159results about How to "Accuracy" patented technology

The invention provides an automatic task allocation method and system for an elevator emergency rescue platform, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining elevator operation state data and environment information data, constructing an emergency task model, and obtaining classified emergency tasks; inputting the classified emergency tasks into a deep reinforcement learning model to generate a task allocation scheme; the deep reinforcement learning model is formed by combining a deep Q network structure with a priority scanning algorithm, and a priority experience playback mechanism is introduced; and constructing a multi-objective optimization module comprising a time constraint, a space constraint and a resource constraint, and optimizing the task allocation scheme by the multi-objective optimization module based on an improved particle swarm algorithm to obtain an optimal task allocation scheme.

The invention discloses a partial discharge positioning method, system and device based on an acoustic-electric combined signal, and relates to the technical field of electrical device detection, and the method comprises the steps: deploying a plurality of sensor nodes based on a target electrical device, and collecting an electromagnetic signal and a sound wave signal generated by partial discharge at the same time; calculating a preliminary positioning result of the partial discharge source based on the collected acoustic-electric combined signal; environment parameter data, historical operation data and electrical equipment structure information in the electrical equipment are collected and input into a preset error correction model, and a corresponding error correction interval is obtained; when a certain preliminary positioning result exceeds the corresponding error correction interval, obtaining a timestamp corresponding to the preliminary positioning result; performing multi-source information fusion positioning based on the environmental parameter data, the historical operation data, the electrical equipment structure information and the corresponding timestamps, and analyzing and determining final positioning coordinates of the partial discharge source; the accuracy of partial discharge signal positioning is improved, and the positioning efficiency is improved.

The invention discloses a flight space directional training auxiliary system and method, and the system comprises a flight task simulation module; the meteorological environment simulation module is used for simulating a meteorological scene corresponding to a flight task, and simulating the meteorological scene by establishing a multi-dimensional meteorological model and combining meteorological parameters; the meteorological environment parameter control module comprises a time step length control module which adopts a preset time step length to input meteorological parameter observation data collected in real time to the meteorological parameter setting module; the specific meteorological event control module is used for transmitting meteorological parameters of a specific meteorological event area to the meteorological parameter setting module when the airplane triggers a specific meteorological event; and the training scheme generation module is used for generating a proper training scheme according to the historical training information of the flight personnel. According to the system and the method, a real and personalized simulation training environment can be provided for the flight personnel, and the professional skills and the capability of coping with various flight conditions of the flight personnel are effectively improved.

The invention discloses a vegetation resistance and resilience evaluation system and method under drought stress, and belongs to the field of global change ecology research. Comprising the following steps: collecting related data of vegetation resistance and restoring force by using a data collection module, and inputting the related data of vegetation resistance and restoring force into a data storage module for storage, a vegetation physiological index processing sub-module, a vegetation structure index processing sub-module and a vegetation function index processing sub-module in the data processing module are called to process the collected and stored data; and calling a lag accumulation correlation coefficient calculation sub-module, a vegetation resistance index and restoring force index evaluation model sub-module and a vegetation resistance and restoring force grade division sub-module in the analysis and evaluation module, and finally outputting vegetation resistance and restoring force evaluation grades. According to the method, comprehensive, systematic and accurate evaluation and grading can be carried out on the resistance and the restoring force of the vegetation under drought stress, the vegetation which is difficult to resist drought or is restored after drought stress in the evaluation area is effectively identified, and the method is simple to operate and easy to realize.

The invention provides a premature delivery prediction method and device, electronic equipment and a readable storage medium, and relates to the technical field of data analysis. The method comprises the following steps: based on a preset model, scoring acquired comprehensive information of a pregnant woman in an early pregnancy stage; wherein the comprehensive information comprises demographic information, clinical information and biochemical index information; and predicting the premature delivery risk of the pregnant woman according to the scoring result. According to the method, the comprehensive information of the pregnant women at the early pregnancy stage is scored through the preset model, the premature delivery risk of the pregnant women is predicted according to the scoring result, quantitative evaluation of premature delivery risk prediction of the pregnant women at the early pregnancy stage is achieved, and objectivity and certain accuracy are achieved; and the difficulty and the cost of obtaining the comprehensive information of the pregnant women in the early pregnancy are relatively low, the difficulty and the cost of predicting the premature delivery risk of the pregnant women in the early pregnancy are reduced, and medical resources are saved.

The invention discloses a data merging optimization method and device for incremental data migration, equipment and a storage medium, and relates to the technical field of databases, and the method comprises the steps: carrying out the data collection of a source end database, and sequentially sending the collected data to a message queue; sequentially pulling the data from the message queue, merging the data, and converting the data into a data packet; performing execution sequence number distribution on the data packets; and based on the execution sequence number, sequentially playing back the data of the data packet to a target end database. According to the method, the effect of concurrent execution of the single-table data is achieved through data merging, the problem that the single-table incremental data cannot be executed in parallel due to the time sequence problem is solved, the method does not depend on the characteristics of the database, can be suitable for data migration synchronization of different database types, and is good in compatibility.

The invention discloses a method for quantitatively evaluating the immune level of subsequent commutation failure of an AC-DC hybrid system, and the method comprises the steps: carrying out the analysis of the transient process of the system based on the transient reactive dynamic characteristics of the AC-DC hybrid system on the basis of achieving the cross-regional power transmission through a high-voltage DC power transmission technology; the essential relation among the electrical quantities in the transient process is obtained; and deducing an online evaluation index GCFII for reflecting the immune level of the subsequent commutation failure of the system in combination with known system parameters and electrical signals acquired by the PMU. According to the method, the influence of the direct current control mode is considered by obtaining the online evaluation index GCFII, and real-time updating can be carried out along with the change of system operation parameters, so that the requirements of real-time performance and accuracy can be met, a theoretical basis is provided for power grid construction and operation of operation personnel, and safe operation of a power grid is effectively ensured.

The invention provides a safety monitoring method and device for a fully mechanized coal mining face roof. The method comprises the steps that a first data set related to a hydraulic support of the fully mechanized coal mining face is obtained, wherein the first data set at least comprises the initial supporting force, the resistance increasing ratio, the weighting step pitch and the safety valve opening proportion of the hydraulic support; micro-seismic of the fully mechanized coal mining face is monitored, a second data set is obtained, and the second data set comprises daily micro-seismic event maximum energy, daily micro-seismic event total energy and daily micro-seismic event total energy increase; and according to the first data set and the second data set, carrying out disaster grade identification on the fully mechanized coal mining face roof so as to determine a target disaster grade of the fully mechanized coal mining face roof. Due to the fact that multiple evaluation indexes are adopted for evaluating and analyzing the roof disaster danger degree, and the adopted evaluation index reference values are all statistical data obtained based on the mining theory and mining practice, the analysis structure of the roof disaster danger degree is more scientific and accurate.

The invention discloses a GOOSE protocol message anomaly detection method based on a generative adversarial network model, and the model training method comprises the following steps: S1, obtaining a large number of network data messages of an electric power industrial control system, and carrying out the preprocessing of the data, including denoising and data cleaning; s2, performing feature extraction on the GOOSE data, including a protocol field value, a source / destination address, a timestamp, a state number, a serial number, an AppID application identifier, a TAG identification value and a data length, so as to establish a proper feature set; and S3, training the GOOSE protocol data feature set through a generative adversarial network model. And continuously adjusting model parameters and training according to a training judgment result during detection, and analyzing and detecting an abnormal GOOSE protocol message. According to the method, effective anomaly detection can be carried out on the existing GOOSE protocol network attack, and safe operation guarantee is provided for an electric power industrial control network system.

The invention provides an automatic updating method and system for a power grid equipment graph model based on data collected and recorded by an unmanned aerial vehicle. The unmanned aerial vehicle is used for regularly patrolling a power grid line and collecting image data and position data of power grid equipment in real time; on the basis of a power grid GIS system and identified equipment information, topology key nodes of the power grid are automatically identified, and data collected by the unmanned aerial vehicle are compared and matched with an existing power grid topology structure through a data matching algorithm; providing a user interaction interface for a user to check a matching result, and performing confirmation or necessary adjustment; based on topological key node information, information of other devices on a line is automatically updated, newly collected data is integrated with an existing power grid device graph model, and corresponding power grid device graph model data is automatically updated. According to the method, the key topological nodes of the power grid can be automatically identified, the equipment information on the power grid line is automatically updated, the updating efficiency and accuracy of the power grid equipment graph model are improved, and real-time updating and sharing of data are realized.

The invention discloses a risk assessment method, device and equipment, a storage medium and a computer program product, and is applied to the technical field of artificial intelligence. The multi-dimensional data at least comprises historical loan data of the to-be-evaluated enterprise, operation data of the to-be-evaluated enterprise and macroeconomic indicators; carrying out data processing on the multi-dimensional data through a target model, and generating a risk probability containing to-be-evaluated enterprise default; according to the risk probability and a risk probability range of a plurality of risk levels corresponding to the business to be handled, determining a risk level corresponding to the enterprise to be assessed under the business to be handled, the business to be handled being a business requested to be handled by the enterprise to be assessed; and determining whether to handle the to-be-handled business for the to-be-assessed enterprise according to the Therefore, the scheme provided by the invention can improve the accuracy of enterprise default risk assessment.

The invention discloses a space surveying and mapping calibration method and system based on synchronous positioning drawing, and relates to the technical field of space surveying and mapping, the space surveying and mapping calibration method based on synchronous positioning drawing comprises the following steps: obtaining environment characteristic data; identifying a space environment and a key landmark; based on a stereoscopic vision technology, obtaining a space environment map according to the identified space environment and the key landmarks; adjusting the motion track of the detection equipment in the space environment by using an optimization algorithm; and calibrating the space environment map. The dynamic change in the space environment is monitored in real time, and the influence of the change on the map is evaluated, so that the environment change can be automatically judged and dealt with, the map is ensured to be always kept in the latest state, the space environment map can be automatically calibrated and updated, the need of manual intervention is reduced, and the working efficiency is improved. And the efficiency and timeliness of map maintenance are improved.

The invention relates to an aging correction method for a nitrogen-oxygen sensor, which comprises the following steps of: measuring and calculating an aging correction coefficient, archiving the aging correction coefficient into an aging correction module, and extracting data in real time by using the aging correction module, so that the output value of the nitrogen-oxygen sensor is corrected, and after a probe is aged, the output value of the nitrogen-oxygen sensor is corrected. A relatively accurate numerical value can still be output, namely, aging of a probe is offset by using a correction part, an aging correction coefficient can be obtained by looking up a table in combination with accumulated working time, accumulated driving mileage and daily driving working conditions after the nitrogen-oxygen sensor is loaded and parameters such as NOx and O2 concentrations measured for a long time, and the value is multiplied by an output coefficient, so that the aging correction coefficient is obtained. And the output precision can be improved.

The invention relates to an electric energy metering method for broadband characteristics of an electric power system. The electric energy metering method comprises the following steps: acquiring electric quantity data corresponding to current and voltage by using a filter built in a power grid system; preprocessing the electric quantity data to obtain effective electric quantity data; calculating the effective electric quantity data through a preset power formula to obtain average power of a sampling period; decomposing the electric quantity data of the electric power system to obtain a wavelet packet frequency band; carrying out adaptive harmonic extraction on the wavelet packet frequency band to obtain a sampling frequency spectrum; and obtaining an electric energy metering value of the power system according to the sampling frequency spectrum and time integration. In the technical scheme of the invention, the result shows that the amplitude and phase information of the waveform are well reserved by the extracted waveform, and electric energy metering can be carried out on each frequency component by direct integration; similarly, the method is used for processing other wavelet packet frequency bands, the same effect is remarkable, and the metering error can be controlled within 3%.

The invention relates to a printed circuit board welding spot temperature cycle acceleration test profile design method, which takes welding spots on an aging test board carrying a test device as research objects, and utilizes simulation software to carry out finite element simulation on a target circuit board under multiple groups of temperature cycle stress conditions. A plurality of groups of fatigue life are calculated through an Engelmaier fatigue life model in combination with a simulation result, the fatigue life obtained through calculation is substituted into a Coffin-Manson acceleration model for fitting calculation of an acceleration factor calculation formula, finally, the acceleration factor calculation formula is utilized, required acceleration time is substituted into the formula for calculation to obtain actual condition design acceleration test section parameters, and the acceleration test section parameters are calculated according to the actual condition design acceleration test section parameters. The obtained acceleration test profile can complete the temperature cycle acceleration test of the printed circuit board within a given time, and the process of the method is as shown in Figure 8. The method belongs to the technical field of reliability evaluation of microelectronics and components.

The invention discloses a two-stage space-time diagram network and multi-source information fusion time sequence probability prediction method and device, and relates to the technical field of artificial intelligence and big data analysis. The method comprises the following steps: acquiring historical wind speed sequence data and corresponding target variable sequence data; preprocessing the data, and constructing a training sample according to a preset time sliding window; constructing a time sequence probability prediction framework of the two-stage space-time diagram network and multi-source information fusion; based on the fluctuation correlation of the historical sequence data, constructing an adjacent matrix between nodes; inputting the training sample and the adjacent matrix between the nodes into a wind speed prediction model, training the model through a designed threshold perception loss function of a wind speed-power nonlinear relationship, and outputting a multi-node wind speed prediction probability of a first stage; and inputting the multi-node wind speed prediction probability and the multi-source environmental factor data into the gradient boosting tree model for second-stage prediction, and outputting a multi-node wind power prediction result. According to the invention, prediction errors can be reduced.

The invention discloses a compact sandstone reservoir diagenetic phase quantitative characterization method which is characterized by comprising the following specific steps: step 1, determining diagenetic phase evaluation parameters and obtaining a parameter sequence; step 2, determining a correlation coefficient of the parameter sequence obtained in the step 1 by applying a grey correlation method, and calculating a correlation degree; step 3, constructing a multivariate comprehensive classification coefficient Feci; 4, dividing lithogenous phase types according to the multivariate comprehensive classification coefficient obtained in the step 3; and 5, performing logging characteristic analysis on the types of the diagenetic facies divided in the step 4, selecting a sensitive logging curve, and obtaining a discrimination function by using a Fisher discrimination method so as to realize quantitative identification of the diagenetic facies. According to the method, evaluation parameters are high in credibility and objective, so that lithogenous phase judgment can be more accurate.

The invention discloses a snow melting salt corrosion test device and a test method, the snow melting salt corrosion test device comprises a test box, a spherical temperature and humidity test chamber with adjustable temperature and humidity is arranged in the test box, the spherical temperature and humidity test chamber is composed of a lower hemisphere chamber and an upper hemisphere chamber which can be separated, and a sample placing rack is arranged in the lower hemisphere chamber; a rotary spraying mechanism is arranged above the sample placing frame, the rotary spraying mechanism comprises a semi-arc-shaped support capable of rotating and nozzles uniformly arranged on the semi-arc-shaped support, the sample placing frame is installed on a lifting support capable of ascending and descending, and the lifting support is constructed to be a weighing support with a gravity sensing function. And the lower hemisphere chamber is configured to be a soaking water tank connected with a water supply system. According to the invention, the corrosion process of the snow-melting salt test sample in an icing-melting circulation state can be simulated.

The invention relates to a meteorological soil factor-based crop pest spatio-temporal prediction method and device. The method comprises the following steps of: acquiring daily pest information and meteorological soil data in a full life cycle of crops; performing data checking and preprocessing on the meteorological soil data to generate research area meteorological soil raster data with consistent spatial resolution; and training an insect pest prediction model by using the training set, and taking the meteorological soil raster data as input data of the insect pest prediction model to realize insect pest quantity prediction. The crop insect pest time-space prediction method and the crop insect pest time-space prediction device provided by the invention have the beneficial effects of effectively solving the problems of excessive dependence on expert experience and complexity in relation between prediction factors and insect pests, surpassing the limitation of previous small-range prediction, and realizing accurate prediction of crop insect pest time sequence and spatial distribution.

The invention provides a retrieval enhancement generation method and system based on long-term memory, electronic equipment and a storage medium, and belongs to the field of artificial intelligence. The method comprises the following steps: performing compression and semantic coding on the content of a whole database by utilizing a large language model to form global memory; generating a preliminary answer on the basis of a query proposed by a user, and taking the preliminary answer as retrieval information to retrieve related information from a database; and generating a final answer based on the retrieved related information and the global memory, and providing the final answer to the user. Firstly, global memory is formed by using a lightweight large language model, retrieval clues are generated based on query proposed by a user, and most valuable information can be quickly and accurately positioned by adjusting the detailed degree and correlation of the retrieval clues. And a final detailed answer is generated by combining the retrieved detailed evidence and the preliminary answer, so that the method has the advantages of high efficiency, accuracy and flexibility, and can also be applied to application scenes needing to process a large amount of text data and complex queries.

The invention belongs to the technical field of medical data information processing, and discloses a human nutrient element deficiency risk intelligent prediction method based on conventional test data, comprising the following steps: acquiring basic personal information and conventional test data of a subject; performing data cleaning on the acquired data information; the individual nutrition status is comprehensively evaluated, and risk labels of nutrient element deficiency are marked in a standardized mode; training a human nutrient element deficiency risk prediction model; determining an optimal threshold value of the human nutrient element deficiency risk prediction model; and evaluating the feasibility of human nutrient element deficiency risk prediction based on conventional test data. By adopting the human nutrient element deficiency risk intelligent prediction method based on the conventional inspection data, the capability of identifying the human nutrient element deficiency risk is comprehensively improved, the detection cost is reduced, the detection time is shortened, dynamic monitoring and immediate feedback of the nutrition status are realized, and the human nutrient element deficiency risk intelligent prediction method is suitable for popularization and application. The method has the characteristics of comprehensiveness, high efficiency, accuracy and real-time dynamic monitoring.

The invention discloses application of a cannabidiol analogue CIAC001 in preparation of a medicine for treating Alzheimer's disease, and relates to the technical field of medicine. The application comprises the following steps: step 1, medicine extraction: extracting CIAC001, an NMDA receptor antagonist and a cholinesterase inhibitor according to a ratio of 1: 5: 5 into a medicine carrying needle cylinder; 2, medicine heating, wherein a heating assembly is started to conduct pulse heating on the medicine in the medicine carrying needle cylinder; 3, nursing the patient: heating and nursing the injection part of the patient by using a nursing assembly; 4, medicine injection: pushing a pushing head, puncturing a needle into the injection part of the patient, and injecting the medicine into the body of the patient; and 5, observing and treating side reactions: taking down the injection device after the drug reaction is complete, observing the side reaction condition of the patient, and starting a temperature regulator to cool the patient. The medicine activity can be effectively improved, side effects of the medicine on patients are reduced, and therefore the treatment effect of the medicine is enhanced.

The invention belongs to the technical field of electric power engineering, and discloses a system and method for measuring the grounding resistance of a tower in a grounding looped network, and the system comprises a data obtaining module which is used for obtaining the current values and voltage values of a grounding line in different switching states in different environments; the first data processing module is used for calculating grounding resistance values in different grounding lines according to the current values and the voltage values of the grounding lines in different switching states in different environments; preprocessing the grounding resistance values in the different grounding lines to obtain grounding resistance data sets in the different grounding lines; the resistance change prediction module is used for training according to the grounding resistance data sets in different grounding lines to obtain a line tower grounding resistance prediction model, and predicting the change trend of the line tower grounding resistance through the line tower grounding resistance prediction model; accurate monitoring of the grounding resistance is achieved, potential problems can be found as early as possible, and the fault risk is reduced.

The present invention discloses an intelligent motor fault diagnosis method based on digital twins, belonging to the field of motor fault diagnosis. In the model training stage, the method selects feature quantities through the random forest method to train an extreme learning machine model with an autoregressive cycle function as a digital twin motor. In the open circuit fault diagnosis stage, the method collects the physical entity motor feature quantity and inputs it into the digital twin virtual motor to obtain the digital twin current, and makes a difference between the actual winding current data and the digital twin winding current to obtain the current residual, and uses the difference value of the current residual as a threshold to perform open circuit fault diagnosis, and adopts the sliding average window method to locate the fault; in the turn-to-turn short circuit fault diagnosis stage, the current symmetrical mean absolute error is used as a judgment standard to diagnose the short circuit fault. The present invention does not require precise design parameters and motor fault data of the motor, but only requires data when the motor is in normal operation to quickly and real-time diagnose the motor open circuit fault and turn-to-turn short circuit fault, and has strong robustness.

The invention belongs to the field of pesticide poisoning prognosis, and particularly discloses a method for establishing an acute diquat pesticide poisoning prediction death risk index, which comprises the following steps: collecting data; establishing a death risk index model based on demographic characteristics and blood routine test results; and carrying out model training, internal verification and external verification. The death risk index provided by the invention comprehensively considers age, diquat blood concentration, leukocyte count and glutamic oxalacetic transaminase concentration, and aims to accurately evaluate the death risk of acute diquat pesticide poisoning patients in an index form. Through deep analysis of large-scale clinical data, the death risk index of the invention not only has scientificity and accuracy on the data level, but also can play an important role in clinical practice, can be applied to construction of acute diquat pesticide poisoning death risk prediction terminal equipment, helps medical teams to distribute medical resources more reasonably, and has a good application prospect. High-risk patients are preferentially processed, so that the overall treatment effect and the survival opportunity of the patients are improved.

The invention is suitable for the field of target detection repelling, and provides a transformer substation bird repelling control method and system based on target detection, and the method comprises the steps: carrying out the preprocessing of a to-be-detected image through an image enhancement module, extracting image features through a backbone network stacking convolution layer, carrying out the feature fusion through a neck structure, and forming a multi-scale feature; outputting an edge feature and a category result of a target object in the to-be-detected image through a detection head module; an alarm signal is generated in response to a driving instruction generated based on the edge features and the category result, the alarm signal is transmitted to an intelligent control system through a communication module, and the intelligent control system issues an action instruction to a gas pipeline electromagnetic valve of an air compression injection system at an alarm point through a DO module after operation of a CPU module. And injecting compressed gas to repel the target object. According to the invention, birds in the transformer substation area can be repelled, and safe operation of the transformer substation can be effectively protected.

The invention discloses a system for eliminating inrush current of a fan, particularly relates to the field of data analysis, and comprises a monitoring time division module, a fan data acquisition module, a fan data analysis module, a comprehensive analysis module, an abnormality judgment module and an inrush current elimination module. By designing the system for eliminating the inrush current of the fan, the problem of the inrush current caused by closing of a high-voltage air switch during operation of the fan is effectively solved, the inrush current phenomenon is accurately judged and an elimination mechanism is triggered by monitoring, collecting and analyzing operation data of the fan, an electrical system is protected from being impacted by transient high current, and the service life of the fan is prolonged. The operation stability and reliability of the fan are improved, the system failure rate and the maintenance cost are reduced, and powerful support is provided for healthy development of the wind power industry.

The application discloses a wind turbine load rapid evaluation method for wind farm collaborative regulation, and relates to the technical field of wind turbine safety regulation. The specific implementation scheme is as follows: standard pulse instructions are applied to the wind turbine to extract vibration characteristic parameters and load oscillation initial amplitude; based on the rate of the candidate regulation instruction and the environmental wind speed, a transient excitation intensity function is set and effective excitation equivalent is calculated; according to the ratio of the effective excitation equivalent and the reference excitation equivalent, the relative impact intensity and the load oscillation amplitude are determined; the envelope function is constructed by combining the vibration characteristic parameters of the wind turbine, and the earliest safety execution time required for attenuation to the safety threshold is solved; the executability of the regulation instruction is determined by comparing with the maximum waiting time set by the rated blade bending moment load, and the load evaluation is completed; the collaborative controller can dynamically and accurately arrange the instruction sequence, and the overall safety of the wind turbine is improved.

The invention provides an F-P spectral imaging system and method, and belongs to the technical field of spectral component measurement, and the system comprises a front lens which is used for collecting light energy and converting the light energy into emergent light meeting the limitation of the clear aperture of an F-P interferometer; the annular band optical filter comprises a central area and a plurality of concentric annular areas surrounding the central area, the bandwidth of each area is the same as the free spectral range of the F-P interferometer, and the annular band optical filter is used for eliminating level aliasing in emergent light of the front lens; the F-P interferometer is used for carrying out wavelength selection on emergent light of the annular band optical filter and outputting an interference field; the imaging mirror is used for projecting the interference field to the detector; and the detector is used for converting the projected optical signal into a digital electric signal. The F-P spectral imaging system which can maintain high spectral resolution and also can realize wide spectrum coverage is constructed by introducing the annular band optical filter which is accurately matched with the FSR of the F-P interferometer.

The invention discloses a qualitative detection method and a quantitative detection method of salmonella based on RPA-CRISPR / Cas12a (recombinase polymerase amplification-CRISPR / CRISPR / Cas12a). The qualitative detection method comprises the following steps: reacting a mixed system at least comprising an RPA reagent, a salmonella upstream primer, a salmonella downstream primer, a CRISPR / Cas12a system and a to-be-detected sample to obtain a reaction product of the to-be-detected sample; and detecting the reaction product of the to-be-detected sample by adopting a fluorescence detection method or an electrochemical test paper detection method, and qualitatively detecting the salmonella in the to-be-detected sample according to an absorption peak at 500-520nm of a fluorescence spectrum or a T line and a current value of electrochemical test paper. According to the salmonella detection method provided by the invention, potential pollution risks in a traditional step-by-step reaction process are avoided, and meanwhile, the detection method shows high stability, relatively high specificity, excellent sensitivity and excellent field detection capability.