Sensitivity data acquisition method and system based on environmental factors

Abstract

The invention discloses a sensitivity data acquisition method and system based on environmental factors, and the method comprises the steps: obtaining a data set in a preset time period, the data set comprising meteorological data, environmental protection data, pollen concentration data and doctor seeing data; performing model training by using the data set to construct a prediction regression model; calculating each index coefficient and intercept based on the prediction regression model, wherein each index coefficient at least comprises a relative humidity coefficient, an air pressure coefficient, a temperature coefficient, a pollutant concentration coefficient, a pollen concentration coefficient, a precipitation coefficient and a wind speed coefficient; obtaining current environment data and future predicted environment data, wherein the current environment data and the future predicted environment data are in one-to-one correspondence with the index coefficients; and calculating the sensitivity data based on the current environmental data, the future predicted environmental data, the index coefficients and the intercept, so that the sensitivity data can be graded, and protection early warning can be provided for users in time according to the grading condition.

Description

Technical Field

[0001] The present invention belongs to the fields of meteorology and environmental science, as well as data science and artificial intelligence technology, and specifically relates to a method and system for acquiring sensitivity data based on environmental factors. Background Art

[0002] With climate change and accelerated urbanization, pollen-induced respiratory allergies have become a global health problem, affecting nearly one-third of the world's population, especially children and the elderly. Allergic symptoms include hay fever, rhinitis, asthma, allergic sinusitis, allergic conjunctivitis and other serious complications, posing a huge challenge to public health.

[0003] With global warming and rising carbon dioxide concentrations in the atmosphere, the pollen season has become longer, the onset of disease has come earlier, and the pollen concentration has increased. This has led to an increasing burden on patients’ allergic symptoms and health. Changes in vegetation and land use in cities have led to a diversification of pollen sources and changes in distribution patterns, posing greater challenges to public health. Solving the problem of pollen allergies has become increasingly urgent.

[0004] Currently, most systems on the market only report on pollen concentrations. No one has conducted in-depth research and analysis on the relationship between meteorology, environmental protection, pollen concentrations and hospital visit data, to analyze which factors pose a greater risk for various allergic symptoms and which factors can cause widespread allergic symptoms.

[0005] Therefore, how to design an accurate model and obtain sensitivity data under the current environment based on this model is a technical problem that needs to be solved. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for acquiring sensitivity data based on environmental factors, so as to solve the problems existing in the prior art.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for acquiring sensitivity data based on environmental factors, comprising:

[0009] Optionally, a data set within a preset time period is obtained, the data set including meteorological data, environmental protection data, pollen concentration data and medical treatment data;

[0010] Using the data set to perform model training and construct a predictive regression model;

[0011] Calculate each index coefficient and intercept based on the prediction regression model, wherein each index coefficient includes at least a relative humidity coefficient, an air pressure coefficient, a temperature coefficient, a pollutant concentration coefficient, a pollen concentration coefficient, a precipitation coefficient and a wind speed coefficient;

[0012] Acquire various environmental data, wherein the various environmental data correspond to various indicator coefficients one by one, and the various environmental data include various current environmental data or various predicted environmental data in the future;

[0013] Sensitivity data is calculated based on the environmental data, the index coefficients and the intercept, and the sensitivity data is graded to provide protection warnings to users.

[0014] Optionally, the sensitivity data is calculated based on the environmental data, the index coefficients and the intercept, and is obtained according to the following formula:

[0015]

[0016] Where S represents the sensitivity data, N represents the number of coefficients of each index coefficient, n represents a positive integer less than or equal to N, and η n represents the nth index coefficient, d n represents the environmental data corresponding to the nth indicator coefficient, and b represents the intercept;

[0017] Among them, the current environmental data at least include current temperature data, current humidity data, current wind speed data, current air pressure data, current pollen concentration data, current precipitation data and current pollutant concentration data, and the future predicted environmental data at least include predicted temperature data, predicted humidity data, predicted wind speed data, predicted air pressure data, predicted pollen concentration data, predicted precipitation data and predicted pollutant concentration data.

[0018] Optionally, after obtaining the data set within the preset time period, the following is also included:

[0019] Performing data cleaning on the acquired data set, wherein the data cleaning is to remove missing values ​​and outliers;

[0020] The cleaned data set is merged in time series, wherein the time series is merged to form a data frame by arranging the meteorological data, environmental protection data, pollen concentration data and medical treatment data in sequence according to the acquired time sequence within a preset time period.

[0021] Optionally, using the data set to perform model training to construct a predictive regression model includes:

[0022] Constructing additional feature data based on the cleaned data set, the additional feature data at least including the season, the average temperature within a preset time period, and / or the average humidity within a preset time period;

[0023] Model training is performed based on the additional feature data and the data set to obtain a new prediction regression model.

[0024] Optionally, using the data set to perform model training to construct a predictive regression model includes:

[0025] Dividing the data set in the data frame after data cleaning into a training set and a test set according to a preset ratio;

[0026] The training set is input into a multivariate linear regression model for model training to construct a predictive regression model, and after the training is completed, the predictive regression model is verified to be qualified using the test set.

[0027] Optionally, use cross-validation methods to assess the stability and accuracy of the predictive regression model.

[0028] Optionally, the method further includes:

[0029] Classify data according to sensitivity and obtain corresponding protection recommendations based on each level;

[0030] The level classification results and corresponding protection recommendations are displayed visually on the terminal.

[0031] The present application also provides a sensitivity data acquisition system based on environmental factors, comprising:

[0032] An acquisition module, used to acquire a data set within a preset time period, wherein the data set includes meteorological data, environmental protection data, pollen concentration data, and medical treatment data;

[0033] A construction module is used to use the data set to perform model training and construct a predictive regression model;

[0034] A data acquisition module, used to calculate various index coefficients and intercepts based on the prediction regression model, wherein the various index coefficients at least include relative humidity coefficient, air pressure coefficient, temperature coefficient, pollutant concentration coefficient, pollen concentration coefficient, precipitation coefficient and wind speed coefficient;

[0035] A data correspondence module is used to obtain various environmental data, wherein each environmental data corresponds to each indicator coefficient one by one, and each environmental data includes current environmental data or future predicted environmental data;

[0036] The sensitivity data acquisition module is used to calculate the sensitivity data based on the environmental data, the index coefficients and the intercept, and to classify the sensitivity data so as to provide protection warnings to users.

[0037] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the method are implemented when the processor executes the computer program.

[0038] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0039] Beneficial effects:

[0040] 1. This application constructs a predictive regression model by acquiring meteorological data, environmental data, pollen concentration data and medical data, and calculates the intercept and various index coefficients through the predictive regression model (the various index coefficients only include humidity coefficient, air pressure coefficient, temperature coefficient, pollutant concentration coefficient, pollen concentration coefficient, precipitation coefficient and wind speed coefficient). The sensitivity data is calculated through various environmental data, the various index coefficients and the intercept, and the sensitivity data is graded so that users can take precautions in advance, such as: 1) Increase indoor air filtration, use air purifiers or change daily activities to reduce the occurrence of allergic symptoms. 2) Patients can buy anti-allergic drugs in advance to avoid being affected by drug shortages during the allergy season. 3) After understanding the peak pollen season, individuals can better arrange outdoor activities and avoid going out during the peak allergy season, thereby improving the quality of life. 4) When users know the upcoming pollen season and possible allergies, they can reduce anxiety and uneasiness.

[0041] 2. Public Health Strategy:

[0042] 1. Public health policy: Governments and relevant agencies can formulate public health measures based on the forecast results, such as issuing health tips, strengthening community publicity and providing protective measures.

[0043] 2. Resource allocation: By predicting the peak allergy season, medical resources can be allocated more effectively, such as adding emergency medicines or providing medical consultation services.

[0044] 3. Scientific research and data analysis:

[0045] 1. Research and development: By collecting and analyzing data on pollen allergies, we can conduct in-depth research on the changing trends of allergens and help scientists and doctors better understand the mechanisms of allergies.

[0046] 2. Impact of climate change: Understand how changes in pollen season respond to climate change, and promote research on environmental protection and climate adaptation strategies.

[0047] 4. Personalized medicine:

[0048] 1. Individualized treatment plan: Based on the predicted results, doctors can develop personalized treatment and prevention plans for allergy patients to improve treatment outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0050] Figure 1 A schematic diagram of a flow chart of a method for acquiring sensitivity data based on environmental factors provided in an embodiment of the present application;

[0051] Figure 2 An interface diagram provided for an embodiment of the present application;

[0052] Figure 3 A schematic diagram of the structure of a sensitivity data acquisition system based on environmental factors provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0055] It should be understood that although the terms first, second, etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another unit. For example, a first unit can be referred to as a second unit, and similarly, a second unit can be referred to as a first unit without departing from the scope of the exemplary embodiments of the present invention.

[0056] It should be understood that the term "and / or" that may appear in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this article describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B can represent two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0057] Embodiment 1:

[0058] like Figure 1 FIG. 1 is a flow chart of a method for acquiring sensitivity data based on environmental factors proposed in an embodiment of the present invention, comprising:

[0059] S1, obtaining a data set within a preset time period, wherein the data set includes meteorological data, environmental protection data, pollen concentration data, and medical treatment data.

[0060] Specifically, a data set within a preset time period is obtained, and the data in the data level includes meteorological data, environmental protection data, pollen concentration data, and medical treatment data. The data set includes two parts:

[0061] Part 1: Data Source

[0062] Meteorological data: temperature, humidity, air pressure, wind speed, precipitation, etc., from the Meteorological Bureau or related meteorological websites. Environmental data: PM2.5, PM10, NO2, SO2, CO, O3 and other pollutant concentrations, from environmental monitoring stations. Pollen concentration: from various pollen concentration detectors. Hospital visit data: medical records obtained from local hospitals, including the number of rhinitis-related cases.

[0063] Part 2: Data Format

[0064] Meteorological data: date, temperature (℃), humidity (%), air pressure (hPa), wind speed (m / s), precipitation;

[0065] Environmental factors: date, PM2.5 (μg / m 3 )、PM10(μg / m 3 )、NO2(μg / m 3 )、SO(μg / m 3 2. CO (μg / m 3 )、O3(μg / m 3 );

[0066] Pollen concentration: Date, pollen concentration (N / cm 3 );

[0067] Hospital visit data: date, number of patients with rhinitis.

[0068] In order to establish an accurate prediction regression model, in a preferred embodiment of the present solution, after obtaining the data set within a preset time period, the following steps are further included:

[0069] Performing data cleaning on the acquired data set, wherein the data cleaning is to remove missing values ​​and outliers;

[0070] The cleaned data set is merged in time series, wherein the time series is merged to form a data frame by arranging the meteorological data, environmental protection data, pollen concentration data and medical treatment data in sequence according to the acquired time sequence within a preset time period.

[0071] Specifically, the acquired data set is cleaned to process missing values ​​and outliers, mainly to check whether the units of the collected data are repeated and whether they are effective. After the cleaning is completed, the cleaned data set is merged in time series. The only common point of data from different sources is the date. Different data sources are merged into one data frame according to the date. Only by the date can various types of data for each day be integrated.

[0072] In order to improve the prediction accuracy of the prediction regression model, in a preferred embodiment of the present invention, the prediction regression model is constructed by using the data set for model training, including:

[0073] Constructing additional feature data based on the cleaned data set, the additional feature data at least including the season, the average temperature within a preset time period, and / or the average humidity within a preset time period;

[0074] Model training is performed based on the additional feature data and the data set to obtain a new prediction regression model.

[0075] Specifically, additional feature data (feature construction) is constructed based on the cleaned data set: additional features are constructed according to needs, such as the season, average temperature and humidity in the previous few days, etc. The feature construction part is mainly used to analyze the high-incidence season. Feature construction creates some new features based on the existing data, enhances them according to the content to be predicted, and improves the prediction accuracy of the prediction regression model.

[0076] S2, using the data set to perform model training and construct a predictive regression model.

[0077] Specifically, the data set is divided into a training set and a test set according to a preset ratio, usually 70%-80% of the training set and 20%-30% of the test set; the training set is input into a multivariate linear regression model for model training to construct a predictive regression model, wherein during the training process, the predictive regression model is qualified by using the test set. This solution also includes a model optimization part, using Lasso regression, ridge regression and other methods to select features and remove unimportant features. The cross-validation method is used to evaluate the stability and accuracy of the predictive regression model.

[0078] S3, calculating each indicator coefficient and intercept based on the predictive regression model.

[0079] Specifically, the coefficients and intercepts of various indicators are calculated based on the predictive regression model combined with the parameter estimation method in the existing multivariate linear regression analysis. The coefficients of various indicators at least include relative humidity coefficient, air pressure coefficient, temperature coefficient, pollutant concentration coefficient, pollen concentration coefficient, precipitation coefficient and wind speed coefficient.

[0080] S4, obtaining various environmental data.

[0081] Specifically, the current environmental data and the future predicted environmental data are obtained, and the current environmental data and the future predicted environmental data correspond to each index coefficient one by one; the current environmental data at least include current temperature data, current humidity data, current wind speed data, current air pressure data, current pollen concentration data, current precipitation data and current pollutant concentration data, and the future predicted environmental data at least include predicted temperature data, predicted humidity data, predicted wind speed data, predicted air pressure data, predicted pollen concentration data, predicted precipitation data and predicted pollutant concentration data. This application can obtain the current and future predicted sensitivity data based on the current environmental data of the Meteorological Bureau and the future predicted environmental data of the Meteorological Bureau combined with the model training results. For example, the sensitivity data of the current and future 5 days are predicted based on the predicted meteorological information of the next 5 days and the current meteorological information combined with the model training results.

[0082] S5, calculating sensitivity data based on the current environmental data and the future predicted environmental data, as well as the index coefficients and intercepts, and classifying the sensitivity data to provide protection warnings to users.

[0083] Specifically, the sensitivity data is calculated based on the current environmental data and the future predicted environmental data, as well as the index coefficients and the intercept, and is obtained according to the following formula:

[0084]

[0085] Where S represents the sensitivity data, N represents the number of coefficients of each index coefficient, n represents a positive integer less than or equal to N, and η n represents the nth index coefficient, d n represents the environmental data corresponding to the nth indicator coefficient, and b represents the intercept;

[0086] Among them, the current environmental data at least include current temperature data, current humidity data, current wind speed data, current air pressure data, current pollen concentration data, current precipitation data and current pollutant concentration data, and the future predicted environmental data at least include predicted temperature data, predicted humidity data, predicted wind speed data, predicted air pressure data, predicted pollen concentration data, predicted precipitation data and predicted pollutant concentration data. The index coefficient represents the degree of influence of each variable (each environmental data) on the sensitization index. b is a constant term (intercept), which represents the baseline value of the allergy incidence rate when all variables are zero.

[0087] In order for the user to take effective prevention measures in time, in a preferred embodiment of the present invention, the method further comprises:

[0088] Classify data according to sensitivity and obtain corresponding protection recommendations based on each level;

[0089] The level classification results and corresponding protection recommendations are displayed visually on the terminal.

[0090] Specifically, the sensitivity data is divided into low risk level, medium risk level and high risk level, and different protective measures are taken according to the level. The results will be displayed in the report interface. Figure 2 According to the future forecast environmental data (the weather data for the next five days) predicted by the Meteorological Bureau, combined with the model training results, the sensitization index for the next few days is predicted. The results are also shown in Figure 2 In the report interface, the prediction results are displayed through the WeChat applet or the report website. By analyzing the time series trend of rhinitis allergy visit data, the allergy risk in the next few days can be predicted to help hospitals and patients prepare in advance. The technology of this application can study the relationship between seasonal factors and allergies, and formulate targeted protective measures for different seasons.

[0091] This application allows users to provide feedback on their allergic reactions and medical treatment, provide real-time data for model optimization, use real-time data to update the model, and provide timely predictions and warnings. After analyzing the relationship between historical meteorological, environmental, pollen concentration and hospital treatment data, a predictive regression model is formed through big data and artificial intelligence. The current and future trends in meteorological, environmental, and pollen concentrations can be used to calculate the model to obtain the current or future "sensitivity data". The sensitivity data can directly reflect the incidence of allergies in the corresponding environmental conditions according to the level reached.

[0092] As an embodiment of the present application, the coefficients of various indicators are obtained by constructing a predictive regression model, including: intercept: 4851.6212368000715; relative humidity coefficient: -0.1; air pressure coefficient: -54.0; temperature coefficient: 6.4; pm2.5 coefficient: -0.2; pollen concentration coefficient: 19.9; precipitation coefficient: -4.1; wind speed coefficient: -9.1.

[0093] Today's current information: temperature (15℃ / 4℃), humidity (92.8%), wind speed (3m / s), air pressure (877.6hPa), pollen concentration (11N / cm 3 )

[0094] Based on the coefficients of each indicator and today's current information, the sensitivity data is: 347.59 (high)

[0095] Figure 3 A schematic diagram of a sensitivity data acquisition system based on environmental factors provided by the present invention includes:

[0096] An acquisition module 10 is used to acquire a data set within a preset time period, wherein the data set includes meteorological data, environmental protection data, pollen concentration data, and medical treatment data;

[0097] A construction module 20 is used to use the data set to perform model training and construct a prediction regression model;

[0098] A data acquisition module 30, for calculating various index coefficients and intercepts based on the prediction regression model, wherein the various index coefficients at least include a relative humidity coefficient, an air pressure coefficient, a temperature coefficient, a pollutant concentration coefficient, a pollen concentration coefficient, a precipitation coefficient and a wind speed coefficient;

[0099] A data correspondence module 40 is used to obtain various environmental data, wherein the various environmental data correspond to various index coefficients one by one, and the various environmental data include current environmental data or future predicted environmental data;

[0100] The sensitivity data acquisition module 50 is used to calculate the sensitivity data based on the environmental data, the index coefficients and the intercept, and classify the sensitivity data to provide a protection warning to the user.

[0101] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 4 As shown, the electronic device 60 includes: a processor 601 (processor), a memory 602 (memory) and a bus 603;

[0102] The processor 601 and the memory 602 communicate with each other via the bus 603;

[0103] The processor 601 is used to call the program instructions in the memory 602 to execute the methods provided by the above-mentioned method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative labor.

[0104] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for obtaining sensitivity data based on environmental factors, characterized in that: include: Acquire a data set within a preset time period, the data set including meteorological data, environmental protection data, pollen concentration data, and medical treatment data; Using the data set to perform model training and construct a predictive regression model; Calculate each index coefficient and intercept based on the prediction regression model, wherein each index coefficient includes at least a relative humidity coefficient, an air pressure coefficient, a temperature coefficient, a pollutant concentration coefficient, a pollen concentration coefficient, a precipitation coefficient and a wind speed coefficient; Acquire various environmental data, wherein the various environmental data correspond to various indicator coefficients one by one, and the various environmental data include various current environmental data or various predicted environmental data in the future; Sensitivity data is calculated based on the environmental data, the index coefficients and the intercept, and the sensitivity data is graded to provide protection warnings to users.

2. The method according to claim 1, characterized in that The sensitivity data is calculated based on the environmental data, the index coefficients and the intercept, and is obtained according to the following formula: Where S represents the sensitivity data, N represents the number of coefficients of each index coefficient, n represents a positive integer less than or equal to N, and η n represents the nth index coefficient, d n represents the environmental data corresponding to the nth indicator coefficient, and b represents the intercept; Among them, the current environmental data at least include current temperature data, current humidity data, current wind speed data, current air pressure data, current pollen concentration data, current precipitation data and current pollutant concentration data, and the future predicted environmental data at least include predicted temperature data, predicted humidity data, predicted wind speed data, predicted air pressure data, predicted pollen concentration data, predicted precipitation data and predicted pollutant concentration data.

3. The method according to claim 1, characterized in that After obtaining the data set within the preset time period, it also includes: Performing data cleaning on the acquired data set, wherein the data cleaning is to remove missing values ​​and outliers; The cleaned data set is merged in time series, wherein the time series is merged to form a data frame by arranging the meteorological data, environmental protection data, pollen concentration data and medical treatment data in sequence according to the acquired time sequence within a preset time period.

4. The method according to claim 3, characterized in that The data set is used to perform model training and construct a predictive regression model, including: Constructing additional feature data based on the cleaned data set, the additional feature data at least including the season, the average temperature within a preset time period, and / or the average humidity within a preset time period; Model training is performed based on the additional feature data and the data set to obtain a new prediction regression model.

5. The method according to claim 3, characterized in that: The data set is used to perform model training and construct a predictive regression model, including: Dividing the data set in the data frame after data cleaning into a training set and a test set according to a preset ratio; The training set is input into a multivariate linear regression model for model training to construct a predictive regression model, and after the training is completed, the predictive regression model is verified to be qualified using the test set.

6. The method according to claim 5, characterized in that The stability and accuracy of the predictive regression models were evaluated using cross-validation methods.

7. The method according to claim 2, characterized in that The method further comprises: Classify data according to sensitivity and obtain corresponding protection recommendations based on each level; The level classification results and corresponding protection recommendations are displayed visually on the terminal.

8. A sensitivity data acquisition system based on environmental factors, characterized in that: include: An acquisition module, used to acquire a data set within a preset time period, wherein the data set includes meteorological data, environmental protection data, pollen concentration data, and medical treatment data; A construction module is used to use the data set to perform model training and construct a predictive regression model; A data acquisition module, used to calculate various index coefficients and intercepts based on the prediction regression model, wherein the various index coefficients at least include relative humidity coefficient, air pressure coefficient, temperature coefficient, pollutant concentration coefficient, pollen concentration coefficient, precipitation coefficient and wind speed coefficient; A data correspondence module is used to obtain various environmental data, wherein each environmental data corresponds to each indicator coefficient one by one, and each environmental data includes current environmental data or future predicted environmental data; The sensitivity data acquisition module is used to calculate the sensitivity data based on the environmental data, the index coefficients and the intercept, and to classify the sensitivity data so as to provide protection warnings to users.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for acquiring sensitivity data based on environmental factors as described in any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for acquiring sensitivity data based on environmental factors as described in any one of claims 1 to 7 are implemented.

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