Intelligent data processing analysis method and system suitable for interior design

Abstract

The invention provides an intelligent data processing and analyzing method and system suitable for interior design, and relates to the technical field of data processing, and the method comprises the steps: collecting indoor environment sensing data to be designed; converting the environment sensing data into a deduplication data set in the to-be-designed room, performing feature extraction on the deduplication data set to obtain an environment feature domain in the to-be-designed room, and constructing an indoor environment flow model in the to-be-designed room according to the environment feature domain; obtaining historical interior design data and a design expected text of a customer, extracting all text feature data items in the design expected text, and determining a data comparison entropy when each text feature data item is subjected to design comparison with the historical interior design data; the corresponding text feature data items are mapped into the indoor environment flow model according to the data comparison entropy, and the design scheme of the interior to be designed is obtained, diversified sensing data can be effectively integrated and processed, and the design scheme of the interior to be designed is generated in combination with the design expectation text of a client.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and more specifically, to an intelligent data processing and analysis method and system suitable for 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 usually involves multiple steps, including data collection, data cleaning, feature extraction, model building and solution generation.

[0003] In the process of model building, the difficulty of accurately capturing user needs and behaviors is also obvious. User needs are often subjective and are affected by personal preferences, cultural background and situational factors. Designers need to use effective algorithms to identify and quantify these complex needs. There are many factors involved in interior design, including space layout, material properties, user behavior, etc. These data come from different sensors and systems and often show high heterogeneity and inconsistency. Therefore, how to effectively integrate and process diversified sensor data and combine it with the customer's design expectation text to generate a design plan for the interior to be designed is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an intelligent data processing and analysis method and system suitable for interior design, which can effectively integrate and process diversified sensor data, and generate a design plan for the interior to be designed in combination with the customer's design expectation text.

[0005] In a first aspect, the present application provides an intelligent data processing and analysis method applicable to interior design, the analysis method comprising 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; The corresponding text feature data items are mapped to the indoor environment flow model according to the data comparison entropy, thereby obtaining a design scheme for the interior to be designed.

[0006] In this embodiment, the environmental sensing data of the room to be designed is collected by sensor equipment.

[0007] In this embodiment, the environmental sensing data includes thermal environment data, light environment data and indoor air data.

[0008] In this embodiment, converting the environmental sensing 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.

[0009] In this embodiment, feature extraction is performed on the redundant data set 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.

[0010] In this embodiment, 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; An indoor environmental flow model of the interior to be designed is constructed based on the environmental feature domain and all feature interaction coefficients.

[0011] In this embodiment, determining the feature interaction coefficient between the indoor environment features 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.

[0012] In this embodiment, 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.

[0013] In this embodiment, the corresponding text feature data items are mapped to the indoor environment flow model according to the data comparison entropy, 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.

[0014] In a second aspect, the present application provides an intelligent data processing and analysis system applicable to interior design for executing an intelligent data processing and analysis method applicable to interior design, the analysis system comprising: 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.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: The method collects environmental sensor data of the interior to be designed; converts the environmental sensor data into a redundant data set of the interior to be designed, performs feature extraction on the redundant data set to obtain an environmental feature domain of the interior to be designed, and constructs an indoor environmental flow model of the interior to be designed according to the environmental feature domain; obtains historical interior design data and the customer's design expectation text, and then extracts all text feature data items in the design expectation text, performs design comparison between each text feature data item and the historical interior design data, and then obtains the data comparison entropy of each text feature data item when performing the design comparison; maps the corresponding text feature data items to the indoor environmental flow model according to the data comparison entropy, and then obtains the design scheme of the interior to be designed.

[0016] It can be seen that in the present application, firstly, converting environmental sensor data into a redundant data set can reduce data redundancy and noise, extracting the environmental feature domain of the interior to be designed is helpful 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 environmental changes; then, by extracting the text features of the expected design text, customer needs can be better understood, including style preferences, functional requirements, etc., so as to formulate a design plan that better meets customer expectations, and by comparing historical interior design data with text feature data items, the effectiveness of the design plan can be evaluated based on 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; finally, according to the data comparison entropy, the corresponding text feature data items are mapped to the indoor environment flow model, which can fully combine the customer's expected design text to generate a design plan for the interior to be designed.

[0017] In summary, the technical solution adopted in this application can effectively integrate and process diversified sensor data, and generate a design plan for the interior to be designed in combination with the customer's design expectation text. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 is an exemplary flow chart of the intelligent data processing and analysis method applicable to interior design provided by the present application; Figure 2 is an exemplary flow chart of determining a redundant data set provided by the present application; Figure 3is an exemplary flow chart of determining text feature data items provided by the present application; Figure 4 It is a module structure diagram of the intelligent data processing and analysis system suitable for interior design provided in this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides an intelligent data processing and analysis method suitable for interior design, the core of which is to collect environmental sensor data of the interior to be designed; convert the environmental sensor data into a redundant data set of the interior to be designed, perform feature extraction on the redundant data set to obtain the environmental feature domain of the interior to be designed, and construct an indoor environmental flow model of the interior to be designed based on the environmental feature domain; 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, 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 the design comparison; map the corresponding text feature data item to the indoor environmental flow model based on the data comparison entropy, and then obtain the design plan of the interior to be designed. The above scheme can effectively integrate and process diversified sensor data, and generate a design plan for the interior to be designed in combination with the customer's design expectation text.

[0022] Embodiment 1 In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of an intelligent data processing and analysis method suitable for interior design according to this embodiment of the present application, and the analysis method includes the following steps: In step S1, environmental sensor data of the room to be designed is collected.

[0023] In the specific implementation, the environmental sensing data of the room to be designed is collected through sensor equipment; first, the sensors are reasonably arranged at different positions in the room to be designed to cover the environmental changes of the entire space, and it is ensured that the sensors are installed firmly and meet the data collection requirements; then, the sensor system is configured to collect data in real time through a wireless network or a wired connection, so as to obtain the environmental sensing data of the room to be designed; it should be noted that in the present 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 of the room to be designed, the light environment data mainly refers to the light intensity of the room to be designed, and the indoor air data mainly refers to the formaldehyde gas data and carbon monoxide data in the room to be designed.

[0024] In step S2, the environmental sensing data is converted into a redundant data set of the room to be designed, and features are extracted from the redundant data set to obtain an environmental feature domain of the room to be designed, and an indoor environmental flow model of the room to be designed is constructed according to the environmental feature domain.

[0025] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flow chart of determining a redundant data set in an embodiment of the present application. In this embodiment, the environmental sensor data is converted into a redundant data set in the room to be designed, which can be implemented by the following steps: First, in step S21, a wavelet transform algorithm is used to perform noise reduction processing on the environmental sensor data; Then, in step S22, a data redundancy threshold set of the indoor environment sensor data to be designed is determined according to the noise-reduced environment sensor data; Finally, in step S23, 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 indoor space to be designed.

[0026] 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.

[0027] 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: 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.

[0028] 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.

[0029] Preferably, in this embodiment, the indoor environment flow model of the room to be designed is constructed according to the environmental feature domain in the following manner, namely: Determining a feature interaction coefficient between each indoor environmental feature in the environmental feature domain; An indoor environmental flow model of the interior to be designed is constructed based on the environmental feature domain and all feature interaction coefficients.

[0030] In the specific implementation, first, the feature interaction coefficient between each indoor environmental feature in the environmental feature domain can be determined, and the feature interaction coefficient is an indicator for measuring the degree of influence between indoor environmental features; then, an indoor environmental flow model of the interior to be designed can be constructed based on the environmental feature domain and all the feature interaction coefficients, that is, the environmental feature domain is used as input data, and each feature interaction coefficient is input as a corresponding weight into the computer-aided design software to construct the indoor environmental flow model of the interior to be designed. The indoor environmental flow model can reflect the relationship between the indoor environmental features to be designed and the dynamic characteristics of the environmental flow.

[0031] In this embodiment, the feature interaction coefficients between the indoor environment features in the environment feature domain may be determined in the following manner, namely: 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.

[0032] In the specific implementation, first, an indoor environment feature is selected in the environmental feature domain, and the feature difference distance between the selected indoor environment feature and other indoor environment features is determined. The feature difference distance represents the degree of difference between the indoor environment features, and the feature difference distance between the selected indoor environment feature and other indoor environment features can be represented by the Euclidean distance; then, the feature interaction coefficient between the selected indoor environment feature and the corresponding indoor environment feature can be determined by the corresponding feature difference distance, that is, the reciprocal of the difference between the corresponding feature difference distance and 1 is used as the feature interaction coefficient between the selected indoor environment feature and the corresponding indoor environment feature. The feature interaction coefficient between each indoor environment feature in the environmental feature domain can be obtained in the above manner.

[0033] It should be noted that converting environmental sensor data into redundant data sets can reduce data redundancy and noise, ensure that the data is more accurate and effective, and improve the reliability of subsequent analysis. Extracting the environmental feature domain of the interior to be designed helps to understand the key factors affecting the indoor environment, thereby making the design more targeted. The indoor environmental flow model constructed based on the environmental feature domain can dynamically simulate indoor environmental changes and help designers optimize spatial layout and functional allocation under different conditions.

[0034] In step S3, historical interior design data and the customer's design expectation text are obtained, and then all text feature data items in the design expectation text are extracted, and each text feature data item is respectively compared with the historical interior design data to obtain the data comparison entropy of each text feature data item during the design comparison.

[0035] In specific implementation, data of past design projects, i.e., historical interior design data, can be extracted from the database of an interior design company or institution. In this application, the historical interior design data is descriptive data used to represent historical interior design plans. Design expectation documents conveyed by customers via e-mail or other documents, i.e., customer's design expectation texts, can be collected.

[0036] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flow chart of determining text feature data items in an embodiment of the present application. In this embodiment, extracting all text feature data items in the expected design text can be specifically implemented by the following steps: First, in step S31, the design expected text is preprocessed to obtain the preprocessed design expected text; Then, in step S32, text feature extraction is performed on the preprocessed design expected text to obtain all text features of the design expected text; Secondly, in step S33, the feature similarity between each text feature is determined; Finally, in step S34, all text features are fused based on the similarities of the respective features, thereby obtaining all text feature data items in the designed expected text.

[0037] In the specific implementation, firstly, the design expected text is preprocessed, that is, the design expected text is cleaned, segmented, stop words are removed and stemming is performed on the design expected text, so as to obtain the preprocessed design expected text, wherein text cleaning is to remove special characters, punctuation marks and extra spaces, word segmentation is to use natural language processing tools to split sentences in the design expected text into single words or phrases, stop word removal is to delete high-frequency words that are irrelevant to text understanding, and stemming is to restore words to their basic form to unify similar words; then, the preprocessed design expected text can be subjected to text feature extraction, that is, the LDA (latent Dirichlet allocation) algorithm is applied to identify First, the themes and associated words in the expected design text are identified, so as to obtain all the text features of the expected design text; secondly, the feature similarity between each text feature can be determined, that is, the similarity between each text feature is calculated using cosine similarity, that is, the feature similarity between each text feature; finally, all the text features are feature fused based on the similarity of each feature, that is, the two text features with feature similarity higher than the set threshold are feature fused to obtain a text feature data item, which is a data item used to represent the key features in the expected design text. All the text feature data items in the expected design text can be obtained in the above manner.

[0038] In this embodiment, each text feature data item is respectively compared with the historical interior design data, and then the data comparison entropy of each text feature data item during the design comparison is obtained in the following manner, namely: Feature alignment is performed on each text feature data item and the historical interior design data, and then design comparison is performed on each text feature data item and the historical interior design data based on a machine learning algorithm, thereby obtaining data comparison entropy of each text feature data item during design comparison.

[0039] In the specific implementation, first, the historical interior design data and the extracted text feature data items are organized into a comparable format to ensure that the two match in structure, so that the features of each text feature data item and the historical interior design data can be aligned; then, a machine learning algorithm (such as decision tree, random forest) can be used to perform a design comparison between each text feature data item and the historical interior design data, that is, each text feature data item is compared with the historical interior design data one by one, and the comparison results are recorded, so that the occurrence probability of each text feature data item in the historical interior design data can be obtained; finally, the data comparison entropy of each text feature data item during the design comparison is determined, and the 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 text feature data item * log occurrence probability of text feature data item. The data comparison entropy of each text feature data item during the design comparison can be obtained in the above manner.

[0040] It should be noted that by extracting the text features of the design expected text, we can better understand customer needs, including style preferences, functional requirements, etc., so as to formulate a design plan that better meets customer expectations. By comparing historical interior design data with text feature data items, we can evaluate the effectiveness of the design plan based on data, and by calculating the data comparison entropy, we can identify the text feature data items that have the greatest impact on design decisions.

[0041] In step S4, the corresponding text feature data items are mapped to the indoor environment flow model according to the data comparison entropy, thereby obtaining a design scheme for the interior to be designed.

[0042] In this embodiment, the corresponding text feature data items are mapped to the indoor environment flow model according to the data comparison entropy, and then the design scheme of the interior to be designed is obtained in the following manner, namely: 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.

[0043] In the specific implementation, first, according to historical data analysis and experience, a comparison entropy threshold is set, which can effectively distinguish important text feature data items from unimportant text feature data items, and can usually be set by the data comparison entropy distribution statistics of historical text feature data items; then, all text feature data items are traversed to screen out text feature data items whose data comparison entropy is higher than the set comparison entropy threshold; then, the screened text feature data items are mapped with 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 the relevant variables in the indoor environment flow model to reflect the customer's design needs, and according to the mapped text feature data items, the settings of the indoor environment flow model are adjusted, such as space layout, airflow distribution, light intensity, etc., to ensure that the model can truly reflect the customer's design expectations. After the mapping is completed, an interior design model that meets the customer's expectations is generated; finally, the interior design model can be used as a design plan for the interior to be designed, and the generated design plan is verified to ensure that it can meet the design goals in actual applications and take into account the availability and comfort of the space.

[0044] It can be seen that in the present application, firstly, converting environmental sensor data into a redundant data set can reduce data redundancy and noise, extracting the environmental feature domain of the interior to be designed is helpful 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 environmental changes; then, by extracting the text features of the expected design text, customer needs can be better understood, including style preferences, functional requirements, etc., so as to formulate a design plan that better meets customer expectations, and by comparing historical interior design data with text feature data items, the effectiveness of the design plan can be evaluated based on 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; finally, according to the data comparison entropy, the corresponding text feature data items are mapped to the indoor environment flow model, which can fully combine the customer's expected design text to generate a design plan for the interior to be designed.

[0045] In summary, the technical solution adopted in this application can effectively integrate and process diversified sensor data, and generate a design plan for the interior to be designed in combination with the customer's design expectation text.

[0046] Embodiment 2 This application provides an intelligent data processing and analysis system suitable for interior design. Figure 4 As shown, this figure is a module structure diagram of the analysis system shown in this embodiment of the present application, and the analysis system includes: The data acquisition module 100 is used to collect the environmental sensor data of the room to be designed; A model building module 200 is used to convert the environmental sensor data into a redundant data set of the room to be designed, perform feature extraction on the redundant data set to obtain an environmental feature domain of the room to be designed, and build an indoor environmental flow model of the room to be designed according to the environmental feature domain; The design comparison module 300 is used to obtain historical interior design data and the client'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 the design comparison; The solution generation module 400 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 solution of the interior to be designed.

[0047] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0049] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

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; The corresponding text feature data items are mapped to the indoor environment flow model according to the data comparison entropy, thereby obtaining a design scheme for the interior to be designed.

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: 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; An indoor environmental flow model of the interior to be designed is constructed based on the environmental feature domain and all feature interaction coefficients.

7. The intelligent data processing and analysis method for interior design as claimed in claim 6, characterized in that: Determining the feature interaction coefficients between the indoor environment features 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.

8. 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.

9. 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.

10. 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 9, 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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