Installation effect evaluation system for home energy storage equipment

Abstract

The present invention discloses an installation effect evaluation system for household energy storage equipment, which relates to the technical field of equipment installation evaluation. The present invention obtains three-dimensional point cloud data, image features, technical parameters and environmental parameters of the installation space through an installation parameter acquisition module, and constructs a structured case database; the comprehensive evaluation module combines aesthetic evaluation and utility evaluation to generate a comprehensive score to evaluate the installation effect; the preferred case library construction module screens high-quality cases, performs feature encoding and constructs a case similarity map, and dynamically maintains a set of top ten cases; the collaborative recommendation module receives new installation requirements, and outputs an optimized installation parameter package through k-nearest neighbor search, parameter fusion optimization and virtual installation verification. The present invention realizes the collaborative optimization of technical performance and humanistic needs, improves the efficiency of installation decision-making, and promotes the development of household energy storage equipment installation towards intelligence and personalization.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment installation evaluation, and in particular to an installation effect evaluation system for household energy storage equipment. Background Art

[0002] With the prevalence of home energy storage devices, the evaluation of their installation effects has become a key link in improving user experience and device performance. Traditional evaluation systems mostly focus on technical parameters (such as device power, heat dissipation efficiency, etc.) and select installation solutions based on static indicators. However, such methods have significant flaws in multi-dimensional comprehensive evaluation and reuse of historical experience: First, there is a lack of quantitative evaluation of the coordination between the installation solution and the home environment, resulting in the equipment meeting functional standards but lacking aesthetics; second, reliance on manual experience-based decision-making makes it difficult to dynamically optimize installation parameters, resulting in repeated trial and error and waste of resources in similar scenarios. Existing technologies have not yet effectively solved the problem of coordinated optimization of technical parameters and human needs, limiting the personalized adaptation capabilities of home energy storage devices.

[0003] Current mainstream solutions suffer from the following core issues: First, there is a lack of an aesthetic evaluation system. Traditional methods lack quantitative models for aesthetic indicators such as decor style matching and color coordination, relying solely on subjective judgments based on visual inspection. This results in inconsistent evaluation results (with an error rate exceeding 40%). For example, they fail to consider the impact of wall material reflectivity on the visual integration of the device's appearance, or ignore the user's personalized aesthetic preferences. Second, case data utilization is low. Historical installation data is stored as isolated cases, lacking an intelligent recommendation mechanism based on similarity retrieval. This results in a reuse rate of less than 15% for high-quality installation solutions. Furthermore, static weight allocation models cannot adapt to dynamic environmental changes (such as furniture layout adjustments and equipment aging), causing evaluation results to lag behind actual needs.

[0004] These deficiencies lead to two major challenges in the installation of home energy storage devices: an imbalance between technical performance and aesthetic requirements, with user satisfaction generally falling short of industry expectations; and inefficient installation decision-making, with an average solution generation time exceeding two hours. While some existing research has attempted to introduce three-dimensional modeling or user rating mechanisms, closed-loop optimization through multi-source data fusion analysis and a self-evolving case library remains unattainable. Therefore, there is an urgent need to develop an intelligent evaluation system that integrates aesthetic assessment, dynamic case matching, and parameter self-optimization to overcome existing technological bottlenecks and promote the intelligent and personalized installation of home energy storage devices.

[0005] In order to solve the above defects, a technical solution is now provided. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems in the existing technology of the lack of multi-dimensional comprehensive evaluation of the installation effect of household energy storage equipment, low utilization of high-quality case data, and difficulty in dynamically optimizing installation parameters, and to propose an installation effect evaluation system for household energy storage equipment.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The installation effect evaluation system for home energy storage equipment includes:

[0009] Installation parameter acquisition module is used to use the equipment to collect 3D point cloud data, image features, technical parameters and environmental parameters of the installation space, and build a structured case database to store the data;

[0010] A comprehensive evaluation module is used to calculate the aesthetic score and utility score through aesthetic evaluation and utility evaluation respectively, and then generate a comprehensive score to evaluate the installation effect of home energy storage equipment;

[0011] Optimize the case library construction module, screen high-quality cases, encode their features, build a case similarity map, and dynamically maintain the top ten case sets to provide reference for recommendations;

[0012] The collaborative recommendation module receives new installation requirements, extracts spatial feature vectors, and outputs the optimized installation parameter package through k-nearest neighbor search, parameter fusion optimization, and virtual installation verification.

[0013] Furthermore, the execution process of the installation parameter acquisition module is as follows:

[0014] Use a laser scanner to obtain 3D point cloud data of the installation space and reconstruct a BIM model including wall structure and furniture layout;

[0015] Use a wide-angle camera to capture a panoramic image of the installation area and extract HSV color space histogram features;

[0016] Record technical parameter sets: including battery pack arrangement, ground clearance, and heat dissipation channel inclination;

[0017] Collect environmental parameters: Use a spectrophotometer to measure the reflectivity of wall materials and the reverberation time of the space;

[0018] Build a structured case database, where each case is stored as a tuple Case = {coordinates (x, y, z), technical parameters T, aesthetic features A, environmental parameters E};

[0019] Finally, semantic data annotation and retrieval are performed.

[0020] Furthermore, the specific steps for semantic data annotation and retrieval in the installation parameter acquisition module are as follows:

[0021] Semantically annotate the stored case data and use the ontology model to define the semantic relationships between the data;

[0022] When building a structured case database, achieve efficient semantic-based retrieval;

[0023] When users can query related cases through natural language descriptions, the search results are sorted according to the degree of matching, with cases with high matching degrees being displayed first;

[0024] Present the case to the user in a visual manner, including: displaying key information of the case in list form, including installation coordinates, technical parameters, aesthetic characteristics and environmental parameter summary.

[0025] Furthermore, the specific process of the comprehensive evaluation module is as follows:

[0026] Aesthetic evaluation: The ResNet-50 model is used to extract deep features of the installation area image. Feature extraction is performed on the input installation area image through multiple convolutional layers, pooling layers, and fully connected layers.

[0027] Calculate the cosine similarity with the preset decoration style template, and assume that the image feature vector of the installation area extracted from the ResNet-50 model is , the preset feature vector of a certain decoration style template is , then the cosine similarity calculation formula between them is: ;in, is a vector and The dot product of and They are vectors and The similarity value range is between [-1,1], and the similarity result is used as the AI ​​score, recorded as ;

[0028] Integrating user subjective ratings, users can view the virtual effects of home energy storage devices in the installation area through the augmented reality interface and rate them from 1 to 5 stars based on their aesthetic feelings. ;

[0029] Output aesthetic score ; Where 0.6 and 0.4 are weight coefficients;

[0030] Utility evaluation: Calculate installation compactness: ;

[0031] Measuring maintenance convenience: The length of the maintenance path is simulated. The A* algorithm calculates the shortest achievable path. When measuring maintenance convenience, the maintenance worker's starting position is used as the starting point, and the part of the home energy storage device that needs maintenance is used as the target point. The shortest achievable path length L calculated by the A* algorithm is the simulated maintenance path length.

[0032] Evaluating thermal management effectiveness: Fluid dynamics simulation is used to calculate the uniformity of air velocity distribution in the heat dissipation channel to evaluate thermal management effectiveness. In fluid dynamics simulation, a physical model of the heat dissipation channel is established, boundary conditions are set, and the Navier-Stokes equations of fluid mechanics are solved to determine the distribution of air velocity within the heat dissipation channel.

[0033] Output utility score: First, standardize the installation compactness C, simulated maintenance path length L, and air flow rate coefficient of variation CV using the linear normalization method: 、 、 ,in are the minimum and maximum values ​​of installation compactness in all evaluated cases, respectively; are the minimum and maximum values ​​of the simulated maintenance path lengths in all evaluated cases, respectively; are the minimum and maximum values ​​of the coefficient of variation of air velocity in all evaluated cases, respectively; are the normalized values ​​of installation compactness C, simulated maintenance path length L, and coefficient of variation CV of air velocity, respectively;

[0034] The utility score calculation formula is: ;in are the dynamic weights of installation compactness, simulated maintenance path length, and air velocity variation coefficient, respectively; and ;

[0035] Generate a composite score: ,in, and is the weight coefficient, and .

[0036] Furthermore, the specific operation steps of the preferred case library construction module are as follows:

[0037] High-quality case screening: Continuously monitor new cases entered into the case database in real time; when each new case is entered into the database, it is judged based on the comprehensive score S given by the comprehensive evaluation module;

[0038] Compare the comprehensive score S with the preset threshold θ; if S>θ, the case is marked as a high-quality case and enters the subsequent processing flow;

[0039] If S≤θ, it will not enter the high-quality case related processing and will only be stored in the case library as an ordinary case for subsequent reference;

[0040] Feature encoding: Dimensionality reduction of three-dimensional spatial coordinates. For high-quality cases, the t-SNE algorithm is used to process the three-dimensional spatial coordinates. The t-SNE algorithm maps high-dimensional data to a low-dimensional space while maintaining the relative distance relationship between data points.

[0041] One-hot encoding of technical parameters: For high-quality case technical parameters T, one-hot encoding is used.

[0042] Constructing a case similarity graph: Based on the encoded high-quality case features, the Mahalanobis distance between the feature vectors of the family scenarios is calculated;

[0043] Set a distance threshold δ. When the calculated Mahalanobis distance d between two cases is less than δ, the two cases are considered similar. At this time, an edge connection is established between the two cases in the case similarity graph to associate them. By continuously calculating and comparing the Mahalanobis distances between all high-quality cases, a graph structure reflecting the case similarity relationship is gradually constructed, making it easier to quickly find similar cases.

[0044] Dynamically maintain the top ten case collection: maintain a collection of only the top ten high-quality cases; use the LRU algorithm to manage the collection;

[0045] Whenever a new high-quality case is added to the collection, when the collection reaches ten cases, the LRU algorithm checks the call time records of each case in the collection; it eliminates old cases that are more than 30 days old and have the lowest frequency of use within 30 days to make room for the new case;

[0046] Through a dynamic maintenance mechanism, we ensure that the top ten case collections always contain the most valuable and high-quality cases, providing case data support that is more in line with actual needs for subsequent collaborative recommendation functions.

[0047] Furthermore, the specific operation steps of the collaborative recommendation module are as follows:

[0048] Receive new installation requirements and extract spatial feature vectors: When there is a new home energy storage device installation requirement, the system first extracts the spatial feature vector from the relevant data of the current home. The data comes from the three-dimensional point cloud data of the installation space obtained by the laser scanner and the panoramic image of the installation area taken by the wide-angle camera;

[0049] Perform k-nearest neighbor search: to extract the spatial feature vector Based on, perform k nearest neighbor search in the case library; calculate The similarity between the spatial feature vectors of all historical cases in the case library can be calculated by using Euclidean distance or cosine similarity. The five most similar historical cases;

[0050] Parameter fusion optimization: For the five most similar historical cases, their technical parameters are weighted averaged. The weight is calculated by multiplying the similarity by the comprehensive score. That is, the higher the similarity between each case and the current requirement and the higher its own comprehensive score, the greater its weight in the weighted average.

[0051] Suppose these five historical cases are ; Five historical cases and The similarities are The comprehensive scores are , the technical parameters are ;

[0052] When analyzing the technical parameters of the battery pack arrangement, assume that the value is series or parallel. When performing weighted average calculation, first express the series and parallel with numerical values, specifically 0 for series and 1 for parallel, and calculate the weighted average. The formula is: ;

[0053] Each technical parameter is calculated by weighted average calculation to finally generate a candidate parameter set ,in The battery pack arrangement is the height above the ground, It is the inclination angle of the heat dissipation channel, which is the parameter combination with the highest weight after considering the similarity and comprehensive score.

[0054] Furthermore, the collaborative recommendation module further includes:

[0055] Virtual installation verification: injecting the generated candidate parameter sets into the digital twin system for conflict detection;

[0056] During the conflict detection process, check whether there is any pipeline collision or safety distance does not meet the requirements of the specification;

[0057] When a problem is detected, parameter adjustment is automatically triggered; the adjustment method is based on preset rules and algorithms to adjust, recalculate and optimize the parameters until the installation requirements are met;

[0058] Output optimized installation parameter package: After virtual installation verification and parameter adjustment, output optimized installation parameter package, which contains the following information:

[0059] Three-dimensional positioning coordinates: Provides the three-dimensional positioning coordinates of the home energy storage device in the installation space;

[0060] Recommended wiring plan: presented as a color-coded path heat map. Different colors represent different wiring paths. The color depth of the heat map reflects the priority or difficulty of the wiring.

[0061] Aesthetic optimization suggestions: Based on the aesthetic evaluation of the installation area and the matching analysis with the preset decoration style, specific aesthetic optimization suggestions are given.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] (1) The present invention integrates functions such as installation parameter collection, comprehensive evaluation, construction of a preferred case library and collaborative recommendation, and innovates the evaluation method of the installation effect of household energy storage equipment. The installation parameter collection module accurately obtains multi-dimensional data of the installation space and builds a structured case database. The comprehensive evaluation module integrates aesthetic and utility evaluation, uses the ResNet-50 model to extract image features, calculates the similarity with the preset style template as an AI score, and combines the user's subjective score to derive an aesthetic score. The utility is evaluated from three aspects: installation compactness, maintenance convenience and thermal management efficiency, and finally a comprehensive score is generated. This series of operations effectively solves the defects of traditional evaluation systems that only focus on technical parameters and lack multi-dimensional comprehensive evaluation, realizes the coordinated optimization of technical performance and humanistic needs, and improves the overall evaluation level of the installation effect of household energy storage equipment.

[0064] (2) In the present invention, the preferred case library construction module continuously screens high-quality cases, constructs a case similarity map through feature coding, and dynamically maintains the top ten case sets to provide support for the collaborative recommendation module; after receiving new installation requirements, the collaborative recommendation module extracts spatial feature vectors, uses k-nearest neighbor search to locate similar cases in the case library, generates candidate parameter sets through parameter fusion optimization, and finally outputs the optimized installation parameter package through virtual installation verification; this process makes full use of historical high-quality cases to achieve intelligent recommendation and optimization of installation parameters, avoids repeated trial and error and waste of resources, significantly improves installation decision-making efficiency, and promotes the development of home energy storage equipment installation towards intelligence and personalization;

[0065] (3) The present invention focuses on the combination of aesthetic evaluation and actual installation effect. Through semantic data annotation and retrieval, users can query related cases through natural language descriptions and present them in a visual way, which is convenient for users to quickly screen and view, thereby improving user participation and satisfaction. In the process of optimizing installation parameters, the aesthetic characteristics of the installation area and the matching degree of the preset decoration style are fully considered, and specific aesthetic optimization suggestions are given to effectively solve the problem that the equipment functionality meets the standards but the aesthetics are insufficient in traditional methods. The installation effect of the home energy storage equipment can better integrate into the home environment while meeting the technical performance requirements, thereby improving users' satisfaction with the installation effect and promoting the development of the home energy storage equipment installation industry in the direction of taking into account both technology and aesthetics. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0067] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0069] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0070] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0071] like Figure 1 As shown, the installation effect evaluation system for household energy storage equipment includes an installation parameter acquisition module, a comprehensive evaluation module, a preferred case library construction module, and a collaborative recommendation module;

[0072] The installation parameter acquisition module uses the equipment to collect the 3D point cloud data, image features, technical parameters and environmental parameters of the installation space, and builds a structured case database to store the data;

[0073] A laser scanner is used to capture 3D point cloud data of the installation space, reconstructing a BIM model that includes wall structure and furniture layout. A wide-angle camera is used to capture panoramic images of the installation area and extract HSV color space histogram features. Technical parameter sets are recorded, including battery pack arrangement (series / parallel), height from the ground (accurate to the millimeter level), and the inclination of the heat dissipation channel. Environmental parameters are collected, including the reflectivity of the wall material (using a spectrophotometer) and the spatial reverberation time (to assess equipment noise propagation).

[0074] Build a structured case database, where each case is stored as a tuple Case = {coordinates (x, y, z), technical parameters T, aesthetic features A, environmental parameters E};

[0075] Semantic Data Annotation and Retrieval: Stored case data is semantically annotated, using ontology models to define semantic relationships between data. This enables efficient semantic-based retrieval when building a structured case database. When users query relevant cases using natural language descriptions, search results are sorted based on the degree of matching, prioritizing cases with high matching scores. Cases are presented to users in a visual manner, such as a list displaying key case information, including installation coordinates, technical parameters, aesthetic features, and environmental parameter summaries, allowing users to quickly filter and view.

[0076] The comprehensive evaluation module calculates the aesthetic score and utility score through aesthetic evaluation and utility evaluation respectively, and then generates a comprehensive score to evaluate the installation effect of home energy storage equipment;

[0077] Aesthetic evaluation: The ResNet-50 model is used to extract deep features of the installation area image. Feature extraction is performed on the input installation area image through multiple convolutional layers, pooling layers, and fully connected layers.

[0078] Calculate the cosine similarity with the preset decoration style template (modern / classical / industrial style), assuming that the installation area image feature vector extracted from the ResNet-50 model is , the preset feature vector of a certain decoration style template is , then the cosine similarity calculation formula between them is: ;in, is a vector and The dot product of and They are vectors and The similarity value range is between [-1, 1]. The closer the value is to 1, the more similar the installation area image is to the preset decoration style template; the closer the value is to -1, the greater the difference between the two. This similarity result is used as the AI ​​score and is recorded as ;

[0079] Integrating user subjective ratings, users can intuitively view the virtual effects of home energy storage devices in the installation area through the augmented reality (AR) interface and rate them from 1 to 5 stars based on their own aesthetic feelings, which is recorded as ; Output aesthetic score ; 0.6 and 0.4 are weight coefficients used to adjust the relative importance of AI ratings and user ratings in aesthetic scoring;

[0080] Utility evaluation: Calculate installation compactness: The higher the installation compactness, the more efficient the equipment is in space utilization.

[0081] Measuring maintenance convenience: The length of the simulated maintenance path is used. The A* algorithm calculates the shortest achievable path. When measuring maintenance convenience, the maintenance personnel's starting position is used as the starting point, and the key parts of the home energy storage device that need maintenance are used as the target points. The shortest achievable path length L calculated by the A* algorithm is the simulated maintenance path length. The shorter the simulated maintenance path length, the smaller the operating space and movement distance required to repair the equipment, and the higher the maintenance convenience.

[0082] Evaluating thermal management effectiveness: Computational fluid dynamics (CFD) simulations are used to calculate the uniformity of air velocity distribution within the heat dissipation channel. In CFD simulations, a physical model of the heat dissipation channel is established, boundary conditions (such as inlet air velocity and temperature) are set, and fluid dynamics equations (such as the Navier-Stokes equations) are solved to determine the distribution of air velocity within the heat dissipation channel.

[0083] Output utility score: First, standardize the installation compactness C, simulated maintenance path length L, and air flow rate coefficient of variation CV using the linear normalization method: 、 、 ,in are the minimum and maximum values ​​of installation compactness in all evaluated cases, respectively; are the minimum and maximum values ​​of the simulated maintenance path lengths in all evaluated cases, respectively; are the minimum and maximum values ​​of the coefficient of variation of air velocity in all evaluated cases, respectively; 、 , are respectively the normalized values ​​of installation compactness C, simulated maintenance path length L, and air velocity variation coefficient CV; the utility score calculation formula is ;in are the dynamic weights of installation compactness, simulated maintenance path length, and air velocity variation coefficient; ;

[0084] Generate a composite score: ,in, and is the weight coefficient, and , initial values ​​α=0.4,β=0.6.

[0085] The optimal case library construction module selects high-quality cases, encodes their features, constructs a case similarity map, and dynamically maintains the top ten case sets to provide reference for recommendations;

[0086] High-quality case screening: Continuously monitor new cases added to the case library in real time. Each new case is evaluated based on the comprehensive score S assigned by the comprehensive evaluation module. This score S is compared with a preset threshold value, θ (the default value is 80). If (S>θ), the case is marked as a high-quality case and enters the subsequent processing flow. If (S≤θ), the case is not processed as a high-quality case and is stored in the case library as an ordinary case for subsequent general reference.

[0087] Feature Encoding: Dimensionality Reduction of Three-Dimensional Coordinates: For cases marked as high-quality, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to process their three-dimensional coordinates. The t-SNE algorithm maps high-dimensional data to a low-dimensional space (in this case, reducing the three-dimensional coordinates to a two-dimensional embedding vector) while minimizing the relative distances between data points for subsequent analysis and visualization.

[0088] One-hot encoding of technical parameters: For high-quality case studies, we use one-hot encoding for the technical parameter T. For example, assuming that "parallel connection_30-degree tilt angle" is one of the possible combinations of technical parameters, we encode it as [0, 1, 0, 0, ..., 1]. In this encoding vector, only the position corresponding to "parallel connection_30-degree tilt angle" is 1; all other positions representing different technical parameter combinations are 0. This encoding converts non-numeric technical parameter data into a numerical vector form that is easy for computer processing and analysis, facilitating subsequent calculation of similarity between cases.

[0089] Constructing a case similarity graph: Based on the encoded high-quality case features, the Mahalanobis distance between the feature vectors of family scenarios is calculated. A distance threshold δ (δ = 0.35) is set. When the calculated Mahalanobis distance d between two cases is less than δ, the two cases are considered similar. At this point, an edge connection is established between the two cases in the case similarity graph to link them. By continuously calculating and comparing the Mahalanobis distances between all high-quality cases, a graph structure reflecting case similarity relationships is gradually constructed, facilitating the rapid search for similar cases.

[0090] Dynamically maintain the top ten case collection: A collection containing only the top ten high-quality cases is maintained. This collection is managed using the LRU (least recently used) algorithm. Whenever a new high-quality case is added to the collection, if the collection is full (reaches ten cases), the LRU algorithm checks the call time records for each case in the collection. Older cases that are more than 30 days old and have the lowest frequency of use (i.e., least recently used) in the past 30 days are eliminated to make room for new cases. This dynamic maintenance mechanism ensures that the top ten case collection always contains the most valuable high-quality cases in recent times, providing case data support that is more tailored to actual needs for subsequent collaborative recommendation functions.

[0091] The collaborative recommendation module receives new installation requirements, extracts spatial feature vectors, and outputs the optimized installation parameter package through k-nearest neighbor search, parameter fusion optimization, and virtual installation verification.

[0092] Receive new installation requirements and extract spatial feature vectors: When there is a new home energy storage device installation requirement, the system first extracts the spatial feature vector from the current home’s relevant data These data come from the three-dimensional point cloud data of the installation space obtained by the laser scanner and the panoramic images of the installation area taken by the wide-angle camera. For example, the laser scanner is used to reconstruct the BIM model containing the wall structure and furniture layout, from which information such as the space size, shape, and obstacle position is extracted. The images taken by the wide-angle camera are used to analyze the color distribution and lighting conditions of the space, and these multi-source information are integrated and feature extracted to finally obtain a vector that can represent the characteristics of the current home installation space. ;

[0093] Perform k-nearest neighbor search: to extract the spatial feature vector Based on this, perform a k-nearest neighbor search (k=5) in the case library; calculate The similarity between the spatial feature vectors of all historical cases in the case library can be calculated by Euclidean distance and cosine similarity. The five most similar historical cases; these historical cases are accumulated from previous installation practices. Their installation solutions and results have been evaluated and verified, and they are of great reference value for current new installation needs.

[0094] Parameter fusion optimization: For the five most similar historical cases found, the system performs a weighted average of their technical parameters. The weight is calculated by multiplying the similarity by the comprehensive score. That is, the higher the similarity between each case and the current demand and the higher its own comprehensive score, the greater its weight in the weighted average. Suppose these five historical cases are ; They are related to The similarities are The comprehensive scores are , the technical parameters are Taking the battery pack arrangement as an example, assuming that it is in series or parallel, when performing weighted average calculation, first represent series and parallel with numerical values ​​(e.g., series is 0, parallel is 1), and then calculate the weighted average. The formula is: ; Calculate each technical parameter by weighted average calculation and finally generate a candidate parameter set ,in The battery pack arrangement is the height above the ground, It is the inclination angle of the heat dissipation channel. The parameter combination with the highest weight after comprehensive consideration of similarity and comprehensive score is considered to be the most suitable parameter combination for the current new installation needs.

[0095] Virtual installation verification: The generated candidate parameter set is injected into the digital twin system for conflict detection. The digital twin system is a virtual model that is highly similar to the actual physical system, simulating various situations in the actual installation process of home energy storage equipment. During the conflict detection process, check whether there are problems such as pipeline collisions or safety distances that do not meet regulatory requirements. For example, simulate the installation location and wiring of the equipment to check whether the pipeline will collide with walls, furniture or other equipment, and whether the safety distance between the equipment and surrounding objects meets relevant standards and specifications. If a problem is detected, parameter adjustment is automatically triggered; the adjustment method is based on preset rules and algorithms to adjust, recalculate and optimize the parameters until the installation requirements are met.

[0096] Output optimized installation parameter package: After virtual installation verification and parameter adjustment, the system outputs the optimized installation parameter package. This parameter package contains the following information:

[0097] Three-dimensional positioning coordinates: Provides precise three-dimensional positioning coordinates of the home energy storage device within the installation space, with an accuracy of up to ±2mm. This ensures accurate placement of the device during installation, avoiding installation issues and subsequent inconvenience caused by positioning deviations. Recommended wiring scheme: Presented as a color-coded path heat map, with different colors representing different wiring paths. The depth of the heat map color reflects the priority or difficulty of the wiring. This visual approach allows installers to clearly understand the recommended wiring method, improving wiring efficiency and accuracy. Aesthetic optimization suggestions: Based on an aesthetic assessment of the installation area and an analysis of its compatibility with the preset decoration style, specific aesthetic optimization suggestions are given. For example, "using a matte shell to improve style matching by 12%" helps users improve the aesthetics of the installation while meeting the functional requirements of the device, allowing it to better integrate into the home environment.

[0098] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The installation effect evaluation system for household energy storage equipment is characterized by: include: Installation parameter acquisition module is used to use the equipment to collect 3D point cloud data, image features, technical parameters and environmental parameters of the installation space, and build a structured case database to store the data; The comprehensive evaluation module is used to calculate the aesthetic score and utility score through aesthetic evaluation and utility evaluation, and then generate a comprehensive score to evaluate the installation effect of the home energy storage device. The specific process of the comprehensive evaluation module is as follows: Aesthetic evaluation: The ResNet-50 model is used to extract deep features of the installation area image. Feature extraction is performed on the input installation area image through multiple convolutional layers, pooling layers, and fully connected layers. Calculate the cosine similarity with the preset decoration style template, and assume that the image feature vector of the installation area extracted from the ResNet-50 model is , the preset feature vector of a certain decoration style template is , then the cosine similarity calculation formula between them is: ;in, is a vector and The dot product of and They are vectors and The similarity value range is between [-1,1], and the similarity result is used as the AI ​​score, recorded as ; Integrating user subjective ratings, users can view the virtual effects of home energy storage devices in the installation area through the augmented reality interface and rate them from 1 to 5 stars based on their aesthetic feelings. ; Output aesthetic score ; Where 0.6 and 0.4 are weight coefficients; Utility evaluation: Calculate installation compactness: ; Measuring maintenance convenience: The length of the maintenance path is simulated. The A* algorithm calculates the shortest achievable path. When measuring maintenance convenience, the maintenance worker's starting position is used as the starting point, and the part of the home energy storage device that needs maintenance is used as the target point. The shortest achievable path length L calculated by the A* algorithm is the simulated maintenance path length. Evaluating thermal management effectiveness: Fluid dynamics simulation is used to calculate the uniformity of air velocity distribution in the heat dissipation channel to evaluate thermal management effectiveness. In fluid dynamics simulation, a physical model of the heat dissipation channel is established, boundary conditions are set, and the Navier-Stokes equations of fluid mechanics are solved to determine the distribution of air velocity within the heat dissipation channel. Output utility score: First, standardize the installation compactness C, simulated maintenance path length L, and air flow rate coefficient of variation CV using the linear normalization method: 、 、 ,in are the minimum and maximum values ​​of installation compactness in all evaluated cases, respectively; are the minimum and maximum values ​​of the simulated maintenance path lengths in all evaluated cases, respectively; are the minimum and maximum values ​​of the coefficient of variation of air velocity in all evaluated cases, respectively; are the normalized values ​​of installation compactness C, simulated maintenance path length L, and coefficient of variation CV of air velocity, respectively; The utility score calculation formula is: ;in are the dynamic weights of installation compactness, simulated maintenance path length, and air velocity variation coefficient, respectively; and ; Generate a composite score: ,in, and is the weight coefficient, and ; Optimize the case library construction module, screen high-quality cases, encode their features, build a case similarity map, and dynamically maintain the top ten case sets to provide reference for recommendations; The collaborative recommendation module receives new installation requirements, extracts spatial feature vectors, and outputs the optimized installation parameter package through k-nearest neighbor search, parameter fusion optimization, and virtual installation verification.

2. The installation effect evaluation system for household energy storage equipment according to claim 1, characterized in that: The execution process of the installation parameter acquisition module is as follows: Use a laser scanner to obtain 3D point cloud data of the installation space and reconstruct a BIM model including wall structure and furniture layout; Use a wide-angle camera to capture a panoramic image of the installation area and extract HSV color space histogram features; Record technical parameter sets: including battery pack arrangement, ground clearance, and heat dissipation channel inclination; Collect environmental parameters: Use a spectrophotometer to measure the reflectivity of wall materials and the reverberation time of the space; Build a structured case database, where each case is stored as a tuple Case = {coordinates (x, y, z), technical parameters T, aesthetic features A, environmental parameters E}; Finally, semantic data annotation and retrieval are performed.

3. The installation effect evaluation system for household energy storage equipment according to claim 1, characterized in that: The specific steps for semantic data annotation and retrieval in the installation parameter acquisition module are as follows: Semantically annotate the stored case data and use the ontology model to define the semantic relationships between the data; When building a structured case database, achieve efficient semantic-based retrieval; When users can query related cases through natural language descriptions, the search results are sorted according to the degree of matching, with cases with high matching degrees being displayed first; Present the case to the user in a visual manner, including: displaying key information of the case in list form, including installation coordinates, technical parameters, aesthetic characteristics and environmental parameter summary.

4. The installation effect evaluation system for household energy storage equipment according to claim 1, characterized in that: The specific operation steps of the preferred case library construction module are as follows: High-quality case screening: Continuously monitor new cases entered into the case database in real time; when each new case is entered into the database, it is judged based on the comprehensive score S given by the comprehensive evaluation module; Compare the comprehensive score S with the preset threshold θ; if S>θ, the case is marked as a high-quality case and enters the subsequent processing flow; If S≤θ, it will not enter the high-quality case related processing and will only be stored in the case library as an ordinary case for subsequent reference; Feature encoding: Dimensionality reduction of three-dimensional spatial coordinates. For high-quality cases, the t-SNE algorithm is used to process the three-dimensional spatial coordinates. The t-SNE algorithm maps high-dimensional data to a low-dimensional space while maintaining the relative distance relationship between data points. One-hot encoding of technical parameters: For high-quality case technical parameters T, one-hot encoding is used. Constructing a case similarity graph: Based on the encoded high-quality case features, the Mahalanobis distance between the feature vectors of the family scenarios is calculated; Set a distance threshold δ. When the calculated Mahalanobis distance d between two cases is less than δ, the two cases are considered similar. At this time, an edge connection is established between the two cases in the case similarity graph to associate them. By continuously calculating and comparing the Mahalanobis distances between all high-quality cases, a graph structure reflecting the case similarity relationship is gradually constructed, making it easier to quickly find similar cases. Dynamically maintain the top ten case collection: maintain a collection of only the top ten high-quality cases; use the LRU algorithm to manage the collection; Whenever a new high-quality case is added to the collection, when the collection reaches ten cases, the LRU algorithm checks the call time records of each case in the collection; it eliminates old cases that are more than 30 days old and have the lowest frequency of use within 30 days to make room for the new case; Through a dynamic maintenance mechanism, we ensure that the top ten case collections always contain the most valuable and high-quality cases, providing case data support that is more in line with actual needs for subsequent collaborative recommendation functions.

5. The installation effect evaluation system for household energy storage equipment according to claim 1, characterized in that: The specific operation steps of the collaborative recommendation module are as follows: Receive new installation requirements and extract spatial feature vectors: When there is a new home energy storage device installation requirement, the system first extracts the spatial feature vector from the current home’s relevant data The data comes from the three-dimensional point cloud data of the installation space obtained by the laser scanner and the panoramic image of the installation area taken by the wide-angle camera; Perform k-nearest neighbor search: to extract the spatial feature vector Based on, perform k nearest neighbor search in the case library; calculate The similarity between the spatial feature vectors of all historical cases in the case library can be calculated by using Euclidean distance or cosine similarity. The five most similar historical cases; Parameter fusion optimization: For the five most similar historical cases, their technical parameters are weighted averaged. The weight is calculated by multiplying the similarity by the comprehensive score. That is, the higher the similarity between each case and the current requirement and the higher its own comprehensive score, the greater its weight in the weighted average. Suppose these five historical cases are ; Five historical cases and The similarities are The comprehensive scores are , the technical parameters are ; When analyzing the technical parameters of the battery pack arrangement, assume that the value is series or parallel. When performing weighted average calculation, first express the series and parallel with numerical values, specifically 0 for series and 1 for parallel, and calculate the weighted average. The formula is: ； Each technical parameter is calculated by weighted average calculation to finally generate a candidate parameter set ,in The battery pack arrangement is the height above the ground, It is the inclination angle of the heat dissipation channel, which is the parameter combination with the highest weight after considering the similarity and comprehensive score.

6. The installation effect evaluation system for household energy storage equipment according to claim 5, characterized in that: The collaborative recommendation module also includes: Virtual installation verification: injecting the generated candidate parameter sets into the digital twin system for conflict detection; During the conflict detection process, check whether there is any pipeline collision or safety distance does not meet the requirements of the specification; When a problem is detected, parameter adjustment is automatically triggered; the adjustment method is based on preset rules and algorithms to adjust, recalculate and optimize the parameters until the installation requirements are met; Output optimized installation parameter package: After virtual installation verification and parameter adjustment, output optimized installation parameter package, which contains the following information: Three-dimensional positioning coordinates: Provides the three-dimensional positioning coordinates of the home energy storage device in the installation space; Recommended wiring plan: presented as a color-coded path heat map. Different colors represent different wiring paths. The color depth of the heat map reflects the priority or difficulty of the wiring. Aesthetic optimization suggestions: Based on the aesthetic evaluation of the installation area and the matching analysis with the preset decoration style, specific aesthetic optimization suggestions are given.

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