2823results about How to "Improve scalability" patented technology

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention relates to the technical field of natural language processing, and discloses a question and answer task processing model training method, device and equipment and a storage medium, and the method comprises the steps: adjusting a question and answer task processing model according to an initial training data set and a first model training algorithm, and obtaining a first intermediate question and answer task processing model; according to the initial training data set and the first intermediate question and answer task processing model, obtaining a first reasoning step and first annotation information for processing the question and answer task, and generating a first target training data set; according to the initial training data set and a second model training algorithm, adjusting a question and answer task processing model to obtain a second intermediate question and answer task processing model; and adjusting the second intermediate question and answer task processing model according to the first target training data set to obtain a target model. The problems that when the model for processing the question and answer task is trained, the accuracy of the model is affected by the quality of the manually-labeled data due to the dependence on the manually-labeled data, and the training process is high in cost and low in efficiency are solved.

The invention provides an interactive question and answer task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including interactive question and answer tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an interactive question and answer agent, and different sub-agents are used for processing different types of sub-tasks; the interactive question-answering agent is controlled to present an interactive graphical user interface corresponding to the interactive question-answering task to the user, an answer result corresponding to the received feedback answer is presented, and the interactive graphical user interface comprises a question and an interactive answer area; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on an answer result output by the interactive question and answer agent, and the associated sub-target agent is a sub-target agent which depends on the answer result and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. Through application of the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost, especially for an interactive question and answer task in which a user needs to subjectively answer a proposed question and the effect is checked according to an answer result; the interactive questioning and answering agent provides the interactive graphical interface containing the question and the interactive answering area for the user, so that the task requirement of the user can be met more quickly in an interactive answering mode, and the task solving effect is improved.

The invention relates to a transportation agent intelligent cargo tracking method, device and equipment and a storage medium. The method comprises the following steps: carrying out multi-source sensor data acquisition on goods and a transport vehicle to obtain an original data set, and carrying out time synchronization and Kalman filtering processing to obtain a fused state vector; performing feature extraction through a deep cargo tracking network to obtain a feature vector; performing distributed model training based on the feature vectors to obtain a global cargo state detection model; inputting the collected real-time data set into a global cargo state detection model, and performing multi-stage anomaly detection processing to obtain an anomaly detection result; and performing trajectory prediction and multi-target optimization by using a space-time diagram neural network model according to an anomaly detection result and a historical transportation route to obtain a target transportation route. According to the embodiment of the invention, comprehensive goods tracking of multi-source data and dynamic optimization paths can be integrated to cope with complex and changeable logistics environments, and more accurate, safer and more efficient goods tracking services are provided.

The invention relates to the field of robot motion, in particular to a multi-modal information fusion robot motion planning method, which comprises the following steps: information acquisition: acquiring multi-modal data of a robot through various sensors; preprocessing: carrying out normalization, denoising and synchronization processing on the multi-modal data; fusion: coding and fusing the preprocessed data by adopting a feature level fusion method, and generating an environment model based on an information fusion algorithm; motion planning: combining an environment modeling and path planning algorithm, and generating a motion path according to the fused multi-modal information; and feedback adjustment: dynamically adjusting the motion track by adopting a feedback control mechanism based on the environment change sensed in real time. The invention provides a multi-modal information fused robot motion planning method and equipment, which are used for improving the perception capability of a robot to a complex environment, enhancing motion planning and precision and improving task execution efficiency and safety by fusing multi-modal information.

The invention provides a data asset multi-dimensional assessment method and system based on a block chain and machine learning, an assessment index system covering dimensions such as data quality, value, risk and security is constructed, a block chain technology is adopted to carry out assessment process and result evidence storage, a machine learning model is introduced to dynamically optimize index weights, and the assessment efficiency is improved. And a data asset marketization pricing mechanism is explored and formed. According to the method, the problems that an existing data asset evaluation method is single in dimension, high in subjectivity and lack of credibility are solved, scientific evaluation, marketization pricing and credible publication of the data asset value are achieved, important reference is provided for data element circulation, and the method has great significance in promoting prosperous development of the data element market.

The invention discloses an enterprise-level knowledge management system based on a large language model. The enterprise-level knowledge management system comprises a knowledge storage module, a knowledge analysis module and a knowledge application module, the knowledge storage module is responsible for managing and storing knowledge from different sources, supporting uploading, updating, deleting and sharing of electronic documents and multimedia documents, and is provided with authority control; the knowledge analysis module is used for carrying out deep understanding and structured processing on text and multimedia contents through a special analyzer by utilizing an advanced large language model technology; the knowledge application module provides intelligent retrieval and intelligent question and answer functions and supports advanced applications such as behavior portrait analysis and standard process supervision. According to the method and the system, the acquisition, understanding and application efficiency of enterprise knowledge is remarkably improved by fusing a large language model technology and a traditional knowledge management method, an intelligent, automatic and safe knowledge management solution is provided for an enterprise, and value mining and innovative application of enterprise knowledge assets are effectively promoted.

The invention discloses an AI model adaptive tuning method based on deep reinforcement learning, and belongs to the technical field of industrial process control, the method comprises the following steps: firstly, constructing a deep reinforcement learning environment comprising a state space, an action space and a reward function, the state space comprising a process variable measurement value, a controller parameter and a performance index; the action space is a parameter adjustment quantity vector, and the reward function comprehensively considers the stability, rapidity and accuracy of the system; then, based on a deep Q learning network of a dual-network structure, training data are stored and sampled by adopting an experience playback mechanism, and network parameters are iteratively optimized through a deep Q learning algorithm; and finally, realizing online adaptive adjustment of controller parameters by using the optimized deep Q network. The method has the advantages of being high in adaptability, good in optimization performance, high in practicability and the like, intelligent optimization control over an industrial process control system can be achieved, and the overall performance of the system is improved.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention provides a deep reinforcement learning optimization algorithm based on an adaptive encryption framework, and the algorithm comprises ten steps: data input preprocessing, data sensitivity evaluation, system resource state acquisition, state vector construction, strategy decision, encryption execution, calculation reward, model updating, result output, and loop optimization. The method aims at solving the problems that encrypted data processing efficiency is low and safety is difficult to consider. According to the method, intelligent optimization of the encryption strategy is realized through the data sensitivity evaluation module, the adaptive encryption strategy adjustment module, the deep reinforcement learning optimization module, the parallel processing module, the resource scheduling module and the like. On the premise that the data security is ensured, the processing efficiency of the system is remarkably improved, and the method is suitable for scenes such as cloud computing, big data processing and the Internet of Things which need to process a large amount of sensitive data.

The invention provides an instant message inquiry type task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including instant information inquiry tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an instant information inquiry agent, and different sub-agents are used for processing different types of sub-tasks; the instant information inquiry agent is controlled to inquire questions from the inquiry object to obtain instant feedback, and the instant information inquiry agent supports question inquiry in a background operation mode; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on instant feedback output by the instant information inquiry agent, and the associated sub-target agent is a sub-target agent which depends on the instant feedback and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. By applying the scheme, a better comprehensive task processing effect can be achieved under the condition that the comprehensive cost is lower, and in particular, an instant information inquiry task which needs to be combined with the instant information to verify whether the actual situation meets the task requirement or not and takes the verification result as instant feedback is obtained; in addition, the instant information inquiry agent also supports question inquiry (namely asynchronous task processing) in a background operation mode, so that the user does not need to stay in a session interface all the time, and other operations of the user are not influenced.

The invention discloses an audio stream adaptive decryption method, system and device and a storage medium. The method comprises the following steps: acquiring an MPEG-DASH list file and analyzing the MPEG-DASH list file; the standard DRM interface is initialized; performing feature extraction on the identified audio segmentation information in the audio segmentation mode, and dynamically adjusting a segmentation request and processing logic according to the extracted features to perform adaptive segmentation downloading and preprocessing; extracting the encryption parameter to identify an encryption mark of the target audio stream platform, and selecting a corresponding decryption algorithm according to the encryption mark to decrypt the current audio segment; and performing audio frame reconstruction on the decrypted data to form an updated audio stream, caching the updated audio stream to a preset cache region based on a self-adaptive buffer strategy, and outputting the final audio stream to ensure the continuity of the final audio stream and realize efficient parallel processing of decryption and playing, so that the decryption efficiency is improved, and the user experience is improved. And particularly, in a high-concurrency scene, the user experience is improved.

The embodiment of the invention provides an access control strategy construction method and device, electronic equipment and a storage medium. The access control strategy construction method comprises the following steps: determining access control elements and attributes thereof of an application based on an acquired resource access scene; based on the access control elements and the attributes thereof, an access control strategy template is created according to different access control scenes, and the access control strategy template comprises preset access control elements and attributes; selecting the access strategy template based on a service demand and assigning a value to the attribute to create a corresponding access control strategy; and performing matching calculation on the access control strategy and the intercepted resource access request based on a strategy calculation engine, and if matching succeeds, executing a corresponding strategy action. Through the steps, access control configuration is more convenient and easier to understand, and access control is more flexible.

The invention provides an expandable block chain identity authentication method and system for a distributed resource aggregation scene, and relates to the technical field of block chain identity authentication, and the method comprises the steps: receiving a user identity authentication request, generating an identity identification string through a distributed Hash algorithm, and selecting a verification node cluster to deploy a smart contract; each verification node executes the smart contract to verify the identity identification string, constructs an access authority control strategy and generates an authentication block; and when the number of the blocks reaches a preset threshold value, selecting a newly added verification node based on the terminal equipment information, deploying an intelligent contract, and realizing distributed storage through the verification node cluster broadcast authentication blocks. According to the invention, the expandability of identity authentication is improved, the dynamic expansion of the verification node is realized, and the reliable storage and efficient propagation of the authentication information are ensured.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and particularly relates to a method for improving the reasoning solution and training speed of a deep operator network model. According to the method, multi-level optimization is carried out on the PI-DeepONet model, and the reasoning solving speed and the training speed of the PI-DeepONet model are improved. According to the method, the SGEMM operator is customized, and compared with the best single-precision matrix multiplication in a basic linear algebraic library of a CUDA (Compute Unified Device Architecture) computing platform, the SGEMM operator can obtain a speed-up ratio of 1.5 times at most in a common model size range of PI-DeepONet. A fusion operator is designed according to the special structure of the modified MLP used in the model, the speed-up ratio of the end-to-end reasoning speed of the model is more than two times, and the calculation time of a graphics processor can be shortened by 5.7 times at most. According to the method, data parallel is used for carrying out distributed training on the model, a communication concealment strategy is used, and the expandability is kept at 83%-85%.

The invention relates to a power grid business intelligent auditing method based on AI enhancement, and the method comprises the following steps: S1, collecting data from a power grid SCADA system and a financial system, integrating historical and real-time data, and constructing an auditing knowledge graph; s2, constructing an instruction recognition model by using RoBERTa to analyze a natural language instruction of an auditor, and combining knowledge graph information with a language model to improve the understanding and reasoning ability of an instruction background; s3, constructing a multi-task deep learning framework to realize processing and fusion of multiple auditing tasks; s4, on the basis of a natural language instruction input by an auditor, extracting query targets, conditions and parameters involved in the instruction according to the instruction recognition model, obtaining corresponding auditing data in the database, and obtaining intention classification; and S5, calling a corresponding auditing model based on the intention classification result and the auditing data, and obtaining an analysis result. According to the invention, efficiency and precision in power grid business auditing work are effectively improved.

The invention provides a dynamic multi-link intelligent management and scheduling system, and relates to the technical field of network communication. According to the system, real-time monitoring, dynamic management and transmission data scheduling of a network link can be realized, and the problems of low bandwidth utilization rate and insufficient transmission efficiency in the prior art are solved. The method comprises the following steps: firstly, acquiring and monitoring state information of each link in real time through an intelligent agent technology below a link layer; and secondly, the system performs link admission control according to real-time monitoring data by using a dynamic regulation rule method, and dynamically judges whether a new relay link needs to be established and provides an optimal relay node selection when the data transmission bandwidth between the two nodes is insufficient. And finally, multi-link transmission data scheduling is carried out by adopting a mode of combining deep reinforcement learning and a heuristic scheduling algorithm, and the bandwidth utilization rate is improved to the maximum extent. On the whole, an efficient and intelligent network link management scheduling scheme is provided through combination of methods of intelligent proxy, dynamic regulation rules, deep reinforcement learning and the like, and the utilization rate of network resources and the quality of data transmission are effectively improved.

The invention provides an ecological hydrological simulation method based on a machine learning model, and the method comprises the steps: obtaining ecological hydrological data, carrying out the preprocessing of the obtained ecological hydrological data, obtaining a data set, building an ecological hydrological model based on an improved ConvLSTM network structure, and training the ecological hydrological model based on the data set. And obtaining a trained ecological hydrological model, inputting to-be-measured data into the trained ecological hydrological model, and performing evapotranspiration prediction. According to the method, evapotranspiration prediction is realized based on various data, advantages of remote sensing data and ground meteorological data are fully utilized, evapotranspiration prediction precision is remarkably improved, real-time data can be processed, real-time performance is high, different applications can be performed according to different areas, flexible adjustment of model input can be realized, expandability is high, and the method is suitable for popularization and application. The method can be applied to multiple fields, and provides real-time support for agricultural and ecological monitoring.

The invention discloses an audit domain knowledge graph ontology framework construction method based on a large language model, which comprises the following steps: collecting audit domain related data, and preprocessing the collected data; pre-training the large language model by using the preprocessed audit field related data; based on an existing general entity extraction and relation extraction data set, performing fine adjustment on the pre-trained large language model; performing knowledge extraction on the audit field text data preprocessed in the first step by using a large language model to obtain a knowledge extraction result of the audit field; performing standardization processing on a knowledge extraction result; and based on the knowledge extraction result after standardization processing, constructing a knowledge graph ontology framework in the audit field. According to the method, the knowledge of the auditing field can be automatically extracted, summarized and sorted, and a set of complete auditing field knowledge graph ontology framework is formed.

The invention relates to the technical field of data sharing, and particularly discloses an electrical equipment supply chain data security access method and system, and the system can intelligently match table structure information in a power industry database based on natural language input of a user, and generates a corresponding SQL query. By combining a natural language processing technology with a large language model, a user can easily access and query required power industry data under the condition that a complex SQL statement or a database structure does not need to be mastered. According to the method, the efficient query task can be automatically generated, the data access difficulty is greatly reduced, meanwhile, a data field level encryption scheme and a user permission intelligent verification mechanism are designed, and the safety of the data access sharing process is ensured; and the method has good expansibility and can be compatible with power enterprise databases of different scales.

A detection method of a power transformer oil leakage detection system based on multi-modal prompt and multi-scale segmentation comprises the steps of data input and preprocessing, feature extraction, multi-modal feature fusion, Transformer decoder decoding, segmentation and classification, model training and fine adjustment and output and result display, and by combining Mask2Former and CLIP models, multi-modal information is fully utilized, and the detection result is more accurate. According to the method, the accuracy and robustness of oil leakage detection are improved, the characteristics of multi-modal information fusion, high-precision target segmentation, high robustness and high expandability are realized, accurate identification and segmentation of an oil leakage area are realized, other transformer fault detection tasks are adapted by simply modifying prompt words, and the method has good universality and adaptability.

The invention discloses a performance pressure test system and method applied to a large model streaming interface, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to comprehensively evaluate the performance of the large model streaming interface in a high-concurrency scene. Comprising a test data management module used for constructing a test data set; the concurrent access simulation module is used for dynamically configuring concurrent access parameters and simulating and generating a concurrent access scene of a plurality of users; the performance monitoring module is used for monitoring and recording the large-model streaming interface in the concurrent access scene; the fault-tolerant processing module is used for analyzing and processing the abnormal condition of the large model interface in the concurrent access scene based on a predefined abnormal processing strategy and forming an error log file; the post-processing module is used for calculating a performance index based on the monitoring data; and the result output module is used for constructing a test result file based on the error log file and the performance index and outputting the test result file.

The invention discloses a multi-level dynamic task decision-making method for a large-scale heterogeneous unmanned aerial vehicle based on a coalition game, and relates to the field of game theory, task decision-making and multi-agent collaborative task planning, and the method comprises the following steps: 1, building a dynamic task decision-making optimization model of the heterogeneous unmanned aerial vehicle under multiple constraints, determining constraint conditions and designing an evaluation function based on the unmanned aerial vehicle income; 2, converting the dynamic task decision process into a coalition game solving process, establishing a coalition game model, determining a preference relationship of the unmanned aerial vehicle based on coalition revenue, designing a coalition switching rule of the unmanned aerial vehicle, giving a form of a coalition game stable solution, and determining a convergence condition of the game; and step 3, designing a multi-level dynamic task decision algorithm based on the established coalition game model. According to the method, a complex dynamic task decision process is decomposed into a plurality of stages, global revenue maximization is realized, and the method has good expandability for heterogeneous unmanned aerial vehicle clusters.

The invention discloses a BIM-based whole house intelligent control system. The system is mainly formed by mutual cooperation of a BIM model data integration unit, an Internet of Things access unit, a control and management unit, an energy consumption analysis and optimization unit and a user interaction and platform integration unit. The invention provides a BIM-based whole house intelligent control system, and the system achieves the deep fusion of a BIM model (a building information model) and intelligent household equipment (i.e., Internet of Things equipment), thereby achieving the bidirectional data interaction between the BIM model and the household Internet of Things equipment, achieving the equipment management, energy consumption optimization, automatic control and remote operation and maintenance, and achieving the intelligent control of the whole house. And the living experience of the user and the equipment management efficiency are improved.

The invention discloses a high-heat-dissipation integrated ducted fan electric propulsion system, which belongs to the technical field of ducted fans and comprises a motor controller, a motor, a motor front bearing, a motor rear bearing, a front fairing, a rear fairing and an integrated fan rotating shaft integrally formed by fan blades and a motor rotating shaft. The integrated nacelle motor shell is integrally formed by a motor shell, a ducted nacelle and a plurality of groups of ducted stationary blades; cooling devices are mounted on the motor shell and the motor controller; the cooling device comprises a cooling water channel and a water cooling cavity; a cooling water channel is arranged on the motor shell; the motor controller is provided with a water cooling cavity, and the cooling water channel is communicated with the water cooling cavity; the front fairing is connected to the left end of the integrated fan rotating shaft, and the rear fairing is connected to the right end of the motor controller. The integrated level is high, the size and the weight are reduced by reducing connecting parts, and the power density is high; the system reliability is high; the system cooling efficiency is high; and the power level compatibility and the expandability are high.

The invention discloses a distributed energy dynamic load adjusting method and system based on a virtual power plant, and relates to the technical field, and the method comprises the following steps: obtaining first data of the virtual power plant, and building a topological structure of the virtual power plant; constructing a distributed energy output prediction model based on an LSTM deep learning algorithm; combining the output of the distributed energy output prediction model and the topological structure to generate an energy scheduling scheme in real time; monitoring real-time data of the distributed energy, comparing the real-time data with the energy scheduling scheme, and performing feedback updating; according to the invention, a virtual power plant topological structure is constructed, a prediction model is constructed based on an LSTM algorithm, an energy scheduling scheme is generated in combination with model output and the topological structure, and a feedback updating technology is carried out. The problems that existing regulation and control based on a static model cannot adapt to dynamic changes of energy production and load requirements in real time, and due to the fact that an effective cooperation mechanism is lacked between distributed energy sources, energy waste is caused, and the regulation efficiency is low are solved.

The invention relates to the technical field of liquid cooling, in particular to a distributed full liquid cooling device which comprises a liquid cooling distribution module, a liquid supply main pipe, a liquid return main pipe and a plurality of liquid cooling modules, and the liquid cooling distribution module adjusts the flow speed of low-temperature cooling liquid in the liquid supply main pipe according to the total heating value, so that the overall heat dissipation balance of a system is guaranteed; the cooling liquid inlet flow of each liquid cooling unit is accurately regulated and controlled based on the inlet and outlet liquid temperature difference and the real-time liquid supply mass flow of the liquid cooling units in the liquid cooling module and the real-time reference power of the electronic equipment, so that the heat dissipation requirement of the single liquid cooling module can be met; meanwhile, the liquid cooling distribution module can control the opening state of the jet flow unit according to the surface temperature of the heating element in the electronic equipment, and local enhanced cooling can be conducted on the heating element. According to the distributed full liquid cooling device, accurate control from a global system to a local system is realized through distributed management, and heat dissipation requirements of different levels can be met.