17542results about How to "Reduce risk" patented technology

The invention provides a big data platform scheduling task and data collaborative smooth migration method and system, and relates to the technical field of big data, and the method comprises the steps: constructing a digital twin environment of a target system, collecting task operation state data of a source system, extracting task features through a deep neural network, and carrying out the clustering and grouping. And deploying the task group to a digital twin environment, analyzing a task association relationship and a resource competition relationship based on a graph neural network, and generating a task stability score in combination with a task operation index. And constructing a migration risk prediction model to perform quantitative evaluation on the migration risk, and determining a migration priority and an execution sequence according to the quantitative evaluation. And making corresponding resource configuration, data synchronization and fault recovery plans for each task group, carrying out migration drilling in a digital twinning environment, and optimizing a migration scheme by utilizing a reinforcement learning algorithm. The migration risk can be effectively reduced, the smoothness and stability of the migration process are guaranteed, and the migration efficiency is improved.

The invention discloses a dam safety monitoring system and method based on digital twinning, and particularly relates to the technical field of digital twinning. The method comprises the following steps: acquiring stress, deformation, displacement, inclination and environmental change data of a dam structure in real time, regularly carrying out high-precision calibration on a sensor, recording a time drift error, analyzing nonlinear sensitivity of a digital twin model under an extreme working condition, simulating the influence of the sensor error and the model sensitivity on dam evaluation, predicting a deviation degree, and calculating the evaluation accuracy of the dam. Based on a prediction result, a dynamic drift compensation algorithm is adopted to correct sensor data in real time, the sensitivity of the model is dynamically adjusted, the optimized data and model are integrated, the structural state and potential risk of the dam under normal and extreme working conditions are accurately evaluated, accurate early warning information and decision scheme are generated, and the accuracy of early warning is improved. The accuracy and reliability of dam safety monitoring and risk assessment are remarkably improved, decision delay or resource waste caused by misjudgment or missed judgment is reduced, and the dam safety guarantee capacity is improved.

The invention provides a cerebral hemorrhage personalized treatment scheme optimization method and system based on big data analysis, and relates to the technical field of data analysis, and the method comprises the steps: constructing a cerebral hemorrhage patient multi-source heterogeneous clinical database, extracting feature subsets, carrying out semantic alignment, carrying out time sequence modeling and fusion, and obtaining a patient panoramic feature representation; constructing a cerebral hemorrhage diagnosis and treatment knowledge graph, adding the cerebral hemorrhage diagnosis and treatment knowledge graph to a preset graph attention network, distributing weights, obtaining knowledge-enhanced patient feature representation and taking the knowledge-enhanced patient feature representation as a state, generating an action space based on an optional treatment scheme, constructing a reward function and determining a candidate treatment scheme; performing medication suitability analysis, performing semantic conflict identification in combination with the patient allergy history and complication, and performing screening to obtain a suitable candidate treatment scheme set; and for each scheme, predicting a multi-dimensional prognosis index, constructing a multi-criterion decision matrix, combining personal influence factors, adjusting the weight of each index, modeling an optimization problem and determining an optimal treatment scheme.

The invention relates to the technical field of logistics transportation, in particular to a logistics transportation route optimization method and system based on digital twinning. The method comprises the following steps: constructing a digital twin logistics base through multi-source heterogeneous data fusion and dynamic logistics network modeling; carrying out regional logistics distribution feature extraction and dynamic time sequence prediction based on a digital twinborn substrate to obtain a predicted regional transportation demand; performing real-time state evaluation on the line, generating a dynamic line performance matrix, and performing vehicle resource allocation in combination with the order task priority; and carrying out multi-objective solution and dynamic adjustment on the transportation scheduling scheme to finally obtain an optimal transportation scheduling scheme. And performing iterative optimization on the digital twin model through real-time deviation analysis and a feedback mechanism, and dynamically adjusting a transportation plan to form a closed-loop optimization process. Through the logistics transportation route optimization technology, the logistics transportation efficiency is remarkably improved, the cost is reduced, and the toughness of the system is enhanced.

The invention provides a pure electric vehicle charging control method and system, and relates to the technical field of electric vehicle energy storage, and the method comprises the steps: collecting real-time charging data, combining historical data to construct a battery state evaluation model, and calculating battery state information in real time; a multi-objective optimization model is established based on the battery state information, and an optimal charging strategy is solved by adopting an improved particle swarm algorithm; and a multi-dimensional safety monitoring system is established, a corresponding safety plan is executed when abnormal charging is detected, and the plan is fed back to the multi-target optimization model for strategy correction.

The invention relates to the technical field of information security, in particular to a power distribution Internet of Things cloud side end collaborative interaction security protection system, which comprises a network flow monitoring module, an abnormal flow identification module, a security threat response module, a network attack early warning module, a behavior pattern analysis module and an access authority management module. According to the method, the network flow data is collected and analyzed in real time, the abnormal flow recognition speed and accuracy are enhanced, the security threat response speed and efficiency are improved, measures are taken in time at the early stage of a problem, the risks of data leakage and network attacks are reduced, and the timeliness of defense measures is improved by combining security threat response and network attack early warning; by analyzing the relevance between threat events, the complex multi-stage attack activity is effectively predicted and identified, the access data and the communication mode of the equipment are monitored in real time, and the access authority and the trust level are adjusted, so that the security protection is more targeted and flexible, and the management efficiency of the risk equipment is improved.

The invention discloses a fan multi-mode fault diagnosis method based on voiceprint feature and vibration data fusion. The method comprises the steps that S1, voiceprint signals and vibration signals generated in the fan operation process are synchronously collected through a plurality of sensors; s2, preprocessing the collected voiceprint signal and the vibration signal; s3, fusing the preprocessed voiceprint signal and vibration signal through a feature level weighted fusion algorithm; s4, performing coarse-grained fault screening under a multi-level framework; s5, performing fine-grained fault diagnosis under a multi-level framework; s6, after the fine-grained fault diagnosis is completed, the system outputs a corresponding fault early warning signal according to the type, the specific part and the severity of the fault; and S7, the system dynamically adjusts the fault diagnosis model by monitoring the running state and the fault condition of the fan in real time. According to the method, the fault type of the fan can be accurately identified, the severity of the fan can be evaluated, and the overall intelligent level and the real-time response capability of a fault diagnosis system are improved.

The invention, which relates to the technical field of the industrial internet, discloses an intelligent equipment data interaction system based on the industrial internet, comprising an industrial internet platform. The industrial internet platform is in communication connection with a data acquisition and preprocessing module, an edge calculation module, a data transmission module, an anomaly analysis module, an intelligent decision module, a feedback control module and a man-machine interaction module, and all the modules are in electric signal connection. According to the intelligent equipment data interaction system based on the industrial internet, the production efficiency is remarkably improved by monitoring and optimizing the production process in real time, data are collected through a sensor and an equipment state monitor, and abnormal states in the production process are quickly recognized and responded through preprocessing and analysis of an edge calculation module; and the intelligent decision-making module generates an optimization control scheme according to an analysis result and feeds back the implementation scheme of the control module, so that the risks of equipment failure and production interruption are reduced.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention discloses an internet-based intelligent bid invitation management platform, which comprises a data integration and analysis module configured to establish an intelligent comprehensive economic analysis model by integrating internal data and / or external data of the intelligent bid invitation platform; the standardized bid invitation file module is configured to construct an industry lexical analysis and language model by utilizing natural language processing and a knowledge graph; the intelligent question-and-answer type bidding file filling module is configured to complete compiling of a bidding file by guiding a bidder to adopt an intelligent question-and-answer mode; the intelligent bid evaluation tool set module is configured to assist the bid evaluation process by establishing an intelligent bid evaluation tool; the risk early warning module is configured to construct a knowledge graph of a supplier, a subject matter and a project; and the large model application module is configured to construct an AI bid evaluation model by using a large model technology. Through intelligent bid invitation management, bid evaluation work efficiency is improved, and fairness and accuracy of bid evaluation results are ensured.

The invention discloses an unmanned aerial vehicle disaster emergency inspection path intelligent planning system and method, and particularly relates to the technical field of unmanned aerial vehicles. A disaster area is divided into sub-disaster monitoring areas, disaster data of each area is acquired and analyzed in real time, and the area deterioration degree and the risk level are dynamically evaluated; an intelligent planning algorithm is combined to generate a preliminary inspection path, the energy consumption and coverage rate of the unmanned aerial vehicle are monitored in real time to assess the accuracy of path planning, the priority of the inspection path is further dynamically adjusted through fuzzy logic, the high efficiency, accuracy and safety of the inspection task of the unmanned aerial vehicle are ensured, and the risk of missing report and false report is reduced. Therefore, the decision quality of disaster emergency response and the rationality of resource allocation are improved.

The invention relates to the field of robot attitude control, in particular to a robot attitude control method and system based on intelligent recognition. The method comprises the following steps: continuously acquiring a real-time sandbox scene video stream based on a high-definition camera; and carrying out time sequence frame segmentation on the real-time sandbox scene video stream, and carrying out global contrast enhancement processing to obtain a global contrast optimization image sequence. Carrying out dynamic object identification on the global contrast optimization image sequence, and carrying out continuous frame optical flow motion tracking to generate a dynamic object optical flow change track; optimizing the image sequence according to the global contrast to extract scene ground and scene obstacle data; performing dynamic trajectory rendering on the scene ground and scene obstacle data based on the dynamic object optical flow change trajectory so as to construct a dynamic scene twinborn model; robot position posture parameters and user operation instructions are recognized. According to the invention, timely attitude adjustment response and accurate movement position control are realized.

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 machine learning, in particular to a heating fault prediction method and system for a high-voltage circuit breaker and an isolating switch of the high-voltage circuit breaker. The method comprises the following steps: carrying out multi-source data acquisition on a high-voltage isolation switch and an associated high-voltage circuit breaker through an SCADA system, and carrying out data preprocessing to obtain a preprocessed data set; performing operation feature correlation analysis on the preprocessed data set to obtain a correlation operation feature set; performing dynamic association network construction on the association operation feature set to obtain a dynamic association network graph; acquiring historical fault data; performing propagation risk path search according to the historical fault data and the dynamic association network diagram to obtain a candidate risk path set; and performing path propagation probability calculation on the candidate risk path set to obtain a path probability data set. According to the method, the influence of the associated high-voltage circuit breaker on the heating risk of the isolating switch is effectively identified, so that the accuracy of fault prediction and the timeliness of early warning are improved, and a more reliable guarantee is provided for safe and stable operation of a power system.

The invention discloses a management system for real-time monitoring and early warning of mine coal and gas outburst parameters, and belongs to the field of safety production, and the management system specifically comprises the steps: collecting gas and ground stress data in a mine in real time through a gas sensor and a microseismic sensor, and carrying out the real-time monitoring and early warning of the mine coal and gas outburst parameters through a time sequence analysis and spatial data mining technology and a multi-scale coupling strategy; identifying the change rule and potential correlation of the crustal stress and the gas parameters; a comprehensive outburst analysis and prediction model is established, the coal and gas outburst risk level is evaluated, and a three-level early warning mechanism and an emergency plan are designed; the method can be flexibly adjusted according to actual conditions of a mine, coal and gas outburst accidents are effectively prevented and controlled, and the safety of the mine is improved.

The invention discloses an access control method and system for data security protection, and relates to the field related to data security, and the method comprises the steps: obtaining user access request information, carrying out sensitivity evaluation, obtaining access data sensitivity information, matching with a security verification mode library, and obtaining a target security verification mode; performing multi-dimensional identity verification on the target user to obtain a security verification result, and when the security verification result is passed, performing dynamic access permission adjustment to obtain accessible permission data information; access monitoring is carried out, user access behavior information is obtained, abnormity analysis is carried out, and abnormal access behavior feature information is obtained; and performing access control analysis to obtain a data security access strategy, and performing access restriction protection. The technical problems that existing data access control is low in security protection effect and has data leakage risks are solved, and the technical effects of improving the data security protection effect and effectively reducing data leakage and illegal access risks are achieved.

The invention provides a real-time data monitoring system in a knitted fabric production process, and relates to the technical field of data monitoring, and the system comprises a real-time monitoring module which is used for directly collecting actual production data from equipment and the environment of a production workshop through various sensors; the data analysis module is used for comparing the initial prediction value of the prediction model with actual production data to output final prediction data and generating optimization parameters according to the final prediction data; the prediction production module is used for establishing a virtual production model according to the actual production data, and the prediction model predicts an initial prediction value of the output variable based on the input data; and the dynamic adjustment module is used for generating a dynamic deviation threshold value according to the deviation trend data and generating a corresponding adjustment strategy according to whether the deviation value falls into the deviation threshold value or not. The precision and stability of the production process are improved, and the influence of human factors is reduced.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses an intelligent advertisement putting processing method and system based on user data, and relates to the technical field of data processing, and the method comprises the steps: putting an advertisement in an advertisement pool based on an edge device, collecting the video information during the advertisement playing period through the edge device, analyzing the stored video information, extracting the user feature information, and storing the user feature information in the advertisement pool; the user feature information is integrated into user feature vectors, the user feature vectors stored in the edge devices are uploaded to the cloud platform, the cloud platform analyzes the user feature vectors uploaded by the edge devices to generate user portrait information, and the user portrait information is displayed on the basis of the user portrait information generated by the cloud platform. And putting corresponding advertisements to the user groups with similar user portrait information, collecting feedback data, and dynamically optimizing the advertisements put by the edge devices and the user groups based on the feedback data. The method has the advantages that the data transmission delay and the cloud computing pressure are reduced through the cloud edge collaboration method.

The invention relates to an intelligent monitoring method and system for an air environment monitoring station, and belongs to the technical field of environment monitoring. The method comprises the following steps: acquiring multi-modal monitoring data, performing data association analysis on the multi-modal monitoring data by adopting a multi-source data association model, and establishing an association relationship among the multi-modal monitoring data; performing data exception monitoring on the multi-modal monitoring data, and triggering and starting an exception type diagnosis event when data exception is monitored; extracting a first data subset related to an exception type from the multi-modal monitoring data; extracting a second data subset associated with the first data subset from multi-modal monitoring data; and performing fault diagnosis based on the first data subset and the second data subset, and outputting an abnormality diagnosis result. According to the method, the multi-source data association model is utilized, the association between different types of data can be effectively analyzed, detailed fault analysis and diagnosis are carried out, the fault response time is shortened, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of temperature control, in particular to a temperature monitoring and adjusting method for a display screen, and the method comprises the following steps: capturing temperature data of the display screen in real time through a temperature sensor, evaluating the difference and complexity of the temperature data through information entropy, and obtaining an entropy analysis result; and identifying abnormal fluctuation in the data according to the entropy analysis result, and generating an abnormal fluctuation monitoring result. According to the invention, by introducing the information entropy to evaluate the difference and complexity of the temperature data, the abnormal fluctuation can be accurately identified, the output of the heat dissipation equipment can be timely adjusted, the response speed and prediction capability of the temperature management of the display screen can be improved, the temperature control precision can be obviously enhanced, and the stability of the equipment in a variable environment can be ensured; the designed temperature trend prediction algorithm utilizes historical and current data to predict future changes, the adaptive capacity is enhanced, the maintenance cost is reduced, the energy use is optimized, the energy consumption is reduced, and the equipment performance is continuously protected by dynamically adjusting a control strategy.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention discloses a flood discharge gate opening degree real-time automatic distribution method for a centralized control center, and relates to the technical field of water conservancy gate control, and the method comprises the steps: setting a monitoring point in each sub-region, and collecting the mechanical state, the vibration stress parameter and the surrounding geological parameter of a flood discharge gate, so as to construct a gate wear index Msz; therefore, the state of the gate can be monitored in real time, and abrasion and fault problems can be found and early warned in time. And establishing a sub-region prediction model, extracting water flow characteristics and river volume characteristics, calculating an average water flow index Slzs and a regional river clogging index Ydzs of each sub-region, correlating the obtained predicted river load index Fzz, and performing evaluation. And setting a first difference threshold value C1 and a second difference threshold value C2, calculating and obtaining a load difference value FCz according to the predicted river load index Fzz and the load threshold value R, generating an opening instruction, and setting a priority distribution mechanism. The opening degree of the gate is optimized, and the accuracy and response speed of flood discharge control are ensured.

The invention relates to the technical field of dynamic scheduling, in particular to a dynamic optimization scheduling algorithm for a photovoltaic energy storage system, which is characterized in that based on a working environment of the photovoltaic energy storage system, environment illumination intensity data is acquired in real time, average power generation under the same illumination intensity is extracted, and weather conditions in a future time period of a current region are received through weather forecast data; and predicting the generating capacity in a future period to obtain generating capacity evaluation information. According to the invention, by capturing the environment illumination condition in real time and combining the historical power generation data, the future power generation condition of the photovoltaic system is effectively predicted, the load change of the power grid is monitored in real time, and the influence brought by specific activities is considered, so that timely response can be made when the demand of the power grid fluctuates, and the supply and scheduling of electric energy are optimized; according to the invention, continuous monitoring and abnormity processing are carried out on the power generation equipment, and the abnormal condition of the equipment is handled by correcting the discharge capacity in real time, so that the risk in operation of the energy storage system is reduced, and solid technical support is provided for stable operation of a power grid.

The invention provides an unmanned aerial vehicle route layout method and device for complex terrains, equipment and a medium, and relates to the technical field of unmanned aerial vehicle aerial photogrammetry. The method comprises the following steps: carrying out preliminary region division on a terrain region to be surveyed, and determining a plurality of region units; clustering the area units according to topographic observation data to obtain survey sub-areas; determining a survey priority corresponding to each survey sub-region, and matching a basic flight route corresponding to the unmanned aerial vehicle according to the survey priority; and splicing the different types of basic flight routes corresponding to the survey sub-regions, and performing optimization adjustment on the spliced basic flight routes to obtain a flight route of the target unmanned aerial vehicle so as to perform topographic survey on the topographic region to be surveyed. According to the technical scheme, the route planning of the unmanned aerial vehicle can adapt to various types of landform features, and the safety and aerial survey efficiency of the unmanned aerial vehicle are ensured while the integrity and accuracy of aerial survey data are improved.

The invention discloses a crop sowing parameter control optimization method applied to multi-target precise fertilization, and relates to the technical field of agricultural crop sowing, and the method comprises the following steps: collecting a soil sample, carrying out spectrum scanning and analysis, combining with known soil nutrient data, building a soil nutrient content prediction model, and calculating a soil nutrient content prediction model; and combining spectral data to predict the nutrient content in the soil. According to the invention, through accurate evaluation of soil nutrient conditions and deep analysis of crop growth demands, fertilization and sowing parameters are optimized, so that crops can obtain required nutrients in the growth process, the optimized sowing depth, row spacing and plant spacing can effectively promote the development of crop roots, the absorption ability of the crops to moisture and nutrients is enhanced, and the quality of the crops is improved. And through real-time monitoring and adjustment of the intelligent fertilization equipment, the fertilization amount can be dynamically adjusted according to actual demands of crops, and the common problem of excessive or insufficient fertilizer in a traditional fertilization method is avoided.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.