Intelligent Data Processing and Analysis Method and System Applicable to Interior Design

Abstract

The present application provides an intelligent data processing and analysis method and system applicable to interior design, which relates to the technical field of data processing. Environmental sensing data of the interior to be designed is collected; the environmental sensing data is converted into a redundancy-eliminated data set of the interior to be designed, feature extraction is performed on the redundancy-eliminated data set to obtain an environmental feature domain of the interior to be designed, and an indoor environmental flow model of the interior to be designed is constructed according to the environmental feature domain; historical interior design data and the design expectation text of the customer are obtained, all text feature data items in the design expectation text are extracted, and the data comparison entropy of each text feature data item when performing a design comparison with the historical interior design data is determined; the corresponding text feature data items are mapped into the indoor environmental flow model according to the data comparison entropy to obtain a design scheme for the interior to be designed. The present application can effectively integrate and process diversified sensing data and generate a design scheme for the interior to be designed in combination with the design expectation text of the customer.

Description

Technical Field

[0001] This application relates to the technical field of data processing. More specifically, this application relates to an intelligent data processing and analysis method and system applicable to interior design. Background Art

[0002] The application of intelligent data processing in interior design aims to achieve more efficient, personalized, and sustainable design solutions by collecting and analyzing data from environmental sensors, user feedback, and historical data. This process typically involves multiple steps, including data collection, data cleaning, feature extraction, model construction, and solution generation.

[0003] During the model construction process, the difficulty of accurately capturing user needs and behaviors is also obvious. User needs are often subjective and influenced by personal preferences, cultural backgrounds, and situational factors. Designers need to use effective algorithms to identify and quantify these complex needs. Interior design involves many factors, including spatial layout, material properties, user behavior, etc. These data come from different sensors and systems and often exhibit high heterogeneity and inconsistency. Therefore, how to effectively integrate and process diverse sensing data and generate a design solution for the interior to be designed in combination with the design expectation text of the customer is a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides an intelligent data processing and analysis method and system applicable to interior design, which can effectively integrate and process diverse sensing data and generate a design solution for the interior to be designed in combination with the design expectation text of the customer.

[0005] In a first aspect, this application provides an intelligent data processing and analysis method applicable to interior design. The analysis method includes the following steps:

[0006] Collect environmental sensing data of the interior to be designed;

[0007] Convert the environmental sensing data into a redundancy-eliminated data set of the interior to be designed, extract features from the redundancy-eliminated data set to obtain an environmental feature domain of the interior to be designed, and construct an interior environment flow model of the interior to be designed according to the environmental feature domain;

[0008] Obtain historical interior design data and the design expectation text of the customer, then extract all text feature data items in the design expectation text, respectively compare each text feature data item with the historical interior design data in terms of design, and then obtain the data comparison entropy of each text feature data item during the design comparison;

[0009] Map the corresponding text feature data item to the interior environment flow model according to the data comparison entropy, and then obtain a design solution for the interior to be designed.

[0010] In this embodiment, environmental sensing data of the indoor space to be designed is collected by a sensor device.

[0011] In this embodiment, the environmental sensing data includes thermal environment data, light environment data, and indoor air data.

[0012] In this embodiment, converting the environmental sensing data into a redundancy elimination data set for the indoor space to be designed specifically includes:

[0013] Using a wavelet transform algorithm to perform noise reduction processing on the environmental sensing data;

[0014] Determining a data redundancy threshold set for the environmental sensing data of the indoor space to be designed based on the noise-reduced environmental sensing data;

[0015] Performing redundancy elimination processing on the noise-reduced environmental sensing data according to the data redundancy threshold set to obtain a redundancy elimination data set for the indoor space to be designed.

[0016] In this embodiment, performing feature extraction on the redundancy elimination data set to obtain an environmental feature domain for the indoor space to be designed specifically includes:

[0017] For each type of sensing data in the redundancy elimination data set, performing feature extraction on the sensing data to obtain indoor environmental features corresponding to each type of sensing data;

[0018] Determining the environmental feature domain of the indoor space to be designed through all the indoor environmental features.

[0019] In this embodiment, constructing an indoor environmental flow model for the indoor space to be designed based on the environmental feature domain specifically includes:

[0020] Determining the feature interaction coefficients between the various indoor environmental features in the environmental feature domain;

[0021] Constructing an indoor environmental flow model for the indoor space to be designed based on the environmental feature domain and all the feature interaction coefficients.

[0022] In this embodiment, determining the feature interaction coefficients between the various indoor environmental features in the environmental feature domain specifically includes:

[0023] Selecting an indoor environmental feature in the environmental feature domain and determining the feature difference distance between the selected indoor environmental feature and other indoor environmental features;

[0024] Determining the feature interaction coefficients between the selected indoor environmental feature and the corresponding indoor environmental features through the corresponding feature difference distances, thereby obtaining the feature interaction coefficients between the various indoor environmental features in the environmental feature domain.

[0025] In this embodiment, extracting all text feature data items from the design expected text specifically includes:

[0026] Perform text preprocessing on the design expected text to obtain the preprocessed design expected text;

[0027] Extract text features from the preprocessed design expected text to obtain all text features of the design expected text;

[0028] Determine the feature similarity between each text feature;

[0029] Perform feature fusion on all text features based on each feature similarity, and then obtain all text feature data items in the design expected text.

[0030] In this embodiment, mapping the corresponding text feature data items to the indoor environment flow model according to the data comparison entropy, and then obtaining the design scheme of the indoor to be designed specifically includes:

[0031] Obtain the set comparison entropy threshold;

[0032] Screen out all text feature data items with a data comparison entropy higher than the comparison entropy threshold;

[0033] Map all the screened text feature data items to the indoor environment flow model to obtain an indoor design model that meets the customer's expectations;

[0034] Use the indoor design model as the design scheme for the indoor to be designed.

[0035] In a second aspect, the present application provides an intelligent data processing and analysis system applicable to indoor design for executing an intelligent data processing and analysis method applicable to indoor design. The analysis system includes:

[0036] A data acquisition module for acquiring environmental sensing data of the indoor to be designed;

[0037] A model construction module for converting the environmental sensing data into a redundancy elimination data set of the indoor to be designed, extracting features from the redundancy elimination data set to obtain an environmental feature domain of the indoor to be designed, and constructing an indoor environment flow model of the indoor to be designed according to the environmental feature domain;

[0038] A design comparison module for obtaining historical indoor design data and the customer's design expected text, then extracting all text feature data items from the design expected text, respectively comparing each text feature data item with the historical indoor design data for design comparison, and then obtaining the data comparison entropy of each text feature data item during design comparison;

[0039] A solution generation module is configured to map corresponding text feature data items into the indoor environment flow model according to the data comparison entropy, so as to obtain a design solution for the indoor environment to be designed.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] By collecting environmental sensing data of the indoor environment to be designed; converting the environmental sensing data into a redundancy elimination data set of the indoor environment to be designed, extracting features from the redundancy elimination data set to obtain an environmental feature domain of the indoor environment to be designed, constructing an indoor environment flow model of the indoor environment to be designed according to the environmental feature domain; obtaining historical indoor design data and the design expected text of the customer, then extracting all text feature data items in the design expected text, respectively comparing each text feature data item with the historical indoor design data, and then obtaining the data comparison entropy when each text feature data item is compared in design; mapping the corresponding text feature data items into the indoor environment flow model according to the data comparison entropy, so as to obtain a design solution for the indoor environment to be designed.

[0042] Thus, in this application, first of all, converting environmental sensing data into a redundancy elimination data set can reduce data redundancy and noise. Extracting the environmental feature domain of the indoor environment to be designed helps to understand the key factors affecting the indoor environment, and the indoor environment flow model constructed based on the environmental feature domain can dynamically simulate indoor environment changes; then, by extracting the text features of the design expected text, it is possible to better understand customer needs, including style preferences, functional requirements, etc., so as to formulate a design solution that better meets customer expectations. By comparing historical indoor design data with text feature data items, the effectiveness of the design solution can be evaluated based on data, and by calculating the data comparison entropy, the text feature data items that have the greatest impact on design decisions can be identified; finally, mapping the corresponding text feature data items into the indoor environment flow model according to the data comparison entropy can fully combine the design expected text of the customer, so as to generate a design solution for the indoor environment to be designed.

[0043] In summary, the technical solution adopted in this application can effectively integrate and process diversified sensing data, and combine the design expected text of the customer to generate a design solution for the indoor environment to be designed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 is an exemplary flowchart of an intelligent data processing and analysis method applicable to interior design provided by this application;

[0046] Figure 2 is an exemplary flowchart of determining a redundancy-eliminated data set provided by this application;

[0047] Figure 3 is an exemplary flowchart of determining text feature data items provided by this application;

[0048] Figure 4 is a module structure diagram of an intelligent data processing and analysis system applicable to interior design provided by this application. Detailed implementation manners

[0049] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0050] The embodiments of this application provide an intelligent data processing and analysis method applicable to interior design. The core is to collect environmental sensing data of the interior to be designed; convert the environmental sensing data into a redundancy-eliminated data set of the interior to be designed, perform feature extraction on the redundancy-eliminated data set to obtain an environmental feature domain of the interior to be designed, and construct an interior environment flow model of the interior to be designed according to the environmental feature domain; obtain historical interior design data and the design expectation text of the customer, then extract all text feature data items in the design expectation text, respectively perform design comparison between each text feature data item and the historical interior design data, and then obtain the data comparison entropy of each text feature data item when performing design comparison; map the corresponding text feature data item to the interior environment flow model according to the data comparison entropy, and then obtain the design scheme of the interior to be designed. Adopting the above solution can effectively integrate and process diversified sensing data and generate the design scheme of the interior to be designed in combination with the design expectation text of the customer.

[0051] Embodiment 1

[0052] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners. Refer to Figure 1 As shown, this figure is an exemplary flowchart of an intelligent data processing and analysis method applicable to interior design shown in this embodiment of this application. The analysis method includes the following steps:

[0053] In step S1, collect environmental sensing data of the interior to be designed.

[0054] In specific implementation, environmental sensing data of the indoor space to be designed is collected through sensor devices. First, sensors are reasonably arranged at different positions in the indoor space to be designed to cover environmental changes in the entire space, ensuring that the sensors are firmly installed and meet the data collection requirements. Then, the sensor system is configured to collect data in real time through wireless or wired connections, thereby obtaining the environmental sensing data of the indoor space to be designed. It should be noted that in this application, the environmental sensing data includes three types of sensing data, namely, thermal environment data, light environment data, and indoor air data. The thermal environment data mainly refers to the temperature and humidity in the indoor space to be designed, the light environment data mainly refers to the light intensity in the indoor space to be designed, and the indoor air data mainly refers to the formaldehyde gas data and carbon monoxide data in the indoor space to be designed.

[0055] In step S2, the environmental sensing data is converted into a redundancy elimination data set for the indoor space to be designed, feature extraction is performed on the redundancy elimination data set to obtain an environmental feature domain for the indoor space to be designed, and an indoor environmental flow model for the indoor space to be designed is constructed based on the environmental feature domain.

[0056] Preferably, in this embodiment, with reference to Figure 2 As shown, this figure is an exemplary flowchart for determining the redundancy elimination data set in the embodiment of this application. The conversion of the environmental sensing data into the redundancy elimination data set for the indoor space to be designed in this embodiment can be specifically implemented by the following steps:

[0057] First, in step S21, the wavelet transform algorithm is used to perform noise reduction processing on the environmental sensing data.

[0058] Then, in step S22, a data redundancy threshold set for the environmental sensing data of the indoor space to be designed is determined based on the noise-reduced environmental sensing data.

[0059] Finally, in step S23, redundancy elimination processing is performed on the noise-reduced environmental sensing data according to the data redundancy threshold set to obtain the redundancy elimination data set for the indoor space to be designed.

[0060] In the specific implementation, first, select an appropriate wavelet basis (such as Haar, Daubechies, etc.), and perform wavelet transform on the environmental sensor data to eliminate noise and interference in the environmental sensor data, so as to obtain the denoised environmental sensor data; then, the data redundancy threshold set of the indoor environmental sensor data to be designed can be determined according to the denoised environmental sensor data, and for each type of sensor data in the denoised environmental sensor data, calculate the mean and standard deviation of the sensor data of this type, so that the difference between the mean and twice the standard deviation is used as the first data redundancy threshold of the sensor data of this type, and the sum of the mean and twice the standard deviation is used as the second data redundancy threshold of the sensor data of this type, so that the data redundancy threshold of the sensor data of this type can be obtained. The data redundancy threshold is the threshold value used to eliminate redundant data. The above method can be used to obtain To the data redundancy threshold of each type of sensor data, thereby obtaining the data redundancy threshold set of the indoor environment sensor data to be designed, and the data redundancy threshold set is a data set containing the data redundancy threshold of each type of sensor data; finally, the denoised environmental sensor data can be de-redundant according to the data redundancy threshold set, that is, for each type of sensor data in the denoised environmental sensor data, the sensor data is de-redundant using the corresponding data redundancy threshold in the data redundancy threshold set, that is, the sensor data with a first data redundancy threshold smaller than the data redundancy threshold and a sensor data larger than the second data redundancy threshold in the sensor data are eliminated, so that the sensor data of this type after redundancy is obtained. Each type of sensor data can be de-redundant in the above manner, so that all types of sensor data after redundancy are combined as the de-redundant data set for the indoor environment to be designed.

[0061] In this embodiment, the feature extraction of the redundant data set to obtain the indoor environment feature domain to be designed can be specifically carried out in the following manner, namely:

[0062] For each type of sensor data in the redundant data set, feature extraction is performed on the sensor data, thereby obtaining indoor environment features corresponding to each type of sensor data;

[0063] The environmental characteristic domain of the interior to be designed is determined through all indoor environmental characteristics.

[0064] In the specific implementation, first, for each type of sensor data in the redundant data set, feature extraction can be performed on the sensor data, that is, binomial fitting can be performed on the selected type of sensor data to obtain a fitting curve corresponding to the sensor data of this type, so that the average slope of the fitting curve can be used as the indoor environment feature corresponding to the sensor data of this type, and the indoor environment feature is used to describe the rate of change of the indoor environment under the sensor data of the corresponding type. The indoor environment features corresponding to each type of sensor data can be obtained in the above manner; then, the feature domain composed of all indoor environment features can be used as the environmental feature domain of the indoor room to be designed.

[0065] Preferably, in this embodiment, the indoor environment flow model of the indoor space to be designed can be specifically constructed according to the environmental feature domain in the following manner, that is:

[0066] Determine the feature interaction coefficients between the indoor environmental features in the environmental feature domain;

[0067] Based on the environmental feature domain and all the feature interaction coefficients, construct the indoor environment flow model of the indoor space to be designed.

[0068] Specifically, first, the feature interaction coefficients between the indoor environmental features in the environmental feature domain can be determined. The feature interaction coefficient is an index used to measure the influence degree between indoor environmental features. Then, based on the environmental feature domain and all the feature interaction coefficients, the indoor environment flow model of the indoor space to be designed can be constructed, that is, taking the environmental feature domain as input data and taking each feature interaction coefficient as the corresponding weight and inputting them into the computer-aided design software to construct the indoor environment flow model of the indoor space to be designed. This indoor environment flow model can reflect the relationship between the indoor environmental features to be designed and the dynamic characteristics of the environmental flow.

[0069] In this embodiment, the feature interaction coefficients between the indoor environmental features in the environmental feature domain can be specifically determined in the following manner, that is:

[0070] Select an indoor environmental feature in the environmental feature domain, and determine the feature difference distance between the selected indoor environmental feature and other indoor environmental features;

[0071] Determine the feature interaction coefficient between the selected indoor environmental feature and the corresponding indoor environmental feature through the corresponding feature difference distance, and then obtain the feature interaction coefficients between the indoor environmental features in the environmental feature domain.

[0072] Specifically, first, select an indoor environmental feature in the environmental feature domain, and determine the feature difference distance between the selected indoor environmental feature and other indoor environmental features. The feature difference distance represents the difference degree between indoor environmental features, and the Euclidean distance can be used to represent the feature difference distance between the selected indoor environmental feature and other indoor environmental features. Then, the feature interaction coefficient between the selected indoor environmental feature and the corresponding indoor environmental feature can be determined through the corresponding feature difference distance, that is, taking the reciprocal of the difference between the corresponding feature difference distance and 1 as the feature interaction coefficient between the selected indoor environmental feature and the corresponding indoor environmental feature. Through the above method, the feature interaction coefficients between the indoor environmental features in the environmental feature domain can be obtained.

[0073] It should be noted that converting environmental sensing data into a redundancy-eliminated data set can reduce data redundancy and noise, ensure the data is more accurate and effective, improve the reliability of subsequent analysis, extracting the environmental feature domain of the indoor space to be designed helps to understand the key factors affecting the indoor environment, making the design more targeted, and the indoor environmental flow model constructed based on the environmental feature domain can dynamically simulate indoor environmental changes, helping designers optimize the spatial layout and function allocation under different conditions.

[0074] In step S3, obtain historical indoor design data and the design expectation text of the customer, and then extract all text feature data items in the design expectation text, respectively compare each text feature data item with the historical indoor design data for design comparison, and then obtain the data comparison entropy of each text feature data item during the design comparison.

[0075] Specifically, when implementing, data of past design projects, that is, historical indoor design data, can be extracted from the database of an indoor design company or institution. In this application, the historical indoor design data is descriptive data used to represent historical indoor design schemes; design expectation documents conveyed by customers through emails or other documents, that is, the design expectation text of the customer, can be collected.

[0076] Preferably, in this embodiment, as shown in Figure 3 This figure is an exemplary flowchart for determining text feature data items in an embodiment of this application. In this embodiment, extracting all text feature data items in the design expectation text can be specifically implemented by the following steps:

[0077] First, in step S31, perform text preprocessing on the design expectation text to obtain the preprocessed design expectation text;

[0078] Then, in step S32, perform text feature extraction on the preprocessed design expectation text to obtain all text features of the design expectation text;

[0079] Secondly, in step S33, determine the feature similarity between each text feature;

[0080] Finally, in step S34, perform feature fusion on all text features based on each feature similarity, and then obtain all text feature data items in the design expectation text.

[0081] In specific implementation, first, text preprocessing is performed on the design expectation text, that is, text cleaning, word segmentation, stop word removal, and stemming are performed on the design expectation text to obtain the preprocessed design expectation text. Among them, text cleaning is to remove special characters, punctuation marks, and redundant spaces. Word segmentation is to use natural language processing tools to split the sentences in the design expectation text into individual words or phrases. Stop word removal is to delete high-frequency words that are irrelevant to text understanding. Stemming reduces words to their basic forms to unify similar words. Then, text feature extraction can be performed on the preprocessed design expectation text, that is, the LDA (Latent Dirichlet Allocation) algorithm is applied to identify the themes and related words in the design expectation text, so as to obtain all text features of the design expectation text. Secondly, the feature similarity between each text feature can be determined, that is, the cosine similarity is used to calculate the similarity between each text feature, that is, the feature similarity between each text feature. Finally, feature fusion is performed on all text features based on each feature similarity, that is, two text features with a feature similarity higher than the set threshold are fused to obtain a text feature data item, which is a data item used to represent the key features in the design expectation text. All text feature data items in the design expectation text can be obtained through the above method.

[0082] In this embodiment, each text feature data item is respectively compared with the historical interior design data for design comparison, and then the data comparison entropy of each text feature data item during design comparison can be obtained. Specifically, the following method can be adopted, that is:

[0083] Align the features of each text feature data item and the historical interior design data, and then based on the machine learning algorithm, each text feature data item is respectively compared with the historical interior design data for design comparison, and then the data comparison entropy of each text feature data item during design comparison can be obtained.

[0084] In specific implementation, first, organize the historical interior design data and the extracted text feature data items into a comparable format to ensure their structural matching, so that each text feature data item can be feature-aligned with the historical interior design data. Then, machine learning algorithms (such as decision trees and random forests) can be used to perform design comparison on each text feature data item and the historical interior design data, that is, compare each text feature data item with the historical interior design data one by one and record the comparison results, so as to obtain the occurrence probability of each text feature data item in the historical interior design data. Finally, determine the data comparison entropy of each text feature data item during the design comparison. This data comparison entropy represents the uncertainty of the text feature data item relative to the historical interior design data. In actual implementation, the data comparison entropy can be determined by the following formula: data comparison entropy = occurrence probability of the text feature data item * log(occurrence probability of the text feature data item). Through the above method, the data comparison entropy of each text feature data item during the design comparison can be obtained.

[0085] It should be noted that by extracting the text features of the design expectation text, the customer needs can be better understood, including style preferences, functional requirements, etc., so as to formulate a design plan that better meets the customer's expectations. By comparing the historical interior design data with the text feature data items, the effectiveness of the design plan can be evaluated based on the data, and by calculating the data comparison entropy, the text feature data items that have the greatest impact on the design decision can be identified.

[0086] In step S4, map the corresponding text feature data items to the indoor environment flow model according to the data comparison entropy, and then obtain the design plan for the indoor to be designed.

[0087] In this embodiment, mapping the corresponding text feature data items to the indoor environment flow model according to the data comparison entropy, and then obtaining the design plan for the indoor to be designed can be specifically implemented in the following way, that is:

[0088] Obtain the set comparison entropy threshold;

[0089] Screen out all text feature data items whose data comparison entropy is higher than the comparison entropy threshold;

[0090] Map all the screened text feature data items to the indoor environment flow model to obtain an indoor design model that meets the customer's expectations;

[0091] Take the indoor design model as the design plan for the indoor to be designed.

[0092] In specific implementation, first, according to historical data analysis and experience, a comparison entropy threshold is set. This comparison entropy threshold can effectively distinguish important text feature data items from unimportant text feature data items, and it can usually be set by statistically analyzing the data comparison entropy distribution of historical text feature data items. Then, all text feature data items are traversed, and the text feature data items with data comparison entropy higher than the set comparison entropy threshold are screened out. Furthermore, the screened text feature data items are mapped to the corresponding parameters in the indoor environment flow model, and the data comparison entropy corresponding to the text feature data items is used as the mapping weight, which can be achieved by adjusting relevant variables in the indoor environment flow model to reflect the design requirements of the customer. According to the mapped text feature data items, the settings of the indoor environment flow model, such as spatial layout, air flow distribution, light intensity, etc., are adjusted to ensure that the model can truly reflect the design expectations of the customer. After the mapping is completed, an indoor design model that meets the customer's expectations is generated. Finally, this indoor design model can be used as the design scheme for the indoor space to be designed, and the generated design scheme is verified to ensure that it can meet the design goals in actual application and consider the usability and comfort of the space.

[0093] Thus, it can be seen that in this application, first, converting environmental sensing data into a redundancy-eliminated data set can reduce data redundancy and noise. Extracting the environmental feature domain of the indoor space to be designed helps to understand the key factors affecting the indoor environment, and the indoor environment flow model constructed based on the environmental feature domain can dynamically simulate indoor environment changes. Then, by extracting the text features of the design expectation text, customer needs, including style preferences, functional requirements, etc., can be better understood, so as to formulate a design scheme that more meets the customer's expectations. By comparing historical indoor design data with text feature data items, the effectiveness of the design scheme can be evaluated based on data, and by calculating the data comparison entropy, the text feature data items that have the greatest impact on design decisions can be identified. Finally, mapping the corresponding text feature data items to the indoor environment flow model according to the data comparison entropy can fully combine the customer's design expectation text, thereby generating the design scheme for the indoor space to be designed.

[0094] In summary, the technical solution adopted in this application can effectively integrate and process diversified sensing data, and combine the customer's design expectation text to generate the design scheme for the indoor space to be designed.

[0095] Embodiment 2

[0096] This application provides an intelligent data processing and analysis system applicable to indoor design. Referring to Figure 4 as shown, this figure is the module structure diagram of the analysis system according to this embodiment of this application. The analysis system includes:

[0097] A data acquisition module 100, which is used to acquire environmental sensing data of the indoor space to be designed;

[0098] The model construction module 200 is configured to convert the environmental sensing data into a redundancy-eliminated data set for the indoor space to be designed, extract features from the redundancy-eliminated data set to obtain the environmental feature domain of the indoor space to be designed, and construct an indoor environmental flow model for the indoor space to be designed according to the environmental feature domain.

[0099] The design comparison module 300 is configured to obtain historical indoor design data and the design expectation text of the customer, then extract all text feature data items in the design expectation text, respectively perform design comparison between each text feature data item and the historical indoor design data, and then obtain the data comparison entropy of each text feature data item during the design comparison.

[0100] The solution generation module 400 is configured to map the corresponding text feature data item to the indoor environmental flow model according to the data comparison entropy, and then obtain the design solution for the indoor space to be designed.

[0101] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one or more flows and / or Figure 1 blocks or multiple blocks.

[0102] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0103] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, commodity or device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. An intelligent data processing and analysis method suitable for interior design, characterized in that: The analytical method comprises the following steps: Collect environmental sensor data of the room to be designed; Converting the environmental sensor data into a redundant data set of the room to be designed, performing feature extraction on the redundant data set to obtain an environmental feature domain of the room to be designed, and constructing an indoor environmental flow model of the room to be designed according to the environmental feature domain; Obtain historical interior design data and a customer's expected design text, and then extract all text feature data items in the expected design text, and perform design comparison between each text feature data item and the historical interior design data, and then obtain data comparison entropy of each text feature data item when performing design comparison; Mapping the corresponding text feature data items to the indoor environment flow model according to the data comparison entropy, thereby obtaining a design scheme for the interior to be designed; Wherein, constructing the indoor environment flow model of the room to be designed according to the environmental feature domain specifically includes: Determining a feature interaction coefficient between each indoor environmental feature in the environmental feature domain; Constructing an indoor environmental flow model of the interior to be designed based on the environmental feature domain and all feature interaction coefficients; Wherein, determining the feature interaction coefficient between each indoor environment feature in the environment feature domain specifically includes: Selecting an indoor environment feature in the environmental feature domain, and determining a feature difference distance between the selected indoor environment feature and other indoor environment features; The feature interaction coefficient between the selected indoor environment feature and the corresponding indoor environment feature is determined through the corresponding feature difference distance, and then the feature interaction coefficient between each indoor environment feature in the environment feature domain is obtained.

2. The intelligent data processing and analysis method suitable for interior design as claimed in claim 1, characterized in that: The environmental sensing data of the room to be designed is collected through sensor equipment.

3. The intelligent data processing and analysis method for interior design as claimed in claim 1, characterized in that: The environmental sensing data includes thermal environment data, light environment data and indoor air data.

4. The intelligent data processing and analysis method for interior design according to claim 1, characterized in that: Converting the environmental sensor data into a redundant data set in the room to be designed specifically includes: Use wavelet transform algorithm to reduce noise of environmental sensor data; Determine a data redundancy threshold set of the indoor environment sensor data to be designed according to the noise-reduced environment sensor data; The noise-reduced environmental sensor data is de-redundantly processed according to the data redundancy threshold set to obtain a de-redundant data set for the room to be designed.

5. The intelligent data processing and analysis method suitable for interior design as claimed in claim 1, characterized in that: The feature extraction of the redundant data set is performed to obtain the indoor environment feature domain to be designed, which specifically includes: For each type of sensor data in the redundant data set, feature extraction is performed on the sensor data, thereby obtaining indoor environment features corresponding to each type of sensor data; The environmental characteristic domain of the interior to be designed is determined through all indoor environmental characteristics.

6. The intelligent data processing and analysis method for interior design as claimed in claim 1, characterized in that: Extracting all text feature data items in the design expected text specifically includes: Performing text preprocessing on the expected design text to obtain the preprocessed expected design text; Performing text feature extraction on the preprocessed design expected text to obtain all text features of the design expected text; Determine feature similarity between various text features; All text features are fused based on the similarity of each feature, thereby obtaining all text feature data items in the designed expected text.

7. The intelligent data processing and analysis method for interior design as claimed in claim 1, characterized in that: According to the data comparison entropy, the corresponding text feature data items are mapped to the indoor environment flow model, and then the design scheme of the interior to be designed is obtained, which specifically includes: Get the set comparison entropy threshold; Screening out all text feature data items whose data comparison entropy is higher than the comparison entropy threshold; Mapping all the filtered text feature data items to the indoor environment flow model to obtain an indoor design model that meets customer expectations; The interior design model is used as a design plan for the interior to be designed.

8. An intelligent data processing and analysis system suitable for interior design, used to execute an intelligent data processing and analysis method suitable for interior design as claimed in any one of claims 1 to 7, characterized in that: The analysis system comprises: A data acquisition module, used to collect environmental sensor data of the room to be designed; A model building module, used for converting the environmental sensor data into a redundant data set of the room to be designed, performing feature extraction on the redundant data set to obtain an environmental feature domain of the room to be designed, and building an indoor environmental flow model of the room to be designed according to the environmental feature domain; A design comparison module is used to obtain historical interior design data and the customer's design expectation text, and then extract all text feature data items in the design expectation text, and respectively perform design comparison between each text feature data item and the historical interior design data, and then obtain the data comparison entropy of each text feature data item when performing design comparison; The scheme generation module is used to map the corresponding text feature data items to the indoor environment flow model according to the data comparison entropy, so as to obtain the design scheme of the interior to be designed.

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