Operation guidance and optimization method, device, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an operation guidance and optimization method, device, equipment and medium, and the method comprises the steps: obtaining multi-source heterogeneous data to construct a dynamic knowledge graph, and generating an initial guidance scheme containing target operation, and in combination with the visual interaction model, presenting a target operation in real time, collecting real-time dynamic data, generating operation correction information based on data differences, updating display contents of the visual interaction model, and dynamically optimizing a guidance scheme according to an effect analysis value. According to the method, the dynamic knowledge graph is constructed by fusing multi-source heterogeneous data, real-time presentation and data acquisition are performed in combination with a visual interaction model, operation correction information is generated based on data differences, and a guidance scheme is dynamically optimized, so that personalized, dynamically updated and real-time feedback guidance scheme generation and optimization in multi-field application are realized; and the operation accuracy and adaptability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and in particular to an operation guidance and optimization method and device, equipment and a storage medium. BACKGROUND

[0002] In the medical and health business field, the existing postoperative rehabilitation guidance scheme generally has the following shortcomings, which seriously affects the scientificity and pertinence of rehabilitation intervention.

[0003] Firstly, the existing rehabilitation guidance scheme has obvious limitations in data integration and analysis. Clinical information systems usually manage multiple source heterogeneous data such as medical records, images, and genetic testing independently, lack a unified data fusion mechanism, making it difficult to form systematic knowledge association based on multi-dimensional information in the process of developing rehabilitation schemes, affecting the comprehensiveness and individualization level of the scheme.

[0004] Secondly, the current rehabilitation scheme generation method mainly depends on experience-based templates or guideline standards, lacks dynamic knowledge graph analysis based on multi-source data, and is difficult to realize personalized guidance content design for different patient characteristics and health status, and the problem of generalization and roughness of the scheme is more prominent.

[0005] At the same time, the existing rehabilitation guidance mode has technical bottlenecks in interactive experience, and the guidance content is mainly presented in the form of simple text and fixed two-dimensional / three-dimensional demonstration, lacking a visual interactive model that can interact with users in real time and dynamically adjust, which cannot effectively improve the action standardization and execution effect in the rehabilitation training process.

[0006] In addition, the rehabilitation scheme lacks a dynamic feedback and optimization mechanism based on real-time data. Although some wearable devices can collect real-time dynamic data such as bioelectric signals and kinematic signals during rehabilitation, existing systems are difficult to analyze rehabilitation effects in real time based on these data, and lack the ability to automatically optimize the guidance scheme, resulting in a serious lag in scheme adjustment and failing to achieve continuous and personalized dynamic guidance optimization.

[0007] In the field of financial technology business, personalized dynamic guidance services for high-risk investment, health insurance, and financial health management scenarios also have similar technical bottlenecks. Multi-source data cannot be effectively integrated, and systematic correlation analysis and knowledge mining are lacking, resulting in a lack of individualized pertinence in developing guidance schemes. At the same time, the guidance content presentation form is single and weak in interactivity, lacking the ability to dynamically feedback and optimize based on real-time data, and unable to meet the technical needs of high adaptability and dynamic adjustment in financial health management, restricting the improvement of overall service effect and customer experience. SUMMARY

[0008] The main purpose of the present application is to provide an operation guidance and optimization method, device, equipment and storage medium, aiming at solving the technical problems that the prior art lacks a dynamic fusion and real-time feedback optimization mechanism based on multi-source heterogeneous data, and cannot realize the generation and interactive presentation of personalized and dynamically updated guidance schemes.

[0009] To achieve the above-mentioned purpose, the present application provides an operation guidance and optimization method, comprising:

[0010] acquiring multi-source heterogeneous data, and constructing a dynamic knowledge graph based on the multi-source heterogeneous data;

[0011] generating an initial guidance scheme containing a target operation based on the dynamic knowledge graph;

[0012] generating a visual interactive model corresponding to the target operation;

[0013] presenting the target operation through the visual interactive model, and collecting real-time dynamic data when the target operation is executed;

[0014] generating operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data;

[0015] generating visual elements based on the operation correction information, and updating the display content of the visual interactive model through the visual elements;

[0016] determining an effect analysis value based on the real-time dynamic data, and adjusting the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition.

[0017] Further, to achieve the above-mentioned purpose, the present application provides an operation guidance and optimization device, comprising:

[0018] a knowledge graph construction module for acquiring multi-source heterogeneous data, and constructing a dynamic knowledge graph based on the multi-source heterogeneous data;

[0019] a guidance scheme generation module for generating an initial guidance scheme containing a target operation based on the dynamic knowledge graph;

[0020] an interactive model construction module for generating a visual interactive model corresponding to the target operation;

[0021] a real-time data collection module for presenting the target operation through the visual interactive model, and collecting real-time dynamic data when the target operation is executed;

[0022] a correction information generation module for generating operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data;

[0023] a visual content updating module configured to generate a visual element based on the operation correction information and update display content of the visual interactive model through the visual element;

[0024] a guidance scheme optimization module configured to determine an effect analysis value based on the real-time dynamic data, and adjust the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition.

[0025] Further, to achieve the above object, the present application also provides a computer device, which comprises a memory, a processor, and an operation guidance and optimization program stored in the memory and executable on the processor, and the operation guidance and optimization program, when executed by the processor, implements the steps of the operation guidance and optimization method.

[0026] Further, to achieve the above object, the present application also provides a computer readable storage medium, which stores an operation guidance and optimization program, and the operation guidance and optimization program, when executed by a processor, implements the steps of the operation guidance and optimization method.

[0027] Beneficial effects: The present application relates to the field of artificial intelligence technology, and can be applied to business scenarios such as financial technology and medical health, and discloses an operation guidance and optimization method, device, equipment and medium, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a dynamic knowledge graph based on the multi-source heterogeneous data, generating an initial guidance scheme containing a target operation based on the dynamic knowledge graph, generating a visual interactive model corresponding to the target operation, presenting the target operation through the visual interactive model, collecting real-time dynamic data when the target operation is executed, generating operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data, generating a visual element based on the operation correction information and updating the display content of the visual interactive model through the visual element, determining an effect analysis value based on the real-time dynamic data, and adjusting the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition. The present application constructs a dynamic knowledge graph by fusing multi-source heterogeneous data, combines real-time presentation and data collection through a visual interactive model, generates operation correction information based on data differences and dynamically optimizes a guidance scheme, realizes personalized, dynamically updated and real-time feedback guidance scheme generation and optimization in multi-field applications, and improves operation accuracy and adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0028] The present application will be further described below in conjunction with the accompanying drawings and embodiments, wherein:

[0029] Figure 1 An application environment schematic diagram of the operation guidance and optimization method in an embodiment of the present application;

[0030] Figure 2 Flowchart of an embodiment of the operation guidance and optimization method of the present application;

[0031] Figure 3 Functional module diagram of a preferred embodiment of the operation guidance and optimization device of the present application;

[0032] Figure 4 Structure diagram of a computer device in an embodiment of the present application;

[0033] Figure 5 Another structure diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0034] It should be understood that the specific embodiments described herein are merely illustrative of the present application and do not limit the present application.

[0035] The operation guidance and optimization method provided by the embodiments of the present application can be applied in an application environment such as Figure 1 , in which a user end communicates with a service end through a network. The service end can obtain multi-source heterogeneous data through the user end, construct a dynamic knowledge graph based on the multi-source heterogeneous data, generate an initial guidance scheme containing a target operation based on the dynamic knowledge graph, generate a visual interactive model corresponding to the target operation, present the target operation through the visual interactive model, collect real-time dynamic data when the target operation is executed, generate operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data, generate visual elements based on the operation correction information, and update the display content of the visual interactive model through the visual elements. The effect analysis value is determined based on the real-time dynamic data. When the effect analysis value meets the optimization trigger condition, the initial guidance scheme is adjusted to generate an updated guidance scheme. The present application constructs a dynamic knowledge graph by fusing multi-source heterogeneous data, combines real-time presentation and data collection through a visual interactive model, generates operation correction information based on data differences and dynamically optimizes the guidance scheme, and realizes the generation and optimization of a personalized, dynamically updated and real-time feedback guidance scheme in multi-field applications, thereby improving the operation accuracy and adaptability. The user end can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The service end can be implemented by an independent server or a server cluster composed of multiple servers. The present application will be described in detail through specific embodiments.

[0036] Please refer to Figure 2 , Figure 2 Flowchart of an embodiment of the operation guidance and optimization method provided by the present application. It should be noted that although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in an order different from that described herein.

[0037] AsFigure 2 As shown, the operation guidance and optimization method provided by the present application comprises the following steps:

[0038] S10, acquiring multi-source heterogeneous data, and constructing a dynamic knowledge graph based on the multi-source heterogeneous data;

[0039] In this embodiment, multi-source heterogeneous data is acquired, and the data sources cover structured, semi-structured and unstructured data types, specifically including but not limited to text information, image information, audio information, sensor data, medical device output data, financial transaction records and user behavior logs, etc. The text information can include electronic medical records, examination reports, diagnosis and treatment records in the medical and health field, account information, transaction flow, customer interaction records in the financial technology field, etc. The image information can include medical images such as X-ray images, CT scan results, nuclear magnetic resonance images, and also include remote identity verification images, contract photograph components, etc. in the financial field. The audio information can come from voice interaction systems, customer call recordings or medical voice recordings. The sensor data sources include physiological index data output by portable health monitoring devices, state information of environmental monitoring devices, and terminal operation environment monitoring data in the financial field. The medical device output data can include data results of vital sign monitoring devices and rehabilitation training instruments. The financial transaction records cover payment data, risk scores, and multi-dimensional data in the credit approval process. The user behavior logs include APP usage records, system operation traces, login behaviors, and other dynamic information.

[0040] The specific implementation mode of acquiring multi-source heterogeneous data includes integrating through a data interface, linking a database, calling a file system, and interfacing an information system, etc. in combination with standardized data conversion rules, to uniformly convert data of different formats and sources into a structured expression form. The text information can be subjected to keyword extraction, semantic analysis and structure mapping operations through a natural language processing engine. The image information can be subjected to image element segmentation, label annotation and structure mapping in combination with image recognition technology. The audio information can be converted into usable text information through a speech recognition and semantic understanding module, and then fused with other data types. The sensor data and device output data are accessed through a real-time data acquisition module or a protocol adaptation module, and data structure analysis is completed. The financial transaction data and behavior logs can be called through a secure data exchange interface, and the data integrity and security are guaranteed in combination with a permission control mechanism.

[0041] A dynamic knowledge graph is constructed based on multi-source heterogeneous data. The dynamic knowledge graph refers to a data network structure with real-time updating, structural self-evolution, and semantic expression capability, including node, relationship, and attribute information. The node is used to express various entity units, including but not limited to patient information, disease diagnosis, treatment plan, rehabilitation action, financial account, customer label, and transaction record. The relationship is used to express the logical, business, causal, semantic, and other multi-dimensional association between different nodes. The relationship type can include upstream and downstream dependence, same type mapping, time sequence association, causal link, and behavior trajectory. The attribute information includes quantified data, text description, and label classification of structured information attached to the node or relationship. The dynamic updating capability is embodied in real-time extension, modification, or reconstruction of nodes, relationships, and attributes according to data input and external events, forming a dynamically evolving data graph structure.

[0042] The specific implementation of the dynamic knowledge graph includes storing and managing the data structure through a graph database or a graph computing framework, combining entity recognition, relationship extraction, and attribute fusion algorithms to map the obtained multi-source heterogeneous data to the graph structure. Text information is identified through entity dictionary matching, syntax analysis, and dependency syntax tree parsing to identify key entities and relationships, and is updated to the knowledge graph. Image information is extracted through target detection, image segmentation, and classification models to map the entity and attribute information in the image to the graph nodes and attributes. Audio information is converted into text content to supplement entities and relationships. Sensor data and device output data update the dynamic attribute values of the nodes or form new nodes and new relationships. Financial transaction data and user behavior logs are combined with rule engines or model analysis to form business logic paths, risk association links, and customer portrait labels, supplementing the graph structure. In the process of dynamic evolution, the knowledge graph structure is self-adaptively optimized, and nodes can be added or deleted, relationships can be reconstructed, and attribute information can be updated according to data changes, to ensure that the graph structure reflects real, complete, and real-time business and data relationships.

[0043] When obtaining multi-source heterogeneous data, in the medical and health field, the data interfaces of hospital information systems (HIS), picture archiving and communication systems (PACS), laboratory information systems (LIS), portable health monitoring devices, and third-party health management platforms can be used to complete the comprehensive acquisition of medical record information, examination results, image data, genetic testing data, and device monitoring data. International standards such as HL7, DICOM, and FHIR can be combined in the data access process to ensure data compatibility and standardized expression. In the financial technology business field, the core banking system, payment platform, risk control system, customer management system, and external data service platform can be used to complete the full acquisition of account information, transaction details, risk scoring, credit approval, and behavior logs. Data encryption and permission management strategies can be combined to ensure the safety and compliance of data acquisition.

[0044] In the medical and health field, based on multi-source heterogeneous data, a patient-centered diagnosis and treatment knowledge graph can be constructed by combining medical special ontology, clinical knowledge base and disease diagnosis and treatment standards. The nodes cover diseases, examinations, treatments, rehabilitation actions and medication information. The relationships cover causal links, diagnosis and treatment paths, and health evolution trends. The dynamic updating process reflects the changes in patient status, treatment plan adjustment and health index fluctuations in real time, and assists medical decision-making and rehabilitation guidance. In the financial technology business field, a business knowledge graph based on customer information, transaction data and risk indicators can be constructed. The nodes include customer identity, account information, transaction events, behavior labels and risk nodes. The relationships express account association, transaction links, behavior patterns and risk paths. The dynamic updating process maps customer behavior changes, risk exposure trends and business strategy adjustments in real time, and assists risk control and precision marketing.

[0045] The embodiment breaks down the problem of scattered and fragmented medical health data and financial business data by obtaining multi-source heterogeneous data, realizes the unified integration and structural expression of data of different sources, formats and types, and ensures the comprehensiveness, accuracy and real-time nature of data acquisition. Based on multi-source heterogeneous data, a dynamic knowledge graph is constructed to form a multi-dimensional data structure with real-time updating and dynamic evolution capabilities. The semantic association and logical path of various data are connected, the correlation analysis and intelligent reasoning capabilities of the data are enhanced, and complete, real-time and accurate data support is provided for subsequent guidance scheme generation and dynamic optimization, thereby improving the system intelligence level and application effect.

[0046] S20, generating an initial guidance scheme containing a target operation based on the dynamic knowledge graph;

[0047] In the embodiment, when generating an initial guidance scheme containing a target operation in the dynamic knowledge graph, the specific meaning and expression form of the target operation need to be first determined. The target operation can be understood as a set of operations with clear execution meaning inferred or recommended based on the current state of the system, user feature information and environmental variables. The target operation not only includes a single action or instruction, but also can involve multi-step combination, complex process, parameter configuration or behavior sequence, and has the characteristics of flexible combination and dynamic adjustment.

[0048] In order to obtain an initial guidance scheme highly matched with the target operation, the system combines the constructed dynamic knowledge graph and uses various structured reasoning and path analysis methods. The node structure, relationship network and attribute information in the knowledge graph together form a multi-level decision basis. The node information reflects multi-element information such as operation object, execution condition, dependent resource and associated event. The relationship network presents the logical dependency and path structure of the operation chain. The attribute information supplements the dynamic state, environmental adaptation information and user features of the nodes or relationships.

[0049] In the generation process, the system maps the user's current state, preference information or behavior data into the graph structure based on the user feature vector and target demand information using embedding expression and semantic mapping methods, forms a high-dimensional semantic association with existing nodes, and ensures that the retrieval and reasoning results take into account both system structure logic and individual difference characteristics. Through path retrieval, logical reasoning, weight calculation or intelligent recommendation mechanisms, the system locates the path combination that best meets the current demand in the graph structure. The path can be composed of multiple nodes, relationships and attributes, and completely describes the implementation path, necessary conditions, dependency information and potential impact of the target operation.

[0050] To improve the adaptability and practicality of the scheme generation, the system selects probabilities or priorities for different paths, dynamically adjusts the weight distribution according to the availability of resources involved in the path, operation risk, execution efficiency, historical feedback and user preferences, and ensures that the scheme recommendation is targeted, flexible and highly adaptable. The final initial guidance scheme integrates the execution instructions, environment adaptation parameters, operation process, individual difference information and dynamic adjustment space of the target operation, facilitating subsequent interactive presentation and real-time updating, and ensuring the efficiency, personalization and dynamic adaptability of the scheme output.

[0051] In the medical and health business field, the generation process is based on a dynamic knowledge graph that includes patient health status, rehabilitation standards, behavior habits, environmental information and medical knowledge. The system dynamically constructs a patient feature vector by collecting physiological indicators, athletic performance and environmental feedback, maps the patient information to the knowledge graph, retrieves and reasons the rehabilitation path combination that meets the current state, and considers the action execution difficulty, physiological risk, rehabilitation stage and external conditions to generate an initial guidance scheme that includes rehabilitation actions, parameter configurations and environment adaptation suggestions, improving the relevance and safety of the scheme.

[0052] In the financial technology business field, the system dynamically constructs a knowledge graph based on customer behavior portraits, account information, transaction history, risk rules and compliance requirements, and generates feature expressions by combining real-time transaction data and external risk information. The system retrieves path combinations that match the operation demand within the graph structure, and the path involves risk control nodes, strategy configuration relationships and external constraint information. The system dynamically calculates the path weight based on operation risk, customer preference and compliance strategy to generate an initial guidance scheme for specific business scenarios, ensuring that the scheme takes into account personalization, compliance and dynamic adaptability.

[0053] This embodiment uses dynamic knowledge graph structure expression and semantic reasoning, combined with user feature expression and environmental variables, to efficiently generate personalized and dynamically adaptive guidance schemes that include target operations, avoiding the problem of static schemes being difficult to cover complex scenarios and individual differences, improving the adaptability, flexibility and execution efficiency of the operation scheme, reducing the need for human intervention, and enhancing the system's intelligence level.

[0054] S30, generating a visual interaction model corresponding to the target operation;

[0055] In this embodiment, for the determined target operation, the system needs to generate a corresponding visual interaction model to realize the intuitive presentation of operation content, execution path and interaction feedback. The target operation is a set of operation instructions formed in combination with the dynamic knowledge graph reasoning result, which has specific execution logic and individual adaptation parameters. In order to enable users to intuitively understand and accurately execute the target operation, the system constructs a visual interaction model with spatial mapping, structural expression and dynamic interaction capabilities.

[0056] In the generation process, the system first analyzes the action type, operation parameter, execution path and environmental constraint information contained in the target operation, and extracts standard action data or operation template. Standard action data can come from medical image acquisition, motion capture system, simulation library or expert experience database, covering spatial trajectory, time rhythm, posture change and physiological constraints of action process.

[0057] Based on the standard action data, the system constructs a three-dimensional spatial expression structure, generates a three-dimensional action model containing kinematic parameters, spatial coordinate system and structural skeleton, and the three-dimensional action model has highly restored action trajectory, posture change and spatial position, ensuring the authenticity and accuracy of the target operation in the virtual environment. To improve the dynamic adaptation capability of the model, the system synchronously constructs a skeletal kinematics constraint model. The constraint model defines the joint activity range, motion chain structure and spatial offset limit, ensuring that the three-dimensional action model has physiological reasonableness and technical executability in the subsequent interaction process, avoiding action paths or operations that do not conform to reality or are out of limits.

[0058] On the basis of the three-dimensional action model and the skeletal kinematics constraint model, the system dynamically generates visual elements according to the prompt requirements, interaction feedback and user understanding level involved in the target operation. The visual elements include trajectory guide lines, position markers, color coding, transparency adjustment and dynamic prompt information. The system dynamically adjusts the type, position and display mode of the visual elements according to user characteristics, operation difficulty and environmental feedback, improving the intuitive interaction and operation accuracy.

[0059] Finally, the system structurally combines the three-dimensional action model, the skeletal kinematics constraint model and the visual elements to construct a visual interaction model with spatial expression, structural constraint and dynamic interaction functions. The model supports dynamic presentation in augmented reality, virtual reality or mixed reality environments, assisting users in understanding the target operation content and execution path, and improving the accuracy and efficiency of the operation.

[0060] In the medical health business, the system acquires standard rehabilitation action data through an optical motion capture system, constructs a three-dimensional action model containing joint range of motion and posture change information based on the current state of the patient and rehabilitation needs, generates a skeletal kinematics constraint model to ensure that the action path meets the physiological structure and postoperative safety requirements. Combined with real-time data and expert rules, the system dynamically generates trajectory guide lines, error prompts and dynamic assistance information, and combines to form a visual interactive model to assist patients in accurately understanding and performing rehabilitation actions in a virtual environment, reducing the risk of misoperation.

[0061] In the financial technology business, the system generates a three-dimensional spatial expression structure of process nodes based on user operation processes, risk control needs and operation paths, constructs multi-level logical relationships and operation constraint information, generates color-coded visual prompts, path guides and dynamic feedback information, and combines to form a visual interactive model for intelligent terminals to help users efficiently and accurately complete target operations in complex business processes, avoiding operation errors and safety hazards due to interface understanding bias.

[0062] This embodiment generates a visual interactive model with spatial expression, structural constraints and dynamic interaction capabilities by analyzing target operations and combining standard data and dynamic parameters, achieving intuitive presentation and dynamic assistance of target operations, improving user operation accuracy, understanding efficiency and interaction experience, reducing the risk of misoperation due to information understanding bias, and enhancing system intelligence and user trust.

[0063] S40, presenting the target operation through the visual interactive model and collecting real-time dynamic data when the target operation is performed;

[0064] In this embodiment, after generating a visual interactive model with three-dimensional spatial expression and structural constraint capabilities, the system presents the target operation based on the model, dynamically displaying the operation path, action points and interaction feedback. The target operation is a collection of operations formed for specific execution scenarios and individual needs based on previous reasoning and parameter configuration, including action type, spatial path, parameter limit and time information. The visual interactive model has spatial mapping, dynamic response and information prompting functions, supporting real-time and intuitive presentation of the operation process.

[0065] The system dynamically presents the target operation process by mapping the visual interactive model to the physical space or virtual environment. This process includes spatial display of three-dimensional action path, posture change of skeletal structure, real-time update of visual elements and information prompts. Users can understand action requirements, adjust posture and optimize path according to visual feedback during the operation process. To ensure accurate acquisition of operation data, the system synchronously starts the data collection module and monitors user operation status in real time with different types of sensors.

[0066] The data acquisition module includes bioelectric sensors and kinematic sensors. The bioelectric sensors are used to monitor muscle activation levels, neural responses, and physiological signals. Common sensing methods include surface electromyography, skin galvanic response, and heart rate variability monitoring. The kinematic sensors are used to obtain spatial position, angle change, and posture information. Common devices include inertial measurement units, optical positioning systems, and depth cameras. The system synchronously acquires bioelectric signals and kinematic signals, and combines timestamps and spatial calibration to achieve unified calibration and fusion of the signals.

[0067] By combining the dynamic presentation of the visual interaction model with the sensing data acquisition process, the system can obtain real-time dynamic data during the execution of the target operation. The data includes spatial trajectory, posture change, physiological response, and execution deviation during the action process, and has high frequency, high accuracy, and multi-dimensional information fusion characteristics. Real-time dynamic data provides basic support for subsequent deviation analysis, effect evaluation, and dynamic optimization, ensuring that the system has continuous monitoring, intelligent adjustment, and adaptive optimization capabilities.

[0068] In medical health applications, the system maps the visual interaction model to the actual operation environment of the patient through wearable devices and AR terminals, dynamically presents rehabilitation action path and posture prompt information, and assists the patient in understanding the action points and path specifications. Combined with surface electromyography sensors and inertial measurement units, the system can acquire muscle activation state, joint angle, and posture change information in real time, generate dynamic data with spatial, temporal, and physiological dimensions, and assist in clinical monitoring and effect evaluation.

[0069] In financial technology applications, the system integrates the visual interaction model into the intelligent terminal operation interface, dynamically presents complex business process structures, risk nodes, and operation feedback information, and assists users in understanding process logic and operation specifications. Combined with the behavior data monitoring module and input device feedback, the system can acquire position change, input content, and behavior deviation information during the operation process in real time, generate multi-source dynamic data, and assist in risk control analysis and operation path optimization.

[0070] This embodiment realizes intuitive display of the target operation process and comprehensive acquisition of real-time dynamic data by dynamic presentation based on the visual interaction model and multi-dimensional sensor data fusion, improves user operation accuracy, understanding efficiency, and system response capability, reduces operation deviation risk, enhances intelligent monitoring and dynamic optimization capability, and ensures safe, stable, and efficient operation process.

[0071] S50, generating operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data;

[0072] In this embodiment, the system presents the target operation and synchronously collects real-time dynamic data, and combines the preset target parameters in the target operation to perform a difference analysis process to generate operation correction information. The target parameters are derived from the target operation generated in the previous step, and specifically include spatial path, action amplitude, angle interval, timing requirement, and physiological index reference value, representing the standard specification and expected effect of the operation process. The real-time dynamic data includes spatial trajectory, posture change, physiological signal, and behavior data obtained through multiple sensors, comprehensively reflecting the actual operation state.

[0073] The system first extracts the target parameters from the target operation, and according to the data type and structure standard, analyzes the position, posture, and physiological index information item by item to form a complete parameter baseline. In combination with the real-time dynamic data, the system analyzes the spatial position, angle change, time characteristics, and physiological response obtained during the actual execution process to generate an actual parameter set, ensuring that the data dimension and structure are consistent with the target parameters.

[0074] In the difference analysis process, the system compares the target parameters with the actual parameters to determine the spatial position deviation, angle overrun, posture anomaly, and physiological index deviation, and forms a quantitative deviation result. The deviation result can include absolute value difference, relative error, trend change, and dynamic stability analysis, reflecting the deviation type, severity, and development trend existing in the operation execution process.

[0075] Based on the deviation analysis result, the system combines the set tolerance threshold and safety standard to determine whether the deviation exceeds the allowed range. If the deviation is within the acceptable range, the system can generate a confirmation prompt information to feedback that the operation state is good, prompting the user to continue to maintain the operation mode. If the deviation exceeds the tolerance threshold, the system generates specific operation correction information based on the deviation type, position, and severity, including spatial adjustment suggestions, action amplitude correction, posture optimization prompts, and physiological index adjustment guidelines.

[0076] The operation correction information has the characteristics of pertinence, real-time, and visual expression, ensuring that the user can intuitively understand the deviation reason and adjustment requirement during the operation process, improving the accuracy, safety, and execution efficiency of the operation process.

[0077] In medical health applications, the system sets target parameters for spatial trajectory, joint angle, and muscle activation state during patient rehabilitation training, obtains real-time dynamic data through surface electromyography sensors and posture tracking devices, and compares and analyzes the actual execution state with the target parameters. If insufficient joint mobility, abnormal muscle activation level, or posture deviation overrun is detected, the system generates operation correction information in real time, such as prompting insufficient joint external rotation angle, slow action rhythm, or abnormal muscle load, guiding the patient to adjust the training posture and movement mode.

[0078] In the financial business scenario, the system sets target parameters in the complex business operation process, covering input path, operation time sequence and behavior specification, real-time operation data is acquired through behavior monitoring and data acquisition module, the system compares and analyzes the actual operation behavior and target parameters, if the path deviates, input is delayed or operation is not standardized, operation correction information is generated, such as prompting operation sequence error, risk node jump abnormality or input content inconsistency, assisting users to adjust operation path and optimize behavior mode.

[0079] In this embodiment, by combining the difference between target parameters and real-time dynamic data with the quantitative analysis and correction information generation process, the system realizes real-time detection, accurate identification and intelligent correction of deviations in the operation execution process, improves the accuracy, stability and safety of the operation process, reduces the risk of execution deviation and the probability of adverse events, and enhances the operation experience of users and the intelligent feedback ability of the system.

[0080] S60, generating a visual element based on the operation correction information, and updating the display content of the visual interaction model through the visual element;

[0081] In this embodiment, based on the generation of operation correction information, the system generates visual elements corresponding to the operation correction information through multi-dimensional information analysis and visual design process, and updates the display content of the visual interaction model based on the visual elements. The operation correction information is derived from the previous step, and the content includes space adjustment suggestion, action optimization prompt, posture correction guide and state feedback information, which represents the real-time adjustment and feedback content that needs to be conveyed to the user in the operation process.

[0082] The system first analyzes the operation correction information, extracts the information type, position attribute, adjustment suggestion and visual expression requirement, and determines the corresponding visual element type. The visual element type includes trajectory guide line, angle mark, dynamic prompt symbol, color coding and text information, and different types of visual elements correspond to different deviation types and operation guidance requirements. The system configures the display parameters of the visual element according to the specific content and type of the operation correction information, and the display parameters include color, transparency, shape, size and animation effect, to ensure that the visual element has good recognition, real-time and visual expression effect.

[0083] After configuration is completed, the system positions the specific position, direction and associated object of the visual element in space based on the three-dimensional coordinate system generated in the previous step, to ensure the coordination and consistency of the visual element with the operation object, user perspective and environment space. Combined with the space coordinate information and display parameters, the system renders and generates the visual element to form a three-dimensional visual object with dynamic feedback and interaction attributes, including space path, position prompt, state identifier and adjustment suggestion information.

[0084] The system associates the rendered visual elements with the visualization interaction model, updates the display content of the visualization interaction model, including overlay, replacement, superposition or addition, to ensure that the updated display content can reflect the operation correction information in real time, accurately and intuitively, and assist users in understanding the deviation reasons, mastering the adjustment method and optimizing the operation behavior. The updating process synchronously considers system performance, user experience and environmental adaptability to ensure the stability, continuity and efficiency of the display content.

[0085] In the medical health application, for the operation correction information generated in the patient rehabilitation training process, the system analyzes the muscle fatigue prompt and posture deviation warning, and generates visual elements such as red dynamic trajectory line, space angle mark and position guide symbol. The system renders visual elements on the patient visualization interaction model in real time based on three-dimensional space coordinates, prompts the patient to adjust the posture and optimize the action through intuitive space path and color change, and improves the accuracy and safety of rehabilitation training.

[0086] In the financial business scenario, the system analyzes the deviation prompt and path warning generated in the high-risk operation process, and generates visual elements such as highlighted path mark, risk node prompt symbol and dynamic text information. The system updates the display content in the business interaction interface and three-dimensional process visualization model, and guides the user to adjust the operation path, avoid risk nodes and optimize the input behavior in time through dynamic change and color coding, to ensure the operation compliance and process safety.

[0087] This embodiment realizes the visualization expression and interactive feedback of deviation information in the operation process through the dynamic association and real-time update of operation correction information and visual elements, improves the understanding efficiency and response speed of users on the operation state and adjustment demand, enhances the intuitiveness, interactivity and intelligent guidance ability of the operation process, reduces the operation deviation risk, and optimizes the accuracy and user experience of the overall operation process.

[0088] S70, determining an effect analysis value based on the real-time dynamic data, and adjusting the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition.

[0089] In this embodiment, the system determines an effect analysis value reflecting the current operation state and execution effect through multi-dimensional data processing and quantitative evaluation combined with the previously generated real-time dynamic data. The effect analysis value is used to measure the completion quality of the target operation, the user state change and the training achievement trend. Real-time dynamic data includes bioelectric signal, kinematic parameter, posture trajectory and system feedback information, which has the characteristics of high frequency, continuity and multi-source fusion, and can fully reflect the instantaneous state of user operation.

[0090] The system extracts key parameters such as muscle activation, joint range of motion, motion stability, and physiological indicator changes through real-time dynamic data analysis. Based on these parameters, a comprehensive effectiveness index system is constructed. The system designs a standardized calculation model based on the index system, quantifies the contribution of different dimension parameters to the operation effect, and finally generates an effectiveness analysis value. The effectiveness analysis value has a numerical expression form, which is convenient for the system to track trends, compare states, and make optimization decisions.

[0091] The system continuously monitors the change trend and historical data of the effectiveness analysis value, sets optimization trigger conditions, and includes cases such as continuous decline, deviation from the reference interval, or fluctuation exceeding the threshold of the effectiveness analysis value. When the system detects that the effectiveness analysis value meets the optimization trigger condition, it determines that the current guidance scheme has reduced matching degree for user state and operation effect, and there is a need for adjustment and optimization.

[0092] The system performs scheme adjustment operations based on real-time dynamic data and the specific performance of the effectiveness analysis value. Scheme adjustment involves operation parameters, training intensity, motion path, and guidance strategies. During the adjustment process, the system combines individual characteristics, real-time data, and historical feedback to dynamically optimize the content and structure of the guidance scheme, ensuring that the adjusted scheme better adapts to the user's current state and operation needs.

[0093] The system generates an updated guidance scheme that integrates real-time optimization results based on the initial guidance scheme, with higher individual matching degree, dynamic adaptability, and operation guidance ability. The system presents the updated guidance scheme in real time through visual interaction models and multi-modal output methods, assisting users in adjusting operation behavior, improving operation effect, and enhancing system intelligent interaction level.

[0094] In the medical and health field, the system collects bioelectric signals and joint activity data during rehabilitation training, and generates an effectiveness analysis value in real time to monitor muscle fatigue and posture deviation levels. When the effectiveness analysis value continuously decreases, the system automatically adjusts the training intensity and motion amplitude, generates an updated guidance scheme, dynamically adapts to the patient's rehabilitation state, and improves training safety and effectiveness.

[0095] In the financial business scenario, the system generates an effectiveness analysis value based on real-time operation data and risk indicators, reflecting user operation stability and process compliance. When the effectiveness analysis value fluctuates beyond the threshold, the system automatically adjusts the operation guidance content and risk prompt strategy, generates an updated guidance scheme, and dynamically optimizes the trading process and user operation experience.

[0096] The embodiment realizes the continuity, individualization and intelligent adaptation of the operation guidance scheme, improves the flexibility of the operation process and the real-time response ability of the user state, reduces the operation deviation and risk, and enhances the intelligent level of human-computer interaction and the overall operation effect of the system through real-time dynamic data driven effect analysis and scheme dynamic optimization.

[0097] The application relates to the technical field of artificial intelligence, can be applied to business scenes such as financial technology and medical health, and discloses an operation guidance and optimization method and device, equipment and a medium, which comprise the following steps: acquiring multi-source heterogeneous data, constructing a dynamic knowledge graph based on the multi-source heterogeneous data, generating an initial guidance scheme containing a target operation based on the dynamic knowledge graph, generating a visual interactive model corresponding to the target operation, presenting the target operation through the visual interactive model, collecting real-time dynamic data when the target operation is performed, generating operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data, generating visual elements based on the operation correction information, updating the display content of the visual interactive model through the visual elements, determining an effect analysis value based on the real-time dynamic data, and adjusting the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization triggering condition. The application realizes the generation and optimization of the personalized, dynamically updated and real-time feedback guidance scheme in multi-field application by fusing multi-source heterogeneous data to construct a dynamic knowledge graph, combining real-time presentation and data collection of the visual interactive model, generating operation correction information based on the data difference and dynamically optimizing the guidance scheme, and improving operation accuracy and adaptability.

[0098] In one embodiment, the step S10 comprises:

[0099] S101, analyzing text data to identify entity features;

[0100] S102, processing image data to extract structure parameters;

[0101] S103, analyzing biological data to determine response paths;

[0102] S104, fusing the entity features, the structure parameters and the response paths to generate an initial knowledge graph;

[0103] S105, acquiring updated evidence of multi-source heterogeneous data sources based on the initial knowledge graph, and analyzing weight factors of the updated evidence;

[0104] S106, expanding the initial knowledge graph based on the weight factors to construct a dynamic knowledge graph.

[0105] In this embodiment, the system faces multi-source heterogeneous data for information acquisition and fusion processing, including structured text, image data and biological data, covering data resources in different business scenarios such as medical health, industrial monitoring and financial risk control. Text data comes from electronic medical records, business logs, contract files or financial transaction texts, with characteristics of various structures and complex semantics. The system analyzes text data through natural language processing and semantic recognition technology, identifies entity features with clear business attributes and entity associations, including people, devices, organizations, geographic locations or other business concepts with associated relationships. Entity extraction, relationship identification and context association techniques are used in the identification process to improve the accuracy and completeness of entity features.

[0106] Image data comes from medical images, monitoring screens, engineering structure diagrams or other image resources. The system extracts structural parameters from image data through image segmentation, edge detection, key point recognition and structure analysis techniques. Structural parameters include anatomical structures, mechanical components, spatial layouts or other data elements with structural representation. The system combines image features and spatial information to ensure accurate expression and multi-dimensional association capabilities of structural parameters.

[0107] Biological data comes from gene sequencing, molecular biology experiments, vital sign monitoring or other biological data sources. The system determines the response paths contained in biological data through biological data analysis, pathway identification and function reasoning techniques. Response paths reflect physiological processes, molecular reaction chains or other data representations with logical associations, ensuring effective use and reasoning capabilities of the system for biological level information.

[0108] The system fuses entity features, structural parameters and response paths to construct an initial knowledge graph. The initial knowledge graph represents entities, attributes and relationships of multi-source data based on graph structure, with multi-level, multi-modal and dynamic scalability. The system uses graph databases, association reasoning and relationship mining techniques to ensure the structural integrity and semantic expression ability of the initial knowledge graph.

[0109] The system continuously acquires updated evidence from multi-source heterogeneous data sources based on the initial knowledge graph. Updated evidence includes new data, data changes or data abnormalities. The system uses data monitoring, difference analysis and data comparison techniques to capture updated evidence in real time and analyze its business weight. The weight factor reflects the importance, reliability and business impact of the updated evidence in the knowledge graph. The system dynamically evaluates the value and impact range of data updates through the weight factor.

[0110] Based on the weight factor result, the system expands the initial knowledge graph, and the expansion process includes adding new entity nodes, supplementing relationship edges, or updating attribute information. The system ensures that the knowledge graph structure maintains coherence, accuracy, and dynamic adaptability during data updating. Finally, a dynamic knowledge graph with real-time updating, dynamic evolution, and multi-source fusion capabilities is formed.

[0111] Through the structured fusion of multi-source heterogeneous data and the real-time expansion of dynamic knowledge graphs, the system breaks down the fragmented state between different data sources, improves the information association ability and business expression integrity across data types, ensures the dynamic updating and continuous optimization of the knowledge system, and enhances the level of data-driven intelligent reasoning, real-time decision-making, and global information perception in complex business environments.

[0112] In one embodiment, the above step S20 includes:

[0113] S201, constructing a user feature vector;

[0114] S202, embedding the user feature vector into the dynamic knowledge graph to generate a feature embedding representation;

[0115] S203, querying a decision path in the dynamic knowledge graph based on the feature embedding representation;

[0116] S204, determining a selection probability based on the decision path;

[0117] S205, generating an initial guidance scheme containing target operations based on the selection probability.

[0118] In this embodiment, during the construction of the initial guidance scheme, the system first extracts and constructs a user feature vector based on the established dynamic knowledge graph. The user feature vector is derived from the user's historical data, behavior records, physiological indicators, preference information, or other data sets related to specific application scenarios. The user feature vector is encoded in a vectorized manner, forming a multi-dimensional data representation with high-dimensional expression ability, structural uniformity, and computational capability. This representation preserves individual differences, behavior patterns, and attribute characteristics of users, ensuring that the scheme generation process is targeted and personalized.

[0119] The user feature vector is embedded into the dynamic knowledge graph to generate a feature embedding representation. The embedding process uses graph neural networks, knowledge representation learning, or embedding mapping algorithms to deeply integrate the user features with entities, attributes, and relationships in the knowledge graph structure. The feature embedding representation has cross-modal and cross-structural association expression capabilities, and can accurately reflect the user's positioning, upstream and downstream dependency relationships, and potential influence paths in the overall structure of the dynamic knowledge graph.

[0120] Based on the feature embedding representation, a decision path is queried in the dynamic knowledge graph. The decision path reflects a multi-level associated path obtained by combining the knowledge graph structure reasoning from the user features. The nodes in the path represent entities, attributes or business states. The edges of the path reflect the logical relationship between entities, data dependency or business process. The system determines a decision path set with high correlation degree and strong business relevance to the user features through path search, relationship reasoning and dependency analysis.

[0121] Based on the decision path, a selection probability is determined. The selection probability reflects the applicability, effectiveness and priority of each path in the current business scenario. The system comprehensively considers the path length, path weight, node importance and historical effect, adopts probability calculation, normalization processing and multi-factor fusion strategy, generates the selection probability of each decision path, and ensures the objectivity and adaptability of the system in multi-path selection, and guarantees the optimality and reliability of the scheme generation result.

[0122] Based on the selection probability, an initial guidance scheme containing target operations is generated. The system selects the operation nodes corresponding to the high probability paths in priority according to the path selection probability, generates an initial guidance scheme with clear operation target, execution parameter and process description according to the business rules, user demand and environmental constraints, and the scheme content includes the execution steps, operation standard and expected effect of the target operation, which ensures the pertinence, scientificity and operability of the scheme.

[0123] In this embodiment, through the deep integration of dynamic knowledge graph and user features, combined with path reasoning, probability calculation and intelligent decision, the system can dynamically generate an initial guidance scheme with individuality, real-time and high adaptability for different users, different environments and different business needs, which significantly improves the accuracy, efficiency and business coverage of the scheme, breaks through the problems of relying on artificial experience, lacking intelligent reasoning and dynamic adaptation in traditional scheme formulation, and enhances the data-driven scheme generation level and decision support ability.

[0124] In one embodiment, the above step S30 comprises:

[0125] S301, analyzing the action instruction of the target operation, and obtaining standard action data based on the action instruction;

[0126] S302, establishing a skeletal kinematics constraint model;

[0127] S303, generating a three-dimensional action model based on the standard action data and the skeletal kinematics constraint model;

[0128] S304, generating a visual element based on the three-dimensional action model;

[0129] S305, combining the visual elements to construct a visual interaction model.

[0130] In this embodiment, the system first analyzes the target operation during the construction of the visual interaction model, obtains the action instruction associated with the target operation, and the action instruction refers to a structured information set containing action type, execution parameter and operation requirement. The action instruction can be derived from user input, historical operation record, system inference result or preset operation template. The process of analyzing the action instruction extracts the explicit operation intention, execution range and parameter requirement through semantic analysis, keyword recognition or structure mapping, ensuring that the subsequent model generation process has accurate operation basis.

[0131] Based on the action instruction, standard action data is obtained, which is a standardized action parameter set conforming to industry standards, medical standards or business process requirements. Standard action data is usually derived from clinical action library, industry operation standard or system built-in model. The data content includes joint range of motion, bone position parameter, action trajectory curve and time control parameter, ensuring that the generated action model has professional, accurate and safe.

[0132] A skeletal kinematic constraint model is established, which reflects the anatomical structure limitation, joint motion range and physiological function boundary of the human body or object in the actual operation process. The model construction process is based on standard anatomical parameters, three-dimensional spatial position relationship and motion chain structure, using kinematic modeling, spatial constraint reasoning or three-dimensional structure reconstruction method to generate skeletal constraint structure conforming to physical laws and physiological characteristics, ensuring that the subsequent action model conforms to the actual motion law and safety standards.

[0133] Based on the standard action data and the skeletal kinematic constraint model, a three-dimensional action model is generated, which is a three-dimensional structure representation with spatial coordinates, structure levels and dynamic behavior. The generation process considers standard action parameters, skeletal structure constraints and operation requirements, and uses three-dimensional modeling, motion mapping and dynamic rendering technology to construct a complete three-dimensional action expression. The three-dimensional action model has real-time rendering capability, dynamic interaction function and multi-angle display effect, ensuring that the visualization process has intuitiveness and immersion.

[0134] Based on the three-dimensional action model, visual elements are generated, including but not limited to trajectory guide line, action node identification, space prompt mark or operation dynamic feedback. The generation process combines the structure characteristics and spatial position of the three-dimensional action model, and uses graphic rendering, visual coding and interaction design technology to form an intuitive, clear and complete visual expression unit.

[0135] The combination of visual elements constructs a visual interaction model. The visual interaction model integrates multiple visual elements to form a three-dimensional display structure with integrity, interactivity and feedback. The interaction model supports users to obtain operation feedback, adjust action details and optimize execution path in real time through visual observation, gesture control or other input methods, ensuring the accuracy, standardization and personalized adaptability of the operation process.

[0136] The embodiment can generate a visual interaction model with professionalism, accuracy and strong interactivity in real time, significantly improve the visual experience, action guidance accuracy and dynamic feedback capability of the operation process, overcome the problems of lack of three-dimensional interaction, insufficient action standard and poor real-time adaptability of traditional guidance systems, and enhance the efficiency, accuracy and user experience level in the operation training, rehabilitation guidance or task execution process.

[0137] In one embodiment, the step S40 comprises:

[0138] S401, rendering a three-dimensional action demonstration process of the target operation through the visual interaction model;

[0139] S402, collecting bioelectric signals through bioelectric sensors;

[0140] S403, collecting kinematic signals through kinematic sensors;

[0141] S404, synchronizing the bioelectric signals and the kinematic signals, and fusing the synchronized bioelectric signals and kinematic signals to obtain multi-source signals;

[0142] S405, generating real-time dynamic data based on the multi-source signals.

[0143] In the embodiment, the three-dimensional action demonstration of the target operation is rendered by the visual interaction model. The three-dimensional action demonstration refers to an action display form with spatial depth information, dynamic behavior expression and visual guidance function. The rendering process is based on the generated visual interaction model structure, combined with the preset perspective, spatial layout and dynamic parameters, uses the three-dimensional rendering engine, graphics processing unit and interaction design logic, and dynamically outputs three-dimensional visual content with spatial positioning, action path and operation feedback, ensuring that users can observe, understand and follow the execution of the target operation in an intuitive and immersive manner.

[0144] In the process of target operation performed by the user or the object, bioelectric signals are collected in real time by bioelectric sensors. The bioelectric signals are electrical signal information reflecting muscle activity, nerve response or other physiological state changes. The bioelectric sensors can include surface electromyography sensors, nerve electrical signal recording devices or other bioelectric monitoring equipment. The sensor layout position, parameter setting and signal collection frequency are determined according to the operation type, object characteristics and data requirements, to ensure the accuracy, real-time performance and physiological safety of the collection process.

[0145] Meanwhile, kinematic signals are collected by kinematic sensors. The kinematic signals are structured data reflecting spatial position, angle change, acceleration characteristics or dynamic behavior patterns. The kinematic sensors include inertial measurement units, attitude tracking devices or other spatial positioning components. The sensor layout mode, measurement dimension and data output format are determined in combination with the operation requirements and spatial structure, to ensure the comprehensiveness, dynamics and high-precision expression of the motion data.

[0146] The system synchronously processes the bioelectric signals and the kinematic signals. Synchronization refers to time alignment and data integration of signals of different sources and multiple dimensions according to a unified time reference, data structure and logical sequence, to ensure the consistency of various signals in space, time and logic, eliminate information misplacement, data drift and synchronization deviation caused by differences in data collection frequency, signal transmission delay or asynchronous events, and ensure that the multi-source data has integrity, timeliness and logical closure.

[0147] The bioelectric signals and the kinematic signals after synchronization are further fused. In the fusion process, various signals are converted into unified multi-source signal expression through data registration, feature mapping and multi-dimensional integration, to reflect physiological state, action execution and spatial behavior characteristics in the operation process. The fusion strategy can adopt data layer fusion, feature layer fusion or decision layer fusion mode, and is flexibly configured in combination with specific data structure, system architecture and performance requirements, to ensure that the fusion result has high information density, comprehensive data expression and real-time analysis capability.

[0148] Real-time dynamic data is generated based on the fused multi-source signals. The real-time dynamic data is a data set dynamically reflecting physiological state, action execution effect and spatial behavior performance of the object in the target operation process. The data content includes biological response indicators, kinematic parameters, operation deviation information or behavior trend analysis results. The system continuously outputs real-time dynamic data with high frequency, high precision and dynamic change through real-time data generation, dynamic updating and immediate feedback mechanism, supports subsequent operation monitoring, correction feedback and optimization decision function, and improves the adaptability, accuracy and individualization level of operation guidance, training execution or rehabilitation process.

[0149] The embodiment can dynamically, real-timely and comprehensively generate real-time dynamic data reflecting the actual operation state of the object by combining the visual output of three-dimensional action demonstration, multi-dimensional acquisition, synchronous integration and deep fusion of bioelectric signals and kinematic signals, solve the problems of lack of physiological state monitoring, spatial behavior tracking and multi-source data fusion in traditional guidance systems, improve the data acquisition efficiency, information integrity and dynamic feedback capability in the operation process, enhance the scientific nature, accuracy and safety of training, rehabilitation or task execution, and meet the needs of personalized guidance and real-time adaptation.

[0150] In one embodiment, the above step S50 comprises:

[0151] S501, extracting a target parameter from the target operation and analyzing an actual parameter from the real-time dynamic data;

[0152] S502, determining the deviation of the target parameter and the actual parameter;

[0153] S503, determining whether the deviation is greater than a preset tolerance threshold;

[0154] S504, generating a trajectory guidance element when the deviation is greater than the preset tolerance threshold;

[0155] S505, generating a confirmation prompt when the deviation is less than or equal to the preset tolerance threshold;

[0156] S506, generating operation correction information based on the trajectory guidance element or the confirmation prompt.

[0157] In the embodiment, first, a target parameter is extracted based on a target operation, the target parameter refers to a standardized, structured and quantitative reference data set preset for a specific target operation, used to represent an ideal operation state, a spatial behavior path or a physiological response standard. The target parameter includes spatial position, attitude angle, action amplitude, physiological indicator threshold or dynamic behavior mode, and the data is derived from a standard database, historical operation record or model generation result. The specific parameter type, dimension and expression mode are dynamically configured according to the structural features, execution requirements and monitoring standards of the target operation, to ensure that the target parameter has high accuracy, clarity and operation guidance value.

[0158] Synchronously, the actual parameter is analyzed from the real-time dynamic data, the actual parameter is a structured data set reflecting the real, physiological, spatial and dynamic state of the object in the specific operation process through multi-source heterogeneous data fusion, dynamic integration and real-time analysis, the data is derived from bioelectric signals, kinematic signals or other sensing systems, and the analysis process includes data extraction, structure mapping and logic recombination, to ensure the high consistency of the actual parameter and the target parameter in data type, structure expression and spatial logic, and to ensure the comparability, accuracy and real-time of the deviation analysis process.

[0159] Based on the extracted target parameters and the analyzed actual parameters, the system determines the deviation between the two, which refers to the absolute difference, relative deviation or logical deviation of the target parameters and the actual parameters in numerical expression, spatial position or physiological indicators. The calculation method selects Euclidean distance, angle difference, proportional change rate or other difference quantification models according to the data type, dynamically adjusts the deviation judgment standard according to the data dimension, structure characteristics and operation requirements, and guarantees the high accuracy, stability and operation adaptability of the deviation calculation result.

[0160] The system further judges whether the deviation is greater than the preset tolerance threshold. The tolerance threshold is the acceptable deviation range set for different target parameter types, operation scenarios and individual differences, indicating that the actual parameter deviation within this range does not constitute a substantial impact on the operation effect, behavior safety or physiological state. The threshold is dynamically determined in combination with clinical standards, engineering specifications or intelligent adjustment logic, supports flexible adjustment based on operation risk level, object ability level and environmental factors, and improves fault tolerance and guidance adaptability.

[0161] When the detection result shows that the deviation is greater than the tolerance threshold, the system generates a trajectory guidance element. The trajectory guidance element is a visual structure that intuitively guides the object to adjust the operation path, optimize the action mode or correct the behavior deviation through spatial visualization, dynamic prompt and real-time feedback. Element types include three-dimensional trajectory lines, spatial guide arrows, dynamic path markers or interactive virtual auxiliary devices. The elements are dynamically configured in combination with target operation structures, spatial layouts and user needs to ensure high visibility, operability and real-time interactivity of the guidance information.

[0162] If the deviation is less than or equal to the tolerance threshold, the system generates a confirmation prompt. The confirmation prompt is an information expression that reflects the normal operation state, standard-compliant behavior or stable physiological state through visual, audio or multi-modal forms. The prompt content includes text information, icon markers, color changes or voice broadcasts, which are flexibly adjusted in combination with operation flow, interaction design and user preferences to enhance positive feedback, confidence reinforcement and behavior stability during operation.

[0163] The system finally generates operation correction information based on the trajectory guidance element or the confirmation prompt. The operation correction information is a data set that comprehensively reflects the object's operation state, deviation result and feedback instruction. The content structure includes deviation data, spatial guidance information, confirmation prompt and operation suggestion. The correction information is output through multi-modal, dynamically updated and real-time pushed mechanism, supporting immediate guidance, dynamic correction and continuous optimization during operation, and guaranteeing the scientificity, accuracy and safety of operation execution.

[0164] The embodiment combines target parameter extraction, actual parameter analysis, deviation quantification, threshold determination and correction information generation process organically, and the system can identify the deviation in the operation process in real time, accurately and dynamically, generate track guidance elements or confirmation prompts, realize efficient, intuitive and personalized operation correction and feedback, solve the problems of lack of real-time deviation analysis, visual guidance and dynamic adaptation in traditional guidance systems, improve the scientificity, interactivity and safety of operation guidance, and enhance user experience, operation accuracy and training rehabilitation effect.

[0165] In one embodiment, the above step S60 comprises:

[0166] S601, analyzing the operation correction information to obtain a visual element type;

[0167] S602, configuring display parameters of the visual element type;

[0168] S603, generating a three-dimensional space coordinate system;

[0169] S604, rendering a visual element with the display parameters in the three-dimensional space coordinate system;

[0170] S605, updating the rendered visual element to the display content of the visualization interaction model.

[0171] In the embodiment, the system analyzes the visual element type based on the operation correction information. The operation correction information is a structured data set generated by combining the target parameters, real-time dynamic data and deviation analysis results of the target operation in the previous steps, and contains multi-dimensional information such as deviation properties, spatial positions, operation states and correction suggestions. The visual element type refers to the visual expression form dynamically matched, selected and defined by the system according to different deviation states, feedback needs and spatial characteristics in the operation correction information. The types include spatial track guidance, path optimization prompt, confirmation information display or risk warning identification, and are flexibly configured according to operation complexity, object ability and interaction needs, to ensure that the visual element type is highly matched with the actual correction needs and the expression is clear.

[0172] The system further configures display parameters of the visual element type. The display parameters are a set of visual expression attributes set for different visual element types, spatial layouts and user needs. The parameter content includes color, size, transparency, dynamic effect, position coordinate or spatial directionality. The parameter configuration is dynamically determined according to the visual element type, operation scene and interaction logic, to ensure that the visual element has high visibility, spatial adaptability and interaction friendliness, and improve the understanding efficiency and operation cooperation degree of the user.

[0173] The system synchronously generates a three-dimensional space coordinate system, which is a spatial reference framework for accurate positioning, spatial expression, and dynamic rendering of visual elements. The coordinate system is dynamically constructed based on the spatial structure, operating environment, and user position of the visualization interaction model, and uses a unified spatial origin, coordinate axis direction, and scale parameter. It supports spatial mapping and visual element positioning across multiple devices and scenes, ensuring spatial accuracy, consistency, and operational adaptability in the visual element rendering process.

[0174] The system renders visual elements with display parameters in the three-dimensional space coordinate system. The rendering process includes visual element type loading, display parameter binding, spatial position mapping, and dynamic effect presentation. The system generates, dynamically adjusts, and accurately renders visual elements in real time based on the operating scene, interaction requirements, and spatial layout, ensuring high real-time performance, spatial accuracy, and optimized interaction experience for visual content expression. This enhances the intuitiveness, accuracy, and engagement of operation guidance.

[0175] The system finally updates the rendered visual elements to the display content of the visualization interaction model. Display content updates include dynamic superposition of visual elements, spatial information redrawing, interactive interface refreshing, and multi-modal information synchronization. This ensures continuous optimization, dynamic adjustment, and real-time updating of information expression, guidance feedback, and spatial presentation during the operation process, enhancing user's operational compatibility, guidance accuracy, and visual perception of training effectiveness.

[0176] Example: In the medical health business field, for postoperative rehabilitation guidance, motor function reconstruction, and rehabilitation intervention process, the system dynamically generates personalized rehabilitation guidance plans by integrating multi-source heterogeneous data, and optimizes the rehabilitation path based on real-time data. The specific process includes:

[0177] The system first acquires multi-source heterogeneous data, including clinical medical records, medical images, genomic information, and wearable device data. Clinical medical record data covers patients' past medical history, surgical records, and rehabilitation stage evaluation results. Medical image data includes MRI, CT, and ultrasound examination results. Genomic information involves patients' gene expression, drug metabolism-related genotypes, and genetic risk factors. Wearable device data includes heart rate, blood oxygen, electromyography, and kinematic data. The system constructs a dynamic knowledge graph based on the above multi-source heterogeneous data. The knowledge graph is centered on the patient, and structures the anatomical information, functional indicators, motion parameters, genetic characteristics, and rehabilitation progress. Entity nodes include muscles, bones, joints, functional scores, and molecular indicators. Relationship edges express tissue function association, motion links, and biological regulation paths, supporting dynamic updating of multi-source data and rehabilitation information correlation analysis.

[0178] The system generates an initial guidance scheme containing target operations based on a dynamic knowledge graph, the target operations including joint function training, muscle strength recovery, and gait correction schemes. The system constructs a patient feature vector, including age, gender, body mass index, rehabilitation stage, injury type, and functional impairment level. The system embeds the patient feature vector into the dynamic knowledge graph to analyze the patient's position and functional defects in the overall rehabilitation network. The system queries the rehabilitation path and training decisions for the patient in the knowledge graph based on the embedding representation. The system determines the recommended probability of each rehabilitation operation based on the path information and generates a personalized rehabilitation guidance scheme containing target operations, including training type, intensity parameters, rhythm arrangement, and precautions.

[0179] The system further generates a visual interaction model corresponding to the target operation, analyzes the action instructions of the target operation, including limb movement range, posture adjustment, and strength output requirements. The system obtains standard action data from rehabilitation standards, normal reference of motor function, and expert experience templates. The system establishes a skeletal kinematics constraint model, including joint range of motion restrictions, muscle stretch range, and safe action boundaries. The system generates a three-dimensional action model based on standard action data and skeletal kinematics constraint model, which accurately represents the spatial position, dynamic trajectory, and key nodes of rehabilitation actions. The system generates visual elements based on the three-dimensional action model, including joint position indication, motion path guidance, and real-time feedback layers. The system combines visual elements to build a visual interaction model, providing an intuitive, dynamic, and interactive rehabilitation guidance interface.

[0180] The system presents the target operation through the visual interaction model and collects real-time dynamic data during the execution process. The system renders a three-dimensional action demonstration of the target operation, showing the standard action process, dynamic trajectory, and spatial guidance. The system collects real-time bioelectric signals of the patient through bioelectric sensors, including electromyographic activity, neural response, and physiological load. The system collects real-time kinematic signals of the patient through kinematic sensors, including limb position, angle change, and action stability. The system synchronizes bioelectric signals and kinematic signals, fuses the synchronized data to generate multi-source signals, and generates real-time dynamic data based on the multi-source signals, reflecting the patient's actual action performance, physiological state, and functional execution effect.

[0181] The system generates operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data. The target parameters include standard action trajectory, force output range, and movement rhythm. The system extracts target parameters from the target operation and analyzes actual parameters from real-time dynamic data. The system determines the deviation between the target parameters and the actual parameters and judges whether the deviation exceeds the preset tolerance threshold. When the deviation is greater than the threshold, the system generates a trajectory guide element, highlights the error path through the visual layer, dynamically guides the correct action, and prompts the correction area. When the deviation is within the threshold range, the system generates a confirmation prompt, feeds back the accurate action, and encourages continuous training. The system generates operation correction information based on the trajectory guide element or the confirmation prompt to guide the patient to adjust the action in time and optimize the rehabilitation effect.

[0182] The system generates visual elements based on operation correction information and updates the display content of the visual interaction model through visual elements. The system analyzes operation correction information, obtains visual element types, including error prompts, correct confirmations, and dynamic guides. The system configures the display parameters of visual elements, sets colors, transparency, positions, and dynamic effects. The system generates a three-dimensional space coordinate system to locate the spatial position of visual elements in the interactive interface. The system renders visual elements with display parameters in the three-dimensional space coordinate system. The system updates the rendered visual elements to the display content of the visual interaction model in real time to ensure that the rehabilitation guidance interface dynamically presents action feedback, path guidance, and status prompts.

[0183] The system determines the effect analysis value based on real-time dynamic data. The effect analysis value is an index for quantifying rehabilitation effect, action accuracy, and functional recovery level. The system monitors the effect analysis value of the continuous training period to analyze recovery trends and training effect changes. When the effect analysis value meets the optimization trigger condition, the system determines that the patient's rehabilitation progress is slow, functional recovery is lagging, or action performance is repeatedly fluctuating. The system adjusts the training parameters and guidance content in the initial guidance scheme to generate an updated guidance scheme. The updated scheme dynamically optimizes training intensity, rhythm arrangement, and guidance feedback to improve the real-time adaptability and rehabilitation effect of rehabilitation intervention, supporting personalized, dynamic, and intelligent rehabilitation guidance for postoperative patients.

[0184] In the field of financial technology business, for financial customer behavior intervention and risk education guidance, the system dynamically generates personalized guidance schemes for financial users and realizes real-time optimization by fusing multi-source heterogeneous data. The specific process includes:

[0185] Firstly, the system obtains the behavior data, transaction data, compliance records, identity information and external data sources of the financial user from multiple source heterogeneous data. The financial user behavior data is derived from the operation log, click behavior and path preference of the user in mobile banking, online financial management and financial terminal. The transaction data includes payment records, fund flow and account activity. The compliance records cover historical violation operations, risk alerts and regulatory information. The identity information includes real-name information, user portrait and risk label. The external data sources involve credit information, anti-fraud data and social behavior information. The system constructs a dynamic knowledge graph based on the above data, which structures and maps the multi-source heterogeneous data into a multi-dimensional entity relationship network. The entity nodes include account, transaction behavior, risk label, identity feature and external risk source. The relationship edges describe account association, transaction link, behavior dependence and risk propagation path. The dynamic knowledge graph supports real-time correlation of multi-source data, dynamic update of relationship and risk chain traceability analysis.

[0186] The system generates an initial guidance scheme containing financial target operations based on the dynamic knowledge graph. The financial target operations include high-risk transaction identification, abnormal account behavior alert and financial knowledge popularization education. The system first constructs a user feature vector. The feature dimensions include account stability, historical behavior preference, transaction complexity, compliance risk level and financial understanding level. The system embeds the user feature vector into the dynamic knowledge graph. The embedding representation reflects the user's behavior position and risk exposure state in the financial risk network. The system queries the risk decision path and financial behavior link related to the user in the knowledge graph based on the embedding representation. The system determines the selection probability of each operation suggestion based on the decision path. The personalized guidance scheme containing the financial target operation is generated according to the selection probability. The guidance scheme content includes operation alert, behavior correction, risk education prompt and financial literacy improvement suggestion.

[0187] The system further generates a visual interaction model corresponding to the financial target operation. The system analyzes the action instructions of the target operation. The action instructions in the financial scenario include account operation reminder, risk prompt content and education interaction instruction. The system obtains standard operation data based on the action instructions. The standard operation data is the financial regulatory requirements, compliance standards and historical excellent operation templates. The system establishes a logic constraint model based on the operation process to ensure that the financial behavior guidance meets the compliance requirements and risk control boundaries. The system generates a three-dimensional operation process model based on the standard operation data and the logic constraint model. The three-dimensional model is expressed in the form of dynamic flowchart, visual path and spatial guidance on the interface. The system generates visual elements based on the three-dimensional model. The visual elements include highlighted operation area, risk alert icon and operation flow dynamic guidance. The system combines the visual elements to build a visual interaction model, which supports dynamic operation guidance, risk prompt and real-time interactive education.

[0188] The system presents the financial target operation through the visual interaction model, and collects real-time dynamic data in the operation process. The operation process collects user input data, click trajectory and operation response through interface interaction, terminal feedback and behavior tracking. The system monitors the user's physiological response to stress through bioelectricity sensors, such as monitoring heart rate, skin electrical response and physiological stress level through smart wearable devices. In combination with the abnormal tension, hesitation or high-risk state in the financial operation process, the system collects the operation intensity, click frequency and kinematics signal of the operation path through terminal sensors. The system synchronizes the bioelectricity signal and kinematics signal, fuses the synchronized multi-source signals, generates real-time dynamic data, and the dynamic data reflects the user's risk perception state, behavior stability and operation compliance in the financial operation process.

[0189] The system generates operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data. The target parameters include the standard operation path, the recommended behavior mode and the risk threshold. The system extracts the target parameters from the target operation and analyzes the actual operation parameters and physiological state parameters from the real-time dynamic data. The system determines the deviation of the target parameters and the actual parameters, and judges whether the deviation is greater than the preset tolerance threshold. When the deviation exceeds the threshold, the system generates a trajectory guide element, such as an interface highlighting an incorrect operation path, a dynamic guide returning to a compliant path, and a pop-up risk reminder. When the deviation is less than or equal to the tolerance threshold, the system generates a confirmation prompt, which feeds back that the operation is correct and guides to continue. The system generates operation correction information based on the trajectory guide element or the confirmation prompt, and adjusts the user's operation behavior and risk education feedback in real time.

[0190] The system generates visual elements based on the operation correction information, and updates the display content of the visual interaction model through the visual elements. The system analyzes the operation correction information to obtain the type of visual elements, which includes risk warning icons, operation path dynamic guides and positive confirmation prompts. The system configures the display parameters of the visual elements, including color, transparency, position and dynamic effect. The system generates a three-dimensional space coordinate system to accurately locate the spatial position of the visual elements. The system renders the visual elements with display parameters in the three-dimensional space coordinate system, and updates the rendered visual elements to the visual interaction model in real time to ensure that the operation interface dynamically expresses the risk state, operation guidance and feedback information.

[0191] The system determines an effect analysis value based on real-time dynamic data, the effect analysis value is an index for quantifying user operation compliance, risk understanding level and education effect, the system monitors the effect analysis value in a continuous operation period, when the effect analysis value meets the optimization trigger condition, for example, the user has long-time high-risk deviation, the operation path repeatedly makes mistakes or the physiological pressure continuously rises, the system adjusts the operation parameters and guidance content in the initial guidance scheme, generates an updated guidance scheme, the updated scheme strengthens risk education, optimizes the operation path and dynamically adjusts the prompt content, and improves the safety, compliance and education effect of the financial user operation.

[0192] The embodiment realizes dynamic generation and real-time update of visual elements by combining analysis of operation correction information, visual element type matching, display parameter configuration and three-dimensional space rendering, and further improves information expression efficiency, space presentation effect and user interaction experience in the operation guidance process by updating the display content of the visual interaction model, solves the problems of lack of dynamic visual feedback, poor space adaptability and weak real-time update capability in traditional systems, strengthens visual guidance, dynamic optimization and operation accuracy guarantee in the operation process, and improves the overall training effect and system application value.

[0193] In an embodiment, an operation guidance and optimization device is provided, which corresponds to the operation guidance and optimization method in the above embodiments. Referring to Figure 3 , Figure 3 The function module schematic diagram of a preferred embodiment of the operation guidance and optimization device of the present application is shown in the figure. The knowledge graph construction module 10, the guidance scheme generation module 20, the interaction model construction module 30, the real-time data acquisition module 40, the correction information generation module 50, the visual content update module 60 and the guidance scheme optimization module 70. The detailed description of each function module is as follows:

[0194] The knowledge graph construction module 10 is used to acquire multi-source heterogeneous data, and construct a dynamic knowledge graph based on the multi-source heterogeneous data;

[0195] The guidance scheme generation module 20 is used to generate an initial guidance scheme containing a target operation based on the dynamic knowledge graph;

[0196] The interaction model construction module 30 is used to generate a visual interaction model corresponding to the target operation;

[0197] The real-time data acquisition module 40 is used to present the target operation through the visual interaction model and acquire real-time dynamic data when the target operation is performed;

[0198] The correction information generation module 50 is used to generate operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data;

[0199] The visual content updating module 60 is configured to generate a visual element based on the operation correction information, and update the display content of the visual interaction model through the visual element.

[0200] The guidance scheme optimization module 70 is configured to determine an effect analysis value based on the real-time dynamic data, and adjust the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition.

[0201] In an embodiment, the knowledge graph construction module 10 is specifically configured to:

[0202] parse text data to identify entity features;

[0203] process image data to extract structure parameters;

[0204] analyze biological data to determine response paths;

[0205] fuse the entity features, the structure parameters, and the response paths to generate an initial knowledge graph;

[0206] obtain updated evidence of a multi-source heterogeneous data source based on the initial knowledge graph, and analyze weight factors of the updated evidence;

[0207] extend the initial knowledge graph based on the weight factors to construct a dynamic knowledge graph.

[0208] In an embodiment, the guidance scheme generation module 20 is specifically configured to:

[0209] construct a user feature vector;

[0210] embed the user feature vector into the dynamic knowledge graph to generate a feature embedding representation;

[0211] query a decision path in the dynamic knowledge graph based on the feature embedding representation;

[0212] determine a selection probability based on the decision path;

[0213] generate an initial guidance scheme containing a target operation based on the selection probability.

[0214] In an embodiment, the interaction model construction module 30 is specifically configured to:

[0215] parse an action instruction of the target operation, and obtain standard action data based on the action instruction;

[0216] establish a skeletal kinematics constraint model;

[0217] generate a three-dimensional action model based on the standard action data and the skeletal kinematics constraint model;

[0218] generating a visual element based on the three-dimensional action model;

[0219] combining the visual element to construct a visual interaction model.

[0220] In an embodiment, the real-time data acquisition module 40 is specifically configured to:

[0221] rendering a three-dimensional action demonstration process of the target operation through the visual interaction model;

[0222] acquiring bioelectric signals through bioelectric sensors;

[0223] acquiring kinematic signals through kinematic sensors;

[0224] synchronizing the bioelectric signals and the kinematic signals, and fusing the synchronized bioelectric signals and kinematic signals to obtain multi-source signals;

[0225] generating real-time dynamic data based on the multi-source signals.

[0226] In an embodiment, the correction information generation module 50 is specifically configured to:

[0227] extracting target parameters from the target operation, and analyzing actual parameters from the real-time dynamic data;

[0228] determining a deviation of the target parameters and the actual parameters;

[0229] judging whether the deviation is greater than a preset tolerance threshold;

[0230] generating a trajectory guide element when the deviation is greater than the preset tolerance threshold;

[0231] generating a confirmation prompt when the deviation is less than or equal to the preset tolerance threshold;

[0232] generating operation correction information based on the trajectory guide element or the confirmation prompt.

[0233] In an embodiment, the visual content update module 60 is specifically configured to:

[0234] analyzing the operation correction information to obtain a visual element type;

[0235] configuring display parameters of the visual element type;

[0236] generating a three-dimensional space coordinate system;

[0237] rendering a visual element with the display parameters in the three-dimensional space coordinate system;

[0238] updating the rendered visual elements to a display content of the visual interaction model.

[0239] In one embodiment, a computer device is provided, which can be a server, and an internal structure diagram thereof can be as shown in Figure 4 The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide determination and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external user terminal through a network connection. The computer program is executed by the processor to implement the functions or steps of the server side of the operation guidance and optimization method.

[0240] In one embodiment, a computer device is provided, which can be a user terminal, and an internal structure diagram thereof can be as shown in Figure 5 The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide determination and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external server through a network connection. The computer program is executed by the processor to implement the functions or steps of the user terminal side of the operation guidance and optimization method

[0241] In one embodiment, a computer device is provided, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the following steps:

[0242] Obtaining multi-source heterogeneous data, and constructing a dynamic knowledge graph based on the multi-source heterogeneous data;

[0243] Generating an initial guidance scheme containing a target operation based on the dynamic knowledge graph;

[0244] Generating a visual interaction model corresponding to the target operation;

[0245] Presenting the target operation through the visual interaction model, and collecting real-time dynamic data when the target operation is performed;

[0246] Generating operation correction information based on the difference between the target parameters of the target operation and the real-time dynamic data;

[0247] generate a visual element based on the operation correction information, and update display content of the visual interaction model through the visual element;

[0248] determine an effect analysis value based on the real-time dynamic data, and adjust the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition.

[0249] In one embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium. The computer program is executed by a processor to implement the following steps:

[0250] obtain multi-source heterogeneous data, and construct a dynamic knowledge graph based on the multi-source heterogeneous data;

[0251] generate an initial guidance scheme containing a target operation based on the dynamic knowledge graph;

[0252] generate a visual interaction model corresponding to the target operation;

[0253] present the target operation through the visual interaction model, and collect real-time dynamic data when the target operation is executed;

[0254] generate operation correction information based on a difference between a target parameter of the target operation and the real-time dynamic data;

[0255] generate a visual element based on the operation correction information, and update display content of the visual interaction model through the visual element;

[0256] determine an effect analysis value based on the real-time dynamic data, and adjust the initial guidance scheme to generate an updated guidance scheme when the effect analysis value meets an optimization trigger condition.

[0257] It should be noted that the functions or steps that the computer readable storage medium or the computer device can implement correspond to the descriptions of the server side and the user side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0258] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0259] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0260] It should be noted that if non-company software tools or components appear in the embodiments of the present application, they are only used for example introduction and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An operation guidance and optimization method, characterized in that: The following steps are involved: Acquire multi-source heterogeneous data and construct a dynamic knowledge graph based on the multi-source heterogeneous data; generating an initial guidance plan including a target operation based on the dynamic knowledge graph; Generate a visual interaction model corresponding to the target operation; Presenting the target operation through the visual interaction model and collecting real-time dynamic data when the target operation is performed; generating operation correction information based on a difference between a target parameter of the target operation and the real-time dynamic data; generating a visual element based on the operation correction information, and updating the display content of the visual interaction model through the visual element; An effect analysis value is determined based on the real-time dynamic data, and when the effect analysis value meets an optimization trigger condition, the initial guidance plan is adjusted to generate an updated guidance plan.

2. The operation guidance and optimization method according to claim 1, characterized in that: Acquiring multi-source heterogeneous data and constructing a dynamic knowledge graph based on the multi-source heterogeneous data, including: Parsing text data to identify entity features; processing image data to extract structural parameters; analyzing biological data to identify response pathways; fusing the entity features, the structural parameters, and the response path to generate an initial knowledge graph; Acquire update evidence from multiple heterogeneous data sources based on the initial knowledge graph, and analyze weight factors of the update evidence; The initial knowledge graph is expanded based on the weight factors to construct a dynamic knowledge graph.

3. The operation guidance and optimization method according to claim 1, wherein: Generating an initial guidance plan including a target operation based on the dynamic knowledge graph, including: Construct user feature vector; Embedding the user feature vector into the dynamic knowledge graph to generate a feature embedding representation; Based on the feature embedding representation, querying the decision path in the dynamic knowledge graph; determining a selection probability based on the decision path; An initial guidance plan including a target operation is generated based on the selection probability.

4. The operation guidance and optimization method according to claim 1, wherein: Generating a visual interaction model corresponding to the target operation, including: Parsing the action instruction of the target operation and obtaining standard action data based on the action instruction; Establish a skeletal kinematic constraint model; Generate a three-dimensional motion model based on the standard motion data and the skeletal kinematic constraint model; generating a visualization element based on the three-dimensional motion model; The visualization elements are combined to construct a visualization interaction model.

5. The operation guidance and optimization method according to claim 1, wherein: Presenting the target operation through the visual interaction model and collecting real-time dynamic data when the target operation is performed include: Rendering a three-dimensional action demonstration process of the target operation through the visual interaction model; Collecting bioelectric signals through bioelectric sensors; Collect kinematic signals through kinematic sensors; Synchronizing the bioelectric signal and the kinematic signal, and fusing the synchronized bioelectric signal and the kinematic signal to obtain a multi-source signal; Real-time dynamic data is generated based on the multi-source signals.

6. The operation guidance and optimization method according to claim 1, wherein: Generating operation correction information based on a difference between a target parameter of the target operation and the real-time dynamic data, including: Extracting target parameters from the target operation and parsing actual parameters from the real-time dynamic data; determining a deviation between the target parameter and the actual parameter; Determining whether the deviation is greater than a preset tolerance threshold; When the deviation is greater than a preset tolerance threshold, generating a trajectory guidance element; When the deviation is less than or equal to a preset tolerance threshold, generating a confirmation prompt; Operation correction information is generated based on the trajectory guidance element or confirmation prompt.

7. The operation guidance and optimization method according to claim 1, wherein: Generating a visual element based on the operation correction information, and updating the display content of the visual interaction model using the visual element, includes: Parsing the operation correction information to obtain the visual element type; Configuring display parameters of the visual element type; Generate a three-dimensional space coordinate system; Rendering a visual element having the display parameters in the three-dimensional space coordinate system; The rendered visual elements are updated to the display content of the visual interaction model.

8. An operation guidance and optimization device, characterized in that: The operation guidance and optimization device includes: A knowledge graph construction module is used to obtain multi-source heterogeneous data and construct a dynamic knowledge graph based on the multi-source heterogeneous data; A guidance scheme generation module, configured to generate an initial guidance scheme including a target operation based on the dynamic knowledge graph; An interaction model building module, used to generate a visual interaction model corresponding to the target operation; A real-time data collection module, configured to present the target operation through the visual interaction model and collect real-time dynamic data when the target operation is performed; a correction information generating module, configured to generate operation correction information based on a difference between a target parameter of the target operation and the real-time dynamic data; a visual content updating module, configured to generate visual elements based on the operation correction information, and update the display content of the visual interaction model using the visual elements; The guidance scheme optimization module is used to determine the effect analysis value based on the real-time dynamic data, and when the effect analysis value meets the optimization trigger condition, adjust the initial guidance scheme to generate an updated guidance scheme.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and an operation guidance and optimization program stored in the memory and capable of running on the processor. When the operation guidance and optimization program is executed by the processor, the steps of the operation guidance and optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores an operation guidance and optimization program, which, when executed by a processor, implements the steps of the operation guidance and optimization method according to any one of claims 1 to 7.

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