184 results about "Context vector" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

Disclosed in the present invention is a non-autoregressive Transformer-based modeling method for a 4-level pulse amplitude modulation high-speed transmitter. The method involves establishing a deep learning model having an encoder-decoder architecture to predict the behavior of a 4-level pulse amplitude modulation transmitter. An encoder processes unordered non-sequential inputs, including an input signal parameter and link parameters, to generate a context vector and then transmit same to a decoder. The decoder uses both the context vector generated by the encoder and a transmitter output signal sequence to generate a categorical probability distribution for each point in the sequence one by one. The model is trained using a random masking strategy, and inference is performed by means of non-autoregressive decoding and filtering, so that the model can perform parallel prediction on an output sequence, and perform a filtering process to predict an output signal. Compared to traditional simulation methods, the present invention achieves a significant acceleration effect, particularly when processing multi-link systems.

The present application provides a large language model-based query statement generation method, apparatus, and device, and a medium. The large language model comprises a plurality of encoders. The method comprises: acquiring a query text; performing vectorization processing on the query text to obtain a target vector representation corresponding to the query text; inputting the target vector representation into the plurality of encoders, to perform encoding processing on the target vector representation by means of weight matrices of the encoders, so as to obtain context vectors corresponding to the encoders, wherein different encoders have different weight matrices; and generating a target query statement on the basis of the context vectors corresponding to the encoders. A model is allowed to focus on different information features in different attention heads, independently capture different aspects of a query text, and process information at multiple abstraction levels, thereby better capturing complex and abstract semantic relationships, effectively distinguishing the importance of information when processing complex and fuzzy questions, improving the accuracy of query statement generation.

The invention discloses a real-time translation recognition system under a cloud service framework, belongs to the technical field of real-time translation, and solves the problems that an existing translation system is insufficient in real-time performance, poor in scene adaptability, weak in privacy protection, slow in model evolution and the like. The dynamic model management engine obtains adaptive slices from a model slice factory according to scenes, equipment states and network quality and distributes the adaptive slices to edges, and the adaptive slices are distributed to a cloud-side collaborative reasoning system; the cloud-side collaborative reasoning system comprises a cloud-side collaborative reasoning system, a cloud-side collaborative reasoning system, a cloud-side collaborative reasoning system and a cloud-side collaborative reasoning system; the multi-modal perception engine fuses audio, images and dialogue history to generate a structured context vector and improve translation context fitting degree, the cloud edge cooperation engine takes an edge model as a core, processes different complexity tasks in combination with a cloud end, and constructs a data closed loop by incremental learning and a federation engine to realize model optimization and privacy protection; according to the system, the real-time performance, accuracy and safety are improved through cloud edge collaboration, dynamic adaptation and continuous learning, and the system is suitable for multi-scene real-time translation.

The invention provides a digital human AGI dialogue system based on a cloud side-end collaborative architecture, and relates to the technical field of digital humans, the system is characterized in that a sensing module, a processing module, a decision module, a rendering module and an output module are deployed at a side end, and a decision module, a driving module and a rendering module are deployed at a cloud end; the sensing module collects and preprocesses an input signal of a user; the processing module is connected with the sensing module and is used for extracting features of the input signals and generating context vectors; the decision-making module is connected with the processing module, and generates a decision-making result containing an answer text and an emotion label according to the context vector; the driving module is connected with the decision module, generates an audio stream and a phoneme sequence according to the answer text, and calculates skeleton driving parameters and mouth shape driving parameters of the digital human; the rendering module is connected with the driving module to generate a rendered picture; and the interaction module is connected with the driving module and the rendering module, and aligns the rendered picture and the audio stream to obtain an output result. And low time delay and high performance are realized by adopting cloud edge collaboration.

The invention discloses a semantic extension matching method and system based on a domain synonym library, and relates to the technical field of data processing, the method comprises the following steps: obtaining a query keyword input by a user, and generating a query context vector according to the query keyword and user context information; if the query keyword does not belong to the category word in the platform service category system, determining an extended synonym set matched with the query keyword from a pre-constructed field synonym library; for each extended synonym in the extended synonym set, calculating a correlation score between the extended synonym and the query context vector, and performing weighting processing to obtain a corresponding weighted extended synonym; and according to each weighted extended synonym and the query keyword, generating an extended query index for search matching. Therefore, dynamic expansion combining the field scene and the synonym library is realized, multi-dimensional semantic expansion can be carried out when complex and non-standardized user query is processed, and the intelligent level of the system is improved.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

The invention discloses a retrieval method based on a semantic enhanced knowledge graph, which relates to the technical field of information, and comprises the following steps: receiving a natural language query of a user, and carrying out deep analysis on the query, including named entity recognition and linking, relationship extraction and query intention classification; and based on an analysis result, extracting a related local sub-graph from the knowledge graph, generating a query context vector, and generating dynamic semantic embedding for the sub-graph through a query-perceived graph attention network to obtain a dynamic enhanced semantic graph. According to the retrieval method based on the semantic enhancement knowledge graph, the retrieval precision and the recall rate are remarkably improved, the limitation of static knowledge representation is solved through a dynamic semantic enhancement mechanism of query intention perception, so that the local semantic representation of the knowledge graph is highly aligned with the query intention of a specific user; and the ability of understanding and answering complex, fuzzy, ambiguous and multi-hop queries is improved.

The invention discloses an attention-based context-aware sparse hybrid expert model routing method, which comprises the following steps of: encoding prompt information input by a user to obtain context vector representation of the prompt information, and introducing a multi-head attention mechanism to obtain multi-scale semantic interaction information; further, constructing a gating network based on attention output, and dynamically selecting Top-K expert networks for reasoning; by introducing a self-adaptive neighbor attention weight and a fusion gating mechanism, expert dispatching and weight fusion of a token level are realized; combining with the sub-output of each expert network, and aggregating according to the weight to obtain the final model output; according to the method, the understanding ability of the model for different semantic contexts is enhanced through a multi-expert structure and a dynamic routing mechanism, and the method is adaptive to multiple rounds of token generation processes, so that the accuracy and diversity of generated texts can be improved.

The invention discloses a multi-role configuration and effective judgment method based on semantic arbitration, and belongs to the technical field of artificial intelligence, and the method comprises the steps: setting a multi-organization role and an arbitration role; receiving and vectorizing a test description text, distributing the test description text as a first clinical test context vector to multiple organizations, randomly assigning a first speaking party and setting an empty discussion pool; the first speaking party generates speaking content based on the clinical test context vector, vectorizes the speaking content and writes the speaking content into a discussion pool, calculates the similarity between a speaking content vector and the clinical test context vector, and calculates an arbitration score in combination with multi-organization attributes; the arbitration role executes three-state judgment according to the arbitration score and the parameters, an arbitration decision is generated, and an effective speaking content vector is determined; fusing, updating and writing the effective vector and the clinical test context vector into a discussion pool; discussion is terminated according to preset conditions, and based on the final vector and the discussion pool, effective judgment is output and a log is recorded; according to the method, a multi-organization discussion scene can be effectively simulated, and the clinical test discussion efficiency and accuracy are improved.

The invention discloses a front-end cache management method, system and device for a lightweight large-model dialogue state and a medium, belongs to the technical field of front-end cache management of a large-model dialogue system, and aims at solving the technical problem of how to overcome the defects that in a traditional scheme, long context cache is low in efficiency, storage redundancy and insufficient in dynamic semantic adaptation capacity, and the large-model dialogue state cannot be managed easily. In order to realize dialogue context volume compression, improve semantic similar request hit rate and reduce cross-end synchronization delay, the adopted technical scheme is as follows: data acquisition and preprocessing: capturing user interaction behaviors in real time through front-end burying points, and performing preprocessing operation on the acquired user behavior data; semantic normalization processing: performing embedded vector conversion and semantic clustering on the text input by the user to generate a unique semantic identifier and a context vector; querying and updating the multi-level cache; and dynamic collaborative updating: dynamically adjusting the cache based on the cache hit rate, the response delay and the user feedback, and optimizing the cache effect in real time.

The invention provides an intelligent teaching method and system fusing a learning track and an attention mechanism, and belongs to the technical field of education, and the method comprises the steps: obtaining a question text inputted by a user and historical learning behavior data of the user; using a current problem encoder to construct an input problem text into a current problem semantic vector; using a user memory encoder to construct the historical learning behavior data into a user memory vector sequence, and updating a user memory state based on the user memory vector sequence; based on the semantic vector of the current problem and the updated user memory state, a context fusion module of a dynamic memory attention mechanism is adopted to generate a memory context vector, and meanwhile, a double-flow user behavior encoder is adopted to obtain double-flow representation; and fusing the double-flow representation and the memory context vector, and obtaining a final teaching answer through a decoder and an output generator. According to the invention, deep modeling and long-term memory integration of the learning track of the user are realized, so that more efficient and personalized teaching services are provided.

The invention discloses a capsule network sentiment analysis method for teaching evaluation. Firstly, teaching evaluation texts are collected, aspect items are extracted through data preprocessing, an aspect category-emotion two-tuple is generated through manual annotation, and a teaching evaluation data set is constructed. Secondly, splicing each evaluation text and all aspect categories, and inputting the spliced evaluation text and all aspect categories into a pre-training language model for encoding to obtain high-dimensional context vector representation; thirdly, text features highly related to a specific aspect are extracted through a cross attention mechanism, and modeling and classification of category sentiment polarity of all aspects are achieved through a capsule network and a dynamic routing mechanism; and finally, judging the existence of aspect categories and the sentiment polarity of the aspect categories through a multi-task classifier, and outputting a plurality of aspect-sentiment two-tuples contained in the sentences. The sentiment analysis accuracy and interpretability in a multi-aspect and multi-sentiment polarity coexistence scene in a teaching evaluation text are effectively improved, and the method is suitable for intelligent analysis of large-scale education evaluation data.

The invention discloses a subject entity labeling method and system fusing image recognition and a knowledge graph, and relates to the technical field of image recognition and natural language processing. The method comprises the following steps: carrying out preprocessing and image-text association on multi-source heterogeneous subject data; detecting a visual entity in the image through an improved YOLO model, and extracting and linking a text entity in combination with a subject dictionary and a knowledge graph; cross-modal collaborative disambiguation is realized by calculating the semantic similarity of visual candidate entities and text context vectors; multi-modal entities are combined, relation reasoning and enrichment labeling are carried out in a knowledge graph, and a deep labeling result containing the entities and a semantic relation network of the entities is generated; the problems of difficulty in multi-source data fusion, inaccurate professional entity recognition and difficulty in semantic ambiguity elimination are effectively solved, the depth and accuracy of subject knowledge semantic understanding are remarkably improved, and key technical support is provided for intelligent education application.

The invention relates to the technical field of syndrome evolution prediction, in particular to a syndrome evolution prediction method, system and device based on multi-modal data driving and a medium, the method comprises the steps that multi-modal syndrome data is acquired and preprocessed, and the multi-modal syndrome data comprises clinical symptom data, tongue condition feature data and pulse condition feature data; extracting bidirectional time sequence characteristics in the preprocessed multi-modal syndrome data through a bidirectional long-short-term memory network, and generating a hidden state sequence; weighting the hidden state sequence by using an attention mechanism to obtain context vectors of contribution weights of different time steps; and inputting the context vector into a hidden Markov model of which the state transition matrix is constrained and corrected by the traditional Chinese medicine theory, performing syndrome state reasoning and evolution trend prediction, and outputting a prediction result. The objective of the invention is to improve the prediction precision and interpretability of syndrome evolution.

A translation method is provided, including: encoding to-be-processed text information to obtain a source vector representation sequence, the to-be-processed text information belonging to a first language; obtaining a source context vector corresponding to a first instance according to the source vector representation sequence, the source context vector indicating to-be-processed source content in the to-be-processed text information at the first instance; determining a translation vector according to the source vector representation sequence and the source context vector; and decoding the translation vector and the source context vector, to obtain target information of the first instance, the target information belonging to a second language.

Embodiments of the present disclosure provide a method, an apparatus and an electronic device for determining a word representation vector, and a computer-readable storage medium, which belong to a field of processing natural languages. The method includes obtaining a set of glyph units of a text; obtaining a context vector of the text based on the set of glyph units; and predicting next word of the text based on the context vector. The method for determining the word representation vector of the present disclosure can effectively obtain a corresponding set of glyph units even for hieroglyphics in which hyperbolic characters are prone to appear or languages evolved from the hieroglyphics, thereby improving an accuracy of determining the word representation vector.

The invention discloses a knowledge graph generation method and system based on RAG deduction, relates to the technical field of knowledge graph generation, and obtains a preliminary candidate text set by extracting an expected entity category, a relationship category and a field context in a knowledge graph generation task, constructing a task structure template and performing dense retrieval in combination with keywords. Then, entity and relation coverage is analyzed, missing information is recognized, retrieval is supplemented, and a text set is optimized; fusing the text set and obtaining state information, calculating a fusion quality disqualification coefficient, and if the coefficient is smaller than a threshold value, generating a structured triple set based on a fusion context vector; otherwise, re-fusing until the generation is completed. According to the method, a fusion mechanism process can be effectively detected, potential problems can be timely identified and corrected, risks of entity errors, relation mismatching and the like in knowledge graph generation are reduced, and the accuracy and usability are improved.

The invention relates to a power distribution network multi-modal fault reasoning method and device and a medium, and the method comprises the steps: collecting original data, extracting multi-modal features based on the original data, and carrying out the alignment and fusion, and obtaining fusion features; based on the fusion features and the original data, introducing power grid physical rule constraints to construct a space-time knowledge graph; obtaining a text description fault phenomenon, and utilizing the pre-training model to execute the following steps for fault reasoning: carrying out word segmentation on the fault phenomenon, searching a target node in a space-time knowledge graph based on each token obtained by the word segmentation, obtaining a neighbor node of the target node, calculating an attention weight based on the target node and the neighbor node, and carrying out fault reasoning; acquiring a graph context vector based on the attention weight; and performing fault reasoning based on the graph context vector. Compared with the prior art, the capability of handling novel and complex faults is improved.

The present invention discloses a method and system for extracting event temporal relationships based on an event knowledge graph. The method comprises the following steps: obtaining a text set to be temporally identified; encoding text sentences of each event in the text set using a pre-trained language model to obtain context vectors of the text sentences of each event; performing temporal prediction analysis on the upper and lower word vectors of event trigger words of each event using a relative time prediction module to obtain time prediction values ​​of the event trigger words of each event; splicing the context word vectors and time prediction values ​​of different events to obtain a splicing vector; performing relational probability analysis on the splicing vector using a time classification module to obtain temporal relationships between different events. The present invention encodes the context vectors of text sentences, fully explores the temporal relationships of implicit events through event time prediction and combined with event temporal relationship probability prediction, can accurately realize event temporal relationship extraction, and can be widely used in the field of data processing technology.

Systems and methods for Role-Based Access Control (RBAC) using Large Language Model (LLM) embeddings are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor. The memory may store program instructions that, upon execution, generate a plurality of distinct unstructured natural language documents from a same portion of structured data of an enterprise, with each document created for a corresponding role in the enterprise. The IHS may concatenate a role context vector that defines an access privilege for a document with a document vector associated with the document to produce a role-integrated document vector. The IHS may also apply pre-attention and post-attention layers to the role context vector to manage access control during document retrieval based on user roles.

The invention provides a fault detection method and system based on multi-source data fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining the health state data of a to-be-detected transformer bushing; preprocessing the health state data, and performing spatial feature extraction and fusion on the health state data by using a CNN network to obtain a fusion feature map; determining each pixel point in the fusion feature map as a time step, and capturing time sequence features in the fusion feature map by using a bidirectional long short-term memory network BiLSTM to obtain a time sequence feature sequence; obtaining an attention score of a time sequence feature corresponding to each time step in the time sequence feature sequence, performing weighted aggregation on the time sequence feature according to an attention weight obtained through normalization processing to obtain a context vector, and splicing the context vector and the time sequence feature sequence to obtain time sequence spliced data; and inputting the time sequence splicing data into the trained prediction model, and generating fault prediction information related to the transformer bushing to be detected.

The invention relates to the field of household appliances, and provides a smart home multi-modal dialogue method, device and equipment and a medium, and the method comprises the steps: inputting equipment dialogue data into a multi-modal dialogue model, and extracting word features of text information in the equipment dialogue data, part-of-speech features corresponding to the word features and relation features between adjacent word features, performing text enhancement on the word features and the part-of-speech features according to the relation features, extracting context vectors according to text information, combining the part-of-speech features and the word features after text enhancement to obtain a decoded word sequence, predicting a user emotional state according to device dialogue data, and combining the decoded word sequence to generate a reply prediction result. According to the method, the defect that a multi-mode dialogue technology is difficult to meet human emotion requirements is overcome, and it can be ensured that the generated reply not only conforms to logic in content, but also is matched with a user state in emotion expression.

A method for querying a large language model (LLM) in a system including a distributed vector database on a plurality of computational storage devices is provided. Each computational storage device of the computational storage devices has a controller and a storage. The method includes modeling a dataset in the storage of each computational storage device to generate vector embeddings, loading the distributed vector database having the vector embeddings on the computational storage devices, generating context vector embeddings for a query, querying the LLM with the query to obtain a query result, and performing a semantic search to retrieve a refined result from the distributed vector database based on the query result and the context vector embeddings.

The invention provides a medical document multi-language intelligent analysis system, relates to the field of language translation, and improves the accuracy and efficiency of professional term translation. The method comprises the following steps of: firstly, performing language recognition on an original text document by a recognition module through text unitization and context vector generation, and matching a corresponding corpus; then, a translation module carries out lexical element alignment on the source lexical elements through term bank injection and an AI model, the translation process is automatically optimized, and accurate translation of the terminologies is ensured; and finally, the reconstruction module accurately replaces corresponding contents in the original text document with translation output through the mapping file, so as to ensure that the document format and typesetting are consistent. Through the automatic and optimized translation process, the quality and efficiency of professional term translation are remarkably improved, manual intervention is reduced, and the translation requirement of a high professional standard is met.

The invention discloses a multi-round dialogue logic optimization method in an intelligent question answering system based on a knowledge graph, and belongs to the technical field of artificial intelligence and natural language processing, and the method comprises the following steps: S1, constructing and dynamically updating a triple knowledge graph; s2, based on a graph neural network and a graph attention mechanism, performing time sequence and emotion perception modeling on cross-round semantic association in the triple knowledge graph, and outputting a dialogue context vector; s3, inputting a dialogue context vector and a current user input feature into the generative model, performing semantic mapping through an independent intention, entity and behavior embedding encoder, and generating a robot behavior in combination with logic constraint and emotion-driven weight adjustment from a triple knowledge graph; s4, optimizing the generative model based on a task-associated adversarial training mechanism; and S5, carrying out multi-modal emotion fusion and consistency verification. According to the method, dynamic, coherent and emotional intelligent multi-round dialogue logic optimization can be realized.

The invention relates to an NL2SQL semantic parsing method and device, and the method comprises the steps: inputting a natural language query statement into a business data model, and obtaining a context vector; fusing the context vector with the natural language query statement to obtain a joint semantic vector; decoding the joint semantic vector and the structured feature based on a constraint decoding strategy to obtain an initial SQL statement, and verifying the initial SQL statement through a grammar parser to obtain a target SQL statement; the context vector of the natural language query statement can be more comprehensively captured through the business data model, and the optimal joint semantic modeling is realized through the context vector and the natural language query statement, so that the problems of information bias and redundancy caused by fixed weight or simple splicing are effectively solved; the context features, the structured features of the database and the constraint decoding strategy are dynamically fused, so that complex service logic and anaphora and omission phenomena in the long dialogue context can be better understood.

The invention discloses a context vector generation method and device, and relates to the technical field of machine learning, and the method comprises the steps: receiving a target vector; a first sub-vector of the target vector is generated according to the first vector information, a second sub-vector generated by a processor of the reasoning equipment is obtained, the first vector information is stored in a first storage space on the acceleration card, and the first sub-vector is used for indicating the relation between the target vector and a first historical vector; the second sub-vector is used for indicating a relationship between the target vector and a second historical vector, and the second vector information is stored in a second storage space on the processor; and generating a context vector of the target vector according to the first sub-vector and the second sub-vector. According to the method and the device, the technical problem that the storage space of the accelerator card is occupied by the historical information is solved, and the technical effect of reducing the storage space occupied by the historical information on the accelerator card under the condition of keeping the model operation precision is achieved.

The invention belongs to the field of natural language processing, and relates to an intelligent memory dynamic evolution method and system based on metadata and two channels, and the method comprises the steps: extracting an entity in an instruction input by a user, and obtaining an instruction entity; querying a metadata index database based on the instruction entity to obtain an entity state; when the entity state is a known state or a non-input state, updating the relational vector database and the graph database through a two-channel mechanism; a user input instruction is matched to the new relational vector database and the new graph database for mixed retrieval, and a preference relation and a hard constraint relation are obtained; constructing a context vector based on the user core portrait, the preference relationship and the hard constraint relationship; processing the user input instruction and the context vector to obtain generated content, and outputting feedback; and the interaction response capability and the engineering landing effect of the large language model intelligent agent in a long-period and complex interaction scene are greatly improved.

The invention discloses a retrieval question and answer method and system combined with a time sequence context, a medium and equipment, and belongs to the crossing field of artificial intelligence and intelligent operation and maintenance of an electric power system. Extracting time sequence semantic elements in the event sequence, and retrieving a corresponding event sub-graph in a time sequence directed graph constructed based on an event sequence, a time sequence and a logic link of the power equipment; performing feature aggregation on the sub-graph nodes to obtain context vectors; performing weighted fusion on the query semantic vector and the context vector to obtain a joint vector; calculating the similarity between the knowledge fragment vector set and each knowledge fragment in the knowledge fragment vector set, and screening out a plurality of most relevant knowledge fragments; and inputting the original query and the knowledge fragments into a text generation model, outputting answers, and completing questions and answers. By implementing the method and the device, the technical problem that an intelligent question and answer retrieval method in the prior art lacks pertinence and professional traceability of retrieval results in a complex event sequence scene in the power field can be solved.