1399 results about "Data interface" patented technology

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention discloses a gas pipeline operation risk dynamic evaluation system based on big data analysis, and relates to the technical field of gas pipeline safety monitoring, and the system comprises a multi-source data processing module, a feature extraction module, a local risk modeling module, a risk evolution prediction module and a dynamic early warning module. The multi-source data processing module is used for acquiring original multi-source time sequence data from a pressure sensor, a flow sensor, a temperature sensor, a geological monitoring device and a third-party construction disturbance data interface of a gas pipeline network, and performing time alignment, abnormity elimination, normalization and characteristic standardization processing on the data; a uniform input feature vector is formed; and the feature extraction module is used for decomposing the normalized time series data into a low-frequency trend component and a high-frequency residual signal by using discrete wavelet decomposition. The gas pipeline risk assessment system solves the problems of low gas pipeline risk assessment precision, insufficient risk propagation modeling and lack of dynamic early warning and emergency strategies in the prior art.

The invention relates to the field of data fusion evaluation, and provides a dynamic quantitative evaluation method for an enterprise development process, which comprises the following steps of: S1, acquiring operation data and compliance data of an enterprise through a multi-source heterogeneous data interface, and executing entity association and conflict resolution operation to generate a structured basic data set; s2, extracting dynamic evaluation features of the structured basic data set through a data analysis engine, wherein the data analysis engine comprises an industry adaptive rule base and / or a deep feature mining network; s3, calculating a multi-dimensional score value of the enterprise development situation based on the dynamic evaluation features; and S4, according to a historical evaluation record, mapping the multi-dimensional score value to a preset grade interval and presenting the multi-dimensional score value. And furthermore, objective and real-time evaluation is carried out on the growth performance and the anti-risk capability of the non-listed companies.

The invention discloses a multi-protocol hardware communication adaptation module based on an energy storage system and a configuration method, the module comprises a protocol template library, an intelligent mapping engine and a model generator, and the method comprises the following steps: storing structured protocol point location information through the protocol template library; an intelligent mapping engine is used for shielding the isomerism of Modbus, MQTT, IEC104 and other underlying protocols, and a unified data interface is provided; intelligent matching among different protocol terms is realized by adopting a natural language processing technology through a semantic level mapping unit; and finally, generating a target data model by a model generator through one key. The method effectively solves the problems that when the energy storage system integrates multi-manufacturer equipment, protocol configuration depends on manpower, efficiency is low, errors are prone to occurring, and expansibility is poor.

Embodiments described herein provide A method of fine-tuning a neural network based model. In some embodiments, a system receives, via a data interface, a training dataset including a plurality of input samples. The system generates, via a pre-trained neural network based model, a first response based on a first input sample of the plurality of input samples, and a second response based on the first input sample. The system generates, via a trained reward model, a first reward score based on the first input sample and the first response, and a second reward score based on the first input sample and the second response. The system computes a loss function based on the first prompt, the first response, the second response, the first reward score, and the second reward score. The system updates parameters of the neural network based model based on the loss function.

The invention discloses a multi-device cooperative control system and method for a construction site intelligent capsule bin, and relates to the technical field of industrial control, and the system comprises a data interface layer, a model construction module, a situation prediction module, a cooperative control unit, a self-adaptive cooperative optimization module, and a safety helmet positioning module. The method comprises a model construction step, a data acquisition step, a simulation prediction step, a situation judgment step, a cooperative control step and a self-adaptive cooperative optimization step. According to the method, unified data fusion and cooperative control are carried out on various devices, dynamic adjustment and strategy optimization of the devices can be carried out in time according to the actual situation of the site, the resource scheduling efficiency is improved, and the utilization efficiency of data is improved.

The invention discloses a lithium iron phosphate battery grading active fire control method and system, and the method comprises the steps: collecting multi-mode perception data through deploying a multi-source sensor network and a BMS data interface, and constructing a battery thermal runaway risk assessment model through employing a Lyapunov fixed time stability theory; a hierarchical prevention and control strategy is dynamically generated in combination with a historical case sensitivity scoring mechanism, and linkage response of first-level early warning heat dissipation, second-level aerosol inhibition and third-level perfluorohexanone fire extinguishing is achieved. According to the invention, the problem that the prior art lacks self-adaptability and BMS deep collaboration is solved, the technical transformation from passive response to active prevention and control is realized, and the accuracy and timeliness of thermal runaway early warning are remarkably improved.

The invention discloses a soil constitutive parameter determination system and method based on an in-situ expansion test and numerical inversion, and belongs to the technical field of geotechnical engineering investigation. The system comprises a data interface and preprocessing module, a configurable numerical calculation engine, an intelligent inversion module and a result output module. A pressure-expansion displacement curve of the soil body is obtained in situ through the integrated probe; selecting a target constitutive model and setting a parameter inversion space; an intelligent optimization algorithm is used for driving a numerical calculation engine to conduct iteration forward modeling, and the optimal constitutive parameter set is automatically searched with the purpose of minimizing the difference between a simulation curve and an actual measurement curve. According to the method, in-situ expansion testing and intelligent numerical inversion are deeply fused, in-situ, accurate and automatic calibration of the whole set of parameters of the HSS model is achieved, and dependence on an empirical formula and a disturbed soil sample is abandoned. Reliable technical support is provided for refined design of major projects such as offshore wind power, and the method has great scientific value and engineering application prospects.

The invention discloses an automatic auditing and checking system and method for substation operation safety. The system comprises an automatic data acquisition module, a data semantic fusion analysis module, a dynamic auditing rule generation module, a risk collaborative prediction module, an automatic information pushing module and a visual interaction configuration module. All the modules are connected through an intranet data interface and a system internal communication protocol, aiming at the prominent problems that the auditing process depends on manpower, the information system is split, the system semantics is difficult to understand, the risk identification is not comprehensive, the rule adaptability is poor, the response and push chain is broken and the like commonly existing in the existing substation operation auditing work. The invention provides an automatic auditing and checking system and method for substation operation safety, and aims to construct a full-process closed-loop intelligent auditing platform with semantic comprehension capability, multi-source data fusion capability, risk collaborative prediction capability and dynamic rule evolution capability.

The application provides a test method for intelligent auxiliary driving test, constructs a test problem description grabbing model, directly collects signals uploaded by an intelligent vehicle in real time through a vehicle-mounted Ethernet and a CAN FD direct connection interface without using other data interfaces, triggers a test problem description reporting process once abnormal signals are monitored, and ensures that the vehicle meets safety requirements of real vehicle road test.

A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

The invention relates to the technical field of artificial intelligence and deep learning, in particular to a material intelligent classification and deletion detection system based on a deep learning model. In an existing insurance claim settlement process, various kinds of materials need to be submitted, and checking and auditing are mostly completed manually. At present, an insurance company starts to use a deep learning technology to assist auditing, the model scale is huge, a large storage space and strong GPU computing power support are needed, and the cost is high. The system is composed of a material preprocessing module, a hierarchical classification recognition module, a deletion detection module and a result output and prompt module, and all the modules interact through standardized data interfaces; the hierarchical classification and recognition module is used for pre-classifying uploaded materials by using a hierarchical cascade classification architecture and a lightweight convolutional neural network, and quickly pre-classifying the uploaded materials according to scene-class and character-class pictures; the lightweight intelligent classification and deletion detection system provided by the invention gives consideration to high precision and high efficiency, meets actual service requirements, and cannot be replaced by a traditional scheme.

The invention relates to the technical field of national asset risk early warning, and discloses a national asset risk intelligent early warning system based on multi-source data fusion. The system comprises a risk stage identification module which obtains enterprise operation parameters through a national capital supervision data interface, divides the life cycle of a national capital enterprise into an operation stable period, an investment expansion period and an asset recombination period, and outputs corresponding risk stage identifiers; a multi-source data fusion module performs staged weight distribution on financial data, public opinion data and compliance data according to identifiers to obtain a differential fusion data set and construct a dynamic risk twinborn model; the risk feature extraction module calculates the deviation between the model and real-time monitoring data, and generates a multi-dimensional risk feature vector containing quantitative indexes of financial abnormality, public opinion popularity and compliance deviation; and the intelligent early warning output module evaluates the vector by using a staged risk propagation tree algorithm, generates a risk level signal and an associated risk source positioning result, and assists national asset risk early warning and supervision.

Embodiments described herein provide a method for training a neural network based language model (LM). The method includes receiving, via a data interface, a training dataset including pairs of user queries and ground-truth responses; generating, via the LM, a plurality of responses based on a query from the training dataset; identifying, from the plurality of responses, a first response having a first similarity metric value below a threshold, based on a similarity metric associated with a corresponding ground-truth response from the training dataset; training the LM using the first response as a negative sample and a second response as a positive sample such that the LM after training is more likely to generate the positive sample and less likely to generate the negative sample; receiving, via a user interface, a query; and generating a response to the query via the trained LM.

The invention relates to the technical field of video image intelligent analysis, in particular to a rockfall detection and early warning method and system based on videos. According to the method, a suspected rockfall target is recognized and judged by collecting a road or side slope monitoring video and combining target detection and motion trail analysis; and after the rockfall is judged, generating early warning information containing the position, time and danger level, storing the early warning information in a database, and triggering an early warning prompt of a user side at the same time. The system is composed of a video acquisition module, a detection and judgment module, an early warning generation module, a log storage module and a front-end interaction module, and all the modules achieve cooperation through data interfaces. Compared with the prior art, the method can still keep high detection accuracy and real-time performance in a complex environment, has the advantages of high automation degree and high expandability, can be widely applied to scenes such as highways, railways and mining areas, and remarkably improves the traffic and engineering safety level.

The invention provides a method and a device for generating a customized report of an SAAS (Software as a Service) system based on dynamic template configuration. The method comprises the following steps: acquiring a customized visual report template for realizing business data decoupling; editing the report template based on a preset editor, and generating a template configuration file; analyzing a structured marker in the template configuration file to generate a metadata description file; querying a business database through a unified data interface according to a data requirement defined by the metadata description file, and obtaining and assembling customized business data corresponding to the tenant ID; and at least based on the customized business data, controlling a rendering process through a dynamic position calculation algorithm, and dynamically filling the data to a corresponding position of the report template to generate a customized report. According to the method, the problems of code redundancy and difficulty in maintenance of the SAAS multi-tenant report are solved, zero code configuration is realized, and report generation efficiency and tenant personalized adaptation capability are improved.

The invention discloses an advertisement putting ROI dynamic optimization method and system based on real-time data driving. The method comprises the steps of collecting advertising data such as advertisement display quantity, click quantity, conversion quantity and consumption cost in a minute-level period through a multi-platform data interface; based on historical and real-time data, utilizing a machine learning model to predict the rate of return on investment (ROI) of each advertisement platform in a future time period; constructing an optimization problem with the overall ROI maximization as a target under the constraint of the total budget, and solving an optimal budget allocation strategy of each platform in each time period through an optimization algorithm; and synchronizing the budget allocation strategy to an advertisement platform for execution through an automatic control module, and recording ROI changes before and after optimization by a monitoring module to realize iterative updating of the model. Compared with the prior art, data acquisition, prediction and optimization can be completed in a minute-level period, dynamic allocation and rolling optimization of advertisement budget are realized, the overall ROI is effectively improved, putting waste is reduced, and the method is suitable for a multi-platform advertisement scene.

The invention discloses a method and device for identifying telecommunication fraud, equipment and a storage medium. The method comprises: in response to a received target user number, obtaining multi-dimensional communication data corresponding to the target user number through a plurality of communication data interfaces, the plurality of communication data interfaces including an operation support system interface, a service support system interface and a network data platform interface; extracting cross-modal fraud features from the multi-dimensional communication data by using a feature recognition model; analyzing the cross-modal fraud features by using a risk assessment model, and determining a risk score of the target user number; and displaying an identification result of the target user number on a front-end interface according to the risk score. According to the method, the big language model is used for carrying out feature extraction analysis on the multi-dimensional communication data, the fraud number can be accurately recognized, and the method has high applicability.

The invention discloses a brightness compensation method, a brightness compensation device, electronic equipment and a storage medium, and belongs to the technical field of image processing. The method is applied to the brightness compensation module, the brightness compensation module comprises a main control chip and an independent display chip, the main control chip is connected with the independent display chip through a data interface, the independent display chip is connected with a display screen, and the method comprises the following steps: according to image data to be counted by the main control chip, calculating the image data to be counted; determining a compensation parameter of the image data, and sending the compensation parameter to the independent display chip through the data interface; the compensation parameter represents the difference between the theoretical display brightness and the actual display brightness of the display screen; brightness compensation is carried out on the original image data through the independent display chip according to the compensation parameters, the compensated image data are sent to the display screen, and the display screen is used for displaying an image according to the compensated image data.

The invention relates to a laser automatic frequency locking control system and method based on cold atom feedback and a matched spectrum, the system comprises a laser module, a spectrum module, a photoelectric detector, a modulation-demodulation module, a digital PID controller and a data acquisition and operation platform, and the output end of the laser is connected with the spectrum module; the detection light output by the spectrum module enters the modulation-demodulation module after passing through the photoelectric detector; an output signal of the modulation-demodulation module enters a digital PID controller, and the digital PID controller is connected with a laser for frequency scanning control and is in two-way communication with a data acquisition and operation platform through a data interface; the data acquisition and operation platform comprises a cross-correlation algorithm processing module, a frequency control logic module, a cold atom feedback processing module and an abnormity management module, and is used for calculating, feeding back, controlling and monitoring the system operation state in real time and completing the automatic frequency locking process. According to the invention, the accuracy of frequency locking point identification and the frequency locking efficiency are improved; and automatic relocking can be achieved, and long-term stable frequency locking operation is guaranteed.

The invention discloses a mass data automatic label generation method based on a large model and a rule engine, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring data through a unified data interface, and then performing data cleaning, data standardization processing and data format conversion to obtain standard input data; s2, constructing a label system framework; s3, generating a large model label; s4, performing intelligent clustering and label refining; s5, rule engine constraint and optimization; s6, evaluating and optimizing label quality; and S7, automatically expanding and updating the tag system. According to the method, automatic label generation of mass multi-source heterogeneous data is realized through a large model and rule engine technology under the condition of no personnel intervention, a large-scale label system containing thousands of labels is constructed, meanwhile, the label system can be rapidly updated along with product iteration and keep timeliness, and the generated labels are accurate and extensible.

In described embodiments, a system for data processing is provided that includes a classic data interface configured to receive data from a classic computing environment. The system also includes a data encoding module configured to encode the received data into a format compatible with the quantum computing environment. A quantum processing module within the quantum computing environment is configured to process the encoded data. The communication interface is configured to transmit the encoded data to the quantum computing environment and retrieve the processed data from the quantum computing environment. The data decoding module is further configured to decode the processed data into a format compatible with the classical computing environment.

The invention provides a meta-universe virtual-reality interaction method based on causal invariance, belongs to the field of meta-universe virtual-reality interaction technologies, and is used for solving the problems of poor cross-domain adaptation, insufficient long-tail scene coverage and low dynamic robustness in related technologies. According to the method, through sensing layer causal kernel quality evaluation and data enhancement, reasoning layer causal invariance learning and cross-domain parameter migration, decision layer causal attention intention alignment, execution layer causal reinforcement learning control and iteration layer double-threshold knowledge updating, full-link parameter collaborative circulation is realized in combination with a causal data interface; and finally, the accuracy and the stability of virtual-real interaction of the element universe are improved, and efficient adaptation of cross-domain, long-tail and dynamic scenes is realized.

The invention discloses a photovoltaic box transformer substation fault prediction method and system, electronic equipment and a storage medium. During operation of a photovoltaic box transformer substation, various problems occur to the box transformer substation due to objective reasons. Sensors are arranged at a transformer, a circuit breaker and a cable joint of a photovoltaic box transformer substation and inlet wire and outlet wire ends of the photovoltaic box transformer substation, and operation data of the box transformer substation are collected and transmitted to a local edge calculation unit; preprocessing the operation data of the box transformer substation; constructing a local embedded photovoltaic box transformer substation fault prediction model, wherein the model outputs a fault diagnosis result, a fault prediction result and a time window; a standardized data interface is constructed in a local edge calculation unit of the photovoltaic box transformer substation, bidirectional data interaction with a power station digital twinborn platform is carried out, and the power station digital twinborn platform calibrates a digital twinborn model and outputs a fault operation and maintenance decision after receiving operation data of the photovoltaic box transformer substation. According to the invention, fault diagnosis and prediction of the photovoltaic box transformer substation are realized, and the operation and maintenance efficiency of the photovoltaic box transformer substation is improved.

The invention relates to the technical field of interdiscipline, in particular to a grain depot equipment intelligent operation and maintenance management system based on SAAS, and the system comprises a data collection unit which obtains the atmospheric temperature provided by an external meteorological data interface, and records the preset grain variety physical attributes; the digital twin modeling unit is used for solving a predicted temperature distribution field of the grain pile; the risk quantitative evaluation unit is used for calculating a comprehensive risk state index based on the predicted temperature distribution field and the real-time grain humidity, and judging a risk level according to a preset risk threshold value; the optimization decision-making unit is used for generating an optimal intervention strategy; when the risk level is the security level, maintaining the current intervention strategy; the model self-adaptive correction unit is used for comparing the predicted temperature output by the digital twin modeling unit with the real-time grain temperature acquired by the data acquisition unit and updating a self-adaptive deviation item in the model according to deviation; according to the invention, the reliable foundation of prediction and decision-making of the whole intelligent operation and maintenance management system is ensured.

The invention discloses a dialogue type business intelligent analysis method based on a natural language, and relates to the technical field of business management systems, and the method mainly comprises the steps: recognizing the intention type of a user intention contained in a natural language request through a general large language model; according to the identified specific intention type, calling a corresponding special analysis module to perform semantic analysis and parameter extraction on the natural language request so as to generate an operable analysis instruction; calling a business intelligent system data interface to execute the analysis instruction, and obtaining corresponding structured data from a business intelligent system data warehouse; and generating a comprehensive analysis report which is matched with the identified intention type and the corresponding structured data and comprises visual elements and analysis explanations based on the identified intention type and the corresponding structured data. According to the method, the intention type of the natural language request of the user is understood through the large language model, so that the user with a non-professional background can easily perform complex data exploration, and the use threshold is reduced.

The invention discloses a critical patient risk prediction method based on big data, and the method comprises the following steps: S1, carrying out the butt joint of a hospital multi-department electronic medical record system, organizing medical information personnel and department experts to formulate a unified data interface standard, and regularly capturing data through an interface; s2, noise data are removed through data mining recognition, a disease association map is constructed and trained through a graph neural network framework, and recessive common disease association is mined; s3, aligning a recessive common disease association time sequence by using a time sequence causal inference model, inputting common disease feature training, identifying and eliminating false association, and retaining a true causal relationship; s4, when the difference between the true causal and the actual causal exceeds a set threshold value, triggering reverse updating of the model, and adjusting a corresponding common disease association weight value in the graph neural network according to a result; therefore, early warning and personalized intervention suggestions can be pushed in real time, medical workers are assisted in timely intervention, and the severe illness treatment efficiency is improved.

The invention discloses an intelligent distribution and inventory monitoring system for surgical consumables. The intelligent distribution and inventory monitoring system comprises a data interface gateway, a central processing server and an intelligent consumable storage unit, operation schedules and unstructured text data in an HIS system, an ORIS system and an EMR system are integrated through a data interface gateway, an NLP analysis module in a central processing server is used for extracting requirements of'operation-special consumables', and a consumable consumption probability distribution model based on multi-dimensional features and a surgeon doctor preference coefficient are combined; using a dynamic target inventory calculation engine to generate a target inventory level that varies with time according to a formula; the inventory strategy module automatically generates an electronic replenishment instruction according to comparison with real-time inventory; the intelligent storage unit realizes accurate tracking of consumable delivery and warehousing through RFID (Radio Frequency Identification Device); and the closed-loop feedback module continuously optimizes model parameters by using actual consumption data. According to the system and the method, prospective inventory management based on future operation requirements can be realized, and the accuracy of consumable supply, the inventory turnover rate and the operation safety guarantee capability are remarkably improved.

The invention discloses a trusted data space-based cross-domain ecological flow self-driven method and a storage medium, right authentication is performed on a user behavior result based on a block chain technology in a trusted data base, and a privacy computing technology is adopted to realize data security sharing; establishing a digital asset incentive mechanism based on a digital asset incentive driving module to carry out value evaluation and capitalization; the user behavior result after right confirmation is converted into digital assets, an income distribution rule is set through an intelligent contract, ecological matrix collaboration is realized based on an ecological matrix and a flow self-circulation module, and cross-scene circulation and appreciation are carried out; data tampering resistance is realized through a block chain technology, and right authentication of user behavior results is realized; secure sharing and cooperative computing of data are realized through a privacy computing technology, and the requirement of privacy protection is met; the contribution value of the user is effectively quantified and realized; by integrating a plurality of application scenes and setting a unified data interface and asset system, autonomous migration and circulation of cross-scene user traffic are realized.

The invention provides a digital intelligent carbon emission integrated measurement management method and platform, and relates to the technical field of carbon emission management, and the method comprises the steps: collecting multi-source carbon emission related data of a target management region through a preset data interface and Internet of Things equipment; correlation and fusion are carried out on multi-source carbon emission related data, and an integrated carbon activity database is constructed; calling a built-in carbon emission factor library, carrying out automatic accounting and dynamic metering on carbon emission of an organization level and a product level, and generating a real-time carbon emission flow map; carbon footprint tracing and hot spot analysis of the whole life cycle are carried out by combining a product bill of material and a production process path, and a key emission link is positioned; and carrying out visual display on the real-time carbon emission flow map and the key emission link. According to the invention, the technical problem that the carbon emission management and control efficiency is further affected due to the fact that a specific emission link cannot be positioned due to data islands and post accounting in the prior art is solved.