29 results about "Business goals" patented technology

The invention discloses a water conservancy operation and maintenance platform based on a digital twinning technology, and relates to the technical field of digital twinning, and the platform comprises a digital twinning model building unit which is used for building a water conservancy facility digital twinning model comprising a physical mechanism model; the health state evaluation unit is used for calculating theoretical performance based on the mechanism model and comparing the theoretical performance with actual performance to obtain a performance residual error so as to accurately quantify the health state of the equipment; the intelligent scheduling decision-making unit adopts a reinforcement learning framework, performs decision-making deduction by using a composite reward function fusing business rewards and quantitative health loss cost in a simulation environment associated with digital twinning, and generates an optimal scheduling instruction giving consideration to business targets and equipment long-term health; and the closed-loop control and feedback unit is used for executing the instruction and forming a dynamic optimization closed loop. According to the method, abstract health loss is converted into computable decision cost, and global optimization of an operation and maintenance strategy is realized.

The invention discloses a generative dialogue optimization method and system based on multi-step intensified simulation, and the method comprises the steps: generating a plurality of candidate replies based on the current dialogue history through a basic dialogue model; using the user simulator to generate a dialogue chain for each candidate reply; calculating a reward for each dialogue chain, selecting K dialogue chains with the highest reward value as high-quality samples, and taking the dialogue chain with the lowest reward value as a negative sample; updating parameters of the basic dialogue model by using the high-quality sample and the negative sample through a contrast learning and reinforcement learning method to obtain a trained basic dialogue model as a customer service model; and the customer service model generates a reply according to a statement input by the user. According to the conversation generation method, multi-round conversation dynamic planning, medical scene adaptability optimization and efficient strategy exploration can be fused, and double optimization of user trust establishment and business goal achievement is achieved while compliance is ensured.

A system and method are disclosed for performing rough cut capacity planning. The method includes receiving supply chain transaction data as transaction tables, generating a base plan and generating updated transaction tables, denormalizing the base plan and the updated transaction tables, generating supply chain network flow paths and supply chain network data, solving a rough cut capacity planning problem based at least in part on the supply chain network flow paths, the supply chain network data and simulation data, repeating at least the generating and solving until business goals of the rough cut capacity planning meet a threshold, and updating the simulation data based on an upsert process. The method further includes relaxing supply chain network constraints, inverting the supply chain network, and traversing a perturbation in the supply chain network constraints as demands in a reverse direction towards customer nodes.

The invention discloses an intelligent test data generation and optimization method and system based on a low-code platform, and belongs to the technical field of software engineering and artificial intelligence, and the method comprises the steps: analyzing a business rule and data model definition information of a low-code application; according to the business rule and the data model definition information, utilizing a generative model to generate initial test data; performing quality evaluation and fault prediction on the generated initial test data; collecting test execution feedback information, including fault types caused by data, performance bottleneck positions and the like; formulating a data generation parameter adjustment strategy according to the test execution feedback information and the service target; and regenerating test data according to the adjustment strategy, and outputting the optimized test data for a subsequent iterative test. And the optimized test data is subjected to effect evaluation, so that the test quality and efficiency are continuously improved. According to the invention, the test data dynamically adapts to business demand changes, the test coverage rate and the fault discovery efficiency are improved, and the labor cost is reduced.

The present application relates to a kind of intelligent interview evaluation and feedback system based on multi-modal data fusion, specifically relates to data processing field, by meta-learning mechanism dynamic perception interview scene and generate initial fusion weight, subsequently utilize the complex interaction relationship between modalities modeled by graph neural network to carry out fine-grained correction to weight, so as to significantly improve the accuracy and scene adaptability of multi-modal evaluation, further introduce reinforcement learning, link evaluation decision and long-term performance of talents, continuously optimize weight generation strategy, ensure that evaluation standard and business goal are aligned, finally, through closed-loop iteration mechanism, make the whole scheme can be updated automatically according to new data and performance feedback, continuous evolution, with strong self-optimizing ability and long-term robustness, realize the fundamental change from static rule to dynamic intelligent decision.

The invention provides an application program monitoring method and system, and relates to the technical field of information, the processing flow of each request response is tracked in detail and the abnormity is detected through a constructed time sequence model and a log detection model, the performance bottleneck and the fault point in an application program can be quickly positioned, and the reliability of the application program is improved. The stable monitoring guarantee of the user according to the own service target and the own platform is realized, so that the stability of the application program is improved, and the user experience is improved.

The invention discloses an e-commerce advertisement putting strategy generation method and system based on quantum computing optimization, and particularly relates to the technical field of quantum computing, comprising parameter coding, quantum optimization, line evolution, strategy resolving, dynamic optimization and strategy verification and output. According to the method, a high-dimensional parallel search mechanism based on a parameterized quantum circuit is constructed by introducing a quantum calculation normal form, the problem of calculation complexity index increase of a classical computer in e-commerce advertisement multi-parameter combination optimization is effectively solved, and the multi-parameter combination optimization of e-commerce advertisements is realized by designing a classical and quantum mixed architecture and a dynamic feedback optimization mechanism. The real-time adaptive adjustment of the advertisement putting strategy is realized, the practicability and robustness of the system are remarkably improved, the precise alignment of the technical means and the commercial target is realized by deeply coupling the quantum optimization process with the specific e-commerce advertisement business index, and a quantifiable business improvement effect is provided.

The invention belongs to the technical field of intelligent factories, and particularly relates to an AI technology-based intelligent steel factory and a construction method thereof. The construction method comprises the steps that industrial robots are arranged in a raw material area, an area before iron making, a steelmaking area, a steel rolling area, a finishing operation area and an inspection and test area of a steel factory; performing unified standardization and quantification on related factors compiled by the production plan to form a factor library and a material type library; setting an optimization control system of various specific business targets; constructing an intelligent database of the steel factory, namely summarizing data information in the whole range of the steel factory into the intelligent database of the steel factory; establishing a production operation management and control platform based on an AI technology, and performing data extraction, cleaning and classified storage on the intelligent database of the steel factory; and intelligent personnel and material scheduling is carried out according to the production operation management and control platform. Through the application of the AI algorithm and in combination with the process standard, the plan yield and the plan making efficiency are improved, the application of the control model is optimized, and the quality consistency management and control is promoted.

This invention provides an artificial intelligence-based software refactoring method. The method includes: acquiring the current code data and user data of the software to be refactored; determining a target refactoring strategy based on the current code data and user data; generating refactoring code data corresponding to the target refactoring strategy based on a large language model; and refactoring the software based on the refactoring code data to obtain the target software. This invention analyzes code data and user data, considering both the code problems of the software to be refactored and user business expectations, to generate a target refactoring strategy that balances technical optimization and business needs. This avoids the problem of the separation between technical optimization and business goals in traditional methods, ensuring that the refactored code accurately matches the user's actual business scenario while optimizing technical indicators, reducing the cost of secondary modifications due to misunderstandings of requirements, and thus improving the accuracy and efficiency of software refactoring.

1. Name of the Design Product: Graphical User Interface for Displaying Business Goal Completion Status for Electronic Devices. 2. Use of the Design Product: An electronic device. 3. Design Key Points of the Design Product: Lies in the content of the graphical user interface on the screen. 4. Picture or Photograph Most Indicating the Design Key Points: The front view of Design 1. 5. As it is a common design, the rear views, left views, right views, top views and bottom views of Design 1 to Design 3 are omitted. 6. Design 1 is designated as the basic design. 7. Use of the Graphical User Interface: A graphical user interface for displaying business goal completion status, facilitating users to view the goal completion status of the current business, and helping users intuitively understand the progress of the tasks being carried out; Click on Card 01 of Task 1 in the front view of Design 1 to enter the interface change state diagram of Design 1; Stay at the front view of Design 2 for a preset time to enter Interface Change State Diagram 1 of Design 2, stay at Interface Change State Diagram 1 of Design 2 for a preset time to enter Interface Change State Diagram 2 of Design 2, stay at Interface Change State Diagram 2 of Design 2 for a preset time to enter Interface Change State Diagram 3 of Design 2, stay at Interface Change State Diagram 3 of Design 2 for a preset time to enter Interface Change State Diagram 4 of Design 2; The front view of Design 2, Interface Change State Diagram 1 of Design 2, Interface Change State Diagram 2 of Design 2, Interface Change State Diagram 3 of Design 2 and Interface Change State Diagram 4 of Design 2 are dynamic pattern changes showing the coherent display of Card 01 of Task 1 from the unpassed state to the passed state when the system recognizes the user's current data and determines that the user has completed Task 1; Click on Card 01 of Task 1 in the front view of Design 3 to enter the interface change state diagram of Design 3. 8. Other Situations Requiring Explanation: Other Explanation: The gray part in the graphical user interface of this design product is the content screen. For example, the gray circle and the gray rectangle part are user avatars or pictures; The X part in the graphical user interface of this design product is variable or belongs to the content screen. For example, X represents text and / or numbers.

The invention relates to a marketing decision system driven by multi-source data, and relates to the technical field of artificial intelligence, and the system comprises a state sensing module which is used for determining user state information representing the current state of a user according to the real-time interaction behavior of the user collected on terminal equipment; the intention acquisition module is used for acquiring marketing intention information representing a commercial target; the content generation module is used for dynamically generating marketing content by utilizing a generative model in response to the user state information and the marketing intention information; and the content presentation module is used for presenting the dynamically generated marketing content on an interface of the terminal equipment. According to the method and the device, the dynamic change of the user in a specific situation can be captured more accurately and more timely, so that the matching degree of the marketing content and the instant state of the user is remarkably improved, the accuracy and the real-time performance of a marketing decision are enhanced, and the flexibility and the intelligent level of the marketing decision are improved.

The present invention relates to the field of human resources technology, and specifically to a human resources intelligent allocation and optimization system, which includes a career analysis and prediction module, a career path planning module, an emergency deployment management module, and a human resources optimization module. The present invention realizes the accurate prediction and dynamic deployment of career paths by integrating the career history and skill data of medical staff. The precise career transition point analysis makes talent matching more accurate and reduces resource waste. The career training recommendations customized for the hospital's business goals improve the pertinence and effectiveness of training, optimize the talent training process, and improve the training effect of professional talents. In an emergency, the system can respond in time through real-time data monitoring and conduct rapid and effective manpower deployment to ensure that the hospital can maintain efficient operation in the face of emergency medical needs. Through continuous data updates and feedback, the human resources allocation strategy is continuously optimized, which improves the hospital's adaptability and management efficiency in a changing environment.

A non-transitory computer readable medium having computer instructions stored therein that when executed by a computer system cause the computer system to perform operations including receiving a first user input defining a dialog session comprising one or more steps of a natural language conversation flow, receiving a second user input defining a positive outcome of the dialog session, receiving, via a chat interface, a natural language input, initiating the dialog session in response to an association between the natural language input and the dialog session, receiving, via the chat interface, a subsequent natural language input, and updating a data entry associated with the custom metric in an outcome log in response the subsequent natural language input being indicative of the positive outcome of the dialog session.

The invention relates to the technical field of data analysis, and particularly discloses an information pushing method and device, computer equipment and a storage medium. The information management platform uniformly manages generation and pushing of the activity information, generates the activity information and the activity participation rule according to the activity information generation model and the matching rule generation model, intelligently matches the users capable of participating in the activity according to the activity participation rule, does not need to independently manage the activity corresponding to each service, does not need manual participation, and improves the user experience. And matching can be performed according to the basic information of the user and the activity participation rule, and the activity information is pushed to the user meeting the rule, so that the generation efficiency of the activity information and the pushing accuracy of the activity information are improved. The method is applied to businesses such as financial insurance sales and medical health information popularization, optimal activity information corresponding to the businesses can be intelligently generated, the optimal activity information is accurately pushed to users meeting participation rules, and the business goal is effectively achieved.

The invention belongs to the technical field of intelligent logistics, and particularly relates to a logic processing method, system and device for an intelligent question and answer model in a freight scene and a medium, and the method comprises the steps: when a user initiates a dialogue, analyzing a business target according to the dialogue content of the user; performing multiple rounds of conversations with the user based on the business target, and collecting related information of the business target from the multiple rounds of conversations; wherein for each round of dialogue of the user, the correlation between the current dialogue content and the service target is evaluated: if the dialogue content is not related to the service target, a chat mode is started to call corresponding data information in a database to make a reply according to the demand of the user; and if the dialogue content is related to the business target, collecting and updating the related information of the business target, and re-evaluating the information collection completion degree of the business target after the information collection completion degree of the business target so as to perform reflection according to the information collection completion degree of the business target and confirm whether a user needs to be guided to provide the related information of business target missing.

The purpose of the present application is to provide a method, device, medium and program product for determining a magic number, the method comprising: determining one or more first indicator features corresponding to a business goal based on product function information of a target application and historical behavioral performance information of a user; evaluating the one or more first indicator features based on statistical results corresponding to multiple dimensions, and determining at least one second indicator feature from the one or more first indicator features based on the evaluation results, wherein the multiple dimensions include a user quantity scale dimension and a historical business effect dimension; inputting at least one second indicator feature into a predetermined feature evaluation model to obtain importance information corresponding to the one or more second indicator features, and determining one or more target indicator features from the one or more second indicator features based on the importance information; determining critical value information corresponding to the one or more target indicator features, and using the critical value information as the magic number corresponding to the business goal.

An implementation method, device and equipment for path searching along an original path as much as possible and a storage medium. In the application, the business goal of "searching along the original path as much as possible" is converted into a quantifiable priority index in the path searching algorithm. By judging the "deviation" degree of the candidate route from the original route in real time during the path searching process, and giving a lower priority to the route that deviates earlier, the search process is systematically guided to preferentially explore the route branch similar to the original path, and the optimal solution is efficiently found.

This invention relates to a dialogue goal optimization device, method, and program for optimizing goal achievement in user interactions. Based on user utterances and actions, it estimates user states, including emotions, resistance, comprehension, and signs of detachment, as well as goal progress indicating the degree of achievement of primary and sub-goals, and maps these to a state space. After restricting the set of candidate response strategies based on constraints such as compliance and cost applied to the dialogue, it selects a response strategy that maximizes the objective function. Furthermore, it performs a self-correction process that dynamically updates the parameters of the response strategy in response to signs of failure detected during the dialogue. This makes it possible to suppress user detachment while adhering to practical constraints and continuously improve the business goal achievement rate.

A system and method are disclosed for performing rough cut capacity planning. The method includes receiving supply chain transaction data as transaction tables, generating a base plan and generating updated transaction tables, denormalizing the base plan and the updated transaction tables, generating supply chain network flow paths and supply chain network data, solving a rough cut capacity planning problem based at least in part on the supply chain network flow paths, the supply chain network data and simulation data, repeating at least the generating and solving until business goals of the rough cut capacity planning meet a threshold, and updating the simulation data based on an upsert process. The method further includes relaxing supply chain network constraints, inverting the supply chain network, and traversing a perturbation in the supply chain network constraints as demands in a reverse direction towards customer nodes.

The invention relates to the technical field of service self-learning, and discloses an alarm false alarm reduction system and method based on service self-learning, and the method comprises the following steps: obtaining a task index and resource time sequence data in a service change log, and carrying out the service intention perception based on the task index and the resource time sequence data; obtaining a forward feature of business intention perception, and obtaining a reverse feature based on manual annotation and a weight reduction feature of adversarial training weight reduction; and constructing a learning cooperative training model according to the forward feature, the reverse feature and the weight reduction feature. According to the method, task indexes and resource time sequence data in a business change log are analyzed, BERT semantic recognition and LSTM periodic analysis technologies are combined, and an intention vector of a quantitative business target is dynamically generated. According to the method, the limitation of a static alarm threshold value is overcome, and the system can sense the real intention of service operation in real time, so that normal service fluctuation and real abnormity are distinguished more accurately, and the false alarm rate caused by service scene change is remarkably reduced.

The invention discloses an electronic information security storage system and method based on cloud computing, and relates to the technical field of security storage, by introducing a behavior log and enterprise mode conjoint analysis mechanism in the step S1, the system can perform scene division and label classification on user operation behaviors, and comprehensive description of behavior semantics in a multi-dimensional context is realized. In the step S2, by constructing similarity analysis of a service semantic vector and a behavior semantic vector and combining a user behavior trajectory similarity matrix, dynamic judgment of the matching degree between the user access behavior and a service target is achieved, so that an intention judgment result with high credibility is obtained, and the recognition rate of the hidden abnormal behavior is further improved. And S3, performing comparative analysis on the intention credibility under the scene label and a dynamic credibility threshold, generating a sub-scene risk level index, and performing back-stepping on a replica redundancy control strategy in combination with behavior log data, thereby effectively suppressing the problem of redundant replica diffusion.

An object of the system according to the embodiment is to analyze various data of a user and make a personalized proposal.SOLUTION: A system includes a user data acquisition unit, an analysis unit, and a proposal unit. The user data acquisition unit acquires data related to the lifestyle, health condition, hobbies, learning, and business goals of the user. The analysis unit analyzes the data acquired by the user data acquisition unit. The suggestion unit makes a personalized suggestion on the basis of the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

A system and method are disclosed for performing rough cut capacity planning. The method includes receiving supply chain transaction data as transaction tables, generating a base plan and generating updated transaction tables, denormalizing the base plan and the updated transaction tables, generating supply chain network flow paths and supply chain network data, solving a rough cut capacity planning problem based at least in part on the supply chain network flow paths, the supply chain network data and simulation data, repeating at least the generating and solving until business goals of the rough cut capacity planning meet a threshold, and updating the simulation data based on an upsert process. The method further includes relaxing supply chain network constraints, inverting the supply chain network, and traversing a perturbation in the supply chain network constraints as demands in a reverse direction towards customer nodes.

The invention belongs to the technical field of big data processing, and relates to a data prediction method, device and equipment and a storage medium thereof.The method includes the steps that firstly, a principal component analysis method is adopted to analyze a small amount of current analysis data, important feature dimensions are determined, feature extraction is conducted on historical data on the important feature dimensions, and a trend line is generated; according to the method, data matrix missing value supplementation is performed in combination with a trend line, a continuous prediction model is constructed by using a data matrix without missing values, and a service prediction model is constructed only in combination with important dimension features, so that processing of unimportant dimension data is avoided, and specific services are more efficiently assisted to perform data analysis and service target setting. The data prediction method is specifically applied to a financial business data analysis scene, and a business target can be set for business personnel more efficiently and quickly.

The embodiment of the invention provides a domain-driven design architecture, a data processing method, equipment and a storage medium, the domain-driven design architecture comprises an interface layer, a logic layer and a domain layer, the domain layer comprises a domain model divided based on at least one of functions, data and business domains, and the domain model is used for packaging and realizing business logic; the interface layer is used for receiving a user request, converting the user request into a command object and calling the logic layer to process the command object; the user request has a corresponding service target; the logic layer is used for calling the domain layer to arrange the business process according to the command object and triggering a transaction for realizing the business process; the domain layer is used for calling a target domain model from the domain models according to the business process so as to realize a business target corresponding to the user request; wherein the business process is driven by business logic in the target domain model. According to the embodiment of the invention, the problem that a domain model and an actual service in a traditional architecture are disjointed can be solved.

The invention provides a dialogue generation method and device driven by a large language model, equipment and a medium, and the method comprises the steps: receiving natural language input of a user; based on the natural language input and the context information, performing semantic analysis through a large language model to obtain intention and slot position information; managing and updating a current dialogue state based on the intention and the slot position information, the dialogue state being migrated according to a predefined state conversion logic; checking the filling condition of a necessary slot position corresponding to the current dialogue state; deciding the next action of the system based on the filling condition and the updated current dialogue state; and based on the next action, calling the large language model to generate a user-oriented natural language reply. By adopting the method, the controllability of the dialogue process, the natural fluency of interaction, the business goal achievement efficiency and the user interaction experience can be improved.

The present disclosure relates generally to an integrated machine learning platform. The machine learning platform can convert machine learning models with different schemas into machine learning models that share a common schema, organize the machine learning models into model groups based on certain criteria, and perform pre-deployment evaluation of the machine learning models. The machine learning models in a model group can be evaluated or used individually or as a group. The machine learning platform can be used to deploy a model group and a selector in a production environment, and the selector may learn to dynamically select the model(s) from the model group in the production environment in different contexts or for different input data, based on a score determined using certain scoring metrics, such as certain business goals.

The invention discloses a fund comprehensive scoring method based on adaptive learning. The fund comprehensive scoring method comprises the steps of data preparation and preprocessing; carrying out model training and initialization processing; carrying out distribution drift detection; a model training and updating mechanism; performing fund score prediction; and carrying out interpretability analysis. According to the method, a multi-dimensional feature system covering styles, earnings and risks is constructed, and comprehensive information input is provided for the model. XGBRanker is selected as a base model, Pairwise is used as a target function, and the relative sequence relation between samples can be learned. The method is highly consistent with a business target of screening out funds with good performance, and can better learn relative advantages and disadvantages between funds compared with a regression or linear scoring model. The fund is divided into ten groups for back testing according to the model score, and the annual yield of the high group is about 41% higher than that of the low group, which shows that the model has effective distinguishing ability.

This invention provides a method and system for training large-scale models in vertical domains, belonging to the field of artificial intelligence technology. Based on a general-purpose large language model, the method sequentially constructs an instruction fine-tuning dataset through data engineering, introduces domain-supervised samples for joint optimization after multi-task pre-training to obtain a base model, employs a two-stage hybrid fine-tuning strategy to activate and consolidate domain knowledge, and finally performs dual-dimensional alignment training of business goal alignment and common sense alignment. The common sense alignment constructs training sample sets and selects appropriate optimization algorithms for four anomaly patterns in the model response: illusion, factual error, security and privacy, and timeliness issues. This invention significantly reduces the training cost of large-scale models in vertical domains, enhances the model's deep understanding of specific domain knowledge and instruction compliance capabilities, and improves the stability and security of performance in engineering applications.