43 results about "Toxicity data" patented technology

The invention relates to the technical field of medical animal models, in particular to a zebrafish brain toxicity modeling and detecting method based on podophyllotoxin and application thereof.The method comprises the steps that S1, 3-day-fertilization (3dpf) wild-type AB-line zebrafish is randomly selected and placed in a beaker, and 30 zebrafish are treated in each cup (experimental group); s2, dissolving the podophyllotoxin in water, carrying out modeling on the podophyllotoxin, setting a normal control group at the same time, setting the volume of each cup to be 20mL, and treating at 28 DEG C for 2 days; s3, performing cognitive function influence analysis on the zebrafish obtained by modeling in the step S2; according to the method, a behavior analyzer is adopted to carry out influence analysis on the cognitive function of the zebra fish, through a cross module experiment, the movement distance proportion of the zebra fish in a blue area is recorded, and the change of the cognitive function is evaluated. The behavioral analysis can provide systematic and comprehensive toxicity data. The influence of podophyllotoxin on a nervous system is evaluated by recording the behavior change of zebra fish in a specific environment, and the comprehensiveness of toxicity research is enhanced.

The invention discloses an ecological risk assessment method for organic pollutants in a natural water body, and belongs to the field of ecological risk assessment of the natural water body, and the method comprises the steps: building a quantitative structure-activity relationship toxicity prediction model of species based on a machine learning and feature screening method; predicting and obtaining biotoxicity data in a to-be-predicted water body by using the quantitative structure-activity relationship model, establishing a species sensitivity distribution model of pollutants in the water body according to the biotoxicity data, and calculating predicted non-effect concentration; on the basis of database retrieval, acute toxicity experiments and an inter-species relationship prediction model, collecting, predicting and supplementing toxicity data of species in the water body, and evaluating the harmfulness of pollutants in the water body by using a risk entropy method and a comprehensive scoring method respectively. According to the method, ecological risk assessment is carried out by utilizing a machine learning method, the toxicity of the persistent organic pollutants in seawater and fresh water can be effectively assessed, and the ecological risk of the organic pollutants in the water body can be effectively assessed.

The invention discloses a method for predicting a composite biological effect of a carbon nanomaterial. The method comprises the following steps: measuring the growth inhibition toxicity of different types of carbon-based nanomaterials to scenedesmus obliquus in the presence of singly; the growth inhibition toxicity is calculated by taking the effect multiple change generated by a treatment group compared with a control group as a toxicity index; based on the obtained growth inhibition toxicity data, constructing an SEML prediction model through a support vector machine regression algorithm or an artificial neural network regression algorithm; and predicting the composite biological effect of the carbon nanomaterial according to the SEML prediction model. According to the method, the effect multiple change FC is adopted as a toxicity index, biotoxicity evaluation is more accurate, meanwhile, the prediction model is directly constructed based on growth inhibition toxicity data, material characteristic parameters of the carbon-based nano material do not need to be measured, and the modeling process is more convenient.

The invention discloses a perturbation mode incremental toxicity data continuous detection method and system, and the method comprises the steps: obtaining to-be-detected texts, carrying out the perturbation of different modes on all to-be-detected texts through employing a toxicity data jailbreak perturbation frame, obtaining jailbreak to-be-detected difficult texts in each perturbation mode, constructing a data set in each perturbation mode, and carrying out the detection of the to-be-detected difficult texts in each perturbation mode; the data set is divided into a training set, a verification set and a test set; constructing a toxicity continuous detection model based on feature playback, training the toxicity continuous detection model by adopting the training set under each disturbance mode, and verifying by adopting the verification set to obtain an optimal toxicity continuous detection model; and sending the test set in each disturbance mode into the optimal toxicity continuous detection model for detection to obtain a corresponding toxicity detection result. According to the method, a jailbreak disturbance framework is provided to generate data sets in different disturbance modes, and a continuous learning method based on feature playback is provided, so that the continuous detection capability of toxic texts in different disturbance modes is effectively promoted.

The invention relates to the technical field of environmental toxicology and ecological risk assessment, in particular to a system and a method for predicting chronic toxicity of bioaccumulation pollutants, and the system comprises a data acquisition and processing module, a model training and optimization module, a toxicity prediction module and an ecological risk assessment module. According to the method, full-process prediction is realized through data standardization, feature engineering, model training and model prediction; tissue residual toxicity data is obtained and standardized, a multi-dimensional characteristic matrix containing pollutant physicochemical properties, species biological characteristics and experimental conditions is constructed, a toxicity prediction model is optimized by using a machine learning algorithm, and tissue residual toxicity prediction of fishes in different life stages is realized. And calculating a risk quotient based on the predicted value, and generating a risk assessment report. The problems that an existing system depends on water body concentration toxicity data, is lack of biological accumulation pollutant chronic toxicity prediction capacity and does not aim at an endangered characteristic optimization model are solved.

The invention discloses a method for predicting toxicological properties of cosmetic raw materials. The invention discloses an organic compound toxicity prediction method and model based on molecular structure characteristics and a machine learning algorithm, and aims to predict four key toxicological properties of cosmetic raw materials, namely mutagenicity, carcinogenicity, eye irritation and skin sensitization. The method comprises the following steps: firstly, collecting structural data and four toxicological property data of a compound, and constructing a toxicity data set; then, dividing the data set into a training set and an external test set, calculating molecular feature fingerprints of the data set, and screening the molecular fingerprints of the training set by using a feature selection method; a toxicity classification prediction model is established by using a machine learning algorithm and training set data, and the prediction accuracy and reliability of the model are verified through external test set data. According to the method, the safety risk of cosmetic raw material research and development can be effectively reduced, the cost of toxicology testing is reduced, and meanwhile guiding significance and practical value are provided for safety risk assessment of cosmetic raw materials.

The invention relates to the technical field of environment informatization, in particular to a new pollutant environment fate simulation and risk assessment system, which comprises a data prediction module used for receiving molecular structures of target new pollutants, space-time emission source item data and basin environment medium parameters, and complementing missing physicochemical property parameters by using a model; the effect analysis module is used for acquiring new pollutant toxicity data and deducing and predicting non-effect concentration and sensitive point risk weight in combination with a mixed toxicity model; the sample generation module is used for simulating various discrete emission scenes based on a mechanism driving model and generating a space-time environment concentration response data set; and the agent model module is used for training a machine learning agent model according to the emission scene and the space-time environment concentration response data set. According to the method, the input and output relation of a deep neural network learning mechanism driving model is utilized through the agent model module, and the problem that a traditional mechanism model cannot support rapid iterative optimization of mass emission scenes due to slow operation is solved.

The invention discloses a method for predicting combined toxicity of a graphene-based material and ionic liquid. The method comprises the following steps: obtaining an adsorption coefficient through an adsorption experiment of the graphene-based material on the ionic liquid; establishing a concentration-reaction relationship of a binary mixture composed of the graphene-based material and the ionic liquid to a specific ecological species, and obtaining a mean effect concentration of the binary mixture; fitting the adsorption coefficient and the mean effect concentration through a machine learning algorithm to obtain a fitting relationship, and obtaining a quantitative structure-activity relationship model according to the fitting relationship; and inputting a to-be-detected adsorption coefficient serving as an input variable into the quantitative structure-activity relation model to obtain the combined toxicity of the binary mixture on the specific ecological species. The adsorption coefficient is used as the characteristic parameter corresponding to the toxicity data, so that the characteristic parameter is easier to obtain, and the time, manpower, material and other costs caused by biological testing when the average effect concentration is obtained are avoided; and through a machine learning method, a fitting relation is constructed, and prediction can be more efficient and accurate.

The present invention discloses a method for assessing the exposure of hazardous waste resource products, including collecting production processes, application scenarios and physical and chemical test data of the whole process of hazardous waste resource products; inputting the physical and chemical test data of the whole process into a toxicity prediction model to obtain toxicity data, classifying the production process and the application scenarios to obtain impact data; obtaining an impact factor according to the correlation of the impact data to the exposure, and establishing a risk index according to the toxicity data and the exposure; constructing an exposure assessment model based on the toxicity data, the impact factor and the risk index, optimizing the parameters of the exposure assessment model, inputting the data to be assessed into the exposure assessment model, and outputting an assessment result. This method can not only improve the accuracy of the exposure assessment of hazardous waste resource products, but also help to better manage and control hazardous waste resource products, achieve optimal utilization of resources, and reduce negative impacts on the environment.

The invention provides an establishment method of a multi-species aquatic organism acute toxicity prediction model and a toxicity prediction method, and belongs to the technical field of environmental science and ecotoxicology. The establishment method of the acute toxicity prediction model of the multi-species aquatic organisms comprises the steps that actual acute toxicity data of multiple compounds on the multi-species aquatic organisms, molecular maps of the multiple compounds and species information of the multi-species aquatic organisms are obtained, and the species information comprises Cytb common subsequences and / or species chemical information of the multi-species aquatic organisms; inputting the molecular maps of the various compounds and the species information of the multi-species aquatic organisms into an initial prediction model to obtain initial acute toxicity prediction data of the various compounds on the multi-species aquatic organisms; and optimizing network parameters of the initial prediction model according to the initial acute toxicity prediction data and the actual acute toxicity data to obtain a trained multi-species aquatic organism acute toxicity prediction model.

The invention relates to the technical field of molecular biology and environmental biology, and discloses a PCR chip, application thereof and a method for evaluating the toxic effect of petroleum pollutants on zebra fish. The PCR chip comprises 22 pairs of target gene primers, each pair of target gene primers comprises an upstream primer and a downstream primer, and the sequences of the 22 pairs of target gene primers are as shown in SEQ ID NO. 1 to SEQ ID NO. 44. According to the invention, 22 specific related genes are specifically selected according to a research result, and a PCR chip for researching the toxic effect of the heavy metal pollutants on the zebra fish is integrated, so that chip customization based on the heavy metal pollutants is realized; the operation method of the PCR chip is mature and simple, a large amount of toxicity data can be obtained at a time, the toxic effect mechanism of the heavy metal pollutants on the zebra fish embryos can be researched more comprehensively and in detail, and data support is provided for deeper research on the toxic mechanism of the heavy metal pollutants.

The present invention provides a method for evaluating the multi-level regional soil ecological risk. The evaluation method is based on the regional rare earth exposure data and species toxicity data, and through the mutually coupled screening risk assessment, semi-probability risk assessment and probability risk assessment, the rare earth elements that need to be prioritized in the region are screened out, and the ecological risk of the prioritized rare earth elements is used to represent the comprehensive ecological risk of the rare earth element mixture. The present invention establishes a set of evaluation methods from qualitative to quantitative, which accurately reflects the ecological risk of regional rare earth pollution.

The invention discloses a method for predicting the toxicity of PFAS to microalgae cells based on molecular docking and molecular dynamics simulation. Collecting a toxicity data set of the PFASs on the microalgae to obtain a half inhibitory concentration toxicity index; collecting structural information of known compounds and structural information of microalgae key proteins and nucleic acid receptors; performing density functional theory calculation based on a B3LYP / 6-31 + G (d, p) base group by adopting Gaussian 09 software, optimizing the geometrical shape and the minimum energy conformation of the PFAS, and determining structural parameters; screening binding sites of the PFASs and a receptor by using an Autolock tool, and selecting the binding site with the minimum binding energy as a docking result; the method comprises the following steps: carrying out molecular dynamics simulation by adopting Gromacs on the basis of determining a complex docking position through molecular docking; using MM / PB (GB) SA to calculate the binding free energy of the PFASs and the receptor; a linear regression model is utilized to analyze the toxicity index of PFASs and combine free energy correlation, and modeling is carried out.

The invention discloses an organic compound toxicity prediction method based on particle swarm optimization and ensemble learning, and relates to the field of organic compound toxicity prediction, and the method comprises the following implementation steps: through setting an acquisition module and an integration module, collecting semi-lethal concentration data of aquatic organisms from an ECOTOX aquatic toxicity database, the real-time detection unit and the auxiliary tracking unit can detect volatilization of toxic organic compounds in real time, the monitoring module receives and processes information and transmits signals to the collection module through wireless transmission, and at the moment, the liquefaction forming unit cools volatile toxic compound gas in a cooling mode. The toxic organic compound gas is cooled and liquefied to form water drops, the cooled and liquefied toxic compound water drops fall into an original non-volatile compound through the vibration sliding-down unit, and the toxic organic compound is stirred and uniformly mixed again through the uniform mixing unit by adopting a mixing and stirring mechanism. The accuracy of toxicity data of the to-be-detected compound is ensured.

The invention discloses a soil micro-plastic ecological risk assessment method based on three-dimensional probability density distribution, and belongs to the technical field of environmental pollution prevention and control. According to the method, soil micro-plastic distribution characteristic data of a target site are collected and screened, statistical models such as power law distribution, bimodal distribution and normal-inverse Gaussian distribution are used for fitting the size, shape and density characteristics of micro-plastic, and a three-dimensional probability density distribution model of the size, shape and density of the micro-plastic is constructed. And optimizing the collected and screened micro-plastic toxicity data based on the constructed three-dimensional probability density distribution model. For special types of sites, customized toxicity data optimization can be carried out based on field sampling and detection results. And in combination with the optimized SSD curve, calculating HC5 and RQ, and carrying out ecological risk assessment on the micro-plastics. The implementation of the invention can provide a scientific basis for environmental ecological risk assessment, pollution prevention and control management and policy making of new pollutants in soil.

The invention relates to the technical field of toxicity detection, and discloses a micro-plastic toxicity prediction method integrating machine learning and meta analysis. The method comprises the following steps: performing multi-dimensional detection on a micro-plastic sample to obtain physicochemical characteristic parameters; carrying out meta analysis on the physicochemical characteristic parameters and the toxicity data to obtain quality correction toxicity data; judging whether the toxicity correlation meets constraint conditions or not, and if yes, performing collaborative learning to obtain a prediction algorithm; inputting characteristic parameters to carry out prediction calculation, and outputting a toxicity prediction value after determining uncertainty; and carrying out risk calculation and mechanism analysis, judging a risk level and generating an evaluation result. According to the invention, the accuracy of micro-plastic toxicity prediction is improved.

The application discloses a soil cadmium environmental ecological toxicity data screening processing method and application, and comprises the following steps: S1, obtaining ecological toxicity data, S2, screening data, S3, processing data, the screening processing method can obtain high-quality data, and the data is processed to a uniform level, which is more convenient for analysis and calculation; through the simulation of the species sensitivity distribution model by using the screened data, fitting parameters can be obtained, and the sensitivity distribution of ecological species to pollutants can be mastered to a large extent according to limited ecological toxicity data.

The invention provides an ecological risk assessment method and device for a PFOA (perfluorooctanoic acid) polluted drainage basin, and electronic equipment, and relates to the technical field of environmental ecological risk assessment. According to a runoff period division result of a to-be-evaluated drainage basin, a biological sensitive period of multiple aquatic organisms at multiple nutrition levels in the to-be-evaluated drainage basin and toxicity data at different toxicity end points, comprehensive threshold values of all toxicity end points in different runoff periods are calculated; after the PFOA exposure concentration of each position in the to-be-evaluated drainage basin at the target moment is determined, the PFOA pollution ecological risk distribution of the to-be-evaluated drainage basin at the target moment is determined in combination with the comprehensive threshold value of the toxicity end point in the corresponding runoff period and a preset risk indication threshold value. According to the method, the runoff period and the biological sensitive period are coupled, so that the technical problem of low space-time accuracy of PFOA ecological risk assessment in an existing assessment method is solved.

The invention relates to a method and a device for acquiring key components of a mixture, a storage medium and electronic equipment, and the method comprises the following steps: acquiring each component in a multi-component mixture system of a target area, and determining factor level numbers of factors corresponding to the components; constructing a track starting point based on the factor level number, and determining that the currently constructed track starting point is not repeated based on the constructed track starting point group; determining a preferred trajectory starting point group for the uniformity deviation of each constructed trajectory starting point group; for each preferable trajectory starting point in the preferable trajectory starting point group, performing conversion according to the factor sequence corresponding to the preferable trajectory starting point, and obtaining a mixture sample for the factor level of one factor in each conversion; and performing a mixture toxicity test based on the mixture sample to obtain mixture toxicity data, and performing global sensitivity analysis on the mixture toxicity data to determine the key component based on a global sensitivity analysis result. And the reliability of a mixture toxicity evaluation result can be improved.

This invention relates to a method, apparatus, storage medium, and electronic device for obtaining key components of a mixture. The method includes: obtaining each component in a multi-component mixture system in a target region; determining the number of factor levels corresponding to each component; constructing trajectory starting points based on the number of factor levels; determining that the currently constructed trajectory starting points are not repeated based on a previously constructed set of trajectory starting points; determining a preferred set of trajectory starting points based on the uniformity deviation of each constructed set of trajectory starting points; for each preferred trajectory starting point in the preferred set of trajectory starting points, performing a transformation according to the factor order corresponding to that preferred trajectory starting point, with each transformation targeting the factor level of one factor, to obtain a mixture sample; conducting a mixture toxicity test based on the mixture sample to obtain mixture toxicity data; performing a global sensitivity analysis on the mixture toxicity data; and determining key components based on the results of the global sensitivity analysis. This can improve the reliability of mixture toxicity assessment results.

The invention discloses a detection method for recessive toxicity multi-modal data and terminal equipment, belongs to the technical field of toxicity data detection, and can solve the problems that an existing model cannot effectively detect recessive toxicity content and is difficult to provide clear explanation and quantify the hiding degree of the recessive toxicity content. The method comprises the following steps: S1, acquiring a plurality of visual nodes from an input image of an input image-text pair, and constructing a visual association tree of the input image; s2, acquiring a plurality of text nodes from an input text of the input image-text pair, and constructing a text association tree of the input text; s3, constructing a cross-modal bipartite graph of the input image-text pair according to the visual association tree and the text association tree; and S4, matching each node pair in the cross-modal bipartite graph with all concept pairs in the toxic concept pair set, and determining a toxic label of the input image-text pair according to a matching result. The method is used for detecting the multi-modal data of the recessive toxicity.

The invention discloses a pre-eclampsia environmental chemical substance risk prediction method based on multi-dimensional data. The method comprises the following steps: firstly, analyzing environmental chemical substance concentration based on exposure science, and calculating exposure risk frequency score; thirdly, comprehensively evaluating the correlation degree of environmental chemicals and preeclampsia based on epidemiology, and calculating an epidemiology risk score; then performing a cytotoxicity experiment based on toxicology of an in-vitro experiment to detect the influence of environmental chemical substances on cell activity; then, on the basis of metabonomics, the interference degree of environmental chemical substances is quantified through pathway enrichment analysis, and a metabolic interference score is calculated; calculating toxicological scores based on the toxicity database; and finally, integrating the various groups of chemical scores, calculating the risk scores of the environmental chemical substances and generating a preeclampsia foreign substance priority list. By fusing multi-omics and multi-source data, the environmental chemical substances are comprehensively and accurately detected and evaluated, and accurate prevention and control of high-risk chemicals and risk early warning of corresponding crowds are facilitated.

The present application relates to the field of environmental science and computational toxicology, and specifically provides a method for constructing species sensitivity distribution and performing soil ecological risk probability assessment based on multi-species quantitative structure-activity relationship model, which constructs QSAR model for each receptor and combines Monte Carlo simulation technology, models three key uncertainty sources of model application domain confidence, model reliability factor and ecological weight as Beta distribution, Logit- Normal distribution and truncated normal distribution respectively, randomly samples and calculates comprehensive weight in each iteration, constructs SSD curve based on weighted toxicity data, and then obtains probability distribution of hazard concentration, and finally, outputs risk probability distribution of pollutants according to comparison between pollutant concentration and HC5 distribution in the environment, which quantitatively the uncertainty of QSAR model and ecological parameters, realizes the transformation from point estimation to probability distribution, and provides a scientific basis with statistical significance for soil ecological risk classification management and remediation decision.

The invention relates to the technical field of chemical screening, in particular to an insecticide screening recommendation method and system.The insecticide screening recommendation method comprises the following steps that on the basis of molecular structure parameters, environment temperature parameters, pH value parameters and soil type parameters of a compound, according to the relevance of the molecular decomposition rate, the environment dynamic stability and the biodegradation behavior, the biodegradation behavior of the compound is determined; and extracting characteristic variables associated with the decomposition rate in the molecular structure, and generating an environment decomposition dynamic data set. According to the method, through correlation analysis of the exposure dose, the exposure time and the survival rate of the non-target organism, key variables of toxicity data are mainly extracted, more accurate evaluation of the risk of the non-target organism is achieved, the probability that the non-target organism is injured is effectively reduced, and the risk of the non-target organism is evaluated by analyzing the cross relation between the action time and the experimental period. The high-precision prediction of the pesticide experiment effect and the optimization of the screening list are realized, and the precision and practicability of the screening result are remarkably improved.

The invention provides a harmful outcome path-based chemical reproductive toxicity transfer learning model prediction method, which comprises the following steps of: collecting test data of male and female reproductive toxicity of chemicals in different levels in a harmful outcome path, and preprocessing the test data; randomly splitting the preprocessed test data into a training set, a verification set and a test set, training a source model by using a deep neural network, and obtaining a target model after migration; evaluating the prediction performance of the target model by taking the area under the curve, the F1 score and the balance accuracy as indexes; representing an application domain of the target model according to the index similarity density and the weighted rugged property; and predicting whether the chemicals in the application domain have reproductive toxicity to the living body or not by using the target model. The transfer learning model fully fuses multi-level in-vitro test toxicity data of chemicals from molecular initial events to harmful outcomes, and can accurately predict in-vivo reproductive toxicity.

This patent relates to an acute intraperitoneal toxicity prediction method based on MACCS bonds and a machine learning framework. This method collects and processes toxicity data of chemical compounds, calculates molecular descriptors using MACCS bonds, and constructs a gradient boosting decision tree model for toxicity prediction through feature selection and removal of highly correlated features. The model's hyperparameters are optimized using a grid search method, and the interpretability of the model is improved using SHAP value analysis. This achieves high-precision and highly interpretable toxicity prediction. The method achieves a coefficient of determination of 0.914, a mean squared error of 0.047, a root mean square error of 0.218, and a mean absolute error of 0.148 on the test set. This invention significantly improves the accuracy of toxicity prediction and has strong generalization ability, making it widely applicable to toxicity screening in new drug development, reducing reliance on traditional animal experiments, and lowering R&D costs and time.

The invention discloses a chemometrics prediction model and method for predicting acute oral toxicity of six typical drug skeleton molecules. Based on interpretable chemometrics and machine learning methods, the invention provides an acute oral toxicity prediction model for six types of drug skeleton molecules (pyrazine, piperazine, thiazole, thiophene, indole and benzimidazole), and aims to reduce animal experiment and drug development costs. According to the method, 12 high-precision QSAR models are constructed by integrating rodent animal toxicity data (rat / mouse LD50) of 1150 compounds, and prediction performance is optimized by combining cross reference (q-RASAR) and seven machine learning algorithms (random forest, SVM, neural network and the like). An early toxicity screening tool is provided for drug research and development, the experiment cost and period are remarkably reduced, meanwhile, environmental risk assessment is supported, and green drug design and chemical supervision decision making are assisted. All models and data are open-sourced and can be applied to toxicity prediction of new compounds through a standardized process.

The invention relates to a compound toxicity comprehensive evaluation method and system based on entropy weight method coupling multi-dimensional data. The invention provides an objective, quantitative and comprehensive toxicity assessment method, which comprises the following steps of: objectively weighting multi-dimensional toxicity data from different software and different end points by introducing an information entropy theory and constructing a mathematical model, and finally integrating the multi-dimensional toxicity data into a single and quantificationally comparable'comprehensive toxicity index '. Therefore, reliable assessment of AOPs degradation paths and intermediate product risks is realized.

The invention discloses a method for predicting toxicity of commercial pesticides to earthworms based on machine learning, which comprises the following steps: acquiring a data set, and constructing a toxicity prediction model based on the data set; the data set comprises the toxicity data of the commercial pesticide to the earthworms, the molecular characterization characteristics of the original pesticide of the commercial pesticide, the assistant type of the commercial pesticide and the physical and chemical properties of soil; based on the toxicity prediction model, obtaining the predicted toxicity level of the commercial pesticide to the earthworms; the method provided by the invention makes up for the defect of neglecting the types of auxiliaries in the prior art, can efficiently and accurately predict the toxicity of commercial pesticides to non-target organisms under actual soil conditions, and provides a scientific basis for safe use of pesticides and environmental risk management.

The invention relates to the field of environmental science, and provides a sensitive species screening method and system for soil Cd environmental quality benchmark derivation. The method comprises the following steps: establishing a multi-dimensional evaluation index system, carrying out weighted scoring processing on candidate species, and screening to obtain a preliminary acceptor species directory; for species in the preliminary receptor species directory, acquiring soil Cd toxicity data, and performing soil property normalization processing on the soil Cd toxicity data to obtain a standardized toxicity data set; inputting the standardized toxicity data set into a species sensitivity distribution model for fitting calculation, and generating a species sensitivity distribution curve; according to the species sensitivity distribution curve, sorting the species according to the sensitivity, selecting sensitive species from a sorting result, and forming a sensitive ecological receptor directory. According to the method, scientificity, accuracy and regional applicability of soil Cd environment reference derivation are improved.