68 results about "Outbreak" patented technology

The invention relates to the technical field of environmental monitoring and water ecological safety, in particular to a eukaryotic algae outbreak early warning method and system based on genus-level specific recognition. The method comprises the following steps: collecting a water body sample at a monitoring position according to a preset sampling plan, collecting an environment measurement value, and respectively obtaining environment parameter data, a water sample sampling identifier and a sampling timestamp; extracting nucleic acid from the water sample at the monitoring position based on the water sample sampling identifier, performing targeted amplification, and performing high-throughput sequencing at the same time to obtain eDNA original sequencing data; therefore, by constructing the eukaryotic algae outbreak early warning process based on genus-level specific recognition, the problems that in a traditional method, sampling disturbance is uncontrollable, sequence judgment precision is insufficient, trend recognition is lagged, and an early warning link is not transparent are solved, and the accuracy, stability and traceability of early judgment of algae outbreak are improved.

The invention discloses a content auditing abnormity monitoring and early warning method and system based on intelligent alarm suppression, and the method comprises the following steps: obtaining real-time content auditing data collected in multiple dimensions, determining multi-dimensional data, carrying out the data cleaning and standardization processing of the multi-dimensional data, and carrying out the monitoring and early warning of the content auditing abnormity. Storing the multi-dimensional data subjected to data cleaning and standardization processing into a database; and constructing a deep reinforcement learning model, determining data input of the deep reinforcement learning model based on the standardized multi-dimensional data, and driving the deep reinforcement learning model to optimize an alarm mode in a training process. According to the method, hidden violation and semantic evolution trends are recognized through semantic clustering, and the traditional detection bottleneck based on a numerical threshold value is broken through; according to the invention, by realizing cross-cycle trend evolution analysis, early signals before abnormal outbreak can be identified in advance; the method has the cross-event semantic comparison capability, and the false alarm rate and the redundant data volume are remarkably reduced.

The invention relates to the technical field of industrial big data and intelligent breeding, and particularly discloses a pig farm breeding management system and method based on industrial big data. The system comprises a multi-source heterogeneous data acquisition module, a data fusion and feature engineering module, a subclinical period anomaly detection module, a causal inference and root cause tracing module and a dynamic resource optimization scheduling module. High-order features are extracted through multi-source data fusion, a double-layer adaptive model is adopted to realize sub-clinical period anomaly detection, then root causes are deduced and positioned in combination with causality, and finally an accurate resource scheduling instruction is generated based on multi-objective optimization. According to the invention, extremely early warning and accurate intervention of the health state of the swinery can be realized, the disease outbreak risk is reduced, and the breeding benefit is improved.

The invention relates to the technical field of medical artificial intelligence and public health data management crossing, in particular to an intelligent tuberculosis screening and data analysis management system which comprises a data acquisition module, an intelligent screening module, a data analysis management module, a clinical interaction module and a system management module. The multi-source data fusion and the dynamic threshold reasoning enable the early-stage tiny focus detection rate to be larger than or equal to 85%, the screening time is shortened to 5 minutes per case (80% higher than that of manual film reading), cross-mechanism and cross-dimension data integrated acquisition and sharing are achieved, 'hospital-disease control-grassroots' data intercommunication is achieved, and the screening efficiency is improved. Complete data support is provided for epidemiological analysis, longitudinal individual tracking and transverse group analysis are combined, the early warning period of regional outbreak is larger than or equal to 2 weeks, and a public health prevention and control gateway is assisted to move forwards.

The invention belongs to the technical field of aquatic pathogen detection, and provides a primer and probe combination with strong specificity and high sensitivity, a kit and a cdPCR detection method in order to solve the problems of dependence of absolute quantification of an existing qPCR technology on a standard curve and low repeatability and stability caused by uncontrollable quality of a standard substance. The primer probe can generate specific amplification on LYCIV and has no cross reaction on other common aquatic pathogens (such as NNV, DIV1 and the like), so that the specificity of a detection result is fundamentally ensured, a false positive result caused by the cross reaction is effectively avoided, and the diagnosis accuracy is improved. The method has the characteristics of extremely high sensitivity and absolute quantification, the lower limit of detection reaches 6.2 copies / mu L, the method has more advantages in quantification of samples with extremely low concentration, early diagnosis and detection of extremely low virus load can be realized, viruses with extremely low content in fish bodies can be detected earlier, precious time is provided for disease early warning and early intervention, and the method is worthy of popularization and application. Disease outbreak is effectively prevented.

The invention provides a fully humanized monoclonal antibody for resisting SARS-CoV and SARS-CoV-2 and application of the fully humanized monoclonal antibody, and belongs to the technical field of immunology, and the amino acid sequence of a heavy chain variable region of the monoclonal antibody is as shown in SEQ ID NO: 1; the amino acid sequence of a light chain variable region of the monoclonal antibody SARS2-1 is as shown in SEQ ID NO: 2; the antibody has a unique CDR partition, can identify S proteins of the two viruses at the same time, and has cross-neutralization activity on pseudoviruses of the two viruses. The antibody has the characteristics of high expression, full human source and good stability, is suitable for industrial production, and has potential application value for coping with outbreak prevalence caused by coronavirus possibly occurring at present and in the future.

The invention relates to the technical field of infectious disease monitoring, and discloses a respiratory infectious disease early warning method and system based on big data. The method comprises the following steps: identifying a first type of change nodes and calculating a second type of change nodes by calculating a patient number change value; a central area and a peripheral area are determined, and when it is monitored that a new case appears in the peripheral area, a first early warning signal is output based on the risk level of the central area; and defining the geographic position of the new case as a new seed location, updating the central area, and outputting a second early warning signal. According to the method, the infectious disease evolution time period is divided into at least one infectious disease development stage, so that differentiated risk assessment can be carried out for different infectious disease development stages, and when the geographical location of the infectious disease is transferred, the monitoring and early warning focus can focus on a new risk outbreak point, and the risk assessment accuracy is improved. The timeliness and geographical accuracy of early warning are ensured, and the utilization efficiency of prevention and control resources is improved.

This invention discloses a remote water quality monitoring system for secondary water supply systems, belonging to the field of water quality testing technology. It solves the problems of Legionella outbreaks caused by the coupling of hydraulic stagnation and high-temperature environments in secondary water supply systems, where the random distribution of pathogens in isolated pipe sections leads to spatial randomness in detection, false negatives, and high costs due to monitoring blind spots. The system includes a main control and computing module, an electrochemical impedance spectroscopy (EIS) measurement module, a microcurrent measurement module, a multi-channel switching module, a temperature sensing module, a bus power supply and communication module, and a three-electrode sensing patch. The main control and computing module coordinates and controls the operation sequence of all modules, executes EIS fitting and microcurrent trend analysis algorithms, and makes risk decisions based on the fusion results of multi-source data. This invention automatically monitors the water quality at the end of each pipeline, measures temperature, performs EIS scanning, analyzes weak metabolic currents, and fuses data to assess the risk of biofilm formation in real time, ultimately uploading early warning information.

This application discloses an indoor artificial rearing method for the rice stem borer, belonging to the field of insect rearing technology. This method addresses the problems of drowning in young larvae and frequent disease outbreaks in older larvae caused by homogeneous rearing across all generations. It constructs a constant-temperature environment and prepares granular dry feed for young larvae and slightly dehydrated flake feed for older larvae based on differences in larval mouthparts. A heterogeneous three-dimensional rearing device is used. In the young larvae stage, a slow-release substrate at the bottom maintains a vertical humidity gradient, and layered feeding prevents mechanical damage to the larvae. In the older larvae stage, substrate-free bare-bottom rearing and full-volume replacement feeding are employed. Timely removal of filamentous netting blocks the breeding environment for soft rot. Furthermore, the method combines pupation induction with corrugated paper induction and non-contact moisturizing, humidification during the adult dark period to delay gelatinous hardening, and overall substrate replacement during the light period to achieve damage-free egg mass harvesting. This application, through precise age-based management, significantly improves the survival rate and population propagation efficiency of the rice stem borer across all generations.

The invention relates to the field of order data analysis, in particular to an order data analysis method and device, equipment and a storage medium. The method comprises the following steps of performing multi-source concurrent acquisition on all-platform orders to obtain available order data streams; performing time sequence behavior trend analysis on the available order data flow to obtain order behavior trend characteristics; performing multi-user purchase identification and behavior interaction influence analysis based on the available order data stream to obtain an order behavior influence effect; performing outbreak critical point precursor learning based on the order behavior influence effect to obtain an order outbreak precursor signal; and performing order outbreak prediction based on the order behavior trend characteristics and the order outbreak precursor signal to obtain an outbreak prediction result. According to the invention, the judgment accuracy of order burst is improved, and the resource utilization efficiency is optimized.

In one aspect, the disclosure relates to compositions and methods for dispersing exiting Salmonella biofilms and inhibiting formation of Salmonella biofilms. In various aspects, the disclosed compositions can be used in methods of treating a persistent Salmonella infection, including an asymptomatic infection. Such infections can colonize a variety of tissues, including the gall-bladder. Also disclosed are methods of treating typhoid fever. Also disclosed are methods for mitigating or preventing secondary outbreaks of typhoid fever by treating asymptomatic subjects who had been symptomatic for typhoid fever at a previous time. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.

The invention relates to the technical field of pest risk prediction, and discloses a soybean aphid outbreak risk prediction method and system based on a population life table, and the method comprises the steps: obtaining historical data and real-time data, carrying out the time alignment and fusion of multi-source data in a sliding time window, and building an environment data set; establishing a life table data set based on the environment data and life table records; constructing a host growth period index HPI through inflection point detection and segmentation fitting of a remote sensing vegetation index, and marking a growth stage; fusing the life table data and the environment data to form a life table extension feature group, generating a population evolution path diagram in a continuous time window, and mining a time sequence ecological causal rule set; and according to the HPI, the evolution path and the causal rule, calculating and dynamically correcting an outbreak risk index, and outputting risk grade distribution and early warning information. According to the method, dynamic, explainable and high-precision prediction of the soybean aphid outbreak risk is realized through fusion of life table parameters and multi-source environment characteristics.

This item is a cover for a box-shaped livestock storage rack that has multiple storage spaces capable of accommodating livestock such as chickens. It is used to prevent the spread of viruses during disease control operations in the event of an outbreak of livestock infectious diseases such as avian influenza. The right and left sides of the cover are each provided with a zipper, one near the left edge and one near the right edge, extending from the bottom edge to near the top edge. As shown in the reference perspective diagram of the usage state, when both zippers are opened, the portion sandwiched between these zippers can be rolled up to expose the livestock storage rack, allowing for work on the rack, such as storing livestock. Each edge of the top surface of the cover is provided with a flap that protrudes outward, and this flap can be grasped to attach or detach the cover.

The invention discloses a mixed nutritional dinoflagellate bloom outbreak evaluation system based on big data analysis, and the system comprises the following units: a dinoflagellate metabolic activity analysis unit which collects water environment parameters, and carries out the metabolic activity correlation analysis of the water environment parameters and mixed nutritional dinoflagellate to obtain dinoflagellate metabolic activity parameters; according to the dinoflagellate metabolic activity parameters, algal group spatial and temporal distribution feature extraction is carried out, and feature data of algal group spatial and temporal distribution are obtained; the invention relates to the technical field of marine ecological early warning. According to the big data analysis-based mixed nutritional dinoflagellate algal bloom assessment system, reliable basic data is obtained through comprehensive and accurate data acquisition and analysis, dinoflagellate dynamics is visually presented through accurate trajectory reconstruction and density modeling, and risk assessment accuracy is improved through scientific critical threshold prediction; effective environmental factor correction enables evaluation to be more practical, and intelligent prediction and early warning use a fusion algorithm to improve prediction precision and realize timely early warning.

The present application relates to the technical field of automatic risk situation assessment, and discloses a risk situation automatic assessment method based on multi-source data access. The method accesses multi-source heterogeneous data streams, generates a uniform spatiotemporal reference risk event set after standardization processing. The cause-and-effect chain between events is established based on the relationship between time and space, and a composite risk trajectory is integrated and generated. The diffusion energy level, influence range and structural stability of the trajectory are calculated in real time, and the trajectory is dynamically assigned a stage label of dormancy, activity or outbreak. By continuously monitoring the stage labels and physical influence ranges of different trajectories, when multiple trajectories simultaneously turn to the active or outbreak state and the spatial ranges overlap, the overlapping area is automatically determined as a high-risk conflict domain and an early warning is generated. The method realizes continuous tracking of the risk evolution process and automatic perception of multi-risk interaction conflicts.

The present invention relates to a Fiber1 gene-based quantitative fluorescence detection method for fowl aviadenoviruses, belonging to the technical field of animal medical tests. The method uses designed primers and probes in combination with a specific reaction system and reaction conditions to detect fowl aviadenoviruses. The method is fast and effective, and has excellent detection sensitivity. By means of the method, fowl aviadenovirus detection and monitoring can be performed periodically, which helps to formulate effective prevention, control and eradication measures in a timely manner, so as to block large-area outbreaks of Angara disease in fowls, thus laying a foundation for prevention, control and eradication of Angara disease in fowls.

The invention discloses a multi-virus aerosol parallel detection method and system based on micro-fluidic impedance. The system comprises a gas conveying unit, a micro-fluidic chip, a sample partitioning unit, a sample detection unit and a data processing and analyzing unit, the gas conveying unit inputs to-be-detected gas into the micro-fluidic chip, the micro-fluidic chip is used for realizing large-particle impurity separation, and the sample partitioning unit and the sample detection unit are used for multi-channel parallel specific detection; and finally, parallel detection and quantitative analysis of various viruses are realized by a virus-impedance quantitative-qualitative model in the data processing and analysis unit. An efficient and automatic real-time detection scheme is provided for an airborne disease and pathogen monitoring system, accurate identification of the airborne diseases of the complex air matrix, which is simple and convenient to operate, is realized, dynamic and high-throughput evaluation on early-stage epidemic diseases is met, and the method can be applied to real-time detection and prevention and control scenes of airborne viruses.

The invention discloses a corn pathogen early warning method and system based on big data, and relates to the technical field of agricultural early warning, the method comprises the following steps: comparing sequences in historical small RNA deep sequencing data and verified viral pathogen monitoring data, and establishing a mapping relationship between the two to obtain a pathogen-small RNA reference corresponding relationship library; matching the small RNA deep sequencing data of the current monitoring period with the pathogen-small RNA reference corresponding relationship library, and extracting sequence fragments consistent with the mapping relationship in the library to obtain a target viral pathogen feature sequence; coupling the target viral pathogen feature sequence with the current environmental factor data; according to the method, the core value in the multi-source data can be effectively mined, the early infection signal of the pathogen can be accurately captured, the early accurate prediction of the transmission risk and potential outbreak of the corn viral pathogen is realized, a timely and reliable decision basis is provided for field prevention and control, and the corn production safety is guaranteed.

This application discloses an indoor artificial rearing method for the rice stem borer, belonging to the field of insect rearing technology. This method addresses the problems of drowning in young larvae and frequent disease outbreaks in older larvae caused by homogeneous rearing across all generations. It constructs a constant-temperature environment and prepares granular dry feed for young larvae and slightly dehydrated flake feed for older larvae based on differences in larval mouthparts. A heterogeneous three-dimensional rearing device is used. In the young larvae stage, a slow-release substrate at the bottom maintains a vertical humidity gradient, and layered feeding prevents mechanical damage to the larvae. In the older larvae stage, substrate-free bare-bottom rearing and full-volume replacement feeding are employed. Timely removal of filamentous netting blocks the breeding environment for soft rot. Furthermore, the method combines pupation induction with corrugated paper induction and non-contact moisturizing, humidification during the adult dark period to delay gelatinous hardening, and overall substrate replacement during the light period to achieve damage-free egg mass harvesting. This application, through precise age-based management, significantly improves the survival rate and population propagation efficiency of the rice stem borer across all generations.

The invention provides a prevention and control method for cynomolgus monkeys based on SRV virus detection, and belongs to the technical field of cynomolgus monkey SRV virus detection, the cynomolgus monkeys after birth are subjected to intensive SRV antigen PCR and SRV antibody ELISA detection and are fed and managed in groups, negative animals after 9 months are subjected to annual quarantine, positive animals stop quarantine and are subjected to positive management by default, and the cynomolgus monkeys after birth are subjected to SRV antigen PCR detection and SRV antibody ELISA detection by default. The antibody positive and / or antigen positive belongs to the positive category. According to the method, SRV antigen PCR and SRV antibody ELISA detection are densely carried out on the cynomolgus monkeys after birth, grouping feeding management is carried out, early prevention and control can cut off a transmission chain, recessive infection can be effectively recognized, partially-infected monkeys are low in antibody reaction and even cannot be detected, but long-term detoxification can be achieved, recessive infection sources can be accurately removed by combining PCR and serological detection in the baby monkey period, and the method has the advantages of high specificity, high sensitivity, high sensitivity and the like. Although the detection cost is invested in the early stage, huge losses such as treatment, animal elimination and experiment repetition caused by disease outbreak in the future can be avoided.

The invention relates to the field of aquaculture monitoring, and discloses an aquaculture pathogenic microorganism early warning method and system and a storage medium. The method comprises the following steps: acquiring environmental data through a sensor to form a preliminary data set; classifying environment modes based on the data set and judging a risk interval, and determining a pathogenic microorganism breeding potential acceleration area; predicting the breeding trend of pathogenic microorganisms and evaluating the risk level; triggering environment simulation according to the risk level, and generating a water nutrition balance scheme; tracking breeding change of pathogenic microorganisms, generating an alarm, and constructing an early warning model; updating environment data, activating a prevention and control protocol, and determining a control threshold value; the breeding density is adjusted to achieve sustainable risk reduction. According to the method and the system, the support vector machine, the random forest and other algorithms are combined with real-time monitoring and simulation analysis, so that the problem of difficulty in timely early warning of outbreak of pathogenic microorganisms in aquaculture is solved, and the accuracy of disease prevention and control and the sustainability of a culture environment are improved.

The invention discloses a biological outbreak risk assessment method for a water intake of a nuclear power plant, and belongs to the field of nuclear power plant operation safety and risk management, and the method comprises the steps: collecting temperature, dissolved oxygen, chlorophyll, salinity and biological abundance monitoring data through a sensor arranged at the water intake of the nuclear power plant, and carrying out the standardization processing; the method comprises the following steps: balancing the number of samples with different biological abundance levels by adopting minority class oversampling, constructing a random forest classification model, optimizing hyper-parameter data preprocessing through random search and cross validation, training a random forest model with the best generalization performance, performing nuclear power hot trap risk assessment, and outputting a biological disaster risk level in the current time period. When the grade is higher, an alarm is given; the biological outbreak risk assessment method for the water intake of the nuclear power plant is higher in adaptability and practicability, the risk level can be recognized in time, the operation and maintenance decision can be assisted, the safety and toughness of a heat trap of the nuclear power plant can be improved, and good expandability and engineering applicability are achieved.

The application provides an early information cascade outbreak prediction method based on global propagation probability.The technical scheme is as follows: firstly, converting the network with known structure, regarding all the undirected edges in the network as bidirectional directed edges; then compressing the nodes in the infected state node set and the nodes in the recovered state node set into one node at a certain moment, and regarding the nodes in the susceptible state node set among all the neighbor nodes of the infected state node set as the neighbors of the compressed node, to obtain the converted network; secondly, iteratively solving the equation set to obtain the probability that all the nodes belong to the giant connected piece, which is called the global propagation probability of the node; and thirdly, calculating the probability and range of the information cascade outbreak at the moment.The application fully utilizes the network structure data and early propagation situation, proposes two equivalent relations, and can quantitatively predict the probability and range of the cascade outbreak.

This invention discloses an early warning method for *Ulva prolifera* green tide disasters based on environmental DNA, belonging to the field of marine ecological disaster monitoring and early warning technology. Monitoring sections and fixed stations are deployed in areas prone to *Ulva prolifera* green tides to periodically collect surface water samples to obtain initial *eDNA* samples, i.e., initial environmental DNA samples. The abundance of *eDNA* for *Ulva prolifera* green tide outbreak genotypes in the water is quantitatively detected, and the rate of change in *eDNA* abundance is calculated. Simultaneous environmental parameters are collected and combined with satellite remote sensing data to construct a joint database of environmental driving factors and green tide biomass. Time series analysis is used to determine the time lead of *eDNA* signals to green tide outbreaks, outputting a green tide risk index. The green tide diffusion path is dynamically simulated to identify risk propagation channels, and early warning information is issued to guide targeted defense. Compared to the lag in traditional remote sensing and on-site observation, which only issue warnings after the algal bloom is visible, *eDNA* signals can be detected before *Ulva prolifera* biomass accumulates on a large scale, achieving early warning during the initial or latent period before a green tide outbreak.

The application provides a kind of anti-SARS-CoV and SARS-CoV-2 fully human monoclonal antibody and its application, belong to immunology technical field, the amino acid sequence of the variable region of heavy chain of the monoclonal antibody is as shown in SEQ ID NO:1;The amino acid sequence of the variable region of light chain of the monoclonal antibody SARS2-1 is as shown in SEQ ID NO:2;The antibody has unique CDR partition, can simultaneously recognize the S protein of the two viruses mentioned above, and has cross-neutralizing activity to the pseudo virus of the two viruses mentioned above.The antibody has the characteristics of high expression, fully human, good stability at the same time, is suitable for industrial production, has potential application value for coping with the outbreak and prevalence caused by coronavirus at present and in the future.

The present invention relates to a carcass treatment system 1. Such a carcass treatment system 1 ferments and decomposes carcasses by hyperthermophile of 85 to 110° C. and aerobic conditions, and comprises a mobile fermentation process unit 3 for transferring to an onset site of disease and treating the livestock carcass by hyperthermophile; or a burial fermentation process unit 5 for excavating a burial site 6 near the outbreak site of disease and treating the livestock carcass by hyperthermophile; a base treatment unit 7 for secondarily processing the livestock carcass treated primarily by the mobile fermentation process unit 3 or the burial fermentation process unit 5 with hyperthermophile, wherein the primary treatment is performed by treating with hyperthermophile at fermentation temperature of 85 to 110° C. for 8-15 days to decompose flesh of the carcass, and the secondary treatment is performed by treating with hyperthermophile for 2-4 weeks.

This invention discloses a two-layer dynamic model of a composite transportation-medical network based on Markov chains, effectively overcoming the shortcomings of traditional complex network failure models in static network construction and the dynamic coupling and integration of disease transmission and infrastructure failure. This model integrates the functional relationships between transportation and medical infrastructure, introduces the background of infectious disease transmission, combines the SEIHR model and Markov chains to describe disease transmission dynamics, and utilizes a load-capacity model to capture the dynamic process of cascading failures, thus forming a two-layer dynamic model. This invention can not only accurately simulate network response and resource allocation driven by individual mobility and medical treatment decisions, but also effectively reveal the triggering mechanism and propagation path of cascading failures, providing scientific support for urban infrastructure planning optimization, public health emergency resource scheduling, and risk early warning. The applicability and analytical accuracy of the model are improved in infectious disease outbreak scenarios.

The application belongs to the technical field of jellyfish prevention and control, and discloses application of DMX-5804 in preparation of a medicine for inhibiting budding reproduction of Aurelia sp.1. By inhibiting budding reproduction of A.coerulea hydrocladium, the outbreak of Aurelia sp.1 can be prevented and controlled, and the adverse effects on marine ecological environment and human coastal economic activities can be reduced. Through experimental verification, 1 μM DMX-5804 can reduce the number of hydrocladium buds without affecting the living state of A.coerulea hydrocladium, which indicates that the compound DMX-5804 can inhibit the asexual reproduction of Aurelia sp.1 without toxicity at this concentration. The application provides a new theory and method for controlling the number of jellyfish, preventing the outbreak of Aurelia sp.1, and studying the biological conservative cell proliferation strategy.

This invention relates to the field of microbial technology, specifically to a highly efficient lysing Vibrio alginolyticus phage derived from abalone, its composition, and its applications. The phage, named RKP-VA24471, has been deposited at the China General Microbiological Culture Collection Center (CGMCC) with accession number CGMCC No. 46426. The phage has a titer of not less than 1 × 10⁻⁶. ^10 With a PFU / mL concentration and an optimal MOI of 0.01, this phage exhibits advantages such as a short latency period, high outbreak rate, good genetic stability, and a wide adaptability to temperature and pH. Lysis spectrum experiments show that this phage has broad-spectrum lytic activity against various Vibrio species, including *Vibrio alginolyticus* and *Vibrio parahaemolyticus*, with an overall lysis rate of 80.6%. Animal experiments have demonstrated that this phage significantly improves the survival rate of abalone infected with *Vibrio alginolyticus*, outperforming antibiotics and fish-derived phages. The phage and its composition provided by this invention can be used for the prevention and treatment of vibriosis in aquatic animals such as abalone, offering a highly efficient and environmentally friendly biological control solution as an alternative to antibiotics.

The invention discloses a specific primer group for simultaneously detecting 54 key pathogens as well as a related method and application thereof, the specific primer group is designed aiming at the 54 key pathogens, a target sequence is amplified by combining multiple PCR (Polymerase Chain Reaction) and a nanopore sequencing technology, and then purification, enrichment, library building and sequencing are carried out, so that the specific primer group can be used for simultaneously detecting the 54 key pathogens. A detection technology system capable of detecting 54 key pathogens at the same time is established, the method is rapid and accurate in detection and high in sensitivity, required reagents and instruments are portable, pathogen detection work can be carried out in a field environment, beneficial data support can be provided for source tracing of the pathogens, analysis of mutation sites can be carried out, and the detection efficiency is improved. The method has very prominent significance on early prevention and control of sudden new infectious diseases caused by high-pathogenicity pathogenic microorganisms.