266 results about "Hotline" patented technology

The invention provides a government affair hotline service knowledge graph construction method and system based on a large language model, and relates to the technical field of natural language processing and government affair informationization, and the method comprises the steps: obtaining a government affair hotline service scene, and defining a mode layer of a multi-scene knowledge graph; unstructured government affair hotline question and answer data are collected, semantic fusion, logic extraction and ambiguity elimination are carried out on the data based on a chained thinking mechanism of a large language model, and a fused government affair hotline question and answer set is generated; driving a large language model to identify knowledge entities in the set by adopting a step-by-step prompt strategy, extracting semantic relationships among the knowledge entities by adopting a semantic association analysis method, constructing triple knowledge data based on the knowledge entities and the semantic relationships, storing the triple knowledge data into a graph database, and performing dynamic updating and multi-dimensional retrieval on the multi-scene knowledge graph to obtain a multi-scene knowledge graph set; therefore, a large language model and a knowledge graph technology can be fused, government affair data are efficiently integrated, and the intelligent level of government affair service is improved.

An electronic device has a substrate, a signal wiring and a heat radiating wiring. The substrate has one surface and the other surface opposite to the one surface. The signal wiring is formed on an insulating layer of the substrate. The heat radiating wiring is formed on the insulating layer and thermally connected to the signal wiring on a same plane as the signal wiring.

The invention relates to the technical field of artificial intelligence, in particular to a government affair hotline complaint classification and automatic order sending system and method based on a large model, and adopts a four-layer architecture to realize full-process intelligent management of government affair complaints from acceptance to disposal. The four-layer architecture comprises a multi-mode sensing layer, an intelligent decision-making layer, a dynamic single-layer dispatching layer and a closed-loop self-optimization layer; the method has the beneficial effects that an efficient, accurate and intelligent government affair hotline complaint classification and automatic order dispatching system is constructed by fusing a large model technology and an innovative data processing and order dispatching strategy, rapid and accurate classification and automatic order dispatching of complaint work orders are realized, the work order processing flow is remarkably optimized, the processing period is shortened, and the working efficiency is improved. The quality and efficiency of government affair services are improved, and the satisfaction degree of the public to the government affair services is enhanced.

The invention discloses an item type self-growth and self-evolution and classification method based on a large model. The method comprises the steps that item type catalogs are added to a large language model, and corresponding item classification cues are constructed; inputting the government affair hotline work order text and the item classification prompt word into the fine-tuned large language model, and quoting an item type directory to output an item type corresponding to the government affair hotline work order text; and outputting the item type output by the large language model as a classification result of the to-be-classified government affair hotline work order text. The item type catalog needs to be periodically updated based on the large language model. Prediction of government affair hotline item types is achieved in the mode that the large language model is combined with the item classification prompt words, meanwhile, fine adjustment is conducted on the large language model so that the large language model can be more focused on the field of government affair hotlines, and item type self-growth and self-evolution of the item type catalog are achieved through the large language model. Therefore, the timeliness and accuracy of government affair hotline item type prediction are improved.

The invention discloses a dynamic turbulence measurement method. A hot-wire anemometer with two hot-wire probes is adopted to measure the wind speed of two points in a space; then setting the sampling frequency fc of the hot-wire anemometer, and counting the average time ts and the space length r; obtaining voltage data output by the hot-wire anemometer, judging whether the voltage data is normal or not, and removing abnormal data; corrected correction voltage data is obtained; calculating the wind speed u according to the corrected voltage data; determining a time interval td according to a preset sampling frequency fc, calculating a speed structure constant # imgabs0 # by combining the wind speeds measured by the two hot-wire probes, and calculating second-order and third-order turbulence dissipation rates; and calculating the kinematic viscosity coefficient v2 at the in-situ measurement position, the internal scale 10 of the atmospheric turbulence and the turbulence I in sequence. The dynamic turbulence measuring method overcomes the defects that a temperature pulsation instrument cannot measure dynamic turbulence and an ultrasonic anemometer platform is poor in applicability, and has the advantages of being easy and convenient to operate, suitable for being carried on a sounding balloon for high-altitude turbulence measurement in a severe environment and the like.

The invention provides a government affair question-answering method and product based on a knowledge-enhanced large language model, and relates to the technical field of artificial intelligence. A current government affair question-answering system mainly adopts a manual reply mode and is low in efficiency, and a system based on RAG lacks a query and discrimination mechanism and cannot recognize intention differences and utilize dialogue contexts, so that the quality of retrieval knowledge is influenced. Aiming at the defects existing in the related technologies, the invention provides a government affair question answering method based on a knowledge enhancement large model, and particularly, aiming at the problem of low manual reply efficiency, a complete government affair hotline service framework containing intelligent question shunting and structured feedback is designed; aiming at the problem of poor knowledge retrieval quality of a traditional RAG system, a multi-stage semantic enhancement retrieval method is designed, cross-domain knowledge is supplemented through networking query, and accurate response and service closed loop of government affair consultation are realized.

The invention relates to the technical field of model training, in particular to a hotline platform label model training method based on a deep neural network. The method comprises the following steps: preprocessing an input hotline work order text, and extracting channel, region, time and other structured metadata; performing semantic coding on the text by adopting a pre-trained BERT model, and dynamically fusing structured metadata and text features through a gating attention mechanism; performing field adaptive pre-training by using a hierarchical encoder, and outputting an enhanced semantic representation vector; constructing a label co-occurrence probability matrix and inputting the label co-occurrence probability matrix into the graph neural network for multi-label collaborative prediction; carrying out adaptive optimization by combining a focus loss function and a meta-learning framework; the teacher model is compressed into a student model through knowledge distillation, and the student model is deployed to an NPU hardware platform, so that real-time reasoning and online feedback closed loop are realized. According to the method, the work order classification accuracy can be remarkably improved, the order sending error rate and the processing delay are reduced, and the hotline service intelligent level and the operation and maintenance efficiency are improved.

The invention provides an electric blanket heating wire state judgment method and system, a medium and a product, and relates to the technical field of safety monitoring. In the method, a control processing unit firstly samples at a first preset frequency and calculates a signal change rate; and when the signal change rate value exceeds the standard rate threshold value, the control processing unit judges that the electric blanket is in a suspected heat accumulation state, and at the moment, the output power of the heating wire is controlled to be reduced to a preset duty ratio. Then, the control processing unit adopts a second preset frequency to collect signals and filter the signals, extracts signal values at the first moment and the second moment, and calculates the characteristic value of the thermal attenuation trend; and finally, the control processing unit realizes the distinguishing between the local wire twisting state and the folding state based on the deviation degree of the characteristic value and the standard thermal attenuation characteristic value. According to the method, the technical problem that the high-temperature cause cannot be distinguished only through the instantaneous temperature is relieved, and accurate recognition of the twisted wire fault and the folding working condition is achieved.

The invention belongs to the technical field of airborne atmospheric detector equipment, and particularly relates to an airborne cloud water content detection system, which comprises a constant-temperature hot wire sensor, a cloud water content detection module, a cloud water content detection module and a cloud water content detection module, the pressure sensor is used for measuring atmospheric pressure; the temperature sensor is used for measuring the temperature in the cloud; the humidity sensor is used for measuring the humidity in the cloud; the data processing module is used for receiving the data of the sensor and processing and analyzing the data through a specific algorithm to determine the cloud water content; the data communication module is used for outputting the measured data and the calculated cloud water content data to a computer or other acquisition detector equipment through the Ethernet or a serial port; and the heating control module is used for keeping the detector in a preset standard working temperature range so as to ensure the measurement accuracy and the reliability of detector equipment, and has the effect of finely detecting the airborne cloud water content.

The invention provides an intelligent government affair hotline group appeal analysis and detection method. The method comprises the following steps: regularly pulling daily work order data; performing vectorization coding on work order content in the work order data; aiming at the vector chemical order data, using a DBSCAN clustering algorithm to obtain a classification cluster; traversing the classification clusters, and expanding the classification clusters by using the DBSCAN clustering algorithm again by using address features and complaint features of the classification clusters and data which are not included in any cluster, so as to obtain more accurate complaint amount and complaint number information; obtaining a complaint summary of each classification cluster through a large model; and writing the data into the data. According to the method, group appeal detection considering semantic and spatial information can be realized, redundant and repeated work of original manual statistics is replaced by an intelligent mode, and similar appeals can be effectively identified and integrated, so that the processing efficiency is improved, the service quality is optimized, and a daily group event list is quickly and efficiently provided.

The invention discloses a government affair hotline appeal knowledge graph construction method based on an agent technology, and the method comprises the following steps: sorting basic data, and building a public entity; regularly arranging result data of entity recognition for performing all-parameter fine tuning so as to enhance the capability of a large language model in an entity recognition task and better correspond an entity recognition result to an existing tag to form an entity recognition large model; compiling an appeal analysis agent program based on the entity identification large model; and self-learning and evolving the knowledge graph. The knowledge graph is constructed for supporting applications such as data analysis and question and answer service in the government affair service field, and has multiple functions such as citizen historical appeal recognition, public opinion early warning and reply text generation. The constructed knowledge graph can be effectively applied to government affair service application based on the knowledge graph, the application capability is effectively enhanced, and the platform operation efficiency is improved.

The invention discloses a heat transfer process simulation method and device in visual flow and a storage medium, and the method comprises the steps: (1) determining a full-field source item f (x) and a thermal function Hb (x) value on a boundary according to the data of a flow velocity field and a temperature field; (2) carrying out collision on the distribution function ni (x, t) under the action of a full-field source item f (x) to obtain # imgabs0 #; (3) processing distribution function collision at a boundary; (4) migrating the distribution function # imgabs1 # after collision to the adjacent grids according to the discrete grid speed; (5) calculating a thermal function H (x, t) = sigma ini (x, t) according to the distribution function; (6) calculating the change of a full-field thermal function, and carrying out termination judgment; and (7) visualizing the hot line distribution according to the thermal function distribution. The method has good parallelism performance and a simple boundary format, and meanwhile has good applicability to boundary non-body-fitted grids.

The invention belongs to the technical field of online government affair service platforms, and relates to a hotline complaint-to-do collaborative application method and a hotline complaint-to-do collaborative application system. According to the method, the public appeal work order is obtained through multiple channels, the key information in the public appeal work order is extracted, and the key information is utilized to form the input work order; according to the TF-IDF algorithm, the input work orders are preliminarily classified, and through combination with the TF-IDF algorithm, accurate classification of the input work orders is facilitated; performing secondary classification on the preliminarily classified input work orders according to an appeal classification model; according to the mapping formula and the conditional probability extraction formula, the input work orders after secondary classification are automatically dispatched, the mapping formula and the conditional probability extraction formula can be automatically matched to the most appropriate processing department or personnel, and the order dispatching efficiency and accuracy are improved. According to the invention, accurate classification and automatic distribution of the crowd appeal work orders can be realized, and the solving efficiency of the crowd appeals is improved.

The invention discloses a multi-modal public hotline rumor detection method based on an interactive attention mechanism and adaptive feature fusion, and the method comprises the following steps: firstly, extracting global visual features of an image through a pre-trained ResNet50 model, and extracting context semantic features described by an original text and an image text through a pre-trained BERT model; secondly, performing deep association on text features and image text description features by utilizing an interactive attention mechanism, and enhancing the expression ability of key semantic information; then, dynamically adjusting feature weights through a two-stage adaptive feature fusion module, and optimizing complementarity of global and local features; and finally, inputting the fused features into a multi-layer perceptron for prediction. According to the method, semantic association is captured through an interactive attention mechanism, feature expression is optimized through adaptive feature fusion, and the rumor detection efficiency and effect are remarkably improved.

The invention provides a government affair hotline work order management method, system and device based on a large model and a medium, and belongs to the technical field of large model application. The method comprises the following steps: recording an incoming call of a government affair hotline, and converting the incoming call into a dialogue text; generating cue words by using the cue word template, and inputting the cue words into the large model to generate a work order abstract; extracting keywords based on the dialogue text, generating an index in combination with a work order abstract, retrieving business knowledge in a government affair hotline business knowledge base, recording the business knowledge in a knowledge list, and retrieving similar work orders in a historical work order knowledge base; according to the knowledge list and the similar work orders, whether work order assignment is carried out or not is judged; if the work order needs to be dispatched, retrieving the department knowledge base and the historical work order knowledge base in sequence according to the work order abstract and the knowledge list, generating a work order distribution suggestion, executing a work order element extraction process, and generating and inputting a work order; and if the work order does not need to be allocated, executing a work order element extraction process, and generating and inputting the work order.

The intelligent plasma turbulence friction resistance reduction control system based on deep reinforcement learning comprises environment building, an upper computer and a controller, and the environment building comprises a high-voltage power supply, a plasma exciter, a state sensor, an evaluation sensor and a signal acquisition board card. The invention further provides an intelligent plasma turbulence friction resistance reduction method based on deep reinforcement learning. According to the invention, a hot line and a hot film are selected as sensors, array dielectric barrier discharge plasma with quick response is used as an exciter, and a control strategy of an intelligent agent is learned and optimized through continuous high-speed interaction between a deep reinforcement learning algorithm deployed in a computer and an FPGA controller and a turbulent boundary layer. And finally, real-time closed-loop control of turbulence friction resistance reduction is realized. Compared with traditional open-loop control, the method has the advantages of being better in adaptability under complex incoming flow, higher in drag reduction energy efficiency and the like.

The invention discloses a work order risk identification method and apparatus, and an electronic device, belongs to the technical field of computers, and aims to solve the problem of lack of a technical scheme for identifying high-risk work orders in government affair hotline work orders at present. The method comprises the following steps: acquiring item contents of a plurality of registered items in a petition item library, and determining at least one group complaint event according to the item contents; obtaining content information of a plurality of target work orders in a government affair hotline work order library; for each target work order, matching the target work order with all group complaint events according to the content information of the target work order to obtain a matching result; the matching result comprises a corresponding relationship between the target work order and the group complaint event; determining a first number of target work orders corresponding to each group complaint event according to a matching result of all the target work orders; and for each group complaint event, determining that the target work order corresponding to the group complaint event is a high-risk work order under the condition that the first number meets a preset condition.

The invention relates to the technical field of multi-parameter sensing, in particular to a multi-parameter sensing method and device. The multi-parameter sensing method comprises the following steps: sending detection light; the signals are sent to the coupler; modulating to obtain first detection light and second detection light; the first detection light is sent to the temperature detection module, and the second detection light is sent to the liquid level detection module; the first reflected light and the second reflected light are sent to a hot-wire type optical fiber, sound waves are generated, and the sound waves cause vibration of a quartz tuning fork and are converted into a first piezoelectric current signal and a second piezoelectric current signal; converting into a first voltage signal and a second voltage signal; demodulating the first voltage signal and the second voltage signal; and carrying out inversion on the temperature and the liquid level. According to the multi-parameter sensing method, multiple parameters can be monitored at the same time, and the detection precision can be improved.

The invention discloses a data processing method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring work order data of a public service hotline; inputting the work order data into a target large language model to obtain hot event information corresponding to the work order data output by the target large language model, the target large language model is a large language model obtained by performing low-rank adaptation fine tuning on the pre-trained large language model according to historical work order data of the target business field, hot event information corresponding to the historical work order data and professional knowledge of the target business field; and sending the hot event information corresponding to the work order data to the target object. According to the method and the device, the technical problem of relatively high training and maintenance cost of the large language model caused by relatively high requirement on the number of parameters participating in training in a related large language model training method is solved.

The invention discloses a linkage command decision-making and early warning method fusing grid patrol events and artificial intelligence, and belongs to the technical field of crossing of smart cities and artificial intelligence. The method aims at solving the problems of low efficiency and insufficient early warning caused by the fact that multi-source data are difficult to fuse and event association depends on manpower in existing urban governance. According to the scheme, multi-source data such as grid patrol, Internet of Things monitoring and hotline are accessed and standardized to form a standardized event in a unified format; performing time-space and semantic fusion analysis on the events by using an artificial intelligence analysis engine, intelligently merging associated events and identifying high-risk events; early warning is automatically generated based on a preset rule base, and a processing task is distributed to a corresponding service system; and finally, the command system carries out unified scheduling. According to the method, intelligent association analysis, active early warning and cooperative command of urban governance events can be realized, and the management efficiency is improved.

The invention provides a government affair hotline resource optimization method and device based on time sequence prediction and reinforcement learning. The method comprises the steps of obtaining historical service work order data of a preset historical time period from a government affair hotline service platform; according to the historical service work order, utilizing a time sequence prediction model to predict future service work order data in a preset future time period; and determining a resource scheduling strategy in the preset future time period by using a reinforcement learning model. According to the technical scheme provided by the invention, in order to improve the working efficiency and reduce the user waiting time and unnecessary troubles in the process that government affair hotlines serve people, the historical work order data is deeply analyzed by using the time sequence prediction model, and possible problem hotspots and service demand peak periods in the future can be more accurately predicted. The distribution strategy of manpower and technical resources is dynamically adjusted in combination with a reinforcement learning algorithm, and it is ensured that the optimal government affair hotline service response speed and service level can be kept in different time periods.

The invention discloses a traffic transportation government affair hotline mining method based on natural language processing, and belongs to the technical field of artificial intelligence and machine learning. According to the method, for structured and unstructured data in a traffic transportation government affair hotline, firstly, a five-class word segmentation dictionary containing cleaning words, noise words, synonyms, additive words and stop words is constructed; converting the unstructured text into structured data through improved data cleaning, text word segmentation and feature representation; carrying out clustering analysis by utilizing an LDA topic model, and extracting public demand topics and high-frequency keywords; hot appeals and trend changes are mined in combination with space-time analysis and association analysis, and finally a visual analysis report is generated. According to the method, the problems of poor model interpretability and insufficient field adaptability in the prior art are solved, the accuracy of hotline data processing and the effectiveness of theme recognition are remarkably improved through multi-dictionary collaborative optimization and field knowledge fusion, and accurate decision support is provided for a traffic transportation management department. A real taxi field case in a certain city is used as an example for research, an experiment proves that the method has an accurate theme identification function, and the complaint and report work order amount in the traffic transportation government affair hotline taxi field in the city is reduced by 20% on year-on-year basis in 2024.

The invention discloses a speed measurement system of a hot-wire anemometer and a control method thereof, and the system comprises a bridge sensor module which comprises a bridge comprising a detection resistor, and when the resistance value of the detection resistor changes along with the wind speed, the voltage difference value of the output end of the bridge is larger than a difference value threshold value; the analog-to-digital conversion circuit is used for converting a voltage analog signal output by the bridge output end into a digital signal; the central processing module is connected with the analog-to-digital conversion circuit and is used for receiving the digital signal and generating a feedback signal based on the digital signal; and the digital-to-analog conversion circuit is used for converting the feedback signal into a bridge top voltage analog signal, inputting the bridge top voltage analog signal to the bridge, and adjusting the bridge top voltage of the bridge to enable the voltage difference value to be smaller than or equal to the difference value threshold value. According to the system, active closed-loop adjustment is realized through a high-speed digital signal processing mode, and compared with a previous negative feedback hot-wire anemometer, the stability of the system is improved, the requirements on the performance of system components are reduced, the anti-electromagnetic interference capability is improved, and measurement errors caused by environmental factors are reduced.

The invention relates to the technical field of artificial intelligence, and particularly provides a tax hotline consultation agent method and device, and the method comprises the following steps: S1, directly obtaining a session audio stream from a telephone end through a hardware telephone line universal demultiplexer RJ9, and carrying out the analysis; s2, converting the voice dialogue between the seat personnel and the taxpayer into a text form in real time, and performing voice transcription in real time; s3, performing deep analysis on the text content generated by real-time voice transcription; s4, constructing a tax knowledge graph; s5, accurate and detailed answers are automatically generated for the tax consultation questions of the clients; s6, extracting key information from the dialogue content, and performing structured processing for subsequent summarization and extraction; and S7, feeding back the generated dialogue summary and question answers to the seat personnel, and evaluating and modifying the summary content and the question answers by the seat personnel. Compared with the prior art, accurate and comprehensive information support can be provided for seat personnel, and the customer service quality and the working efficiency are improved.

The invention belongs to the technical field of communication network management, and discloses a network fault order sending method and device, equipment and a storage medium. The method comprises the following steps: receiving a fault reporting request initiated by a fault reporting user through a customer hotline, and obtaining fault reporting information of the fault reporting user during a fault reporting call; obtaining interaction signaling data of the fault reporting telephone number during the fault reporting call, and determining an estimated position of a fault reporting user based on the interaction signaling data; obtaining measurement report data of the fault reporting telephone number, and determining position information of the fault reporting user based on the measurement report data and the estimated position; marking on a map based on the position information of the fault reporting user to form a network space situation map; and generating fault reporting work orders of different emergency degrees based on the cyberspace situation map, and distributing the fault reporting work orders to maintenance personnel in a preset range. The efficiency and accuracy of network fault order sending can be improved.

The invention discloses a 12345 hotline incoming call service insight method and system, and the method comprises the steps: S1), carrying out the number recognition after an incoming call is accessed; s2) if the incoming call is the first incoming call, recording basic information, entering voice navigation, and skipping to S6) after the processing is finished; if the incoming call is not the first incoming call, generating an intelligent label, and judging a work order type; s3) if the work order is a work order with a problem to be solved, calling an intelligent work order management module for processing; s4) if no work order with the problem to be solved exists, judging whether the user is a vehicle moving work order user; s5) if the user is a vehicle moving work order user, automatically querying a previous vehicle moving record, and querying whether to add a call or not; if not, switching to a seat skill group intelligent distribution module for processing; and S6) calling a call data statistical feedback module for processing, and updating the user portrait. The application solves the problems of tedious process, low efficiency and the like of a traditional system, improves the working efficiency and the service quality of the 12345 hotline, improves the user experience, and reduces the pressure of customer service.

The utility model provides a double-partition PTC (Positive Temperature Coefficient) and NTC (Negative Temperature Coefficient) heater double-core heating wire four-wire four-pin circuit, which comprises a controller and a blanket body, the blanket body comprises a first partition and a second partition, the first partition comprises a heating wire, a temperature sensing wire, a first partition A-PTC heating wire end, a first partition A-NTC temperature sensing wire end, a first partition B-PTC heating wire end and a first partition B-NTC temperature sensing wire end; the second subarea comprises a heating wire, a temperature sensing wire, a second subarea A-PTC heating wire end, a second subarea A-NTC temperature sensing wire end, a second subarea B-PTC heating wire end and a second subarea B-NTC temperature sensing wire end; the temperature sensing wire ends of the first subarea and the second subarea are connected in series; the controller comprises an AC power supply input module, a fuse, an EMI module, a 5V voltage stabilizing circuit, a main control circuit, a first PTC sampling temperature control module, a first heating drive, a second PTC sampling temperature control module, a second heating drive, an NTC over-temperature protection short circuit protection module, a zero cross detection module, a display, a key module, a controller over-temperature protection circuit and an AC voltage sampling temperature control circuit. And the pins H1-H4 are connected.

The invention relates to a dialogue type voice emotion automatic labeling method and system based on a graph language model, and aims to solve the problems of low efficiency, high subjectivity and high cost of artificial voice emotion labeling in government affair service hotlines. The method comprises the following steps: collecting diversified government affair service hotline dialogue voice data; preprocessing the collected data, and converting the collected data into voice-text bimodal data; extracting text emotion features by using a large language model and optimizing a classification result through multi-task learning; inputting the map sequence into a voice emotion recognition model, and generating a voice emotion predicted value through a backbone network and an emotion relationship mining module; and carrying out confidence score evaluation on the text and speech emotion prediction result, if the confidence score is higher than a preset threshold, constructing a high-quality emotion annotation data set, otherwise, iteratively optimizing the model. The text and voice multi-modal fusion is realized, the emotion labeling automation and accuracy are remarkably improved, the cost and subjectivity are reduced, and powerful support is provided for large-scale emotion analysis of dialogue scenes.

The invention provides a test system, a calibration device and a test method. The test system comprises a hot wire probe which comprises an electric heating wire, a thermocouple and a supporting rod; the heating wire and the thermocouple are fixedly connected to the supporting rod; the hot-wire probe is used for performing flow velocity testing on fluid at a target position in a target flow field to obtain a flow velocity signal and performing temperature testing on the fluid at the target position to obtain a temperature signal; the electric heating wire and the thermocouple are located on the same vertical line, a first distance exists between the electric heating wire and the thermocouple, and the first distance enables the electric heating wire and the thermocouple not to influence each other under the conditions of flow velocity testing and temperature testing; the acquisition unit is connected with the hot-wire probe through a circuit; the temperature sensor is used for converting a flow velocity signal into a first electric signal and converting a temperature signal into a second electric signal; the calculating unit is connected with the collecting unit and used for determining the target temperature and the target flow speed of the target position based on the first electric signal and the second electric signal.