Building consultation system based on cloud platform

Abstract

The invention discloses a building consultation system based on a cloud platform, and relates to the technical field of building consultation. The system comprises a user terminal, a cloud platform server and a consultation service terminal. The user terminal is used for submitting consultation demand information, querying progress and receiving a result; the cloud platform server comprises a data storage module, a demand analysis module, a service matching module, a progress tracking module, a result feedback module and the like, intelligent analysis of consultation demands, accurate matching of service personnel, real-time monitoring of a consultation process and result compliance auditing can be realized, and meanwhile, a building industry knowledge base and historical case data are stored; the consultation service terminal is used for service personnel to receive requirements, upload progress, submit results and look up a knowledge base and historical cases. The system can expand functions of user evaluation, automatic data updating, multi-terminal cooperation, risk early warning and the like, improves consultation service quality and industry cooperation efficiency, and is suitable for consultation scenes of various building projects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of architectural consultation, and particularly relates to a cloud platform-based architectural consultation system. BACKGROUND

[0002] In traditional architectural engineering construction management, architectural consultation services are key links for ensuring project compliance, safety and economy, and the service mode and efficiency of the architectural consultation services directly affect the progress of the architectural project. At present, the traditional architectural consultation services mainly rely on offline communication or single online file transmission mode, and the following significant problems exist: Low efficiency of demand transmission and analysis: the user needs to communicate multiple times through emails, telephone calls and the like to clearly understand the consultation demand, and the demand description is easy to be ambiguous, which leads to deviation of the understanding of the core demand by the consultation service personnel, and increases the subsequent rework cost. Inaccurate service resource matching: architectural consultation involves multiple professional fields such as design, construction, regulations, materials and the like, and in the traditional mode, the service personnel is mainly recommended by manual, and it is difficult to efficiently match the professional attributes, urgency of the demand and the professional ability and idle state of the service personnel, and the situation of “inappropriate professional” or “coexistence of idle resources and accumulated demand” is easy to occur. Lack of transparent monitoring in the consultation process: the user cannot obtain the consultation progress in real time, and can only understand the situation through stage communication, and if there is a delay in a certain link, it is difficult to timely find and coordinate the solution, which leads to the extension of the overall consultation period and affects the project progress. Lagging update of industry knowledge: architectural design specifications, construction technology standards, regulations and policies will be continuously updated with the development of the industry, and in the traditional mode, the service personnel needs to rely on personal initiative to query the latest knowledge, and the problem of the consultation scheme not meeting the current requirements due to the lagging knowledge is easy to occur. Insufficient information security and collaboration: sensitive information such as architectural project drawings and business secrets lacks effective encryption protection in the transmission and storage process, and when multiple professionals collaborate in the consultation, the information sharing and document editing among the service personnel are difficult to be efficiently synchronized, which further reduces the consultation efficiency. Therefore, it is urgent to develop an architectural consultation system capable of realizing intelligent analysis of consultation demand, accurate matching of service resources, transparent monitoring of the process, real-time update of knowledge and guarantee of information security, so as to solve the pain points of the prior art and promote the digital transformation of the architectural consultation industry. SUMMARY

[0003] To solve the above problems, the present application provides a cloud platform-based architectural consultation system, which realizes intelligent analysis of consultation demand, accurate matching of service resources, transparent monitoring of the process, real-time update of knowledge and guarantee of information security, so as to solve the pain points of the prior art.

[0004] To achieve the above object, the technical scheme adopted by the present application is as follows: a building consultation system based on a cloud platform is provided, comprising: a user terminal, a cloud platform server and a consultation service terminal, the user terminal and the consultation service terminal are respectively connected with the cloud platform server in communication; The user terminal is used for submitting consultation demand information, consultation progress query and consultation result receiving by a building related user, the consultation demand information comprises project basic information, consultation type information and demand description information; The cloud platform server comprises a data storage module, a demand analysis module, a service matching module, a progress tracking module and a result feedback module; The demand analysis module is used for receiving the consultation demand information submitted by the user terminal, and performing semantic analysis and keyword extraction on the consultation demand information to obtain a demand analysis result, the demand analysis result comprises a core demand point, a demand urgency degree and a required professional field; The service matching module is used for screening at least one consultation service personnel matched with the consultation demand information according to the demand analysis result and combining the consultation service personnel information data stored in the data storage module, adopting a fuzzy matching algorithm and a priority sorting algorithm, and pushing the consultation demand information to the consultation service terminal corresponding to the matched consultation service personnel; The consultation service terminal is used for receiving consultation demand information, uploading consultation progress information and submitting consultation result information by a consultation service personnel, and the consultation service terminal has the function of checking the building industry knowledge base data and historical consultation case data in the data storage module.

[0005] As a preferred, in the cloud server platform: The data storage module is used for storing building industry knowledge base data, consultation service personnel information data and historical consultation case data, the building industry knowledge base data comprises building design specification data, construction technology standard data, building material performance data and building regulations and policies data; The progress tracking module is used for real-time obtaining the consultation progress information fed back by the consultation service terminal, dividing the consultation process into stages and monitoring the time nodes, generating a consultation progress report, and sending the consultation progress report to the corresponding user terminal in real time; the result feedback module is used for receiving the consultation result information submitted by the consultation service terminal, performing format checking and compliance auditing on the consultation result information, sending the consultation result information to the corresponding user terminal after the auditing is passed, and storing the consultation result information and the corresponding consultation demand information in the data storage module to form new historical consultation case data; The cloud server platform further comprises a user evaluation module for the user to score and evaluate the service quality of the consulting service personnel after receiving the consulting result information, and the score and evaluation information is stored in the data storage module and serves as a reference basis for the service matching module to match the service personnel.

[0006] More preferably, the progress tracking module divides the consulting process into a demand confirmation stage, a scheme development stage, a scheme review stage, and a result delivery stage, each stage being provided with a corresponding time threshold, and when the time consumption of a certain stage exceeds the corresponding time threshold, the progress tracking module sends an overtime reminder information to the corresponding consulting service terminal and synchronizes the overtime situation to the user terminal.

[0007] As a preferred, the consulting demand information submitted by the user terminal further comprises attachment information, the attachment information including building project drawing files, project feasibility study report files, and on-site photo files, and the data storage module of the cloud platform server stores the attachment information in an encrypted manner and supports online browsing and downloading by the consulting service terminal.

[0008] As a preferred, the fuzzy matching algorithm in the service matching module specifically comprises: calculating the similarity between the core demand points in the demand analysis result, the required professional field, and the professional field tags and the project type tags that the consulting service personnel are good at in the consulting service personnel information data, and when the similarity is greater than a preset threshold, the consulting service personnel is included in the candidate list; the priority sorting algorithm specifically comprises: according to the historical service score, the historical consulting case completion efficiency, and the current idle state of the consulting service personnel in the candidate list, the candidate list is sorted by priority, and the consulting service terminals corresponding to the top 3 consulting service personnel with the highest priority are taken as the push targets.

[0009] More preferably, the specific steps of the similarity calculation are as follows: ; Among them, represents the similarity, A represents the demand weighted vector, B represents the personnel weighted vector, represents the module of the demand weighted vector, represents the module of the personnel weighted vector, represents the core demand point weight of the demand, represents the weight of the corresponding synonym tag of the personnel, k represents the semantic correlation coefficient, represents the related cases, represents the total cases.

[0010] As preferred, the cloud platform server further comprises a data updating module, which is used for obtaining the latest building design specification data, construction technology standard data and building regulation policy data from an official database of the building industry, an industry association publishing platform, and updating the building industry knowledge base data in the data storage module, and the update record is stored to the data storage module, supporting the user terminal and the consultation service terminal to query the data update history.

[0011] As preferred, the cloud platform server further comprises a warning module, which is used for identifying and warning the risks that may occur in the consultation process, including the risk of conflict between the consultation scheme and the existing building regulation policy, and the risk of abnormal consultation result data, and when the risk is identified, the warning module sends the risk warning information to the corresponding consultation service terminal, and provides the reference suggestion of the risk solution, and the risk warning information and the reference suggestion of the solution are stored in association in the data storage module.

[0012] Compared with the prior art, the building consultation system based on the cloud platform has the following remarkable beneficial effects: improving the accuracy and efficiency of consultation demand analysis: through the demand analysis module of the cloud platform server, the consultation demand information submitted by the user is subjected to semantic analysis and keyword extraction, the core demand point, the urgency and the required professional field are automatically identified, the ambiguity and inefficiency of artificial communication are avoided, and a precise foundation is laid for subsequent service matching. Realizing efficient and accurate matching of service resources: the service matching module combines the fuzzy matching algorithm and the priority sorting algorithm, deeply associates the demand with the professional field of the service personnel, the historical service quality and the idle state, filters out the optimal service personnel, solves the problem of "resource mismatch", and improves the resource utilization rate of the service personnel and the consultation experience of the user. Ensuring the transparency and controllability of the consultation process: the progress tracking module divides the consultation process into stages and monitors the time nodes, generates a real-time progress report and synchronizes it to the user terminal, automatically sends a reminder when the timeout condition occurs, lets the user master the consultation progress throughout the process, reduces the project delay risk caused by information asymmetry. Ensure real-time update and efficient reuse of industry knowledge: the data updating module regularly obtains the latest design specifications, technical standards and regulation policies from official channels to update the building industry knowledge base; at the same time, historical consultation cases and consultation results are stored in association, supporting service personnel to review and reuse, improving the compliance and professionalism of the consultation scheme, and reducing the cost of repetitive labor. Strengthen information security and multi-terminal collaboration ability: the system uses SSL encryption transmission protocol and AES-256 encryption algorithm to protect sensitive data such as user identity information and project business secrets; the online collaborative editing function of the consulting service terminal, the offline editing of the mobile terminal and the automatic synchronization function support efficient collaboration of multi-professional service personnel and adapt to different use scenarios, further improving the flexibility and efficiency of consulting services. Form an industry service quality closed-loop management: the user evaluation module collects user ratings and evaluations of service personnel, which serves as a reference for service matching, promotes service personnel to improve service quality, and provides a reliable reference for users to select services, forming a "demand-service-evaluation-optimization" closed loop to promote the overall service level of the building consulting industry. BRIEF DESCRIPTION OF DRAWINGS

[0013] Fig. 1 is a building consulting system framework based on a cloud platform.

[0014] Fig. 2 is a cloud platform structure framework schematic diagram of a better embodiment.

[0015] Fig. 3 is a progress tracking module stage schematic diagram of a better embodiment. DETAILED DESCRIPTION

[0016] Referring to Figs. 1-3 The present application provides a building consulting system based on a cloud platform, which comprises: A user terminal, a cloud platform server and a consulting service terminal, the user terminal and the consulting service terminal are respectively connected with the cloud platform server to establish a communication connection; The user terminal is used for submitting consulting demand information, consulting progress query and consulting result receiving for building related users, and the consulting demand information includes project basic information, consulting type information and demand description information; The cloud platform server includes a data storage module, a demand analysis module, a service matching module, a progress tracking module and a result feedback module; The demand analysis module is used for receiving the consulting demand information submitted by the user terminal, and performing semantic analysis and keyword extraction on the consulting demand information to obtain a demand analysis result, the demand analysis result includes core demand points, demand urgency and required professional fields; The service matching module is used for screening at least one consulting service personnel matched with the consulting demand information according to the demand analysis result and combining the consulting service personnel information data stored in the data storage module, using fuzzy matching algorithm and priority sorting algorithm, and pushing the consulting demand information to the consulting service terminal corresponding to the matched consulting service personnel; The data storage module is configured to store building industry knowledge base data, consultant information data, and historical consulting case data, wherein the building industry knowledge base data includes building design specification data, construction technology standard data, building material performance data, and building regulation and policy data. The progress tracking module is configured to obtain real-time consulting progress information fed back by the consulting service terminal, divide the consulting process into stages and monitor time nodes, generate a consulting progress report, and send the consulting progress report to the corresponding user terminal in real time. The result feedback module is configured to receive consulting result information submitted by the consulting service terminal, perform format checking and compliance auditing on the consulting result information, send the consulting result information to the corresponding user terminal after the auditing is passed, and store the consulting result information in association with the corresponding consulting demand information in the data storage module to form new historical consulting case data. The cloud server platform further includes a user evaluation module configured to allow users to score and evaluate the service quality of the consulting service personnel after receiving the consulting result information. The scoring and evaluation information is stored in the data storage module and serves as a reference basis for the service matching module to match service personnel.

[0017] The stage division of the progress tracking module on the consulting process includes a demand confirmation stage, a scheme development stage, a scheme auditing stage, and a result delivery stage. Each stage is provided with a corresponding time threshold. When the time consumption of a certain stage exceeds the corresponding time threshold, the progress tracking module sends an overtime reminder information to the corresponding consulting service terminal and synchronizes the overtime situation to the user terminal.

[0018] The consulting service terminal is configured to allow the consulting service personnel to receive consulting demand information, upload consulting progress information, and submit consulting result information. The consulting service terminal is also provided with the function of checking the building industry knowledge base data and historical consulting case data in the data storage module.

[0019] The system data storage module pre-imports building industry knowledge base data, including the latest specifications such as “Concrete Structure Design Specification” (GB50010-2020) and “Concrete Strength Inspection and Evaluation Standard” (GB / T50107-2010), as well as performance parameters of concrete of different brands and strength grades (such as the compressive strength, initial setting time, and applicable environment of C30 and C35 concrete); The information data of the service personnel B is entered, including professional field tags, “concrete technology” and “construction quality control”, historical service score 4.9 / 5.0, current idle state “available for order”, and historical consulting cases: 30 cases of concrete-related consulting, with an average completion period of 2 working days. The system is deployed on the Aliyun server and supports web and mobile (iOS / Android) access.

[0020] User A logs in to the system web page through a notebook computer browser and enters the "demand submission" page, filling in the consultation demand information: Project basic information: Project name "XX Garden No. 3, Guangzhou", project phase "main structure construction", project address "XX Road, Tianhe District, Guangzhou"; Consultation type information: Select "construction technology consultation-material selection"; Demand description information: "The concrete strength grade of beam and slab components of No. 3 building on 10-15 floors of this project is C35. Considering the local building material supply situation, it is planned to replace it with commercial concrete of the same strength grade. It is necessary to confirm whether the performance of the selected commercial concrete meets the specification requirements and the maintenance measures that need to be paid attention to during construction"; Attachment information: Upload the 10th floor structure construction drawing of No. 3 building (PDF format, 10MB), and the C35 concrete performance test report provided by the local commercial concrete supplier (Word format, 2MB).

[0021] After user A submits the demand, the system automatically pushes a "demand submission success" message to the Android mobile terminal application of user A, reminding the user. At the same time, the data storage module encrypts the attachment information and generates a unique demand number REQ2025090800X.

[0022] The cloud platform server demand analysis module receives the information corresponding to the demand number REQ2025090800X, extracts the keywords "C35 concrete" "performance specification requirements" "construction maintenance measures" through semantic analysis, determines the core demand point as "commercial concrete performance compliance verification + maintenance scheme suggestion", the demand urgency is "general" (user did not mark the urgency, the system defaults), and the required professional field is "concrete technology + construction technology"; The service matching module starts the fuzzy matching algorithm, matches the core demand with the professional field label and service personnel information library, finds that the "concrete technology" label of service personnel B has a similarity of 96% with the demand professional field, which is higher than the preset threshold of 80% (the preset threshold can be customized), and is included in the candidate list; The similarity calculation steps are as follows: ; Where, represents the similarity, A represents the demand weighted vector, B represents the personnel weighted vector, represents the norm of the demand weighted vector, represents the norm of the personnel weighted vector, represents the core demand point weight of the demand, represents the weight of the personnel corresponding to the synonym label, k represents the semantic correlation coefficient, represents the related cases, This represents the total number of cases.

[0023] The specific steps are as follows: Based on the "core requirement points + required professional fields" output by the requirement analysis module, a "requirement feature vector" is constructed. Taking the example, the requirement feature vector is: [C35 concrete, performance compliance verification, construction and maintenance measures, concrete technology, construction technology], where the first 3 items are core requirement points, and the last 2 items are required professional field labels. Each feature item is assigned a unique code, such as "C35 concrete" is coded as TC001, and "concrete technology" is coded as FD003. Service personnel tag standardization: Professional information of service personnel B is extracted from the data storage module to construct a "personnel tag vector". In this example, the tag vector for service personnel B is: [Concrete Technology, Construction Quality Control, C30 / C35 Concrete Performance Analysis, Concrete Curing Scheme Design, Interpretation of Construction Technical Specifications]. Similarly, each tag is assigned a code consistent with the required characteristics, such as "Concrete Technology" coded as FD003 and "C35 Concrete Performance Analysis" coded as TC002, ensuring cross-vector feature comparability.

[0024] Step 1: Construct the feature word frequency matrix: Using all unique features from the "demand feature vector" and "personnel tag vector" as columns and the two vectors as rows, construct a 2x8 word frequency matrix. Count the number of times each feature appears in the corresponding vector, with 1 marked as appearing and 0 as not appearing. In this embodiment, the matrix is ​​as follows: ; The first column is the demand feature vector, and the second column is the personnel label vector; the first row is C35 concrete, the second row is performance compliance verification, the third row is construction and maintenance measures, the fourth row is concrete technology, the fifth row is construction technology, the sixth row is construction quality control, the seventh row is C30 / C35 concrete performance analysis, and the eighth row is concrete curing scheme design.

[0025] Step 2: Considering that "professional domain tags" have a higher priority for matching than "specific needs" (e.g., matching within the domain of "concrete technology" is more decisive than matching a single need like "C35 concrete"), we need to assign weights to different types of features to obtain the following: Demand weighted vector: [0.4,0.4,0.4,0.6,0.6,0,0,0]; Personnel weighted vector: [0,0,0,0.6,0,0.6,0.4,0.4].

[0026] Step 3: Cosine similarity formula calculation: Cosine similarity is used to calculate the cosine value of the included angle of two vectors, and the closer the value is to 1, the more consistent the vector direction is (the higher the matching degree is), and the formula is: ; Wherein, represents the cosine value of the included angle of two vectors, A represents the demand weighted vector, B represents the personnel weighted vector, represents the norm of the demand weighted vector, represents the norm of the personnel weighted vector, and the norm is calculated by the square root of the sum of squares of each element.

[0027] Combined with the example data calculation: Calculate the dot product : (0.4x0)+(0.4x0)+(0.4x0)+(0.6x0.6)+(0.6x0)+(0x0.6)+(0x0.4)+(0x0.4)=0.36; Calculate ||A||: √(0.4 2 +0.4 2 +0.4 2 +0.6 2 +0.6 2 +0 2 +0 2 +0 2 ) =√(0.16+0.16+0.16+0.36+0.36)=√1.2=1.095; Calculate ||B||: √(0 2 +0 2 +0 2 +0.6 2 +0 2 +0.6 2 +0.4 2 +0.4 2 ) =√(0.36+0.36+0.16+0.16)=√1.04 =1.02; Calculate cosθ: 0.36 / (1.095x1.02)≈0.36 / 1.117≈0.322; Step 4: Feature semantic association correction: The above basic calculation does not consider the characteristics of "semantically similar but expressed differently", such as "construction maintenance measures" in the demand and "concrete maintenance scheme design" in the personnel label, which need to be corrected through "industry semantic dictionary": The system has built-in building industry semantic dictionary, pre-stored near-synonymous word groups (such as "construction maintenance measures" and "concrete maintenance scheme design" are near-synonymous word groups, with a correlation coefficient of 0.8; "performance compliance verification" and "C35 concrete performance analysis" are near-synonymous word groups, with a correlation coefficient of 0.9); The basic calculation results are corrected: find the near-synonymous word groups in the demand and personnel label, calculate the correction increment, the formula is: Correction increment = Σ (demand core demand point weight × personnel corresponding near-synonymous word label weight × semantic correlation coefficient); The correction increment calculation in the embodiment is: "Construction maintenance measures (0.4)" × "Concrete maintenance scheme design (0.4)" × 0.8 = 0.128 "Performance compliance verification (0.4)" × "C35 concrete performance analysis (0.4)" × 0.9 = 0.144 The total correction increment is: 0.128 + 0.144 = 0.272 Basic calculation result + correction increment = 0.322 + 0.272 = 0.594; Final similarity = basic calculation result + correction increment + historical case matching degree; Historical case matching degree = relevant cases / total cases; The similarity in the final embodiment is 92%, and the "historical case matching degree" needs to be corrected, such as personnel B has 30 concrete cases, of which 11 are related to C35, with a case matching degree coefficient of 0.4, and finally 0.594 + 0.366 = 0.9606, i.e. 96%.

[0028] The priority sorting algorithm combines the historical score of service personnel B (4.9), the current idle state (available for order) and the concrete related case experience to determine that the priority of service personnel B is the first, and the system automatically pushes the demand information (including access permission of attachments) to the desktop web page and iOS tablet mobile terminal of service personnel B.

[0029] The iOS tablet mobile terminal of service personnel B receives the "new demand reminder", checks the demand information and attachments through the application, confirms the demand, and clicks "accept demand" in the system, and the system synchronously pushes the "service personnel has accepted the order" message to the Android mobile phone of user A; Service personnel B accesses the data storage module through the desktop computer web page, checks the compressive strength requirements (axial compressive strength design value 16.7 N / mm 2 ) of C35 concrete in the Concrete Structure Design Specification (GB50010-2020), compares the measured compressive strength (18.2 N / mm 2 ) of the batch of C35 concrete in the supplier's test report, and confirms compliance with performance; Service personnel B creates a consultation progress document in the system, marks "Stage: Scheme development (50%)", uploads the preliminary sorted specification clauses and test report comparison table, and the system progress tracking module records the progress and synchronizes it to user A's terminal; Service personnel B combines historical cases (C35 concrete curing scheme for a Guangzhou residential project) to develop curing measures for this project (such as "cover and keep moist within 12 hours after pouring, curing time not less than 7 days, and take insulation measures when environmental temperature is lower than 5℃"), completes the initial draft of the consultation scheme, and submits "Stage: Scheme review (80%)" in the system; After the review is correct, service personnel B submits the final consultation results (including compliance conclusion, curing scheme document, and specification reference list) in the system and marks "Stage: Result delivery (100%)".

[0030] The cloud platform server result feedback module receives the consultation results, automatically checks the document format (confirms no garbled code, complete attachments), reviews the content compliance (confirms accurate specification reference, no obvious technical errors in the scheme), and pushes the "consultation results have been delivered" message to user A's terminal after the review is passed; user A checks the consultation results through the notebook computer, confirms that the requirements are met, and evaluates service personnel B in the system: score 4.8 / 5.0, evaluation content "high professional level, detailed scheme, solved the concrete selection question"; The system user evaluation module stores the evaluation, and stores the demand information, result document, and evaluation information of this consultation in the data storage module, forming a new historical case (case number CASE20250908001), supporting subsequent service personnel to check and reuse. Four, special scene response example (optional) If the service personnel B in this embodiment delays for 1 working day in the "scheme making stage" due to temporary tasks, the system progress tracking module detects that the time consumption of this stage has exceeded the preset threshold (1 working day), and automatically sends a "timeout reminder: the scheme making stage has exceeded the time limit, please handle it in priority" to the terminal of the service personnel B, and at the same time, pushes a synchronous message "the consultation progress is slightly delayed, and the result is expected to be delivered after 1 working day" to the user A, so as to ensure information transparency; if the user A needs to supplement the project geological report as a reference, the user A can upload the attachment through the mobile terminal application, the system automatically pushes the attachment to the terminal of the service personnel B, and updates the demand state to "supplementing materials", so as to ensure efficient communication.

[0031] Through the application of this embodiment, the user A obtains professional consultation results within 1.5 working days, which is more than 60% higher than the traditional offline consultation (3-5 working days on average); the service matching process does not need manual intervention, and the service personnel with professional matching is accurately matched, so as to avoid the problem of "unmatched specialty"; the user can query the progress in real time throughout the whole process, and the information transparency is significantly improved; the consultation result refers to the latest specification, and is associated with historical cases, so that the technical reliability and reusability are guaranteed, and the practicability and innovativeness of the system are fully verified.

[0032] The above embodiments only describe the preferred embodiments of the present application, and do not limit the scope of the present application, and various modifications and improvements to the technical solutions of the present application made by the ordinary engineering technical personnel without departing from the design spirit of the present application shall fall within the protection scope determined by the claims of the present application.

Claims

1. A cloud-based architectural consulting system, characterized in that, include: The system includes a user terminal, a cloud platform server, and a consulting service terminal, wherein the user terminal and the consulting service terminal establish a communication connection with the cloud platform server respectively. The user terminal is used for building-related users to submit consultation request information, query consultation progress, and receive consultation results. The consultation request information includes basic project information, consultation type information, and request description information. The cloud platform server includes a data storage module, a demand parsing module, a service matching module, a progress tracking module, and a result feedback module. The requirement parsing module is used to receive the consultation requirement information submitted by the user terminal, and to perform semantic analysis and keyword extraction on the consultation requirement information to obtain the requirement parsing result. The requirement parsing result includes the core requirement points, the urgency of the requirement, and the required professional field. The service matching module is used to select at least one consulting service personnel who matches the consulting demand information based on the demand parsing result and the consulting service personnel information data stored in the data storage module, using a fuzzy matching algorithm and a priority sorting algorithm, and push the consulting demand information to the consulting service terminal corresponding to the matched consulting service personnel. The consultation service terminal is used for consultation service personnel to receive consultation request information, upload consultation progress information, and submit consultation result information. The consultation service terminal also has the function of accessing the construction industry knowledge base data and historical consultation case data in the data storage module.

2. The cloud-based architectural consulting system according to claim 1, characterized in that, In the cloud server platform: The data storage module is used to store construction industry knowledge base data, consulting service personnel information data, and historical consulting case data. The construction industry knowledge base data includes building design code data, construction technology standard data, building material performance data, and building laws and policies data. The progress tracking module is used to obtain consultation progress information from the consultation service terminal in real time, divide the consultation process into stages and monitor time nodes, generate a consultation progress report, and send the consultation progress report to the corresponding user terminal in real time. The result feedback module is used to receive consultation result information submitted by the consultation service terminal, perform format verification and compliance review on the consultation result information, and send the consultation result information to the corresponding user terminal after the review is passed. At the same time, the consultation result information is associated with the corresponding consultation demand information and stored in the data storage module to form new historical consultation case data. The cloud server platform also includes a user evaluation module, which allows users to rate and evaluate the service quality of consulting service personnel after receiving consultation results. The rating and evaluation information is stored in the data storage module and serves as a reference for the service matching module to match service personnel.

3. The cloud-based architectural consulting system according to claim 2, characterized in that, The progress tracking module divides the consultation process into stages, including the requirement confirmation stage, the solution development stage, the solution review stage, and the result delivery stage. Each stage has a corresponding time threshold. When the time taken for a certain stage exceeds the corresponding time threshold, the progress tracking module sends a timeout reminder to the corresponding consultation service terminal and synchronizes the timeout status to the user terminal.

4. The cloud-based architectural consulting system according to claim 1, characterized in that, The consultation request information submitted by the user terminal also includes attachment information, which includes architectural project drawings, project feasibility study reports, and on-site photos. The data storage module of the cloud platform server encrypts and stores the attachment information, and supports online viewing and downloading by the consultation service terminal.

5. The cloud-based architectural consulting system according to claim 1, characterized in that, The fuzzy matching algorithm in the service matching module is as follows: the core requirements and required professional fields in the requirements analysis results are compared with the professional field tags and project type tags in the information data of the consulting service personnel. When the similarity is greater than a preset threshold, the consulting service personnel are included in the candidate list. The priority ranking algorithm is as follows: the candidate list is ranked according to the historical service rating, historical consultation case completion efficiency and current idle status of the consulting service personnel in the candidate list. The consulting service terminals corresponding to the top 3 consulting service personnel with the highest priority are used as the push targets.

6. A cloud-based architectural consulting system according to claim 5, characterized in that, The specific steps for similarity calculation are as follows: ； in, Let A represent the demand-weighted vector and B represent the personnel-weighted vector, indicating similarity. The magnitude of the demand weighted vector is represented. The modulus of the personnel weighted vector is represented. This indicates the weight of the core demand points. This indicates the weight of the synonym tags corresponding to the personnel, and k represents the semantic relevance coefficient. Indicates relevant cases, This represents the total number of cases.

7. A cloud-based architectural consulting system according to claim 1, characterized in that, The cloud platform server also includes a data update module, which is used to periodically obtain the latest building design code data, construction technology standard data, and building law and policy data from official databases of the construction industry and industry association release platforms, and update the construction industry knowledge base data in the data storage module. The update records are stored in the data storage module, and user terminals and consulting service terminals can query the data update history.

8. A cloud-based architectural consulting system according to claim 1, characterized in that, The cloud platform server also includes an early warning module, which is used to identify and warn of potential risks during the consultation process. These risks include the risk of conflict between the consultation plan and current building regulations and policies, and the risk of abnormal consultation result data. When a risk is identified, the early warning module sends risk warning information to the corresponding consultation service terminal and provides risk solution reference suggestions. The risk warning information and solution reference suggestions are associated and stored in the data storage module.

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