A method and system for managing the process of smart home whole-house customization projects.

Abstract

This invention relates to the field of smart home whole-house customization project management technology, and discloses a method and system for managing the process of smart home whole-house customization projects. The method acquires multi-dimensional personnel attribute data of project participants in real time, and combines this with personnel on-duty status and workload to accurately monitor the execution status of key project tasks. When a designated task leader is unable to perform their duties, causing a blockage in a key task, the method can identify the blockage status and collect task requirement information. Based on this, the system can filter and match alternative experts or automatically form multi-person collaborative teams based on task requirements and the professional fields, experience levels, permission scopes, real-time workloads, and on-duty status of candidate personnel. Finally, the blocked task is automatically transferred to the recommended alternative expert or collaborative team, and a remote collaborative work environment is simultaneously set up, recording task processing data and decision results throughout the process, achieving closed-loop control of the project process.

Description

Technical Field

[0001] This invention relates to the field of smart home whole-house customization project management, and more specifically, to a method and system for managing the process of smart home whole-house customization projects. Background Technology

[0002] In the implementation of smart home whole-house customization services, project management systems are widely used for task prioritization and scheduling. These systems typically calculate task priorities based on preset deadlines, project phases, and critical paths manually marked by the project manager. In the early stages of a project, when resources are plentiful and the environment is stable, this effectively guides the execution of routine tasks, ensuring initial coordination across design, procurement, and construction phases.

[0003] However, in actual project implementation, existing systems have failed to fully consider the profound impact of unforeseen circumstances on project processes. For example, when a senior designer responsible for core system integration is temporarily absent due to an emergency, causing critical design tasks to be blocked, existing systems often cannot obtain and interpret the actual on-duty status of key personnel and their substantial impact on critical tasks in real time. The system may only mark the task as "pending" instead of "blocked" or "high-risk delay," resulting in downstream tasks that rely on this approval result, such as material procurement and equipment ordering, not being adjusted in time, forming a critical bottleneck in the project's main workflow. Summary of the Invention

[0004] This invention provides a method and system for managing the process of smart home whole-house customization projects, aiming to solve the problems of project delays, resource waste, and decreased customer satisfaction caused by the deficiencies in the existing smart home whole-house customization project management system in terms of task priority calculation, real-time resource allocation, and cross-departmental information synchronization mechanisms under emergency situations.

[0005] The technical solution of this application is as follows:

[0006] Firstly, this application discloses a method for managing the process of a smart home whole-house customization project, the method comprising:

[0007] Real-time acquisition of multi-dimensional personnel attribute data for each participant in the smart home whole-house customization project. This multi-dimensional personnel attribute data includes the corresponding personnel's professional field, experience level, permission scope, real-time workload, and on-duty status.

[0008] By combining the on-the-job status of each project participant with the key task binding relationship corresponding to the workload, the execution status of key tasks of the project is monitored in real time. When it is detected that the person in charge of a designated task is unable to perform his / her duties normally, the blocking status of the corresponding key task is accurately identified and the requirement information of the key task is collected.

[0009] Based on the requirements of this key task, the job qualifications are matched with the professional fields, experience levels and authority of the candidates. At the same time, the real-time workload and on-duty status of the candidates are linked to screen and match alternative experts, or automatically adapt and form a multi-person collaborative team that meets the requirements of task execution.

[0010] The system automatically transfers critical tasks that are blocked to recommended alternative experts or established collaborative teams, and simultaneously builds a remote collaborative work environment adapted to the task scenario. This supports the alternative experts or collaborative teams to efficiently advance and handle critical tasks, and records all data and final decision results throughout the task processing process, thereby achieving closed-loop management of the project process.

[0011] Furthermore, this application proposes to screen suitable alternative experts, specifically including steps for verifying differences in ideas and resolving disputes:

[0012] Retrieve historical work data of all potential alternative experts with matching qualifications to obtain the design philosophy tendencies of each potential alternative expert;

[0013] By combining the current project requirements with the standards for whole-house customization design, we can accurately compare and identify the differences in design concepts among potential alternative experts.

[0014] When differences in design concepts are identified, a structured input guidance mechanism is activated to guide potential alternative experts to input core design principles in a structured manner.

[0015] Based on the core design principles input by various experts, an intelligent design concept influence map is generated, visually demonstrating the impact of different design concepts on key project dimensions such as project cost, implementation effect, and adaptability.

[0016] Adaptively adjust the priority configuration parameters of project stakeholders, dynamically observe the real-time changes of the influence map of design concepts, and verify the effectiveness of concept adaptation;

[0017] If, after multiple parameter adjustments, the experts still cannot reach a consensus on their design concepts, a summary of the conceptual disputes will be automatically generated, and corresponding intelligent mediation suggestions will be output to match the project scenario, assisting in the closed-loop handling of disputes.

[0018] More specifically, in some implementation schemes, the design philosophy preferences of potential alternative experts are obtained, including:

[0019] Collect and analyze historical project work records of potential replacement experts to identify the project name, smart home subsystem type, and key information of the actual application scenario of the corresponding historical projects.

[0020] Based on the identified project scenarios and system type information, historical text data is refined and archived.

[0021] Keyword extraction and deep semantic analysis are performed on each segment of historical text after classification to quantify the design concept tendency score in each scenario.

[0022] Link expert identity information, store the distribution data of the tendency scores of each potential alternative expert in different project backgrounds and subsystem scenarios, and establish a database of personal ideological characteristics;

[0023] For currently pending critical tasks that are blocked, match the corresponding scenario and subsystem type, and accurately call the tendency score in the corresponding context as the matching basis;

[0024] If there is no matching historical scenario data for the current task, the bias scores of multiple highly relevant backgrounds are retrieved and weighted averaged to obtain the equivalent bias score suitable for this task.

[0025] Building upon the above, this application further proposes identifying differences in design philosophies among members based on their design concept preferences, including:

[0026] Get the task type and customer's personalized requirements of the currently blocked task, and retrieve the preset project priority weight configuration rules;

[0027] Based on the type of smart home subsystem corresponding to the current task, the system calls the preset evaluation dimensions and default weight parameters of the design concept adapted to the subsystem.

[0028] By combining the project priority weight configuration with the subsystem default weight, the design concept tendency scores of each potential alternative expert are adaptively corrected.

[0029] Based on the revised propensity score, the degree of difference in design concepts among experts was recalculated to accurately pinpoint the points of conflict and the degree of difference in concepts.

[0030] Preferably, this application also proposes guiding potential alternative experts to input core design principles in a structured manner, including:

[0031] Obtain the task type and complexity level of the current critical task, and adaptively generate a multi-dimensional design principle decomposition framework that is suitable for this task.

[0032] Based on the decomposition framework, potential alternative experts are guided to select options or input custom content for the parameters of each sub-dimension within the framework.

[0033] Real-time collection of expert input, simultaneous semantic analysis and intelligent matching, and real-time push of contextual semantic prompts to help standardize input content;

[0034] Perform global consistency checks and cross-dimensional conflict pre-analysis on all input core design principles;

[0035] When logical conflicts or rule contradictions are detected in the input content, real-time content clarification feedback is issued to guide experts to make corrections and adjustments.

[0036] By combining the current task type with the client's core needs, we accurately push high-quality historical reference cases of similar scenarios to assist experts in unifying design standards.

[0037] Building upon the above, this application further proposes pre-analysis of global consistency detection and cross-dimensional conflict, including:

[0038] Retrieve the complete list of subsystems, integration interface specifications of each subsystem, and device data exchange standards for the current smart home whole-house customization project;

[0039] For the design principles of the inputs within a single subsystem, conduct local logical consistency checks and investigate rule inconsistencies within the subsystem.

[0040] Identify the relationships and dependencies between the design principles of different subsystems, and analyze the cross-subsystem linkage logic;

[0041] Conduct a comprehensive chain reaction analysis on the identified cross-subsystem dependencies to predict the systemic impact of rule adjustments;

[0042] If a conflict of principles is detected across systems or within a system, an intelligent warning will be issued, accurately marking the subsystems involved in the conflict, the conflicting principle entries, and the corresponding chain of negative impacts.

[0043] Based on the above, this application further proposes that the method also includes:

[0044] Obtain the real-time data transmission paths, node device processing capacity parameters, and communication protocol standards of each smart home subsystem in the project;

[0045] By combining equipment operating parameters and transmission rules, the data transmission latency and business processing latency of each subsystem are accurately calculated.

[0046] Based on data transmission latency, business processing latency, and system linkage, a dynamic simulation environment for whole-house smart home is constructed.

[0047] In a dynamic simulation environment, the system data flow and control flow operation states after different design principle adjustments are simulated, and the impact of design adjustments on various performance indicators of other related subsystems is observed.

[0048] The system compares the various performance indicators obtained from the simulation with the preset thresholds in real time. When the performance indicators exceed the legal threshold range, it is determined that there is a systemic conflict. The current system operating status and core parameters are recorded synchronously for subsequent source tracing analysis.

[0049] Based on the above, this application further proposes that the method also includes: identifying conflicts;

[0050] Multi-dimensional correlation analysis was conducted on multiple performance indicators that exceeded preset thresholds to uncover common triggering factors and systemic cascading effects that caused the anomalies.

[0051] Based on common triggering factors and cascading impact links, a complete conflict event propagation chain is constructed, clearly showing the conflict's starting point, the layer-by-layer propagation path, and the final scope of impact.

[0052] Conflicts are classified and categorized according to their scope of impact, severity, and difficulty of remediation, and then matched with corresponding priority levels.

[0053] The conflict characteristic information is associated with and stored uniformly in the project subsystem operation logs, device network traffic data, and hardware device status data.

[0054] Based on the full amount of data stored in the associated database, a root cause analysis report of the conflict is automatically generated, providing data support for conflict remediation and process optimization.

[0055] Based on the above, this application further proposes a multi-dimensional correlation analysis, including:

[0056] Continuous real-time data streams of performance indicators from various smart home subsystems are collected in real time to construct a monitoring dataset for all time periods;

[0057] Perform refined time series analysis on real-time data streams to accurately identify instantaneous abnormal peak data, and perform data smoothing and noise reduction processing to eliminate invalid interference caused by occasional fluctuations in equipment;

[0058] Periodically sample the performance index data after noise reduction and smoothing, and compare the sampled data with the system's preset baseline data item by item to accurately identify the continuous data deviation state;

[0059] For performance indicators that exhibit persistent deviations, conduct multi-dimensional correlation analysis to accurately identify the intrinsic relationship between indicator anomalies and interaction patterns of specific subsystems and devices;

[0060] By combining the magnitude of the deviation, the duration of the deviation, and the scope of the correlation, it is determined whether the current persistent deviation is a common triggering factor or a cascading effect result corresponding to a systemic conflict.

[0061] Secondly, this application also discloses a smart home whole-house customization project process management system for executing any of the above-mentioned smart home whole-house customization project process management methods. The system includes:

[0062] The information acquisition module is used to collect and acquire multi-dimensional personnel attribute data of project participants in real time, including their professional fields, experience levels, scope of authority, real-time workload, and on-duty status, so as to provide data support for subsequent personnel matching and task scheduling.

[0063] The task monitoring module communicates with the information acquisition module and is used to monitor the project task execution status in real time by combining the key task binding relationship between personnel on-duty status and workload. When the designated person in charge is unable to perform their duties, the module can identify the key task blocking status and extract the key task requirement information.

[0064] The expert screening module communicates with the task monitoring module and is used to complete the qualification screening based on key task requirements information, combined with personnel's professional fields, experience levels, and authority scope. It also links the workload and on-the-job status of candidate personnel to intelligently recommend the best alternative experts or automatically form a compliance collaboration team.

[0065] The task assignment and recording module communicates with the expert screening module to automatically assign critical tasks that are blocking the project to recommended alternative experts or collaborative groups, build a dedicated collaborative working environment to support task processing, record task processing data and final decision results throughout the process, and achieve closed-loop management of project processes.

[0066] This technical solution provides an intelligent management system that integrates information acquisition, task monitoring, expert screening, and task transfer and recording. It effectively supports the process management of smart home whole-house customization projects, improves project execution efficiency, and enhances the ability to respond to emergencies.

[0067] Beneficial effects

[0068] The smart home whole-house customization project process management method and system disclosed in this application can accurately monitor the execution status of key project tasks by acquiring multi-dimensional personnel attribute data of project participants in real time and combining it with personnel on-duty status and workload. When it is detected that the designated task leader is unable to perform their duties, causing the key task to be blocked, the method can identify the blocking status and collect task requirement information. On this basis, the system can screen and match alternative experts or automatically form multi-person collaborative teams based on task requirements and the professional fields, experience levels, permission scope, real-time workload and on-duty status of alternative personnel. Finally, the blocked task is automatically transferred to the recommended alternative expert or collaborative team, and a remote collaborative working environment is set up simultaneously, recording task processing data and decision results throughout the process, realizing closed-loop control of the project process. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating a smart home whole-house customization project process management method provided in an embodiment of the present invention;

[0070] Figure 2 This is a flowchart of a method for screening and matching alternative experts provided by an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram of the structure of a smart home whole-house customization project process management system provided in an embodiment of the present invention. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Reference Figure 1 , Figure 1 This is a flowchart illustrating a method for managing the process of a smart home whole-house customization project provided by an embodiment of the present invention. The method includes:

[0074] S11, real-time acquisition of multi-dimensional personnel attribute data of each participant in the smart home whole-house customization project. The multi-dimensional personnel attribute data includes the corresponding personnel's professional field, experience level, permission scope, real-time workload and on-duty status.

[0075] S12, combining the on-the-job status of each project participant with the key task binding relationship corresponding to the workload, monitor the execution status of key tasks in real time, and when it is detected that the designated task leader cannot perform his / her duties normally, accurately identify the blocking status of the corresponding key task and collect the requirement information of the key task.

[0076] S13. Based on the requirements of the key tasks, the job qualification matching is completed by combining the professional fields, experience levels and authority of the candidates. At the same time, the real-time workload and on-duty status of the candidates are linked to screen and match alternative experts, or automatically adapt and form a multi-person collaborative team that meets the task execution requirements.

[0077] S14 automatically transfers critical tasks that are blocked to recommended alternative experts or established collaborative teams, and simultaneously builds a remote collaborative work environment adapted to the task scenario to support alternative experts or collaborative teams in efficiently advancing and handling critical tasks. It records the entire process of task processing data and final decision results to achieve closed-loop management of project processes.

[0078] This application aims to effectively address unforeseen circumstances and personnel changes in smart home whole-house customization projects through real-time and intelligent management methods, ensuring smooth project processes and improving project management efficiency and customer satisfaction.

[0079] To better understand the smart home whole-house customization project process management method proposed in this application, we will first explain some key terms involved.

[0080] "Multi-dimensional personnel attribute data" refers to a comprehensive dataset describing project participants, encompassing multiple dimensions such as professional field, experience level, access scope, real-time workload, and on-duty status. Specifically, "professional field" refers to a person's technical or business expertise, such as intelligent lighting, HVAC, or security monitoring; "experience level" reflects a person's level of expertise in that field, ranging from junior to intermediate to senior; "access scope" limits a person's operational permissions and the range of data they can access within the project management system; "real-time workload" indicates the amount of work and workload a person currently undertakes; and "on-duty status" indicates whether a person is in a normal working state, such as "online," "offline," or "on leave." This data forms the basis for accurate personnel matching and task scheduling.

[0081] "Key tasks" refer to tasks in smart home whole-house customization projects that have a significant impact on project progress, quality, or cost, and their completion directly affects the overall progress of the project.

[0082] "Blocked status" refers to a state in which a critical task cannot proceed because the designated person in charge is unable to perform their duties properly.

[0083] "Alternative experts" refers to professionals who can take over and effectively handle tasks when the person in charge of a critical mission is unable to perform their duties, as selected intelligently by the system.

[0084] A "multi-person collaboration team" refers to a team of multiple candidates who work together to complete a critical task that is currently blocked.

[0085] A “remote collaborative work environment” refers to a virtual workspace that provides online collaboration tools and resources to replace experts or collaborative groups, enabling them to handle tasks efficiently across geographical boundaries.

[0086] The core of the smart home whole-house customization project process management method proposed in this application lies in effectively addressing uncertainties in project execution through real-time data-driven and intelligent decision-making.

[0087] During the project initiation phase, it's crucial to acquire multi-dimensional personnel attribute data for all participants in the smart home whole-house customization project in real time. This data forms the basis for subsequent personnel matching and task scheduling. For example, this can be achieved by automatically synchronizing personnel information through integration with an enterprise's internal Human Resource Management (HRM) or Project Management Information System (PMIS). Furthermore, individual profiles can be created within the project management platform, allowing project participants to self-fill and update information such as their professional fields and experience levels. Real-time workload and on-duty status can be obtained by integrating attendance systems, scheduling tools, or through the status update function on the project management platform. For instance, when a designer logs into the project management system and begins processing tasks, their on-duty status can automatically update to "online," and their workload can be calculated based on their currently assigned tasks.

[0088] During project execution, it's necessary to combine the on-duty status of each project participant with the critical task binding relationship corresponding to their workload to monitor the execution status of key tasks in real time. When a designated task leader is detected as unable to perform their duties, the system accurately identifies the blocked status of the corresponding key task and collects the requirement information for that key task. For example, the system can continuously monitor the on-duty status of task leaders. If a designer responsible for intelligent lighting system design is "offline" for an extended period, and the key design task under their responsibility has not been updated for a long time, the system can determine that the task may be blocked. At this time, the system will automatically trigger a task requirement information collection process, for example, guiding the project manager or relevant personnel to input detailed information such as the specific requirements of the design task, the progress completed, and the required resources through a pop-up form.

[0089] Once a critical task is identified as being blocked and complete requirement information is collected, the system will match job qualifications based on the task's requirements, combined with the professional fields, experience levels, and access permissions of candidate personnel. Simultaneously, it will consider the real-time workload and on-duty status of candidate personnel to screen suitable alternative experts or automatically adapt and form a multi-person collaborative team that meets the task execution requirements. For example, for a blocked intelligent security system integration task, the system will first screen all candidate personnel based on the task requirements, selecting those with a professional field of "security system integration," at least an "intermediate" experience level, and "system configuration" permissions. Based on this, the system will further consider the real-time workload of these candidate personnel, prioritizing those with lower workloads or available time. The system will also consider the on-duty status of candidate personnel to ensure that recommended personnel can immediately begin work. If a single alternative expert cannot fully meet the task requirements or the task is highly complex, the system will intelligently form a multi-person collaborative team composed of experts from different professional fields based on multiple sub-requirements of the task. For example, a team might include a security expert, a network engineer, and a software developer.

[0090] Finally, critical tasks in a blocked state are automatically transferred to recommended alternative experts or established collaboration teams. Simultaneously, a remote collaborative work environment adapted to the task scenario is set up to support the alternative experts or collaboration teams in efficiently advancing and handling critical tasks. The entire task processing data and final decision results are recorded throughout the process, achieving closed-loop management of the project workflow. For example, once an alternative expert or collaboration team is identified, the system automatically transfers detailed information about the blocked task, relevant documents, and historical communication records to them. At the same time, the system automatically configures a remote collaborative work environment based on the task type, such as integrating online document collaboration tools, video conferencing systems, and code version management tools, ensuring that the alternative expert or collaboration team can seamlessly take over and work efficiently. During task processing, the system records every operational step, communication content, document modification history, and final decision results. This data will be used for subsequent project reviews, experience accumulation, and process optimization, thereby achieving closed-loop management of the project workflow.

[0091] The smart home whole-house customization project process management method proposed in this application forms an efficient and intelligent project management closed loop through the close cooperation of the above-mentioned links. When key tasks in the project are hindered due to personnel changes, the system can respond quickly, accurately identify the problem, and intelligently match or assemble suitable alternative resources.

[0092] refer to Figure 2 , Figure 2 This is a flowchart of a method for screening and matching alternative experts provided in an embodiment of the present invention. S13 includes:

[0093] S131, retrieve the historical work data of all potential alternative experts with matching qualifications, and obtain the design concept tendency characteristics of each potential alternative expert;

[0094] S132, combining the current project task requirements with the whole-house customization design standards, accurately compares and identifies the differences in design concepts among potential alternative experts;

[0095] S133, When a difference in design philosophy is identified, a structured input guidance mechanism is activated to guide potential alternative experts to input the core design principles in a structured manner;

[0096] S134, based on the core design principles input by various experts, intelligently generates a design concept influence map, visually displaying the impact relationship between the design concept and key project dimensions such as project cost, implementation effect, and adaptability.

[0097] S135, Adaptively adjust the priority configuration parameters of project stakeholders, dynamically observe the real-time changes of the design concept's influence map, and verify the concept's adaptation effect;

[0098] S136 If, after multiple parameter adjustments, the experts still cannot reach a consensus on their design concepts, a summary of the conceptual disputes will be automatically generated, and corresponding intelligent mediation suggestions will be output to match the project scenario, assisting in the closed-loop handling of disputes.

[0099] Specifically, the concept difference verification refers to identifying the similarities and differences in core concepts, style preferences, and technology choices among different potential alternative experts in smart home design through systematic analysis and comparison. The historical work data can be understood as textual and non-textual data such as design schemes, technical documents, customer feedback, and project summary reports from past smart home projects participated in by the expert. The purpose is to construct a profile of the expert's individual design concepts through data mining and analysis. The design concept tendency characteristics are quantitative indicators extracted from historical work data that reflect the expert's preference or style characteristics in specific design dimensions (e.g., minimalism, technological feel, practicality, energy conservation, and environmental protection).

[0100] Furthermore, the precise comparison and identification of design concept differences among potential alternative experts refers to the system performing cross-analysis and quantitative evaluation of the acquired expert design concept tendency characteristics based on preset smart home whole-house customization design standards (e.g., industry norms, company standards, customer-specific requirements, etc.) and the specific requirements of the current project task, thereby calculating the degree of difference in concepts among experts and locating specific conflict points.

[0101] When differences in design philosophies are identified, the structured data entry guidance mechanism aims to provide a standardized interface or process. This guides experts to input their core design principles for the task in a unified, easily parseable format (e.g., checkboxes, dropdown menus, text input boxes combined with keyword prompts). This includes their design focus and technology preferences for subsystems such as intelligent lighting, environmental control, and security monitoring. The goal is to transform the experts' subjective design intentions into quantifiable and comparable data, laying the foundation for subsequent analysis and adjustments.

[0102] The design concept impact map can be understood as a visualization tool that graphically represents the potential impact of different design concepts on key project dimensions (such as total project cost, visual presentation of the final result, system compatibility with user habits, and construction difficulty), for example, through node diagrams, heatmaps, or radar charts. Its purpose is to help project teams and stakeholders intuitively understand the advantages and disadvantages of different design concepts, thus aiding in decision-making.

[0103] In practical applications, the adaptive adjustment of project stakeholder priority configuration parameters refers to dynamically adjusting the importance weights of different project dimensions (e.g., cost control, aesthetics, functionality, construction period, etc.) based on the actual project situation and customer needs. For example, if the customer values ​​cost control more, the system will correspondingly increase the weight of the cost dimension and reassess the impact of the design concept. The purpose is to verify the adaptability of the design concept and find the optimal balance point by simulating the impact under different priorities.

[0104] If, after multiple parameter adjustments, the experts still cannot reach a consensus on their design concepts, the "Concept Dispute Summary" is a report automatically generated by the system. It clearly summarizes the core issues of the dispute, the experts involved, the main viewpoints of each party, and the potential impact on the project. Simultaneously, the "Intelligent Mediation Suggestions" are specific strategies or solutions provided by the system based on historical cases, industry best practices, and an expert knowledge base to resolve the current dispute. For example, it may suggest adopting a compromise solution, introducing third-party review, or adjusting task allocation. Its purpose is to assist the project team in efficiently resolving disputes and preventing project stagnation.

[0105] This application's solution effectively addresses the potential conflicts arising from neglecting design concept differences in traditional expert selection methods by introducing a concept difference verification and dispute mediation process. Through this technical solution, the application significantly improves the accuracy and effectiveness of expert selection for smart home whole-house customization projects. The solution not only considers the objective qualifications of experts but also delves into the compatibility of their design concepts, effectively avoiding initial project disagreements and subsequent rework caused by incompatible design philosophies. By using visualized impact maps and priority parameter adjustments, the project team can more comprehensively assess the potential impact of different design concepts and make more informed decisions. Furthermore, when disputes arise, the system's intelligent mediation suggestions assist the project team in efficiently resolving conflicts, shortening the decision-making cycle, and ensuring the smooth progress of the project. This not only improves the efficiency and quality of project management but also significantly enhances customer satisfaction and reduces project risks and costs.

[0106] Specifically, the design philosophy preferences for obtaining potential alternative experts can be carried out in the following ways.

[0107] The design philosophy bias for acquiring potential alternative experts includes:

[0108] Collect and analyze historical project work records of potential replacement experts to identify the project name, smart home subsystem type, and key information of the actual application scenario of the corresponding historical projects.

[0109] Based on the identified project scenarios and system type information, historical text data is refined and archived.

[0110] Keyword extraction and deep semantic analysis are performed on each segment of historical text after classification to quantify the design concept tendency score in each scenario.

[0111] Link expert identity information, store the distribution data of the tendency scores of each potential alternative expert in different project backgrounds and subsystem scenarios, and establish a database of personal ideological characteristics;

[0112] For currently pending critical tasks that are blocked, match the corresponding scenario and subsystem type, and accurately call the tendency score in the corresponding context as the matching basis;

[0113] If there is no matching historical scenario data for the current task, the bias scores of multiple highly relevant backgrounds are retrieved and weighted averaged to obtain the equivalent bias score suitable for this task.

[0114] Among them, collecting and parsing the historical project work text records of potential replacement experts refers to the system automatically collecting and processing relevant text data of smart home whole-house customization projects that experts have participated in in the past from channels such as project management platforms, document libraries, and email systems, such as design plans, communication records, and technical reports.

[0115] By using Natural Language Processing (NLP) technology, we can identify key information in these texts, such as project names, types of smart home subsystems (e.g., lighting, security, audio-visual, environmental control), and actual application scenarios (e.g., villas, apartments, commercial spaces), with the aim of providing a raw data foundation for subsequent design concept analysis.

[0116] Furthermore, based on the identified project scenarios and system type information, the historical text data is refined into categories and archives. This refers to the structured organization and classification of the collected text data according to its project scenario and smart home subsystem type. For example, all texts about "villa lighting system design" can be grouped into one category, and texts about "apartment security system design" can be grouped into another category. The purpose is to facilitate accurate analysis of specific scenarios and subsystems in the future.

[0117] Building upon this foundation, keyword extraction and deep semantic analysis are performed on each categorized historical text segment to quantify design philosophy preference scores for each scenario. Specifically, the system utilizes text mining techniques to extract keywords representing design style, technological preferences, and cost control tendencies from the categorized text. Simultaneously, a deep learning model is used to perform semantic analysis on the text, understanding the design philosophies and preferences expressed by experts in different scenarios and quantifying them into specific preference scores, such as the degree of preference for "minimalism" or the inclination towards "high-tech integration." The aim is to transform abstract design philosophies into calculable and comparable numerical values.

[0118] Furthermore, by linking expert identity information, the system stores the distribution data of the preference scores of each potential alternative expert in different project backgrounds and subsystem scenarios, establishing a personal philosophy characteristic database. This means that the system will bind the quantified design philosophy preference scores with the corresponding expert identity information and record the distribution of the expert's preference scores in different project types and different smart home subsystem designs, thereby constructing a comprehensive personal philosophy characteristic database. The purpose is to provide personalized philosophy references for subsequent expert matching.

[0119] For currently pending critical tasks that are causing obstacles, the system matches the corresponding scenarios and subsystem types, and accurately uses the relevant propensity scores as the matching criteria. Specifically, when a new obstacle task arises, the system first analyzes the project scenario and smart home subsystem type to which the task belongs. Then, it precisely retrieves and uses the historical propensity scores of experts that best match the current task context from the personal philosophy characteristic database as the evaluation criteria. The purpose is to ensure the accuracy and relevance of the philosophy matching.

[0120] As a preferred implementation, if there is no matching historical scenario data for the current task, the system retrieves the propensity scores from multiple highly relevant backgrounds and performs a weighted average calculation to obtain an equivalent propensity score suitable for the current task. This means that when an expert does not have direct historical data for a specific task scenario, the system will not simply give up, but will intelligently identify other historical scenarios highly relevant to the current task and perform a weighted average of the propensity scores in these scenarios to generate an equivalent propensity score that represents the expert's design philosophy in this type of task. The purpose is to improve the coverage and flexibility of philosophy matching.

[0121] The proposed solution systematically collects and analyzes the historical project work records of potential replacement experts, thereby gaining a comprehensive understanding of the experts' design practices and conceptual expressions in past projects.

[0122] In some of the embodiments described above in this application, a method for screening and matching alternative experts is proposed, specifically including steps for verifying differences in design philosophies and mediating disputes. This requires accurately comparing and identifying the differences in design philosophies among potential alternative experts. To identify these differences in design philosophies more accurately and precisely, this application further proposes a specific method for identifying differences in design philosophies among members based on their design philosophies tendencies.

[0123] The above-mentioned identification of design philosophy differences among members based on design philosophy preferences includes:

[0124] Get the task type and customer's personalized requirements of the currently blocked task, and retrieve the preset project priority weight configuration rules;

[0125] Based on the type of smart home subsystem corresponding to the current task, the system calls the preset evaluation dimensions and default weight parameters of the design concept adapted to the subsystem.

[0126] By combining the project priority weight configuration with the subsystem default weight, the design concept tendency scores of each potential alternative expert are adaptively corrected.

[0127] Based on the revised propensity score, the degree of difference in design concepts among experts was recalculated to accurately pinpoint the points of conflict and the degree of difference in concepts.

[0128] Specifically, when identifying differences in design philosophies, the first step is to obtain detailed information about the currently blocked task, such as the task type and the client's personalized needs. This information is crucial for understanding the task context and client expectations. Simultaneously, the system will retrieve preset project priority weighting rules, which define the importance of different factors in project decisions. For example, some projects may focus more on cost control, while others may prioritize user experience or technological advancement.

[0129] Secondly, based on the type of smart home subsystem involved in the current task, the system will invoke preset evaluation dimensions and default weight parameters tailored to that subsystem's design philosophy. Different subsystems (such as lighting, security, and audio-visual systems) may have different emphases and evaluation standards in their design philosophies. For example, a security system may prioritize reliability and safety, while an audio-visual system may prioritize immersion and user experience. These evaluation dimensions and default weight parameters provide a basic framework for subsequent design philosophy comparisons.

[0130] Furthermore, the acquired project priority weights are superimposed on the subsystem's default weights to adaptively adjust the design philosophy preference scores of each potential alternative expert. This superposition mechanism ensures that the evaluation of design philosophies considers not only the characteristics of the subsystem itself but also the overall macro-priority of the project. For example, if the project priority rules emphasize cost control, then when adjusting preference scores, the scores of experts who prefer high-cost, high-configuration solutions will be reduced accordingly, while the scores of experts who prefer economical and practical solutions will be increased.

[0131] Finally, based on the revised propensity score, the system recalculates the degree of difference in design philosophies among the experts. This quantitative calculation allows for precise identification of points of conflict and the degree of difference in ideas. For example, it can identify which experts have significant disagreements on the "cost-effectiveness" dimension, or which experts hold different views on the "level of intelligence." This refined calculation of differences facilitates subsequent mediation and decision-making.

[0132] The proposed solution adaptively corrects the design philosophy preference scores of potential alternative experts by comprehensively considering task type, customer needs, project priority, and characteristics of smart home subsystems, thereby making the identification of design philosophy differences more accurate and comprehensive.

[0133] In some embodiments described above in this application, when differences in design concepts are identified, a structured input guidance mechanism needs to be activated to guide potential alternative experts to input core design principles in a structured manner. Specifically, guiding potential alternative experts to input core design principles in a structured manner includes the following steps:

[0134] Obtain the task type and complexity level of the current critical task, and adaptively generate a multi-dimensional design principle decomposition framework that is suitable for this task.

[0135] Based on the decomposition framework, potential alternative experts are guided to select options or input custom content for the parameters of each sub-dimension within the framework.

[0136] Real-time collection of expert input, simultaneous semantic analysis and intelligent matching, and real-time push of contextual semantic prompts to help standardize input content;

[0137] Perform global consistency checks and cross-dimensional conflict pre-analysis on all input core design principles;

[0138] When logical conflicts or rule contradictions are detected in the input content, real-time content clarification feedback is issued to guide experts to make corrections and adjustments.

[0139] By combining the current task type with the client's core needs, we accurately push high-quality historical reference cases of similar scenarios to assist experts in unifying design standards.

[0140] Specifically, in guiding potential alternative experts to input core design principles in a structured manner, the system first obtains the task type and complexity level of the current critical task. Task types may include design scheme review, material selection, and construction process determination, while the task complexity level may be categorized based on the number of subsystems involved, technical difficulty, and the degree of personalization of client needs. Based on this information, the system can adaptively generate a multi-dimensional design principle decomposition framework adapted to this task. This decomposition framework aims to break down complex design principles into smaller, more easily understood and input sub-dimensions, such as aesthetic design principles, functional design principles, cost control principles, and environmental protection and energy conservation principles.

[0141] Furthermore, based on the generated decomposed framework, the system guides potential alternative experts to select options or input custom content for each sub-dimensional parameter within the framework. This means that experts can choose parameters that align with their design philosophy from a preset list of options, or they can customize the input details according to specific circumstances. For example, under aesthetic design principles, experts can choose style options such as "modern minimalism" or "new Chinese style," or customize specific descriptions such as "emphasizing lines and material contrast."

[0142] During the expert input process, the system collects this input in real time and simultaneously performs semantic analysis and intelligent matching. Semantic analysis aims to understand the deeper meaning and intent of the expert's input, while intelligent matching compares this content with a pre-set knowledge base, industry standards, or historical data. Based on this, the system provides real-time contextual semantic prompts. For example, when the expert's input may deviate from a certain standard, the system will immediately provide a prompt to help the expert standardize the input and ensure it meets project requirements and industry standards.

[0143] In addition, all input core design principles undergo global consistency checks and cross-dimensional conflict pre-analysis. Global consistency checks ensure that all expert-input design principles are consistent and unified, without contradictions. Cross-dimensional conflict pre-analysis focuses on potential conflicts between different sub-dimensions of design principles; for example, an overemphasis on aesthetic design may conflict with cost control principles.

[0144] When logical conflicts or rule contradictions are detected in the input content, the system will issue real-time clarification feedback to guide experts in making corrections and adjustments. This feedback mechanism can promptly identify the problem and provide corrective suggestions, preventing potential design flaws from becoming entrenched in the early stages.

[0145] Finally, to further assist experts in unifying design standards, the system will accurately recommend high-quality historical reference cases for similar scenarios based on the current task type and the client's core needs. These cases provide experts with concrete practical references, helping them better understand and apply design principles, thereby promoting convergence of design concepts among different experts and improving the overall quality and consistency of the solutions.

[0146] This application's solution effectively addresses the challenge of experts efficiently and accurately expressing and unifying core design principles when design philosophies differ. By providing a structured input framework and an intelligent guidance mechanism, it addresses the issue of experts struggling to efficiently and accurately express and unify core design principles in smart home whole-house customization projects. Through this technical solution, the application significantly improves the standardization and normalization of design principle input in these projects. It not only effectively guides potential alternative experts to clearly and accurately express their core design principles but also, through an intelligent detection and feedback mechanism, promptly identifies and resolves logical conflicts and rule contradictions within design philosophies, thereby drastically reducing communication costs and rework risks caused by differences in design concepts. Furthermore, by providing abundant historical reference cases, this application helps promote the unification of design standards among experts, ensuring the overall coordination and high quality of the final design, ultimately improving project execution efficiency and customer satisfaction.

[0147] Specifically, the aforementioned global consistency check and cross-dimensional conflict pre-analysis of all input core design principles may include the following steps:

[0148] Retrieve the complete list of subsystems, integration interface specifications of each subsystem, and device data exchange standards for the current smart home whole-house customization project;

[0149] For the design principles of the inputs within a single subsystem, conduct local logical consistency checks and investigate rule inconsistencies within the subsystem.

[0150] Identify the relationships and dependencies between the design principles of different subsystems, and analyze the cross-subsystem linkage logic;

[0151] Conduct a comprehensive chain reaction analysis on the identified cross-subsystem dependencies to predict the systemic impact of rule adjustments;

[0152] If a conflict of principles is detected across systems or within a system, an intelligent warning will be issued, accurately marking the subsystems involved in the conflict, the conflicting principle entries, and the corresponding chain of negative impacts.

[0153] The process of retrieving the complete list of subsystems for the current smart home whole-house customization project, the integration interface specifications of each subsystem, and the device data exchange standards refers to the system acquiring all the basic configuration information required to build the whole-house smart home system. This information is the cornerstone for consistency checks and conflict pre-analysis, ensuring the comprehensiveness and accuracy of subsequent analyses. For example, the subsystem list may include lighting systems, security systems, and environmental control systems; the integration interface specifications define the protocols and formats for data interaction between different subsystems; and the device data exchange standards specify the specific rules for communication between devices.

[0154] Furthermore, the system performs local logical consistency checks on the design principles input within a single subsystem to identify and resolve internal rule contradictions. This means the system will first check whether the design principles within each independent subsystem are self-consistent and whether there are any conflicting rules. For example, in a lighting system, if there are design principles that simultaneously state "automatically turn off all lights at night" and "keep corridor lights on when someone moves at night," the system will identify potential contradictions and prompt experts to make adjustments.

[0155] Furthermore, it's crucial to identify the relationships and dependencies between the design principles of different subsystems and to clarify the cross-subsystem linkage logic. This step aims to uncover potential interactions and influences between different subsystems. For example, the arming status of a security system might affect the ventilation mode of an environmental control system, or the opening of a smart door lock might trigger the welcome mode of a lighting system. Clarifying these linkage logics provides a basis for subsequent cross-system analysis.

[0156] Based on this, a comprehensive chain reaction analysis is conducted on the identified cross-subsystem dependencies to predict the systemic impact of rule adjustments. This means that when the design principles of a subsystem change, the system can predict the potential impact of this change on other related subsystems, including functionality, performance, and user experience. For example, if the response threshold of a temperature sensor in an environmental control system is modified, the system will analyze whether this will affect the linkage logic of other temperature-related subsystems (such as curtain systems and ventilation systems).

[0157] Ultimately, if a cross-system or intra-system principle conflict is detected, the system will issue an intelligent warning, accurately identifying the subsystems involved, the conflicting principle entries, and the corresponding cascading negative impacts. This warning mechanism can promptly inform experts of potential design problems and provide detailed conflict information, including the specific location of the conflict, the principles involved, and the potential negative consequences, thereby assisting experts in quickly locating and correcting the issue.

[0158] Through the aforementioned technical solutions, this application effectively enhances the rigor and accuracy of the design phase in smart home whole-house customization projects. Its unique global consistency detection and cross-dimensional conflict pre-analysis capabilities comprehensively identify logical contradictions and systemic conflicts within design principles from both macro and micro perspectives. This not only significantly reduces rework rates and project costs caused by design flaws but also ensures that the final delivered smart home system possesses higher stability, compatibility, and user experience. Through precise intelligent early warnings and detailed conflict annotations, experts can quickly locate and correct problems, thereby guaranteeing project design quality and accelerating project progress.

[0159] The above methods also include:

[0160] Obtain the real-time data transmission paths, node device processing capacity parameters, and communication protocol standards of each smart home subsystem in the project;

[0161] By combining equipment operating parameters and transmission rules, the data transmission latency and business processing latency of each subsystem are accurately calculated.

[0162] Based on data transmission latency, business processing latency, and system linkage, a dynamic simulation environment for whole-house smart home is constructed.

[0163] In a dynamic simulation environment, the system data flow and control flow operation states after different design principle adjustments are simulated, and the impact of design adjustments on various performance indicators of other related subsystems is observed.

[0164] The system compares the various performance indicators obtained from the simulation with the preset thresholds in real time. When the performance indicators exceed the legal threshold range, it is determined that there is a systemic conflict. The current system operating status and core parameters are recorded synchronously for subsequent source tracing analysis.

[0165] Specifically, obtaining the real-time data transmission paths, node device processing capabilities, and communication protocol standards of each smart home subsystem in the project refers to collecting the specific path information of data flow between the various subsystems that make up the smart home system (such as lighting systems, security systems, environmental control systems, etc.), including the transmission link from the source device to the target device; at the same time, obtaining the performance parameters such as computing power and response speed of each node device (such as sensors, controllers, actuators, etc.) when processing data; and the communication protocol specifications (such as Zigbee, Wi-Fi, Bluetooth, KNX, etc.) followed by the data exchange between the subsystems. This information is the foundation for building a simulation environment to accurately simulate the system behavior in the real world.

[0166] In this process, by combining equipment operating parameters and transmission rules, the precise calculation of data transmission latency and business processing latency for each subsystem can be understood as using the acquired information such as transmission paths, equipment processing capabilities, and communication protocols, and through mathematical models or simulation algorithms, to quantitatively evaluate the time required for data transmission between different subsystems, as well as the time required for each subsystem to complete its corresponding business logic processing after receiving the data. For example, factors such as network bandwidth, equipment load, and protocol overhead can be considered in calculating latency. The purpose is to provide accurate latency data for subsequent dynamic simulations to reflect the system's response speed in real-world operation.

[0167] In practical applications, constructing a dynamic simulation environment for a whole-house smart home, based on data transmission latency, business processing delay, and system linkage, refers to using the latency data calculated above, combined with the logical connections and interdependencies between various subsystems, to build a virtual platform capable of simulating the operational state of the entire smart home system. This simulation environment can reproduce the data flow, control command transmission, and interaction processes between various devices and subsystems within the system, thereby allowing for the rehearsal of system behavior without affecting the actual project deployment.

[0168] Furthermore, in a dynamic simulation environment, the system's data flow and control flow are simulated under different design principle adjustments. The impact of these adjustments on the performance indicators of other related subsystems is observed. This involves inputting or modifying specific design principles in the simulation environment (e.g., adjusting the response priority of a subsystem, changing the data acquisition frequency, etc.), then running the simulation model to observe how these adjustments affect the data transmission path, data volume, and timeliness of control commands of the entire smart home system. Simultaneously, close attention is paid to other related subsystems that have not been directly modified. For example, will design adjustments to a lighting system lead to increased data processing latency in the security system or decreased response speed in the environmental control system? Performance indicators may include, but are not limited to, data throughput, response time, resource utilization, and error rate.

[0169] Therefore, by comparing various performance indicators obtained from simulation with preset thresholds in real time, a systemic conflict is identified when a performance indicator exceeds the legal threshold range. The current system operating status and core parameters are simultaneously recorded for subsequent source analysis. This means that during simulation, various performance indicators are continuously monitored and compared with pre-set performance thresholds that meet project requirements or industry standards. Once any performance indicator (such as latency, throughput, etc.) is found to exceed the acceptable range, a potential systemic conflict is considered to exist. At this time, the system automatically records the specific operating status, relevant design parameters, and simulation data that led to the conflict, so as to facilitate in-depth analysis and location of the root cause of the conflict.

[0170] This application's solution overcomes the shortcomings of traditional static logic analysis in assessing the performance impact of complex smart home systems by introducing a dynamic simulation environment. Through this technical solution, the application effectively addresses the problem that static logic analysis alone cannot comprehensively evaluate the impact of design principle adjustments on smart home system performance. By constructing a dynamic simulation environment, it is possible to predict in advance the data transmission delay, business processing latency, and cascading effects on the performance indicators of other related subsystems that may result from design adjustments, thereby identifying and mitigating potential systemic conflicts in the early stages of the project. This not only improves the robustness and reliability of the design solution, avoiding rework and increased costs caused by discovering performance problems only after actual deployment, but also provides valuable data support for subsequent conflict tracing analysis and optimization by synchronously recording the system's operating status and core parameters when conflicts occur, significantly enhancing the risk management capabilities and implementation efficiency of smart home whole-house customization projects.

[0171] The steps for identifying conflicts mentioned above include:

[0172] Multi-dimensional correlation analysis was conducted on multiple performance indicators that exceeded preset thresholds to uncover common triggering factors and systemic cascading effects that caused the anomalies.

[0173] Based on common triggering factors and cascading impact links, a complete conflict event propagation chain is constructed, clearly showing the conflict's starting point, the layer-by-layer propagation path, and the final scope of impact.

[0174] Conflicts are classified and categorized according to their scope of impact, severity, and difficulty of remediation, and then matched with corresponding priority levels.

[0175] The conflict characteristic information is associated with and stored uniformly in the project subsystem operation logs, device network traffic data, and hardware device status data.

[0176] Based on the full amount of data stored in the associated database, a root cause analysis report of the conflict is automatically generated, providing data support for conflict remediation and process optimization.

[0177] Specifically, when multiple performance indicators exceed preset thresholds in a dynamic simulation environment or during actual operation, the system will no longer simply record data but will initiate a deeper analysis process. Multi-dimensional correlation analysis refers to comprehensive data mining of these abnormal indicators, aiming to identify the common causes of these anomalies and how these causes, through interactions within the system, trigger a series of chain reactions—that is, cascading effects. For example, an increase in latency in one subsystem may not be an isolated event but may be triggered by a data transmission bottleneck in another subsystem, further affecting the business processing latency of a third subsystem.

[0178] Furthermore, based on the identified common triggering factors and cascading effects, the system will construct a complete conflict propagation chain. This chain, visualized clearly, shows where the conflict initially occurred, how it gradually spreads across different subsystems or modules, and the final scope and depth of its impact. This helps the project team intuitively understand the evolution of the conflict.

[0179] Furthermore, to manage and resolve conflicts more effectively, this application also classifies and categorizes conflicts. Specifically, conflicts are assessed and assigned corresponding priority levels based on multiple dimensions, including the scope of their impact on the overall project, the potential degree of damage they may cause, and the technical resources and time required to resolve them. For example, conflicts that affect core functionality and are difficult to fix are given the highest priority.

[0180] During conflict analysis and resolution, all relevant conflict characteristic information, such as conflict type, priority, and propagation chain, will be associated and bound with underlying operational data such as project subsystem operation logs, device network traffic data, and hardware device status data, and stored uniformly. This associated storage ensures that all conflict-related data can be traced and integrated.

[0181] Ultimately, based on the full amount of data stored in these interconnected databases, the system can automatically generate a detailed root cause analysis report of the conflict. This report not only points out the symptoms of the conflict but also delves into its underlying causes and may provide preliminary remedial suggestions. This report provides the project team with solid data support and a solid basis for decision-making in conflict remediation, optimizing design principles, and improving project processes.

[0182] This application's solution, based on the determination of systemic conflicts and the recording of relevant data, further introduces a systemic conflict identification and analysis mechanism, thereby overcoming the limitations of relying solely on conflict determination and data recording to efficiently locate the root cause of problems and understand the scope of impact. Through the above technical solution, this application enables in-depth analysis and closed-loop management of systemic conflicts occurring in smart home whole-house customization projects. Compared to basic solutions that only determine the existence of conflicts and record data, the additional technical features of this application allow project teams to accurately identify common triggering factors and cascading effects of conflicts, clearly grasp the propagation path and scope of impact, thus avoiding blind troubleshooting and repeated trial and error in complex systems. By scientifically classifying and prioritizing conflicts, project managers can allocate resources more effectively, prioritizing the resolution of issues with the greatest impact on the project, significantly improving problem-solving efficiency and the scientific nature of decision-making. Furthermore, the automatically generated conflict root cause analysis report provides strong data support and decision-making basis for subsequent conflict remediation, design principle adjustments, and project process optimization, greatly reducing project risks and ensuring the smooth implementation and high-quality delivery of smart home whole-house customization projects.

[0183] In some embodiments described above in this application, a multi-dimensional correlation analysis is proposed to analyze multiple performance indicators exceeding preset thresholds in order to uncover common triggering factors and systemic cascading effects that cause anomalies. Specifically, the aforementioned multi-dimensional correlation analysis includes:

[0184] Continuous real-time data streams of performance indicators from various smart home subsystems are collected in real time to construct a monitoring dataset for all time periods;

[0185] Perform refined time series analysis on real-time data streams to accurately identify instantaneous abnormal peak data, and perform data smoothing and noise reduction processing to eliminate invalid interference caused by occasional fluctuations in equipment;

[0186] Periodically sample the performance index data after noise reduction and smoothing, and compare the sampled data with the system's preset baseline data item by item to accurately identify the continuous data deviation state;

[0187] For performance indicators that exhibit persistent deviations, conduct multi-dimensional correlation analysis to accurately identify the intrinsic relationship between indicator anomalies and interaction patterns of specific subsystems and devices;

[0188] By combining the magnitude of the deviation, the duration of the deviation, and the scope of the correlation, it is determined whether the current persistent deviation is a common triggering factor or a cascading effect result corresponding to a systemic conflict.

[0189] The continuous real-time data stream collecting performance indicators from various smart home subsystems refers to the system continuously acquiring key performance data such as operating status, sensor readings, and response times from each subsystem (e.g., lighting system, security system, environmental control system), and aggregating them into a continuous data stream. This allows the construction of a full-time monitoring dataset, containing the performance of all subsystems at different points in time during the project's operation, providing a comprehensive data foundation for subsequent analysis.

[0190] Furthermore, conducting refined time-series analysis on real-time data streams refers to using specialized data analysis techniques to perform in-depth time-dimensional analysis on continuously collected performance data. Its purpose is to accurately identify instantaneous abnormal peak data, such as sudden increases or decreases in performance indicators caused by momentary network fluctuations or brief equipment failures. Simultaneously, data smoothing and noise reduction processing is performed, using algorithms such as moving averages and exponential smoothing, to eliminate invalid interference from occasional equipment fluctuations, ensuring the accuracy of subsequent analysis and avoiding misjudging transient, non-systematic anomalies as conflicts.

[0191] Furthermore, periodic fixed-point sampling of the noise-reduced and smoothed performance data involves extracting representative data points from the processed data stream within a preset time interval. The sampled data is then compared item by item with the system's preset baseline data, which refers to the normal range or average value of various performance indicators determined through long-term monitoring and statistical analysis under normal and stable system operation. This comparison allows for the accurate identification of persistent data deviations—that is, situations where performance indicators deviate from the normal baseline for extended periods—which are often early signals of systemic conflicts.

[0192] Specifically, conducting multi-dimensional correlation analysis on performance indicators exhibiting persistent deviations means not only focusing on the anomaly of a single indicator, but also comprehensively analyzing data from multiple related performance indicators, subsystem states, and equipment operating modes. The aim is to accurately identify the intrinsic correlation between indicator anomalies and the interaction patterns of specific subsystems or devices. For example, it might reveal that the response delay of a lighting system consistently coincides with excessive load on a specific model of gateway device, or is correlated with a specific operating mode of a security subsystem.

[0193] Finally, by combining the deviation magnitude, duration, and scope of impact, it is determined whether the current persistent deviation is a common triggering factor or a cascading effect of a systemic conflict. Here, deviation magnitude refers to the degree to which performance indicators deviate from the baseline; duration refers to the length of time the deviation persists; and scope of impact refers to the number of subsystems or devices affected. By comprehensively considering these factors, it is possible to more accurately determine whether the anomaly is an isolated event or a manifestation of a systemic problem, and its specific role in the conflict propagation chain.

[0194] This application's solution provides a solid data foundation for conflict analysis by continuously and in real-time collecting performance index data from various smart home subsystems and constructing a comprehensive monitoring dataset. Through this technical solution, the application can achieve more accurate and comprehensive identification and analysis of systemic conflicts in smart home whole-house customization projects. Compared to simple multi-dimensional correlation analysis, this solution significantly improves the accuracy and robustness of conflict identification by introducing continuous real-time data stream acquisition, refined time series analysis, data smoothing and noise reduction, periodic fixed-point sampling and baseline comparison, and multi-dimensional correlation analysis. This not only effectively avoids misjudgments caused by instantaneous fluctuations but also accurately locates persistent and systemic performance deviations and deeply reveals their intrinsic relationship with the interaction patterns of specific subsystems or devices. Therefore, it can more accurately identify common triggering factors and cascading effects of conflicts, providing more reliable data support and decision-making basis for subsequent conflict classification, root cause analysis, and remediation, thereby improving the efficiency and effectiveness of closed-loop project process management.

[0195] The aforementioned smart home whole-house customization project process management method provides an efficient and intelligent project management framework. However, in practical applications, without the support of an integrated system, many steps of this method may require extensive manual operation, making it susceptible to human factors and difficult to achieve real-time, automated data collection, task monitoring, and intelligent decision-making. This can lead to low project process execution efficiency, difficulty in ensuring data consistency, and potential delays in the processing of critical tasks, thereby affecting the overall project schedule and quality.

[0196] In response, this application proposes an intelligent home whole-house customization project process management system, aiming to provide an automated and intelligent operating platform for the aforementioned methods. Through modular design, this system concretizes the various functions of the methods into executable software components, thereby achieving comprehensive and efficient management of the project process.

[0197] refer to Figure 3 , Figure 3 This is a schematic diagram of a smart home whole-house customization project process management system provided in an embodiment of the present invention, used to execute the above-described smart home whole-house customization project process management method. The system includes:

[0198] The information acquisition module is used to collect and acquire multi-dimensional personnel attribute data of project participants in real time, including their professional fields, experience levels, scope of authority, real-time workload, and on-duty status, so as to provide data support for subsequent personnel matching and task scheduling.

[0199] The task monitoring module, which is connected in communication with the information acquisition module, is used to monitor the execution status of project tasks in real time by combining the key task binding relationship between personnel on-duty status and workload, identify the key task blocking status when the designated person in charge is unable to perform their duties, and extract the key task requirement information.

[0200] The expert screening module is connected to the task monitoring module and is used to complete the qualification screening based on key task requirements information, combined with personnel's professional fields, experience levels, and authority scope. It also links the workload and on-the-job status of candidate personnel to intelligently recommend the best alternative experts or automatically form a compliance collaboration team.

[0201] The task transfer and recording module communicates with the expert screening module and is used to automatically transfer blocked critical tasks to recommended alternative experts or collaborative groups, build a dedicated collaborative working environment to support task processing, record the entire process data and final decision results of task processing, and realize closed-loop management of project process.

[0202] Specifically, the information acquisition module can be understood as the system's data entry point. Its main function is to continuously and dynamically collect multi-dimensional information related to project participants. This information includes, but is not limited to, the personnel's professional field, such as intelligent lighting, security monitoring, and audio-visual entertainment; experience level, such as junior, intermediate, and senior engineers or experts; scope of permissions, i.e., the functions they can operate and the data access level in the system; real-time workload, reflecting the amount of tasks currently being processed and the time spent; and on-duty status, indicating whether the personnel are in a working state. The real-time collection and updating of this data provides a solid data foundation for subsequent task allocation, personnel scheduling, and intelligent decision-making.

[0203] The task monitoring module communicates with the information acquisition module to obtain the latest personnel status data. The core function of this module is to track the execution status of key project tasks in real time. By combining the on-duty status and workload of project participants with preset key task binding relationships, the system can continuously monitor task progress. When it detects that a designated task leader is unable to perform their duties normally for any reason, such as no task updates for an extended period, abnormal status, or explicit leave, the task monitoring module can accurately identify the blocked status of the corresponding key task. Once a task is identified as blocked, the module immediately initiates the requirement information collection process to ensure that complete requirement details of the blocked task are obtained, providing a basis for subsequent alternative solutions.

[0204] Furthermore, the expert screening module communicates with the task monitoring module to receive the demand information for blocked tasks. Based on the collected key task demand information, this module combines the professional fields, experience levels, and authority of candidate personnel with qualification data such as their expertise, experience level, and scope of authority to perform preliminary job qualification matching. Simultaneously, this module links the real-time workload and on-duty status of candidate personnel for comprehensive evaluation. Through intelligent algorithms, the system can screen out the best-matched alternative experts, or automatically adapt and form multi-person collaborative teams that meet the task execution requirements based on the complexity of the task and the required skill set, ensuring that the task can be taken over efficiently and with high quality.

[0205] Therefore, the task assignment and recording module communicates with the expert screening module to receive screening results. This module is responsible for automatically assigning critical tasks that are blocked to recommended alternative experts or established collaborative teams. Simultaneously with task assignment, the system builds a remote collaborative work environment adapted to the task scenario, providing shared documents, online communication tools, and progress management interfaces to support alternative experts or collaborative teams in efficiently advancing and handling critical tasks. Furthermore, this module records all data throughout the task processing process, including but not limited to task takeover time, processing steps, communication records, modification logs, and final decision results, thereby achieving closed-loop management of the project process and providing detailed data for subsequent auditing, optimization, and knowledge accumulation.

[0206] This application's solution modularizes and integrates various functions of the smart home whole-house customization project process management method into a single system, achieving automated and intelligent management of the project process. The information acquisition module, as the data source, provides real-time and accurate personnel attribute data for subsequent decision-making. The task monitoring module can promptly detect and respond to task blockages, avoiding project delays caused by personnel changes. The expert screening module utilizes multi-dimensional data for intelligent matching, ensuring that tasks are taken over by the most suitable personnel or teams. Finally, the task transfer and recording module not only achieves seamless task transitions but also ensures transparency and traceability of the project process through full recording, effectively solving problems such as low efficiency, lack of transparency, and untimely response in traditional management.

[0207] Through the aforementioned technical solutions, the smart home whole-house customization project process management system of this application can significantly improve the automation level and response speed of project management. Systematic data collection and analysis capabilities enable more precise and efficient personnel matching and task scheduling, effectively reducing the risk of human error and project delays. Furthermore, the full-process recording and closed-loop control mechanism not only ensures the integrity and traceability of project data but also provides solid data support for the accumulation of project experience and continuous optimization of management processes, thereby comprehensively improving the management efficiency and success rate of smart home whole-house customization projects.

[0208] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for managing the process of a smart home whole-house customization project, characterized in that, The method includes: Real-time acquisition of multi-dimensional personnel attribute data of all participants in the smart home whole-house customization project. The multi-dimensional personnel attribute data includes the corresponding personnel's professional field, experience level, scope of authority, real-time workload and on-duty status. By combining the on-the-job status of each project participant with the key task binding relationship corresponding to the workload, the execution status of key tasks of the project is monitored in real time. When it is detected that the designated task leader is unable to perform his / her duties normally, the blocking status of the corresponding key task is accurately identified, and the requirement information of the key task is collected. Based on the requirements of the key tasks, the job qualifications are matched with the professional fields, experience levels and authority of the candidates. At the same time, the real-time workload and on-duty status of the candidates are linked to screen and match alternative experts, or automatically adapt and form a multi-person collaborative team that meets the task execution requirements. The system automatically transfers critical tasks that are blocked to recommended alternative experts or established collaborative teams, and simultaneously builds a remote collaborative work environment adapted to the task scenario. This supports the alternative experts or collaborative teams in efficiently advancing and processing critical tasks, and records all data and final decision results throughout the task processing process, thereby achieving closed-loop management of the project process.

2. The method for managing the process of a smart home whole-house customization project according to claim 1, characterized in that, The selected alternative experts include: Retrieve historical work data of all potential alternative experts with matching qualifications to obtain the design philosophy tendencies of each potential alternative expert; By combining the current project requirements with the standards for whole-house customization design, we can accurately compare and identify the differences in design concepts among potential alternative experts. When differences in design concepts are identified, a structured input guidance mechanism is activated to guide potential alternative experts to input core design principles in a structured manner. Based on the core design principles input by various experts, an intelligent design concept influence map is generated, visually demonstrating the impact of different design concepts on key project dimensions such as project cost, implementation effect, and adaptability. Adaptively adjust the priority configuration parameters of project stakeholders, dynamically observe the real-time changes of the influence map of design concepts, and verify the effectiveness of concept adaptation; If, after multiple parameter adjustments, the experts still cannot reach a consensus on their design concepts, a summary of the conceptual disputes will be automatically generated, and corresponding intelligent mediation suggestions will be output to match the project scenario, assisting in the closed-loop handling of disputes.

3. The method for managing the process of a smart home whole-house customization project according to claim 2, characterized in that, The acquisition of design philosophy preferences for each potential alternative expert includes: Collect and analyze historical project work records of potential replacement experts to identify the project name, smart home subsystem type, and key information of the actual application scenario of the corresponding historical projects. Based on the identified project scenarios and system type information, historical text data is refined and archived. Keyword extraction and deep semantic analysis are performed on each segment of historical text after classification to quantify the design concept tendency score in each scenario. Link expert identity information, store the distribution data of the tendency scores of each potential alternative expert in different project backgrounds and subsystem scenarios, and establish a database of personal ideological characteristics; For currently pending critical tasks that are blocked, match the corresponding scenario and subsystem type, and accurately call the tendency score in the corresponding context as the matching basis; If there is no matching historical scenario data for the current task, the bias scores of multiple highly relevant backgrounds are retrieved and weighted averaged to obtain the equivalent bias score suitable for this task.

4. The method for managing the process of a smart home whole-house customization project according to claim 2, characterized in that, The precise comparison and identification of design concept differences among potential alternative experts includes: Get the task type and customer's personalized requirements of the currently blocked task, and retrieve the preset project priority weight configuration rules; Based on the type of smart home subsystem corresponding to the current task, the system calls the preset evaluation dimensions and default weight parameters of the design concept adapted to the subsystem. By combining the project priority weight configuration with the subsystem default weight, the design concept tendency scores of each potential alternative expert are adaptively corrected. Based on the revised propensity score, the degree of difference in design concepts among experts was recalculated to accurately pinpoint the points of conflict and the degree of difference in concepts.

5. The method for managing the process of a smart home whole-house customization project according to claim 2, characterized in that, The guidance provided to potential alternative experts in inputting core design principles in a structured manner includes: Obtain the task type and complexity level of the current critical task, and adaptively generate a multi-dimensional design principle decomposition framework that is suitable for this task. Based on the decomposed framework, each potential alternative expert is guided to select options or input custom content for each sub-dimensional parameter within the framework. Real-time collection of expert input, simultaneous semantic analysis and intelligent matching, and real-time push of contextual semantic prompts to help standardize input content; Perform global consistency checks and cross-dimensional conflict pre-analysis on all input core design principles; When logical conflicts or rule contradictions are detected in the input content, real-time content clarification feedback is issued to guide experts to make corrections and adjustments. By combining the current task type with the client's core needs, we accurately push high-quality historical reference cases of similar scenarios to assist experts in unifying design standards.

6. The method for managing the process of a smart home whole-house customization project according to claim 5, characterized in that, The process of performing global consistency checks and cross-dimensional conflict pre-analysis on all input core design principles includes: Retrieve the complete list of subsystems, integration interface specifications of each subsystem, and device data exchange standards for the current smart home whole-house customization project; For the design principles of the inputs within a single subsystem, conduct local logical consistency checks and investigate rule inconsistencies within the subsystem. Identify the relationships and dependencies between the design principles of different subsystems, and analyze the cross-subsystem linkage logic; Conduct a comprehensive chain reaction analysis on the identified cross-subsystem dependencies to predict the systemic impact of rule adjustments; If a conflict of principles is detected across systems or within a system, an intelligent warning will be issued, accurately marking the subsystems involved in the conflict, the conflicting principle entries, and the corresponding chain of negative impacts.

7. The method for managing the process of a smart home whole-house customization project according to claim 6, characterized in that, The method further includes: Obtain the real-time data transmission paths, node device processing capacity parameters, and communication protocol standards of each smart home subsystem in the project; By combining equipment operating parameters and transmission rules, the data transmission latency and business processing latency of each subsystem are accurately calculated. Based on the data transmission delay, the business processing delay, and the system linkage relationship, a dynamic simulation environment for whole-house smart home is constructed. In the dynamic simulation environment, the system data flow and control flow operation states after different design principle adjustments are simulated, and the impact of design adjustments on various performance indicators of other related subsystems is observed. The system compares the various performance indicators obtained from the simulation with the preset thresholds in real time. When the performance indicators exceed the legal threshold range, it is determined that there is a systemic conflict. The current system operating status and core parameters are recorded synchronously for subsequent source tracing analysis.

8. The method for managing the process of a smart home whole-house customization project according to claim 7, characterized in that, The method further includes conflict identification, which includes: Multi-dimensional correlation analysis was conducted on multiple performance indicators that exceeded preset thresholds to uncover common triggering factors and systemic cascading effects that caused the anomalies. Based on the common triggering factors and cascading impact links, a complete conflict event propagation chain is constructed, clearly showing the conflict's starting point, the layer-by-layer propagation path, and the final scope of impact. Conflicts are classified and categorized according to their scope of impact, severity, and difficulty of remediation, and then matched with corresponding priority levels. The conflict characteristic information is associated with and stored uniformly in the project subsystem operation logs, device network traffic data, and hardware device status data. Based on the full amount of data stored in the association, a root cause analysis report of the conflict is automatically generated, providing data support for conflict remediation and process optimization.

9. The method for managing the process of a smart home whole-house customization project according to claim 8, characterized in that, The multi-dimensional correlation analysis includes: Continuous real-time data streams of performance indicators from various smart home subsystems are collected in real time to construct a monitoring dataset for all time periods; A refined time series analysis is performed on the continuous real-time data stream to accurately identify instantaneous abnormal peak data, and data smoothing and noise reduction processing is performed to eliminate invalid interference caused by occasional fluctuations in the equipment. Periodically sample the performance index data after noise reduction and smoothing, and compare the sampled data with the system's preset baseline data item by item to accurately identify the continuous data deviation state; For performance indicators that exhibit persistent deviations, conduct multi-dimensional correlation analysis to accurately identify the intrinsic relationship between indicator anomalies and the interaction patterns of specific subsystems and devices; By combining the magnitude of the deviation, the duration of the deviation, and the scope of the correlation, it is determined whether the current persistent deviation is a common triggering factor or a cascading effect result corresponding to a systemic conflict.

10. A smart home whole-house customization project process management system, characterized in that, The system is used to execute the smart home whole-house customization project process management method according to any one of claims 1-9, the system comprising: The information acquisition module is used to collect and acquire multi-dimensional personnel attribute data of project participants in real time, including their professional fields, experience levels, scope of authority, real-time workload, and on-duty status, so as to provide data support for subsequent personnel matching and task scheduling. The task monitoring module, which is connected in communication with the information acquisition module, is used to monitor the execution status of project tasks in real time by combining the key task binding relationship between personnel on-duty status and workload, identify the key task blocking status when the designated person in charge is unable to perform their duties, and extract the key task requirement information. The expert screening module is connected to the task monitoring module and is used to screen qualifications based on key task requirements information, combined with personnel's professional fields, experience levels, and authority scope. It also links the workload and on-duty status of candidate personnel to screen matching alternative experts or automatically adapt and form multi-person collaborative teams that meet the task execution requirements. The task transfer and recording module communicates with the expert screening module and is used to automatically transfer blocked critical tasks to the alternative experts or the multi-person collaboration group, build a dedicated collaborative working environment to support task processing, record task processing data and final decision results throughout the process, and realize closed-loop management of project processes.

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