266 results about "Big data management" patented technology

The invention provides a prospecting prediction system based on multi-scale big data fusion, and the system comprises a geological big data management module and a big data mining analysis module which are respectively used for carrying out the management and mining analysis of geological, geophysical, geochemical, remote sensing and mineral data; the multi-element extraction module is used for carrying out geological element extraction on the mining analysis result output by the big data mining analysis module; the multi-scale grid calculation module is used for carrying out subdivision and assignment on the geological elements through a multi-scale multi-element grid technology; the intelligent prospecting prediction module is used for compiling a result map; the intelligent prospecting evaluation module is used for compiling an optimal decision diagram; the auxiliary decision-making module generates a prospecting result report; and the prospecting dispatching command module is used for synchronously updating the data in the prospecting dispatching command module. By integrating the prospecting prediction basic data set, the knowledge base, the model base and the algorithm base, flexible scheduling and convenient use of various data, knowledge, models and algorithm tools are achieved.

The invention belongs to the technical field of task allocation and progress tracking, and particularly discloses and provides a detection task allocation and progress tracking method based on a big data management system, which comprises the following steps: firstly, calculating the matching degree of task and processing end multi-aspect information through a weighted fusion algorithm, and selecting a processing end with the highest matching degree as a main node; judging whether a constraint processing task is met or not, collecting related data during task execution, performing coupling analysis to obtain a progress standard-reaching rate, marking the processing end of which the standard-reaching rate is smaller than a set value as abnormal, and calculating a secondary allocation abnormal processing end task through a matching degree in combination with the progress and residual resource information of the processing end; according to the method, the parameters of the to-be-detected task and the processing end are synthesized through the weighted fusion algorithm, the matching degree including calculation, storage, time matching degree and task type correlation degree is analyzed, global optimal matching of the task and the processing end in the aspects of computing power, storage, task types and adaptability is accurately evaluated, and the problem that the task and the processing end are not matched is solved.

According to the big data management-based data labeling method and system provided by the invention, efficient, accurate and intelligent data labeling is realized by integrating multi-virtual machine collaborative labeling, multi-model fusion labeling, adaptive data preprocessing and hierarchical cleaning and a multi-dimensional task scheduling and dynamic load balancing mechanism. The method comprises the steps that self-adaptive preprocessing and layered cleaning are conducted on original big data, noise is removed, missing values are filled up, and format conversion is completed; dividing the preprocessed data into a plurality of subtasks according to data characteristics; distributing the sub-tasks to different virtual machine instances for parallel processing by utilizing a multi-virtual machine technology; labeling the same data fragment on each virtual machine by adopting a multi-model labeling mechanism; adopting a weighted fusion or voting mechanism to generate a comprehensive annotation result for the output results of the multiple models in each virtual machine; and summarizing the labeling results of the virtual machines to form a global data labeling result.

The invention provides a project cost big data management and analysis system, and relates to the technical field of data management, and the system comprises a data collection and preprocessing module which is used for collecting original cost data from a heterogeneous data source, and carrying out the preprocessing of the original cost data, and obtaining the preprocessed cost data; the semantic feature extraction module is used for converting the preprocessed cost data into a multi-dimensional feature vector based on a multi-level feature extraction system; the similarity calculation module is used for calculating similarities among different cost data based on the multi-dimensional feature vectors to obtain a similarity matrix; and the data storage and management module is used for storing the cost data, the multi-dimensional feature vector and the similarity matrix by adopting a mixed storage architecture, and providing retrieval and recommendation functions of cost projects based on a multi-level feature space index structure. According to the method, the limitation that a traditional method only depends on keyword matching is solved, and the system can recognize the deep incidence relation between the items.

The invention relates to the technical field of intelligent agriculture, in particular to a multi-source heterogeneous big data governance method and device.The multi-source heterogeneous big data governance method comprises the following steps that S1, multi-source heterogeneous data collection and semantic alignment are conducted, hidden semantic conflicts are eliminated; s2, performing dynamic data source adaptation and quality monitoring; s3, performing cross-domain permission penetration control; s4, building an industrial model driven by federal learning; and S5, double-chain block chain evidence storage and traceability are carried out. The method is used for disambiguating dialect term conflict, federated learning cross-domain privacy modeling and double-chain block chain evidence storage traceability through a dynamic semantic alignment neural network, and systematically solves the problems of dialect semantic segmentation, data islands and sensitive information leakage caused by small scattered distribution of peasant households in the field of traditional Chinese medicinal material planting. And a trust closed loop of government-enterprise-peasant household three-party cooperative treatment is constructed.

The invention discloses an energy big data right confirmation method and system based on a hierarchical hash tree and dynamic authorization, and belongs to the field of energy big data management and information security, and the method comprises the steps: obtaining and standardizing original data in an energy scene, and dividing the original data into a data block set according to equipment and time; constructing a hierarchical hash tree and generating root hash; writing the root hash and the associated metadata into the block chain to realize right confirmation and evidence storage; executing proxy re-encryption according to the access token, and converting the ciphertext into a decryptable format; verifying the data consistency through the Hash path and the root Hash, and completing the access; and recording the access behavior and distributing transaction earnings by the smart contract based on the contribution degree. Hash calculation and local path updating of a data block level are supported through a layered Hash tree structure, so that when large-scale energy data is frequently updated, a new root Hash value can be quickly generated and right confirmation updating can be completed only by carrying out local Hash recalculation on a changed path.

The invention discloses a field emergency rescue medical big data management system, and relates to the technical field of medical data management. The system comprises a data acquisition module, a data transmission module, a data storage module, an analysis processing module and a rescue decision support module, wherein the acquisition module acquires physiological and environmental data by using a multi-modal sensor array and performs edge preprocessing; the transmission module constructs a multi-link communication architecture and encrypts transmission; the storage module adopts distributed encryption and block chain storage; the analysis processing module combines depth migration and federal learning to mine data; the decision support module provides a scheme based on a knowledge graph and reinforcement learning, and VR / AR auxiliary decision is fused. The system comprehensively and accurately collects data, stably transmits and stores data in various field environments, deeply mines data values to assist in accurate decision making, supports remote cooperation, realizes functions of intelligent early warning, equipment management, authority control and the like, and comprehensively improves the medical efficiency and accuracy of field emergency rescue.

The invention relates to the technical field of big data management, in particular to a platform saturation management method and system based on multi-objective optimization, and the method comprises the steps: collecting platform, task, resource and path data in real time through a resource coupling modeling module; a resource conflict strength value and a path conflict strength value are calculated based on a warehouse operation resource task queue and a key passage path occupation state, a dynamic resource coupling weight matrix integrating three-dimensional conflicts is constructed, and the problem of conflict data splitting is solved; the resource mismatch response module is used for triggering a degradation task priority instruction and iteratively outputting a platform allocation scheme set of which the resource conflict intensity value reaches the standard when the resource conflict intensity value exceeds a preset threshold value by taking the platform occupation state as a physical basis and combining with an operation timeliness requirement; and the secondary congestion suppression module performs weighting on the path conflict intensity value by multiplexing the matrix, screens the lowest congestion risk final case in the scheme set, blocks a secondary congestion spreading chain, and realizes collaborative optimization of resource utilization and path smoothness.

The invention belongs to the technical field of smart tourism, and discloses a destination global tourism big data management platform based on data processing, and the platform comprises a sensing qualified point gathering module which collects global tourism data, constructs a geographic grid point network, sets a spatial grid point reconstruction rule, and carries out the automatic refining or merging of grid points; a track triggering mechanism is introduced, and when it is detected that the behavior frequency in the preset target area reaches a preset behavior frequency threshold value, fine-grained modeling is started to generate a geographic sensing structure; the behavior deconstruction coding module is used for analyzing continuous behaviors of tourists in a geographical perception structure and constructing a multi-dimensional situation coding vector; reconstructing a complete behavior chain of the tourist through a behavior jigsaw type deconstruction method and an implicit behavior compensation method; and a solid support is provided for deep analysis and intelligent service of global tourism data.

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.

The invention relates to the technical field of big data management, in particular to a bioinformatics data classification method and system based on big data, and the method comprises the following steps: obtaining time sequence recognition trend reversal and positioning fragments, extracting recognition difference positions inside and outside a frequency band data division region, screening samples with consistent features, and rearranging path labels; connecting nodes are cut off to generate fracture indexes, and label states are updated and written into sample fields to form a classification result set. According to the method, a labeling area is constructed by extracting trend inversion points in a time sequence, sample fragments are divided by combining data fluctuation positions in a disturbance frequency band, label numbers are arranged according to the fluctuation sequence of samples in a path, a corresponding sequence of a label chain connection relation and the sample positions is established, and label section boundaries are positioned and limited by fracture nodes. And the updated label state is synchronously written into a sample field, and the path label is bound according to a chain sequence, so that the sample identifier is kept coherent in sequence change, and the continuous coverage capability of the path information in classified output is improved.

The present invention provides a visualization intelligent big data urban planning decision support management method and system, which relates to the technical field of big data management. The method includes: acquiring multi-dimensional data related to urban planning; preprocessing the multi-dimensional data; establishing a correlation analysis model between urban elements, and calculating the interaction influence factors between different urban element data through it to form an urban element correlation matrix; extracting features from the urban element correlation matrix, screening out urban element combinations, and calculating the weight values of different urban element combinations by using a causal inference method; performing site selection optimization calculation on the target planning area, and calculating the comprehensive fitness scores of each alternative area; sorting each alternative area, selecting the alternative area with the highest comprehensive fitness score as the target area, and generating a visualization urban planning decision report. The present invention can provide dynamic analysis content, which is convenient for providing decision-making basis for urban planning.

The invention relates to the technical field of big data management and artificial intelligence application, in particular to an automatic data quality inspection system and method based on a big model and data flow orchestration, and the method comprises the following steps: system architecture construction: employing a three-layer architecture design which comprises a data access layer, an intelligent quality inspection layer and a flow orchestration layer; the method has the beneficial effects that the full-link automatic quality inspection of structured and unstructured data is realized by integrating the intelligent analysis capability of a large language model (LLM) and the data processing pipeline of a low-code process arrangement tool. The system combines a rule engine and large model dynamic reasoning, supports customized quality inspection rule generation, abnormal data intelligent identification and quality inspection process adaptive optimization, improves the automation level and complex scene adaptability of data quality detection, and is suitable for data governance scenes of multiple industries such as government, finance, medical treatment, e-commerce and the like.

The invention relates to the technical field of big data management, in particular to a collaborative optimization decision-making system for electric power-environment coupling big data, and the system comprises a drainage basin coupling module which is used for fusing multi-source ecological data obtained in real time with a preset mechanism model, and constructing a drainage basin water-energy-grain-ecological coupling model; the hierarchical decision-making module comprises a coordination layer agent representing a top layer strategy target and execution layer agents representing different benefit parties; the decision attribution analysis module is used for analyzing the collaborative scheduling strategy and acquiring a key driving factor causing a preset scheduling behavior; the anti-fact deduction optimization module is used for defining an anti-fact scheduling scene based on the key driving factors, calling a watershed water-energy-grain-ecology coupling model to perform rapid deduction, quantitatively comparing the comprehensive influence of the anti-fact scheduling scene and the collaborative scheduling strategy on water, energy, grain and ecology under a preset time scale, and performing collaborative scheduling on the water, the energy, the grain and the ecology. And optimizing the collaborative scheduling strategy.

The invention discloses a traffic service management big data comprehensive analysis, research and judgment system, and the system comprises an access module which is used for accessing a multi-source big data flow in real time according to the traffic service demands, and generating a cleaned real-time data flow; the loading module is used for loading the risk rule to the rule engine, converting the risk rule into an executable rule model, and performing dynamic adjustment to generate an initialized rule engine; the matching module is used for performing real-time risk identification and rule matching according to the real-time data stream and the rule engine, and generating a risk identification result set based on a matching result; and the early warning module is used for carrying out early warning triggering according to the risk identification result set and pushing corresponding early warning information to the user in real time so as to realize comprehensive analysis, research and judgment of the traffic service management big data. By using the embodiment of the invention, the potential risk in the traffic business process can be effectively analyzed, researched and judged, and the accuracy and timeliness of risk identification are improved.

The invention relates to the technical field of big data management, in particular to a cloud-based steel structure big data management system and method, and the system comprises a data collection and preprocessing module, a structure performance analysis module, a crack prediction and warning module and a maintenance management support module. According to the method, through real-time data collection and dynamic data analysis, the timeliness and accuracy of data are enhanced, and the data quality is enhanced through timestamp sorting and data integrity verification, so that the system can quickly identify the structure problem, thereby effectively early warning in advance, and the analysis of historical and real-time data is combined, so that the accuracy of crack prediction is improved, and the accuracy of crack prediction is improved. The method has the advantages that the early warning response speed is increased, accurate data analysis supports enterprises to make decisions quickly, resource allocation is optimized, maintenance cost is reduced, structural safety is improved remarkably, dependence on traditional physical detection is lowered remarkably, working pressure is relieved, and risks possibly caused by data processing errors are avoided.

The invention relates to the technical field of remote sensing agricultural data management, and discloses a remote sensing agricultural big data management system based on a block chain. According to the system, image data is subjected to radiation, atmosphere and geometric correction preprocessing to generate a standardized remote sensing image; the feature extraction module performs multi-scale segmentation on the crop feature region, identifies the crop feature region, extracts texture, spectrum and shape feature values, and fuses the feature values to form a multi-dimensional feature vector; the block chain storage module performs hash operation on the vector to generate a feature fingerprint, constructs a new block in combination with a timestamp and a preorder block hash value, and forms a non-tampering agricultural data chain after verification of a consensus mechanism; a path analysis module traverses the block sequence, extracts a storage path and screens a high-relevance feature path set; and the intelligent classification module extracts feature tags, and generates an agricultural data classification structure through a multi-dimensional matching algorithm. The system realizes standard processing, safe storage and intelligent application of remote sensing agricultural data, and meets the requirements of agricultural modernization development.

The invention relates to the technical field of e-commerce data management, in particular to a big data management system based on cross-border e-commerce, which comprises a data acquisition module, a multi-layer graph construction module, a multi-layer graph processing module and a multi-layer graph application module, and is characterized in that the data acquisition module collects commodity data used by users, including the users, commodities, operation behaviors and user emotions; the multi-layer graph construction module is used for forming a commodity recommendation multi-layer graph and constructing nodes and edges in emotion, behavior and time layer graphs so as to realize fine modeling of a complex relation; the multi-layer graph processing module calculates embedded vectors of all layers of graphs, and the embedded vectors are fused into aggregation embedded vectors through a full connection layer; and the multi-layer graph application module optimizes the training of a recommendation model by using an aggregation embedded vector, identifies potential interested commodities of the user by predicting a recommendation list, and particularly improves the relevance and individuation of recommendation in combination with an emotion layer graph weight, so that the problem that a recommendation system cannot timely and accurately respond to user requirements is solved, and the user experience is improved. And the user satisfaction and the platform viscosity are improved.

The invention relates to the technical field of big data management, in particular to a data tracing method based on big data mining, which comprises the following steps of: acquiring an unstructured operation log, analyzing the unstructured operation log into a structured event record, and associating to generate a heterogeneous operation session set; performing access mode association analysis on the session set to extract a read-write path mode, and combining association strength and a time decay factor to determine a dependency weight and construct a blood relationship map; eliminating a loop of the graph to generate a directed acyclic graph, constructing a spanning tree serving as a retrieval trunk and a node index coding interval, and mapping a cross-branch associated edge to a bitmap index to generate a probability skeleton tree index; and positioning a trunk path by using the interval, calling a bitmap to perform multi-path verification, and outputting a traceability path and confidence. A probability consanguinity map and a probability skeleton tree index are constructed through mining logs, and data consanguinity reconstruction and large-scale node low-delay retrieval in the heterogeneous black box environment are achieved.

The invention provides a product marketing management system and method based on big data, and relates to marketing big data management. According to the invention, through comprehensive collection and standardization processing of marketing data and quantitative evaluation of the relevance of marketing indexes by means of a correlation coefficient analysis method, three levels of relevance of strong, medium and weak are divided, and potential rules and problems among marketing indexes are revealed for enterprises; secondly, first-level indexes with strong association are further screened out and visually presented, so that an enterprise can focus on core indexes to carry out key management; meanwhile, hidden associated data such as user comment emotional tendency and market trend inflection points are mined by utilizing natural language processing and a long-short term memory network technology, and deeper market insight is provided for marketing strategy adjustment; finally, based on implicit associated data, evaluation indexes are screened out, a periodic change report is generated, an enterprise is assisted to visually grasp key index dynamic and implicit factor influences, and good support is provided for optimizing a decision process.

The invention discloses a total-factor integrated large Token data management system, and the system comprises an identity credential collection unit which is used for obtaining an identity label and associated business data from a plurality of social subjects; the identifier abstraction unit is used for mapping the identity identifier into a standardized Token primary key according to a main body type and structuring the associated business data into a corresponding digital asset package; the total element aggregation unit is used for aggregating and packaging the digital asset package into a single verifiable large Token data structure, and the large Token comprises a Token primary key and a dynamic trust level; the dynamic trust engine is used for calculating and updating the trust level of each main body in real time and binding the trust level to the corresponding large Token; and the intelligent service execution unit is used for responding to an external business instruction and calling the large Token to realize intelligent service.

The invention relates to the technical field of intelligent regulation and control and energy conservation of hospital environment air quality, in particular to a big data management and intelligent evaluation system for the hospital environment air quality. The system comprises a multi-modal data acquisition module, a data processing module, a dynamic transmission modeling module, a dynamic Bayesian network risk modeling module and a prospective risk hedging and energy consumption optimization module. The system calculates a dynamic air transmission coefficient by collecting environment and people flow data, and constructs a dynamic Bayesian network model to calculate a cross-region propagation risk probability; the method is characterized in that when the risk probability exceeds a threshold value, a system actively solves a multi-objective optimization problem with minimization of energy consumption as an objective, and an optimal HVAC control instruction is generated; according to the method, the conversion from lagging evaluation to prospective risk hedging is realized, and the risk can be actively identified and regulated before the pollution exceeds the standard.

The invention relates to the technical field of big data management, in particular to a big model metadata authority management method and system based on an RBAC-ABAC fusion model, and the method comprises metadata annotation, data product release, big model and user access, semantic perception, dynamic authority adjustment, row-level authority control and authority life cycle management. The method has the beneficial effects that by fusing the advantages of the RBAC model and the ABAC model, the defects of a single authority management model are overcome, the efficient, dynamic and fine-grained authority management of the large model metadata is realized, the security of the large model metadata is improved, and the authority management requirement of the large model for accessing the dynamic business data is met.

The invention relates to a new energy locomotive big data management system and method, and belongs to the technical field of new energy locomotives, the new energy locomotive big data management system comprises a data acquisition module, a management module, a big data analysis module and a display module; the data acquisition module is used for determining a data acquisition interface and acquiring locomotive data in real time based on the data acquisition interface; the management module is used for managing the data acquisition interface and performing classified management on the locomotive data; the big data analysis module is used for constructing a big data model based on the locomotive data, predicting the health state and the health trend of the locomotive based on the big data model, obtaining prediction data, setting an alarm threshold value, and generating an early warning signal based on the alarm threshold value and the locomotive data; the display module is used for displaying the locomotive data, the prediction data and the early warning signal, unified management of the new energy locomotive is achieved, and efficient and safe operation of the new energy locomotive is guaranteed.

The invention discloses a medical big data management system and method based on cloud edge collaboration, and relates to the technical field of medical data management. Efficient data management is achieved through cooperation of multiple modules, and the heterogeneous data fusion module collects multi-source data in a unified mode and constructs a knowledge graph; the privacy protection calculation module guarantees data security by using homomorphic encryption and differential privacy; the edge intelligent analysis module deploys a lightweight model to realize real-time analysis; the cloud edge collaborative decision-making module dynamically allocates tasks according to the data and the equipment state; in addition, data quality evaluation, knowledge graph updating and other mechanisms are included, and the data reliability and timeliness are improved. Efficient and safe management of medical data is realized, the data processing efficiency is greatly improved, and the cloud cost and energy consumption are reduced; the data privacy is guaranteed, and the leakage risk is almost zero; the model diagnosis accuracy is improved, and clinical decision making is assisted; the diagnosis time is shortened, the error rate is reduced, and the medical service quality is remarkably optimized.

The invention relates to the field of smart campuses, and discloses a smart campus big data management platform, which comprises an intelligent teaching management system established based on a Spring framework, a campus intelligent security system and a campus life service system, and is characterized in that the intelligent teaching management system comprises a course management module, a teaching interaction module and a learning analysis module; the campus intelligent security and protection system is divided into a video monitoring module, an access control management module and an alarm early warning module, and the campus life service system is divided into an intelligent catering module, an intelligent dormitory management module and a campus navigation module. Through intelligent teaching management, campus intelligent security and protection and a campus life service system, teaching resource distribution is optimized, teaching interactivity is enhanced, teaching effects and student learning experience are improved, campus safety conditions are monitored in real time, abnormal conditions are quickly responded, safety risks are reduced, safety of teachers and students is guaranteed, and convenient catering, dormitory management and campus navigation services are provided. The convenience and efficiency of campus life are improved, and the satisfaction degree of teachers and students is improved.

The invention relates to the technical field of data management, in particular to a supply chain-based manufacturing industry big data management system, which comprises an event attribution module, a data integration module, an inventory trend module, a batch judgment module and a parameter tracing module. According to the method, automatic association analysis of multi-dimensional parameters such as process, logistics, inventory and environment is realized through time sequence synchronous acquisition of multi-source data and dynamic linkage of business events, and accurate circulation of whole-process data is promoted by adopting an event-level feature recognition and parameter screening mechanism; furthermore, positioning and traceability of an abnormal fluctuation process are supported in a process traceability and trend discrimination mode, information interconnection and parameter relation chain establishment of each manufacturing link are enhanced, synchronous processing and hierarchical clustering of multi-parameter heterogeneous data are realized, and the real-time decision support capability and traceability precision of the data are improved; and the response speed of an abnormal process and inventory fluctuation in a manufacturing scene and the intelligent level of data application are effectively enhanced.

The invention provides a method for detecting the furnace temperature uniformity of a roller hearth type heat treatment furnace. The method is applied to a roller hearth type heat treatment furnace system comprising a furnace body, a heating device, a temperature sensor array and a medical service big data management system. The method comprises the steps that a temperature sensor array containing 14 measuring points is arranged at a specific position of a furnace body, and a furnace temperature data recording black box is fixed to the lower right corner of the furnace body; controlling the heating device to heat to a target process temperature of 920 DEG C and keeping the temperature constant for 30 minutes; the temperature data of the whole furnace area is collected every five minutes; and calculating a reference temperature and an absolute deviation value set of each measurement point through a medical service big data system, and evaluating the temperature field uniformity of the furnace body. And when the deviation exceeds a threshold value, the system generates a correction parameter group containing a heating zone power compensation coefficient and a refractory material density gradient value, and executes power redistribution or thermal insulation layer enhancement operation. High-precision detection and dynamic optimization of the temperature field uniformity of the roller hearth type heat treatment furnace can be realized, and the stability of a heat treatment process and the consistency of product quality are improved.

The invention discloses a medical big data management method and system based on cloud edge collaboration, and relates to the technical field of cloud edge collaboration. The method comprises the following steps: constructing a cloud data computing center; the cloud data computing center performs feature extraction on cloud data to train a medical diagnosis model; the medical diagnosis model is combined with a federal cloud edge collaborative algorithm to carry out model training on an edge server; the cloud data computing center obtains a detection report or real-time vital signs uploaded by the patient; processing the acquired data, and inputting the processed data into a medical diagnosis model; and outputting a result by the medical diagnosis model, and performing vital sign abnormal value calculation, similarity value calculation and comprehensive similarity value calculation. The medical diagnosis model is trained through the cloud edge coordination algorithm, the vital sign abnormal value calculation, the vital sign similarity value calculation and the comprehensive similarity value calculation are performed according to the output result of the medical diagnosis model, accurate analysis of the patient is realized, and the medical rescue efficiency and quality are improved.

The invention discloses an agricultural big data management platform information acquisition system and method, and the method comprises the steps: obtaining a farmland image, and dividing the image into different planting regions according to plants; based on the images of different planting areas, dividing the images into a plurality of sub-areas, obtaining soil samples in each sub-area, and analyzing the samples to obtain soil fertility in the sub-areas; based on the land fertility of different time nodes in each sub-region, obtaining a land fertility change condition; and comprehensively evaluating and analyzing the soil fertility change condition of the planting area based on the soil fertility change condition in each sub-area. The farmland soil fertility monitoring system has the advantages that accurate monitoring and dynamic tracking of farmland soil fertility are realized through image analysis and soil sample collection, and scientific decision support is provided for agricultural production. Through analysis of the soil fertility change trend, optimization of a fertilization scheme is facilitated, the land utilization efficiency is improved, and sustainable agricultural development is promoted.