674results about How to "Improve trust" patented technology

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention discloses a method for an AI model maternal and infant assistant from question answering to accurate recommendation, belongs to the technical field of artificial intelligence, and comprises the steps of user demand input and analysis, user portrait construction and dynamic updating, commodity recommendation and optimization, feedback optimization and strategy adjustment. According to the method and the system, the change of user demands and interests can be flexibly coped with, the recommendation accuracy and the user satisfaction degree can be improved, accurate understanding and processing of complex demands are ensured, the real-time performance and accuracy of user portraits are ensured, the change of the user demands and interests can be flexibly coped with, and more accurate and personalized recommendation is realized. The recommendation accuracy and the user satisfaction are improved, and the user can understand what a certain commodity is recommended, so that the trust of the user to the recommendation system is enhanced.

The invention relates to the technical field of supply chain management, in particular to a block chain-based culture product tracing method and system, and the method comprises the following steps: collecting the type, producing area and growth cycle data of culture products, distributing an identifier for each data item, coding the data and the associated identifiers in an upload format, and storing the data and the associated identifiers in a database; and uploading the data to a block chain network through an encryption channel, and generating a coded data record. According to the method, the timestamps and the data records are bound, each record is processed by using the hash function, and the security of each supply chain node is verified, so that not only is the integrity of data processing improved, but also an effective verification means is provided for consumers, and the security of the consumers is improved by decrypting and verifying the encrypted data. The security and privacy protection of data in storage and transmission are ensured, the production and circulation process of products is tracked through a block chain query function, the food security is improved, the supervision compliance is enhanced, and a powerful tool is provided for solving fraud and pollution problems in a supply chain.

The invention belongs to the technical field of natural language processing. According to the zero resource framework-based fine-grained large model illusion processing method and system, multi-dimensional anomaly detection is carried out on a preprocessed text through a large language model, and a multi-dimensional anomaly detection result is obtained; performing anomaly correction priority ranking according to a multi-dimensional anomaly detection result; dynamically adjusting a context window range according to a syntactic analysis result and the generated syntactic dependency tree; performing priority correction on high-risk vocabularies and sentences according to an abnormal correction priority ranking result in combination with the adjusted context window range; and evaluating the corrected content, and obtaining a corrected final text when a termination condition is met. According to the method, the excessive confidence, subtle semantics and grammar deviation illusion detection of the detection content is realized, and the illusion detection processing capability of a large language model, especially the detection processing capability of a fine-grained illusion problem, is improved.

The invention discloses a shear capacity analysis method for a reinforced concrete beam with stirrups based on machine learning, and the method comprises the steps: constructing a training set and a test set based on a shear capacity database of the beam with stirrups; establishing an interpretable prediction model by utilizing machine learning, setting hyper-parameters for the interpretable prediction model, and performing training; obtaining a shear bearing capacity prediction value of the samples in the test set by using the trained prediction model; and quantifying the contribution of each key anti-shearing characteristic to the anti-shearing bearing capacity predicted value, and analyzing and judging the reasonability of the anti-shearing bearing capacity predicted value output by the prediction model according to the anti-shearing theory. According to the method, the problems of high prediction difficulty, high discreteness, lack of reliable physical models, large calculation amount of high-precision numerical simulation and the like in machine learning are solved for the first time; the prediction model can accurately reflect the complex nonlinear relation between the shear bearing capacity and the influence factors, and meanwhile, the model reasonability is verified and improved in combination with a prediction result and priori knowledge, so that the man-machine interaction trust in practical application is improved.

The invention relates to the technical field of offshore wind power operation risk analysis, and discloses an offshore wind power operation risk analysis system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting wind power equipment operation state data and environment data of multiple types of sensors, and carrying out the cleaning and preprocessing of the data; the multi-modal data fusion and feature extraction module is used for extracting and fusing multi-modal features from different data sources; and the real-time risk prediction and intelligent decision module is used for analyzing the operation state of the wind power equipment in real time based on a deep learning model, and performing prediction and alarm when a fault trend is detected. By introducing a self-attention mechanism and a Transform architecture, deep fusion analysis is performed on multi-modal data from wind power equipment, and information of different data sources can be weighted and integrated in a unified feature space, so that the system can adaptively focus on important features, and the analysis precision and fault detection capability of the multi-modal data are remarkably improved.

The invention provides a medical AI question and answer method based on retrieval enhancement, which comprises the following steps: acquiring a medical professional knowledge base, and constructing an entity recognition model and a medical knowledge graph; the method comprises the following steps: splitting a medical professional knowledge base, and creating a vector index and a full-text retrieval reverse index; intention classification is carried out, and initial processing is completed; performing multi-path mixed retrieval on the medical professional knowledge base to obtain a user question related text block; a filtering rule is preset, and the user question related text blocks are filtered; answering the user question according to a preset second prompt word, and answering the user question according to a preset third prompt word when the user question exceeds the range of the medical professional knowledge base or the multi-path mixed retrieval has no result. According to the method, related context information can be accurately searched, reliable reference information is provided for a generative large model to answer professional questions, the interactive experience of a medical question answering system is improved, and the accuracy and specialty of answer generation of the system are remarkably improved.

The invention relates to a text semantic conflict detection method for constructing context association based on a large model. The method comprises the following steps: S1, segmenting an original text by using a segmentation function; s2, performing triple information extraction on each segmented text by using a large model to generate a data set of an entity corresponding relationship; s3, extracting data groups of all entity pairs and relationships, and performing confidence evaluation; s4, aggregating all triads, constructing a affair graph, and pointing out a contradictory relationship with relatively low confidence in the affair graph; and S5, for the found contradictory relationship, performing semantic conflict repair in the text in combination with the large model. According to the method, the accuracy of information generation of the large language model is remarkably improved in a context association mode.

The invention discloses a fresh flower selling management system and method based on a block chain technology, and relates to the technical field of fresh flower management. A data acquisition module acquires real-time data of a fresh flower planting link and a logistics transportation link, transmits the real-time data to a block chain storage module, and binds an identifier of a fresh flower; the block chain storage module is used for storing the fresh flower information and storing the data of the full life cycle of the fresh flowers in a distributed account book; the user access module is used for checking a display legend of the fresh flower and related information of the fresh flower at a user side, and the intelligent contract module is used for generating order information and carrying out payment settlement and automatic account division; the inspection module is used for inspecting flower quality and flower information; and the abnormity monitoring and feedback module is used for timely recording and sending notifications when abnormity is found in the planting, transportation or sales process, and quickly positioning problem nodes. According to the method, the transparency of the whole flow of fresh flower planting and transportation is improved, the data security and credibility are ensured, and rapid traceability is realized.

The invention discloses a milk traceability method based on NFC, relates to the technical field of milk traceability, and comprises the following steps: combining an NFC tag with a block chain, performing encryption transmission on data of each link, storing through a distributed account book technology of the block chain, ensuring that the data is transparent and cannot be tampered, and based on the NFC tag, obtaining a milk traceability result; the monitoring data of each stage of milk production, processing and transportation is updated to the block chain, the traceability efficiency is significantly improved, manual participation is reduced, a deep learning model is introduced, prediction and identification of potential quality problems of milk are realized, enterprises are helped to take corrective measures in advance, loss is reduced, a risk early warning mechanism is provided for a supervision mechanism, and the risk of the supervision mechanism is reduced. The overall safety of the supply chain is improved, the consumer can check the complete traceability information of the product by using the NFC scanning device through the NFC label and the block chain technology, the sense of trust of the consumer to the product is greatly improved, and the credibility of the brand can be enhanced.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention discloses an intelligent bid evaluation method and system based on a large model, and a related product, relates to the technical field of purchase bid evaluation systems, and solves the problems that an existing intelligent bid evaluation system is difficult to understand a complex logical relationship and semantic connotation and cannot provide a comprehensive and accurate scoring result. According to the technical scheme, the method comprises the steps that S1, bidding and tendering files are obtained; s2, performing disassembly processing based on the bidding and tendering file to obtain a plurality of bidding and tendering data pairs; s3, performing instruction construction on the bidding and tendering data pair based on a historical bidding and tendering database to obtain a prompt instruction of the bidding and tendering data pair; s4, processing the prompt instruction of the bidding and tendering data pair based on the large model after training and fine tuning to obtain the score of the bidding and tendering data pair, and accumulating the scores to obtain the score of the bidding file; through the method based on the large model, the scoring effect is effectively improved in the intelligent bid evaluation work, the efficiency is improved, manpower and material resources are saved, and meanwhile interference caused by human factors is avoided.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

The invention discloses an intelligent financial question and answer system based on a large language model, and relates to the technical field of financial question and answer, the system solves the problems of financial term ambiguity and context ambiguity through cooperative work of a plurality of modules, and the system comprises the following modules: a context recognition module, a query module and a query module, a natural language processing algorithm is combined with a context analysis technology in the financial field to automatically extract context clues in user questions; and a financial term knowledge base module. According to the method, context recognition and a financial term knowledge base are combined, and the accuracy of understanding the term ambiguity and the complex context is improved through the graph neural network. And the intelligent recommendation module dynamically provides personalized financial suggestions by using collaborative filtering and content recommendation algorithms in combination with user history and market data, and performs real-time adjustment. The risk assessment and prompt module assesses risks through a Bayesian network and a logistic regression model, and the adaptive learning module continuously optimizes risk prediction and helps users to avoid financial risks.

The invention provides an unknown environment adaptive navigation control system based on deep learning, and aims to improve the autonomous navigation capability of a machine in a complex and unknown environment. According to the system, an advanced sensing module is adopted, multi-sensor data are combined, and a dynamic environment is quickly and accurately sensed through a deep learning algorithm. The online environment modeling mechanism can update the environment information in real time without depending on a pre-constructed map. And by utilizing a reinforcement learning algorithm, the system can autonomously learn and optimize path planning, and meanwhile, energy consumption, time efficiency and safety are considered. An innovative multi-mode obstacle avoidance strategy is adopted, and obstacle avoidance behaviors are dynamically adjusted according to environment complexity and machine states. The intelligent decision support system combines a decision tree and fuzzy logic, and provides fast and accurate decision ability. The adaptive motion control algorithm optimizes path tracking performance and adapts to dynamic characteristics and environmental conditions of a machine. In addition, the collaborative job mechanism supports efficient communication and task collaboration in a multi-machine system.

The invention provides a massage robot control method and system, a medium, a program product and a terminal. Through a massage robot control algorithm of global active planning and local passive planning, personalized, accurate, safe and effective massage treatment is realized. A massage path and a target area are defined by global active planning, and the overall massage direction and range are determined. Passive planning is adopted locally, and the robot adjusts in real time according to feedback and physical conditions of a patient. The self-adaptive technology is adopted, massage parameters and force are adjusted in real time, and the optimal massage effect is ensured. Compared with a traditional massage robot, the treatment precision and safety are improved, and the safety risk caused by the fact that the robot is not suitable for individual differences is reduced. Meanwhile, through application of the self-adaptive technology, the massage effect is balanced and optimized, and the technical defects that an existing massage robot achieves accurate control and adapts to the body types and muscle tissue characteristics of different patients are overcome.

The invention relates to the field of legal service management, and discloses a legal service management system based on a block chain, and the system comprises the following modules: a block chain network, an intelligent contract module, a data storage module, a user interface module, a feedback and monitoring module and a user management module, the non-tampering property and the transparency are ensured; the intelligent contract module automatically executes contract terms and supports intelligent management; the data storage module adopts a hierarchical structure, and on-chain data and off-chain data are stored in a distinguished manner, so that the storage and access efficiency is optimized; and the user interface module provides user-friendly operation experience through a visual tool. By using the block chain technology, all contract execution and compliance audit data are encrypted and stored in a distributed mode, it is ensured that tampering is avoided and whole-course traceability is achieved, and a user and related mechanisms can verify the authenticity and integrity of the contract data at any time.

The invention discloses a mobile game scene interaction intelligent optimization method based on a generative adversarial network, and aims to improve the game experience of players. Comprising the following steps: S1, collecting behavior data and interaction information of a player in a mobile game, and generating a preliminary behavior feature model; s2, constructing a generative adversarial network model; s3, generating diversified game scene interaction examples according to the trained generator; s4, evaluating and optimizing the generated scene interaction example by using a discriminator; s5, real-time feedback of the player is received, and the behavior feature model of the player is updated; s6, dynamically adjusting a generation strategy based on the updated behavior feature model, and optimizing a scene interaction effect; s7, environment data are integrated, scene interaction is optimized in real time, and the immersion of players is enhanced; and S8, providing user-defined scene interaction options for the players, and allowing the players to adjust according to personal preferences. The interactive experience and game fun of players can be effectively improved.

The invention provides a real-time multi-scene automatic driving system based on AI multi-sensor fusion. The system can realize multi-scene seamless high-precision real-time automatic driving of the automatic driving vehicle. Adopted key technologies comprise a multi-source heterogeneous fusion automatic driving technology based on AI assistance and an AI enabling enhanced decision execution mechanism. Aiming at the defects of the current automatic driving technology in the aspect of vehicle full situation awareness, the technology improves the multi-source fusion efficiency and accuracy through deep learning, directly maps from multi-sensor original data to a driving decision through an end-to-end learning model to improve the reaction speed, and deeply fuses multiple sensors from an information layer. Meanwhile, aiming at the contradiction between big data processing and real-time computing requirements under the current automatic driving vehicle-machine man-cloud interconnection, the edge computing technology, cloud processing and local computing are used for assistance, so that the power consumption is reduced and the real-time requirements are met. Furthermore, through access integration of an intelligent traffic system, deep combination of traffic big data and AI automatic driving is realized, and relative positioning between vehicles is realized through traffic big data interconnection characteristics. The purposes of responding to complex road conditions in real time and reducing the power consumption of a processing algorithm are achieved through information characteristics such as the vehicle machine, the man-machine and the accurate position between the vehicle machine and the specific marker. In a real-time multi-scene automatic driving system based on AI multi-sensor fusion, the generalization ability of the system is continuously enhanced, and the applicability and transportability of the system are concrete so as to adapt to wider driving scenes.

The invention discloses a medical care data management system based on big data calculation, and relates to the technical field of data management, and the system comprises a data collection module, a data security module, a data storage module, a care quality analysis module, a care aid decision support module and a data visualization module. According to the medical care data management system based on big data calculation, by integrating and analyzing massive medical care data, more scientific and accurate decision support is provided for nursing personnel, multi-dimensional nursing data of a patient is collected and is compared and analyzed with a standardized nursing knowledge base, differences and defects in a nursing plan are quickly identified, and the medical care data management system is suitable for medical care. And the deviation of the nursing plan of the patient is determined through data analysis, and prospective decision-making suggestions are provided for nursing personnel, so that the subjectivity and uncertainty of manual judgment are reduced, and the efficiency and accuracy of nursing decision-making are improved.

The invention discloses an AI-driven complete-cycle scoliosis management system, which belongs to the field of scoliosis management systems and comprises a data fusion module, a feature extraction module, a text processing module, a diagnosis inference module and an output presentation module. Wherein the data fusion module is used for performing deep fusion on sensor data of the wearable equipment and X-ray / CT medical image data to generate comprehensive fusion prediction data; the feature extraction module uses a pre-trained fusion encoder to perform feature extraction and encoding on the fusion prediction data to generate potential feature representation; the text processing module adopts a pre-trained text encoder to encode free text diagnosis information provided by a doctor and a patient and extract semantic features; and the diagnosis inference module is used for fusing the fusion features and the text features. According to the invention, the sensing data of the wearable device and the medical image data can be effectively integrated to give full play to the complementary advantages thereof, so that the diagnosis accuracy is improved.

The invention discloses an intelligent customer service question and answer optimization method and system based on sentiment analysis, and relates to the technical field of language data processing, and the specific steps are as follows: reading voice data, and carrying out sentiment analysis and feature extraction on the voice data; judging the priority level of the voice data based on the extracted features; performing demand prediction on the voice data in sequence based on the priority level; and generating personalized reply content according to the extracted features, the prediction demand and the historical interaction record. By introducing emotion recognition, the emotion change of the user can be accurately captured, the reply tone and content are adjusted accordingly, more intimate service is provided, the negative emotion of the user can be relieved, and the trust feeling and satisfaction degree of the user to a power enterprise are increased.

The invention relates to a construction method of a customer complaint processing system for coping with supervision complaints, which belongs to the field of customer service and supervision compliance management, and comprises the following steps: (1) establishing diversified complaint channels, and integrating the complaint channels to a unified platform; (2) complaint information is comprehensively collected and intelligently analyzed; (3) establishing a quick response and processing mechanism based on an analysis result of the step (2); (4) performing closed-loop control and continuous improvement on complaints; (5) technical support and informatization construction; and (6) personnel training and ability improvement. The method has the advantages that the processing efficiency is improved, manual intervention is reduced through the automatic processing process, and the complaint response time is shortened; (2) the processing quality is enhanced: the complaint is ensured to be professionally and timely processed by a standardized processing flow and an intelligent distribution mechanism; and (3) resource configuration optimization: tasks are intelligently allocated according to the processing capability, and waste of human resources is avoided. (4) customer satisfaction is improved, complaints are quickly responded and effectively solved, and customer credibility and loyalty are enhanced.

The invention relates to the technical field of big data analysis and data processing, and provides an intelligent power utilization load management method based on big data analysis, which constructs an accurate power utilization load prediction model by integrating historical power utilization data, climate data, a power grid equipment state and user behavior data. According to the method, the security and traceability of prediction data are improved by adopting a block chain technology, and automation of an electrical load management process is realized by means of an intelligent contract. The method comprises the specific steps of data acquisition and preprocessing, load prediction model construction and verification, prediction data storage and hash value generation, intelligent contract writing and execution, and process node data processing and evaluation. The method effectively solves the problems of load data abnormity and model errors, and improves the stability and management efficiency of a power grid system.

The invention relates to a critical disease inter-hospital referral patient scoring and resource allocation method, system and device, and a medium. The method comprises the following steps: determining referral demands of patients; constructing a resource matching model in combination with the illness state and the geographic position of the patient and the medical specialist information of the hospital to generate a recommended hospital list, and determining a transfer-in hospital and a referral time window; calculating an optimal referral path and the shortest time required by referral in the referral time window according to the transfer-in hospital and the referral time window in combination with the geographic information and the real-time road condition data; and allocating the ambulances to the patients within the available range of the ambulances based on the globally optimized resource allocation model. The advanced scoring rule and the resource allocation algorithm are introduced, the treatment efficiency of critical patients is improved, more scientific and objective decision support is provided, and unnecessary referral treatment and repeated examination are reduced, so that the medical cost is effectively reduced, medical errors are reduced, and the satisfaction degree of the patients is greatly improved.

The invention belongs to the technical field of chronic wound depth analysis, and relates to a chronic wound depth analysis system based on image recognition. According to the method, more comprehensive and richer wound information is provided by acquiring the chronic wound image of the target patient, misdiagnosis and missed diagnosis can be reduced, the depth of the chronic wound can be judged more accurately, and by analyzing the initial depth level of the chronic wound image of the target patient, the accuracy of the chronic wound is improved. The chronic wound in-depth detection method of the target patient is determined, the in-depth detection requirement of the chronic wound of the target patient is judged, a more personalized detection scheme can be formulated, unnecessary examination and treatment are reduced, the economic burden and psychological stress of the patient are reduced, and the detection accuracy is improved by determining the chronic wound in-depth detection method of the target patient and performing detection and correction. The final depth grade of the chronic wound image of the target patient is analyzed, interference factors in the image can be recognized and corrected, the diagnosis accuracy is improved, and an important basis is provided for formulating a personalized treatment scheme.

The invention discloses an electric power material supply management and control system and a method thereof. The electric power material supply management and control system comprises a master control center, a sub-control center, a material warehouse, a mobile control terminal and a data processing platform. The electric power material supply management and control method comprises the following steps: S1, material demand prediction; s2, intelligent scheduling; s3, performing real-time monitoring; s4, risk assessment and early warning; and S5, emergency response is carried out. The electric power material supply management and control system and the method thereof can improve the material supply efficiency, enhance the decision support capability, make the decision more accurate and efficient, help to optimize resource configuration, improve the overall operation efficiency, improve the transparency and visualization of a supply chain, enhance risk management, guarantee the safe and stable operation of a power grid, improve the emergency response speed, and improve the power supply efficiency. The resource allocation is optimized, the inventory cost is reduced, the fund utilization efficiency is improved, the optimal allocation of resources is realized, and the electric power material requirements of each region can be better met.

The invention discloses a transparent logistics supply chain tracing management system fused with a block chain technology. The transparent logistics supply chain tracing management system comprises a data acquisition module, a block chain storage module, an intelligent contract module, a user interface module and a data analysis module, the data acquisition module is responsible for collecting real-time data of each link of a supply chain and transmitting the data to the block chain storage module; the block chain storage module uses SHA-256 and ECDSA algorithms to ensure the security and authenticity of the data; according to the method, the blockchain technology is utilized, data of all links of the supply chain is subjected to chaining storage, the data are open, transparent and non-tampering, and the credibility of the data is enhanced; through an SHA-256 Hash algorithm and an ECDSA signature algorithm, the security and integrity of the data in the transmission and storage process are ensured, and the data are prevented from being tampered or forged; and transactions and operations in the supply chain are automatically executed through the intelligent contract module according to a preset rule, so that human intervention is reduced, and the automation level and the operation efficiency of the system are improved.

The invention relates to the technical field of electric power data transaction, in particular to an integrated energy service electric power transaction method and system. The method comprises the following steps: acquiring power use data; performing consumer behavior pattern graph construction on the power use data to generate a consumer energy use behavior pattern graph; performing comprehensive energy demand prediction on the standard power use data through the consumer energy use behavior mode map to generate consumer comprehensive energy demand prediction data; and building a block chain energy transaction platform based on the comprehensive energy demand prediction data of the consumer, and generating a block chain energy transaction contract. According to the invention, through personalized energy demand prediction, block chain transaction platform construction, electric energy type analysis, fluctuation response strategy, automatic transaction monitoring and data visualization, the flexibility and efficiency of electric power transaction are improved.