Data processing method and system, electronic equipment and storage medium

Abstract

The invention provides a data processing method and system, electronic equipment and a storage medium, and belongs to the field of data processing.The method comprises the steps that in response to a received automatic control instruction, multiple pieces of target standard information are determined from multiple pieces of standard information based on room information; determining a first control model based on the matching degree between the basic information of the target person and multiple pieces of target standard information; the standard information is basic information of historical personnel; inputting the behavior characteristics of the target person in the target room into a first control model to obtain a control strategy of the target room; the target room is a room in which the target personnel live; and controlling the intelligent equipment in the target room based on the control strategy. According to the data processing method and system, the electronic equipment and the storage medium provided by the invention, the accuracy and reliability of hotel control can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of data processing, and more specifically, relates to a data processing method and system, an electronic device, and a storage medium. Background Art

[0002] In today's fiercely competitive hotel industry, improving the quality and intelligence of guest room services has become a key development trend. With the rapid advancement of technology, hotel rooms are equipped with more and more smart devices, such as smart air conditioners, smart lighting systems, smart curtains, etc., aiming to provide guests with a more comfortable, convenient and personalized stay experience.

[0003] Traditional guest room equipment control methods are often simple and fixed, mostly based on preset schedules or simple sensor threshold triggers, which makes it difficult to accurately adapt to the diverse needs of different guests under different environmental conditions.

[0004] Therefore, an accurate and reliable data processing method is urgently needed. Summary of the invention

[0005] The purpose of the present disclosure is to provide a data processing method and system, an electronic device, and a storage medium to improve the accuracy and reliability of hotel control.

[0006] A first aspect of an embodiment of the present disclosure provides a data processing method, including: In response to receiving the automatic control instruction, determining a plurality of target standard information from a plurality of standard information based on the room information; Determine the first control model based on the matching degree between the basic information of the target person and a plurality of target standard information; the standard information is the basic information of the historical person; Inputting the behavior characteristics of the target person in the target room into the first control model to obtain the control strategy of the target room; the target room is the room where the target person lives; Control the smart devices in the target room based on the control strategy.

[0007] A second aspect of the embodiments of the present disclosure provides a data processing system, including: a standard information determination module, configured to determine a plurality of target standard information from a plurality of standard information based on the room information in response to receiving the automatic control instruction; A model determination module, used to determine a first control model based on the matching degree between the basic information of the target person and a plurality of target standard information; the standard information is the basic information of the historical person; A control strategy output module is used to input the behavior characteristics of the target person in the target room into the first control model to obtain the control strategy of the target room; the target room is the room where the target person lives; The control module is used to control the smart devices in the target room based on the control strategy.

[0008] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned data processing method when executing the computer program.

[0009] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned data processing method are implemented.

[0010] The beneficial effects of a data processing method and system, electronic device, and storage medium provided by the embodiments of the present disclosure are: The disclosure can filter target standard information that meets the room from a preset standard information library based on the room information, thereby narrowing the scope of the filter and making the filtered target standard information more consistent with the actual situation of the current room, thereby improving the pertinence of data processing. The disclosure takes the environmental characteristics of the target room as input and outputs a control strategy through a first control model, thereby enabling the disclosure to adjust the control strategy according to the real-time changing environmental characteristics, thereby ensuring that the environment of the target room is always in the best state, thereby improving the accuracy and reliability of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, 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 some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flowchart of a data processing method provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of a data processing system provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0014] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 A flowchart of a data processing method provided in an embodiment of the present disclosure includes: S101: In response to receiving an automatic control instruction, determine a plurality of target standard information from a plurality of standard information based on room information.

[0016] In this embodiment, the automatic control instruction may be an instruction indicating that the resident has checked in, for example, the room status may be detected as checked-in and the door opening information may be received. Whether the room status is checked-in may be determined through the hotel's registration system, and the door opening signal may be detected through the smart door lock. Considering that when the room is idle, the hotel's sanitation cleaning staff may also enter the room for cleaning, and since the room status is idle at this time, the control will not be started at this time, thus avoiding waste of resources.

[0017] Room information can be room specifications, area, house orientation, etc., and standard information is historical basic information of people. Considering that the size, orientation, specifications, etc. of a room may lead to different control methods in the room, standard information suitable for the room can be determined from multiple standard information based on the room information, that is, target standard information, that is, the target standard information is the basic information of residents who have stayed in the room or this type of room. Both standard information and target standard information are basic information of residents.

[0018] For example, when the room area is relatively small, the air conditioning has better heating and cooling effects, and residents tend to adjust the temperature of the air conditioning frequently. When the room faces south, the sunlight in the room is relatively dazzling, and residents tend to control the curtains to be partially closed during the day. When the room specifications are more luxurious, residents have higher expectations for service quality and personalized experience, and the number of smart devices is also different from that of standard rooms.

[0019] In this embodiment, detailed information of all rooms is obtained from the hotel's management system, including room specifications, such as luxury suites, standard rooms, single rooms, etc.; area, square meters; house orientation, east, south, west, north, etc. At the same time, basic information of historical personnel is collected as standard information, which may include the guest's age, gender, occupation, geographical origin, frequency of stay, etc. If the current room is a luxury suite, the target standard information should give priority to the basic information of guests who have stayed in the luxury suite. This is because guests in luxury suites may have higher expectations for service quality, equipment functions, and personalized experience, which are different from the needs of guests in ordinary rooms.

[0020] Different intervals are divided according to the size of the room. For example, the room area is divided into three intervals: less than 20 square meters, 20-30 square meters, and more than 30 square meters. If the current room area is between 20-30 square meters, the basic information of guests who have stayed in rooms of similar size is selected as the target standard information.

[0021] Different matching rules are formulated for different house orientations. If the room is south-facing, due to the abundant sunlight, the target standard information should focus on selecting the basic information of guests who have lived in south-facing rooms and have a record of controlling sunlight entering the room.

[0022] The basic information of the residents who have stayed in the room can be used as the target standard information, and the basic information of the residents who have stayed in similar rooms can also be used as the target standard information. Whether it is a similar room can be judged by calculating the similarity between the room features of the room and the room features of other rooms. The similarity between the room features of the room and the room features of other rooms can be calculated by the Euclidean distance method or the cosine similarity method. Different calculation methods have different judgment criteria, i.e., thresholds.

[0023] For example, in response to the value calculated by the Euclidean distance method being greater than a first distance threshold, the room characteristics of the occupied room are not similar to the room characteristics of the comparison room; In response to the value calculated by the Euclidean distance method being less than or equal to the first distance threshold, the room characteristics of the occupied room are similar to the room characteristics of the comparison room, and the occupied room is the living room of the target person.

[0024] The first distance threshold may be determined according to actual needs.

[0025] S102: Determine a first control model based on the matching degree between the basic information of the target person and a plurality of target standard information; the standard information is the basic information of the historical person.

[0026] In this embodiment, the target person is the resident staying in the hotel. The basic information may include age, gender, occupation and geographical origin. The basic information of the resident can be obtained through the information provided during reservation or registration at the hotel front desk. Since residents of different ages, genders, occupations and geographical origins have different perceptions and adjustment degrees for temperature, humidity, light intensity, etc., the control model cannot be determined solely based on the above-mentioned room information.

[0027] In this embodiment, the similarity between the basic information of the target person and each standard information can be calculated as the matching degree, and the control model corresponding to the standard information with the highest matching degree can be used as the first control model, that is, each standard information corresponds to a control model, but a control model can correspond to multiple standard information. The control model is based on the basic information of historical occupants, and is trained with the behavioral characteristics of historical occupants and the historical environmental characteristics of the occupant rooms as the training set. The control model can be a support vector machine (SVM) model.

[0028] Behavioral characteristics refer to the behavioral characteristics and habits of historical personnel in the room related to the use of various smart devices, which can reflect the guests' needs and preferences for the room environment and equipment functions. For example, behavioral characteristics can include the frequency and amplitude of the guests' switching operations on and off the lights and adjusting the light brightness at different time periods during the day; the temperature range set when using the air conditioner and the number of times the temperature is adjusted; whether the curtains are often closed or opened, and at what time periods. These behavioral characteristics can be collected and obtained through sensors installed in the room (such as light sensors, temperature sensors, etc.) and the operation data recorded by the smart devices themselves. Historical environmental characteristics include the room's historical temperature, humidity, light intensity, and external sound intensity.

[0029] S103: Inputting the environmental characteristics of the target room into the first control model to obtain a control strategy for the target room; the target room is a room where the target person lives.

[0030] In this embodiment, the target room is the room where the target person lives, and the first control model is the most suitable control model selected based on the target person and the target room, and is selected from multiple control models based on the basic information of the target person and the room characteristics of the target room.

[0031] Environmental characteristics include room temperature, humidity, light intensity, and external sound intensity, which can be obtained based on temperature sensors, humidity sensors, light intensity sensors, and sound intensity sensors installed in the room.

[0032] The control strategy is based on the analysis and calculation of the first control model, and gives specific operating instructions and adjustment methods for each smart device in the target room to achieve reasonable control of the room environment and meet the expectations and needs of the target personnel.

[0033] For example, the control strategy may be to set the air conditioning temperature to a specific degree during a certain time period, adjust the light brightness to a certain percentage, control the curtains to be opened or closed to a specific degree, and whether to close the windows, etc. It is a comprehensive operation plan specific to each device.

[0034] S104: Control the smart devices in the target room based on the control strategy.

[0035] In this embodiment, the smart device is a device installed in the target room, which has intelligent functions such as automatic control, remote operation, and the ability to flexibly adjust its own operating state according to preset conditions or external instructions. The smart device can be a smart air conditioner, smart curtains, smart lighting system, smart humidifier, smart window, etc.

[0036] This embodiment can control the corresponding smart device to perform corresponding actions according to the generated control strategy.

[0037] It can be concluded from the above that the present disclosure can filter target standard information that meets the room from a preset standard information library based on the room information, thereby narrowing the scope of the filter and making the filtered target standard information more consistent with the actual situation of the current room, thereby improving the pertinence of data processing. The present disclosure takes the environmental characteristics of the target room as input and outputs the control strategy through the first control model, so that the present disclosure can adjust the control strategy according to the real-time changing environmental characteristics, thereby ensuring that the environment of the target room is always in the best state, thereby improving the accuracy and reliability of data processing.

[0038] In one embodiment of the present disclosure, the basic information includes first information of multiple dimensions; the target standard information includes second information of multiple dimensions; The number of dimensions of the target standard information is greater than or equal to the number of dimensions of the basic information; Determining a first control model based on the matching degree between the basic information of the target person and the plurality of target standard information includes: Calculate the matching degree of the first information of each dimension of the target person and the second information of the corresponding dimension respectively, and obtain a plurality of matching degrees of each dimension; Perform weighted calculation on the matching degrees of each dimension to obtain multiple target matching degrees; The control model corresponding to the target standard information with the highest target matching degree is used as the first control model.

[0039] In this embodiment, the basic information is a collection of various types of data used to describe the characteristics of the target person (that is, the guest currently staying in the hotel room). It is characterized from multiple different angles or aspects. These different angles are the so-called dimensions, and the specific data content under each dimension is the first information.

[0040] For example, for a guest staying in a hotel, the dimensions of his or her basic information may include age, gender, occupation, geographical origin, whether or not he or she is a member, etc. Each dimension has corresponding specific content to reflect the guest's situation in that aspect.

[0041] The target standard information is the part of information related to the room where the target person is located, which is selected from the basic information of many historical personnel. It is also described from multiple dimensions, and the specific content corresponding to each dimension is the second information. These dimensions are somewhat repeated with the dimensions of the basic information, but they cover more dimensions, so the number of their dimensions is greater than or equal to the number of dimensions of the target person's basic information.

[0042] Because the target standard information is the information we want to filter out, its dimensions should be richer and more comprehensive, so as to prevent the dimensions in the basic information from being larger than those in the target standard information. Since the comprehensiveness of the information reserved by each guest when filling out the reservation information is different, that is, the number of dimensions is different, the information of historical residents with comprehensive basic information should be filtered out during the selection and training process.

[0043] Calculating the matching degree of each dimension of the first information of the target person and the second information of the corresponding dimension respectively to obtain the matching degree of each dimension means that each dimension should be matched with the information of the same dimension. For example, the basic information includes: age, gender and regional origin, and the standard information includes: age, gender, regional origin and occupation. When calculating the matching degree, the dimension corresponding to the age dimension should be age, not gender and regional origin.

[0044] The age matching degree can be calculated by setting the matching degree corresponding to the age difference. For example, if the age difference is between 0 and 1 year, the matching degree is 1. If the age difference is set within 1 to 5 years, the matching degree can be assigned a value of 0.8. If the age difference is between 5 and 10 years, the matching degree is assigned a value of 0.6. If the age difference is greater than 10 years, the matching degree is assigned a value of 0.2.

[0045] The calculation of gender matching is defined as 1 if the gender is the same and 0 if the gender is different.

[0046] Both the occupation matching degree and the regional origin matching degree can be obtained through a preset mapping table. For example, the mapping table for the regional origin matching degree includes the matching degrees between various regions, as shown in Table 1: Table 1 Regional source matching mapping table (partial)

[0047] The above mapping table is only a part. The complete mapping table should have the matching degree corresponding to all cities or be judged by certain rules. For example, the matching degree of the same city is 1, and the regional matching degree of the same province but different provinces and cities is 0.8, etc. Alternatively, the matching degree can be measured by cosine similarity or Euclidean distance method.

[0048] After obtaining the matching degree corresponding to each dimension, the matching degree between the basic information and each standard information can be obtained by weighted calculation. The control model corresponding to the target standard information with the highest matching degree can be used as the first control model. When performing weighted calculation, weights can be assigned based on experience, or by calculating the correlation between each dimension and the behavioral characteristics, and adjustments can be made. For example: Determine the initial weight corresponding to each dimension; The weights corresponding to the dimensions whose correlation with the behavioral characteristics is greater than the first correlation threshold are increased according to the first correlation step to obtain the second weights; and the weights corresponding to other dimensions are reduced according to multiple second correlation steps to obtain multiple third weights.

[0049] The initial weight corresponding to each dimension can be set in advance. When the correlation between a dimension and a behavior feature is greater than the first correlation threshold, it means that the correlation between the dimension and the behavior feature is relatively large. When performing weighted calculations, this dimension should be given priority. The correlation between the dimension and the behavior feature can be obtained by calculating the Pearson correlation coefficient. It should be noted that the value calculated by the Pearson correlation coefficient should take the absolute value as the correlation value.

[0050] The first correlation threshold, the first correlation step and the second correlation step can be obtained based on experiments, wherein the first correlation step is equal to the sum of all the second correlation step, in order to ensure that the sum of the weights after adjustment is still 1.

[0051] It can be concluded from the above that the present disclosure divides both the basic information and the target standard information into multiple dimensions and calculates the matching degree of each dimension respectively, so that the present embodiment can more comprehensively capture the similarity between the target person and the target standard information, which helps to more accurately reflect the personalized needs of the target person, thereby providing a control model that better meets their needs. The present disclosure can more flexibly adjust the influence of each dimension in the decision-making process by assigning different weights to different dimensions, thereby enhancing the accuracy and rationality of the decision and improving the accuracy and reliability of data processing.

[0052] In one embodiment of the present disclosure, the first control model is a support vector machine model trained based on historical environmental characteristics, basic information of historical personnel, and historical behavioral characteristics of historical personnel; Before determining a plurality of target standard information from a plurality of standard information based on the room characteristics in response to receiving the automatic control instruction, the method further includes: Determine the kernel function of the support vector machine model based on the historical environmental characteristics and the historical behavioral characteristics of historical personnel; Determine the penalty parameters of the support vector machine model based on the historical environmental characteristics and the historical behavioral characteristics of historical personnel; Based on the historical environment characteristics, the basic information of the historical personnel and the historical behavior characteristics of the historical personnel, the support vector machine model is trained to obtain multiple control models; the first control model is one of the multiple control models.

[0053] In one embodiment of the present disclosure, the kernel function of the support vector machine model is determined based on the historical environment characteristics and the historical behavior characteristics of the historical personnel, including: In response to the absolute value of the correlation coefficient between the historical environmental feature and the historical personnel's behavior feature being greater than a first correlation threshold, using the first type of kernel function as a kernel function of the support vector machine model; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being less than or equal to the first correlation threshold, using the second type of kernel function as the kernel function of the support vector machine model; The first and second kernel functions have different degrees of linear relationship.

[0054] In one embodiment of the present disclosure, the penalty parameter of the support vector machine model is determined based on the historical environmental characteristics and the historical behavioral characteristics of the historical personnel, including: Determine the initial penalty function; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being greater than a second correlation threshold, increasing the initial penalty function by a first penalty step length to obtain a penalty function of a support vector machine model; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being less than or equal to a second correlation threshold, the initial penalty function is reduced by a second penalty step length to obtain a penalty function of the support vector machine model.

[0055] In this embodiment, the first control model is a support vector machine model trained according to historical environmental characteristics, basic information of historical personnel and historical behavior characteristics of historical personnel. SVM has a penalty parameter and multiple kernel functions, wherein the kernel function may include: linear kernel function, polynomial kernel function and radial basis function kernel function. The linear kernel function mainly processes data of linear relationship, while the polynomial kernel function and radial basis function kernel function mainly process data of nonlinear relationship.

[0056] The penalty parameter is used to balance the model's fitting ability and generalization ability. If the penalty parameter is too large, the model will try to correctly classify all samples in the training data, which may cause the model to overfit. For example, for guests' occasional abnormal equipment operation (possibly misoperation), if the penalty parameter is too large, the model will also learn these abnormal situations as normal behavior patterns. On the contrary, when the penalty parameter is too small, the model may be too simple and unable to accurately capture the complex relationship between environmental factors and guest behavior, resulting in underfitting. For example, in the temperature control model, if the penalty parameter is too small, the model may not be able to learn the guests' different needs for temperature in different seasons and weather conditions, but simply adopt a fixed control strategy.

[0057] Therefore, the selection of kernel function and penalty parameter are both based on the correlation between environmental features and behavioral features. When the correlation between environmental features and behavioral features is high, a linear kernel function can be used as the kernel function of SVM to better express the relationship between environmental features and behavioral features. At the same time, a higher penalty parameter can be selected to make the model fit the data more strictly and minimize misclassification. On the contrary, when the correlation between environmental features and behavioral features is low, a nonlinear kernel function can be used as the kernel function of SVM to make the model focus more on generalization ability and make the model relatively simple.

[0058] Therefore, this embodiment sets up a detailed adjustment method, namely, selecting a suitable kernel function by comparing the correlation coefficient of historical environmental characteristics and historical personnel behavior characteristics with a predetermined first correlation threshold, wherein the first type of kernel function is a linear kernel function, and the second type of kernel function is a nonlinear kernel function, and the degree of linear relationship between the two types of kernel functions is different.

[0059] The correlation coefficient between historical environmental characteristics and historical personnel behavior characteristics can be obtained by calculating the Pearson correlation coefficient. It should be noted that the result obtained by calculating the Pearson correlation coefficient has a value range of The closer it is to 1, the greater the positive correlation of the data; the closer it is to -1, the greater the negative correlation of the data; the closer it is to 0, the lower the correlation of the data.

[0060] The first correlation threshold and the second correlation threshold can be obtained through multiple experiments on historical data. For example, when the first correlation threshold is greater than 0.8, the effect of using a linear kernel function is better, and when it is less than 0.8, the effect of a nonlinear kernel function is better. Therefore, the first correlation threshold can be 0.8, and the second correlation threshold can be determined in the same way.

[0061] The initial penalty parameter of SVM can be a value with high applicability, or it can be an initial value preliminarily determined based on the complexity of the data. For example, when the amount or dimension of the data is greater than the preset value, the initial penalty parameter can be assigned a higher value to make the output more accurate.

[0062] The first penalty step length and the second penalty step length can be determined according to actual conditions, or according to the difference between the absolute value of the correlation coefficient and the second correlation threshold value. For example, the first penalty step length is determined based on the difference between the correlation coefficient of historical environmental characteristics and historical personnel behavior characteristics and the second correlation threshold value and the first linear formula. Determine the second penalty step length based on the difference between the correlation coefficient of the historical environmental characteristics and the historical personnel's behavior characteristics and the second phase relationship threshold value, and the second linear formula; The first linear formula can be: , the second linear formula can be: ,in represents the first penalty step length, represents the second penalty step length, Both represent slopes and can be determined based on actual conditions. The difference between the correlation coefficient representing the historical environmental characteristics and the historical personnel behavior characteristics and the second correlation threshold value can be used to dynamically adjust the first penalty step size and the second penalty step size according to the size of the correlation coefficient through the first linear formula and the second linear formula.

[0063] It can be concluded from the above that the present disclosure, by comparing the correlation coefficient of historical environmental features and historical personnel behavior features with the first correlation threshold, enables this embodiment to intelligently select a linear kernel function or a nonlinear kernel function as the kernel function of the SVM, thereby enabling the model to better adapt to the data features in different situations, thereby improving the generalization ability and adaptability of the model. This embodiment compares the correlation coefficient with the second correlation threshold and adjusts the penalty function accordingly. This embodiment can balance the fitting ability and generalization ability of the model, avoid overfitting or underfitting, and enable the model to more flexibly respond to data of different complexities, thereby improving the robustness and stability of the model. This embodiment calculates the correlation coefficient and performs model selection and parameter adjustment accordingly. This embodiment can more accurately capture the intrinsic relationship between the data, thereby improving the prediction performance and adaptability of the model.

[0064] In one embodiment of the present disclosure, in response to the correlation coefficient between the historical environmental feature and the historical personnel behavior feature being absolutely less than or equal to the first correlation threshold, the second type of kernel function is used as the kernel function of the support vector machine model, including: In response to the historical environmental characteristics and the historical personnel's behavioral characteristics satisfying the polynomial condition, using the first sub-kernel function as the kernel function of the support vector machine model; In response to the historical environmental characteristics and the historical personnel's behavioral characteristics not satisfying the polynomial condition, the second sub-kernel function is used as the kernel function of the support vector machine model; The linear relationship between the first sub-kernel function and the second sub-kernel function is different.

[0065] In one embodiment of the present disclosure, determining an initial width parameter of the second sub-kernel function; In response to the historical environment characteristics and the historical personnel behavior characteristics not satisfying the polynomial condition, and the historical environment characteristics and the historical personnel behavior characteristics satisfying the first distribution condition, reducing the initial width parameter according to the first width step; In response to the historical environment characteristics and the historical personnel behavior characteristics not satisfying the polynomial condition, and the historical environment characteristics and the historical personnel behavior characteristics not satisfying the first distribution condition, the initial width parameter is increased according to the second width step.

[0066] In this embodiment, the second type of kernel function includes a polynomial kernel function and a radial basis function kernel function. The first sub-kernel function is a polynomial kernel function, and the second sub-kernel function is a radial basis function kernel function. Both kernel functions are nonlinear kernel functions, but the polynomial kernel function tends to distribute data in a polynomial distribution state. The polynomial condition can be determined by drawing a two-dimensional scatter plot or a three-dimensional scatter plot of environmental characteristics and behavioral characteristics and a trained convolutional neural network model to determine whether the scatter plot meets the polynomial characteristics. The above-mentioned convolutional neural network model has been trained with a large number of two-dimensional scatter plots and three-dimensional scatter plots that meet the polynomial distribution.

[0067] In this embodiment, whether the polynomial condition is met can also be determined by calculating the goodness of fit index, such as calculating the determination coefficient. The closer the determination coefficient is to 1, the better the polynomial model fits the data. If the determination coefficients obtained by fitting the first number of fitting times are all less than the first fitting threshold, the polynomial condition is not met. The first number of fitting times and the first fitting threshold can be determined based on actual conditions.

[0068] Among them, if the SVM model is the second sub-kernel function, that is, the radial basis function kernel function, an initial width parameter can be determined first, and then the initial width parameter can be adjusted according to the distribution of historical environmental characteristics and historical personnel's behavioral characteristics.

[0069] In this embodiment, the expression of the radial basis function kernel function is: , the width parameter is , Represents historical environmental characteristics, Represents the behavioral characteristics of historical personnel, where the width parameter Determines the influence range of each data point. If the hotel room data is scattered, for example, different guests have very different requirements for temperature, and the distribution range of data points in the feature space is wide, then it can be appropriately reduced. , because a smaller value will make the influence range of the data point larger, the model will pay more attention to the overall data distribution, which is helpful to deal with such scattered data. The first width step can be determined empirically.

[0070] On the contrary, if the data distribution is relatively concentrated, for example, the guests' operation behaviors on a certain device are very similar under similar environmental conditions, and the data points are relatively clustered, then the , which can make the model pay more attention to local data features and better fit such concentrated data.

[0071] The density of data points in the entire feature space can be calculated by statistical methods. For example, the space composed of environmental characteristics (temperature, humidity, light intensity) and behavioral characteristics (frequency of device use, combined use of devices) is divided into multiple small areas, and the number of data points in each small area is calculated. If the number of these data points in each small area is relatively uniform, and there is no obvious density peak area, then it can be preliminarily judged that the data is scattered. On the contrary, if the density of data points in some small areas is significantly higher than that in other areas, forming high-density areas, this may mean that the data is concentrated in these areas.

[0072] For example, the feature space is divided into a first number of divided spaces to obtain a plurality of second feature spaces; Calculate the density of data points in each second feature space respectively; In response to the difference between the maximum density and the minimum density of the data points in the plurality of second feature spaces being greater than or equal to the first density threshold, the historical environmental features and the historical personnel behavior features satisfy the first distribution condition; In response to the difference between the maximum density and the minimum density of the data points in the plurality of second feature spaces being less than the first density threshold, the historical environmental features and the historical personnel behavior features do not satisfy the first distribution condition.

[0073] The first division quantity and the first density threshold can be determined according to experiments. The feature space is a space composed of historical environmental features and historical personnel behavior features.

[0074] In this embodiment, the data points in the feature space may also be clustered using a clustering algorithm, for example: Based on the clustering algorithm, the data points in the feature space are clustered to obtain the clustering results; In response to the number of clusters in the clustering result being greater than a first clustering threshold, the historical environmental features and the historical personnel behavior features satisfy a first distribution condition; In response to the number of clusters in the clustering result being less than or equal to the first clustering threshold, the historical environmental features and the historical personnel behavior features do not meet the first distribution condition.

[0075] When the number of clusters in the clustering result is large, that is, greater than the first clustering threshold, it means that the data distribution is more dispersed, otherwise, it means that the data distribution is more concentrated. The first clustering threshold can be determined according to actual conditions.

[0076] It can be concluded from the above that the present disclosure dynamically adjusts the width parameter by calculating the density and clustering results of data points, so that the model can better fit the local or overall characteristics of the data, so that the model can maintain optimal performance under different data distribution conditions. The present disclosure determines whether the polynomial condition and the first distribution condition are met in a variety of ways, thereby improving the accuracy and reliability of data processing.

[0077] A data processing method corresponding to the above embodiment, Figure 2 This is a structural block diagram of a data processing system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The data processing system 20 includes: a standard information determination module 21, a model determination module 22, a control strategy output module 23 and a control module 24.

[0078] The standard information determination module 21 is used to determine a plurality of target standard information from a plurality of standard information based on the room information in response to receiving the automatic control instruction; A model determination module 22, for determining a first control model based on the matching degree between the basic information of the target person and a plurality of target standard information; the standard information is the basic information of the historical person; The control strategy output module 23 is used to input the behavior characteristics of the target person in the target room into the first control model to obtain the control strategy of the target room; the target room is the room where the target person lives; The control module 24 is used to control the smart devices in the target room based on the control strategy.

[0079] In one embodiment of the present disclosure, the target standard information includes second information of multiple dimensions; The number of dimensions of the target standard information is greater than or equal to the number of dimensions of the basic information; The model determination module 22 is specifically used to calculate the matching degree of the first information of each dimension of the target person and the second information of the corresponding dimension respectively, and obtain a plurality of matching degrees of each dimension; Perform weighted calculation on the matching degrees of each dimension to obtain multiple target matching degrees; The control model corresponding to the target standard information with the highest target matching degree is used as the first control model.

[0080] In one embodiment of the present disclosure, the first control model is a support vector machine model trained based on historical environmental characteristics, basic information of historical personnel, and historical behavioral characteristics of historical personnel; A data processing system 20 further includes: a model building module; A model building module, used for determining the kernel function of the support vector machine model based on the historical environment characteristics and the historical behavior characteristics of the historical personnel; Determine the penalty parameters of the support vector machine model based on the historical environmental characteristics and the historical behavioral characteristics of historical personnel; Based on the historical environment characteristics, the basic information of the historical personnel and the historical behavior characteristics of the historical personnel, the support vector machine model is trained to obtain multiple control models; the first control model is one of the multiple control models.

[0081] In one embodiment of the present disclosure, the model building module is specifically configured to use the first type of kernel function as the kernel function of the support vector machine model in response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being greater than a first correlation threshold; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being less than or equal to the first correlation threshold, using the second type of kernel function as the kernel function of the support vector machine model; The first and second kernel functions have different degrees of linear relationship.

[0082] In one embodiment of the present disclosure, the model building module is further specifically used to determine an initial penalty function; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being greater than a second correlation threshold, increasing the initial penalty function by a first penalty step length to obtain a penalty function of a support vector machine model; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being less than or equal to a second correlation threshold, the initial penalty function is reduced by a second penalty step length to obtain a penalty function of the support vector machine model.

[0083] In one embodiment of the present disclosure, the model building module is further specifically used to use the first sub-kernel function as the kernel function of the support vector machine model in response to the historical environmental characteristics and the historical personnel behavior characteristics satisfying the polynomial condition; In response to the historical environmental characteristics and the historical personnel's behavioral characteristics not satisfying the polynomial condition, the second sub-kernel function is used as the kernel function of the support vector machine model; The linear relationship between the first sub-kernel function and the second sub-kernel function is different.

[0084] In one embodiment of the present disclosure, the model building module is further specifically used to determine an initial width parameter of the second sub-kernel function; In response to the historical environment characteristics and the historical personnel behavior characteristics not satisfying the polynomial condition, and the historical environment characteristics and the historical personnel behavior characteristics satisfying the first distribution condition, reducing the initial width parameter according to the first width step; In response to the historical environment characteristics and the historical personnel behavior characteristics not satisfying the polynomial condition, and the historical environment characteristics and the historical personnel behavior characteristics not satisfying the first distribution condition, the initial width parameter is increased according to the second width step.

[0085] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 24 are shown.

[0086] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0087] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0088] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0089] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of a data processing method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0090] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0091] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0092] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0093] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In the several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0095] 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 embodiments of the present disclosure.

[0096] In addition, each functional unit in each embodiment of the present disclosure 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0097] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: include: In response to receiving the automatic control instruction, determining a plurality of target standard information from a plurality of standard information based on the room information; Determine a first control model based on the matching degree between the basic information of the target person and the plurality of target standard information; the standard information is the basic information of the historical person; Inputting the environmental characteristics of the target room into the first control model to obtain the control strategy of the target room; the target room is the room where the target person lives; The smart devices in the target room are controlled based on the control strategy.

2. A data processing method according to claim 1, characterized in that: The basic information includes first information of multiple dimensions; the target standard information includes second information of multiple dimensions; The number of dimensions of the target standard information is greater than or equal to the number of dimensions of the basic information; The determining of the first control model based on the matching degree between the basic information of the target person and the plurality of target standard information includes: Calculating the matching degree of the first information of each dimension of the target person and the second information of the corresponding dimension respectively, and obtaining a plurality of matching degrees of each dimension; Performing weighted calculation on the matching degrees of each of the multiple dimensions to obtain multiple target matching degrees; The control model corresponding to the target standard information with the highest target matching degree is used as the first control model.

3. A data processing method according to claim 1, characterized in that: The first control model is a support vector machine model trained according to historical environmental characteristics, basic information of historical personnel and historical behavior characteristics of historical personnel; Before determining a plurality of target standard information from a plurality of standard information based on room characteristics in response to receiving the automatic control instruction, the method further includes: Determine the kernel function of the support vector machine model based on the historical environmental characteristics and the historical behavioral characteristics of historical personnel; Determining a penalty parameter of a support vector machine model based on the historical environmental characteristics and the historical behavioral characteristics of the historical personnel; Based on the historical environmental characteristics, the basic information of the historical personnel and the historical behavioral characteristics of the historical personnel, a support vector machine model is trained to obtain multiple control models; the first control model is one of the multiple control models.

4. A data processing method according to claim 3, characterized in that: The kernel function of the support vector machine model is determined based on the historical environment characteristics and the historical behavior characteristics of the historical personnel, including: In response to the absolute value of the correlation coefficient between the historical environmental feature and the historical personnel's behavior feature being greater than a first correlation threshold, using the first type of kernel function as a kernel function of the support vector machine model; In response to the absolute value of the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being less than or equal to the first correlation threshold, using the second type of kernel function as the kernel function of the support vector machine model; The first type of kernel function and the second type of kernel function have different degrees of linear relationship.

5. A data processing method according to claim 3, characterized in that: The step of determining the penalty parameter of the support vector machine model based on the historical environmental characteristics and the historical behavioral characteristics of the historical personnel includes: Determine the initial penalty function; In response to the absolute value of the correlation coefficient between the historical environmental feature and the historical personnel behavior feature being greater than a second correlation threshold, increasing the initial penalty function with a first penalty step length to obtain a penalty function of a support vector machine model; In response to the absolute value of the correlation coefficient between the historical environmental feature and the historical personnel behavior feature being less than or equal to the second correlation threshold, the initial penalty function is reduced by a second penalty step length to obtain a penalty function of a support vector machine model.

6. A data processing method according to claim 4, characterized in that: In response to the correlation coefficient between the historical environmental characteristics and the historical personnel behavior characteristics being absolutely less than or equal to the first correlation threshold, using the second type of kernel function as the kernel function of the support vector machine model includes: In response to the historical environmental characteristics and the historical personnel's behavioral characteristics satisfying the polynomial condition, using the first sub-kernel function as the kernel function of the support vector machine model; In response to the historical environmental characteristics and the historical personnel's behavioral characteristics not satisfying the polynomial condition, the second sub-kernel function is used as the kernel function of the support vector machine model; The linear relationship between the first sub-kernel function and the second sub-kernel function is different.

7. A data processing method according to claim 6, characterized in that: Also includes: Determining an initial width parameter of the second sub-kernel function; In response to the historical environment characteristics and the historical personnel behavior characteristics not satisfying a polynomial condition, and the historical environment characteristics and the historical personnel behavior characteristics satisfying a first distribution condition, reducing the initial width parameter according to a first width step; In response to the historical environment characteristics and the historical personnel's behavior characteristics not satisfying the polynomial condition, and the historical environment characteristics and the historical personnel's behavior characteristics not satisfying the first distribution condition, the initial width parameter is increased according to a second width step.

8. A data processing system, characterized in that: include: a standard information determination module, configured to determine a plurality of target standard information from a plurality of standard information based on the room information in response to receiving the automatic control instruction; A model determination module, used to determine a first control model based on the matching degree between the basic information of the target person and the plurality of target standard information; the standard information is the basic information of the historical person; A control strategy output module, used to input the behavior characteristics of the target person in the target room into the first control model to obtain the control strategy of the target room; the target room is the room where the target person lives; A control module is used to control the smart devices in the target room based on the control strategy.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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