Intelligent home environment management system based on data analysis

Abstract

The invention discloses a smart home environment management system based on data analysis, which relates to the field of smart home and comprises a data acquisition module, a data analysis module and a control module. The data acquisition module is used for acquiring travel behavior data of each family member; acquiring home environment data of each family member at each moment when the family member is at home; the data analysis module is used for predicting the predicted home returning time of each family member according to the travel behavior data; analyzing a suitable environment parameter range of each family member according to the home environment data; and the control module is used for determining a target home environment parameter range of each time period according to the predicted home returning time of each family member and the suitable environment parameter range, controlling each household appliance to work and adjusting the indoor environment. According to the invention, home environment management can be carried out more intelligently, and the experience of each family member is comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and more specifically to a smart home environment management system based on data analysis. Background Art

[0002] Smart home is a home system that uses the house as a platform and integrates various devices and systems in the home using integrated wiring, network communication, automatic control and other technologies to improve the intelligence, comfort and safety of home life. Among them, the environmental management system is one of the core parts of the smart home. It can automatically monitor and control various environmental indicators such as temperature, humidity, air quality, etc. in the home to ensure that the home environment is in the best condition.

[0003] However, the existing smart home environment management system often only controls various electrical appliances according to different preset modes based on the parameters monitored by sensors, the parameters set by users and time. However, this control method is relatively fixed for the control of the home environment, ignoring the different comfort levels felt by different family members for different environmental parameters, resulting in some family members being in an uncomfortable home environment. In addition, the existing smart home environment management system starts control after detecting someone, and cannot immediately put people in a comfortable home environment after they go home; and maintaining the indoor environment at all times increases unnecessary energy consumption.

[0004] Therefore, how to manage the home environment more intelligently is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides a smart home environment management system based on data analysis, which analyzes the travel data and home environment data of each family member to predict the time of each member's return home and the appropriate environmental parameter range, and intelligently controls the home environment.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention discloses a smart home environment management system based on data analysis, comprising: a data acquisition module, a data analysis module and a control module;

[0008] The data acquisition module acquires the travel behavior data of each family member; acquires the home environment data of each family member at each time when they are at home;

[0009] The data analysis module predicts the predicted return home time of each family member based on the travel behavior data; and analyzes the suitable environmental parameter range of each family member based on the home environment data;

[0010] The control module determines the target home environment parameter range for each time period according to the predicted return home time of each family member and the suitable environment parameter range, controls the operation of each household appliance, and adjusts the indoor environment.

[0011] Furthermore, the data acquisition module acquires the travel displacement data of each family member through the mobile terminal worn by each family member; determines the time when each family member goes out and comes home through the face recognition device set at the door and the door switch sensor on the door, and sorts out the time series of each family member going out and coming home;

[0012] The travel behavior data includes: the travel displacement data, the outgoing time series and the returning home time series;

[0013] The data acquisition module detects the temperature, humidity, carbon dioxide concentration and particulate matter concentration of the indoor air at each moment through a temperature sensor, a humidity sensor, a carbon dioxide concentration sensor and a laser particle counter to obtain the home environment data.

[0014] Furthermore, the data analysis module includes a home time prediction unit, which trains a corresponding home time prediction model based on each member's historical travel displacement data and each member's historical outgoing time series and returning time series;

[0015] The home time prediction unit predicts the home time of each family member based on the travel displacement data of each family member after the current trip, as well as the departure time series and home time series of a set time length before the current moment, using the trained home time prediction model of each family member.

[0016] Furthermore, the home time prediction model is a TreeCNN-LSTM network model, including a TreeCNN network, a first LSTM network, a second LSTM network, and a fully connected network;

[0017] The TreeCNN network performs convolution and feature extraction on the trajectory tree in the input sample, and outputs a feature sequence to the first LSTM network in the time order of the features;

[0018] The first LSTM network outputs a first prediction time according to the input feature sequence;

[0019] The second LSTM network outputs a second predicted time according to the input going out time sequence and returning home time sequence;

[0020] The fully connected network performs weighted calculation on the first prediction time and the second prediction time, and outputs a final prediction time.

[0021] Furthermore, the TreeCNN network includes an input layer, a tree convolution layer, and a fully connected layer connected in sequence; the input layer is used to input the trajectory tree of a family member; the tree convolution layer convolves each data node in the trajectory tree to obtain a feature representation of each data node in the trajectory tree; the fully connected layer generates the feature sequence according to the time information of the feature representation;

[0022] The first LSTM network and the second LSTM network include an input layer, an LSTM layer, a fully connected layer, and an output layer connected in sequence; the input layer is used to input the feature sequence, or the outgoing time sequence and the returning time sequence; the LSTM layer processes the input sequence and extracts features; the fully connected layer maps the extracted features to the final predicted returning home time, and the output layer outputs the features to the fully connected network;

[0023] The fully connected network maps the predicted home time of the first LSTM network and the second LSTM network to obtain a final predicted home time.

[0024] Furthermore, the trajectory tree is obtained by:

[0025] The travel displacement data of a family member's trip is sampled to obtain three-dimensional tensor data, where the first dimension represents the sampling time, the second dimension represents the two-dimensional plane coordinates of the family member at the sampling time, and the third dimension is the speed of the family member at the sampling time;

[0026] A gridded plane coordinate system is used to divide each slice into grids according to the second dimension of each slice in the three-dimensional tensor data, and to count the slice density of each grid;

[0027] According to the set density threshold, key grids are selected from all grids as child nodes of the trajectory tree;

[0028] Taking home as the root node of the trajectory tree, the connection relationship of each node is determined according to whether there is a slice with a sampling time difference less than a set threshold between two nodes, and each node is connected in time sequence to obtain a trajectory tree.

[0029] Furthermore, the data analysis module further includes a suitable environment parameter analysis unit, which performs sampling based on the home environment data and the time period of each family member at home according to a set step length to obtain a number of environmental parameter point data of each family member, and then obtain an environmental parameter point set of each member;

[0030] Eliminate points in the environmental parameter point sets that have manual environmental control within a set time period before the sampling time;

[0031] Perform Kmeans clustering on the set of environmental parameter points after the elimination operation, take k cluster center points as the key points of the set of environmental parameter points, and use the maximum and minimum values ​​of air temperature, humidity, carbon dioxide concentration and particulate matter concentration represented by all key points to determine the appropriate environmental parameter range of the family members corresponding to the set of environmental parameter points;

[0032] Eliminate and cluster each set of environmental parameter points one by one to determine the appropriate environmental parameter range for each family member.

[0033] Furthermore, the determination of the target home environment parameter range for each time period specifically includes:

[0034] Step 1: According to the predicted return time of each family member, determine the family members who will be indoors in each future time period;

[0035] Step 2: Initialize the proportional coefficient p and the amplification coefficient of each family member;

[0036] Step 3: Determine whether the suitable environmental parameter ranges of the family members determined in step 1 have an intersection. If so, the intersection is used as the target home environmental parameter range. Otherwise, proceed to step 4.

[0037] Step 4: p=p+0.01, amplify each of the suitable environmental parameter ranges according to the proportional coefficient and the amplification coefficient, and return to step 3.

[0038] Furthermore, the range of each suitable environmental parameter is enlarged, and the formula is:

[0039]

[0040] in, denote the upper and lower bounds of the jth environmental parameter of the ith family member, respectively; j = 1, 2, 3, 4 denote air temperature, humidity, carbon dioxide concentration, and particulate matter concentration, respectively; q i is the magnification factor of the ith family member.

[0041] Furthermore, the amplification factor is calculated based on the variance of each environmental parameter in each set of environmental parameter points, and the formula is:

[0042]

[0043] in, represents the jth environmental parameter value of the nth environmental parameter point in the i-th environmental parameter point set, where N is the total number of environmental parameter points; is the mean variance of each environmental parameter of the i-th family member, and e is a natural constant.

[0044] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a smart home environment management system based on data analysis. By analyzing the travel behavior data of each family member, it is possible to predict the time of return home according to the current travel displacement and historical travel behavior data of each family member, and automatically adjust the home environment before returning home, so as to ensure that they have a comfortable environment after returning home; and it is possible to analyze the corresponding appropriate environmental parameter range according to the predicted historical environmental data of each member, and intelligently adjust electrical equipment according to the situation of family members in the room at each time period, so as to realize intelligent adjustment of the home environment and improve the comfort of each family member. The present invention can manage the home environment more intelligently and comprehensively improve the experience of each family member. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0046] Figure 1 It is a schematic diagram of the overall structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments 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.

[0048] The embodiment of the present invention discloses a smart home environment management system based on data analysis, such as Figure 1 As shown, it includes: a data acquisition module, a data analysis module and a control module;

[0049] The data acquisition module acquires the travel behavior data of each family member; acquires the home environment data of each family member at each time when they are at home;

[0050] The data analysis module predicts the predicted return home time of each family member based on the travel behavior data; and analyzes the suitable environmental parameter range of each family member based on the home environment data;

[0051] The control module determines the target home environment parameter range for each time period based on the predicted return home time of each family member and the suitable environmental parameter range, controls the operation of each household appliance, and adjusts the indoor environment.

[0052] In a specific embodiment, the data acquisition module obtains the travel displacement data of each family member through the mobile terminal (such as a mobile phone, a smart watch, etc.) worn by each family member; determines the time when each family member goes out and comes home through the face recognition device set at the door and the door switch sensor on the door, and sorts out the time sequence of each family member going out and coming home;

[0053] Travel behavior data include: travel displacement data, departure time series, and return home time series;

[0054] The data acquisition module detects the temperature, humidity, carbon dioxide concentration and particulate matter concentration of the indoor air at each moment through a temperature sensor, a humidity sensor, a carbon dioxide concentration sensor and a laser particle counter to obtain home environment data.

[0055] In a specific embodiment, the data analysis module includes a home time prediction unit, which trains a corresponding home time prediction model based on each member's historical travel displacement data and each member's historical outgoing time series and returning time series;

[0056] The home time prediction unit predicts the home time of each family member based on the travel displacement data of each family member after the current trip, as well as the departure time series and home time series of the set time length before the current moment, using the trained home time prediction model of each family member.

[0057] In a specific embodiment, the home time prediction model is a TreeCNN-LSTM network model, including a TreeCNN network, a first LSTM network, a second LSTM network, and a fully connected network;

[0058] The TreeCNN network performs convolution and feature extraction on the trajectory tree in the input sample, and outputs the feature sequence to the first LSTM network in the time order of the features;

[0059] The first LSTM network outputs the first prediction time according to the input feature sequence;

[0060] The second LSTM network outputs the second predicted time based on the input time series of going out and returning home;

[0061] The fully connected network performs weighted calculation on the first prediction time and the second prediction time and outputs the final prediction time.

[0062] In a specific embodiment, the TreeCNN network includes an input layer, a tree convolution layer, and a fully connected layer connected in sequence; the input layer is used to input a trajectory tree of a family member; the tree convolution layer convolves each data node in the trajectory tree to obtain a feature representation of each data node in the trajectory tree; the fully connected layer generates a feature sequence according to the time information of the feature representation;

[0063] The first LSTM network and the second LSTM network include an input layer, an LSTM layer, a fully connected layer, and an output layer connected in sequence; the input layer is used to input a feature sequence, or a time sequence of going out and a time sequence of returning home; the LSTM layer processes the input sequence and extracts features; the fully connected layer maps the extracted features to the final predicted time of returning home, and the output layer outputs the results to the fully connected network;

[0064] The fully connected network maps the predicted home time of the first LSTM network and the second LSTM network to obtain the final predicted home time.

[0065] In a specific embodiment, the trajectory tree is obtained by:

[0066] The travel displacement data of a family member's trip is sampled to obtain three-dimensional tensor data, where the first dimension represents the sampling time, the second dimension represents the two-dimensional plane coordinates of the family member at the sampling time, and the third dimension is the speed of the family member at the sampling time;

[0067] Gridding plane coordinate system, dividing each slice into grids according to the second dimension of each slice in the three-dimensional tensor data, and counting the slice density of each grid;

[0068] According to the set density threshold, key grids are selected from all grids as child nodes of the trajectory tree;

[0069] Taking home as the root node of the trajectory tree, the connection relationship of each node is determined according to whether there is a slice with a sampling time difference less than a set threshold between two nodes, and each node is connected in time sequence to obtain a trajectory tree.

[0070] Specifically, after sampling the displacement data, the travel trajectory can be effectively simplified by gridding the plane coordinate system and determining the key grids, and the differences in the travel trajectory for different travel purposes can be highlighted, which is convenient for subsequent analysis. And the connection relationship is determined according to the time node, and the trajectory tree is constructed so that the feature representation of each key position in position, time, and speed can be extracted using the TreeCNN network, thereby further outputting the feature time series. The first LSTM network is then used to analyze the antecedent relationship of the feature time series to obtain the first prediction time. The second LSTM network predicts the current home time based on the continuous outgoing time series and homecoming time series of family members over a period of time (such as one month or one week) to obtain the second prediction time. Since the travel behavior characteristics of each family member are different, some family members have a large periodicity in their travel and homecoming time, and the second prediction time has a higher accuracy; while some family members have a large randomness in their travel and homecoming time, and the first prediction time has a better accuracy. By setting a fully connected network to allocate the weights between the first and second prediction times of each member, the final predicted time of homecoming is more accurate.

[0071] When training the TreeCNN-LSTM network model, the travel displacement data of a family member is first divided, and a trajectory tree is constructed as the trajectory feature of a sample based on the complete travel displacement data of going out and returning home for one day; a fixed-length door time series and home time series before the day are sampled from the time series of going out and returning home through a sliding window as the time series feature of the sample; the time of returning home on the day is used as the sample label to obtain a training sample. Multiple training samples constitute the training sample set of the member for training, and the prediction model of the member's return home time is obtained. By training, an independent prediction model of the return home time for each family member is established, which can accurately predict the return home time of each family member.

[0072] In a specific embodiment, the data analysis module further includes a suitable environment parameter analysis unit, which performs sampling based on the home environment data and the time period of each family member at home according to a set step length to obtain a number of environmental parameter point data of each family member, and then obtain a set of environmental parameter points of each member;

[0073] Eliminate points in each environmental parameter point set that have manual environmental control within a set time period before the sampling time;

[0074] Perform Kmeans clustering on the set of environmental parameter points after the elimination operation, take k cluster center points as the key points of the set of environmental parameter points, and use the maximum and minimum values ​​of air temperature, humidity, carbon dioxide concentration and particulate matter concentration represented by all key points to determine the appropriate environmental parameter range of the family members corresponding to the set of environmental parameter points;

[0075] Eliminate and cluster each set of environmental parameter points one by one to determine the appropriate environmental parameter range for each family member.

[0076] In a specific embodiment, determining the target home environment parameter range for each time period specifically includes:

[0077] Step 1: According to the predicted return time of each family member, determine the family members who will be indoors in each future period;

[0078] Step 2: Initialize the proportional coefficient p and the amplification coefficient of each family member;

[0079] Step 3: Determine whether the suitable environmental parameter ranges of the family members determined in step 1 have an intersection. If so, the intersection is used as the target home environmental parameter range. Otherwise, proceed to step 4.

[0080] Step 4: p=p+0.01, enlarge the range of each suitable environmental parameter according to the proportional coefficient and the amplification coefficient, and return to step 3.

[0081] In a specific embodiment, the range of each suitable environmental parameter is enlarged, and the formula is:

[0082]

[0083] in, denote the upper and lower bounds of the jth environmental parameter of the ith family member, respectively; j = 1, 2, 3, 4 denote air temperature, humidity, carbon dioxide concentration, and particulate matter concentration, respectively; q i is the magnification factor of the ith family member.

[0084] In a specific embodiment, the amplification factor is calculated based on the variance of each environmental parameter in each environmental parameter point set, and the formula is:

[0085]

[0086] in, represents the jth environmental parameter value of the nth environmental parameter point in the i-th environmental parameter point set, where N is the total number of environmental parameter points; is the mean variance of each environmental parameter of the i-th family member, and e is a natural constant.

[0087] Specifically, the proportional coefficient p is used to control the speed of amplification. The initial value is usually 0.1, and it is gradually increased in each iteration to increase the proportion of the environmental parameter range amplification so that the comfortable environmental parameter ranges of each family member have an intersection. Each family member has different sensitivities to comfortable environmental conditions. Some people can adapt to a wider range of environmental parameters, so the environmental parameter point data in their corresponding environmental parameter point set often have a large degree of discreteness. Some people are more sensitive to comfortable environments. When they feel uncomfortable, they often manually adjust various electrical equipment. In the environmental parameter point set, the environmental parameter point data within a period of time after manual adjustment is eliminated, so that the comfortable environmental parameter range to which they adapt can be determined more accurately. Since the comfortable range to which they adapt is small, the environmental parameter point data in the corresponding environmental parameter point set has a low degree of discreteness. By setting the amplification coefficient q of each family member i , adjusting the ratio between the amplification speeds of each family member can better balance the sensitivity of each family member to the comfortable environment and improve the overall feeling of each family member indoors.

[0088] After the control module determines the control range of temperature, it controls the air conditioner to adjust the temperature; after determining the control range of humidity, it controls the air conditioner to dehumidify or controls the humidifier to humidify; after determining the control range of carbon dioxide concentration, it controls the fresh air fan to work for ventilation; after determining the particulate matter concentration range, it controls the air purifier to work so that the concentration of particulate matter in the air is reduced to a predetermined range.

[0089] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0090] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart home environment management system based on data analysis, characterized in that: include: Data acquisition module, data analysis module and control module; The data acquisition module acquires the travel behavior data of each family member; acquires the home environment data of each family member at each time when they are at home; The data analysis module predicts the predicted return home time of each family member based on the travel behavior data; and analyzes the suitable environmental parameter range of each family member based on the home environment data; The control module determines the target home environment parameter range for each time period according to the predicted return home time of each family member and the suitable environment parameter range, controls the operation of each household appliance, and adjusts the indoor environment.

2. The smart home environment management system based on data analysis according to claim 1, characterized in that: The data acquisition module acquires the travel displacement data of each family member through the mobile terminal worn by each family member; determines the time when each family member goes out and comes home through the face recognition device set at the door and the door switch sensor on the door, and sorts out the time series of each family member going out and coming home; The travel behavior data includes: the travel displacement data, the outgoing time series and the returning home time series; The data acquisition module detects the temperature, humidity, carbon dioxide concentration and particulate matter concentration of the indoor air at each moment through a temperature sensor, a humidity sensor, a carbon dioxide concentration sensor and a laser particle counter to obtain the home environment data.

3. The smart home environment management system based on data analysis according to claim 2, characterized in that: The data analysis module includes a home time prediction unit, which trains a corresponding home time prediction model based on each member's historical travel displacement data and each member's historical outgoing time series and returning time series; The home time prediction unit predicts the home time of each family member based on the travel displacement data of each family member after the current trip, as well as the departure time series and home time series of a set time length before the current moment, using the trained home time prediction model of each family member.

4. The smart home environment management system based on data analysis according to claim 3, characterized in that: The home time prediction model is a TreeCNN-LSTM network model, including a TreeCNN network, a first LSTM network, a second LSTM network, and a fully connected network; The TreeCNN network performs convolution and feature extraction on the trajectory tree in the input sample, and outputs a feature sequence to the first LSTM network in the time order of the features; The first LSTM network outputs a first prediction time according to the input feature sequence; The second LSTM network outputs a second predicted time according to the input going out time sequence and returning home time sequence; The fully connected network performs weighted calculation on the first prediction time and the second prediction time, and outputs a final prediction time.

5. The smart home environment management system based on data analysis according to claim 4, characterized in that: The TreeCNN network includes an input layer, a tree convolution layer, and a fully connected layer connected in sequence; the input layer is used to input the trajectory tree of a family member; The tree convolution layer performs convolution on each data node in the trajectory tree to obtain the feature representation of each data node in the trajectory tree; A fully connected layer generates the feature sequence according to the time information represented by the feature; The first LSTM network and the second LSTM network include an input layer, an LSTM layer, a fully connected layer, and an output layer connected in sequence; the input layer is used to input the feature sequence, or the outgoing time sequence and the returning time sequence; the LSTM layer processes the input sequence and extracts features; The fully connected layer maps the extracted features to the final predicted time of return home, and the output layer outputs the results to the fully connected network. The fully connected network maps the predicted home time of the first LSTM network and the second LSTM network to obtain a final predicted home time.

6. The smart home environment management system based on data analysis according to claim 4, characterized in that: The trajectory tree is obtained in the following way: The travel displacement data of a family member's trip is sampled to obtain three-dimensional tensor data, where the first dimension represents the sampling time, the second dimension represents the two-dimensional plane coordinates of the family member at the sampling time, and the third dimension is the speed of the family member at the sampling time; A gridded plane coordinate system is used to divide each slice into grids according to the second dimension of each slice in the three-dimensional tensor data, and to count the slice density of each grid; According to the set density threshold, key grids are selected from all grids as child nodes of the trajectory tree; Taking home as the root node of the trajectory tree, the connection relationship of each node is determined according to whether there is a slice with a sampling time difference less than a set threshold between two nodes, and each node is connected in time sequence to obtain a trajectory tree.

7. The smart home environment management system based on data analysis according to claim 2, characterized in that: The data analysis module also includes a suitable environment parameter analysis unit, which performs sampling based on the home environment data and the time period of each family member at home according to a set step length to obtain a number of environmental parameter point data of each family member, and then obtain an environmental parameter point set of each member; Eliminate points in the environmental parameter point sets that have manual environmental control within a set time period before the sampling time; Perform Kmeans clustering on the set of environmental parameter points after the elimination operation, take k cluster center points as the key points of the set of environmental parameter points, and use the maximum and minimum values ​​of air temperature, humidity, carbon dioxide concentration and particulate matter concentration represented by all key points to determine the appropriate environmental parameter range of the family members corresponding to the set of environmental parameter points; Eliminate and cluster each set of environmental parameter points one by one to determine the appropriate environmental parameter range for each family member.

8. The smart home environment management system based on data analysis according to claim 7, characterized in that: Determining the target home environment parameter range for each time period specifically includes: Step 1: According to the predicted return time of each family member, determine the family members who will be indoors in each future time period; Step 2: Initialize the proportional coefficient p and the amplification coefficient of each family member; Step 3: Determine whether the suitable environmental parameter ranges of the family members determined in step 1 have an intersection. If so, the intersection is used as the target home environmental parameter range. Otherwise, proceed to step 4. Step 4: p=p+0.01, amplify each of the suitable environmental parameter ranges according to the proportional coefficient and the amplification coefficient, and return to step 3.

9. The smart home environment management system based on data analysis according to claim 8, characterized in that: The formula for enlarging the range of each suitable environmental parameter is: in, denote the upper and lower bounds of the jth environmental parameter of the ith family member, respectively; j = 1, 2, 3, 4 denote air temperature, humidity, carbon dioxide concentration, and particulate matter concentration, respectively; q i is the magnification factor of the ith family member.

10. The smart home environment management system based on data analysis according to claim 9, characterized in that: The amplification factor is calculated based on the variance of each environmental parameter in each environmental parameter point set, and the formula is: in, represents the jth environmental parameter value of the nth environmental parameter point in the i-th environmental parameter point set, where N is the total number of environmental parameter points; is the mean variance of each environmental parameter of the i-th family member, and e is a natural constant.

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