115 results about "Continual improvement process" patented technology

The invention discloses a casting surface treatment quality evaluation method and system, and relates to the technical field of intelligent manufacturing and quality control, and the method comprises the steps: carrying out the multi-modal data collection of a casting, and carrying out the preprocessing; analyzing the preprocessed data, and constructing a comprehensive quality evaluation data set; a quality evaluation model is constructed, abnormal conditions in surface processing are identified, and a quality evaluation result is generated; performing quality classification on the casting according to a quality evaluation result, generating an evaluation report, and providing quality state judgment and related optimization suggestions; the casting surface treatment process is adjusted and optimized, the overall manufacturing quality is improved, and continuous improvement of quality evaluation is achieved. According to the method, the detection precision of the casting surface treatment quality and the process optimization capability are improved. By adopting data-driven closed-loop control, quality abnormity can be accurately identified, optimization suggestions can be provided, intelligentization, stability improvement and long-term quality improvement of the manufacturing process are realized, the defect rate is effectively reduced, and the manufacturing consistency is improved.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention relates to the technical field of supply chain management, in particular to a supply chain middle station collaborative management method and system based on an AI intelligent decision engine. Comprising the steps of accessing multi-source data of each participant of a supply chain; utilizing a deep learning model to predict market demands and dynamically adjust the market demands; based on the prediction result, using an optimization algorithm to realize global optimization configuration and scheduling of resources; a machine learning risk assessment model is constructed, various risks are monitored in real time, and early warning is performed in time; a collaborative decision-making platform is established, online communication, negotiation and decision-making of participants are supported, and real-time data sharing is achieved; defining a performance indicator system, evaluating the performance of the supply chain in real time, and automatically adjusting a strategy for continuous improvement. The method can improve the demand prediction accuracy, optimize the resource configuration, enhance the risk response capability, improve the collaborative decision-making efficiency, realize the continuous optimization of the supply chain, and effectively solve the problems of unsmooth data circulation, low collaborative efficiency and the like in the traditional supply chain management.

The invention discloses a scheduling method for crushing station multi-machine cooperative control based on edge calculation, and belongs to the technical field of mining mechanical equipment automation control. All devices in the crushing station serve as edge nodes; and the edge node communicates with other edge nodes to share the result and perform multi-machine cooperative control to pre-judge the running state and fault of the equipment. According to the method, efficient operation of the crushing station multi-machine equipment under different working conditions can be kept, self-adaption and cooperative work can be achieved, continuous improvement of dynamic scheduling and control strategies can be achieved, and global optimization of equipment starting and stopping and speed optimization is ensured. Meanwhile, historical data and real-time data are utilized to the maximum extent through reinforcement learning, continuous improvement of equipment fault prediction and state maintenance is achieved, and non-planned downtime is shortened. The innovation and practicability of the multi-machine cooperative control and scheduling method of the mine crushing station remarkably improve the production efficiency and economic benefits of the system.

The invention discloses a feed online production intelligent monitoring method and system based on data analysis, and relates to the technical field of intelligent monitoring, original data RAW is collected and preprocessed to obtain an operation feature vector VPS, and a data foundation is laid for accurate analysis. Based on the operation feature vector VPS and a historical database HIS, a predicted quality feature vector VPQ containing specific indexes such as starch gelatinization degree Ysg is generated in real time, a comprehensive feed quality index SIFQ is calculated, a decision signal Sig is generated, multi-dimensional overall evaluation of product quality is achieved, and a clear basis is provided for regulation and control. And when the decision signal Sig indicates that the quality does not reach the standard, an adjustment instruction AIR is generated, and scientific and efficient process adjustment is ensured. And adjusting equipment according to an adjustment instruction AIR, continuously monitoring and updating a historical database HIS, and based on this, carrying out iterative optimization on an inherent coefficient set SCA, endowing the system with self-learning and continuous improvement capabilities, and ensuring long-term adaptability of the model and stable production optimization.

The present invention pertains to an advanced workforce management system that integrates multiple cutting-edge technologies to optimize human capital management and enhance employee engagement. The system comprises an AI-enabled performance dashboard, a blockchain-based gamification module issuing Non-Fungible Tokens (NFTs), a shared human resources service platform, and an augmented reality / virtual reality (AR / VR)-based training platform. These components are interconnected through a novel data processing framework that synchronizes real-time performance data, reward mechanisms, resource allocation, and personalized training. Proprietary machine learning algorithms deliver predictive analytics and actionable insights, while advanced security protocols ensure data integrity and privacy. The blockchain module facilitates transparent and secure NFT rewards, fostering a culture of meritocracy and continuous improvement. Additionally, the AR / VR training platform provides immersive and adaptive learning experiences, enhancing employee skills and preparedness. This integrated system offers a scalable and flexible solution applicable across various industries, significantly improving organizational efficiency and employee satisfaction.

The invention, which relates to the technical field of the electric power supervision management and control system, discloses a supervision management and control system based on improvement of the electric power marketing service level, and the system integrates four modules of data acquisition, data analysis, supervision management and control and feedback improvement. Basic information, service requests and service process records of clients are collected through the data acquisition module; the data analysis module identifies problems and bottlenecks in the service process; the supervision and control module comprises a real-time monitoring and early warning system sub-module, a service quality evaluation model sub-module, a process monitoring and optimizing sub-module and the like and can supervise and control the service process in real time, and the feedback improvement module collects customer feedback and service evaluation and continuously improves and optimizes the service. The system also has a self-learning upgrading capability, continuously optimizes the service process through an internal feedback mechanism, improves the service quality, effectively solves the defects of an existing power marketing service system in the aspects of supervision and control, and provides a more efficient and convenient service solution for the power industry.

The invention discloses a liquid cooling system energy efficiency optimization scheduling method of a charging module group, and relates to the technical field of liquid cooling system energy efficiency optimization. The method comprises the following components: digital twin model construction and data acquisition preparation, real-time data acquisition and system state simulation, energy efficiency real-time prediction and evaluation, scheduling strategy formulation and execution, and loop optimization and continuous improvement. By constructing the digital twin model and combining real-time data acquisition and system state simulation, the actual operation state of the liquid cooling system can be accurately reflected, high-precision prediction is performed on the energy efficiency of the liquid cooling system in a future period of time by using a machine learning algorithm, comparison analysis is performed with a preset standard, energy efficiency bottleneck factors are found in time, and the energy efficiency prediction accuracy is improved. By means of the process, energy efficiency management is more scientific and accurate, and corresponding adjustment strategies can be made according to different energy efficiency bottlenecks.

The invention discloses a large-arrangement laying design method for a laying ship, and relates to the technical field of ship operation. The problems that the influence of multiple factors is not comprehensively considered, the operation of the geotextile laying ship is prone to exceeding the capacity range, resource waste is caused, the quality progress is affected, risks are increased, and problems are difficult to find in advance and laying is difficult to scientifically guide are solved. According to the method, parameters such as the ship speed, the tension and the laying angle are accurately configured and dynamically adjusted by integrating multiple factors, efficient and stable operation of the geotextile laying ship is ensured, the laying efficiency and quality are improved, actually monitored key target items are compared with a preset large-layout laying characteristic database, unconventional items are marked, effective prediction of the laying problem is achieved, and the laying efficiency and quality are improved. Coping strategies with clear priorities are formulated according to different risk levels, potential problems can be solved in advance, operation risks can be reduced, after laying is completed, multi-source feedback data are obtained, laying results are analyzed, an unconventional item weight distribution strategy is established, and subsequent laying operation can be continuously optimized by continuously improving a mechanism.

The invention relates to the technical field of thermal power plant equipment maintenance, and discloses a thermal power overhaul platform fusing equipment information, overhaul standards and GIS dynamic grids, the platform is integrated with an equipment information acquisition module, and the equipment state, operation parameters and appearance images are acquired in real time; accurate positioning and space management of equipment are realized through a GIS dynamic grid construction module; the maintenance standard matching module automatically matches maintenance standards according to equipment characteristics and generates a task list; the data analysis module deeply analyzes data, predicts faults and preferentially sorts maintenance tasks; the maintenance plan generation module automatically generates a high-efficiency maintenance plan according to the information; a supervisory feedback module ensures plan execution and facilitates continuous improvements. According to the platform, through intelligent and refined management, the efficiency and quality of equipment maintenance of the thermal power plant are remarkably improved, the stability and safety of power supply are guaranteed, and the platform has high practical value and application prospects.

The invention discloses a business process control method and system based on a decision engine, and relates to the technical field of business process control. The method comprises the following steps: collecting environmental change data in a business process in real time, classifying and filtering the data, and dividing the data into a significant change group and a tiny fluctuation group to obtain an environmental change core set; strategy matching analysis is carried out on the significant change group, and an adaptive decision adjustment scheme set is determined; then, the execution efficiency and stability of all the schemes are evaluated through historical execution records, comprehensive sorting is carried out, and candidate decision combinations are determined; and finally, evaluating the applicability of the candidate scheme in combination with environmental characteristics. According to the method, quick response to environment change is realized, through historical data analysis and multi-dimensional evaluation, the decision making process is optimized, the decision making accuracy and execution efficiency are improved, the applicability of a complex and changeable business environment is improved, and dynamic optimization and continuous improvement of decision making are realized.

The invention relates to the technical field of intelligent manufacturing, and discloses a production scheduling system of a production line and a scheduling method thereof. According to the system and method, key information such as production orders, equipment states and raw material inventory is collected in real time through the data collection unit, basic data is provided for subsequent analysis, the data analysis unit can rapidly evaluate the production capacity and the resource utilization rate, the orders are subjected to priority ranking, and it is ensured that the production efficiency is improved in the dynamically changing market environment. The production plan can preferentially meet the most emergency and important requirements, the scheduling decision-making unit rapidly generates an optimized preliminary schedule by applying an advanced scheduling algorithm, the manual intervention time is shortened, the response speed is improved, the scheduling execution unit ensures that a production instruction is issued and executed in time, the production progress is monitored in real time, and the production efficiency is improved. And the optimization unit continuously improves a scheduling strategy by evaluating and feeding back an execution result, so that the production efficiency is improved, the cost is reduced, and meanwhile, the timeliness of delivery and the customer satisfaction are ensured.

The invention discloses a tightening tool parameter optimization system and method based on deep learning, and relates to the technical field of tool parameter optimizing.Firstly, cleaning, standardization and feature extraction of real-time data are conducted through an edge calculation module, and optimal tightening parameters are predicted through an RNN model; the technological parameter optimization module dynamically adjusts parameters in combination with real-time working conditions, and can quickly adapt to complex environment changes; secondly, the cloud analyzes load balance and operation sequence among multiple tools through a Transform model and optimizes a task allocation strategy, and meanwhile, the IIOT platform realizes optimization parameter sharing among the multiple tools, so that the collaboration efficiency of the production line is remarkably improved; besides, a feedback module collects tightening operation performance and operation parameters and uploads the data to a cloud end, cooperation and historical data between tools are continuously analyzed through a deep learning model, an optimization algorithm is dynamically updated, and the adaptability and continuous improvement capacity of the system to dynamic working conditions are improved.

The invention relates to an information management method for a supply chain comprehensive management platform, and the method comprises the steps: obtaining and sorting supply chain related enterprises, and determining a plurality of block chain nodes; obtaining operation data of supply chain associated enterprises, and storing the operation data to corresponding block chain nodes; label set mapping is carried out on the operation data based on the sorting result; respectively inputting the operation data into the prediction model and the optimization model; performing association analysis on the operation demand prediction information and the operation optimization information to generate an operation optimization strategy; the operation optimization strategy is stored in the block chain node correspondingly associated with the operation optimization strategy; according to the method, the blockchain technology is introduced, so that the transparency and traceability of supply chain information are realized, and the problems of information asymmetry and data islands are solved; demand prediction and process optimization are carried out through the pre-training model, so that the real-time response capability to market changes and risks is improved; and by generating and storing an optimization strategy, a basis and a safety guarantee are provided for continuous improvement of the supply chain.

The invention discloses an intelligent construction equipment control method based on a multi-modal large model, and relates to the technical field of intelligent construction and construction equipment control, and the method comprises the steps: collecting concrete pumping flow, pipeline pressure, construction area point cloud and multi-angle images, obtaining a voice instruction of an operator at the same time, and carrying out the noise suppression and voiceprint recognition; inputting the trained multi-modal large model to carry out cloth coverage precision judgment, calculating a regional granularity factor, comparing the regional granularity factor with a coverage threshold value, and automatically generating a task label or triggering a voice correction strategy; a dynamic feeding stability coefficient is constructed by calculating the standard deviation of flow and pressure data, and intelligent monitoring and regulation of the running state and the construction environment of the material distributing machine are achieved in combination with a load balancing coefficient and a construction risk probability; the system also establishes a full-link tracing mechanism, realizes traceability of operation instructions, personnel identities and equipment states, ensures controllable construction quality and clear responsibility, and realizes task process optimization and continuous improvement.

The invention provides a multi-agent cooperation and dynamic feedback long text generation system and method, and the system comprises a performance agent which is used for receiving a theme or an outline inputted by a user, planning and scheduling a long text generation process, coordinating the interaction of an author agent and an evaluation agent, and dynamically adjusting a generation strategy based on an evaluation result; the author agent is used for generating text content according to an instruction of the director agent and correcting a generation result; the evaluation agent is used for performing multi-dimensional quality evaluation on the text content generated by the author agent and returning optimization suggestions; and the memory bank is used for storing the historical generation information and the global setting information and providing context support in subsequent text generation. Through the synergistic effect of the director agent, the author agent, the evaluation agent and the memory bank, a dynamic feedback closed-loop mechanism is established, so that the text generation process has continuous improvement capability, and the overall quality and stability of long text generation are improved.

The invention relates to a real-time reaction monitoring device based on intelligent sensing and a feedback optimization method, and aims to solve the problems that multi-source heterogeneous data is difficult to fuse, the dynamic optimization efficiency is low and abnormal working conditions are difficult to diagnose in the industrial reaction process. According to the device, an intelligent sensor array is constructed by integrating various physical and chemical sensors, integrated packaging of the sensors is realized by adopting a micro-fluidic chip technology, and preprocessing and digitization of data are supported. In combination with deep learning and reinforcement learning algorithms, the method realizes feature extraction, fusion analysis and dynamic optimization control of multi-sensor data, and has the functions of abnormal working condition diagnosis, real-time feedback and closed-loop optimization. According to the system architecture, edge computing and cloud service collaboration are adopted, distributed optimization control, multi-device collaboration and industrial interconnection standard integration are supported, and efficient and reliable man-machine interaction and data visualization functions are provided. By constructing a knowledge management mechanism, optimization rules and models accumulated in the production process are precipitated, and continuous improvement and rapid adaptation are achieved.

The present invention discloses a tightening tool parameter optimization system and method based on deep learning, which relates to the technical field of tool parameter optimization. The present invention first uses an edge computing module to clean, standardize and extract features of real-time data, and uses an RNN model to predict the optimal tightening parameters. The process parameter optimization module dynamically adjusts parameters in combination with real-time working conditions, and can quickly adapt to complex environmental changes; secondly, the cloud uses a Transformer model to analyze the load balancing and operation sequence between multiple tools, and optimizes the task allocation strategy. At the same time, the IIoT platform realizes the sharing of optimization parameters between multiple tools, significantly improving the collaborative efficiency of the production line; in addition, the feedback module collects tightening operation performance and operating parameters, and uploads the data to the cloud, uses a deep learning model to continuously analyze the collaboration between tools and historical data, dynamically updates the optimization algorithm, and improves the system's adaptability to dynamic working conditions and continuous improvement capabilities.

The invention relates to the field of distribution network industry material management, in particular to a distribution network industry expansion engineering material use data accurate tracking management method, which is deeply connected with an enterprise internal ERP system, construction cost structured data and a data medium table, constructs a distributed material management account book based on a block chain technology, realizes real-time query of material information and improves working efficiency. A recurrent neural network is utilized, a first supply material demand prediction model is established in combination with multi-source data, the material demand of each construction stage is accurately predicted, and resource allocation is optimized. Through coding material and project information, making a material distribution plan and approval record, establishing a two-way tracing mechanism and combining the Internet of Things technology to track materials in real time, the standardization and safety of material management are guaranteed. Meanwhile, materials are checked regularly, and the reporting process is standardized. The method effectively solves the problems of difficult material information query, inaccurate demand estimation and difficult data tracing, promotes continuous improvement and optimization of enterprise material management, and has significant economic benefits and management values.

The invention discloses a special gas and chemical operation and maintenance service management system, and relates to the technical field of chemical management. In order to solve the problem of unreasonable resource allocation or inventory shortage caused by lack of intelligent optimization of demand prediction and inventory management of special gases and chemicals in the prior art. According to the method, material supply and demand balance is realized through adaptive demand prediction and a dynamic inventory optimization algorithm, resource supply of operation and maintenance tasks is ensured, supply chain interruption risk assessment is combined to enhance supply chain elasticity, material supply stability is ensured, early warning and emergency response support are provided, and the risk assessment efficiency is improved. According to task priority dynamic sorting and a path and resource scheduling optimization algorithm, the task completion rate and execution quality are ensured, meanwhile, cross-module information linkage is achieved, the operation and maintenance efficiency is improved, the compliance and efficiency of the operation and maintenance process are comprehensively evaluated, a continuous improvement strategy is provided, and continuous improvement of system performance is promoted.

The invention discloses a multi-stage analysis-based project management monitoring method and system, and relates to the technical field of project management and monitoring, and the method comprises the steps: determining a project management monitoring target and range, collecting project management data information, and recognizing and analyzing key indexes related to a project. Comparing the key indexes with historical project data to predict a potential trend, performing analysis stage division on the key indexes, designing an analysis template in each stage, collecting and arranging related data, evaluating the key indexes in each stage according to the collected data, making a decision based on an evaluation result, and adjusting a project plan. Adjusting information is fed back to the next analysis stage, a continuous improvement process is established, and a project management strategy is dynamically adjusted. According to the method, through multi-stage data collection and analysis and deep identification and prediction of key indexes, the project state can be monitored more accurately, potential risks can be predicted, and a project management strategy can be adjusted dynamically.

Disclosed herein is a system and method for generating real-time project estimates and providing field assistance in the construction and home improvement industries. Utilizing AI and NLP, the system processes project requests, accesses a database of real-time material and labor costs, and generates detailed project estimates. It offers an interactive interface for voice and text inputs, dynamic estimate adjustments, and integrates with third-party software. Field assistance features installation tips, compliance information, and a feedback loop mechanism for continuous improvement.

The invention relates to the technical field of data processing, and discloses a lean production management method and system, and the system comprises a data collection module, a data preprocessing module, a data analysis module, a data prediction module, a lean production analysis module, and a man-machine interaction module. By arranging the data analysis module and the data prediction module, production schemes of bearing products are arranged by acquiring the production condition of the bearing, a foundation is laid for subsequently acquiring a real-time optimal production scheme, and by acquiring the real-time optimal production scheme, the flexibility of value flow graph analysis can be improved, the lag phenomenon is avoided, and the production efficiency is improved. Meanwhile, according to the change of the daily production quantity of bearing products, a production scheme and improvement measures are flexibly adjusted, the product production scheme is updated in real time to continuously improve the production process and improve the product quality and the production efficiency, meanwhile, a neural network model is integrated, the nonlinear and multi-modal data modeling capability of the neural network is fully exerted, and the production efficiency is improved. And a complex data relationship is effectively captured.

The invention provides an 8D quality improvement collaboration method and device based on process engine driving and a medium, and can improve the problem solving speed, enhance the quality improvement effect, improve team collaboration and communication and enhance the continuous improvement ability, and the method comprises the steps: creating an 8D process corresponding to each production stage of the whole production process of a product; when it is determined that a quality problem is found in a first production stage, a target 8D process is automatically matched, the first production stage is any one of the production stages, and the target 8D process is an 8D process corresponding to the first production stage; the driving process engine executes the target 8D process according to a specified node sequence constraint mechanism, in the process, the driving process engine uses an experience library to automatically fill an 8D report field, and the 8D report field is a field needing to be filled in corresponding steps in the target 8D process; and the process engine is driven to uniformly store process data generated when the process engine executes the target 8D process into a specified storage device.

The invention discloses a big data analysis and automatic evaluation system in project cost consultation, and relates to the technical field of big data analysis and evaluation. By constructing a unified data acquisition framework and a dynamic updating mechanism, efficient integration and real-time interaction of multi-party heterogeneous data are realized, data transparency and reliability are remarkably improved, a solid data basis is provided for project cost management, the system quickly and accurately optimizes various variables by utilizing comprehensive analysis and multivariable optimization technologies, and the system has a good application prospect. The system adapts to the dynamic environment, enhances the anti-risk capability of the project, ensures that the project cost management meets the real-time requirement and realizes the globally optimal solution, and in addition, through resource allocation strategy generation, view updating and decision recording and a feedback loop mechanism, the system realizes the generation and continuous improvement of a resource allocation strategy oriented to global benefit balance. The resource allocation efficiency and the long-term competitiveness of the project are improved, and powerful support is provided for sustainable development of urban construction.

The embodiment of the invention discloses an assembly quality evaluation method for a product, and the method comprises the steps: monitoring the quality fluctuation of an assembly process according to a quality fluctuation model, and obtaining the quality data of the assembly process; de-noising the quality data based on Kalman filtering to obtain characteristic parameter data; determining a joint quality control chart according to the characteristic parameter data; determining a capability index value of the assembly process according to the joint quality control chart; and evaluating the assembly quality of the product according to a matching relationship between the capability index value of the assembly process and a preset range. According to the method, the evaluation effect of the product assembly quality is comprehensively improved through monitoring of the quality fluctuation of the assembly process, efficient data processing, a visual quality control view and a quantitative quality evaluation standard, the product quality is ensured by an enterprise, and powerful support is provided for quality control and continuous improvement of the enterprise.

The invention relates to the technical field of cloud computing operation and maintenance, in particular to a cloud platform fault intelligent research and judgment system and method based on multi-dimensional evaluation and topology analysis, and the system comprises a multi-dimensional evaluation matrix construction module, an influence diffusion model design module and an emergency index calculation model. The method has the beneficial effects that the system can dynamically predict the influence range of the fault by constructing the service dependency graph and adopting the improved PageRank algorithm. In addition, the system further introduces an emergency index calculation model to quantify the emergency degree of the fault and formulate a corresponding response strategy according to the emergency degree. Through continuous improvement of a mechanism, such as establishment of a fault knowledge graph and reinforcement learning optimization of model parameters, the system can continuously improve the accuracy of fault research and judgment. According to the scheme, the fault grading accuracy is remarkably improved, the influence range prediction error rate and the average repair time are reduced, and meanwhile the requirement for excessive resource capacity expansion is reduced.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes such as financial science and technology and medical health, and discloses an action strategy optimization method and device based on an execution track, equipment and a medium, and the method comprises the steps: obtaining a task request, analyzing a task intention, and generating a preliminary execution plan based on the task intention and a preset action strategy; and recording the execution action and the state change in the execution process to form an execution track sequence, comparing the execution track sequence with a standard operation specification to identify an execution deviation, analyzing a deviation root cause of the execution deviation, generating a correction instruction based on the execution deviation and the deviation root cause, and updating the action strategy by applying the correction instruction to obtain an optimized action strategy. According to the method, a feedback strategy optimization mechanism is constructed through execution track recording, deviation recognition and root cause analysis, so that an action strategy can be continuously improved according to actual task performance, and the accuracy and consistency of task execution are improved.

The invention discloses a business process optimization execution management system, which comprises a process design module used for designing a business process through a graphical interface; the process execution engine is used for automatically executing a business process according to a pre-designed process model; the task management and distribution module is used for managing and distributing tasks in the process to related participants; the flow monitoring module is used for monitoring the execution condition of the flow in real time, providing flow state, progress and processing time information and supporting setting of monitoring indexes and an early warning mechanism; and the process optimization module is used for improving and optimizing the existing process according to the result of the process analysis, including adjusting the process structure, optimizing the process rule and improving the automation degree of the process. The system supports graphical process design, has a powerful process execution engine and a real-time monitoring capability, can perform deep analysis and optimization according to process execution data, and helps enterprises to realize continuous improvement and efficient operation of business processes.

Systems and methods disclosed can evaluate security detection rules in a network security computing environment. Results for a processed log of security events can be retrieved. The results can identify determined outcomes for instances triggering security detection rules. The security detection rules can detect specific behavior on a network by being processed against a log of security events. Scores for the security detection rules can be determined based on the results of the processed log of security events and the determined outcomes. The security detection rules can be ranked based on the scores, from highest to lowest score. The highest score can indicate that a corresponding rule is performing worst among the security detection rules and the lowest score can indicate that a corresponding rule is performing best among the security detection rules. A rules score report can be generated based on the ranked rules.