56 results about "Service model" patented technology

The invention provides an API automatic generation method and system based on model design, and belongs to the technical field of software development. The method comprises the steps that metadata is extracted based on a service model created by a first user; generating a corresponding API based on the metadata and a preset generation rule; and providing the API for a second user. According to the model design-based API automatic generation method and system provided by the invention, the development efficiency of the API can be improved.

The embodiment of the invention discloses a data processing method and device, a computer, a storage medium and a program product, and the method comprises the steps: carrying out the data enhancement of a source business sample through employing a target business sample, and obtaining a business enhancement feature; predicting a first prediction result of the business enhancement feature through the first initial model, and performing parameter adjustment on the first initial model according to the first prediction result and an initial feature distance between the source business sample and the target business sample to obtain a first business model; predicting a second prediction result of the target service sample through the first service model, predicting a third prediction result of the target service sample through the second initial model, and performing parameter adjustment on the second initial model according to the second prediction result and the third prediction result to obtain a second service model; the first business model and the second business model have the same model structure. According to the invention, the accuracy and efficiency of data processing can be improved.

The invention relates to the technical field of artificial intelligence, in particular to a verbal skill confrontation training generation method and device based on a large model, equipment and a storage medium, and the method comprises the steps: generating a simulation inquiry based on a preset user portrait, then obtaining a response of a service large model, and comprehensively evaluating the response quality; and the specific defect type of the service model in logic understanding or verbal skill expression is diagnosed through the cognitive deviation between the response and the user expectation by another evaluation model, so that a targeted optimization strategy is generated, the service model is finally driven to carry out directional reinforcement learning, and the verbal skill ability of the large service model is iteratively improved. According to the method, an adversarial sample is dynamically generated, and deep diagnosis is carried out in combination with cognitive deviation, so that a service model can understand deep intentions and emotion demands of different user groups fundamentally. Therefore, the strain capacity and the communication accuracy of the service model in a real interaction scene are remarkably improved, and the user satisfaction is effectively improved.

The business travel service management method based on the LLM is applied to a service system constructed based on the LLM. Services provided by the service system for users comprise a plurality of service fields; the service system manages a plurality of service models in one-to-one correspondence with a plurality of service fields; comprising the following steps: receiving a user question related to business travel service input by a user; retrieving target structured knowledge from a target sub-knowledge base related to the user questions in a plurality of sub-knowledge bases in one-to-one correspondence with the plurality of service fields, and acquiring historical context information from historical user questions input by the user; generating a cue word based on the user question, the target structured knowledge and the historical context information, and inputting the cue word to an LLM, so that the LLM generates structured intention description information based on the cue word; and analyzing the intention description information, determining a target service field corresponding to the user intention, and distributing the user question to a target service model corresponding to the target service field.

The invention discloses a code-free application construction method and device based on a multistage service model, and relates to the field of code-free application construction. The method comprises the following steps: receiving a service demand of a user to obtain a demand feature tag; based on the demand feature tags, matching service models meeting conditions to obtain a multi-stage service model set; analyzing and instantiating the multi-stage business model set through a generation engine, and outputting an initial application component; receiving a configuration instruction of a user, and adjusting the initial application component to obtain a target application component; calling a data bridging interface, a permission matrix interface and a cross-component collaboration interface, and establishing a data flow relationship and a permission mapping rule among the components in the target application component to obtain an initial application and integrated link; and verifying the initial application, if the verification result meets the application release condition, deploying the initial application to the target operation environment, and outputting the target application. By implementing the technical scheme provided by the invention, the application construction efficiency and stability can be remarkably improved.

Embodiments of the present application provide a data processing method and a related apparatus. The method comprises: obtaining a plurality of pieces of sample data about a service system, wherein each piece of sample data comprises feature data, label data, and a confidence level; on the basis of a plurality of pieces of first sample data, generating second sample data of which the sample type is "black sample", wherein the first sample data comprises sample data having the confidence level higher than a preset threshold among the plurality of pieces of sample data; and generating a new training sample set on the basis of a historical data training sample set and the second sample data, wherein the training sample set is used for training a service model, and the service model is used for performing sample type prediction on the basis of service data in an operation process of the service system. By using the embodiments of the present application, the quality of the training sample set can be improved, thereby improving the performance index of the trained model.

The invention provides a model training method, a medium, electronic equipment and a program product, and relates to the technical field of machine learning, and the method comprises the steps: sending a business model to a client, enabling the client to train the business model according to the business data of the client, and obtaining a trained first business model, according to the method and the device, the first business model is acquired, the vector representation corresponding to the business data is acquired from the first business model, the vector representation is returned to the server side, the server side trains the business model based on the vector representation, and the trained second business model is acquired, so that the business data can be more effectively acquired on the premise of ensuring the privacy of the business data of the client side. The training of the service model of the server is completed, so that the service model of the server can better provide services to the outside, and the model structures of the service models, used for federated learning, of the server and the client can be kept consistent, so that the cognition of the server and the client on the service models is kept consistent, and the service experience of the client is improved. Therefore, the online service model of the server can have a better effect.

The invention discloses an intelligent low-carbon service model and method based on block chain fusion, particularly relates to the technical field of low-carbon management, and is used for solving the problem that the credible recognition and settlement of carbon behaviors are not clear. According to the method, identity verification, time marking and caliber standardization are carried out on energy consumption and travel multi-source data through a trusted data gateway, a carbon behavior voucher containing a signature and an abstract is generated, minimum disclosure under an index upper chain and an original text chain is adopted, it is ensured that the source and the caliber are verifiable and cannot be tampered, and then merging is carried out through a unified event model; and performing conflict resolution and consistency verification to form a one-event one-key event, attaching a consistency label and a credibility index, performing calculation by a rule base and a scene parameter set, performing intelligent contract accounting, and providing rollback correction by using a unique identifier of the event, enabling the credibility index to participate in integral and right confirmation, performing low-credibility entering and re-verification, and suppressing abnormities. And the fusion accuracy and the auditing efficiency are improved.

The invention discloses a confidence evaluation method and device for a large language model, equipment and a storage medium, and the method comprises the steps: obtaining a cue word set which comprises a plurality of cue words; sequentially inputting a plurality of cue words in the cue word set into the to-be-evaluated large language model to obtain a target sequence generated by the to-be-evaluated large language model based on each cue word, and obtaining the confidence coefficient of each target sequence according to the confidence coefficient of each lexical element in each target sequence; and obtaining a generation number of the target sequences, determining a confidence degree distribution result of the to-be-evaluated large language model according to the confidence degrees of all the target sequences when the generation number reaches a preset number, and generating an evaluation result of the to-be-evaluated large language model according to the confidence degree distribution result. Therefore, the problems that it is difficult for a user to effectively monitor the open source large language service model and it is difficult to provide evidence that the service model has degradation behaviors are solved, and the service quality of the open source large language model can be monitored on the user side.

The embodiment of the invention provides a business model intelligent adjustment method and device, and relates to the technical field of business model adjustment technologies. The method comprises the following steps: acquiring business data and model data; based on the business data and the model data, constructing a model risk potential energy distribution diagram used for indicating risk potential energy distribution in the business model; based on the model risk potential energy distribution diagram, performing model derivation processing on the business model by constructing a directional correction path to obtain a derivation model; and based on analysis of the performance loss of the derivative model, performing stabilization processing on the derivative model to obtain a target business model. According to the method and the device, the problem of low model adjustment efficiency is solved, and the effect of improving the model adjustment precision and efficiency is further achieved.

The present application relates to the technical field of data processing, and discloses a configuration data migration method, device, equipment, storage medium and program product, the configuration data migration method comprises: obtaining first configuration data from a preset database, and determining a data point position for marking the first configuration data; in a manner of dictionary query, determining a control point position corresponding to the data point position, and determining a first control instruction marked by the control point position; encapsulating the first configuration data and the first control instruction as a second control instruction, the second control instruction containing second configuration data; and issuing the second control instruction to a service model in an online running mode, so that the second configuration data takes effect in the service model. The present application transfers the initialization configuration data in the service model in an offline running mode to the service model in an online running mode, thereby overcoming the problems of low efficiency and easy errors in manual input of the initialization configuration data.

The one or more embodiments of the application provide a method, device and storage medium for evaluating running of a privacy computing algorithm, the method comprising: obtaining algorithm running information corresponding to a privacy computing algorithm to be evaluated and device configuration information of a device for running the privacy computing algorithm; inputting the algorithm running information and the device configuration information into a service model of a large language model, to predict, by the service model of the large language model, a running condition of the privacy computing algorithm on the device based on the algorithm running information and the device configuration information; the service model of the large language model being a model obtained by performing supervised fine-tuning training on a base model of the large language model based on training samples including the algorithm running information and the device configuration information and labeled with the running condition; obtaining the running condition of the privacy computing algorithm on the device predicted by the service model of the large language model, and outputting the running condition.

The embodiment of the application discloses a network change-based service model optimization method, device, equipment and medium. The method belongs to the technical field of Internet of Things and comprises the following steps: querying the network access attachment state of a communication module; if the network access attachment state is attachment failure, entering a network access failure abnormal processing scene; if the network access attachment state is attachment success, querying the wireless signal parameter of the communication module, and detecting whether the current coverage scene is a weak coverage scene according to the wireless signal parameter; if it is a weak coverage scene, entering the weak coverage scene; if it is not a weak coverage scene, querying the data sending result of the communication module; if the sending result is sending failure, entering a busy communication scene; optimizing the service model according to the different scenes to obtain a target service model; and executing the target service model. The embodiment of the application can improve the communication success rate, avoid network congestion, reduce network resource waste, and further improve the communication function of the Internet of Things.

A system can comprise a near real-time radio access network intelligent controller that comprises an application and a service model component, wherein the service model component is configured to receive respective indications of supported service models and key performance indicators from respective network nodes of a group of network nodes that is part of a radio access network, wherein the service model component is configured to receive a request from the application for a key performance indicator, wherein the service model component is configured to subscribe to a network node of the group of network nodes for the key performance indicator, wherein the network node supports a service model that is unsupported by the application and that is supported by the service model component, and wherein the key performance indicator is returned to the application via a communications protocol that is supported by the application.

The invention provides a building energy consumption analysis method and system based on an MCP service model and an intelligent agent, and relates to the technical field of building energy saving and artificial intelligence, and the method comprises the steps: constructing and packaging an MCP service model, and abstracting the MCP service model into a standardized function interface after packaging; acquiring and storing operation equipment and equipment operation parameters in the building; an input request of a user is obtained, a task intention in the input request is recognized, the task intention is converted into specific API calling according to the task intention, and the API obtains structured energy consumption data by calling an MCP function interface; combining the structured energy consumption data returned by the MCP with the cue word template, transmitting the combined Agents to the large language model, and outputting to obtain a report; and visualizing or exporting the report. According to the invention, full-automatic generation from complex building energy consumption data to a structured report containing natural language analysis and visual charts is realized.

An embodiment of this specification discloses a service processing method, apparatus, and device. The method is applied to a service processing device, and the service processing device includes a trusted execution environment, including: obtaining service data for a target service, where the target service corresponds to a service model for processing the service data, the service model is divided into a first sub-model and a second sub-model, the second sub-model includes model parameters to be protected recorded in the model protection rules corresponding to the target service, and the second sub-model is set in the trusted execution environment and can run in the trusted execution environment; inputting the service data into the first sub-model divided from the service model corresponding to the target service to obtain a first output result corresponding to the service data; transmitting the first output result to the trusted execution environment through the application corresponding to the target service, and in the trusted execution environment, inputting the first output result into the second sub-model to obtain a model prediction result corresponding to the service data.

An operation control method includes: in the process of an electronic device running a target application, in response to a target triggering event, launching a first target service model, where the first target service model is configured to provide a target functional service; monitoring the adaptation value between the first target service model and the target application; and, when the adaptation value changes to fall within a target threshold range, controlling the electronic device to switch from running the first target service model to launching a second target service model, where the quality of the functional service provided by the second target service model is better than that of the first target service model, and / or the user experience of the second target service model is better than that of the first target service model.

The invention discloses a model updating method, device, system and equipment, a medium and a product, relates to the technical field of computers, and can give consideration to personalized model training of a business scene and high-precision model training based on high-performance computing power resources. The method comprises the following steps: a client receives a first service model obtained by training of a server; processing the service data of the client by using a first service model obtained by training of the server to obtain a service processing result; displaying the service processing result, and obtaining user feedback information in response to a feedback operation of the user on the service processing result; and under the condition that the user feedback information indicates that the service processing result is incorrect, updating the first service model based on the user correction result to obtain an updated second service model.

The invention relates to a service mode self-recognition and parameter self-tuning method and device and electronic equipment. The method comprises the following steps: receiving archived log data from a source end database and performance index data from an execution engine, performing metadata level analysis on the archived log data, and extracting multi-dimensional service features to construct a dynamic fingerprint vector; inputting the performance index data and the dynamic fingerprint vector into a pre-trained machine learning model, so that the machine learning model outputs a target service mode label corresponding to the current service load according to the performance index data and the dynamic fingerprint vector; generating a target parameter configuration according to the target service mode label and a corresponding parameter generation strategy; and the target parameter configuration is issued to the execution engine, so that the execution engine adjusts the running state of the execution engine according to the target parameter configuration, and the synchronized data change is written into the target database. The technical problem that the service model change cannot be self-adapted in the prior art is solved.

This application discloses a data processing method performed by a computer device. Obtained service data is inputted into a pre-trained model and an initial service model to obtain first fully-connected layer outputs of the pre-trained model and second fully-connected layer outputs of the initial service model. The pre-trained model and the initial service model construct modeling units with different granularities, first modeling units of the pre-trained model are matched with second modeling units of the initial service model, fully-connected layer outputs of the second modeling units have the same sequence length as fully-connected layer outputs of matched target first modeling units so that with the assistance of the pre-trained model, the target service model can be obtained through the initial service model, the knowledge distillation of the models including modeling units with different granularities is realized, thereby effectively improving the service performance of the service model.

Various embodiments of the present technology generally relate to systems and methods for providing a service model engine. In an aspect a method includes identifying, by a service model engine, a swagger file from a network vendor and parsing the swagger file for API endpoints. The service model engine may generate resource specifications based on the API endpoints and may generate delivery actions for each API endpoint. Each delivery action may include delivery parameters for each API endpoint, The service model engine may also map each of the delivery parameters to corresponding characteristics in the resource specifications to generate parameter mappings. The service model engine may generate a service delivery model including the resource specifications, the delivery actions, and the parameter mappings, where the service delivery model is used by a service orchestration system to deliver respective services.

The invention discloses a business processing method and device, equipment and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring service processing associated data, service processing party data, service processing constraint data and service feedback data of a to-be-processed service, and generating a current multi-dimensional feature vector; a weight calculation routing network is adopted to analyze the current multi-dimensional feature vector, and the current weight of each target service model is obtained; respectively analyzing the current multi-dimensional feature vector by adopting each target service model to obtain a current service detection result of each target service model on the to-be-processed service; and according to the current weight of each target service model, fusing each current service detection result to obtain a target fusion result, and according to the target fusion result, generating a current service processing instruction. According to the technical scheme of the embodiment of the invention, the service processing efficiency and the applicability of the service processing method are improved.

The embodiment of the present application provides a power consumption portrait method, device, data center and storage medium. In the embodiment of the present application, a reference model and a reference service of a new service model can be acquired; a historical power consumption portrait of the reference model running the reference service, a power consumption dictionary of the reference model and a power consumption dictionary of the new model are acquired; further, an initial power consumption portrait of a to-be-tested model running a to-be-tested service (the new service model) can be determined according to the historical power consumption portrait. Further, actual power consumption data generated by the to-be-tested model running the to-be-tested service (the new service model) deployed in a cabinet can be acquired; and the initial power consumption portrait is corrected by using the actual power consumption data, so that adaptive adjustment of the new service model portrait is realized, the obtained power consumption portrait of the new service model is as close as possible to the actual power consumption portrait, and the accuracy of the power consumption portrait is improved.

The invention discloses a rendering test method and system for 3dtiles model service, and belongs to the technical field of rendering test, and the method comprises the steps: carrying out the initial rendering loading according to 3dtiles model service data, obtaining a 3dtiles service model, carrying out the screenshot of the 3dtiles service model, and obtaining a 3dtiles service model rendering reference graph and a second system index parameter; obtaining an evaluation graph sequence and a first system index parameter intercepted in a re-rendering loading process; comparing the 3dtiles service model rendering reference graph with the evaluation graph sequence one by one to obtain rendering completion time and troubleshooting early warning reminding; and based on different preset camera shooting parameters, repeating the above steps to obtain a test result. The test process and technology adopted by the method are simple, the collected data size is small, the maintenance cost is low, and the stability and reliability of the 3dtiles service model are further improved.

An embodiment of the present application provides a method, apparatus, computer-readable storage medium, and electronic device for adjusting the configuration of a storage system. The method includes: obtaining the current configuration of the storage system; determining the working performance parameters of the storage system when processing a target service model according to the current configuration; determining the risk indicators of the storage system when it is in the working performance parameters according to the current configuration of the storage system and the working performance parameters; and adjusting the current configuration of the storage system when the risk indicators are greater than a preset threshold, so that the risk indicators corresponding to the adjusted current configuration are less than or equal to the preset threshold. Through the closed-loop feedback mechanism and configuration adjustment, the system can dynamically and adaptively optimize its own configuration to cope with changing service models and potential failure risks. Thus, in different scenarios, the storage system can maintain stable performance and high availability, improving the reliability and business continuity of the storage system.

The embodiment of the invention provides a business model execution method of a vehicle control system, and relates to the technical field of vehicle mode management. The method comprises the following steps: in response to a starting operation of a control process of a vehicle control system, loading at least two business models; sorting the at least two business models to obtain a business model queue; if it is detected that the request queue is not empty, outputting an execution result of the service model according to the service model queue and the request queue; wherein the request queue is a queue formed by the request service model requested by the user. By adopting the technical scheme, the service model can be intelligently scheduled and executed, so that the scheduling time of the service model is shortened, and the user experience is improved.

The invention provides a business processing method and system based on digital employees, and the method comprises the steps: collecting language and / or text data, and carrying out the lexical analysis, syntactic analysis and semantic analysis; learning the marked service data through supervised learning, establishing different service models for different service scenes, and establishing a linear regression model in combination with the service data in each service system; performing parameter extraction, analysis and statistics on specific business data through a front-end visual interface, screening and filtering the data based on a set rule index, and generating a business process in a configuration manner; generating a service function module through page service introduction; simulating human operation on a computer based on RPA, and executing a repetitive and regular business process according to a preset rule; data encryption, access control, data backup and data recovery are carried out, and data security protection is carried out. According to the invention, the development cost of customized demands of enterprises is reduced; digital employees are highly decoupled in each module and can be selected and matched by themselves, and the application range is wide.

The embodiment of the invention discloses a model quantification method and computing equipment, and the method comprises the steps: training equipment quantifies a pre-training model, and obtains a pre-training quantification model; the training device builds a first original business model based on the pre-training quantification model; the training device performs quantitative perception training on the first original business model based on the N pieces of business data to obtain N target business models; the t-th target service model in the N target service models comprises a shared quantization weight parameter and a t-th service quantization weight parameter in the N target service models; one business corresponds to one target business model; t is an integer from 1 to N. According to the embodiment of the invention, the quantification effect of each service can be improved, and the deployment process of training during model change is optimized.

The invention relates to a universal task query device compatible with various service characteristics, which comprises a process engine interface module, a service model registration center, a service data aggregation module, a task context assembly module and a unified output module, and is characterized in that service data is automatically pulled from a dispersed MIS system by registering a service model, and the task context assembly module and the unified output module are integrated. And fusing with a process task to generate an operable rich to-do task with complete information. The method has the advantages that the key business data are directly displayed in the to-be-processed list, a user does not need to skip for the second time, and the decision process is accelerated; and a new type of to-do task can be accessed or the existing logic can be changed by configuring the service model, and the core device does not need to be changed, so that decoupling with specific services is realized.

The application discloses a kind of based on disassembling component decomposition and combination service process dynamic assembly method, according to each maintenance service demand, the method for dynamically generating this maintenance service process.Its operation includes service model dynamic creation, process dynamic assembly.It specifically includes node object initialization step;Register object service fragment step;Initialization service boundary step;Explicitly listen to the rule of each object step;Explicit dynamic process arrangement rule step;Process output step;And configuration form data step.The application provides a kind of service process dynamic assembly method, to reduce the dependence on artificial when product maintenance service, to reduce cost, reduce complexity, improve efficiency.