30 results about "Service model" patented technology

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 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.

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 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.

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.

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 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.

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.

The invention discloses an outsourcing service system based on BPM (business process management) and an implementation method, and belongs to the technical field of business processes. Abnormal detection is performed on encrypted data sent by a plurality of enterprise subsystems, and enterprise data is preprocessed based on a detection result; constructing a business process enterprise database based on the preprocessed enterprise data, performing matching judgment by using a specification attribute matching algorithm, if a matching judgment result is not null, constructing a service model based on the business process enterprise database, and performing data fusion on a detection result and the service model to obtain a detection result; and updating the dynamic enterprise portrait in real time based on the fused data, performing multi-target optimization routing decision according to the current network state and the updated dynamic enterprise portrait, and generating and executing an optimized routing decision instruction. The method can improve the response speed, adjust the routing decision in real time, optimize the execution path of the business process, reduce the execution delay and cost of the business process, accurately identify the abnormal traffic, and ensure the data security.

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 dynamic management method and system for service inventory, and the method comprises the following steps: abstracting service resources with service capability into a standardized service component comprising a time slot, a service skill and a service area; based on a preset business rule, combining one or more service skills with the service duration, and defining the combined service skills and the service duration as a service model; receiving a service request containing target service time, a service area and the service model; in response to the service request, retrieving components matched with various requirements of the service request in the standardized service components; and based on the retrieved matching result, allocating a preemption state to the service request. According to the invention, the traditional inventory calculation is improved into an intelligent decision engine supporting refined operation and business growth, and the market competitiveness and operation efficiency of service-oriented enterprises are significantly enhanced.

A code development method, comprising: receiving update information of a user for a service model to obtain an updated service model, and then generating first metadata on the basis of the updated service model, the first metadata comprising a file formed by serializing the updated service model; comparing the first metadata with second metadata of a second code selected by a user to obtain a metadata difference comparison result; generating a first code on the basis of the first metadata; and comparing the first code with the second code on the basis of the metadata difference comparison result to obtain a difference code between the first code and the second code, and processing the second code on the basis of the difference code to obtain a processed code. By means of the method, difference codes before and after metadata update can be automatically identified on the basis of a metadata difference comparison result, without requiring manual comparison, and the difference codes can be merged into existing codes on the basis of code merging capabilities, thereby improving development efficiency. In the method, code generation capabilities of low-code platforms are fully utilized to achieve forward iterative development.

The application discloses a kind of compression method, device, equipment and storage medium of time series data.The method comprises: obtaining the set of service model to be compressed, determines compression step;Based on temporary service model and the compression step, traverse the set of service model to be compressed, and update the temporary service model in the process of traversal, and record parent class time data, wherein the temporary service model is used to cache temporary time series data in compression process, and the parent class time data is used to indicate that the original time data corresponding to the original index data meeting the preset characteristic requirement in the unit compression range corresponding to the compression step;According to the traversal result, determine the parent class time data set and determine the service model compression result set.The technical scheme of the embodiment of the application orderly compresses a large amount of time series data, not only guarantees the compression efficiency of time series data, but also records the parent class time data, to provide user to consult original time data.

The present invention relates to the technical field of computers. Disclosed are a storage backend performance optimization method, apparatus and system, and an electronic device. The method comprises: acquiring a protocol component cascading depth of a storage backend; on the basis of the protocol component cascading depth of the storage backend, configuring a basic arbitration parameter of the storage backend; collecting service model statistics on the storage backend, so as to obtain a service model statistical result, wherein the service model statistical result comprises traffic volume information of a service model of the storage backend within a preset period and a distribution proportion corresponding to each traffic volume; and on the basis of the service model statistical result, iteratively optimize the basic arbitration parameter, so as to obtain a target arbitration parameter. An arbitration parameter is iteratively optimized on the basis of an actual service scenario that is represented by a service model statistical result of a storage backend, so as to ensure that a finally applied target arbitration parameter matches the actual service scenario of the storage backend, thereby improving the performance of the storage backend, and laying a foundation for improving the overall performance of a storage device.

The invention provides a service model configuration method and a data processing method and system, and relates to the technical field of data management. According to the specific implementation scheme, in response to a detected configuration instruction for a target business model in a business model library, a model configuration page of the target business model is displayed in a user interface; generating configuration information corresponding to the target business model according to a pre-stored data dictionary and input information in the model configuration page; and configuring the target service model according to the configuration information corresponding to the target service model. According to the scheme of the invention, the business model used for carrying out data verification on different businesses can be configured in the data management system, the configuration modification of the business model is supported through the data dictionary, and adaptive adjustment can be carried out on business changes without manually writing corresponding codes again; the data quality control capability of the database on multiple service lines is optimized, and the data management efficiency and the data circulation reliability are improved.

The invention discloses a data processing method and device, equipment and a readable storage medium. The method comprises the steps of obtaining a dialogue request sent by an object; the dialogue request comprises dialogue content; selecting a first service model according to the dialogue request, and performing dialogue reasoning on the dialogue content through the first service model to obtain a first reasoning result; if the confidence coefficient of the first inference result is smaller than a confidence coefficient threshold value, or the object provides negative feedback for a historical inference result provided by the first service model, object feature parameters of the object are obtained, and model selection is carried out between the first service model and the second service model based on the object feature parameters; and if the selected second service model is the service model for replying the dialogue request, performing dialogue reasoning on the dialogue content through the second service model to obtain a second reasoning result, and providing the second reasoning result to the object. By adopting the method, the balance between the computing resource consumption and the reasoning accuracy can be kept in the reasoning service.

The application discloses a kind of construction method, device and equipment of training sample and storage medium.The method comprises: obtaining at least two sample data groups;Wherein, sample data group includes original sample data and the enhanced sample data corresponding to the original sample data;At least two sample data groups are enhanced in feature, and multiplex sample data is obtained;Multiplex sample data is input to the service model set in advance, and the multiplex sample latent feature that service model generates in training process based on multiplex sample data is obtained;Multiplex sample latent feature is decomposed in feature, and enhanced latent feature is obtained;According to enhanced latent feature and the business category to which service model belongs, based on the loss function corresponding to business category, service model is iteratively updated.The model parameter sparse problem is solved while the training efficiency of network model is improved in the embodiment of the application.

The invention relates to a data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring business data of a target business, business guidance information and a pre-trained business model; in the process of analyzing and processing the service data and the service guidance information by the service model, determining a prejudice feature in an activation matrix of the service model based on a sparse non-negative matrix factorization algorithm, and generating a prejudice feature hierarchical tree; the activation matrix is a structured set of output states of each neuron of the service model; based on the prejudice feature hierarchical tree, positioning a target neuron corresponding to the prejudice feature in the business model; and based on a context-aware affine concept editing algorithm, correcting the prejudice features of the target neuron code to obtain a service result of the target service. By adopting the method, the accuracy of the output result of the large language model can be improved.

The application discloses a kind of effectiveness analysis method, equipment and storage medium of new service data.The method comprises the following steps: when detecting the evaluation request of new service data, determine new candidate features from the new service data, calculate the correlation coefficient between the candidate feature vector corresponding to new candidate features and the label vector of service model, and obtain the coefficient vector of new candidate features;The distance between the coefficient vector of new candidate features and each type of cluster center vector is calculated, and the target feature class cluster to which the new candidate features belong is determined, to determine the effectiveness of each service model according to the inventory features in the target feature class cluster.The calculation complexity of the effectiveness analysis method of new service data of the application is lower, and the effectiveness analysis efficiency of new service data can be improved.

The invention relates to the field of industrial control, and provides an industrial control service model updating iteration method and system for resisting confrontation sample attacks, and the method comprises the steps: obtaining a service mirror image model of an online service model; obtaining a basic use case data set of the production business, and performing performance and efficiency test on the business mirror image model by using the basic use case data set to generate a stability reference index; a new data stream of the production service is discriminated by using the service mirror image model, and an adversarial sample is identified and an incremental data set is obtained through similarity matching between a discrimination mode and an attack feature; the incremental data set is fused into the basic use case data set in stages, and iterative fine tuning is performed on the business mirror image model according to deviation evaluation of the stability reference index; and updating the parameters of the online business model and the basic use case data set based on the fine-tuned business mirror image model and the incremental data set. According to the method, an adversarial sample detection mechanism is introduced in the business model updating process, so that wrong guidance of adversarial sample attacks to the business model can be effectively avoided.

The invention relates to the technical field of model construction, is suitable for the financial field, and provides a sample expansion method, computer equipment and a computer readable storage medium, and the method comprises the steps: obtaining an expanded sample based on the information type of each sample content in a real sample; calculating the semantic similarity between the expansion sample embedding vector and a vector in a real data embedding index corresponding to the real sample; determining a structural consistency score based on the structured data in the expanded sample; determining semantic stability based on the output embedded representation of the extended sample in the plurality of models; determining the credibility of the expanded sample based on the semantic similarity, the structural consistency score, the semantic stability and the output result confidence of the current initial model; and if the credibility is greater than or equal to a preset credibility threshold, determining that the expanded sample is an effective sample. According to the scheme, training data is constructed for rare class services in a targeted manner, and more diversified reliable training samples are provided for a downstream service model.

The invention provides a digital twin system building method and device for simulating automobile financial business processes. The method comprises the following steps: carding the automobile financial business processes to obtain a flow chart; the flow chart comprises a plurality of business process levels, a plurality of business handling nodes corresponding to the business process levels and a plurality of business handling paths formed by connecting the business handling nodes; for each business process level, constructing a corresponding business scene by using a visual low-code platform and a digital twinning technology; for each business handling node, constructing a business model corresponding to the business handling node and a visual panel for displaying business information by using a 3D modeling technology; for the business model corresponding to each business handling node, drawing the business model into a corresponding business scene; and on the basis of each business handling path, drawing the path between the business models in each business scene to obtain the constructed digital twin system, thereby realizing the purpose of holographic visualization of the whole process and business information of the automobile financial business.

The application relates to a method and system for automatically constructing a stress test service model, and belongs to the field of software testing. The method collects and analyzes log information of a tested service by constructing a log collection platform and a log aggregation and analysis platform, obtains hourly service call distribution information and daily service call distribution information of the tested service in a first preset time period, and aggregates and calculates an average call volume model M1 in hourly units. Meanwhile, the highest call volume model M2 and the model M3 with the largest call volume difference are aggregated and calculated in daily units. The model M2, the model M3 and the average call volume model M1 are combined to respectively calculate the service TPS structure with the highest call volume and the service TPS structure with the largest call volume difference, and the TPS proportion of each service is configured in a stress test script. The application enables a tester to obtain an actual production service model, accurately specify the TPS distribution of a corresponding service, and simulate a stress test scene conforming to the actual production situation.