A house fine decoration quality evaluation system and evaluation method

Abstract

The application provides a housing fine decoration quality evaluation system and method, and relates to the technical field of building engineering quality detection. The system is composed of an index system module, a data acquisition module, an AI intelligent analysis module, a quantitative scoring module, a result output module, a whole-process tracing module, a dynamic adjustment module and a calibration module. Key data of fine decoration is obtained through the fusion of visual acquisition and sensor detection. After AI algorithm analysis and processing, the dynamic weight determined based on the analytic hierarchy process is used to realize the quantitative evaluation of the whole-dimensional quality of walls, floors, water and electricity, kitchens and toilets, soft decoration and the like. The evaluation result of the application is consistent with professional detection personnel with a consistency of greater than or equal to 95%, the evaluation efficiency is improved by more than 80% compared with the traditional method, the quantitative scoring precision is less than or equal to 0.5 points, and the application is suitable for various fine decoration houses such as commercial houses, apartments and villas, and provides objective, efficient and traceable quality evaluation basis for owner acceptance, real estate quality control and supervision detection.

Description

Technical Field

[0001] This invention relates to the field of building engineering quality testing technology, specifically to a system and method for evaluating the quality of interior decoration of houses. Background Technology

[0002] Fully furnished apartments have become the mainstream delivery model in the real estate industry, and their quality directly affects the homeowner's living experience and the real estate company's brand reputation.

[0003] Traditional home renovation quality evaluation mainly relies on manual inspection, supplemented by simple tools (ruler, formaldehyde detector), which has many prominent pain points; The evaluation results are highly subjective, with indicators such as wall color difference and soft furnishing compatibility relying on the experience of the testing personnel. This leads to inconsistent evaluation results and can easily cause disputes between homeowners and real estate companies. The indicator system is one-sided: it focuses too much on surface works such as walls and floors, while neglecting key indicators such as hidden works of water and electricity systems, functionality of kitchen and bathroom facilities, and environmental friendliness of soft furnishings, resulting in incomplete evaluation coverage; The evaluation process is inefficient: It takes 2 to 3 hours to manually inspect a 100㎡ fully furnished house, and it cannot achieve large-scale batch inspection, which is not suitable for the needs of real estate companies to deliver in batches. Lack of quantitative standards: The standard adopts a binary judgment of "qualified or unqualified" without precise quantitative scoring, making it impossible to distinguish the differences in quality levels; Lack of traceability: The testing data and evaluation process are not fully recorded, making it difficult to determine responsibility after quality problems occur, and there is a lack of objective evidence; Insufficient testing accuracy: Simple tool testing is greatly affected by human operation, and the testing error of indicators such as wall flatness and tile hollow rate is large, which cannot meet the requirements of high-precision quality control.

[0004] While some existing technologies offer intelligent detection tools for single-stage processes (such as wall defect detection apps), they have not formed an integrated system that combines "full-dimensional indicators + multi-source data collection + AI analysis + quantitative scoring + traceability," thus failing to address the core pain points of traditional evaluation methods.

[0005] Therefore, developing an objective, comprehensive, efficient, and traceable evaluation system and method for the quality of interior decoration of houses has become an urgent need in the field of construction engineering quality control. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a housing interior decoration quality evaluation system and method. It addresses the pain points of traditional housing interior decoration quality evaluation, such as severe subjectivity, incomplete indicator systems, low evaluation efficiency, lack of quantitative standards, and lack of full-process traceability. It innovatively constructs an integrated evaluation system of "multi-dimensional indicator system + AI fusion detection + quantitative scoring + full-process traceability." This solves the problem that while existing technologies may have some intelligent detection tools for single stages, they lack an integrated system of "multi-dimensional indicators + multi-source data collection + AI analysis + quantitative scoring + traceability," thus failing to address the core pain points of traditional evaluation methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A quality evaluation system for interior decoration of houses, the system includes an indicator system module, a data acquisition module, an AI intelligent analysis module, a quantitative scoring module, a result output module, a full-process traceability module, a dynamic adjustment module, and a calibration module; The indicator system module constructs and stores a complete indicator library for evaluating the quality of fine decoration of houses, clarifies the judgment criteria and thresholds of each indicator, and provides a unified and quantitative basis for evaluation. The data acquisition module synchronously acquires image data and physical parameter data of the fully furnished house through multi-source acquisition devices, providing raw materials for AI analysis; The AI ​​intelligent analysis module automatically processes and analyzes the collected raw data, identifies quality defects, determines whether indicators meet the standards, and transforms the raw data into effective information that can be used for scoring. The quantitative scoring module calculates the scores of each level of indicators and the overall score according to unified rules based on indicator weights and AI analysis results, thereby achieving quantitative evaluation of quality. The results output module generates a visual evaluation report that intuitively presents the evaluation results, defect information, and rectification suggestions, making it easy for users to quickly understand and apply them. The end-to-end traceability module records information across the entire evaluation process, forming an immutable data chain that provides a basis for quality responsibility identification and traceability. The dynamic adjustment module adapts and adjusts the weights of indicators according to the housing type and decoration standards to achieve personalized evaluation in different scenarios. The calibration module periodically calibrates the acquisition equipment and AI algorithms to ensure detection accuracy and analysis precision, and to guarantee the stability of the evaluation results.

[0008] Furthermore, the indicator system module includes 5 primary indicators (such as wall engineering, floor engineering, water and electricity system, kitchen and bathroom facilities, and soft furnishing configuration) and 28 secondary indicators, specifying the national standard thresholds for each indicator (such as wall flatness ≤3mm or 2m, formaldehyde concentration ≤0.08mg or m³), ​​providing a unified standard for evaluation; The primary indicators are wall construction (weight 25%~30%), floor construction (weight 20%~25%), water and electricity systems (weight 15%~20%), kitchen and bathroom facilities (weight 15%~20%), and soft furnishing configuration (weight 10%~15%). The secondary indicators include wall flatness, tile hollowness rate, water and electricity pipeline qualification rate, formaldehyde concentration, and soft furnishing suitability. The indicators cover all key aspects of high-end decoration.

[0009] Furthermore, the data acquisition module includes a visual acquisition unit, a sensor unit, and a data synchronization unit; Visual acquisition unit: Composed of a 4K high-definition camera, handheld gimbal, and drone, it acquires images of wall or floor surfaces, exterior images of kitchen and bathroom facilities, and soft furnishing configurations. It supports 360° panoramic shooting with an image resolution of ≥3840×2160, ensuring that defect details are clearly captured. Sensor unit: integrates laser flatness sensor (measurement accuracy ±0.1mm or m), formaldehyde or VOC sensor (detection limit ≤0.01mg or m³), ​​water and electricity detection sensor (voltage accuracy ±1%, water pressure accuracy ±1%), and tile hollow detector, synchronously collecting physical parameter data; Data synchronization unit: Through timestamps and location coordinates, it achieves a one-to-one correspondence between image data and sensor data, ensuring the location accuracy of the analysis results.

[0010] Furthermore, the AI ​​intelligent analysis module includes an image preprocessing unit, a defect recognition unit, and a parameter analysis unit; Image preprocessing unit: performs noise reduction, geometric correction, and brightness equalization on the acquired images to improve the accuracy of subsequent analysis; Defect recognition unit: It uses the YOLOv8 target detection algorithm to identify defects such as wall cracks, hollow tiles, color difference, and scratches. It quantifies the area or length of defects through image segmentation algorithm, with an accuracy rate of ≥98%. Parameter analysis unit: Performs noise reduction and normalization processing on data such as flatness, formaldehyde concentration, and water and electricity parameters collected by sensors, and compares them with the indicator thresholds to determine whether the indicators meet the standards.

[0011] Furthermore, the quantitative scoring module includes a weight calculation unit, a graded scoring unit, and a total score calculation unit; Weight calculation unit: The basic weights are determined based on the Analytic Hierarchy Process (AHP), and the dynamic adjustment module is adapted to the house type. Grading and scoring unit: Secondary indicators are scored from 0 to 10 points according to "compliance status + degree of defect". 10 points are awarded for compliance and no defects, 1 to 2 points are deducted for minor defects, 3 to 5 points are deducted for general defects, and 6 to 10 points are deducted for serious defects. Total score calculation unit: The comprehensive score is calculated according to "first-level indicator score = Σ (second-level indicator score × corresponding weight) and total score = Σ (first-level indicator score × corresponding weight)", with a scoring accuracy of ≤0.5 points.

[0012] Furthermore, the result output module includes a report generation unit, a defect visualization unit, and a rectification suggestion unit; The report generation unit outputs a complete report containing text descriptions, score details, and indicator radar charts. It supports PDF export and printing, and the report generation time is ≤5 minutes. The defect visualization unit marks the location, type, and extent of defects in the report (e.g., "1 hollow spot of 8cm² on the kitchen wall"), and together with the original image, allows users to intuitively locate the problem; The rectification suggestion unit provides targeted suggestions for non-compliant indicators (such as "re-tiling in hollow areas"), offering users actionable directions for quality improvement and enhancing the practicality of the evaluation.

[0013] Furthermore, the end-to-end traceability module includes a data storage unit, a traceability code generation unit, and a query interface unit; The data storage unit uses blockchain technology or an encrypted database to store information throughout the entire process, including data acquisition (such as device number, time, and location), AI analysis (such as algorithm logs and defect judgment criteria), scoring calculation (such as weights and deduction details), and calibration records. The traceability coding generation unit generates a unique traceability code for each house, allowing owners, real estate companies, supervisors and other parties to query the complete evaluation process through the code. The data is tamper-proof, which solves the problem of lack of objective evidence when resolving quality disputes. The query interface unit enables long-term retention of evaluation data, facilitating real estate companies to trace common quality issues in batches of delivered houses and optimize construction processes.

[0014] Furthermore, the dynamic adjustment module includes a scene recognition unit and a weight adaptation unit; The scene recognition unit automatically identifies housing types (such as commercial housing, apartments, and villas) and decoration standards (such as basic decoration, fine decoration, and luxury decoration) without human intervention. The weighting adaptation unit dynamically adjusts the weights of primary indicators. For example, for villas, the weight of kitchen and bathroom facilities is increased to 22% and the weight of soft furnishings is increased to 15%. For commercial housing, the focus is on the basic engineering of walls or floors. This solves the problem of the traditional "one-size-fits-all" evaluation and improves the relevance of the evaluation.

[0015] Furthermore, the calibration module includes a standard sample unit, a device calibration unit, and an algorithm calibration unit; Equipped with standard flatness templates, standard gas samples, and standard water and electricity parameter generators, the sensor accuracy calibration and AI algorithm recognition accuracy calibration are completed every 5 houses tested or when the ambient temperature changes by ≥5℃, ensuring that the detection error is ≤2%.

[0016] Furthermore, a method for evaluating the quality of interior decoration of a house, the method comprising the following steps: Step 1: Evaluation of Preprocessing The indicator system module loads the housing decoration quality evaluation indicator library (including primary and secondary indicators), and the dynamic adjustment module adapts dynamic weights according to housing type (such as commercial housing, apartments, villas) and decoration standards; the calibration module completes equipment and algorithm calibration through standard test samples to ensure data accuracy. Step 2: Multi-source data acquisition The data acquisition module uses visual acquisition units (such as high-definition cameras and drones) and sensor units (such as flatness sensors, formaldehyde sensors, and water and electricity detection sensors) to simultaneously collect image data and physical parameter data of walls, floors, water and electricity systems, kitchen and bathroom facilities, and soft furnishings. Step 3: AI Intelligent Analysis The AI ​​intelligent analysis module processes the collected data. Image data is used to identify surface defects (such as cracks, hollow areas, and color differences) through target detection and image segmentation algorithms. Sensor data is processed by noise reduction and normalization to extract key indicator parameters (such as flatness, formaldehyde concentration, and water and electricity qualification rate). Step 4: Quantitative scoring calculation The quantitative scoring module is based on the indicator weights determined by the analytic hierarchy process and combined with the AI ​​analysis results. It calculates the comprehensive score according to the formula "first-level indicator score = Σ (second-level indicator score × corresponding weight) and total score = Σ (first-level indicator score × corresponding weight)", with a score range of 0 to 10. Step 5: Result Output and Traceability The results output module generates a visual evaluation report, including defect location, score details, and rectification suggestions. The full-process traceability module records the entire chain of information from data collection, analysis, and scoring, forming an immutable quality evaluation data chain.

[0017] This invention provides a system and method for evaluating the quality of interior decoration in residential buildings. It offers the following advantages: 1. This invention provides a quality evaluation system and method for interior decoration of houses. It constructs a complete indicator library of "5 primary indicators + 28 secondary indicators", covering all key aspects of walls, floors, water and electricity, kitchen and bathroom, and soft furnishings. It quantifies quality from multiple dimensions such as surface defects, physical parameters, functionality, and environmental protection, solving the problem of the one-sidedness of traditional evaluation. It integrates 4K visual acquisition and multiple types of sensors, and through YOLOv8 target detection, image segmentation, and sensor data noise reduction algorithms, it realizes the automation and high precision of defect identification and parameter detection, with a detection accuracy of ≥98%, which is 30% higher than the accuracy of manual detection.

[0018] 2. This invention provides a housing decoration quality evaluation system and method. Based on the analytic hierarchy process (AHP), the basic weights of the indicators are determined and dynamically adjusted according to housing type and decoration standards to achieve personalized evaluation in different scenarios. This solves the problem of the "one-size-fits-all" approach in traditional evaluation. The system uses blockchain or encrypted database to store the entire evaluation process, ensuring that the data is tamper-proof and providing objective traceability evidence for quality disputes.

[0019] 3. This invention provides a quality evaluation system and method for interior decoration of houses, which generates a visual report containing defect location, score details and rectification suggestions. It supports export in multiple formats, making the evaluation results intuitive and easy to understand. At the same time, it provides accurate rectification guidance. By regularly calibrating sensors and AI algorithms with standard samples, it ensures that the detection error is ≤2% and the scoring accuracy is ≤0.5 points, thus ensuring the stability and reliability of the evaluation results. Attached Figure Description

[0020] Figure 1 This is the overall architecture diagram of the housing interior decoration quality evaluation system of the present invention; Figure 2 This is a flowchart of the method for evaluating the quality of interior decoration of a house according to the present invention; Figure 3 This is a schematic diagram of the housing interior decoration quality evaluation index system of the present invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0022] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0024] In the description of this embodiment, the terms "upper," "lower," "right," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used only for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first" and "second" are used only for distinction in description and have no special meaning.

[0025] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0026] like Figures 1-3 As shown, this embodiment of the invention provides a housing decoration quality evaluation system, which takes "multi-source data collection - intelligent analysis - quantitative scoring - result output - traceability" as the core process. The modules work together to achieve a full-dimensional, high-precision, and high-efficiency quality evaluation. The system includes an indicator system module, a data collection module, an AI intelligent analysis module, a quantitative scoring module, a result output module, a full-process traceability module, a dynamic adjustment module, and a calibration module. The indicator system module constructs and stores a complete indicator library for evaluating the quality of fine decoration of houses, clarifies the judgment criteria and thresholds of each indicator, and provides a unified and quantitative basis for evaluation. The indicator system module includes 5 primary indicators (such as wall engineering, floor engineering, water and electricity system, kitchen and bathroom facilities, and soft furnishing configuration) and 28 secondary indicators, which clearly define the national standard thresholds for each indicator (such as wall flatness ≤3mm or 2m, formaldehyde concentration ≤0.08mg or m³), ​​providing a unified standard for evaluation; The primary indicators are wall construction (weight 25%~30%), floor construction (weight 20%~25%), water and electricity systems (weight 15%~20%), kitchen and bathroom facilities (weight 15%~20%), and soft furnishing configuration (weight 10%~15%). The secondary indicators include wall flatness, tile hollowness rate, water and electricity pipeline qualification rate, formaldehyde concentration, and soft furnishing suitability. The indicators cover all key aspects of high-end decoration.

[0027] The data acquisition module synchronously acquires image data and physical parameter data of the fully furnished house through multi-source acquisition devices, providing raw materials for AI analysis; The data acquisition module includes a vision acquisition unit, a sensor unit, and a data synchronization unit; Visual acquisition unit: Composed of a 4K high-definition camera, handheld gimbal, and drone, it acquires images of wall or floor surfaces, exterior images of kitchen and bathroom facilities, and soft furnishing configurations. It supports 360° panoramic shooting with an image resolution of ≥3840×2160, ensuring that defect details are clearly captured. Sensor unit: integrates laser flatness sensor (measurement accuracy ±0.1mm or m), formaldehyde or VOC sensor (detection limit ≤0.01mg or m³), ​​water and electricity detection sensor (voltage accuracy ±1%, water pressure accuracy ±1%), and tile hollow detector, synchronously collecting physical parameter data; Data synchronization unit: Through timestamps and location coordinates, it achieves a one-to-one correspondence between image data and sensor data, ensuring the location accuracy of the analysis results.

[0028] The AI ​​intelligent analysis module automatically processes and analyzes the collected raw data, identifies quality defects, determines whether indicators meet the standards, and transforms the raw data into effective information that can be used for scoring. The AI ​​intelligent analysis module includes an image preprocessing unit, a defect recognition unit, and a parameter analysis unit; Image preprocessing unit: performs noise reduction, geometric correction, and brightness equalization on the acquired images to improve the accuracy of subsequent analysis; Defect recognition unit: It uses the YOLOv8 target detection algorithm to identify defects such as wall cracks, hollow tiles, color difference, and scratches. It quantifies the area or length of defects through image segmentation algorithm, with an accuracy rate of ≥98%. Parameter analysis unit: Performs noise reduction and normalization processing on data such as flatness, formaldehyde concentration, and water and electricity parameters collected by sensors, and compares them with the indicator thresholds to determine whether the indicators meet the standards.

[0029] The quantitative scoring module calculates the scores of each level of indicators and the overall score according to unified rules based on indicator weights and AI analysis results, thereby achieving quantitative evaluation of quality. The quantitative scoring module includes a weight calculation unit, a graded scoring unit, and a total score calculation unit; Weight calculation unit: The basic weights are determined based on the Analytic Hierarchy Process (AHP), and the dynamic adjustment module is adapted to the house type. Grading and scoring unit: Secondary indicators are scored from 0 to 10 points according to "compliance status + degree of defect". 10 points are awarded for compliance and no defects, 1 to 2 points are deducted for minor defects, 3 to 5 points are deducted for general defects, and 6 to 10 points are deducted for serious defects. Total score calculation unit: The comprehensive score is calculated according to "first-level indicator score = Σ (second-level indicator score × corresponding weight) and total score = Σ (first-level indicator score × corresponding weight)", with a scoring accuracy of ≤0.5 points.

[0030] The results output module generates a visual evaluation report that intuitively presents the evaluation results, defect information, and rectification suggestions, making it easy for users to quickly understand and apply them. The results output module includes a report generation unit, a defect visualization unit, and a rectification suggestion unit; The report generation unit outputs a complete report containing text descriptions, score details, and indicator radar charts. It supports PDF export and printing, and the report generation time is ≤5 minutes. The defect visualization unit marks the location, type, and extent of defects in the report (e.g., "1 hollow spot of 8cm² on the kitchen wall"), and together with the original image, allows users to intuitively locate the problem; The rectification suggestion unit provides targeted suggestions for non-compliant indicators (such as "re-tiling in hollow areas"), offering users actionable directions for quality improvement and enhancing the practicality of the evaluation.

[0031] The end-to-end traceability module records information across the entire evaluation process, forming an immutable data chain that provides a basis for quality responsibility identification and traceability. The end-to-end traceability module includes a data storage unit, a traceability code generation unit, and a query interface unit; The data storage unit uses blockchain technology or an encrypted database to store information throughout the entire process, including data acquisition (such as device number, time, and location), AI analysis (such as algorithm logs and defect judgment criteria), scoring calculation (such as weights and deduction details), and calibration records. The traceability coding generation unit generates a unique traceability code for each house, allowing owners, real estate companies, supervisors and other parties to query the complete evaluation process through the code. The data is tamper-proof, which solves the problem of lack of objective evidence when resolving quality disputes. The query interface unit enables long-term retention of evaluation data, facilitating real estate companies to trace common quality issues in batches of delivered houses and optimize construction processes.

[0032] The dynamic adjustment module adapts and adjusts the weights of indicators according to the housing type and decoration standards to achieve personalized evaluation in different scenarios. The dynamic adjustment module includes a scene recognition unit and a weight adaptation unit; The scene recognition unit automatically identifies housing types (such as commercial housing, apartments, and villas) and decoration standards (such as basic decoration, fine decoration, and luxury decoration) without human intervention. The weighting adaptation unit dynamically adjusts the weights of primary indicators. For example, for villas, the weight of kitchen and bathroom facilities is increased to 22% and the weight of soft furnishings is increased to 15%. For commercial housing, the focus is on the basic engineering of walls or floors. This solves the problem of the traditional "one-size-fits-all" evaluation and improves the relevance of the evaluation.

[0033] The calibration module periodically calibrates the acquisition equipment and AI algorithms to ensure detection accuracy and analysis precision, and to guarantee the stability of evaluation results. The calibration module includes a standard sample unit, a device calibration unit, and an algorithm calibration unit; Equipped with standard flatness templates, standard gas samples, and standard water and electricity parameter generators, the sensor accuracy calibration and AI algorithm recognition accuracy calibration are completed every 5 houses tested or when the ambient temperature changes by ≥5℃, ensuring that the detection error is ≤2%.

[0034] The method for evaluating the quality of a house's interior decoration includes the following steps: Step 1: Evaluation of Preprocessing Load Indicator Library: The indicator system module loads complete primary or secondary indicators and their corresponding national standard thresholds; Dynamic weighting: The dynamic adjustment module determines the weight allocation based on the housing type (such as commercial housing): wall construction 28%, floor construction 23%, water and electricity system 18%, kitchen and bathroom facilities 19%, and soft furnishing configuration 12%. Equipment and Algorithm Calibration: The calibration module performs dual calibration of the sensor and AI algorithm using a standard flatness template (error ±0.05mm or m) and a standard formaldehyde sample (0.08mg or m³), ​​and records the calibration data.

[0035] Step 2: Multi-source data acquisition Visual acquisition: Operators use handheld 4K high-definition gimbal cameras to acquire images in the order of "walls → floors → kitchen and bathroom → soft furnishings". Drones assist in acquiring images of high walls and ceilings to ensure no blind spots. Sensor data acquisition: Simultaneously use laser flatness sensors to detect the flatness of walls or floors (1 point every 2m, ≥5 points for each wall or floor), formaldehyde sensors to detect indoor formaldehyde or VOC concentration (data collected after closing doors and windows for 12 hours, data collected from 3 different locations), and water and electricity detectors to detect voltage stability, water pressure compliance rate, and pipeline sealing. Data synchronization: The data synchronization unit associates and stores image data with sensor data using timestamps and location coordinates.

[0036] Step 3: AI Intelligent Analysis Image processing: The image preprocessing unit performs noise reduction and geometric correction on the acquired images; Defect identification: The defect identification unit uses the YOLOv8 algorithm to identify wall cracks (length ≥ 0.5cm is considered a defect) and hollow tiles (area ≥ 5cm²). 2 Defects are identified by their color difference (greyscale difference ≥ 10 is considered a defect), and defect parameters are quantified. Parameter Analysis: The parameter analysis unit performs noise reduction processing on the sensor data, calculates the average flatness of the wall or floor and the average formaldehyde concentration, and determines whether the parameters of the water and electricity system are within the standard threshold range.

[0037] Step 4: Quantitative scoring calculation Secondary indicator scoring: Based on the AI ​​analysis results, each of the 28 secondary indicators is scored. For example, if the wall surface is flat and there are no defects, 10 points are awarded. If there is a minor crack, 1 point is deducted, and the score is 9 points. Primary indicator scoring: The score for the primary indicator is calculated according to its weight. For example, the score for wall surface engineering = wall flatness score × 30% + wall color difference score × 25% + wall hollow rate score × 45%; Overall score calculation: Total score = Wall work score × 28% + Floor work score × 23% + Water and electricity system score × 18% + Kitchen and bathroom facilities score × 19% + Soft furnishing configuration score × 12%. The final score is rounded to one decimal place.

[0038] Step 5: Result Output and Traceability Report generation: The results output module generates a visual evaluation report, marking the location, type, and score details of defects, and providing rectification suggestions for non-compliant indicators (e.g., "There are 2 hollow spots on the kitchen wall, it is recommended to re-tile"). Data storage traceability: The full-process traceability module records the acquisition device number, acquisition time, detection location coordinates, AI analysis logs, scoring process, calibration records, and generates a unique traceability code; Results delivery: The evaluation report and traceability code will be delivered to the user, supporting PDF export and printing. Users can query the complete evaluation process through the traceability code.

[0039] Implementation Case: ① Implementation Scenarios The application scenario is "quality evaluation of the interior decoration of a 120㎡ commercial apartment". The decoration standard is "luxury decoration" and the purpose of the evaluation is for the owner's acceptance inspection.

[0040] ②System Configuration Visual acquisition equipment: 4K high-definition gimbal camera (resolution 3840×2160), small drone (supports 4K shooting); Sensor equipment: Laser flatness sensor (measurement accuracy ±0.1mm or m), formaldehyde or VOC sensor (detection limit ≤0.01mg or m) 3 Hydropower Comprehensive Testing Instrument (voltage accuracy ±1%, water pressure accuracy ±1%); AI algorithms: YOLOv8 object detection algorithm (trained with 10,000+ images of defects in interior decoration), analytic hierarchy process weight calculation model; Calibration samples: Standard flatness template (error ±0.05mm or m), standard formaldehyde sample (0.08mg or m) 3 ), standard hydroelectric parameter generator.

[0041] ③ Implementation steps Step 1: Evaluation of Preprocessing Load the indicator library and the weights of commercial housing (walls 28%, floors 23%, water and electricity 18%, kitchen and bathroom 19%, soft furnishings 12%), complete the calibration through standard samples, and after calibration, the sensor detection error is ≤1.5% and the AI ​​defect recognition accuracy is ≥98.5%.

[0042] Step 2: Multi-source data acquisition A handheld gimbal camera captured 120 images of the walls (4 sides), floor, kitchen, bathroom, and soft furnishings (furniture, appliances, curtains); a drone captured 15 images of the ceiling; a laser flatness sensor detected 30 points on the walls or floor; a formaldehyde sensor detected three locations in the living room, master bedroom, and secondary bedroom (collected after 12 hours with doors and windows closed); and a water and electricity detector detected the total voltage, socket voltage stability, water pipe pressure, and pipeline sealing.

[0043] Step 3: AI Intelligent Analysis After image preprocessing, the YOLOv8 algorithm identified one hollow area (8cm²) on the kitchen wall and one minor crack (1.2cm long) on ​​the master bedroom wall, with no other defects. After sensor data processing, the average flatness of the wall or floor was 2.2mm or 2m (compliant), the average formaldehyde concentration was 0.06mg or m³ (compliant), and all water and electricity system parameters met the standards.

[0044] Step 4: Quantitative scoring calculation Secondary indicator scoring: 2 points deducted for hollow walls in the kitchen (out of 8 points), 1 point deducted for cracks in the master bedroom walls (out of 9 points), and all other secondary indicators scored 10 points. Primary indicator scores: Wall work 9.7 points, Floor work 10 points, Water and electricity system 10 points, Kitchen and bathroom facilities 9.5 points, Soft furnishing configuration 10 points; Total score: 9.7×28%+10×23%+10×18%+9.5×19%+10×12%=9.76 points (Excellent level).

[0045] Step 5: Result Output and Traceability Generate a visual evaluation report, marking the location and size of hollow areas in the kitchen walls and cracks in the master bedroom walls, and providing rectification suggestions such as "re-tiling is recommended for hollow areas in the kitchen walls, and repair of cracks in the master bedroom walls with a repair agent"; generate a unique traceability code "JZ-PJ-20250601-001", which users can use to query the entire evaluation data. Implementation effect

[0046] Evaluation results: The overall score was 9.76 (excellent level), which is 99.6% consistent with the average score of 3 professional testers (9.8 points). Evaluation efficiency: The entire process took 28 minutes, an improvement of 81.3% compared to traditional manual inspection (2.5 hours); Accuracy: No defects are missed; the error between sensor detection data and professional equipment detection results is ≤1.2%; scoring accuracy is 0.01 points. Practicality: The homeowner quickly identified two minor defects through the evaluation report, and after addressing them according to the rectification suggestions, the second evaluation scored 10 points, meeting the acceptance requirements.

[0047] The following points should be noted in this article: 1. The accompanying drawings of the embodiments disclosed herein only relate to the structures involved in the embodiments disclosed herein; other structures can be referred to in a general design.

[0048] 2. Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0049] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A system for evaluating the quality of interior decoration of houses, characterized in that, The system includes an indicator system module, a data acquisition module, an AI intelligent analysis module, a quantitative scoring module, a result output module, a full-process traceability module, a dynamic adjustment module, and a calibration module. The indicator system module constructs and stores a complete indicator library for evaluating the quality of fine decoration of houses, clarifies the judgment criteria and thresholds of each indicator, and provides a unified and quantitative basis for evaluation. The data acquisition module synchronously acquires image data and physical parameter data of the fully furnished house through multi-source acquisition devices, providing raw materials for AI analysis; The AI ​​intelligent analysis module automatically processes and analyzes the collected raw data, identifies quality defects, determines whether indicators meet the standards, and transforms the raw data into effective information that can be used for scoring. The quantitative scoring module calculates the scores of each level of indicators and the overall score according to unified rules based on indicator weights and AI analysis results, thereby achieving quantitative evaluation of quality. The results output module generates a visual evaluation report that intuitively presents the evaluation results, defect information, and rectification suggestions, making it easy for users to quickly understand and apply them. The end-to-end traceability module records information across the entire evaluation process, forming an immutable data chain that provides a basis for quality responsibility identification and traceability. The dynamic adjustment module adapts and adjusts the weights of indicators according to the housing type and decoration standards to achieve personalized evaluation in different scenarios. The calibration module periodically calibrates the acquisition equipment and AI algorithms to ensure detection accuracy and analysis precision, and to guarantee the stability of the evaluation results.

2. The housing interior decoration quality evaluation system according to claim 1, characterized in that, The indicator system module includes 5 categories of primary indicators and 28 secondary indicators, and clarifies the national standard thresholds for each indicator, providing a unified standard for evaluation. The primary indicators include wall construction, flooring, water and electricity systems, kitchen and bathroom facilities, and soft furnishings; the secondary indicators include wall flatness, tile hollowness rate, water and electricity pipeline qualification rate, formaldehyde concentration, and soft furnishing suitability. The indicators cover all key aspects of high-end decoration.

3. The housing interior decoration quality evaluation system according to claim 1, characterized in that, The data acquisition module includes a visual acquisition unit, a sensor unit, and a data synchronization unit; Visual acquisition unit: Composed of a 4K high-definition camera, handheld gimbal, and drone, it acquires images of wall or floor surfaces, exterior images of kitchen and bathroom facilities, and soft furnishing configurations. It supports 360° panoramic shooting with an image resolution of ≥3840×2160, ensuring that defect details are clearly captured. Sensor unit: integrates laser flatness sensor, formaldehyde or VOC sensor, water and electricity detection sensor, and tile hollow detector, and synchronously collects physical parameter data; Data synchronization unit: Through timestamps and location coordinates, it achieves a one-to-one correspondence between image data and sensor data, ensuring the location accuracy of the analysis results.

4. The housing interior decoration quality evaluation system according to claim 1, characterized in that, The AI ​​intelligent analysis module includes an image preprocessing unit, a defect recognition unit, and a parameter analysis unit; Image preprocessing unit: performs noise reduction, geometric correction, and brightness equalization on the acquired images to improve the accuracy of subsequent analysis; Defect recognition unit: It uses the YOLOv8 target detection algorithm to identify defects such as wall cracks, hollow tiles, color difference, and scratches. It quantifies the area or length of defects through image segmentation algorithm, with an accuracy rate of ≥98%. Parameter analysis unit: Performs noise reduction and normalization processing on data such as flatness, formaldehyde concentration, and water and electricity parameters collected by sensors, and compares them with the indicator thresholds to determine whether the indicators meet the standards.

5. The housing interior decoration quality evaluation system according to claim 1, characterized in that, The quantitative scoring module includes a weight calculation unit, a graded scoring unit, and a total score calculation unit; Weight calculation unit: The basic weights are determined based on the Analytic Hierarchy Process (AHP), and the dynamic adjustment module is adapted to the house type. Grading and scoring unit: Secondary indicators are scored from 0 to 10 points according to "compliance status + degree of defect". 10 points are awarded for compliance and no defects, 1 to 2 points are deducted for minor defects, 3 to 5 points are deducted for general defects, and 6 to 10 points are deducted for serious defects. Total score calculation unit: The comprehensive score is calculated according to "first-level indicator score = Σ (second-level indicator score × corresponding weight) and total score = Σ (first-level indicator score × corresponding weight)", with a scoring accuracy of ≤0.5 points.

6. The housing interior decoration quality evaluation system according to claim 1, characterized in that, The result output module includes a report generation unit, a defect visualization unit, and a rectification suggestion unit; The report generation unit outputs a complete report containing text descriptions, score details, and indicator radar charts. It supports PDF export and printing, and the report generation time is ≤5 minutes. The defect visualization unit marks the location, type, and severity of defects in the report, and, together with the original image, allows users to intuitively locate the problem; The rectification suggestion unit provides targeted suggestions for non-compliant indicators, offering users actionable directions for quality improvement and enhancing the practicality of the evaluation.

7. The housing interior decoration quality evaluation system according to claim 1, characterized in that, The full-process traceability module includes a data storage unit, a traceability code generation unit, and a query interface unit; The data storage unit uses blockchain technology or an encrypted database to store information from the entire process, including data collection, AI analysis, scoring calculation, and calibration records. The traceability coding generation unit generates a unique traceability code for each house, allowing owners, real estate companies, supervisors and other parties to query the complete evaluation process through the code. The data is tamper-proof, which solves the problem of lack of objective evidence when resolving quality disputes. The query interface unit enables long-term retention of evaluation data, facilitating real estate companies to trace common quality issues in batches of delivered houses and optimize construction processes.

8. The housing interior decoration quality evaluation system and method according to claim 1, characterized in that, The dynamic adjustment module includes a scene recognition unit and a weight adaptation unit; The scene recognition unit automatically identifies the house type and decoration standard without human intervention; The weighting adaptation unit dynamically adjusts the weights of primary indicators. For example, it strengthens the kitchen and bathroom facilities and soft furnishings for villa types, while focusing on the basic wall or floor engineering for commercial housing. This solves the problem of the traditional "one-size-fits-all" evaluation and improves the relevance of the evaluation.

9. The housing interior decoration quality evaluation system and method according to claim 1, characterized in that, The calibration module includes a standard sample unit, a device calibration unit, and an algorithm calibration unit. Equipped with standard flatness templates, standard gas samples, and standard water and electricity parameter generators, the sensor accuracy calibration and AI algorithm recognition accuracy calibration are completed every 5 houses tested or when the ambient temperature changes by ≥5℃, ensuring that the detection error is ≤2%.

10. A method for evaluating the quality of interior decoration of houses, characterized in that, The method includes the following steps: Step 1: Evaluation of Preprocessing The indicator system module loads the housing decoration quality evaluation indicator library, the dynamic adjustment module adapts dynamic weights according to housing type and decoration standards, and the calibration module completes equipment and algorithm calibration through standard test samples to ensure data accuracy. Step 2: Multi-source data acquisition The data acquisition module uses a visual acquisition unit and a sensor unit to simultaneously collect image data and physical parameter data of walls, floors, water and electricity systems, kitchen and bathroom facilities, and soft furnishings. Step 3: AI Intelligent Analysis The AI ​​intelligent analysis module processes the collected data. Image data is used to identify surface defects through target detection and image segmentation algorithms, while sensor data is processed by noise reduction and normalization to extract key indicator parameters. Step 4: Quantitative scoring calculation The quantitative scoring module is based on the indicator weights determined by the analytic hierarchy process and combined with the AI ​​analysis results. It calculates the comprehensive score according to "first-level indicator score = Σ (second-level indicator score × corresponding weight) and total score = Σ (first-level indicator score × corresponding weight)", with a score range of 0 to 10. Step 5: Result Output and Traceability The results output module generates a visual evaluation report, including defect location, score details, and rectification suggestions. The full-process traceability module records the entire chain of information from data collection, analysis, and scoring, forming an immutable quality evaluation data chain.

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