64 results about "Seed sample" patented technology

The invention discloses a seed vigor prediction model construction method based on spectral indexes and a detection system, and relates to a method and a system for rapidly and nondestructively detecting seed vigor by using a visible-near infrared spectrum technology. The problem that in the prior art, the requirement for accurate and quantitative evaluation of seed vigor in modern agriculture cannot be met is solved. The method comprises the following steps: firstly, acquiring spectral data and actual germination rate data of a seed sample, and carrying out abnormal value elimination and pretreatment on the spectral data; then extracting one-dimensional, two-dimensional and three-dimensional spectral indexes from the preprocessed spectral data, and screening out an optimal spectral index by using a correlation matrix method; and finally, by taking the optimal spectral index as input and the actual germination rate as output, establishing a seed activity prediction model based on the optimal spectral index. The prediction model is embedded in the system, and seed vigor prediction is achieved. The method is suitable for the fields of seed quality detection and grading, precise sowing, agricultural production and the like.

The invention discloses a self-learning method of a credit review dialogue model, and the method comprises the steps: constructing a seed sample which is used for marking a reasoning process and dialogue data of a question corresponding to the reasoning process; inputting the dialogue data in the seed sample into a current credit examination dialogue model, outputting a prediction problem and a reasoning process corresponding to a prediction question by the credit examination dialogue model, and forming a series of sample data by the dialogue data, the reasoning process and the corresponding prediction question; evaluating the quality of currently generated sample data, screening out high-quality sample data as seed samples, and putting the seed samples into a seed sample database; and training the trust dialogue model based on the seed samples in the seed sample database. By iterating a small amount of dialogue data of questions marked with the reasoning process, progressive self-optimization of the credit review dialogue model is realized, and the logical reasoning ability of the credit review dialogue model is improved.

The invention relates to the technical field of agricultural machinery intellectualization, in particular to a small seed precision sowing monitoring method and system. The method comprises the following steps: setting a time window, and acquiring first signal data passing through a surface laser sensor and second signal data passing through an infrared sensor based on the time window; respectively correcting the first signal data and the second signal data to obtain first optimized data and second optimized data; performing matching degree calculation according to a preset seed sample database by utilizing the first optimization data and the second optimization data to obtain a fusion matching degree score; and calculating the actual pulse frequency of the time window according to the fusion matching degree score, and determining miss-seeding and reseeding by using the actual pulse frequency. According to the invention, the problem that reseeding and miss-seeding are difficult to monitor due to the fact that small seeds are difficult to identify during mechanical seeding in the prior art is solved, and the identification capability of the small seeds is greatly improved.

The invention discloses a legal affair large model construction method, a legal affair information analysis method and a computer device. The legal affair large model construction method comprises the following steps: constructing a legal domain knowledge graph comprising a multi-level organization knowledge architecture, obtaining a preset amount of manually labeled seed samples, extracting structural features of the seed samples, and generating a target template; according to the legal domain knowledge graph pair, filling the target template, and generating diversified training samples; a multi-level sample generation strategy is configured according to training samples and requirements, a large language model is trained in a layered mode according to the strategy, and the trained large language model serves as a legal affair large model. According to the method, diversified high-quality legal training samples are automatically generated through a legal domain knowledge graph and a small number of seed samples, dependence on a large amount of annotation data and computing power resources is effectively reduced, model training is conducted through a multi-level sample generation strategy, it is ensured that complex legal knowledge and reasoning can be gradually mastered, and the method is high in practicability. And the utilization rate of data and the accuracy of model training are effectively improved.

The utility model relates to the field of crushers, and discloses a crop seed sample crusher which comprises a crushing shell, a bottom shell arranged at the bottom end of the crushing shell, a motor arranged in the bottom shell, an output shaft arranged at the output end of the motor, a crushing cavity and a powder cavity respectively arranged at the upper part and the lower part of the crushing shell, and a filter screen movably connected between the crushing cavity and the powder cavity, the output shaft penetrates through the powder cavity and penetrates into the crushing cavity, the part, located in the crushing cavity, of the output shaft is connected with a crushing roller, the bottom face of the filter screen is provided with a first vibration part, the output shaft is provided with a second vibration part, the output shaft in the powder cavity is connected with a scraper, a baffle is arranged in the powder cavity, and the movement track of the scraper intersects with the baffle. The powder filtering device has the advantages that the filter screen slides up and down in a reciprocating mode under the combined action of the first elastic piece, vibration of the filter screen is achieved, meanwhile, the output shaft drives the scraper to rotate, powder is brought out under the combined action of the baffle and the second elastic piece, and the powder filtering efficiency and the discharging efficiency are improved.

The utility model provides a layered sampling mechanism of a grain seed sampler, and belongs to the technical field of grain seed sampling. Comprising a sampling assembly, the sampling assembly is arranged in a supporting column, the sampling assembly comprises a baffle fixed in the supporting column, a sliding plate is arranged in the baffle, a supporting plate is arranged in the supporting column, a sliding groove is formed in the inner wall of the supporting plate, and the sliding groove is communicated with the sliding plate; a fixing rod is arranged at the top of the supporting plate, a guide groove is formed in the fixing rod, the supporting column accommodates the sampling assembly and is inserted into a grain pile and the sampling assembly through a bottom drill bit, the baffle is matched with the sliding plate, and under the action of the contraction assembly, the sampling assembly can stretch out of the sliding plate. Sliding is achieved through the guide groove of the fixing rod and the sliding groove of the supporting plate, resetting is assisted through the elastic piece, and therefore opening and closing of the material receiving open groove in the supporting column are controlled, and the effects of sampling in a layered mode and preventing grain leakage are achieved.

The invention relates to the technical field of artificial intelligence and software engineering, and discloses a small sample individual fairness testing method based on a domain adaptive flow model. Aiming at the problems of difficulty in obtaining target model training set data, low discrimination sample generation efficiency, insufficient sensitive attribute decoupling and the like in the existing black box fairness test, the invention provides the following technical scheme: firstly, constructing a gating residual domain adaptive flow model, and performing dynamic fusion on a frozen source domain projection matrix and a trainable low-rank adapter to obtain an adaptive flow model; domain adaptation of the flow model is realized under the condition of not depending on a target model training set; secondly, a two-stage anti-fact generation strategy is adopted, a seed sample set is constructed by utilizing a dynamic radius attenuation mechanism, and discriminated samples are efficiently expanded in combination with triple disturbance; finally, gradient orthogonal constraint is applied to the hidden space of the flow model, and decoupling control over the sensitive attributes and the semantic features is achieved. The black box fairness testing method can be used for black box fairness testing of classification models such as tables, texts and images, and has good practical significance and actual effect.

The application provides a cotton seed quality online detection method, device, system and storage medium, comprising: in the case that the quality category of the to-be-detected cotton seed is not the first quality category, acquiring an air-coupled ultrasonic signal for detecting the quality of the to-be-detected cotton seed; generating a target image based on the air-coupled ultrasonic signal; inputting the target image into a trained cotton seed quality detection model to output a target quality category of the to-be-detected cotton seed; the trained cotton seed quality detection model is obtained after training according to a target image sample; the target image sample is obtained by acquiring a target image of a cotton seed sample of a second quality category and a target image of a cotton seed sample with an intact epidermis; the epidermis damage degree of the cotton seed of the first quality category is greater than that of the cotton seed of the second quality category. The application can effectively detect slightly damaged cotton seeds, effectively screen intact cotton seeds without damage, and greatly improve the efficiency of cotton seed online sorting.

This invention discloses a molecular marker for rapid detection of highly homozygous fresh soybean germplasm. By carefully selecting specific editing sites, and then designing corresponding LAMP amplification primer sets and OSD probe sets based on these editing sites, the LAMP amplification primer sets and OSD probe sets designed in this invention can detect fresh soybean samples from different sources, verifying their specific recognition ability for autonomously edited improved germplasm. Wild-type OSD probes can also be used to detect seed samples from autonomous germplasm resources, verifying their accurate recognition ability for mixed wild-type seeds, ensuring that the seed purity detection error is ≤1%, meeting the requirements for production and planting.

According to the present disclosure, disclosed are a learning operating method based on a federated distillation, a learning operating server, and a learning operating terminal which calculate a local average logit by collecting data samples by the terminal, transmit the local average logit and seed samples to an uplink of a server, perform distillation of a global model based on the seed sample and the local average logit by the server to solve the problems of the privacy and communication overhead generated in the distributed network.

The invention relates to the technical field of seed sample splitting, in particular to a sample splitting and sampling device for crop seed detection, which comprises a sample splitter shell, a base, a top feeding unit, a plurality of collecting boxes and a plurality of shunting assemblies, each shunting assembly comprises two movable diffusion plates, an inclined conversion unit, a channel body, a channel sealing plate and a pressure sensor, different pressure threshold values can be set for a pressure sensor of each flow dividing assembly, after the threshold values reach a preset value, a gap between the two movable diffusion plates is closed through an inclined conversion unit, so that corn seeds cannot continue to fall, meanwhile, a channel sealing plate is rotated, corn crop seeds accurately collected on the pressure sensors fall, and the corn seeds are separated from the channel sealing plate. Storing and collecting by a collecting box; the seeds are respectively used for detecting the moisture, germination rate, cleanliness and authenticity of the seeds and storing backup samples, so that the channel bodies can be closed after the collection quantity meets the detection requirement, and the accurate collection of the corn seeds in each channel body is realized.

The application discloses a method for identifying the producing area of coix seed, a terminal device and a storage medium, and comprises the following steps: S1: collecting a plurality of coix seed samples of the producing area to be identified, and obtaining a three-dimensional data array of the excitation emission matrix (EEM) fluorescence spectrum of the coix seed; S2: converting the three-dimensional data array of the EEM fluorescence spectrum into a two-dimensional data array of the sample set; S3: obtaining a training set and a test set by using the two-dimensional data array of the sample set; S4: training a model constructed by using an improved random forest algorithm by using the training set, and obtaining a producing area identification model; and S5: evaluating the performance of the model by using the test set and a prediction set. The method for identifying the coix seed from different producing areas based on the fluorescence spectrum technology realizes the classification of the coix seed from different producing areas by using the model constructed by combining the improved random forest algorithm and the fluorescence spectrum.

The invention discloses an amorpha fruticosa aerial seeding seed pelleting treatment method and equipment for increasing the germination rate, and particularly relates to the field of forest tree seed treatment.The amorpha fruticosa aerial seeding seed pelleting treatment method.The amorpha fruticosa aerial seeding seed pelleting treatment method comprises the steps that S1, amorpha fruticosa seeds are cleaned, disinfected and soaked, washed with clear water after soaking is completed, and then naturally aired; s2, collecting a fresh koelreuteria paniculata seed sample to prepare an endophyte solution, preparing an adhesive, and uniformly stirring the adhesive to fully dissolve; s3, adding the amorpha fruticosa seeds and an adhesive into a stirrer, uniformly stirring, then adding the endophyte liquid, continuously stirring, then selecting medical stone and bentonite as carriers, and quickly and uniformly wrapping the seeds to obtain a mixture; and step S4, mixing the mixture, and then carrying out granulation treatment. Through the synergistic effect of multi-step pretreatment and the pelleting process, the germination rate and the survival rate of the amorpha fruticosa seeds are remarkably improved, the physical characteristics of the seeds are optimized through pelleting treatment, and the aerial seeding adaptability is greatly improved.

The invention relates to an easy-to-handle tool for non-invasive indirect measurement of seed parameter values based on acquisition and computer implemented analysis of an image of a small seed sample collected in the field. The solution can be implemented as portable device or as system comprising one or more portable image acquisition tools connected to a remote computer system for image analysis.

The invention relates to the field of broccoli cultivation, in particular to a broccoli disease-resistant disease-prevention pollination cultivation method which comprises the following steps: acquiring a seed sample, exposing the seed sample to the sun, soaking the seed sample in warm water, sterilizing the soaked seed sample, and mixing imidacloprid wettable powder with the seed sample to obtain pretreated seeds; sandy loam with good drainage is selected, the PH value of the sandy loam is adjusted and controlled within a standard range, a greenhouse of the sandy loam is built, an insect-proof net and an EVA film are selected and covered, and an experimental field where the pretreated seeds are planted is obtained; the handheld electric vibrator is used for pollination, operation is easy and convenient, and efficiency is high. Compared with natural pollination, the method is not limited by climate and pollination insects, can ensure sufficient scattering and uniform spreading of pollen, improves the pollination success rate, reduces the rate of deformed flower balls, increases the fruiting amount and commodity of broccoli, and brings higher economic benefits to farmer households.

This invention discloses a deep neural network-based fuzzy testing method and system based on cross-modal generation and distribution awareness, belonging to the fields of software testing and artificial intelligence security. Addressing the problems of insufficient semantic diversity in generated test cases, uneven data distribution, and poor adaptability to high-resolution image datasets in existing testing methods, this invention uses clustering to select seed samples and extract abstract text embedding vectors. It then generates prompt words using a scene library, adjusts the generation quantity for each category according to a distribution awareness strategy, filters invalid data to obtain a valid test set, and inputs it into the model to statistically analyze error samples. This invention improves the diversity and effectiveness of test cases, increases test coverage and the ability to detect defects in few sample categories, and enhances model robustness.

The utility model provides a kind of agricultural product seed sample disinfection device.It relates to agricultural seed processing technical field, including box, jar, conveying belt, turnover assembly, feed hopper, cleaning assembly and support movement component;The jar is fixedly installed at the top of box, the bottom of jar is fixedly installed with discharge pipe, conveying belt is provided in the box, and the bottom end of discharge pipe is located directly above conveying belt, the rear side of box is equipped with the discharge port compatible with conveying belt, the turnover assembly is set on jar and extends to discharge cylinder, the feed hopper is fixedly installed at the top of jar, the cleaning assembly is set on box and jar, the support movement component is set on box.The utility model design is reasonable, easy and simple to operate, can realize efficient turnover, convenient discharge, comprehensive cleaning and flexible movement, to effectively improve the uniformity of seed disinfection, efficiency and equipment applicability.

A method and apparatus for generating an evaluation set for an intelligent marketing agent. The method includes: acquiring at least one seed sample; performing at least one sample generation based on the seed sample until a stopping condition is met; wherein, in one sample generation: generating candidate samples based on the seed sample and historical sample information; for each candidate sample, using multiple first-type agents to score the candidate sample, obtaining the scoring results and scoring reasons output by the first-type agents; using second-type agents to determine qualified samples that pass the screening and reference samples that fail the screening, and determining the reasons why the reference samples fail the screening, using the reasons as reference information for the reference samples; adding the reference samples and reference information to historical sample information, and adding qualified samples to a qualified sample set; in response to meeting the stopping condition, obtaining an evaluation set for the intelligent marketing agent based on the qualified sample set.

The invention discloses a rice seed germination detection method based on image recognition, and belongs to the field of image recognition, and the method comprises the steps: obtaining an original image sequence of a rice seed germination process based on a rice seed sample, and carrying out the preprocessing of the sequence, and obtaining an enhanced image sequence; processing the enhanced image sequence by adopting an image segmentation algorithm and an edge detection technology in sequence to obtain an independent feature map of a multi-layer structure; rice germination features are extracted from the independent feature map, the rice germination features comprise the coleoptile cracking degree, the endosperm density change and the total root length and the total root number, and the rice germination features are integrated into a comprehensive dynamic vector through a time sequence fusion method; calculating a consumption rate based on a coleoptile cracking degree and endosperm density change to obtain a correlation index of surface layer and middle layer change; and carrying out classification processing on the correlation indexes by adopting a convolutional neural network, judging the synchronism of multi-level change by integrating the total length and the total root number of the root system, and determining the overall health degree score of the germination state.

The invention provides a relation extraction method and device. The method comprises the following steps: determining a relation to be extracted and a seed sample; the seed sample comprises a labeled head entity, a labeled tail entity and a sentence containing the head entity and the tail entity; based on the to-be-extracted relation, at least one target character fragment is selected from the seed sample to serve as a template, and the target character fragment comprises a head entity or a tail entity; determining a target head entity and a target tail entity from the to-be-extracted text, and combining the template with the target head entity and the target tail entity to generate a cue word; performing similarity comparison on the cue word and the to-be-extracted text, and if the similarity is higher than a threshold value, adding a sentence containing a target head entity and a target tail entity into a seed sample to obtain an updated seed sample; and the relationship extraction step is repeated based on the updated seed sample and the to-be-extracted relationship until the to-be-extracted text does not contain the new entity pair, so that the manual annotation cost and errors are reduced, and the relationship extraction efficiency is improved.

This invention belongs to the technical field of salt-processed dodder seed quality evaluation, and discloses a method and system for salt-processed dodder seed quality evaluation based on multi-source information fusion. Traditional salt-processed dodder seed quality evaluation mainly relies on morphological identification and single-component content determination, which are highly subjective and cannot reflect the overall quality of the salt-processed dodder seed. This invention, based on salt-processed dodder seed samples with different processing degrees, obtains data on the appearance, color, taste profile, odor response, flavonoid content, and volatile component characteristics of the samples. Based on these five types of characteristic data, multi-source information fusion analysis is performed to construct a processing degree discrimination model. Finally, the processing degree discrimination model is used to determine the processing grade of the finished salt-processed dodder seed, thereby determining the quality grade of the medicinal material. This invention achieves comprehensive and multi-dimensional digital characterization of salt-processed dodder seed, deeply reflecting the impact of processing technology on the overall quality of the medicinal material, and overcoming the one-sidedness of single-component detection or subjective judgment.

The application provides a method for rapidly detecting seed germination rate, comprising: obtaining a plurality of seed samples with known germination rates, performing volatile organic compound detection on each sample to obtain volatile component data, and after data cleaning and arrangement, constructing a model of a machine learning algorithm of an integrated model, a linear model and a kernel method; using standardized feature data to establish an optimal prediction model of seed germination rate, which can be used for testing of unknown seed germination rate samples. Compared with a traditional manual statistical detection method, the detection period of the traditional method is compressed to within 2 hours, the detection efficiency is greatly improved, the manual germination and grain-by-grain counting operations are omitted, the labor input is significantly reduced, the human subjective error is reduced, the objectivity and repeatability of the detection result are greatly improved, and the error with the actual measurement value is within ± 5%. The method can be widely applied to seed quality detection scenes of various crops, and has good industry application and promotion value.

The invention discloses an annona squamosa seed vigor grading detection method, and relates to the technical field of annona squamosa seed vigor detection, and the method comprises the following steps: 1, collecting a seed sample; 2, seed pretreatment; 3, measuring the constant weight of the seeds; 4, measuring the water absorption rate of the seeds; 5, modeling after detection, and detecting and recording multiple data of the annona squamosa seeds through a TTC staining method, a conductivity determination test and a germination test; and step 6, vigor grading standard formulation. According to the method, multiple annona squamosa seed groups are adopted for detection, and multiple data of annona squamosa seeds detected through the water absorption rate, the TTC dyeing method, the conductivity determination test and the germination test are recorded, so that the method can be more convenient and more accurate when modeling comparison is performed on the data of the annona squamosa seed groups. Therefore, when the method is used for detecting and grading the activity of the annona squamosa seeds, the method can be more accurate, and the accuracy of the method during detection is improved.

The utility model discloses a seed detecting and sampling device which comprises a storage tank, a feed port and a discharge port are arranged at the upper end and the lower end of the storage tank, and a plurality of samplers are arranged on the side wall of the storage tank along the circumferential direction. According to the utility model, the samplers are arranged at all positions on the side wall of the storage tank, so that seeds at all area positions in the storage tank can be sampled randomly according to requirements, the sampled seeds can better reflect the quality of the seeds in the storage tank after being detected, the sampling is convenient and fast, and the sampling efficiency is improved. And the detected data after sampling is more accurate.

The invention discloses an oversampling and software defect prediction method based on space mapping and probability constraint. The method comprises the following steps: firstly, constructing a minority class probability score model by using dual-kernel density estimation, and realizing layered operation on a preprocessed defect sample; secondly, calculating the number of synthetic samples based on layered weight, performing three-dimensional space mapping and adaptive spherical domain nonlinear sampling on the seed samples, and reconstructing high-dimensional synthetic samples through inverse mapping; and finally, introducing manifold consistency and likelihood ratio dual probability constraints to perform quality verification on the samples, and triggering a radius feedback adjustment mechanism for unqualified samples until a balanced data set is constructed. And training the classifier by using the balanced data set to realize software defect prediction of an unknown sample. By selecting the high-quality seed samples and controlling the synthetic samples, the class imbalance problem is further processed from the data level, and the overall prediction performance of the model is remarkably improved.

The application discloses a kind of data generation method and system of member privacy risk detection of credit investigation model, comprising: respectively estimating the average prediction confidence of member data and non-member data on each credit investigation category as judgment threshold;Randomly sample a sample from the sample space of the target credit investigation model to be measured, and for each feature dimension of the sample, a new feature value is randomly sampled from the feature value space of the feature dimension to replace the current feature value, and a seed sample is generated;The seed sample is input into the target credit investigation model to be measured, and the posterior probability prediction output is obtained;When the prediction probability of the seed sample in target label c satisfies the judgment threshold condition of member data or non-member data, then it corresponds as member detection sample or non-member detection sample;Generation satisfies the sample quantity of detection data.The application can more accurately capture the performance difference of credit investigation model on two different data, and guarantee the effectiveness of the generated detection data.

The application discloses a zero sample generation method and device based on expert knowledge and Gaussian SMOTE, generates initial sample data by using a Gaussian noise addition method according to radar rules and expert knowledge accumulated through public data inquiry or other channels, further discriminates and screens the initial sample by using a K nearest neighbor algorithm to form seed samples, and finally expands the seed sample data by using an oversampling technology to form sample data suitable for intelligent algorithm training, so as to improve the generalization ability and applicability of the intelligent algorithm, improve the identification accuracy of the zero sample radar model, and solve the problem that the artificial intelligence identification method based on sample training cannot adapt to the situation that part of radar signals are less collected, and sample is missing in electronic reconnaissance and electromagnetic spectrum processing.

The utility model relates to the field of biological sample storage, in particular to a seeding type sample access system. The storage system comprises a mobile robot, a storage tank and an uncovering module; the mobile robot comprises an access module, a first mobile module, a second mobile module and a base; the uncovering module comprises a mechanical guide rail and an uncovering manipulator assembly; and the second moving module comprises a first-stage moving part and a second-stage moving part. The storage area and the mechanical execution area are effectively separated, the mechanical arm uncovering assembly is used for moving on the mechanical guide rail to uncover different storage tanks, the mobile robot can directly achieve storing and taking operation when reaching the positions of the corresponding storage tanks, the automatic utilization rate is increased, and the working efficiency is improved. Resource waste and energy consumption caused by vacancy of equipment are reduced, the sample access efficiency is improved, and the ever-increasing sample storage requirements are met.

The invention discloses an epimedium seed stratification treatment method, and belongs to the technical field of traditional Chinese medicinal material cultivation, and the method comprises the following steps: S1, seed initial physiological status evaluation: collecting Wushan epimedium seeds of the same batch, selecting a part of seed samples for respiration intensity determination, placing the part of seeds in a closed respiration chamber, and carrying out primary physiological status evaluation on the Wushan epimedium seeds; a high-precision gas analyzer is used for continuously monitoring the change of gas components in the breathing chamber, and the release amount of CO2 and the consumption amount of O2 in unit time are calculated; and after the respiration intensity measurement is completed, continuously using the hyperspectral imaging technology to obtain spectral reflectivity images of the part of seed samples, obtaining spectral reflectivity images of the seeds under different wavelengths, processing the spectral reflectivity images under different wavelengths, and extracting the spectral reflectivity under the alpha-amylase absorption wavelength. The seed deposition treatment effect can be improved, and the stability and consistency of the seed deposition treatment effect of each batch can be improved according to the physiological state.

The invention discloses an incremental learning-oriented adversarial sample sustainability enhancement method and system, and mainly solves the problem that an existing adversarial sample loses efficacy due to domain drift caused by model updating in an incremental learning process. The scheme comprises: 1) training based on an incremental learning method to obtain an initial target model; 2) screening out a non-target sample without target category semantic tendency from the potential space as a seed sample, and performing feature enhancement; 3) extracting general text semantic features of a target category by using a pre-trained vision-language model, and constructing a semantic correction module in combination with gradient information of a target model; and 4) taking the seed sample and the enhanced sample as initial input, and generating an adversarial sample through iterative optimization under the guidance of a semantic correction module. According to the method, the attack sustainability of the adversarial sample after multiple rounds of updating of incremental learning can be remarkably improved, the generation efficiency and quality of the adversarial sample are improved, and the method can be used for security assessment and defense of a deep learning model.