A method and device for assessing the combined exposure risk of VOCs in children's products

Abstract

The present invention provides a method and device for assessing the combined exposure risk of VOCs in children's products. The method includes: obtaining data on the types and usage durations of children's products; establishing a usage association model between children's products, including: establishing a Copula function between the continuous usage duration of children's products and the children's products to be used at the next moment, and calculating the conditional probability density model of the children's products to be used at the next moment under different continuous usage duration conditions; establishing a cellular automaton model of the usage scenarios of children's products based on the usage association model between children's products, and combining with the volatilization rate data of VOCs to calculate the combined exposure dose of VOCs; using a multi-level combined action decoupling method based on the combination index to convert the combined exposure dose of VOCs into the equivalent doses of independent actions of each component substance and summing them up to evaluate the combined exposure risk of VOCs. The present invention can accurately assess the combined exposure risk of VOCs in the usage scenarios of children's products.

Description

Technical Field

[0001] The present invention relates to the field of safety engineering technology, and in particular to a method and device for assessing the combined exposure risk of VOCs in children's products. Background Art

[0002] With the widespread use of children's products in family life, people are paying more and more attention to their safety and environmental friendliness. Volatile Organic Compounds (VOCs) are a common type of pollutants that may be present in children's products, such as cribs, toys, and clothing. Children are in the growth and development stage and are particularly sensitive to pollutants in the environment. Long-term inhalation of VOCs may have adverse effects on their health, such as respiratory problems and neurological development disorders.

[0003] At present, although there are some emission limit standards for VOCs in children's products, these limits are usually determined based on the length of time children use a single product. However, in real scenarios, the interaction between children and a certain product can be easily affected by other products. For example, children may also use erasers when using pencils, and may use correction fluid when using fountain pens. The use association between these products will significantly affect the length of time children use the products, thereby affecting VOCs exposure. Existing methods for calculating VOCs inhalation exposure levels are generally applicable to simple exposure scenarios with a single substance, constant volatile concentration, and stable product use frequency. However, the use scenarios of children's products are usually more complex, which may contain multiple VOCs and the volatilization rate will change dynamically. In addition, there is a large uncertainty in the interaction between children and products, which makes it difficult for current exposure dose calculation methods to accurately estimate the inhalation exposure level of VOCs in such complex dynamic exposure scenarios. In addition, there may be combined effects (such as synergy and antagonism) between multiple VOCs, and the impact on children's health shows a nonlinear change with increasing dose. The traditional hazard index method (Hazard Index, HI) usually assumes that the harmful effects of multiple substances are relatively independent, and simply adds the hazard quotient (Hazard Quotient, HQ) of multiple VOCs. This makes it difficult to accurately characterize the exposure risk to children caused by the combined action of the mixture.

[0004] The VOCs exposure risk assessment method mainly consists of four parts: hazard identification, exposure dose assessment, dose-effect response assessment, and risk characterization. For VOCs exposure dose assessment, the existing VOCs inhalation exposure level calculation method usually sets the concentration of VOCs as a constant, and uses the duration of use × frequency of use to characterize the exposure frequency of pollutants (usually set as a constant), which is suitable for simple exposure scenarios with a single substance, constant volatile concentration, and stable product use frequency. However, the use scenarios of children's products are usually more complicated, children's use behavior of products is highly random, the order of item use also has a certain correlation, and the volatilization rates of various VOCs will change dynamically. At present, there is a lack of children's product use scenario simulation methods that take into account both randomness and correlation, making it difficult to accurately assess the inhalation exposure dose of VOCs in such complex dynamic exposure scenarios. For VOCs risk characterization, the current exposure risks of various VOCs are usually characterized by the HI method, that is, the HQ of each component substance is calculated and added to obtain the overall exposure risk. However, the combined effects of multiple VOCs will cause their overall harmful effects to vary nonlinearly with exposure dose, and the traditional HI method is difficult to accurately estimate the exposure risk of VOCs to the human body in this case. In addition, the dose-effect data of various VOCs on the human body are very limited, which makes it difficult to quantitatively evaluate the intensity of their combined effects.

[0005] Therefore, this application mainly optimizes the two aspects of VOCs exposure dose assessment and risk characterization. A VOCs joint exposure risk assessment method for children's products is proposed to simulate the interaction between children and products, evaluate the joint exposure dose caused by the dynamic volatilization of multiple VOCs in the product, and reasonably characterize the exposure risk of multiple VOCs to children under the joint effect. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to provide a method and device for assessing the joint exposure risk of VOCs in children's products. A two-dimensional Gaussian kernel density estimation method is used to construct a Copula function that describes the usage association of multiple products, and a cellular automaton is used to simulate the usage process of children's products to calculate the joint exposure dose of VOCs in the scenario. Based on the combined index of the toxic effects of VOCs on microorganisms, a multi-level joint action decoupling method is used to convert the joint action of VOCs into the sum of the independent actions of each component, so as to achieve an accurate assessment of the joint exposure risk of VOCs in the children's product usage scenario.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In one aspect, a method for joint exposure risk assessment of VOCs in children's products is provided, the method comprising the following steps:

[0009] S1. Select a specific scene, shoot a video of children using children's products, and extract data on the types and duration of use of children's products;

[0010] S2. Establishing a usage association model between children's products based on the categories and usage duration data of children's products, including: establishing a Copula function between the continuous usage duration of children's products and the children's products used at the next moment, and calculating the conditional probability density model of the children's products used at the next moment under different continuous usage duration conditions;

[0011] S3. Based on the usage association model between children's products, a cellular automaton model of the children's product usage scenario was established, and combined with the VOCs volatilization rate data, the combined exposure dose of VOCs in this scenario was calculated;

[0012] S4. Using the multi-level joint action decoupling method based on the combination index, the combined exposure dose of VOCs is converted into the equivalent dose of each component substance acting independently and added together to evaluate the combined exposure risk of VOCs.

[0013] Optionally, the step S1 specifically includes:

[0014] Assume that there are m kinds of children's products in the scene, and the set Indicates that c0 does not use any children's products, and c0 to c m Encode with natural numbers 0 to m;

[0015] Select children of a certain age and gender, set a time range, and film the process of them using children's products. Identify and record the types of children's products used every second to form time series data. ;

[0016] The design structure is [K c ,c'] to indicate the continuous use of a children's product K c The probability of using other children's products after 1 second, among which K c is the continuous use time of children's product c, and c' is the children's product to be used at the next moment.

[0017] Optionally, the step S2 specifically includes:

[0018] Assume that n usage time data of children's product p have been obtained (k i , c i ), where k i is the observed value of the continuous use time K of children's products p, which is a continuous random variable; c i is the observed value of the children's product C used at the next moment, which is a discrete random variable; i=1,2,…,n;

[0019] To establish the Copula function for variables K and C, first convert the observed values ​​of K and C to the [0,1] interval and establish their cumulative probability distribution functions as shown below:

[0020] (1)

[0021] Where I is the indicator function, which is used to determine whether the continuous use time of a children's product is not less than k i Second:

[0022] (2)

[0023] Let u = F K (k), v = F C (c) Estimate the Copula density using a two-dimensional Gaussian kernel:

[0024] (3)

[0025] Among them G K and G C are Gaussian kernel functions of K and C, respectively, with bandwidths of σ K and σ C , is a set of hyperparameters; based on empirical rules, the best estimate of the bandwidth σ is:

[0026] (4)

[0027] in is the sample standard deviation of σ; the duration data (k i , c i ) are substituted into formula (3) one by one to obtain the Copula function about u and v;

[0028] Based on Sklar's theorem, any multidimensional joint distribution can be represented by its marginal distribution function and a copula function. Therefore, the conditional probability density function of C with respect to K is for:

[0029] (5)

[0030] According to formulas (3) and (5), corresponding Copula functions and conditional probability density models of their continuous use duration and the children's products to be used at the next moment are established for all children's products, which are used for cellular automaton simulation of the next children's product usage scenario.

[0031] Optionally, the step S3 specifically includes:

[0032] Establish a grid coordinate system with time as the horizontal axis and children's products as the vertical axis. If c children's products are used at time t, the corresponding grid will be filled, representing the usage status of the children's products at that moment.

[0033] The entire coordinate system is regarded as a cellular automaton model. After initializing the usage state at time t0, the conditional probability density obtained in formula (5) is used to iteratively simulate the state at each moment until the set time boundary is reached, thus completing the construction of the children's product usage scenario.

[0034] Optionally, the process of step S3 is as follows:

[0035] 3-1) Initialization: Randomly select children's products c at the initial time t0;

[0036] 3-2) Obtain the conditional probability density function: For the current time t, obtain the conditional probability function corresponding to children's products c ;

[0037] 3-3) Continuous use duration judgment: judge how long the currently selected children's product has been used continuously, that is, k seconds;

[0038] 3-4) Conversion matrix construction: based on the children's products c used at the current moment, the continuous use time k and , construct the transformation matrix M;

[0039] 3-5) Cellular automaton simulation: Based on the probability of various children's products being used in M, the Monte Carlo simulation method is used to randomly simulate the children's products c' used at the next moment t+1;

[0040] 3-6) Update continuous use time: compare whether the children's product c at the current moment is the same as the children's product c' at the next moment; if they are the same, update k to k+1, otherwise reset k to 1;

[0041] 3-7) Repeat simulation: Repeat steps 3-2) to 3-6) until the set time limit t is reached. E ;

[0042] After completing a round of operations from step 3-1) to step 3-7), a given time range t is completed. E The simulation results of the use scenarios of children's products are expressed as a matrix S, called the scenario matrix:

[0043] (6)

[0044] In which, each column vector Indicates the usage of children's products at time t. An element value of 1 indicates that the children's product corresponding to the element index is used, and an element value of 0 indicates that the children's product corresponding to the element index is not used.

[0045] Optionally, step S3 further includes:

[0046] The target VOCs is denoted as α. Through the volatilization rate detection experiment of VOCs in children's products, the volatilization rate data of substance α from product c is obtained, which is denoted as ; Assuming that there are q types of target VOCs, the rate of VOCs volatilization from each children's product at time t is recorded as the variable matrix :

[0047] (7)

[0048] When the scenario matrix S is determined, the corresponding VOCs combined exposure dose is recorded as the vector ED S :

[0049] (8)

[0050] Where η is the child's respiratory rate, B is the child's weight; any element in the vector represents the joint exposure dose of substance α in scene S.

[0051] Optionally, the step S4 specifically includes:

[0052] Assume that the target VOCs constitute the set Λ, and λ is a proper subset of Λ composed of different substances, that is, , the specific process of calculating the combined index of VOCs in different material combinations λ is as follows:

[0053] 4-1) Measurement of the half-inhibitory dose EC of the substance α-independently acting on microorganisms α,50 The EC value of the dose at which the inhibition rate is x% α,x , x can take any value;

[0054] 4-2) Based on the law of mass action, as shown in formula (9), according to the inhibition results of the above microorganisms, the dose-effect curve morphological parameter δ of the independent action of substance α is fitted α , and the corresponding dose-effect relationship of substance α is obtained, as shown in formula (10):

[0055] (9)

[0056] (10)

[0057] where f α Represents the degree of microbial inhibition, dα is the dose of substance α;

[0058] 4-3) Measure and calculate the inhibitory concentration and dose-effect curve of each substance under independent action, and compose these substances into mixed substances λ according to the equivalent dose ratio with inhibition rate x%, and prepare mixed solutions of different concentrations and add them to the microbial culture solution, and measure the dose EC of each substance when the inhibition rate reaches x% λ,x ;

[0059] 4-4) Combined with formula (9), calculate the combination index of inhibition rate x% and substance combination λ :

[0060] (11)

[0061] in, is the dose of component α when the substance combination λ causes x% inhibition rate; , , They represent the synergistic, additive and inhibitory effects of the combined action of the components in λ. Repeat steps 4-3) and 4-4) to calculate the combination index of all mixed substances with respect to the inhibition rate x%;

[0062] 4-5) Combined exposure dose ED in actual scenario S S In the above equation, the decoupling method of multi-level joint action is used to calculate the equivalent dose of each component substance corresponding to its independent action; the cumulative independent action equivalent dose of each component substance in the mixture Λ is: ;

[0063] Query or calculate the reference dose RfD of component substance α α , the calculation method is shown in formula (12):

[0064] (12)

[0065] Among them, NOAEL means the dose level at which no adverse effects are observed, and LOAEL, the lowest dose level of adverse effects, can be used instead; UF i is the uncertainty factor;

[0066] calculate The hazard quotient of the concentration of each component substance in the VOC is calculated and summed up to use the hazard index to characterize the inhalation combined exposure risk of VOCs:

[0067] (13)

[0068] Among them, HI S is the hazard index regarding the exposure scenario S; is the hazard quotient of component substance α in exposure scenario S.

[0069] On the other hand, a device for joint exposure risk assessment of VOCs in children's products is provided, which is used to implement any of the above methods, and the device comprises:

[0070] A data extraction module is used to select a specific scene, shoot a video of children using children's products, and extract data on the types of children's products and the duration of use;

[0071] A model building module is used to establish a usage association model between children's products based on the categories and usage time data of children's products, including: establishing a Copula function between the continuous usage time of children's products and the children's products used at the next moment, and calculating the conditional probability density model of the children's products used at the next moment under different continuous usage time conditions;

[0072] The calculation module is used to establish a cellular automaton model of the use scenario of children's products based on the use association model between children's products, and calculate the combined exposure dose of VOCs in this scenario in combination with the volatilization rate data of VOCs;

[0073] The assessment module is used to convert the combined exposure dose of VOCs into the equivalent dose of each component substance acting independently and add them up using a multi-level joint action decoupling method based on the combination index to assess the combined exposure risk of VOCs.

[0074] In another aspect, an electronic device is provided, the electronic device comprising:

[0075] processor;

[0076] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are loaded and executed by the processor, the steps of the above-mentioned evaluation method are implemented.

[0077] On the other hand, a computer-readable storage medium is provided, wherein program code is stored in the computer-readable storage medium, and the program code can be called by a processor to execute the steps of the above-mentioned evaluation method.

[0078] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0079] The present invention adopts a Copula-cellular automaton model to estimate the exposure frequency of VOCs. By collecting sample data of children's interactive behaviors with various products, the binary Gaussian kernel density estimation method is used to fit the Copula function of the continuous use duration of children's products and the type of children's products used at the next moment, and combined with the cellular automaton model, the use of children's products within a given time range is simulated. Compared with the traditional exposure frequency estimation method, this method simultaneously considers the use frequency of children's products, the use association characteristics between products, and the dynamic volatilization rate of VOCs, and can more reasonably estimate the exposure dose of each component substance in VOCs.

[0080] In addition, the present invention proposes a multi-level joint action decoupling method based on the mixture combination index. By calculating the minimum effect dose corresponding to each component in the mixture in turn, calculating the equivalent dose of each component relative to its independent action, and iteratively updating the mixture dose until the mixture dose is zero, the cumulative independent action equivalent dose of each component substance is obtained, and the HI of VOCs is calculated to characterize its inhalation combined exposure risk to children.

[0081] Compared with traditional exposure risk assessment methods, the present invention can simultaneously consider the randomness of the interaction between children and products, the dynamics of the VOCs volatilization rate, and the impact of the nonlinearity of the combined action of VOCs on the final combined exposure risk, providing an effective VOCs inhalation combined exposure risk assessment method for complex scenarios of children's product use. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are 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.

[0083] Figure 1 It is a flow chart of a method for joint exposure risk assessment of VOCs in children's products provided by an embodiment of the present invention;

[0084] Figure 2 is a flowchart of extracting usage time data of children's products provided by an embodiment of the present invention;

[0085] Figure 3 is a schematic diagram of a simulation process of a use scenario of a children's product provided by an embodiment of the present invention;

[0086] Figure 4 It is a structural schematic diagram of a VOCs joint exposure risk assessment device for children's products provided by an embodiment of the present invention;

[0087] Figure 5It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the word "exemplary" is used to present concepts in a specific way.

[0090] The embodiment of the present invention provides a method for assessing the joint exposure risk of VOCs in children's products based on scenario simulation, which mainly includes the following steps: (1) acquiring the usage time data of children's products; (2) establishing a usage association model between children's products; (3) calculating the joint exposure dose of VOCs; and (4) characterizing the joint exposure risk of VOCs.

[0091] As a specific embodiment of the present invention, Figure 1 As shown, the method includes:

[0092] S1. Select a specific scene (such as a classroom, bedroom, etc.), shoot videos of children using children's products, and extract data on the types of children's products and usage duration.

[0093] Assume that there are m kinds of children's products in the scene, and the set Indicates that c0 does not use any children's products, and c0 to c m Use natural numbers 0 to m to encode; select children of a certain age and gender, set a time range, take photos of the process of them using children's products, identify and record the types of children's products used every second, and form time series data .

[0094] There may be certain correlations in the use of different children's products, which can be expressed as "continuous use of a product K c In order to quantitatively describe this association feature, a structure is designed as [K c ,c'] to indicate the continuous use of a children's product K cThe probability of using other children's products after 1 second, among which K c is the continuous use time of children's product c, c' is the children's product used at the next moment, and the data extraction method is as follows Figure 2 shown.

[0095] S2. Establish a usage association model between children's products based on the categories and usage time data of children's products, including: establishing a Copula function between the continuous usage time of children's products and the children's products used at the next moment, and calculating the conditional probability density model of the children's products used at the next moment under different continuous usage time conditions.

[0096] Assume that n usage time data of children's product p have been obtained (k i , c i ), where k i is the observed value of the continuous use time K of children's products p, which is a continuous random variable; c i is the observed value of the children's product C used at the next moment, which is a discrete random variable; i=1,2,…,n;

[0097] Establish the Copula function about variables K and C. Since the domain of the Copula function is the unit cube [0,1] 2 So first convert the observed values ​​of K and C to the interval [0,1] and establish their cumulative probability distribution functions as follows:

[0098] (1)

[0099] Where I is the indicator function, which is used to determine whether the continuous use time of a children's product is not less than k i Second:

[0100] (2)

[0101] Let u = F K (k), v = F C (c) Estimate the Copula density using a two-dimensional Gaussian kernel:

[0102] (3)

[0103] Among them G K and G C are Gaussian kernel functions of K and C, respectively, with bandwidths of σ K and σ C , is a set of hyperparameters; based on empirical rules, the best estimate of the bandwidth σ is:

[0104] (4)

[0105] in is the sample standard deviation of σ; the duration data (k i , c i ) are substituted into formula (3) one by one to obtain the Copula function of u and v.

[0106] Based on Sklar's theorem, any multidimensional joint distribution can be represented by its marginal distribution function and a copula function. Therefore, the conditional probability density function of C with respect to K is for:

[0107] (5)

[0108] According to formulas (3) and (5), corresponding Copula functions and conditional probability density models of their continuous use duration and the children's products to be used at the next moment are established for all children's products, which are used for cellular automaton simulation of the next children's product usage scenario.

[0109] S3. Based on the usage association model between children's products, a cellular automaton model of the children's product usage scenario is established, and combined with the VOCs volatilization rate data, the combined exposure dose of VOCs in this scenario is calculated.

[0110] A grid coordinate system is established with time as the horizontal axis and children's products as the vertical axis. If c children's products are used at time t, the corresponding grid will be filled, representing the usage status of the children's products at that moment. In order to quantitatively express "the possibility of using other children's products after using a certain children's product for t seconds continuously" and visualize the usage scenarios of children's products, the entire coordinate system is regarded as a cellular automaton model. After initializing the usage status at time t0, the conditional probability density obtained in formula (5) is used to iteratively simulate the status at each moment until the set time boundary is reached, completing the construction of the children's product usage scenario.

[0111] like Figure 3 As shown in the figure, the simulation process of the use scenario of children's products is as follows:

[0112] 3-1) Initialization: Randomly select children's products c at the initial time t0;

[0113] 3-2) Obtain the conditional probability density function: For the current time t, obtain the conditional probability function corresponding to children's products c ;

[0114] 3-3) Continuous use duration judgment: judge how long the currently selected children's product has been used continuously, that is, k seconds;

[0115] 3-4) Conversion matrix construction: based on the children's products c used at the current moment, the continuous use time k and , construct the transformation matrix M;

[0116] 3-5) Cellular automaton simulation: Based on the probability of various children's products being used in M, the Monte Carlo simulation method is used to randomly simulate the children's products c' used at the next moment t+1;

[0117] 3-6) Update continuous use time: compare whether the children's product c at the current moment is the same as the children's product c' at the next moment; if they are the same, update k to k+1, otherwise reset k to 1;

[0118] 3-7) Repeat simulation: Repeat steps 3-2) to 3-6) until the set time limit t is reached. E ;

[0119] After completing a round of operations from step 3-1) to step 3-7), a given time range t is completed. E The simulation results of the use scenarios of children's products are expressed as a matrix S, called the scenario matrix:

[0120] (6)

[0121] In which, each column vector Indicates the usage of children's products at time t. An element value of 1 indicates that the children's product corresponding to the element index is used, and an element value of 0 indicates that the children's product corresponding to the element index is not used.

[0122] The volatilization rate of VOCs in the environment usually reaches a volatilization peak in a short period of time and then slowly decreases, and then maintains a lower volatilization rate, which is called steady-state volatilization. It takes days or even weeks to reach a steady-state volatilization state. However, the use scenarios of children's products are usually free of background pollution. Therefore, various target children's products are placed in a closed environmental chamber for volatilization rate detection experiments. A sampling interval is set to sample VOCs that volatilize in a short period of time (such as within 2 hours) to obtain time series data of their volatilization concentrations. The collection and detection methods of VOCs volatilization samples can adopt existing methods and are not described in detail here.

[0123] The target VOCs is denoted as α. Through the volatilization rate detection experiment of VOCs in children's products, the volatilization rate data of substance α from product c is obtained, which is denoted as ; Assuming that there are q types of target VOCs, the rate of VOCs volatilization from each children's product at time t is recorded as the variable matrix :

[0124] (7)

[0125] When the scenario matrix S is determined, the corresponding VOCs combined exposure dose is recorded as the vector ED S :

[0126] (8)

[0127] Where η is the child's breathing rate, in L / s; B is the child's weight, in kg; any element in the vector It represents the combined exposure dose of substance α in scenario S, in mg / kg.

[0128] S4. Using the multi-level joint action decoupling method based on the combination index, the combined exposure dose of VOCs is converted into the equivalent dose of each component substance acting independently and added together to evaluate the combined exposure risk of VOCs.

[0129] Since there may be a combined effect between the harmfulness of various VOCs to the human body, such as synergistic and antagonistic effects, in order to reasonably assess the risks to children caused by combined exposure to multiple substances, the present invention calculates the combined index of different VOCs combinations based on microbial toxicity experiments, and based on this, converts each element in the exposure dose into its equivalent dose when it acts independently.

[0130] The subjects of microbial toxicity experiments can be sulfide bacteria, luminous bacteria, etc., and the lethal dose (ED) or inhibitory dose (EC) is selected as the toxicity index. Different doses of VOCs independent solution and mixed solution are added to their culture solutions to observe the dose difference when the two cause equivalent toxicity. For the microbial toxicity experimental methods, please refer to the "Luminous Bacteria Method for Determination of Acute Toxicity of Water Quality" (GB / T 15441-1995) and other related contents, which will not be described in detail here.

[0131] Assume that the target VOCs constitute the set Λ, and λ is a proper subset of Λ composed of different substances, that is, , the specific process of calculating the combined index of VOCs in different material combinations λ is as follows:

[0132] 4-1) Measurement of the half-inhibitory dose EC of the substance α-independently acting on microorganisms α,50 The EC value of the dose at which the inhibition rate is x% α,x , x can take any value.

[0133] 4-2) Based on the law of mass action, as shown in formula (9), according to the inhibition results of the above microorganisms, the dose-effect curve morphological parameter δ of the independent action of substance α is fitted α , and the corresponding dose-effect relationship of substance α is obtained, as shown in formula (10):

[0134] (9)

[0135] (10)

[0136] where f α Represents the degree of microbial inhibition, d α is the dose of substance α.

[0137] 4-3) Measure and calculate the inhibitory concentration and dose-effect curve of each substance under independent action, and combine these substances into a mixed substance λ according to the equivalent dose ratio with an inhibition rate of x% (for example, if the given inhibition rate is 30%, The three were mixed at a dosage of 1:2:3) and prepared into mixed solutions of different concentrations and added to the microbial culture solution to measure the dosage EC of each substance when the inhibition rate reached x%. λ,x .

[0138] 4-4) Combined with formula (9), calculate the combination index of inhibition rate x% and substance combination λ :

[0139] (11)

[0140] in, is the dose of component α when the substance combination λ causes x% inhibition rate; , , They represent the synergistic, additive and inhibitory effects of the combined action of the components in λ. Repeat steps 4-3) and 4-4) to calculate the combination index of all mixed substances with respect to the inhibition rate x%.

[0141] 4-5) Combined exposure dose ED in actual scenario S S In the method, the dosage of each component substance is usually mixed in any proportion, not necessarily in a proportion of an equivalent dosage of a certain inhibition rate x%, which makes it difficult to directly decompose the dosage of the mixture into the equivalent concentration of each component substance acting independently through the combination index method. Therefore, the present invention proposes a decoupling method using multi-level combined effects to calculate the equivalent dosage of each component substance corresponding to its independent effect, and the specific process is as follows:

[0142]

[0143] The cumulative independent action equivalent dose of each component substance in the mixture Λ is obtained: .

[0144] Query or calculate the reference dose RfD of component substance α αThis value can refer to the authoritative toxicity databases published by health management agencies in various countries, such as the Agency for Toxic Substances and Disease Registry (ATSDR), Integrated Risk Information System (IRIS), European Chemicals Agency Chemical Database (ECHA IUCLID), Shanghai Institute of Organic Chemistry Toxicity Database, etc. In addition, RfD can also be calculated based on animal experiments or clinical data, as shown in formula (12):

[0145] (12)

[0146] Among them, NOAEL represents the dose level at which no adverse effects are observed, and LOAEL, the lowest dose level for adverse effects, can be used instead, with the unit of mg / (kg∙d).

[0147] UF i It is an uncertainty factor. When considering individual differences in the population, UF should be 10; when extrapolating experimental animals to humans, UF should be 10; when using the NOAEL of subchronic studies to estimate chronic RfD, UF should be 10; in some studies where the exposure dose is intermittent rather than continuous, when LOAEL is used instead of NOAEL, UF should be 10. The determination methods of NOAEL and LOAEL can refer to existing methods and will not be described in detail here.

[0148] calculate The hazard quotient of the concentration of each component substance in the VOC is calculated and summed up to use the hazard index to characterize the inhalation combined exposure risk of VOCs:

[0149] (13)

[0150] Among them, HI S is the hazard index regarding the exposure scenario S; is the hazard quotient of component substance α in exposure scenario S.

[0151] In summary, the present invention adopts a Copula-cellular automaton model to estimate the exposure frequency of VOCs. By collecting sample data of children's interactive behaviors with various products, the binary Gaussian kernel density estimation method is used to fit the Copula function of the continuous use duration of children's products and the type of children's products used at the next moment, and combined with the cellular automaton model, the use of children's products within a given time range is simulated. Compared with the traditional exposure frequency estimation method, this method simultaneously considers the use frequency of children's products, the use association characteristics between products, and the dynamic volatilization rate of VOCs, and can more reasonably estimate the exposure dose of each component substance in VOCs.

[0152] In addition, the present invention proposes a multi-level joint action decoupling method based on the mixture combination index. By calculating the minimum effect dose corresponding to each component in the mixture in turn, calculating the equivalent dose of each component relative to its independent action, and iteratively updating the mixture dose until the mixture dose is zero, the cumulative independent action equivalent dose of each component substance is obtained, and the HI of VOCs is calculated to characterize its inhalation combined exposure risk to children.

[0153] Compared with traditional exposure risk assessment methods, the present invention can simultaneously consider the randomness of the interaction between children and products, the dynamics of the VOCs volatilization rate, and the impact of the nonlinearity of the combined action of VOCs on the final combined exposure risk, providing an effective VOCs inhalation combined exposure risk assessment method for complex scenarios of children's product use.

[0154] Accordingly, an embodiment of the present invention further provides a device for assessing the risk of VOCs combined exposure to children's products, such as Figure 4 As shown, the device comprises:

[0155] The data extraction module 201 is used to select a specific scene, shoot a video of a child using a child product, and extract data on the type and duration of use of the child product;

[0156] The model building module 202 is used to establish a usage association model between children's products based on the categories and usage time data of children's products, including: establishing a Copula function between the continuous usage time of children's products and the children's products to be used at the next moment, and calculating a conditional probability density model of the children's products to be used at the next moment under different continuous usage time conditions;

[0157] The calculation module 203 is used to establish a cellular automaton model of the children's product usage scenario based on the usage association model between children's products, and calculate the combined exposure dose of VOCs in the scenario in combination with the volatilization rate data of VOCs;

[0158] The evaluation module 204 is used to evaluate the VOCs joint exposure risk by converting the VOCs joint exposure dose into the equivalent dose of each component substance acting independently and adding them up using a multi-level joint action decoupling method based on a combination index.

[0159] For ease of explanation, Figure 4 Only the main components of the device are shown. The device of this embodiment can be used to perform Figure 1 The technical solution of the method embodiment shown has similar implementation principles and technical effects, which will not be repeated here.

[0160] In an exemplary embodiment, the present invention further provides an electronic device, the electronic device comprising:

[0161] processor;

[0162] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are loaded and executed by the processor, the steps of the above-mentioned evaluation method are implemented.

[0163] Figure 5 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 5 As shown, the electronic device 300 may include a processor 3001 and a memory 3002. Optionally, the electronic device 300 may further include a transceiver 3003. The processor 3001, the memory 3002 and the transceiver 3003 may be connected, for example, via a communication bus. The memory 3002 stores computer-readable instructions, which, when executed by the processor 3001, implement the steps of the above-mentioned evaluation method.

[0164] In a specific implementation, as an embodiment, the processor 3001 may include one or more CPUs, such as Figure 5 CPU0 and CPU1 are shown in FIG.

[0165] In a specific implementation, as an embodiment, the electronic device 300 may also include multiple processors, such as Figure 5 3001 and processor 3004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0166] The memory 3002 is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor 3001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0167] The transceiver 3003 is used to communicate with a network device or a terminal device.

[0168] Optionally, the transceiver 3003 may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0169] Optionally, the transceiver 3003 may be integrated with the processor 3001 , or may exist independently and be coupled to the processor 3001 via an interface circuit of the electronic device 300 , which is not specifically limited in the embodiment of the present invention.

[0170] It should be noted that Figure 5 The structure of the electronic device 300 shown in the figure does not constitute a limitation on the electronic device. The actual electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In addition, the technical effects of the electronic device 300 can refer to the technical effects of the above-mentioned method embodiment, which will not be repeated here.

[0171] In an exemplary embodiment, the present invention further provides a computer-readable storage medium, wherein at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is loaded and executed by a processor to implement the steps of the above-mentioned evaluation method. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0172] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0173] References in the specification to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments", etc. indicate that the described embodiments may include a particular feature, structure, or characteristic, but not every embodiment may include the particular feature, structure, or characteristic. In addition, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the relevant art to implement such feature, structure, or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0174] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0175] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0176] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0177] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0178] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0180] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0181] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0182] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for joint exposure risk assessment of VOCs in children's products, characterized in that: The following steps are involved: S1. Select a specific scene, shoot a video of children using children's products, and extract data on the types and duration of use of children's products; S2. Establishing a usage association model between children's products based on the categories and usage duration data of children's products, including: establishing a Copula function between the continuous usage duration of children's products and the children's products used at the next moment, and calculating the conditional probability density model of the children's products used at the next moment under different continuous usage duration conditions; S3. Based on the usage association model between children's products, a cellular automaton model of the children's product usage scenario was established, and combined with the VOCs volatilization rate data, the combined exposure dose of VOCs in this scenario was calculated; S4. Using the multi-level joint action decoupling method based on the combination index, the combined exposure dose of VOCs is converted into the equivalent dose of each component substance acting independently and added together to evaluate the combined exposure risk of VOCs.

2. The method for joint exposure risk assessment of VOCs in children's products according to claim 1, characterized in that: The step S1 specifically includes: Assume that there are m kinds of children's products in the scene, and the set Indicates that c0 does not use any children's products, and c0 to c m Encode with natural numbers 0 to m; Select children of a certain age and gender, set a time range, and film the process of them using children's products. Identify and record the types of children's products used every second to form time series data. ; The design structure is [K c ,c'] to indicate the continuous use of a children's product K c The probability of using other children's products after 1 second, among which K c is the continuous use time of children's product c, and c' is the children's product to be used at the next moment.

3. The method for joint exposure risk assessment of VOCs in children's products according to claim 1, characterized in that: The step S2 specifically includes: Assume that n usage time data of children's product p have been obtained (k i , c i ), where k i is the observed value of the continuous use time K of children's products p, which is a continuous random variable; c i is the observed value of the children's product C used at the next moment, which is a discrete random variable; i=1,2,…,n; To establish the Copula function for variables K and C, first convert the observed values ​​of K and C to the [0,1] interval and establish their cumulative probability distribution functions as shown below: （1） Where I is an indicator function, which is used to determine whether the continuous use time of a children's product is not less than ki seconds: （2） Let u=FK(k), v=FC(c), and use the two-dimensional Gaussian kernel to estimate the Copula density: （3） Among them G K and G C are Gaussian kernel functions of K and C, with bandwidths of σ K and σ C , is a set of hyperparameters; based on empirical rules, the best estimate of the bandwidth σ is: （4） in is the sample standard deviation of σ; the duration data (k i , c i ) are substituted into formula (3) one by one to obtain the Copula function about u and v; Based on Sklar's theorem, any multidimensional joint distribution can be represented by its marginal distribution function and a Copula function. Therefore, the conditional probability density function of C with respect to K is for: （5） According to formulas (3) and (5), corresponding Copula functions and conditional probability density models of their continuous use duration and the children's products to be used at the next moment are established for all children's products, which are used for cellular automaton simulation of the next children's product usage scenario.

4. The method for joint exposure risk assessment of VOCs in children's products according to claim 3, characterized in that: The step S3 specifically includes: Establish a grid coordinate system with time as the horizontal axis and children's products as the vertical axis. If c children's products are used at time t, the corresponding grid will be filled, representing the usage status of the children's products at that moment. The entire coordinate system is regarded as a cellular automaton model. After initializing the usage state at time t0, the conditional probability density obtained in formula (5) is used to iteratively simulate the state at each moment until the set time boundary is reached, thus completing the construction of the children's product usage scenario.

5. The method for joint exposure risk assessment of VOCs in children's products according to claim 4, characterized in that: The process of step S3 is as follows: 3-1) Initialization: Randomly select children's products c at the initial time t0; 3-2) Obtain the conditional probability density function: For the current time t, obtain the conditional probability function corresponding to children's products c ; 3-3) Continuous use duration judgment: judge how long the currently selected children's product has been used continuously, that is, k seconds; 3-4) Conversion matrix construction: based on the children's products c used at the current moment, the continuous use time k and , construct the transformation matrix M; 3-5) Cellular automaton simulation: Based on the probability of various children's products being used in M, the Monte Carlo simulation method is used to randomly simulate the children's products c' used at the next moment t+1; 3-6) Update continuous use time: compare whether the children's product c at the current moment is the same as the children's product c' at the next moment; if they are the same, update k to k+1, otherwise reset k to 1; 3-7) Repeat simulation: Repeat steps 3-2) to 3-6) until the set time limit t is reached. E ; After completing a round of operations from step 3-1) to step 3-7), a given time range t is completed. E The simulation results of the use scenarios of children's products are expressed as a matrix S, called the scenario matrix: （6） In which, each column vector Indicates the usage of children's products at time t. An element value of 1 indicates that the children's product corresponding to the element index is used, and an element value of 0 indicates that the children's product corresponding to the element index is not used.

6. The method for joint exposure risk assessment of VOCs in children's products according to claim 5, characterized in that: The step S3 further comprises: The target VOCs is denoted as α. Through the volatilization rate detection experiment of VOCs in children's products, the volatilization rate data of substance α from product c is obtained, which is denoted as ; Assuming that there are q types of target VOCs, the rate of VOCs volatilization from each children's product at time t is recorded as the variable matrix : （7） When the scenario matrix S is determined, the corresponding VOCs combined exposure dose is recorded as the vector ED S : （8） Where η is the child's respiratory rate, B is the child's weight; any element in the vector represents the joint exposure dose of substance α in scene S.

7. The method for joint exposure risk assessment of VOCs in children's products according to claim 6, characterized in that: The step S4 specifically includes: Assume that the target VOCs constitute the set Λ, and λ is a proper subset of Λ composed of different substances, that is, , the specific process of calculating the combined index of VOCs in different material combinations λ is as follows: 4-1) Measurement of the half-inhibitory dose EC of the substance α-independently acting on microorganisms α,50 The dose EC at which the inhibition rate is x% α,x , x can take any value; 4-2) Based on the law of mass action, as shown in formula (9), according to the inhibition results of the above microorganisms, the dose-effect curve morphological parameter δ of the independent action of substance α is fitted α , and the corresponding dose-effect relationship of substance α is obtained, as shown in formula (10): （9） （10） Where fα represents the degree of microbial inhibition, and dα is the dose of substance α; 4-3) Measure and calculate the inhibitory concentration and dose-effect curve of each substance under independent action, and compose these substances into mixed substances λ according to the equivalent dose ratio with inhibition rate x%, and prepare mixed solutions of different concentrations and add them to the microbial culture solution, and measure the dose EC of each substance when the inhibition rate reaches x% λ,x ; 4-4) Combined with formula (9), calculate the combination index of inhibition rate x% and substance combination λ : （11） in, is the dose of component α when the substance combination λ causes x% inhibition rate; , , They represent the synergistic, additive and inhibitory effects of the combined action of the components in λ. Repeat steps 4-3) and 4-4) to calculate the combination index of all mixed substances with respect to the inhibition rate x%; 4-5) Combined exposure dose ED in actual scenario S S In the above equation, the decoupling method of multi-level joint action is used to calculate the equivalent dose of each component substance corresponding to its independent action; the cumulative independent action equivalent dose of each component substance in the mixture Λ is: ; Query or calculate the reference dose RfD of component substance α α , the calculation method is shown in formula (12): （12） Among them, NOAEL means the dose level at which no adverse effects are observed, and LOAEL, the lowest dose level of adverse effects, can be used instead; UF i is the uncertainty factor; calculate The hazard quotient of the concentration of each component substance in the VOC is calculated and summed up to use the hazard index to characterize the inhalation combined exposure risk of VOCs: （13） Among them, HI S is the hazard index regarding the exposure scenario S; is the hazard quotient of component substance α in exposure scenario S.

8. A device for evaluating the combined VOCs exposure risk of children's products, the device being used to implement the method according to any one of claims 1 to 7, characterized in that: The device comprises: A data extraction module is used to select a specific scene, shoot a video of children using children's products, and extract data on the types of children's products and the duration of use; A model building module is used to establish a usage association model between children's products based on the categories and usage time data of children's products, including: establishing a Copula function between the continuous usage time of children's products and the children's products used at the next moment, and calculating the conditional probability density model of the children's products used at the next moment under different continuous usage time conditions; The calculation module is used to establish a cellular automaton model of the use scenario of children's products based on the use association model between children's products, and calculate the combined exposure dose of VOCs in this scenario in combination with the volatilization rate data of VOCs; The assessment module is used to convert the combined exposure dose of VOCs into the equivalent dose of each component substance acting independently and add them up using a multi-level joint action decoupling method based on the combination index to assess the combined exposure risk of VOCs.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are loaded and executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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