65results about How to "Improve identification efficiency" patented technology

The invention relates to the field of power systems, in particular to a power system load type intelligent identification method, system and equipment. The method comprises the steps that clustering centers of a plurality of load type subgroups are determined respectively, and each clustering center represents a typical load type calibrated in advance; utilizing a dynamic time warping (DTW) algorithm to calculate a DTW distance between the to-be-identified load node and each clustering center; according to the DTW distance obtained through calculation, distributing the to-be-identified load node to a subgroup of the nearest clustering center, and determining the load type of the to-be-identified load node; and periodically scanning and identifying load type changes of the load units in the domain, and updating a load type identification result. The invention further comprises a power system load type intelligent identification system and electronic equipment, the identification accuracy of the multi-type industrial load composite bus is improved, and misjudgment and deviation are reduced. The bus load type can be dynamically identified and changed and can be updated in time.

The invention relates to the technical field of HVAC system modeling and simulation, and discloses a HVAC model parameter identification method based on a GPS-PSO algorithm. The method comprises the following steps: determining key parameters that affect the accuracy of a HVAC model based on expert knowledge and association rules; performing sensitivity analysis on the key parameters, and sorting them from high to low according to the sensitivity, to construct a parameter set of the HVAC model to be identified; collecting actual operation data related to the parameter set of the HVAC model to be identified, and performing preprocessing; dividing the preprocessed actual operation data into a calibration data set and a verification data set according to a preset ratio; based on the calibration data set, taking the normalized mean error as the optimization target, and performing parameter identification using a GPS-PSO two-stage optimization algorithm; and using the verification data set to verify the effectiveness of parameter identification, thereby improving the efficiency and accuracy of HVAC model parameter identification.

The invention belongs to the technical field of fish genetic identification, discloses a multi-site large yellow croaker wild domestication population identification system based on an agarose gel electrophoresis technology, and particularly discloses a group of InDel molecular markers for large yellow croaker wild and domestication population identification, and the InDel molecular markers comprise InDel11-5053756, InDel20-14004876, InDel3-28345321 and InDel21-17327392. The InDel molecular marker detection primer is used for amplification to obtain a PCR product, agarose gel can be used for detecting strip polymorphism, extra restriction enzyme digestion or complicated polyacrylamide gel electrophoresis operation is not needed, and identification of large yellow croaker wild and domesticated populations can be rapidly and accurately achieved.

The invention discloses a method and a system for rapidly identifying a photo-thermo-sensitive sterile line of forage grass sorghum, and relates to the technical field of agricultural remote sensing. The method comprises the following specific steps: taking a suspected photo-thermo-sensitive sterile line as a material to be detected; acquiring hyperspectral data of the to-be-detected material after heading and before pollen formation by using a hyperspectral imaging technology; extracting identification characteristics related to the photo-thermo-sensitive sterile line of the forage grass sorghum from the hyperspectral data; and establishing an identification model based on the identification characteristics, and inputting a to-be-detected sample into the identification model for rapid identification of the photo-thermo-sensitive sterile line. The method is based on a hyperspectral imaging technology and is combined with a machine learning algorithm to realize rapid, lossless, visual and accurate identification of the photo-thermo-sensitive sterile line of the forage grass sorghum, and the method can greatly improve the identification efficiency and accuracy.

The invention discloses an InDel marker primer for identifying authenticity of sisal hemp interspecific hybridization F1 generation and application of the InDel marker primer. According to re-sequencing data of parent varieties of sisal hemp, an InDel marker primer for identifying the authenticity of a hybrid F1 generation of sisal hemp '292' and 'Induced 7' is developed, identification is carried out in a seedling stage of the hybrid F1 generation, and a result shows that offspring which can simultaneously amplify male parent and female parent characteristic bands is a true hybrid; the hybrid seeds are regarded as false hybrids when any one stripe is lacked or two stripes are lacked, so that the detection purity of the F1-generation real hybrid seeds is improved, and the identification time is advanced. Meanwhile, compared with a traditional hybrid identification method, the method for identifying the authenticity of the sisal hemp variety interspecific hybridization F1 hybrid is rapid, convenient, accurate, reliable, short in breeding period and low in breeding cost, breeding target offspring can be screened in advance, the identification efficiency of sisal hemp interspecific hybrids is effectively improved, and the method is suitable for popularization and application. And a more effective method is provided for rapidly identifying the authenticity of sisal hemp interspecific filial generations.

The invention discloses an online identification method for kinetic parameters of a joint type series robot, which comprises the following steps: S1, for a to-be-identified joint type series industrial robot, establishing a robot kinetic model considering friction; s2, linearization is carried out on the joint type series industrial robot dynamic model; s3, designing an optimal excitation track for an identification experiment, and obtaining an excitation track by using five finite Fourier series and solving a constrained nonlinear optimization problem; and S4, the series robot runs the excitation track, meanwhile, the position information of each joint of the robot and the driving current of each joint at each moment are collected, and to-be-identified parameters are calculated on line at each moment. According to the method, online identification of the robot kinetic parameters can be carried out, the identification efficiency of the robot kinetic parameters is improved, and the method can be used for industrial scenes such as robot moving and carrying, polishing and grinding and heavy-load hydraulic pressure.

The invention discloses a parameter identification method of a lithium battery electrochemical model based on a machine learning classifier and a heuristic algorithm, relates to the parameter identification method of the lithium battery electrochemical model, and aims to solve the technical problem that the existing parameter identification method of the lithium battery electrochemical model is low in identification efficiency. The method comprises the following steps: carrying out sensitivity analysis on parameters of an electrochemical model, classifying the parameters, and selecting high-sensitivity parameters as identification objects; initializing a heuristic algorithm, collecting parameter set data used for classifier training and corresponding convergence labels, and calculating correlation between each parameter in the parameter set and the convergence of the electrochemical model; constructing a classifier based on a machine learning algorithm, training the classifier by using the parameter set data with high correlation, and then finely adjusting the classifier by using the parameter set data with low correlation; and the heuristic algorithm is initialized again, iteration is carried out until the set times or the precision meets the condition, and accurate parameter identification is completed. The method can be used in electrochemical field.

The invention provides a method for creating a pop corn inbred line, and belongs to the technical field of new variety cultivation. The invention provides a method for creating a pop corn inbred line, which comprises the following steps: selecting pop corn germplasm to hybridize with other types of corns to obtain F1-generation seeds; burst corn germplasm is used as a female parent for backcrossing, then BC1F2 or BC2F2 seeds are obtained through planting and selfing, and bursting type grain seeds are selected; hybridizing the burst type grain seeds with a maize haploid induction line containing Ga1-s; hybridized clusters are taken after pollination, embryos are separated and cultured, and haploid embryos with haploid characteristics are selected; inducing the haploid embryo into a seedling, then inducing chromosome doubling, and selfing to obtain the pop corn selfing line. By utilizing the method, the pop corn inbred line can be efficiently, accurately and quickly created, the generation probability of haploid false positive is greatly reduced, the identification efficiency is remarkably improved, and the breeding cost is reduced.

The invention belongs to the technical field of robots, and particularly relates to a friction identification method based on a moss growth algorithm. The method comprises the steps of 1, designing a movement track; 2, executing the motion trail in the step 1 in a position control mode, collecting the speed and moment data of the robot, and extracting the friction moment of the robot; step 3, filtering the collected friction torque; 4, establishing a friction force and Coriolis force coupling model; 5, identifying parameters of the friction force and Coriolis force coupling model by using a moss growth algorithm; and 6, removing the Coriolis force model to obtain a final friction model. According to the method, the problem that a traditional genetic algorithm is prone to falling into a local optimal solution in the parameter identification process is solved, and the identification efficiency and precision are improved.

The invention provides a method and device for screening compounds with biological accumulation potential in a to-be-detected sample, and the method comprises the following steps: analyzing the to-be-detected sample by using a comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain a plurality of primarily screened compounds in the to-be-detected sample and retention time information of each primarily screened compound; respectively calculating a first orthogonal vector of each primary screening compound in the one-dimensional gas chromatographic column and a second orthogonal vector of each primary screening compound in the two-dimensional gas chromatographic column according to the retention time information; inputting the first orthogonal vector and the second orthogonal vector into a first target distribution coefficient prediction model to obtain a first target distribution coefficient of each primary screening compound; the first orthogonal vector and the second orthogonal vector are input into a second target distribution coefficient prediction model, a second target distribution coefficient of each preliminary screening compound is obtained, and the first target distribution coefficient prediction model and the second target distribution coefficient prediction model have different model parameters; and according to the first target partition coefficient, the second target partition coefficient and a biological accumulation potential discrimination condition, screening the multiple preliminary screening compounds to obtain a target compound.

The invention discloses a sesame fusarium wilt identification method based on a molecular marker, and relates to the technical field of phytopathology, the identification method comprises the following steps: S100, RNA extraction and purification: extracting total DNA and protein pollution RNA from a sesame sample to be detected, purifying the extracted RNA by using an efficient RNA extraction kit, removing existing impurities and inhibitors, and determining the content of the protein pollution RNA in the sesame sample to be detected; according to the method, specific long-chain non-coding RNA is adopted as a molecular marker, the molecular marker has high specificity and can accurately distinguish the sesame wilt from other diseases and healthy plants, the probability of misjudgment can be greatly reduced through data analysis, and the detection accuracy is improved. The specific long-chain non-coding RNA has the advantages that the specific long-chain non-coding RNA can be used for detecting the sesame fusarium wilt, reliable guarantee is provided for accurate identification of the sesame fusarium wilt, meanwhile, the specific long-chain non-coding RNA has good stability under different environmental conditions, and detection of a large number of samples can be completed within a short time through PCR amplification and high-throughput sequencing.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to the salt-tolerant character of barley, a primer or a probe, a KASP primer of the SNP molecular marker related to the salt-tolerant character of barley, a barley SNP chip, a reagent or a kit, a barley haplotype and application. The SNP molecular marker and the KASP primer provided by the invention have the advantages that the identification efficiency and accuracy of the barley salt-tolerant variety can be improved, the application range is wide, and the SNP molecular marker and the KASP primer are not influenced by the environment.

The invention belongs to the technical field of information, and particularly relates to a loan contract evidence chain authenticity identification system, which comprises an evidence acquisition module, a data processing and analysis module, an authenticity identification module and a result output module, according to the loan contract evidence chain authenticity identification system provided by the invention, the OCR algorithm, the convolutional neural network algorithm and the recurrent neural network algorithm are integrated, so that the system can automatically identify and process text and image information in a loan contract, the time and cost of manual auditing are greatly reduced, and the identification efficiency is improved.

The invention discloses an auxiliary system and method based on data mining for technology identification, and relates to the field of data matching analysis. The invention trains and tests a recurrent neural network model to construct a final recurrent neural network model, and provides a model for extracting keywords from patent document data to be analyzed later; trains and tests an SVM classification model to construct a final SVM classification model, and provides a classification model for classifying keywords from patent document data to be analyzed extracted by the final recurrent neural network model later; and simultaneously stores the queried technology identification data in separate libraries, and allocates different permissions to different users according to the number of times different users call these different types of technology identification data, thereby reducing invalid calls by users, avoiding resource occupation, and improving the efficiency of calls.

The application provides a battery management method, relates to the technical field of battery application, and can improve the identification accuracy and efficiency of electrochemical parameters of a liquid metal battery. The method comprises the following steps: determining to-be-identified parameters of the liquid metal battery and parameter ranges of the to-be-identified parameters; analyzing the sensitivity of each parameter to voltage output under target and non-target discharge working conditions, and grouping the parameters into high-sensitivity and low-sensitivity parameter sets according to the sensitivity; inputting the measured voltage of the target working condition into a pre-trained CNN to output preliminary estimated values of the parameters; based on the preliminary estimated values, under the non-target working condition, searching for the high-sensitivity parameter set in the parameter range of the high-sensitivity parameter set to obtain a stage identification result, with the root mean square error of the simulated and measured voltages being minimized as the target; under the target working condition, taking a preset range of the preliminary estimated values as a search boundary, allowing the stage identification result to change within a fine-tuning interval, searching for the remaining parameters, and obtaining a target identification result; and determining a battery health state based on the target identification result.

The invention relates to a method for identifying lysimachia christinae hance and desmodium styracifolium, in particular to a method for simultaneously and rapidly identifying the lysimachia christinae hance and the desmodium styracifolium, and aims to solve the problems that the desmodium styracifolium and the desmodium styracifolium are easy to confuse and have many similarities in the aspects of characters, microscopic characteristics and the like, the character identification and microscopic identification processes are complicated and are not easy to observe, and the Therefore, the method is difficult to distinguish from characters and microscopes, and is not easy to operate. A liquid phase content detection and identification method is high in cost and long in time; in order to overcome the defects of difficulty in operation, tedious and complicated process and unstable detection result by adopting a thin-layer identification method, the method for simultaneously and quickly identifying the lysimachia christinae hance and the desmodium styracifolium comprises the steps of ultrasonic extraction, thin-layer sample application, developing by utilizing a developing solvent, developing and inspecting, and the lysimachia christinae hance and the desmodium styracifolium are directly identified and distinguished. Used materials are convenient to obtain, the process is simple, and the efficiency of thin-layer identification is effectively improved.

The invention discloses a primer, a kit and a method for identifying Cobitidae fish. The identification primers include a first primer pair and a second primer pair, the upstream and downstream of the first primer pair respectively have nucleotide sequences shown in SEQ ID NO.1 and SEQ ID NO.2, and the upstream and downstream of the second primer pair respectively have nucleotide sequences shown in SEQ ID NO.3 and SEQ ID NO.4. The method for identifying Cobitidae fish includes: S1: obtaining DNA of a Cobitidae fish sample; S2: performing PCR amplification on the DNA obtained in S1 with the first primer pair to obtain a first PCR amplification product; S3: performing nested PCR amplification with the second primer pair, using the first PCR amplification product as a template to obtain a second PCR amplification product; S4: performing agarose gel electrophoresis on the second PCR amplification product obtained in S3, cutting and purifying the gel, and obtaining a purified second PCR amplification product; and S5: sequencing the purified second PCR amplification product, comparing the mitochondrial gene sequence of the known species of Cobitidae fish in the NCBI database according to the base sequence of the sequencing result, and confirming the species of the Cobitidae fish sample.

The utility model relates to the technical field of coin identification, in particular to a coin identification instrument capable of generating an identification report, which comprises an identification box, a coin slot is arranged at the top end of the identification box, a weighing hole communicated with the coin slot is arranged in the identification box, and a display screen is arranged at the front end of the identification box. According to the utility model, coins can be conveyed through the first electric telescopic rod and the conveying belt, the scanned coins can move into the inclined groove along the conveying belt and further fall into the blanking box through the inclined groove, so that the coins can be automatically moved to corresponding detection positions; according to the coin identification device, the energy and time consumed by a worker for transferring coins can be reduced, the identification efficiency can be improved, a first gear at the output end of a second motor can drive a second gear to rotate, the coins on a clamping table can be turned over below a supporting table, the thickness of the coins can be conveniently measured, meanwhile, other faces of the coins can be conveniently scanned, and the identification efficiency is improved. And the identification precision can be improved.

The invention provides a pattern recognition method and device and a pattern feature size measurement method. The pattern recognition method comprises the following steps: providing a distinguishing feature relationship of CDSEM recognition information of a convex pattern and a concave pattern in a reference structure; cDSEM identification information of the semiconductor structure including a morphology distribution diagram and a characteristic curve diagram is obtained; identifying and cutting the identification signal edge in the semiconductor structure characteristic curve graph, and judging whether the identification signal edge corresponds to the convex pattern or the concave pattern according to the distinguishing characteristic relation of the reference structure. According to the method, the identification characteristic relation of the identification signal edge is calibrated and identified, so that the identification degree of the convex pattern and the concave pattern is improved, the reverse measurement risk is reduced, and the CDSEM pattern identification precision and measurement accuracy are improved; meanwhile, the distinguishing feature relation is obtained through the lowest point height of the reference signal edge, and the pattern recognition efficiency is improved; in addition, the size relation between the slope absolute value and the area integral value of the reference signal edge is used as a preset detection feature, the fitting failure risk is further reduced, and the pattern recognition success rate is improved.

The invention provides a 20K liquid phase chip for simultaneously and accurately identifying various goose varieties and application, the 20K liquid phase chip covers 22005 SNP molecular markers, and the SNP molecular markers are specifically composed of a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt, a genetic differentiation index FSTgt The SNP molecular marker is composed of 4991 genetic differentiation specific sites, 12 important character associated sites and 17002 whole genome background sites of 22005 goose varieties, and the specific positions of the 22005 SNP molecular marker sites are shown in a table 1 in the specification. According to the method, 16 goose varieties can be accurately distinguished, the identification efficiency is high, the detection process can be completed within 24 hours, 48-96 samples can be detected at a time, and the technical repetition rate is gt; the cost of the chip is far lower than that of a high-density chip, and the chip is suitable for carrying out multi-variety and large-scale germplasm monitoring and identification work in a breed conservation field, a research and development unit and the like, and is high in practicability.

The invention discloses an electric power system load model parameter identification method based on an improved bat algorithm, and relates to the field of electric power systems and automation of the electric power systems. And fitness sharing and hybrid local search strategies are applied to the bat algorithm, so that the algorithm can jump out of a local optimal solution, and the problem of global optimization is solved. The method comprises the following steps of: firstly, traversing all solution spaces through chaotic mapping, initializing the solutions, applying a bat algorithm, calculating the fitness of each solution in the algorithm application process, sharing the fitness for the solutions near a peak value in the solution spaces, and after the algorithm runs for a certain number of cycles, determining the fitness of the solutions near the peak value in the solution spaces, wherein the more the solutions exist in a certain range, the lower the fitness is; and carrying out secondary search by using a simplex method. According to the method, the problem that a traditional bat algorithm is easily limited to a local optimal solution is solved, so that the load model parameter identification method has higher identification precision.

The invention discloses an identity recognition method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: in response to a received use request for target equipment, obtaining a target image; under the condition that a target object included in the target image wears target protective clothing and the target protective clothing does not completely cover the face area of the target object, based on the target image, comparison with a plurality of synthetic reference images is carried out, a first comparison result is determined, and the plurality of synthetic reference images are based on the corresponding initial reference images; the protective clothing is obtained by synthesizing the protective clothing and reference protective clothing. When the first comparison result indicates that the target image is matched with any one of the plurality of synthetic reference images, determining that the image recognition result of the target image is passing; and based on the image recognition result, determining that the use request has the use permission for the target equipment. The technical problem that the identity recognition efficiency is not ideal in the prior art is solved.

This invention relates to the field of spectral analysis and identification of genuine and counterfeit Chinese medicinal materials, and discloses a method for identifying counterfeit Cordyceps sinensis based on multispectral fusion. This method first collects multispectral image data of samples under standard conditions; eliminates equipment and environmental noise through standardized preprocessing to establish a reliable data foundation; then, it fuses extracted spectral derivative features and spatial texture features to construct a lightweight neural network identification model. Its core innovation lies in introducing a physical interference stripping model, which can actively correct spectral distortion caused by moisture and mold, obtaining intrinsic spectra to improve model robustness; and through dynamic verification and incremental learning mechanisms, the model can continuously evolve, effectively identifying new counterfeiting methods. Finally, the entire model is integrated into a multispectral portable terminal, achieving rapid screening within seconds and outputting a complete report including authenticity determination, type inference, and confidence level. Ultimately, this solves the industry problems of low efficiency, reliance on experience, and inability to be applied on-site by traditional methods.

This invention discloses a SNP molecular marker associated with mung bean resistance to the bean weevil and its application in genetic breeding. The problem this invention aims to solve is how to identify mung bean resistance to the bean weevil. The SNP molecular marker is a locus (SNP site) on mung bean chromosome 4, with a nucleotide type of T or C, located at nucleotide position 164 of sequence 3 in the sequence listing. This invention provides the application of substances for detecting polymorphisms or genotypes of SNP sites in the mung bean genome in identifying or assisting in the identification of mung bean resistance to the bean weevil. This invention can be used to assist in the identification of mung bean resistance to the bean weevil, for early screening of mung beans, and for marker-assisted breeding of mung beans.

This invention discloses an autonomous inspection method for equipment protection based on a distribution network diagram, belonging to the field of power equipment inspection technology. The method includes: obtaining an electrical coupling observation matrix and a state transition matrix based on historical electrical operation data and historical power grid topology; constructing a hidden state vector through the states of suspected changing nodes and suspected changing branches; using a hidden Markov model based on the state transition matrix and the electrical coupling observation matrix to obtain effective changing nodes and effective changing branches; reconstructing the power grid topology based on the effective changing nodes and effective changing branches; obtaining power grid equipment parameters and line parameters according to real-time power grid measurement data and the power grid topology to reconstruct a real-time distribution network diagram; and obtaining theoretical coordination protection constraint values ​​and theoretical protection settings across equipment, comparing them with actual coordination protection constraint values ​​and theoretical protection settings to obtain the inspection status of protection equipment. This method solves the technical problem that existing technologies struggle to improve the reliability and comprehensiveness of protection equipment inspection.

The application discloses a kind of based on complex-valued neural network three-phase grid-connected inverter admittance identification method, it is related to power electronics field.The method includes: after constructing the small signal flow graph of three-phase grid-connected inverter in synchronous coordinate system, the port admittance matrix under steady-state operating point is constructed, the corresponding characteristic polynomial is determined and factorized, and the unified pole set is obtained;Based on the pole-residual expansion of the port admittance matrix of unified pole set, the transfer function of the elements in the port admittance matrix is determined;Complex-valued neural network and its loss function are constructed;Complex-valued neural network is trained by loss function;The operating condition information and frequency point of grid-connected point are input into complex-valued neural network, and the port admittance under current operating condition is obtained.The method has the technical advantages of low calculation amount, high identification efficiency, supporting multi-condition fast identification and high physical interpretability of fitting result.

The embodiment of the application provides a drug component identification method and related equipment, and belongs to the technical field of drug identification. The method comprises the following steps: obtaining preset drug sample data, including positive drug sample data and negative drug sample data, wherein the positive drug sample data comprises a first thin layer chromatogram, and the negative drug sample data comprises a second thin layer chromatogram; constructing a model training atlas set through the first thin layer chromatogram, so as to train a preset twin neural network model, and obtain a first twin neural network model; constructing a model test atlas set through the first thin layer chromatogram and the second thin layer chromatogram, so as to optimize parameters of the model in combination with the model training atlas set, and obtain a target neural network model; and inputting a to-be-identified thin layer chromatogram into the target neural network model for component identification analysis, and obtaining a component identification result. The embodiment of the application can try not to use control samples and control medicinal materials in drug identification, so that the economic, green and environmentally-friendly purposes are achieved.

The invention discloses a substance identification device, a database calling method, equipment, a medium and a product, the substance identification device is provided with a detection module, a storage module and a data processing module, and the data processing module is configured to perform first similarity comparison on a protein fingerprint spectrum of a to-be-detected object and a standard protein fingerprint spectrum; obtaining a first matching set; verifying a second interference feature in the protein fingerprint spectrum of the to-be-detected object based on the first matching set; if the verification is passed, performing second similarity comparison on the protein fingerprint spectrum of the to-be-detected object and the standard protein fingerprint spectrum to obtain a second matching set; and determining an identification result of the to-be-detected object based on the first matching set and the second matching set. Similarity comparison is directly performed on the protein fingerprints of the to-be-detected object based on the predetermined related characteristics of the protein fingerprints, so that identification complexity is reduced while identification time consumption is shortened, and substance identification efficiency is improved.

The invention discloses a traditional Chinese medicinal material identification and collection device and method, and belongs to the technical field of traditional Chinese medicinal material identification. The identifying and collecting device for the traditional Chinese medicinal materials comprises a chassis, and a walking mechanism and a collecting mechanism which are respectively connected to the bottom and the upper surface of the chassis, and further comprises a pesticide spraying mechanism and an identifying mechanism which are fixedly connected to the chassis, and an ultraviolet lamp irradiation or sunlight irradiation atmosphere covering the upper parts of the traditional Chinese medicinal materials; the spraying mechanism sprays medicine to the surfaces of the traditional Chinese medicinal materials; the collection mechanism collects the fluorescent dominant traditional Chinese medicinal materials identified by the identification mechanism, and based on the fluorescent dominant mechanism of the traditional Chinese medicinal materials, through the cooperation of the collection mechanism and the identification mechanism, the problems of high cost and low efficiency required by identification of the cortex fraxini traditional Chinese medicinal materials in the prior art are solved.

The invention provides a wind power plant parameter identification method based on a deep reinforcement learning algorithm PPO. The wind power plant parameter identification method comprises the following steps: S1, establishing an equivalent mathematical model which is the same as a wind power plant operation environment in BPA software; s2, according to the current state st of the to-be-identified parameter, performing stable and transient calculation on BPA software by using an equivalent mathematical model to obtain reactive power and active power data; s3, calculating a reward R according to the calculated reactive power and active power data and the measured RTLAB hardware-in-the-loop real-time data; s4, judging whether the reward R reaches an error standard or not; if so, outputting a current state st as a wind power plant parameter; if not, using a PPO algorithm to carry out parameter identification in the next step; and S5, performing wind power plant parameter identification by using a PPO algorithm. The problems of poor stability and difficulty in convergence frequently occurring in a traditional algorithm are solved, the identification precision of parameters having great influence on the controller is improved, and the identification efficiency is improved.