with the Dijkstra algorithm.

[0010] Preferably, the process optimization and organizational resource allocation analysis further includes: Calculate the time difference between the role's actual intervention time and the task's planned start time; Forming a character-node-offset three-dimensional model; The model is used to output intervention optimization recommendations.

[0011] Preferably, the process optimization and organizational resource allocation analysis includes: Extract project text description and convert it into semantic vector; Comparing the semantic vector with a technical knowledge base; Use semantic vector matching algorithms to identify technical difficulties involved in the project and output pre-research process prompts.

[0012] Preferably, the process optimization and organizational resource allocation analysis further includes: In the project establishment stage, the CBB library is used to recommend results; Check the number of citations, quality labels and applicability of the recommended results; Support the binding of performance indicators to knowledge sharing behaviors and set up a review mechanism for reused results.

[0013] Preferably, the information module includes: A three-level planned task form is constructed using an online editable interface. The form supports field-level structuring, and field contents are subjected to image recognition and task type inference using a convolutional neural network. The fields include task name, task type, planned working hours, task input, task output and knowledge number; The task form supports genetic algorithms for field configuration optimization, version management, node review and approval control, and authority setting functions. The genetic algorithm is used to optimize the execution order and role allocation of tasks through a fitness function.

[0014] Preferably, the information module further includes: Project collaborative planning function; The function supports multiple roles to collaboratively set task start time, end time and dependency order online; The system generates task Gantt charts and dependency chain diagrams for critical path identification.

[0015] Preferably, the information module further includes a CBB achievement management module, and the CBB achievement management module includes: Support calling CBB results and binding them to task node input during project execution; After the project is executed, support the submission of mature results for review and write back to the CBB library; The system automatically updates the CBB entry status and records the reference relationship.

[0016] The present invention provides a method for improving tissue energy efficiency, which has the following beneficial effects: 1. The present invention adopts process modeling and automatic scheduling solutions to realize automatic allocation and optimization of tasks according to rules, achieving the technical effect of intelligent task management. Compared with the traditional method of allocating tasks based on manual experience, it solves the problems of task duplication and progress confusion, and significantly improves execution efficiency.

[0017] 2. The present invention introduces task clustering and path optimization mechanisms to achieve the effects of automatic process disassembly and path shortening. Compared with the existing solutions with fragmented plans and unclear execution routes, it solves the problems of process discontinuity and resource waste.

[0018] 3. The present invention utilizes feedback mechanism and deviation correction algorithm to achieve real-time tracking and automatic adjustment of task status. Compared with the traditional process with strong rigidity and delayed adjustment, it solves the problems of execution interruption and task postponement and enhances the organization's adaptability.

[0019] 4. The present invention constructs a knowledge sharing and experience reuse mechanism, realizing the centralized management and continuous accumulation of organizational knowledge. Different from the problems of knowledge dispersion and delayed updating in the existing technology, it solves the problems of difficult experience sharing and large repeated investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A method diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 , an embodiment of the present invention provides a method for improving organizational energy efficiency, comprising the following steps: S1. Introduce the concept of improving organizational effectiveness and integrate it into all aspects of enterprise management. Analyze each process link to identify areas for improving effectiveness and use it to build a standard process framework for organizational operations. The process framework is constructed using the Analytic Hierarchy Process (AHP) and includes the goal layer, module layer, and task layer. S1 of this embodiment is to introduce the idea of improving organizational effectiveness and systematically integrate it into all aspects of enterprise management. By conducting a detailed analysis of the existing processes, identifying points for improving effectiveness, and building a standardized organizational operation framework. The core purpose of this step is to provide the organization with a scientific and reasonable framework to further optimize resource allocation, reduce ineffective links, and thus improve overall work efficiency. In order to achieve this goal, this step adopts the Analytic Hierarchy Process (AHP) to construct the organization's standard process framework. The Analytic Hierarchy Process is an effective multi-level decision-making method that can decompose complex problems into multiple manageable parts through a hierarchical structure and perform quantitative evaluation on each part.

[0023] In this embodiment, the standard process framework for organizational operations is divided into three layers: the goal layer, the module layer, and the task layer. The goal layer defines the overall goal of improving organizational effectiveness, such as increasing efficiency, reducing costs, and optimizing resource utilization. The module layer rationally divides various work tasks and determines which work modules are critical to achieving the goals. The task layer further refines the specific tasks and operations within each module, clarifying the responsible personnel and execution details for each task. Through this structured hierarchical analysis, each task and module can be closely aligned with the organization's strategic goals, ensuring the achievement of overall goals.

[0024] Generally, the application of the AHP method first requires constructing a decision matrix to quantitatively evaluate the factors at the goal, module, and task levels for improving organizational effectiveness. At the goal level, this method assigns a weight to each goal, indicating its importance to overall effectiveness. At the module and task levels, weights are assigned layer by layer according to the principles of the AHP method, ensuring that the goals at each level are accurately reflected in the specific tasks to be executed.

[0025] Specifically, to calculate the weight value of each goal, module and task, the present invention will use the following formula for weighted averaging: Where: w i Represents the final weight value of the i-th module or task; a i is the score of the i-th module or task, indicating its performance under a certain standard, usually obtained through expert ratings or historical data; b i is the weight coefficient of the i-th module or task, reflecting its relative importance to the goal of improving organizational effectiveness; n is the number of modules or tasks to be evaluated.

[0026] In this formula, the score and weight coefficient are multiplied and summed, and then the sum is normalized to obtain the final weight value for each module or task. This process helps decision makers understand the relative contribution of each module or task to improving organizational effectiveness, thereby prioritizing tasks and modules with higher weights in subsequent resource allocation and decision-making.

[0027] Alternatively, when evaluating tasks and modules, the present invention can consider using a weighted decision matrix and further adjust the weighting coefficients based on historical data from different industries and organizations. In this way, the AHP can better meet actual business needs and avoid bias caused by over-reliance on a single expert opinion.

[0028] In one possible implementation, the present invention can combine other optimization methods, such as the K-means clustering algorithm, with the refinement of each module at the module level. This approach allows modules to be divided not only based on their performance goals but also to be reasonably clustered based on similarities in historical projects. For example, clustering algorithms can be used to further group technology and marketing modules based on similar execution methods and requirements, thereby optimizing module resource allocation.

[0029] At the task level, the results of the AHP process provide clear priorities for each task. Based on these priorities, tasks can be assigned to different resource pools. Each resource pool allocates manpower, time, and materials based on the task's requirements, ensuring that each task is completed within the scheduled timeframe and achieves the desired objectives.

[0030] In the implementation of this step, in addition to the basic AHP method, quantitative analysis of tasks may also be involved, especially the dependencies and time allocation of tasks. In order to accurately estimate the execution time of each task, the following formula can be used: Where: T i represents the total execution time of the i-th task; T ij represents the time requirement of the i-th task in the j-th stage; pj represents the weight coefficient of the j-th stage, reflecting the impact of this stage on the execution of the task; n1 represents the number of stages involved in the task execution process.

[0031] This formula helps the present invention calculate the total time required for each task more accurately, and optimizes tasks according to their dependencies and priorities, ensuring that tasks can be executed efficiently.

[0032] By introducing the Analytic Hierarchy Process (AHP) to build a standardized organizational process framework, enterprises can obtain a clear hierarchical structure of tasks and goals, thus providing a scientific basis for subsequent process optimization and resource allocation. This method can help organizational leaders: Allocate resources reasonably to ensure that high-priority tasks are adequately supported by resources; Optimize workflows and reduce duplication and conflicts between tasks through clear hierarchical divisions; Improve decision-making efficiency and make the decision-making process more transparent and fair through quantitative evaluation and weighted allocation.

[0033] S2. Analyze process optimization and organizational resource allocation based on efficiency improvement points to develop an executable organizational optimization plan. Process optimization uses the K-means clustering algorithm to classify and tailor processes. The resulting flowcharts define node execution roles, task time windows, and dependencies. In step S1, the present invention uses the Analytic Hierarchy Process (AHP) to determine the organizational goals and the priority of task modules. It also uses an effective evaluation system to clarify the effectiveness and resource requirements of each task. The following step, S2, focuses on further optimizing the task execution process and resource allocation based on these results. Using data analysis and intelligent optimization algorithms, it ensures that the process is completed efficiently in the shortest possible time, minimizing resource waste.

[0034] In this embodiment, during the process optimization phase, the present invention uses a K-means clustering algorithm to classify tasks. This aims to analyze the similarity of task characteristics and rationally group similar tasks together for centralized optimization and resource allocation. This approach can effectively reduce conflicts and redundancies between tasks, thereby improving the overall execution efficiency of the process.

[0035] The K-means clustering algorithm clusters tasks by minimizing the distance between their features. Specifically, each task's feature vector may include parameters such as task type, execution time, and resource consumption. By calculating these feature vectors, the K-means algorithm can group tasks into several categories. Tasks within each category have similar characteristics, facilitating subsequent optimization.

[0036] In general, the goal of the K-means algorithm is to reasonably classify tasks by minimizing the distance between each task point and the center point of its cluster. The formula is as follows: Where: C j represents the jth cluster, into which tasks are divided according to their similarity; x i is the feature vector of the i-th task, including the task execution time, resource requirements, task type, etc.; μ j is the center point of the jth cluster, that is, the mean feature of the tasks in this category; I(x i ∈C j ) is the indicator function, when task x i Belongs to category C j When , the value is 1, otherwise it is 0; ||·|| 2 represents the Euclidean distance between feature vectors.

[0037] The goal of this formula is to divide tasks into several clusters by minimizing the distance between the task feature vector and its cluster center. The tasks within each cluster are similar in characteristics, which helps with subsequent resource allocation and process optimization.

[0038] In actual applications, tasks will be assigned to corresponding categories, and tasks in each category will be processed centrally in the subsequent optimization stage to reduce the waste of processing time and resources.

[0039] Alternatively, after task classification is complete, the present invention uses a depth-first search (DFS) algorithm to analyze and optimize the dependencies between tasks. The DFS algorithm can effectively traverse task nodes, ensuring that task dependencies are correctly handled and avoiding sequence errors during task execution.

[0040] Specifically, the DFS algorithm recursively visits each task node, records the execution order and dependency paths of the tasks, and ensures that the tasks are executed in the correct order. The operation of DFS can be expressed as follows: DFS(v) = {v, traverse (adjacent nodes)}; Among them: v is the current task node, which means the task being executed; adjacent nodes are other task nodes that have dependencies with the current task; the traversal operation means visiting the nodes that have dependencies with the current task in sequence until all dependencies are traversed.

[0041] Through the DFS algorithm, task nodes can be traversed to ensure that the dependencies of each task are met.

[0042] The key to this algorithm is to recursively visit the dependent nodes of each task to ensure that tasks are executed in the correct order and avoid dependency conflicts.

[0043] In one possible implementation, to further optimize the order in which tasks are executed, the present invention introduces the Dijkstra algorithm to calculate the shortest path between tasks, minimizing the overall task execution time. The Dijkstra algorithm can find the optimal execution order between multiple tasks, reducing time wasted during execution.

[0044] In general, the Dijkstra algorithm is used to calculate the shortest path from one task node to other task nodes. The specific formula is as follows: d(v)=min(d(v),d(u)+w(u,v)); Where: d(v) represents the shortest path length from the starting task to node v; w(u,v) represents the weight from task node u to task node v, which usually represents the time dependency or resource consumption between the two tasks; u and v are task nodes, representing the connection between tasks.

[0045] This formula is used to calculate the shortest path from one task node to another during task execution. The purpose is to optimize the task execution order through the shortest path, thereby reducing the overall execution time.

[0046] By continuously updating the shortest path of each task node, the Dijkstra algorithm can find the optimal execution path and ensure that the time during task execution is minimized.

[0047] As an option, the present invention uses a genetic algorithm (GA) in the resource allocation of tasks. The genetic algorithm simulates the process of natural selection and searches for the optimal resource allocation scheme through genetic operations (such as selection, crossover, and mutation) to improve task execution efficiency and resource utilization, which can be expressed as: Where: x represents the individual in the current population (i.e., the task resource allocation scheme); represents the execution time or required resources of the i-th task. The optimization goal is to minimize the execution time or maximize the resource utilization efficiency; n is the number of tasks, that is, the total number of tasks that need to be optimized in this resource allocation scheme.

[0048] In this implementation, each generation of individuals represents a resource allocation plan, and the fitness of the individuals is determined by factors such as task execution time and resource consumption. Through multiple generations of optimization, the most suitable resource allocation plan is finally selected.

[0049] By using the K-means clustering algorithm for task classification, the depth-first search (DFS) algorithm for dependency analysis, the Dijkstra algorithm for path optimization, and the genetic algorithm (GA) for resource allocation optimization, this embodiment significantly improves task execution efficiency, reduces resource waste, and ensures smooth process execution. Furthermore, through comprehensive analysis and optimization of task characteristics, the system can adjust resource allocation in real time, avoiding redundant resource consumption and execution delays.

[0050] S3. The informationization module integrates organizational efficiency improvement concepts into each process node to achieve automated process execution, role collaboration, and knowledge reuse. The informationization module supports convolutional neural networks for task type identification and uses genetic algorithms to optimize task field configuration. In step S2, through task clustering, dependency identification, and the establishment of a preliminary scheduling strategy, the system has achieved preliminary process-level optimization, forming an executable task execution sequence and resource allocation model. However, in actual execution, due to interference from uncontrollable factors such as the external environment, personnel status, and resource fluctuations, the original strategy is often difficult to maintain optimality. Therefore, in step S3, it is necessary to further introduce a dynamic execution control mechanism and a real-time feedback system to continuously monitor the execution status, resource usage, and completion quality of tasks, and to adjust parameters to achieve adaptive closed-loop optimization throughout the entire process.

[0051] In this embodiment, by building a real-time task state feedback model and introducing a target deviation-driven dynamic adjustment algorithm, we achieve immediate response and adaptive parameter adjustment during task execution. This step focuses on identifying and correcting execution deviations during task runtime, centered around the closed-loop control logic of "execution-feedback-correction-re-execution."

[0052] To ensure that the task execution path remains close to optimal in real-world scenarios, a state feedback control strategy is introduced. This feedback mechanism dynamically calculates the adjustment range based on the deviation between the current state of the task (such as progress, resource consumption, and execution efficiency) and the target state.

[0053] The adjustment behavior follows the following control formula: in: Update execution parameters (such as estimated remaining time, resource usage, etc.) for the i-th task at time step t+1; is the execution parameter value of the i-th task at the current time step t; is the target parameter value of the i-th task, that is, the task parameter that should be achieved in the optimal state under the current resource configuration; γ is the feedback gain coefficient, which is used to control the trade-off between response speed and system stability.

[0054] Specifically, this formula is used to dynamically adjust task execution parameters to minimize deviations. Through continuous feedback iteration, the preset execution plan is "regressed" during actual execution, preventing divergent execution paths.

[0055] In some embodiments, to improve response sensitivity, the parameter γ can be dynamically assigned based on task type or priority. For example, for tasks on the critical path, the feedback strength (i.e., γ) can be set to a higher value to ensure that the deviation converges quickly.

[0056] During the continuous execution of tasks, if continuous deviations occur, they may lead to cumulative errors, resulting in resource congestion and execution time overlap for subsequent tasks. To this end, a correction optimization mechanism based on the minimum cumulative deviation is introduced. Its optimization objective can be formalized as follows: Where: R represents the resource allocation strategy of the current task set (for example, processor allocation plan, personnel scheduling ratio, etc.); is the actual execution time of the i-th task under resource configuration R; is the expected execution time of the i-th task, that is, the time budget value of the task in the planning task graph; n3 is the number of monitored tasks in the current scheduling cycle.

[0057] As an option, in practical applications, weights can be set for different tasks to form a weighted error function to improve the execution accuracy of key tasks. Its extended form is as follows: Where: iis the weight factor of the i-th task, which is used to indicate the sensitivity or importance of the task to the overall goal. It is usually obtained through task priority, hierarchical weight, etc.

[0058] This formula indicates that under the premise of maintaining the overall stability of the system, the deviation between the actual task execution time and the planned value should be minimized as much as possible to avoid task chain imbalance caused by execution time delays.

[0059] During the actual execution process, when it is detected that a task delay may cause cascading failures of subsequent tasks, the system can proactively trigger the resource rescheduling model to reconfigure some reallocatable resources. This rescheduling process can be modeled as an optimization problem to minimize resource migration overhead: Where: r ij Indicates whether resource unit j is allocated to task i, which is a decision variable of 0 or 1; c ij The migration cost (such as context switching and load migration time) caused by allocating resource unit j to task i; n4 is the number of tasks currently in the optimization state; m4 represents the number of schedulable resource units (such as personnel, CPU cores, GPU time slices, etc.).

[0060] Generally, this optimization model will run in conjunction with the execution deviation model: when the execution time deviation of a task exceeds the threshold and may affect its subsequent task nodes, the system will enter the rescheduling mode to achieve task recovery with the minimum resource migration cost.

[0061] To prevent frequent system oscillations during execution, an adjustment window mechanism is introduced to limit the frequency of parameter updates. Specifically, the system performs a feedback evaluation every δ time steps to avoid resource oscillations or scheduling repetitions caused by overly frequent adjustments.

[0062] In addition, in actual deployment, in order to support the continuous operation of the adjustment mechanism, it is necessary to record the execution data and feedback results of each round to form a historical database of task behavior trajectories and provide sample data for the generation of subsequent version strategies.

[0063] In some embodiments, the system can also combine external unstructured data (such as personnel emotion monitoring, equipment load logs) as auxiliary parameter input to fine-tune the scheduling parameters. For example: Calculate adjustable task weights based on the degree of human-machine collaboration; Dynamically adjust resource migration cost c based on machine health status ij .

[0064] This type of expansion mechanism enables the system to maintain good task execution resilience in highly dynamic environments.

[0065] Through the above-mentioned modeling and parameter adjustment mechanism, the present invention realizes adaptive control, rapid response to execution deviations and resource reconfiguration capabilities during task execution in step S3, establishes a dynamic, multi-level execution optimization system, and provides stable, flexible and responsive operation support for organizational-level task execution.

[0066] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for improving organizational energy efficiency, characterized in that: The following steps are involved: S1. Introduce the concept of improving organizational effectiveness and integrate it into all aspects of enterprise management. Analyze each process link to identify areas for improving effectiveness and build a standard process framework for organizational operations. The process framework is constructed using the Analytic Hierarchy Process (AHP) and includes a goal layer, a module layer, and a task layer. S2. Based on the efficiency improvement points, process optimization and organizational resource allocation analysis are performed to form an executable organizational optimization plan. The process optimization is performed through process classification and tailoring using the K-means clustering algorithm. The flowchart generated by the tailoring process defines node execution roles, task time windows, and dependencies; S3. Through the information module, the idea of improving organizational efficiency is solidified into each node of the process to achieve automated process execution, role collaboration and knowledge reuse. The information module supports convolutional neural networks to identify task types and uses genetic algorithms to optimize task field configurations.

2. The method for improving organizational energy efficiency according to claim 1, characterized in that: The organizational process standardization construction process includes: Building a three-level planning structure based on the analytic hierarchy process, the three-level structure includes the target layer, the module layer and the task layer; Set the name, type, planned working hours, input, output and knowledge fields in each process task node; The task fields are structured and edited in a visual editing interface using a decision tree algorithm and saved to a system database. The editing process uses an ID3 algorithm to divide field information. The decision tree algorithm further supports automatic optimization and selection of subsequent nodes.

3. The method for improving tissue energy efficiency according to claim 2, characterized in that: The standard process framework for building organizational operations includes: Set up cross-departmental collaboration nodes in the three-level plan template to clarify the input and output flow logic; Use visual editing controls to implement structured editing of task fields; The plan template supports node review flow settings and version control mechanisms.

4. The method for improving organizational energy efficiency according to claim 1, characterized in that: The process optimization and organizational resource allocation analysis includes: The dependency relationship is verified through the depth-first search algorithm for path connectivity, and the Dijkstra algorithm is combined to optimize the task execution path.

5. The method for improving organizational energy efficiency according to claim 4, characterized in that: The process optimization and organizational resource allocation analysis further includes: Calculate the time difference between the role's actual intervention time and the task's planned start time; Forming a character-node-offset three-dimensional model; The model is used to output intervention optimization recommendations.

6. The method for improving organizational energy efficiency according to claim 4, characterized in that: The process optimization and organizational resource allocation analysis includes: Extract project text description and convert it into semantic vector; Comparing the semantic vector with a technical knowledge base; Use semantic vector matching algorithms to identify technical difficulties involved in the project and output pre-research process prompts.

7. The method for improving tissue energy efficiency according to claim 4, characterized in that: The process optimization and organizational resource allocation analysis further includes: In the project establishment stage, the CBB library is used to recommend results; Check the number of citations, quality labels and applicability of the recommended results; Support the binding of performance indicators to knowledge sharing behaviors and set up a review mechanism for reused results.

8. The method for improving organizational energy efficiency according to claim 1, characterized in that: The information module includes: A three-level planned task form is constructed using an online editable interface. The form supports field-level structuring, and field contents are subjected to image recognition and task type inference using a convolutional neural network. The fields include task name, task type, planned working hours, task input, task output and knowledge number; The task form supports genetic algorithms for field configuration optimization, version management, node review and approval control, and authority setting functions. The genetic algorithm is used to optimize the execution order and role allocation of tasks through a fitness function.

9. The method for improving tissue energy efficiency according to claim 8, characterized in that: The information module further includes: Project collaborative planning function; The function supports multiple roles to collaboratively set task start time, end time and dependency order online; The system generates task Gantt charts and dependency chain diagrams for critical path identification.

10. The method for improving organizational energy efficiency according to claim 8, characterized in that: The information module also includes a CBB achievement management module, which includes: Support calling CBB results and binding them to task node input during project execution; After the project is executed, support the submission of mature results for review and write back to the CBB library; The system automatically updates the CBB entry status and records the reference relationship.

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