1562 results about "Disease prevention" patented technology

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

The invention discloses a poultry breeding monitoring management system based on big data, and relates to the technical field of poultry breeding supervision, the poultry breeding monitoring management system monitors the environment in a poultry house in real time, immediately triggers a behavior monitoring module when abnormality is detected, generates a specific environmental abnormality score, and combines visual monitoring and infrared thermal imaging to realize the monitoring and management of poultry breeding. According to the method, individual behavior characteristics of each poultry are effectively extracted, an individual behavior data stream is generated, individual potential health risks are identified, a health archive is adopted to accumulate long-term behaviors and environmental data of each poultry, and a time sequence analysis model is utilized to identify individual behavior modes and health change trends. Possible health risks are predicted based on historical data and models, management perspectiveness and disease prevention and control efficiency are effectively improved, meanwhile, abnormal features with the most influence are automatically recognized through comprehensive risk scoring and contribution degree analysis and are pushed to management personnel, the health archive of each poultry is updated in real time, and the management efficiency is improved. And the overall operation efficiency of the farm is improved.

The invention belongs to the technical field of aquaculture automation control, and particularly discloses an aquaculture environment intelligent disease early warning system and a storage medium, the system comprises a data acquisition layer, a data transmission layer, a processing platform layer and an application service layer; the data acquisition layer comprises an environment monitoring unit, a water quality sensor group, portable detection equipment and a data acquisition module; the data transmission layer comprises a wireless transmission module and an edge computing node; the processing platform layer comprises a machine learning prediction model and an environment factor correlation analysis model; the application service layer comprises an early warning information push module and a prevention and control scheme generation module. According to the intelligent disease early warning system for the aquaculture environment and the storage medium, a correlation model of environmental factors and disease occurrence is innovatively established, the early warning accuracy is 85% or above by adopting a machine learning technology, comprehensive monitoring and real-time early warning in the aquaculture process can be achieved, and accurate and efficient disease prevention and control are supported.

The invention discloses a crop growth prediction system and method based on an agricultural unmanned aerial vehicle remote sensing technology, and relates to the technical field of agricultural information and intelligent monitoring, the system uses an unmanned aerial vehicle remote sensing module and a ground Internet of Things sensor module to collect multi-source data of banana planting, fish pond breeding and hog house breeding, and the multi-source data is used as a data expression recording carrier. The edge computing node module preprocesses data, and the cloud intelligent platform module is internally provided with a multi-modal data fusion model, a growth prediction model and the like as processing record carriers to realize data depth analysis and prediction. And realizing automatic control of the production process according to the data through block chain evidence storage and the intelligent contract. And constructing a disease prevention and control model by using the hyperspectral image of the unmanned aerial vehicle and related data. According to the invention, the collection, processing and application capabilities of agricultural production data are improved, accurate prediction and intelligent regulation are realized, the resource utilization efficiency is improved, the disease loss is reduced, and the agricultural intelligent development is promoted.

The invention discloses an intelligent pest and disease damage identification and targeted spraying method and system based on machine vision, and the method comprises the steps: inputting a multispectral image sequence and environment sensing data into a processing system, and obtaining a high-quality fusion image through spectrum-space joint correction and environment adaptive compensation; constructing a region growing algorithm based on a topological flow theory, and realizing accurate extraction of a target region in combination with curvature flow boundary optimization; integrating the spatial features, the time sequence features and the context information by adopting a multi-modal feature fusion method, and establishing a pest state evaluation and behavior prediction model; and according to an evaluation result, a spraying track is optimized through an environment constraint field, a self-adaptive parameter control system is adopted to adjust spraying parameters, and closed-loop optimization is realized through real-time monitoring. The technical problems of low pest and disease identification precision, poor spraying effect and the like in a complex environment are solved, and the accuracy and efficiency of prevention and treatment are improved.

The invention is suitable for the technical field of agricultural intellectualization, and provides an intelligent blueberry disease detection method and system based on multi-modal unsupervised learning, and the method comprises the following steps: carrying out the feature extraction and clustering of preprocessed multi-modal data based on an adaptive contrast deep clustering framework, and obtaining a feature extraction result; obtaining a multi-modal preliminary feature and a preliminary clustering result; based on a multi-modal complementary feature fusion mechanism, according to the multi-modal preliminary features and the preliminary clustering result, carrying out adaptive weighted fusion on the multi-modal preliminary features to obtain multi-modal fusion features; performing unsupervised clustering optimization and disease type identification on the multi-modal fusion features to obtain an unsupervised learning model; and performing deployment and incremental learning on the unsupervised learning model, and detecting the blueberry diseases. According to the method, early-stage accurate detection of blueberry diseases is realized through an unsupervised learning algorithm, a new normal form is provided for intelligent accurate management of blueberries, and the disease prevention and control efficiency and industrial economic benefits are effectively improved.

The invention relates to the technical field of Internet of Things and artificial intelligence, in particular to a livestock disease prevention, control, monitoring and early warning system based on Internet of Things, which comprises a data acquisition module, a data preprocessing module, a self-adaptive data analysis module, an early warning generation module and a user interface module, wherein the data acquisition module is used for acquiring physiological data, behavior data and environment data of livestock; the data preprocessing module is used for cleaning and processing the data to eliminate noise and interference; the self-adaptive data analysis module is used for dynamically adjusting monitoring parameters and early warning threshold values and identifying potential disease risks; and the early warning generation module is used for generating early warning information of different levels and sending early warning. According to the invention, by combining the Internet of Things, machine learning and deep learning technologies, real-time monitoring of livestock health, accurate identification of disease risks and dynamic adjustment of monitoring parameters and early warning thresholds are realized, and the accuracy and timeliness of disease prevention and control are significantly improved.

The invention relates to the technical field of health monitoring, in particular to a health monitoring system based on wearable equipment, which comprises a data acquisition module, a health data analysis module, an emotion and sound analysis module and a health prediction feedback module. According to the health monitoring system, the prediction accuracy of the health monitoring system is effectively improved by combining time sequence analysis with a processing strategy of environmental factors, and more accurate health condition early warning, disease prevention and management strategy optimization and emotion and sound analysis integration are provided by comprehensively analyzing long-term health data and real-time environmental changes of the user, so that the user experience is improved. The emotion change is clarified through acoustic feature analysis, the understanding of the emotion state of the user is improved, the health monitoring dimension is enhanced, the participation degree and satisfaction degree of the user are improved through personalized health suggestion and real-time feedback, the practicability and effectiveness of the system are enhanced, and health management is more accurate and has interactivity.

A method, belonging to the field of road disease prevention technologies, for constructing a simulation model for roadbed diseases, a test method, and a system, where a simulation model for roadbed diseases is first constructed, and based on the model, an experimental method is performed to simulate diseases such as roadbed settlement, cracks, loosening, and pipeline breakage. Data of multi-sensing devices is associated with different diseases in different control groups, data statuses of various roadbed diseases in complex scenarios are acquired, and data patterns are summarized through control tests, to obtain sensor thresholds when the diseases occur in actual roadbed conditions, thereby providing a basis for road disease prevention.

The invention provides a drug target affinity prediction algorithm based on protein three-mode and multi-feature fusion, and the algorithm makes full use of sequence information and two-dimensional and three-dimensional structural features of drugs and proteins, and realizes deep integration of data. The technical innovation point lies in that a multi-modal data fusion mechanism and an advanced graph neural network technology are introduced, and the sensitivity and recognition capability of the model to key biomarkers are enhanced through a dynamic attention mechanism. According to the technical advantage, the method has higher accuracy and stability when processing complex biological data, and compared with the prior art, the generalization ability and the interpretability are remarkably improved. In the aspect of technical maturity, the algorithm has passed multiple rounds of verification and shows good prediction performance and adaptability. The method is simple in operation and wide in market application prospect, can assist in new drug discovery and accelerate the drug design process, can also promote the development of the precision medical field, and particularly has important value in targeted therapy and disease prevention strategy formulation.

The invention discloses a disease prevention management and monitoring system design method based on an adaptive algorithm. The method comprises the following steps: S1, obtaining health data of a user; s2, performing feature extraction and fusion on the health data by using a hypergraph neural network; s3, constructing a disease risk prediction model based on the generative reasoning network, and introducing an anti-fact generation module to generate a health change hypothesis scene; s4, adaptively adjusting a personalized intervention strategy through a nested game model, and adjusting the intervention intensity in the first layer; s5, in the second layer, generating an optimized intervention combination strategy based on a Nash equilibrium driven multi-target adaptive game; s6, risk weighted scoring is carried out on the health data through a structured clustering algorithm, and a feedback event is generated; and S7, dynamically optimizing a disease prevention management strategy through a chained feedback mechanism, and managing node permission. According to the method, the self-adaptive algorithm is combined with the multi-layer game and the distributed anomaly detection, so that personalized disease prevention management and monitoring are realized.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention relates to the field of microorganisms, in particular to a mortierella alpina strain MA with a plant growth promoting function and application of the mortierella alpina strain MA. The strain has remarkable nitrogen fixation, siderophore production, auxin (IAA) production and cellulase activity, can inhibit various soil-borne pathogenic bacteria such as phytophthora capsici and fusarium, and remarkably improves the disease resistance and growth performance of crops such as pepper. The strain has various functions and broad-spectrum action, is suitable for replacing part of chemical fertilizers and pesticides in intensive agriculture, and realizes green and efficient planting of crops.

The invention relates to the technical field of environmental monitoring, in particular to a water body pathogenic microorganism source analysis method and system, and the method comprises the following steps: based on water body sample collection information, identifying a plurality of moisture sources of a target water body through the sampling position of the water body, calculating the contribution degree of a plurality of source water bodies to a sampling point, and generating a water body position analysis result. According to the method, the prediction accuracy of the pollution risk area is improved by identifying the multiple sources of the water body, calculating the contribution degrees of the multiple water body sources to the sampling points and combining real-time meteorological and topographic data to simulate the diffusion path of pollution, and the prediction capability of the pollution source type is improved by evaluating the microbial population distribution mode in the water body sample. Data support is provided for pollution source position identification, multiple source positions of pathogenic microorganisms are identified through comparison with microorganism samples of multiple pollution sources, a scientific basis is provided for taking effective water quality protection and disease prevention measures, and the quality and efficiency of environment management are improved.

The invention discloses a citrus orchard interplanting pest and disease prevention and control method, which comprises the following steps: installing a drip irrigation facility in a citrus orchard, and uniformly distributing drippers of the drip irrigation facility between each citrus tree and green manure planting rows; soil humidity sensors are buried in multiple positions of the citrus orchard to monitor soil humidity data in real time through the soil humidity sensors; and determining whether to start the drip irrigation facility based on the soil humidity data acquired by the plurality of soil humidity sensors and the position relationship among the plurality of soil humidity sensors. Therefore, whether the irrigation facility is started or not can be automatically decided by monitoring the soil humidity of different positions in the citrus garden in real time, and the intelligent control mode is beneficial to timely discovering and processing the soil water shortage condition; the problems that in a traditional mode, whether irrigation is carried out or not cannot be judged according to the actual humidity condition of soil, and irrigation hysteresis exists are solved, and then a basis is provided for citrus orchard interplanting pest and disease prevention and control management.

The invention provides a primer probe combination and a kit for detecting porcine diarrhea related viruses, and belongs to the technical field of virus detection. The combination comprises specific primers and fluorescent probes aiming at a porcine epidemic diarrhea virus (PEDV), a porcine group A rotavirus (PoRV-A), a porcine transmissible gastroenteritis virus (TGEV) and a porcine delta coronavirus (PDCoV). The primer probe combination disclosed by the invention can be used for simultaneously detecting and identifying the four viruses mainly causing porcine diarrhea in one PCR reaction, and has the characteristics of high specificity, high sensitivity, simplicity and convenience in operation, rapidness and high efficiency. Therefore, according to the invention, rapid and accurate detection of porcine diarrhea related viruses can be realized, and disease prevention and control and epidemiological investigation of pig farms can be effectively supported.

The invention discloses a shrimp health state early warning method and system based on water quality monitoring, and aims to improve the disease prevention and control efficiency in a shrimp culture process. The method comprises the following steps: collecting historical breeding disease data and water quality change data of a target shrimp breeding water area, and constructing a water quality pathopoiesis knowledge graph; then water quality change data in a preset monitoring time period is obtained, space-time tracing is carried out by using the water quality change prediction model, and a water quality anomaly propagation path diagram is generated; formulating a water quality monitoring strategy based on the propagation path diagram, and optimizing the layout and frequency of monitoring points; and finally, monitoring the water quality in real time according to the established monitoring strategy, and realizing dynamic early warning of the health state of the prawns by combining real-time monitoring data and a water quality pathopoiesis knowledge graph. Water quality related diseases can be effectively prevented and controlled, and the stability and yield of shrimp culture are improved.

The invention belongs to the field of microbial secondary metabolites, and particularly relates to bacillus velezensis as well as metabolites and application thereof. The bacillus velezensis is classified and named as Bacillus velezensis Z-1, and is preserved in the Guangdong Microbial Culture Collection Center, the preservation number is GDMCC 64975, and the preservation date is August 12, 2024. The bacillus velezensis Z-1 provided by the invention has a relatively strong inhibition effect on mango colletotrichum gloeosporioides in vitro, and the bacteriostasis rate of fermentation liquor can reach 73.33%. The prepared Z-1 bacterial liquid is used for soaking mango fruits, the bacterial liquid can effectively prolong the shelf life of the picked mangoes, the storage period can reach 24 days, in addition, the softening rate of the mangoes is obviously reduced, the fruit skin is full, the mangoes are fresh and turquoise, and the sense organ and the storage quality are maintained at a relatively high level. The method provides a certain new theoretical basis for disease control, storage and fresh-keeping of the harvested mangoes.

The invention discloses a compound microbial agent for nitrogen fixation, carbon sequestration, synergism and stress resistance and application, and belongs to the technical field of functional microbial preparations, the compound microbial agent provided by the invention is prepared by taking combined nitrogen-fixing bacteria and high-yield urease inhibitor bacteria as cores and compounding salt-resistant, biocontrol, growth-promoting and carbon sequestration bacteria. The compound microbial agent provided by the invention can efficiently wake up nitrogen-fixing flora of a root system, accelerate nitrogen conversion in the atmosphere and play a role in nitrogen fixation and synergism on one hand, and can increase the content of organic carbon in soil and improve the fertility of the soil on the other hand, so that the quality of agricultural products is improved, and the purposes of increasing yield and income are achieved. In addition, the compound microbial agent also has stress resistance and disease prevention functions, and can relieve abiotic stress caused by a saline-alkali soil environment, prevent plant diseases caused by bacteria or fungi, improve the survival rate of crop cutting seedlings and enhance the continuous cropping resistance of crops.

The invention discloses an intelligent pest and disease damage monitoring system based on multi-modal data fusion of an unmanned aerial vehicle, and the system carries a multi-source heterogeneous data collection module through the unmanned aerial vehicle, obtains optical, multispectral, thermal infrared and other multi-modal data in parallel, and stores the data in a ground end after wireless link coding transmission and format adaptation. According to the system, an improved YOLO algorithm is used to construct a multi-level feature pyramid and an adaptive convolution kernel architecture, and feature extraction and disease and pest candidate region generation are carried out on multi-modal data; time sequence dynamic analysis and depth feature mining are realized by means of an optimized long-short-term memory network in combination with parameters such as a pest and disease damage growth cycle; and through a weight adaptive adjustment fusion strategy, decision-level fusion is carried out on results of the two algorithms, and final monitoring data is output. Besides, various correlation formulas are designed in each link of the system, so that parameters in multiple aspects of data acquisition, algorithm processing and the like are mutually coordinated, efficient and accurate monitoring of diseases and insect pests is realized, and technical support is provided for prevention and control of agricultural diseases and insect pests.

The invention relates to a layout image generation method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the element analysis of original image data, and obtaining image element information; performing feature extraction on the image element information to obtain image features and text features; performing hypergraph structure construction according to a preset design rule, the image features, the text features and the image element information to obtain hypergraph structure information; performing style parameter adjustment and coordinate adjustment on the hypergraph structure information according to the matching similarity corresponding to the element embedding vector to obtain to-be-rendered information; and performing image layout rendering according to the to-be-rendered information to obtain an effective layout image. The method can be applied to application scenes of financial science and technology and digital medical systems, and insurance product propaganda images or disease prevention propaganda images which effectively express image elements and features can be effectively, accurately and quickly generated according to original insurance product images or health education images in the system.

The invention belongs to the field of prevention and treatment of tilletia controversa Kuhn, and particularly relates to a tilletia controversa Kuhn effect protein g16561 and application thereof. An amino acid sequence of the Tilletia controversa Kuhn effect protein is shown as SEQ ID NO: 2. The effect protein plays an important role in the process of inhibiting plant defense reaction, and has an interaction relationship with a wheat protein in the process of infecting wheat by Tilletia controversa Kuhn. According to the invention, the pathogenesis of Tilletia controversa Kuhn can be deeply researched, and the excavation of wheat disease-resistant genes and the cultivation of wheat disease-resistant varieties can be promoted.

The invention discloses application of a ZmFS1 gene in corn disease prevention and control, and belongs to the technical field of crop disease prevention and control. The nucleotide sequence of cDNA of the ZmFS1 gene is as shown in SEQ ID NO.1. By inhibiting expression of the gene, resistance of corn to various diseases including southern leaf blight, northern leaf blight, southern rust disease, anthracnose, stem rot, seed rot and the like can be remarkably enhanced, a new solution thought is provided for preventing and treating corn diseases from the perspective of molecular biology, and the application prospect is broad. The method can be applied to corn disease-resistant breeding.

The invention relates to the technical field of health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

The invention relates to a Citrus Huanglongbing detection device and detection method based on a visual image. According to the method, a visual image and a biophysical image of a to-be-detected citrus leaf are obtained, targeted preprocessing and feature extraction are performed on the visual image and the biophysical image to form a comprehensive feature vector, then the comprehensive feature vector is input into a trained citrus huanglongbing detection model, and finally a detection result is obtained. According to the method, multi-source image information can be fully fused, the limitation that early lesion features are not obvious when the citrus leaf huanglongbing is detected only by depending on a visual image in the immature fruit period is overcome, and multi-dimensional features of leaf appearance, physiological metabolism and cell structure changes caused by the huanglongbing are accurately captured; therefore, the accuracy and the early diagnosis capability of citrus huanglongbing detection are remarkably improved, the citrus planting industry is effectively assisted in timely disease prevention and control, the economic loss is reduced, and the fruit quality and yield are guaranteed.

The invention discloses a Burkholderia sp. Strain and an application thereof in prevention and treatment of eucalyptus blight. The Burkholderia sp. AS-SF-1 disclosed by the invention has the preservation number of GDMCC (Graphical December Microorganism Center) No. 66223; the culture and volatile matter of the strain can inhibit growth of various forest pathogenic bacteria, the eucalyptus has a remarkable control effect on branch blight, siderophore can be generated, absorption and utilization of iron can be promoted, various types of carbon sources or nitrogen sources can be utilized, and the strain can grow within the pH range of 4-9. The burkholderia sp. AS-SF-1 disclosed by the invention has an important application value on planting of eucalyptus and prevention of diseases.

The invention relates to the field of prevention and treatment of respiratory diseases, and discloses a preparation method of a human respiratory syncytial virus preventive mRNA vaccine. The main components of the mRNA vaccine comprise mRNA for coding an antigen and lipid nanoparticles. According to the mRNA sequence provided by the invention, an independently researched and developed 5 'UTR / 3' UTR sequence and a codon optimization mode are adopted, and efficient translation of target protein can be realized. According to the mRNA vaccine provided by the invention, RSV F protein is selected as an antigen, and the RSV F protein sequence is optimally designed, so that stable pre-fusion conformation RSV F protein can be expressed, and after a mouse is immunized, the mouse can be induced to generate high-level RSV F protein specific binding antibody titer and neutralizing antibody titer.

The invention discloses a multifunctional peoriae paenibacillus and application thereof, and relates to the technical field of microorganisms, and the key point of the technical scheme is as follows: Paenibacillus peoriae S6330 is separated from rhizosphere soil; the peoriae paenibacillus has a good inhibition effect on pathogenic bacteria, pathogenic fungi and oomycetes; meanwhile, the peoriae paenibacillus has the functions of dissolving potassium, producing protease, producing amylase, producing cellulase, producing siderophore and the like, has a growth promoting effect on various crops such as wheat, corn, watermelons, small oilseed rapes, tobaccos and the like, and can remarkably improve indexes such as chlorophyll content, plant height, fresh weight, overground part weight, underground part weight and the like of plants; in addition, the peoriae paenibacillus can be used for effectively inhibiting the tobacco bacterial wilt; the peoriae paenibacillus has no environmental pollution problem, is simple in culture condition, and has wide disease prevention and growth promotion application prospects in agricultural production.

The invention discloses a bacillus velezensis strain and an application thereof in disease prevention and growth promotion, the strain is preserved in the China General Microbiological Culture Collection Center on May 14, 2024, the preservation number is CGMCC No.30619, and the strain is named as bacillus velezensis. The invention further discloses a preparation method of the bacillus velezensis strain, and the application of the bacillus velezensis strain in disease prevention and growth promotion of the bacillus velezensis strain in disease prevention and growth promotion of the bacillus velezensis strain in disease prevention and growth promotion of the bacillus velezensis strain. When the strain is applied to the field of agricultural biological prevention and control, a biological membrane can be formed and stably colonized at the rhizosphere of a plant, a relatively strong and broad-spectrum inhibition effect can be generated on various plant pathogenic fungi, and substances such as IAA and siderophores can be generated to promote plant growth. The bacillus velezensis suspension has a prevention effect of 56.36% on cucumber fusarium wilt potted plants, has a certain growth promoting effect on cucumbers, is a biocontrol strain with excellent performance, and has good development and application prospects.

The invention relates to a microbial strain and application thereof, in particular to bacillus brevis, a microbial agent prepared from the bacillus brevis and application of the microbial agent. The invention provides the cold-resistant bacillus subtilis which has multiple functions of generating capsular polysaccharide, being high in salt resistance and cold resistance, promoting growth and disease resistance of plants in saline-alkali soil and the like. The bacterial strain can generate capsular polysaccharide, experiments prove that the bacterial strain has high capabilities of dissolving phosphorus, fixing nitrogen and producing siderophore, IAA, ACCD and cytokinin, and has strong antagonistic action on fusarium, curvularia and microplasmodium, and the generated capsular polysaccharide can promote salt tolerance of plants, so that the yield of the plants is increased, and the yield of the plants is increased. The bio-fertilizer is suitable for production of multifunctional bio-fertilizers with disease prevention, growth promotion, salt and alkali resistance, cold resistance and the like, is applied to production of saline-alkali soil grain crops, pasture and greenhouse commercial crops, has remarkable effects of promoting cold damage resistance, root development, prevention and control of root diseases, improvement of crop yield and the like, can reduce the use of chemical fertilizers and pesticides, and has a wide application prospect.