55 results about "Blind Sample" patented technology

The invention relates to a method for identifying human lower limb surface EMG signals (electromyographic signals). The method comprises the following main steps: acquiring surface EMG signals of corresponding muscle blocks under the stimulation of movement actions in real time; carrying out pretreatment on the collected EMG signals, thus obtaining the EMG signals with artifact signals eliminated; carrying out decomposition on the obtained EMG signals by adopting a discrete wavelet transform method, thus obtaining a low-frequency coefficient vector and a high-frequency coefficient row; carrying out singular value decomposition on obtained wavelet components by adopting a filtering method combining time domain with frequency domain, and constituting a characteristic matrix by adopting the singular values obtained by decomposition; carrying out training on a characteristic sample by adopting a support vector machine, and generating a support vector machine classifier for carrying out classification and identification on a blind sample. The method has the beneficial effects that compared with the prior art, the novel lower limb EMG signal preprocessing method is provided.

The invention relates to a Wuyi rock tea production place identification method through combination of four detection technologies, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the partial least square discrimination model, the near infrared characteristic spectrum data, the stable isotope data, the trace element data and the amino acid data of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate is high, achieves 100.0%, and is higher than the PLSDA determining result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

The invention relates to a method for identifying the producing area of Wuyi rock tea through a combination of near infrared spectroscopy and trace element detection, belonging to the technical field of identification of the authenticity of products of geographical indication. The objective of the invention is to overcome the problems that single detection data cannot represent all the key information in tracing of a producing area and that data matching is hard to realize for combined usage of detection data of different types in metrological method and other problems in the prior art. According to the invention, near-infrared characteristic spectroscopic data and trace element data of rock tea from different producing areas (including rock tea from and not from a geographically indicated producing area) are fused together for establishing a least squares support vector machine (LS-SVM) analysis model on the basis of a LS-SVM model, and the model is used for objectively and accurately determining the producing area of an extracted sample; the method has the highest identification rate, as high as 100.0% and much higher than LS-SVM determination results composed of single near-infrared or trace element data; and the method has a blind sample identification rate of 100%, so the method has good application prospects.

The invention relates to an identification method for the producing area of a plant based on multiple detection technologies. The method provided by the invention identifies the producing area of Wuyi rock tea by using a combination of near-infrared spectroscopic data, stable isotope mass spectrometric data and trace element data and belongs to the technical field of identification of the authenticity of products of geographical indication. The objective of the invention is to overcome the problems that single detection data cannot represent all the key information in tracing of a producing area and that data matching is hard to realize for combined usage of detection data of different types in metrological method and other problems in the prior art. According to the invention, near-infrared spectroscopic data, stable isotope mass spectrometric data and trace element data are fused together for modeling analysis on the basis of a least squares support vector machine (LS-SVM) model, and a highest identification rate is obtained, as high as 100.0% and much higher than the determination results of LS-SVM established on the basis of single data; and the identification method has a blind sample identification rate of 100%, so the method has good application prospects. The identification method is also applicable to identification of the producing areas of other plant samples like Chinese torreya and lotus root starch; and the method achieves a producing area identification rate of 99.8% or above in identification of plants except tea.

The invention relates to a Wuyi rock tea production place identification method through combination of isotope detection and trace element detection, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use and analysis in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the partial least square discrimination model, the isotope data and the trace element data of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate achieves 100.0% and is far higher than the PLSDA result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

The invention relates to a Wuyi rock tea production place identification method through combination of three detection methods such as near-infrared spectrum detection, stable isotope mass spectrum detection and catechin detection so as to establish a Wuyi rock tea production place identification method, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the least square support vector machine model, the near infrared characteristic spectrum data, the stable isotope data and the catechin data of the rock teas from different production places are integrally fused, the LS-SVM analysis model is established, the recognition rate is highest, achieves 100.0%, and is higher than the LS-SVM result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

The invention discloses a method for measuring an edible oleic acid value based on conductivity, which utilizes a conductivity meter as a detection tool. The method comprises the following steps: when in measurement, fully mixing alkali liquor having a certain low concentration with an oil sample according to a certain mixing proportion; standing for delamination; and then measuring the conductivity of the alkali liquor layer, and predicting the edible oleic acid value through the change of the conductivity of the alkali liquor layer and by using a model. The measuring step is divided into modeling, model verifying, blind sample verifying, model predicting application and the like. The method has simple application without poisonous organic solvents. An oil-alkali mixing proportion can be regulated according to the practical detection requirements so as to meet different acid value measuring scopes and different precision requirements. The method is a novel method for measuring the edible oleic acid value.

The invention relates to a Wuyi rock tea production place identification method based on deep learning, and belongs to the technical field of authenticity identification of geographical indication products. The method aims at solving the problems that a single kind of detection data can not represent complete key information about production place tracing, and different kinds of detection data are not matched when used in combination in a metrological method. According to the method, based on a neural network ELM model with a deep learning function, an ELM analysis model is established for stable isotope data, microelement data, electronic tongue data and fused data of the three kinds of data of rock tea from different production places with the same method; after sample extraction, the production places of the rock tea are subjectively and accurately judged with the model. The model established with the fused data of the three kinds of data has the highest identification rate which reaches 100.0% and is far higher than that of the discrimination result of a neural network model established with a single kind of data; besides, the identification rate on blind samples reaches 100%. The method has good application prospects and can serve as a technical method for tracing identification of Wuyi rock tea production places.

A blind display comprises a frame; a display opening in the frame; a series of display panel supporting members; and a series of display panels supported by the series of display panel supporting members. At least some of the display panels from the series of display panels are adapted to receive a display sample adapted to display a blind sample. At least some of the display panels from the series of display panels are displaceably, e.g. slidably mounted to the series of display panel supporting members and being visible through the display opening. A method for displaying blind samples and a kit are equally disclosed.

The invention relates to an identification method for the producing area of Wuyi rock tea and with a deep learning function, belonging to the technical field of identification of the authenticity of products of geographical indication. The objective of the invention is to overcome the problems that single detection data cannot represent all the key information in tracing of the producing area and that data matching is hard to realize for combined usage of detection data of different types in metrological method and other problems in the prior art. According to the invention, near-infrared characteristic spectroscopic data, stable isotope mass spectrometric data, trace element data and electronic tongue data of rock tea from different producing areas (including rock tea from and not from a geographically indicated producing area) are fused together for modeling analysis on the basis of an ELM neural network discrimination model, and the model is used for objectively and accurately determining the producing area of an extracted sample; the identification method has the highest identification rate, as high as 100.0% and higher than the ELM determination results of single data; and the identification method has a blind sample identification rate of 100%, so the method has good application prospects and is applicable as a technical identification method for tracing the producing area of the Wuyi rock tea.

The invention relates to a machine learning method for identifying origin of Wuyi rock tea automatically. The method combines five data types including near infrared, stable isotope, trace element, amino acid and electronic tongue and the like to establish a neural net ELM Wuyi rock tea origin analytic model. The present invention belongs to the technical field of geographical trademark product authenticity identification, and aims at solving the problem that a single test data type cannot represent all the key information of an origin, and different types of test data need to match when jointly used in a metrological method. The method is based on an ELM model with a machine learning function, and integrates five data types including the near infrared, stable isotope, trace element, animo acid and electronic tongue and the like. The ELM model combining all five data types has the highest identification rate which reaches 100.0%, andfar exceeds the identification results derived from a neural net comprising only a single data type, and the identification rate of blind samples can attain 100%, thus rendering the method with a promising application prospect.

The invention relates to an intelligent identification method for the producing area of Wuyi rock tea based on multiple inspection techniques, belonging to the technical field of identification of the authenticity of products of geographical indication. The objective of the invention is to overcome the problems that single detection data cannot represent all the key information in tracing of the producing area and that data matching is hard to realize for combined usage of detection data of different types in metrological method and other problems in the prior art. According to the invention, near-infrared characteristic spectroscopic data, stable isotope mass spectrometric data, trace element data, amino acid data, catechin data and electronic tongue data of rock tea from different producing areas (including rock tea from and not from a geographically indicated producing area) are fused together for modeling analysis on the basis of a partial least squares discrimination model, and the model is used for objectively and accurately determining the producing area of an extracted sample; the intelligent identification method has the highest identification rate, as high as 100.0% and higher than the PLSDA determination results of single data; and the intelligent identification method has a blind sample identification rate of 100%, so the method has good application prospects and is applicable as a technical identification method for tracing the producing area of the Wuyi rock tea.

The invention relates to a Wuyi rock tea production place identification method based on partial least square discrimination, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the partial least square discrimination model, the near infrared characteristic spectrum data, the stable isotope data, the trace element data and the catechin data of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate is highest, achieves 100.0%, and is higher than the PLSDA determining result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

The invention provides an agricultural product quality analysis method and device. The agricultural product quality analysis method comprises the steps that spectral data are acquired, and reference value data are determined; the data are divided into a correction set and an external verification set; a composite variable selection algorithm is combined with the Monte Carlo sampling method to select a key variable; and a mathematical model is established based on the selected variable, and blind sample verification is carried out by using the external verification set. Based on the method, anagricultural product quality analysis device based on a digital light processing core is developed, and comprises an optical system, a control system, a circuit system and a data storage and processing system. The device stores data collected by the optical system, extracts key variable data according to the variable selection result, calls the mathematical model to perform data calculation basedon the key variable for a sample to be tested to acquire quality data, and carries out analyzing / classifying according to the quality data. According to the invention, spectral data information can begreatly simplified; the working efficiency of an analysis instrument is improved; and a technical reference is provided for non-destructive rapid and accurate classification of the agricultural product quality.

The invention relates to an electric power material storage and inspection integrated system and method, and the system comprises a storage management unit which is used for achieving the storage, warehouse-in and warehouse-out and inventory checking of electric power materials; a blind sample manufacturing unit which is used for manufacturing blind samples of the electric power materials in the warehouse management unit; a material quality detection unit which is used for detecting the submitted blind samples; and an automatic transportation unit which is used for material transportation in the warehouse management unit and between the warehouse management unit and the blind sample manufacturing unit and between the warehouse management unit and the material quality detection unit. According to the invention, electric power material warehousing-sampling-submission inspection-test detection-warehousing storage, i.e., warehousing-detection integrated automatic operation, is realized, the material sampling inspection period of the central warehouse is effectively shortened, and the material turnover rate of the central warehouse is improved. For engineering urgent materials, inspection can be carried out when arriving, qualification can be achieved when entering a warehouse, use can be achieved when leaving the warehouse, engineering construction requirements are responded to atthe highest speed, and the central warehouse material supply guarantee capacity is improved.

The invention relates to a combined detection method used for identification of the producing area of Wuyi rock tea, i.e., a method for identifying the producing area of Wuyi rock tea through a combination of near infrared spectroscopy, stable isotope mass spectrometry and electronic tongue detection, belonging to the technical field of identification of the authenticity of products of geographical indication. The objective of the invention is to overcome the problems that single detection data cannot represent all the key information in tracing of a producing area and that data matching is hard to realize for combined usage of detection data of different types in metrological method and other problems in the prior art. According to the invention, near-infrared characteristic spectroscopic data, stable isotope mass spectrometric data and electronic tongue data of rock tea from different producing areas are fused together for establishment of a least squares support vector machine (LS-SVM) analysis model on the basis of a LS-SVM model; the method has the highest identification rate, as high as 100.0% and much higher than LS-SVM results composed of single data; and the method has a blind sample identification rate of 100.0%, so the method has good application prospects and is applicable as a technical identification method for tracing the producing area of the Wuyi rock tea.

The invention relates to a method for identifying specific geographical indications of a Wuyi rock tea and belongs to the technical field of authenticity recognition of geographical indication products. The method solves the problem that single detection data cannot represent all the key information of places of origin and different types of detection data have poor matching ability to data used in the metrological method. The method comprises fusing stable isotope data and trace element and catechin data of rock teas in different places of origin based on a neural network ELM model, buildingan analysis model, extracting a sample, and objectively and accurately determining places of origin of rock teas through the model, wherein the model built by three fused data has the highest recognition rate of 100.0% much higher than the ELM results of single data and the blind sample recognition rate of 100%. The method has a good application prospect and can be used as the Wuyi rock tea originidentifying technology.

The invention discloses a high-identification crude oil dactylogram constructing and identifying method. The method comprises the specific steps that under the same condition, a spectrum including fluorescence and Raman features is collected on a guide sample and a blind sample; based on sample data, variables not significant to distinguishing are removed, a rule discrimination function set is constructed based on the principle that the same class deviation is the minimum, and the inter-class deviation is the maximum, and intensity data corresponding to reserved significant variable sets is projected to low-dimensional space; barycentric coordinates of a guide sample and a blind sample of the low-dimensional space are subjected to blind sample distinguishing through the system clustering methodology; or columnar stacking diagrams are drawn based on front three-dimensional barycentric coordinates, front four-dimensional barycentric coordinates and front five-dimensional barycentric coordinates respectively, a novel dactylogram of the guide sample and the blind sample is constructed, and blind sample identification is carried out through dactylogram comparison. According to the method, identification efficiency, accuracy and persuasion are remarkably improved. The method has wide application prospects in the fields of lossless identification of artware, cultural objects, jewelry and criminal investigation physical evidence, producing area identification of geo-authentic crude drugs and marine products and medical disease diagnosis.

The invention relates to a Wuyi rock tea production place identification method through combination of electronic tongue and a chromatographic separation technology, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the least square support vector machine discrimination model, the electronic tongue data and the chromatographic separation data (including amino acids, catechin and the like) of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the LS-SVM analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate of the model formed through the data fusion is highest, achieves 100.0%, and is higher than the LS-SVM result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

The invention provides an automatic electric power material sample sealing system. The sample sealing system involves a cabinet body automatic conveying device, a label recognition system and a multi-axis machine grabbing device; the label recognition system comprises an image recognition system and a positioning system, the image recognition system is used for finding the characteristics of an image and capturing and recognizing outstanding features, then the effective information in the image is recognized, the specific height of a nameplate is judged through three-dimensional coordinate positioning of the positioning system, and an instruction is issued to a cooperative sample sealing robot for automatic sample sealing so as to realize automatic acquisition of position information of the nameplate and a two-dimensional code; a quantum cloud code anti-counterfeit blind sample label is attached to a to-be-sealed electric power material through the cooperative sample sealing robot; "double-blind-sample management" of sampling and detecting is carried out, all links of materials sample sealing are subjected to layer-by-layer decomposition, so that integration of automatic sampling, sample sealing and sample feeding of the materials is realized, the quality level of the materials is improved, the operation stability of electric power facilities is ensured, the quality monitoring and sampling inspection work of the materials is reinforced.

The invention relates to a blind sample detection method for lab samples based on the QR code detection technology, and the method comprises the steps: creating blind sample information of a sample, and carrying out the matching of the blind sample information with the QR code information of the sample; enabling a sample creator to seal and store the matched sample through a QR code facing slip, and uploading the blind sample information to a cloud side; enabling a sample receiver to carry out the code scanning and sample collection after the receiving of the sample, and distributing the sample to a tester for testing; automatically collecting and reading a testing result of the sample after testing, carrying out the matching with the sample information, and providing a test report. According to the invention, the advanced network technology is employed for replacing the manual recognition mode, thereby effectively avoiding the counterfeiting of a tested sample block or manual intervention, and provides a feasible and effective solution for a detection organization for the management of samples.

The invention discloses a method for rapidly detecting the content of polysaccharides in rice bran based on an ATR-FTIR technology, which comprises the following steps: (1) quantitatively weighing fresh rice bran subjected to impurity removal treatment, and measuring the optical properties of the fresh rice bran by combining an attenuated total reflection Fourier transform infrared spectrometer; (2) immediately analyzing the polysaccharide content in the rice bran by adopting a gas chromatography technology; (3) based on a partial least squares regression method, establishing a quantitative prediction model according to the polysaccharide content in the sample and the spectral information thereof, and selecting optimal modeling parameters to ensure the robustness and stability of a regression prediction model; and (4) carrying out an independent experiment, and carrying out nondestructive detection on the rice bran polysaccharide content of the blind sample in combination with the constructed ATR-FTIR model. The method can realize nondestructive quantitative detection of xylose, arabinose and other polysaccharide components in the rice bran based on the near infrared spectrum technology, the detection precision Rp2 is greater than 0.90, the detection precision RPD is greater than 2.0, and the timeliness of polysaccharide content detection in the rice bran is greatly improved.

The invention relates to a Wuyi rock tea production place deep studying system based on five-hiding layer, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the neural network ELM model, the near infrared characteristic spectrum data, the stable isotope data, the trace element data, the catechin data and the electronic tongue data of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate is highest, achieves 100.0%, and is higher than the ELM result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

The invention discloses a cable blind sample manufacturing device for testing. The device comprises an automatic pay-off part, an identification position detection part, a spraying part and an automatic take-up part, wherein the automatic pay-off part and the automatic take-up part are driven by a motor, and a to-be-tested cable keeps certain tension between the automatic pay-off part and the automatic take-up part; the identification position detection part is arranged between the automatic pay-off part and the automatic take-up part and used for detecting the starting point and the end pointof a detection identifier; the spraying part starts spraying or stops spraying according to a detection result of the identification position detection part. According to the device, a blind sample of an outer cable sample can be manufactured efficiently at low cost, the labor resource cost is reduced, the situation that manually manufactured blind samples are not sufficiently sprayed or even missing spraying occurs is eliminated, and the phenomenon that quality of the blind samples is not uniform due to the fact that a manual manufacturing technology is uncontrollable is eliminated; meanwhile, winding of the sample with the manufactured blind sample can be standard and meets testing requirements.

The invention discloses a method for performing rapid semi-quantitative determination on aluminum content in pyrotechnic composite blind samples for fireworks and crackers and belongs to the technicalfield of analytical tests. The method comprises the following steps: separating pyrotechnic composite samples from firework and cracker products, grinding, screening, drying, and cooling to a room temperature; taking an energy dispersion type X-ray fluorescence spectrometer as a detection instrument, adopting an analytical method by a strength method, filling the samples into instrument sample cups, placing a to-be-detected sample into a test chamber of the detection instrument, recording a fluorescence intensity level of an aluminum element characteristic spectral line displayed on the instrument, and estimating the content of the aluminum element in the samples; further selecting an appropriate analytical instrument to perform accurate quantitative analysis on the pyrotechnic compositesfor fireworks and crackers. The method disclosed by the invention is adopted for performing semi-quantitative analysis on the aluminum content and is simple in operation and high in efficiency, and the estimated aluminum content in the sample is slightly different from the actual content. Moreover, a data basis can be effectively provided for further selecting a method for accurately and quantitatively analyzing the aluminum content in the sample.

The invention relates to a water quality quick measuring method for indirectly measuring BOD5 (five-day biochemical oxygen demand) in water. The water quality quick measuring method comprises the following steps of diluting a BOD5 quality control sample or a standard sample into concentration which is the same with the concentration of a blind sample, respectively selecting 2ml of the BOD5 qualitycontrol sample or the standard sample and 2ml of the blind sample, adding into a CODcr (chemical oxygen demand) testing reagent, uniformly mixing, powering on a CODcr tester, and respectively respectively measuring the BOD5 CODcr content in the quality control sample or the standard sample and the BOD5 CODcr in the blind sample; calculating the BOD5. The water quality quick measuring method has the advantages that the limitation by water quality detection conditions is avoided, and the accurate and reliable detection result can be obtained within 2 to 3h; the influence to the measuring resultby various factors in the measuring process of the BOD5 blind sample is eliminated.

The invention discloses a blind sampling anti-exchange sample detection method which comprises the following five steps: manufacturing an encrypted two-dimensional code label or an encrypted two-dimensional code label with an RFID (Radio Frequency Recognition Device) chip, manufacturing a sample, inputting sample information, sending the sample to a detection unit, detecting the sample and outputting a report, so that blind sampling management in a sample detection process is realized, wherein the encrypted two-dimensional code label with the RFID chip is divided into four functional areas, namely a two-dimensional code, a plain code number, a secret code number and the RFID chip, and a mutual corresponding relation database is established for a two-dimensional code character string, the plain code number, the secret code number and a number in the RFID chip, so that information is convenient to input and scan. In order to prevent a taken sample from being exchanged; an RFID tag with atwo-dimensional code is directly implanted or bound when a sample is sampled or manufactured on site. Meanwhile, an recognition pattern is manufactured on the sample to serve as a unique identity card of the sample, and whether objects corresponding to the two photos are the same sample or not is confirmed through comparison of the front photo and the rear photo, so that whether the sample is exchanged or not is effectively judged.

The invention relates to an anti-theft airtight quick sampling valve, which includes a micro ball valve, a sampling tube and a sampling control tube, and is characterized in that an inlet male thread of the micro ball valve is connected with a female thread of a sampling connector on a sampling pipeline; a female thread thereof is connected with the sampling control tube; an outlet of the micro ball valve is in threaded connection with a pressing cap with a backstop annular groove; an annular rubber seal gasket is arranged between the pressing cap and the outlet of the micro ball valve; the sampling tube passes through the pressing cap, the annular rubber seal gasket and a ball core hole, and is inserted into the sampling control tube. When the anti-theft airtight quick sampling valve is used for sampling, no blind sample region exists, airtight sampling without evacuation can be realized, qualified samples can be sampled, accuracy of sample test analysis data can be ensured, reliable parameters can be provided for development and analysis of oil gas fields, and requirements of formulation of a development scheme of oil gas fields can be met.

The invention relates to a fingerprint spectrum building and identification method of high-resolution multi-constituent complicated oil. The method comprises the steps of sampling spectrum, comprisingfluorescent and raman characteristic, from a standard sample and a blind sample; solving two-dimensional synchronous correlated spectrum of each standard sample and blind sample; sampling the two-dimensional synchronous correlated spectrum again under the condition that the main characteristic of fingerprint spectrum is maintained, and then converting the two-dimensional synchronous correlated spectrum to one-dimensional array; removing a variable which is not important to distinguishing by a step-by-step distinguishing method, and only maintaining a variable group with high distinguishing capability; and projecting intensity data corresponding to the preferably-selected variable group to a new low-dimensional space, drawing a columnar stack diagram for intuitive comparison according to projection coordinate in the new low-dimensional space or drawing three-dimensional space projection diagram for fingerprint characteristic comparison. The method is high in identification efficiency and is verified with an existing crude oil identification method, and the accuracy and the persuasion of crude oil identification are improved.