50 results about "Multiple factor analysis" patented technology

The invention discloses a slope disease identification method and device based on an unmanned aerial vehicle and a machine vision technology, and the method comprises the steps: carrying out the multi-angle image collection of a slope region on a preset flight path through an unmanned aerial vehicle carrying a high-resolution camera; preprocessing the image, and performing feature extraction and image enhancement; recognizing and classifying disease features by combining an improved YOLOv8-seg instance segmentation algorithm, wherein slope diseases such as cracks, landslides, collapse and the like are covered; quantifying the risk level of the slope disease by using a rule model or a multi-factor analysis method based on the identification result; and carrying out dynamic change prediction on the acquired disease time sequence image by adopting a long-short term memory network (LSTM), and predicting the development trend and the potential instability time of the disease. On the basis of image data collected by the unmanned aerial vehicle, real-time monitoring and dynamic analysis are carried out on the slope diseases, potential hidden danger areas are found in time, disease features can be accurately recognized, and subjective errors possibly caused by manual judgment in a traditional method are avoided.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention belongs to the technical field of eye detection, and particularly relates to a myopia progress risk prediction system, which comprises a data acquisition module used for collecting a plurality of biological index data related to myopia progress, including but not limited to ocular axis length, corneal curvature, diopter, age, gender, lifestyle, genetic information and the like; the multi-factor analysis module is used for comprehensively analyzing multiple pieces of biological index data and modeling the individualized myopia progress risk based on a multi-factor interaction relationship; the regression algorithm module is used for collecting multiple pieces of biological index data; by analyzing multiple biological indexes at the same time, comprehensive evaluation of the myopia progress risk is achieved, compared with a traditional method which only depends on a single index for prediction, the system considers interaction of multiple factors, individual differences and environmental influences can be captured more comprehensively, and therefore prediction accuracy and reliability are remarkably improved.

The invention relates to the field of electrolytic copper production, in particular to a copper electrolysis production quality closed-loop management and control method, which comprises the following steps: collecting cathode copper surface quality data and process parameters in real time, firstly carrying out data cleaning and normalization, and realizing automatic defect classification in combination with univariate probability density analysis; then, adopting Graphical Lasso multivariable association mining to lock core process parameters, and fusing a Bayesian network and a CART decision tree to construct an anomaly prediction model; and dynamically tracing an abnormal root based on a graph computing technology and generating a key path, and finally outputting a data-driven process parameter adjustment suggestion. By applying a single-factor and multi-factor analysis method and combining a decision tree model and a graph calculation technology, abnormal conditions are identified and early warned, factors with the strongest association are found out, reason tracing is carried out, and auxiliary guidance suggestions of process parameters are provided for teams and groups, so that the overall quality of cathode copper in the electrolysis production process is improved.

The invention discloses a propping agent injection method based on particle size distribution optimization, which comprises the following steps: comprehensively analyzing geometrical morphology parameters of cracks, property parameters of rocks and pressure parameters changing in real time in a fracturing process; determining propping agent particle size distribution ranges corresponding to different areas of the crack through a data analysis model and an algorithm; dividing the particle size distribution range of the proppant particles into a plurality of particle size adaptation intervals according to the width measurement values and permeability test results of different areas of the crack; in the proppant injection process, real-time data monitoring is conducted through an underground sensor, the area with abnormal proppant injection and distribution in the crack is analyzed and judged according to real-time data, and dynamic particle size adjustment is conducted. According to the method, the problem that a traditional propping agent injection mode is uneven in distribution in a complex underground environment is solved, key factors such as the geometrical morphology of a crack, rock properties and fracturing pressure are accurately controlled by constructing a multi-factor analysis model, and fine regulation and control of particle size distribution of the propping agent are achieved.

The invention relates to a method and system for evaluating acceleration factors influencing lithium ion battery aging, and the method comprises the steps: carrying out the parameter setting of a plurality of acceleration factors, and obtaining the test conditions of different acceleration factor combinations; under the test condition, performing an aging test on the lithium ion battery and performing real-time monitoring to obtain a battery aging process data set; and performing multi-factor analysis on the battery aging process data set to obtain influence degree evaluation results of different acceleration factors on the battery aging rate. According to the method, multiple acceleration factors are considered, and the comprehensiveness and accuracy of the battery aging test are remarkably improved.

This invention relates to the field of data processing technology, specifically to a method for evaluating the effectiveness of fish passage in an eco-friendly fishway based on multi-factor analysis. The method involves acquiring multi-dimensional data, a multi-factor evaluation calculation model, and engineering parameters of each batch of fish swimming through a test section within each monitoring period. In each period, the theoretical environmental pressure rate is calculated using the model based on water temperature and flow velocity sequences. The total time consumed is compared with a preset baseline time to determine the energy consumption index. Effective swimming weights are determined by combining the deviation of the swimming trajectory time series and engineering parameters, reducing the weight of ineffective time consumption data in the backwater rest area. Multiple indicators from all batches of fish are integrated to determine long-term evaluation deviation indicators, and effective error periods are selected. An environmental parameter constraint space is constructed, and relevant data from each batch of fish within the effective error period are used for traversal optimization to eliminate data contamination in the rest area and improve the model's adaptability and predictive accuracy to different environments and fish states.

The embodiment of the invention provides a lease fee determination method and system and a storage medium, and belongs to the technical field of budget management. The method comprises the following steps: acquiring depreciation cost and interest cost of stock assets; determining depreciation cost and interest cost of newly added assets based on a multi-factor modeling mode according to historical data of the lease contract and external market environment data; and determining the lease expense in an attribution analysis mode according to a set adjustment factor, the depreciation expense and the interest expense of the newly added assets and the depreciation expense and the interest expense of the stock assets. According to the technical scheme, related cost prediction is adjusted through attribution analysis and according to multi-factor analysis, and the method has the advantage that budget cost planning of the next year can be more accurately carried out.

The invention provides a construction method and equipment of a fracture risk prediction model, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring basic data of a T2DM osteoporosis patient as a training set sample; the training set samples are divided into a fracture group and a fracture-free group; performing statistical analysis on the basic data, representing continuous variables conforming to normal distribution by using mean + / -standard deviation, and comparing the difference between the two groups through independent sample t inspection; adopting median and quartile to describe continuous variables which do not conform to normal distribution; through single-factor and multi-factor analysis, screening fracture influence characteristics of the osteoporosis patient from the basic data; and constructing a prediction model of the fracture risk of the osteoporosis patient based on the prediction variables. A special group of postmenopausal female diabetes is used as a research object, difference characteristics are analyzed, a prediction model is constructed, and early prevention and treatment of osteoporosis fracture patients are achieved.

The invention discloses a garment production order accurate quotation system based on multi-factor analysis, which belongs to the technical field of garment production and comprises a data acquisition module, a material unit price setting module, a rule coefficient setting module, a quotation calculation module and a quotation output module. According to the invention, the real-time unit price of the material is formulated based on the collected market dynamic data, then the real-time coefficient of the customer and the real-time coefficient of the order are formulated based on the collected customer related information data and the order feature information data, and then a plurality of order quotations are calculated based on the formulated real-time unit price of the material, the real-time coefficient of the customer and the real-time coefficient of the order. Compared with the prior art, the order quotation can be quickly obtained, adjustment can be performed according to the real-time condition of the market, and the rationality and timeliness of quotation are ensured.

The present invention discloses a proppant injection method based on particle size distribution optimization, comprising: comprehensively analyzing the geometric parameters of the fracture, the property parameters of the rock, and the pressure parameters that change in real time during the fracturing process, and determining the proppant particle size distribution range corresponding to different areas of the fracture through a data analysis model and algorithm; dividing the proppant particle size distribution range into a plurality of particle size adaptation intervals according to the width measurement values ​​and permeability test results of different areas of the fracture; during the proppant injection process, using downhole sensors to perform real-time data monitoring, and judging the areas in the fracture where proppant injection and distribution abnormalities exist based on the real-time data analysis, and performing dynamic particle size adjustment. The present invention solves the problem of uneven distribution of traditional proppant injection methods in complex underground environments. By constructing a multi-factor analysis model, key factors such as fracture geometry, rock properties, and fracturing pressure are accurately controlled to achieve refined regulation of proppant particle size distribution.

The invention provides a wind power plant fan optimization arrangement system and method based on multi-factor analysis, and relates to the technical field of data optimization, and the system comprises a data collection module which collects wind speed data and topographic slope data of each monitoring point in a wind power plant region, and builds a basic data set; the region determination module is used for extracting core performance parameters of a target fan, performing threshold screening on wind speed data and slope values in the basic data set, determining an effective wind energy region and a gentle terrain region, and further generating a candidate arrangement region; the area analysis module is used for determining the minimum spacing required by operation of a single fan according to the blade size, and determining the maximum number upper limit of the fans capable of being arranged in each area in combination with the area and the shape of the candidate arrangement area; the scheme determination module is used for generating a plurality of groups of arrangement schemes for each candidate arrangement area; and the scheme screening module is used for determining a final fan optimization arrangement scheme based on all the schemes. The overall power generation efficiency of the wind power plant is improved, and the energy loss is reduced.

The application discloses a kind of geophysical response characterization methods and systems of element enrichment degree in coal rock, and characterization method includes the following steps: obtaining the original sample of the same research block coal rock, and dividing subdivision layer;The sample of the subdivision layer is processed to obtain a variety of sizes of cube samples and its remaining material to meet different test conditions;Collect remaining material and acidolysis into solution, select target element and determine the element enrichment degree of acidolysis solution;Determine the multiple geophysical responses of the cube sample;Calculate geophysical derived parameters;According to the test and calculation of the above steps, a plurality of geophysical responses corresponding to the element content are obtained for a certain number of subdivision layer samples, and a geophysical response characterization model of element enrichment degree is constructed using a multi-factor analysis method;Different characterization models constructed by different multi-factor analysis methods are optimized, and the model with high precision and easy operation is determined as the final characterization model.

The invention relates to a bromhidrosis postoperative wound infection risk prediction method and a bromhidrosis postoperative wound infection risk prediction system, aims to solve the problems that an existing clinical evaluation method depends on subjective experience and lacks a standardized tool, and performs quantitative prediction on the individual infection risk of a patient by utilizing a column graph model constructed based on multi-factor analysis. The method comprises the following steps: systematically collecting clinical index data of a patient, wherein the clinical index data comprises general data and operation-related indexes; inputting the data into a pre-constructed column graph model, and obtaining a comprehensive risk score by matching a corresponding score for each index and performing accumulation; and finally, the risk level is divided into a low-risk class and a high-risk class according to a preset threshold value, and an intuitive basis is provided for clinical decision making. According to the method, the operation process is standardized, the result output is visual, the method can be effectively integrated into the clinical working process, and a practical auxiliary tool is provided for medical staff to identify high-risk patients and implement targeted prevention and nursing measures, so that the quality and efficiency of postoperative management are expected to be improved.

The invention provides a tool for intelligently evaluating the dystrophy risk of a patient with nasopharyngeal carcinoma, and the tool for evaluating the specific dystrophy of nasopharyngeal carcinoma is constructed by integrating indexes such as social demographic data, physique measurement, tumor staging, chemoradiotherapy conditions, recent adverse reactions and hematology indexes, so that high-precision risk evaluation is realized. Through single-factor and multi-factor analysis, the variable weight is optimized, and the discrimination capability and stability of the model are improved. An intelligent applet based on artificial intelligence is developed, patient data are extracted, nutrition risk calculation is completed within seconds, evaluation time is shortened, and clinical working efficiency is improved. The system also has the functions of dynamic monitoring and intelligent early warning, automatically updates the evaluation result along with the treatment progress of the patient, and provides individualized intervention suggestions. The defects that a traditional nutrition evaluation tool is high in subjectivity, low in efficiency, lack of tumor specificity and low in informatization degree are overcome, and an efficient, accurate and automatic intelligent evaluation solution is provided for clinic.

A software and / or hardware facility for scoring the quality of traffic to a site accessible via the Internet or other network. The facility extracts data from one or more data logs that are obtained from a publisher, advertiser, or third party. A multi-factor analysis in the form of a rule set is applied by the facility to the data. The analysis of the data identifies agent actions that are desirable to a publisher, advertiser, or third party. The facility generates a relative score for each agent action. The score may be used to assess the quality of traffic received by a network site. Lower scores are indicative of traffic having little value, whereas higher scores are indicative of traffic having greater value. The score may be provided to an advertising network and used to charge a variable amount for advertisements based on the quality of traffic that the advertisements receive.

The application discloses a water body peculiar smell olfactory traceability method and device based on multi-factor analysis and a medium, and can be applied to the water body peculiar smell traceability technical field. After the peculiar smell olfactory module, the olfactory unit and the odor sampling unit which interact with the control end are respectively arranged at appropriate positions of each monitoring water area, the olfactory unit is controlled to perform odor olfaction on the monitoring water area, when the odor olfaction result shows that the odor of the corresponding monitoring water area is abnormal, the odor sampling unit in the monitoring water area is controlled to perform gas sampling to obtain a gas sample, then the target substance information detection result of the peculiar smell corresponding to the odor sample is obtained, the water quality sampling module in all the monitoring water areas is controlled to perform water body sampling to obtain a water body sample, then the water sample detection data of the odor-causing target substance is obtained, and finally, the water body peculiar smell olfactory traceability is performed according to the water sample detection data, so that the source position of the water body peculiar smell can be accurately found.

The invention provides a multi-modal intelligent security event analysis method and system, and the method comprises the steps: obtaining monitoring data in a monitoring region, the monitoring data comprising sensing data collected by a plurality of sensors and video data collected by a plurality of cameras; when an alarm signal sent by a target sensor is received, calculating a weighted score of the alarm event through a composite weighting algorithm based on the equipment attribute of the target sensor, historical alarm data, whether a collaborative alarm signal exists or not and a current environmental factor; carrying out feature extraction and classification on the video data of the region where the target sensor is located by adopting a two-section multi-factor analysis technology so as to identify whether an abnormal behavior exists or not; and determining the risk level of the alarm event according to the weighted score and the identified abnormal behavior, and executing a corresponding grading response operation according to the risk level, thereby reducing emergency resource waste caused by false alarm.

The invention discloses a drainage pipe network assessment method and system based on multi-factor analysis, and relates to the field of drainage pipe network management, and the method comprises a collection module which is used for collecting the historical operation data of a drainage pipe network and the real-time monitoring data of a to-be-assessed time period, and forming an original data set with a space-time label; the processing module is used for carrying out preprocessing of time alignment, space calibration and anomaly correction on the original data set, and generating a time-space correlation standardized data set comprising historical sample data and to-be-evaluated sample data; historical operation and real-time monitoring data of the drainage pipe network can be accurately integrated, deviation is corrected through data calibration, features are extracted by integrating structure, hydraulic power and environmental factors, evaluation weights are dynamically adjusted according to pipe network building, stable operation and aging stages, a current health level, a bearing threshold value and a future risk trend are accurately output, early warning is carried out when the value exceeds a limit, and the safety of the drainage pipe network is guaranteed. Hidden dangers can be checked in time, and stable operation of a pipe network is guaranteed.

The invention relates to the technical field of mattress temperature control, and discloses a jade mattress temperature monitoring control system based on a temperature sensor. A temperature data monitoring module of the system monitors the temperature of the mattress in real time and obtains a temperature value; the temperature state analysis module compares the temperature value with a preset interval, and generates a temperature abnormal signal if the temperature value is not in the interval; the temperature influence analysis module monitors the environment humidity based on the abnormal signal, obtains a temperature influence value and compares the temperature influence value with a threshold value to generate an influence size signal; the temperature correlation evaluation module obtains a temperature change trend value and a similarity value based on the influence magnitude signal, and obtains a temperature correlation value through correlation calculation; the temperature adjusting module obtains an adjusting coefficient based on the correlation value and further adjusts the heating temperature of the mattress. According to the system, real-time monitoring, anomaly recognition, multi-factor analysis and accurate adjustment of the temperature of the jade mattress are achieved through cooperation of multiple modules, the use comfort and reliability are improved, and the system adapts to the temperature adjustment requirements in different environments.

The invention relates to the technical field of data management, in particular to a data element income distribution and management method and system based on multi-factor analysis. The method comprises the following steps: acquiring element source basic data, and performing data auditing to obtain element source data; obtaining element use frequency feature data and element use sensitivity feature data according to the element source data; performing desensitization processing according to the element use sensitivity characteristic data and the element use frequency characteristic data to obtain element desensitization use frequency data, and obtaining desensitization weight data according to the element desensitization use frequency data; obtaining sensitive weight data according to the element use frequency feature data and the element use sensitivity feature data; and obtaining element revenue matrix data according to the desensitization weight data and the sensitive weight data, and obtaining distribution strategy matrix data according to the element revenue matrix data so as to perform data element revenue management operation. According to the invention, scientific management of data element income distribution is realized.

The application relates to the technical field of brake systems, solves the technical problems that the prior art is susceptible to environmental interference, is not high in accuracy of multi-factor analysis, and is low in early warning accuracy, in particular to a passenger and freight vehicle brake risk combined early warning method and system, the method comprises the following steps: S1, collecting original data in a passenger and freight vehicle, and obtaining a dynamic calibration value by preprocessing the original data; S2, calculating a mixed filtering value according to the dynamic calibration value, and calculating a feature enhancement value for realizing feature decoupling based on the mixed filtering value, the application can clearly and explicitly express fault logic through a causal diagram model, can accurately determine a fault position in combination with a Bayesian network model, can display a fault path, can provide clear and accurate analysis basis for maintenance personnel to trace decisions, and can vividly and visually display the type of the fault in combination with AR image display, and the maintenance efficiency is improved.

The invention discloses a data cross-region migration method and device, electronic equipment and a storage medium. The technical problems that a current data cross-region migration strategy is single in consideration factor, low in migration efficiency and high in operation cost are solved. The method comprises the steps of obtaining to-be-migrated data and at least one target storage area of data cross-region migration; based on the to-be-migrated data, performing multi-factor analysis on each target storage area to obtain a multi-factor analysis result of each target storage area; on the basis of the multi-factor analysis result of each target storage area, optimal scheduling combining divide-and-conquer and parallel genetic evolution is carried out on the to-be-migrated data, and an optimal data migration scheme is obtained; and migrating the to-be-migrated data to at least one target storage area based on the optimal data migration scheme.

The invention discloses a food production safety multi-source heterogeneous data acquisition and processing method based on data mining, and relates to the technical field of food production safety data processing, and the method comprises the following steps: collecting and storing multi-source heterogeneous data in a food production safety process; converting the multi-source heterogeneous data into effective data; integrating the effective data, and dividing the effective data into a variable group and a dependent group; effective data in the variable group are converted into a unified format; constructing a multi-factor analysis complex based on the variable group and the dependent group, analyzing effective data, and mining an influence relationship between the dependent group and the variable group; the food production safety is monitored in real time based on the influence relation; the method is used for solving the problem that the safety of produced food cannot be guaranteed due to the fact that data with common influence cannot be recognized and analyzed in an existing food production safety data processing technology.

The application relates to a method for predicting the risk of postoperative complications of an acute abdomen patient, comprising the following steps: collecting clinical data of the acute abdomen patient after receiving surgical treatment, and preprocessing the clinical data; screening relevant risk factor variables through single factor analysis and multi-factor analysis, and constructing a characteristic variable set; constructing a postoperative complication risk prediction model based on the characteristic variables; the prediction model is based on an XGBoost base model, a moderate greedy algorithm is used for step-by-step parameter optimization, a plurality of optimal parameter subsets are selected to construct a plurality of XGBoost sub-models, and the MGA-XGBoost combined prediction model is combined through a weighted integration mode; the MGA-XGBoost combined prediction model is used for predicting the risk probability of postoperative complications of the patient, and important risk factors in the top list are prompted. The scheme provided by the application can accurately capture the nonlinear and high-dimensional risk correlation in the clinical data of the acute abdomen patient, improve the prediction accuracy of the risk of postoperative complications, and improve the clinical applicability.

The invention relates to the technical field of braking systems, solves the technical problems that the prior art is easily interfered by the environment, the accuracy of multi-factor analysis is not high, and the early warning accuracy is relatively low, and particularly relates to a combined early warning method and system for the braking risk of a van, and the method comprises the following steps: S1, collecting original data in the van, preprocessing the original data to obtain a dynamic calibration value; s2, a hybrid filtering value is calculated according to the dynamic calibration value, and a feature enhancement value used for achieving feature decoupling is calculated based on the hybrid filtering value, the fault logic can be clearly expressed through causal graph model construction, the fault position can be accurately determined in combination with a Bayesian network model, the fault path can be displayed, and the fault detection accuracy is improved. A clear and accurate analysis basis is provided for the retrospect decision of maintenance personnel, and the AR image display is combined, so that the fault type can be vividly displayed, and the maintenance efficiency is improved.

The present application relates to a kind of cold region tunnel portal section negative temperature zone length determination method.The existing cold region tunnel portal section negative temperature zone length determination method is insufficient to consider factor, and tunnel is prone to freeze injury.The present application obtains the air temperature correction value influenced by tunnel portal microtopography condition according to the azimuth of the slope where the portal is located, the slope angle, the geographic latitude and the portal shielding coefficient;The portal air temperature calculation value is calculated according to the altitude, the average temperature of the coldest month, the extreme minimum temperature, the altitude of the tunnel portal, the air temperature correction value;The length of the negative temperature zone of the tunnel portal section is calculated according to the portal air temperature calculation value.The present application is aimed at the characteristics that the length of the negative temperature zone of the tunnel portal section in cold region is easily influenced by the altitude of the tunnel site, the microtopography condition of the portal and the extreme minimum temperature, and the result obtained by multi-factor analysis and calculation has more guiding significance for optimizing the selection of cold region tunnel cold prevention and drainage measures and the length setting, and can improve the accuracy of the cold prevention measure setting of the tunnel portal section in cold region.

The invention discloses a hydrogen pipeline sealing joint temperature and humidity coupling leakage early warning method and system, and relates to the field of hydrogen pipeline safety monitoring. The method comprises the following steps: S1, acquiring temperature, humidity and environment pressure disturbance data to generate a temperature and humidity coupling disturbance quantity; s2, calculating the internal pressure elastic variation by combining the internal pressure change and the material stress; s3, constructing a leakage critical potential energy function based on the internal pressure elastic variation and the heat and humidity energy circulation amount; s4, calculating the hidden leakage expansion strength according to the critical potential energy and the diffusion lag rate; s5, calculating a leakage risk value according to the expansion strength and the structure retardation effect; s6, calculating the coupling disturbance confidence coefficient according to the temperature and humidity coupling disturbance quantity and the environmental pressure disturbance; and S7, when the risk value and the confidence coefficient are both greater than a preset threshold value, determining and outputting a dominant leakage risk type according to the heat and humidity energy transfer gradient and the temperature and humidity dominant relationship. Through temperature and humidity coupling multi-factor analysis, sealing monitoring, critical failure description and leakage inducement identification are realized, and the leakage early warning sensitivity is improved.

Disclosed software and / or hardware score the quality of traffic to a site accessible via the Internet or other network. The facility extracts data from data logs from a publisher, advertiser, or third party. A multi-factor analysis in the form of a rule set is applied by the facility to the data. The analysis of the data identifies agent actions that are desirable to a publisher, advertiser, or third party. The facility generates a relative score for each agent action. The score may be used to assess the quality of traffic received by a network site. In some embodiments, lower scores are indicative of traffic having little value and higher scores are indicative of traffic having greater value. In some embodiments, lower scores indicate higher value. Some embodiments provide the scoring to an advertising network in order to charge a variable amount for advertisements based on the quality of traffic received.

The present invention discloses a radioactive source equipment safety assessment method and system based on multi-factor analysis, which relates to the technical field of equipment safety assessment. A radioactive source equipment safety assessment system based on multi-factor analysis includes: a data acquisition module, a data analysis module, a cognitive intelligence assessment module, a self-organizing management module, an environmental adaptability simulation module, and a decision optimization module. The present invention adopts a Bayesian causal network combined with neural symbolic artificial intelligence, which can not only automatically analyze the operating status of radioactive source equipment, but also combine expert knowledge to achieve higher interpretability and prediction accuracy, thereby optimizing safety decisions; adopts blockchain combined with decentralized autonomous organizations to ensure the non-tamperability of radioactive source equipment data, and realizes automatic tracking of equipment status, authority management, and security policy execution through smart contracts, thereby improving management transparency and security and reducing risks caused by human intervention.