Whole-process engineering consultation method, server, storage medium and program product

Abstract

A whole-process engineering consultation method, a server, a storage medium and a program product relate to the field of engineering consultation, and the method comprises the following steps: obtaining engineering planning data, engineering working condition data, engineering acceptance data and user consultation information of a target project; extracting focus point data from the project planning data, the project working condition data and / or the project acceptance data, wherein the focus point correlation degree of the focus point data exceeds a preset correlation degree threshold value; determining possible problem points of the target project based on the focus point data and the historical engineering data; in a preset project solution database, searching for an improvement scheme corresponding to the possible problem point; simulating and implementing the improvement scheme to obtain an improvement result; and returning the user consultation information, the improvement scheme and the improvement result to the user. By implementing the method, the accuracy and efficiency of the consultation service can be improved.

Description

Technical Field

[0001] The present application relates to the field of engineering consulting, and in particular to a full-process engineering consulting method, server, storage medium and program product. Background Art

[0002] With the increase in the scale of projects and the complexity of their structures, the demand for consulting services throughout the entire project process has increased significantly. In the entire process from planning to acceptance of engineering projects, a large amount of data processing and information flow is involved, such as engineering planning data, engineering condition data, and engineering acceptance data. These data are crucial to ensuring the quality and safety of engineering projects, and are also the basis for providing accurate consulting services. Therefore, how to effectively integrate and utilize these data to provide accurate and real-time engineering consulting services has become the key to improving the success rate of engineering projects.

[0003] At present, the full-process engineering consulting method usually collects various types of engineering-related data, and then processes and analyzes them through a centralized data management system to identify key performance indicators and determine deviations in the engineering execution process, providing users with the function of monitoring and evaluating engineering projects.

[0004] However, these methods are inadequate in handling complex user inquiries and conducting personalized data analysis. Especially when users have specific concerns, these methods often find it difficult to quickly and accurately extract information that is highly relevant to user concerns from a large amount of engineering-related data, making it difficult to meet users' needs for efficient and targeted consulting, affecting the efficiency and quality of consulting services. Summary of the invention

[0005] The present application provides a full-process engineering consulting method, server, storage medium and program product for improving the accuracy and efficiency of consulting services.

[0006] In a first aspect, the present application provides a full-process engineering consulting method, which is applied to a server, and the method includes: obtaining engineering planning data, engineering working condition data, engineering acceptance data and user consulting information of a target project, wherein the engineering planning data is used to represent the planning and design information of the target project before construction, the engineering working condition data is used to represent the real-time status information of the target project during construction, and the engineering acceptance data is used to represent the safety and quality information of the target project after construction, and the user consulting information includes the user's focus on the target project; extracting focus point data whose correlation with the focus point exceeds a preset correlation threshold from the engineering planning data, the engineering working condition data and / or the engineering acceptance data; determining possible problem points of the target project based on the focus point data and historical engineering data, wherein the historical engineering data includes historical engineering planning data, historical engineering working condition data, historical engineering acceptance data and historical risk problems of historical projects; searching for improvement plans corresponding to the possible problem points in a preset project solution database; simulating the implementation of the improvement plan to obtain improvement results; and returning the user consulting information, the improvement plan and the improvement results to the user.

[0007] By adopting the above technical solution, first, the server obtains the full-cycle data of the target project (engineering planning data, engineering condition data and engineering acceptance data) and the user's concerns about the target project, so that it can extract the focus data that is highly relevant to the focus in a targeted manner. Secondly, based on historical engineering data, the server can accurately identify possible problem points of the target project and quickly match improvement plans from the preset solution database. Then, the server uses digital simulation technology to predict the improvement results after the implementation of the improvement plan, and provide users with more reliable and forward-looking consulting advice. This comprehensive and accurate consulting process significantly improves the efficiency and quality of engineering consulting, enables consulting services to better meet the personalized needs of users, and also provides strong support for the smooth implementation of engineering projects.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the focus point data whose correlation with the focus point exceeds a preset correlation threshold is extracted from the project planning data, the project operating condition data and / or the project acceptance data, specifically including: performing natural language processing on the project planning data, the project operating condition data and the project acceptance data to extract project keywords; calculating the correlation between the project keywords and the focus point; marking the data corresponding to the project keywords whose correlation exceeds the preset correlation threshold as the focus point data.

[0009] By adopting the above technical solution, the server performs natural language processing on the project planning data, the project working condition data and the project acceptance data and extracts project keywords, which can more intelligently and efficiently process a large amount of complex project data, thereby accurately screening out the focus data that is highly relevant to the user's focus. This data extraction method based on semantic understanding greatly improves the accuracy and pertinence of data analysis. Compared with traditional keyword matching or manual screening methods, it can not only process large-scale data, but also capture potential semantic associations, thereby discovering important information that may be overlooked.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the possible problem points of the target project are determined based on the focus point data and historical engineering data, and the historical engineering data include historical engineering planning data, historical engineering working condition data, historical engineering acceptance data and historical risk issues of historical projects. Specifically, the following steps are performed: determining the historical risk data in the historical engineering planning data, the historical engineering working condition data and the historical engineering acceptance data according to the historical risk issues, and the historical risk issues correspond to the historical risk data; calculating a first similarity between the historical risk data and the focus point data; and determining the historical risk issues corresponding to the historical risk data whose first similarity exceeds a preset first similarity threshold as possible problem points of the target project.

[0011] By adopting the above technical solution, the server compares and analyzes the focus data of the target project with the historical risk data of historical projects, and can effectively identify situations that are highly similar to historical risks, thereby accurately predicting the problems that the target project may face and realizing intelligent early warning of potential problems. This risk prediction method based on historical experience significantly improves the accuracy and timeliness of problem identification, and can make more comprehensive use of historical project cases compared to traditional experience judgment or single data analysis.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the simulation implements the improvement plan to obtain an improved result, specifically including: constructing a digital twin model of the target project; converting the improvement plan into an executable instruction set; applying the executable instruction set to the digital twin model of the target project to obtain the target project progress data after the implementation of the improvement plan; and converting the target project progress data into the improved result.

[0013] By adopting the above technical solutions, the server builds a digital twin model of the target project, realizing the virtual implementation and effect prediction of the improvement plan. This simulation method based on digital twin technology provides a powerful decision support for engineering consulting, which not only greatly reduces the implementation risk, but also significantly improves the efficiency of solution optimization.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of applying the executable instruction set to the digital twin model of the target project to obtain the target project progress data after the implementation of the improvement plan, the method also includes: determining the target performance indicator value in the target project progress data; comparing the target performance indicator value with a preset performance indicator threshold; if the target performance indicator value is lower than the preset performance indicator threshold, returning an improvement plan error prompt.

[0015] By adopting the above technical solution, the server sets the target performance indicator value and preset performance indicator threshold, and realizes the automatic evaluation of the effect of the improvement plan. This performance indicator-based evaluation mechanism provides an objective and quantitative standard for the quality control of the improvement plan, so that it can automatically identify the improvement plan that does not meet the standard and give error prompts in time, which greatly improves the efficiency and reliability of the consulting process, avoids providing unqualified improvement plans to users, and ensures the quality of consulting services.

[0016] In combination with some embodiments of the first aspect, in some embodiments, before the step of determining possible problem points of the target project based on the focus point data and historical engineering data, where the historical engineering data includes historical engineering planning data, historical engineering operating conditions data, historical engineering acceptance data and historical risk issues of historical projects, the method also includes: calculating a second similarity between the completed project and the target project based on a preset completed project database; determining a completed project whose second similarity exceeds a preset second similarity threshold as the historical project; and obtaining the historical engineering planning data, historical engineering operating conditions data, historical engineering acceptance data and historical risk issues of the historical project from the completed project database.

[0017] By adopting the above technical solution, the server calculates the similarity between the completed project and the target project, realizing the intelligent screening and application of historical data. This historical project matching mechanism based on similarity greatly improves the accuracy and relevance of the application of historical experience, so that historical project cases that are highly similar to the target project can be automatically identified, thereby extracting the most relevant historical engineering data.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of returning the user consultation information, the improvement plan and the improvement result to the user, the method also includes: receiving feedback information from the user on the improvement plan; and updating the preset project solution database based on the feedback information.

[0019] By adopting the above technical solution, the server collects user feedback information and updates the preset project solution database, thereby realizing continuous optimization and self-learning of the full-process engineering consulting method. This dynamic update mechanism based on user feedback enables the full-process engineering consulting method to continuously adapt to new project requirements, identify efficient and inefficient solutions, and thus optimize the content quality of the preset project solution database. In the second aspect, an embodiment of the present application provides a server, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0020] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a server, enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] It is understandable that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. First, the server obtains the full-cycle data of the target project (engineering planning data, engineering condition data, and engineering acceptance data) and the user's concerns about the target project, so that it can extract the focus data that is highly relevant to the focus in a targeted manner. Secondly, based on historical engineering data, the server can accurately identify possible problem points of the target project and quickly match improvement plans from the preset solution database. Then, the server uses digital simulation technology to predict the improvement results after the implementation of the improvement plan, and provide users with more reliable and forward-looking consulting advice. This comprehensive and accurate consulting process significantly improves the efficiency and quality of engineering consulting, enables consulting services to better meet the personalized needs of users, and also provides strong support for the smooth implementation of engineering projects.

[0024] 2. By adopting the above technical solutions, the server builds a digital twin model of the target project, realizing the virtual implementation and effect prediction of the improvement plan. This simulation method based on digital twin technology provides a powerful decision-making support for engineering consulting, which not only greatly reduces the implementation risk, but also significantly improves the efficiency of solution optimization.

[0025] 3. By adopting the above technical solution, the server sets the target performance indicator value and preset performance indicator threshold, and realizes the automatic evaluation of the effect of the improvement plan. This performance indicator-based evaluation mechanism provides an objective and quantitative standard for the quality control of the improvement plan, so that it can automatically identify the improvement plan that does not meet the standard and give error prompts in time, which greatly improves the efficiency and reliability of the consulting process, avoids providing unqualified improvement plans to users, and ensures the quality of consulting services. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the whole process engineering consulting method in the embodiment of the present application; Figure 2 is another flow chart of the full-process engineering consulting method in an embodiment of the present application; Figure 3 It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] The following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the full-process engineering consulting method in an embodiment of the present application.

[0030] S101, obtaining engineering planning data, engineering working condition data, engineering acceptance data and user consultation information of a target project, wherein the engineering planning data is used to represent the planning and design information of the target project before construction, the engineering working condition data is used to represent the real-time status information of the target project during construction, the engineering acceptance data is used to represent the safety and quality information of the target project after construction, and the user consultation information includes the user's concerns about the target project; Among them, the target project refers to a specific engineering project that currently requires full-process engineering consultation. Engineering planning data is used to represent the planning and design information of the target project before construction, such as architectural design drawings, construction organization design, etc. Engineering condition data is used to represent the real-time status information of the target project during construction, including construction progress, material usage, equipment operation status, etc. Engineering acceptance data is used to represent the safety and quality information of the target project after construction, including quality inspection reports, safety assessment results, etc. User consultation information represents the specific consulting needs raised by users, and the focus may include specific issues or requirements in terms of construction period, cost, quality, safety, etc.

[0031] After receiving the user's consultation information, the server first performs this step to obtain comprehensive project information and user needs. Specifically, the server connects with multiple data sources such as the project management system, construction site monitoring system, and acceptance management system to obtain the engineering planning data, engineering condition data, and engineering acceptance data of the target project in real time. At the same time, the server receives user consultation information through the user interface or API interface, and the user consultation information includes the user's concerns about the target project.

[0032] S102, extracting, from the project planning data, the project working condition data and / or the project acceptance data, the focus point data whose relevance to the focus point exceeds a preset relevance threshold; Among them, the focus data refers to specific engineering data that is highly relevant to the user's focus. The relevance indicates the degree of association between the engineering planning data, engineering condition data, and engineering acceptance data and the focus, which can be calculated through semantic analysis, keyword matching, etc. The preset relevance threshold refers to the pre-set relevance judgment standard, which is used to screen highly relevant engineering data.

[0033] After obtaining the engineering planning data, the engineering condition data, the engineering acceptance data and the focus points, the server immediately performs this step to extract the engineering data most relevant to the focus points. Specifically, first, the server performs natural language processing on the engineering planning data, engineering condition data and engineering acceptance data to extract engineering keywords. Then, the server calculates the correlation between these engineering keywords and the focus points. The calculation of the correlation may involve methods such as the TF-IDF algorithm and cosine similarity. Next, the server compares the calculated correlation with the preset correlation threshold, and only data whose correlation exceeds the preset correlation threshold will be marked as focus point data. This process may require multiple iterations to ensure that the extracted data is both sufficient and accurate.

[0034] Optionally, under normal circumstances, extracting the focus point data whose correlation with the focus point exceeds a preset correlation threshold from the project planning data, the project operating condition data and / or the project acceptance data can be achieved in the following way: performing natural language processing on the project planning data, the project operating condition data and the project acceptance data to extract project keywords; calculating the correlation between the project keywords and the focus points; marking the data corresponding to the project keywords whose correlation exceeds the preset correlation threshold as focus point data.

[0035] S103, determining possible problem points of the target project based on the focus point data and historical engineering data, where the historical engineering data includes historical engineering planning data, historical engineering working condition data, historical engineering acceptance data, and historical risk issues of historical projects; Among them, historical engineering data refers to the full-cycle data and experience summary of completed projects. Historical engineering planning data, historical engineering working condition data, and historical engineering acceptance data correspond to the data of historical projects before, during, and after construction, respectively. Historical risk issues refer to various types of problems and risks that actually occurred in historical projects. Possible problem points refer to potential risks or problems that the target project may face as predicted through data analysis.

[0036] After extracting the point of interest data, the server immediately performs this step to identify potential risks. Specifically, first, the server retrieves historical engineering data similar to the target project. The judgment of similarity may be based on multiple factors such as project type, scale, and geographical location. Then, the server conducts an in-depth comparative analysis of the point of interest data of the target project with the historical engineering data. This process may involve technologies such as pattern recognition and machine learning. The server will focus on the parts of the historical projects that are similar to the point of interest data, and analyze whether these similar parts have caused problems or risks in the historical projects. In this way, the server is able to identify possible problem points in the target project. These possible problem points include potential risks of construction delays, cost overruns, quality risks, etc. The server will evaluate and sort these possible problem points to determine their importance and urgency.

[0037] Optionally, under normal circumstances, based on the focus point data and historical engineering data, determining the possible problem points of the target project can be achieved in the following ways: determining historical risk data in historical engineering planning data, historical engineering operating condition data, and historical engineering acceptance data based on historical risk issues, where the historical risk issues correspond to the historical risk data; calculating a first similarity between the historical risk data and the focus point data; and determining the historical risk issues corresponding to the historical risk data whose first similarity exceeds a preset first similarity threshold as possible problem points of the target project.

[0038] S104, searching for an improvement solution corresponding to the possible problem point in a preset project solution database; The preset project solution database refers to a pre-established structured data set containing solutions to various engineering problems. Possible problem points refer to potential risks or problems that the target project may face as predicted through preliminary analysis. Improvement plans refer to specific optimization or solution measures for possible problem points.

[0039] After the server determines the possible problem points of the target project, it immediately performs this step to find an appropriate solution. Specifically, first, the server optimizes the index of the preset project solution database, which may use technologies such as inverted index or multidimensional index to improve query efficiency. Then, the server converts each possible problem point into a structured query statement, which may involve natural language processing technology to extract the key features of the possible problem point. Next, the server uses these query statements to perform multi-dimensional matching in the preset project solution database, and the factors considered may include problem type, project stage, scope of impact, etc. The matching process may use technologies such as fuzzy query or semantic similarity calculation to improve the flexibility and accuracy of the search. For each possible problem point, the server may retrieve multiple potential improvement solutions. These improvement solutions will be sorted according to factors such as their relevance, implementation difficulty, and historical success rate. If a possible problem point cannot find a completely matching improvement solution, the server may use a combined reasoning method to intelligently combine multiple related solutions. Finally, the server generates a detailed list of all possible problem points and their corresponding improvement solutions to prepare for the next simulation implementation.

[0040] S105, simulating the implementation of the improvement plan to obtain an improvement result; Among them, simulation implementation refers to the process of executing the improvement plan in a virtual environment and observing its effect. The improvement plan refers to the optimization measures for possible problem points selected from the preset project solution database. The improvement result refers to the change of the target project after the simulation implementation of the improvement plan.

[0041] After determining the improvement plan, the server immediately performs this step to evaluate the feasibility and effect of the improvement plan. Specifically, first, the server builds a digital target project model based on the existing data of the target project. This digital target project model includes not only the physical structure of the target project, but also various engineering parameters, resource allocation, schedule and other information. The construction process may involve BIM technology, system dynamics modeling, etc. After completing the basic digital target project model, the server will calibrate the model to ensure that it can accurately reflect the status of the actual target project. Then, the server converts each improvement plan into an operation sequence that can be executed on the digital target project model. These operation sequences may include modifying certain parameters, adjusting resource allocation, updating schedules, etc. The server will execute these operation sequences one by one on the digital target project model, and monitor and record the changes of various key indicators in real time. The simulation process will take into account time factors and may be repeated multiple times to observe the short-term and long-term effects of the improvement plan. For complex improvement plans, the server may use Monte Carlo simulation and other technologies to generate multiple possible result scenarios. During the simulation process, the server will also consider various uncertainties and risk events to evaluate the robustness of the improvement plan. Finally, the server will generate a detailed simulation report, including information such as the expected effect, implementation difficulty, and cost-benefit analysis of each improvement plan.

[0042] S106: Return the user consultation information, the improvement plan and the improvement result to the user.

[0043] Among them, the improvement plan refers to the specific solution measures proposed for the user's consultation information. The improvement result refers to the expected effect of the plan obtained by simulating the implementation of the improvement plan. Return refers to the process of delivering the user's consultation information, the improvement plan and the improvement result to the user in an appropriate form.

[0044] After completing the simulation evaluation of the improvement plan, the server immediately performs this step to provide complete consultation feedback to the user. Specifically, first, the server organizes and formats the user consultation information, improvement plan and improvement results. The user consultation information will be reorganized to ensure that the consultation focus and original needs are accurately reflected. The improvement plan will be organized into a structured form, usually including a plan overview, specific implementation steps, required resources, etc. The improvement results will be converted into an easy-to-understand form, which may include data charts, trend analysis, comparison matrices, etc. The server selects an appropriate presentation method, which may be a text report, an interactive dashboard, or a multimedia presentation. Finally, the server will securely transmit these user consultation information, improvement plans, and improvement results to the user through a preset communication channel (such as email, API interface, or user interface). During the transmission process, the server will ensure the integrity and confidentiality of the data.

[0045] By adopting the above technical solution, first, the server obtains the full-cycle data of the target project (engineering planning data, engineering condition data and engineering acceptance data) and the user's concerns about the target project, so that it can extract the focus data that is highly relevant to the focus in a targeted manner. Secondly, based on historical engineering data, the server can accurately identify possible problem points of the target project and quickly match improvement plans from the preset solution database. Then, the server uses digital simulation technology to predict the improvement results after the implementation of the improvement plan, and provide users with more reliable and forward-looking consulting advice. This comprehensive and accurate consulting process significantly improves the efficiency and quality of engineering consulting, enables consulting services to better meet the personalized needs of users, and also provides strong support for the smooth implementation of engineering projects.

[0046] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the full-process engineering consulting method in an embodiment of the present application.

[0047] S201, obtaining engineering planning data, engineering working condition data, engineering acceptance data and user consultation information of a target project, wherein the engineering planning data is used to represent the planning and design information of the target project before construction, the engineering working condition data is used to represent the real-time status information of the target project during construction, the engineering acceptance data is used to represent the safety and quality information of the target project after construction, and the user consultation information includes the user's concerns about the target project; For details, please refer to step S101, which will not be described in detail here.

[0048] S202, extracting, from the project planning data, the project operating condition data and / or the project acceptance data, the focus point data whose relevance to the focus point exceeds a preset relevance threshold; For details, please refer to step S102, which will not be described in detail here.

[0049] S203, calculating a second similarity between the completed project and the target project based on a preset completed project database; The preset completed project database refers to a structured data set containing information on engineering projects completed in the past. Completed projects refer to engineering projects that have been completed and have detailed records. Target projects refer to specific engineering projects that currently require full-process engineering consulting. The second similarity refers to a numerical value that quantitatively measures the degree of similarity between two projects in a specific dimension.

[0050] After obtaining the engineering planning data, engineering condition data, and engineering acceptance data of the target project, the server immediately performs this step to find similar historical projects. Specifically, the server first extracts features from the target project, which may include information on multiple dimensions such as project type, scale, geographic location, technical requirements, and budget range. Then, the server designs a multi-dimensional similarity calculation model, which may involve mathematical methods such as vector space model, cosine similarity, and Euclidean distance. For different feature dimensions, the server may assign different weights to reflect their importance in similarity judgment. Next, the server traverses each completed project in the completed project database and compares each dimension one by one. During the comparison process, the server may use fuzzy matching or semantic analysis technology to process the features of text descriptions. For numerical features, the server may perform standardization to eliminate the influence of dimensional differences. During the calculation process, the server may also consider time factors and give higher weights to newer completed projects. Finally, the server combines the comparison results of various dimensions to obtain a comprehensive second similarity. This second similarity is usually standardized to between 0 and 1, where 1 represents complete similarity and 0 represents complete dissimilarity. The server may verify and adjust the calculation results multiple times to ensure the accuracy and reliability of the similarity calculation.

[0051] S204, determining the completed project whose second similarity exceeds a preset second similarity threshold as the historical project; The second similarity refers to a quantitative index of the project similarity calculated in step S203. The preset second similarity threshold represents a preset similarity judgment standard. A historical project refers to a completed project that is considered to have a high degree of similarity with the target project and can provide a reference for the current consultation.

[0052] After the server completes the similarity calculation of all completed projects, it immediately performs this step to filter out the most relevant historical projects. Specifically, first, the server determines an appropriate preset second similarity threshold based on industry standards, expert experience, or historical data analysis. The setting of the preset second similarity threshold requires a balance between accuracy and recall, ensuring that the selected historical projects are similar enough and that there are a sufficient number of reference projects. Then, the server sorts the second similarities of all completed projects, usually in descending order. The server compares the second similarity of each completed project with the preset second similarity threshold one by one, and marks the completed projects that exceed the preset second similarity threshold as historical projects.

[0053] S205, obtaining historical engineering planning data, historical engineering working condition data, historical engineering acceptance data, and historical risk issues of the historical project from the completed project database; Among them, historical project planning data is used to represent the planning and design information of historical projects before construction, such as architectural design drawings, construction organization design, etc. Historical project working condition data is used to represent the real-time status information of historical projects during construction, including construction progress, material usage, equipment operation status, etc. Historical project acceptance data represents the safety and quality information of historical projects after construction, including quality inspection reports, safety assessment results, etc. Historical risk issues refer to the various risks and problems encountered during the implementation of historical projects and their solutions.

[0054] After the server determines the relevant historical projects, it immediately performs this step to collect detailed information about these historical projects. Specifically, the server retrieves the historical project planning data, historical project working condition data, historical project acceptance data, and historical risk issues of the historical projects from the completed project database. For historical project planning data, the server may obtain information such as the initial design drawings, resource allocation plans, schedules, and budget estimates of historical projects. These data are usually stored in the form of structured documents or CAD files. When obtaining historical project working condition data, the server extracts various monitoring data during the implementation of historical projects, such as equipment operating parameters, material usage, and environmental monitoring data. These data may be stored in the form of time series, and the server needs to consider the time dimension and continuity of the data. For historical project acceptance data, the server obtains information such as quality inspection reports, performance evaluation results, and user satisfaction surveys after the completion of historical projects. These data may include both quantitative and qualitative types. When extracting historical risk issues, the server pays attention to information such as various challenges encountered during the implementation of historical projects, descriptions of risk events, countermeasures taken, and their effects. This information may be scattered in multiple data sources such as project logs, problem reports, and experience summaries. When acquiring this data, the server needs to ensure the integrity and consistency of the data, and may perform data cleaning and format conversion. For data from different sources, the server may need to integrate and align the data to ensure the consistency of the information. At the same time, the server will pay attention to the privacy and security issues of the data to ensure that sensitive information is properly protected. Finally, the server will organize all the acquired data into a structured form for subsequent analysis and utilization.

[0055] S206, determining possible problem points of the target project based on the focus point data and historical engineering data, where the historical engineering data includes historical engineering planning data, historical engineering working condition data, historical engineering acceptance data, and historical risk issues of historical projects; For details, please refer to step S103, which will not be described in detail here.

[0056] S207, searching for an improvement solution corresponding to the possible problem in a preset project solution database; For details, please refer to step S104, which will not be described in detail here.

[0057] S208. Build a digital twin model of the target project; The digital twin model of the target project refers to the digital representation of the target project created in a virtual environment. Construction refers to the process of creating, designing and implementing the digital twin model of the target project. The digital twin model not only contains the static information of the target project, but also reflects the dynamic changes and operating status of the target project in real time.

[0058] The server performs this step immediately after collecting all the existing data of the target project to create a virtual project environment. Specifically, the server selects appropriate modeling tools and platforms, which may involve technologies such as BIM (Building Information Modeling) and CAD (Computer-Aided Design). The server creates a three-dimensional geometric model based on the physical structure of the target project, while embedding various engineering parameters and attribute information. For complex target projects, the server may use multi-scale modeling technology to build layer by layer from macro to micro. On the basis of the geometric model, the server adds virtual representations of various sensors and monitoring points to simulate real-time data collection. The server also establishes logical relationships and data flows between various subsystems to ensure that the target project digital twin model can accurately reflect the overall behavior of the target project. During the modeling process, the server continuously verifies and calibrates to ensure the consistency of the target project digital twin model with the actual target project. Finally, the server establishes an interactive interface for the target project digital twin model, allowing subsequent steps to be easily simulated and analyzed.

[0059] S209, converting the improvement scheme into an executable instruction set; Among them, the improvement plan refers to the optimization measures for possible problem points selected from the preset project solution database. The executable instruction set represents a series of operation commands that can be directly executed in the digital twin model of the target project. Conversion refers to the process of converting abstract improvement measures into specific and operational instructions.

[0060] After the server has completed the construction of the digital twin model of the target project and the formulation of the improvement plan, it will immediately perform this step to prepare for the simulation implementation. Specifically, first, the server will conduct a detailed analysis of the improvement plan to identify the key action points and change items. Then, the server will map each key action point and change item to a specific object or parameter in the digital twin model of the target project. For measures involving physical structure changes, the server will generate corresponding geometric transformation instructions. For measures involving parameter adjustment, the server will create corresponding numerical modification instructions. The server will also consider the timing of the implementation of the improvement measures and generate a sequence of instructions containing time information. In order to handle complex improvement measures, the server may use a rule engine or expert system to convert high-level strategies into low-level operational instructions. During the conversion process, the server will perform a legality check to ensure that the generated instructions do not violate physical constraints or engineering specifications. For measures that may have a chain reaction, the server will generate a series of interrelated instructions to simulate the entire change process. The server will also add error handling and rollback mechanisms to each instruction to deal with abnormal situations that may occur during the simulation. Finally, the server will organize all instructions into a structured executable instruction set and assign them a unique identifier for subsequent execution and tracking.

[0061] S210, applying the executable instruction set to the digital twin model of the target project to obtain progress data of the target project after the improvement plan is implemented; Among them, application refers to the process of executing executable instruction sets on the digital twin model of the target project. The target project progress data represents the changes in various indicators of the target project after the simulation implementation of the improvement plan.

[0062] After the server has completed the preparation of the executable instruction set, it immediately performs this step to simulate the implementation effect of the improvement plan. Specifically, first, the server will take a snapshot backup of the target project digital twin model so that it can restore the initial state when needed. Then, the server will start the simulation engine and start executing the executable instruction set one by one. For each instruction, the server will first check whether its execution conditions are met, and then perform corresponding operations in the target project digital twin model, which may include modifying the geometry, updating parameter values, adjusting resource allocation, etc. During the execution process, the server will monitor the state changes of the target project digital twin model in real time and record the dynamic changes of various key indicators. For instructions involving time series, the server will simulate the advancement of time, and may use discrete event simulation or continuous time simulation technology. The server will also consider the influence of random factors and may perform multiple Monte Carlo simulations to obtain more reliable results. During the simulation process, if an abnormal situation is encountered, the server will handle it according to the preset error handling mechanism and roll back to a safe state if necessary. For complex target projects, the server may use parallel computing technology to simulate the changes of multiple subsystems at the same time. During the entire simulation process, the server will continue to collect and organize various progress data, including but not limited to project progress, resource utilization, quality indicators, cost data, etc. Finally, the server will generate a detailed simulation report, including comparison data before and after improvement, status information of key nodes, potential risk warnings, etc.

[0063] S211, determining the target performance indicator value in the target project progress data; After obtaining the simulated target project progress data, the server immediately performs this step to evaluate the effect of the improvement plan. Specifically, first, the server will determine the key performance indicators (KPIs) that need to be focused on as target performance indicators based on the type of target project and the improvement goal. The target performance indicators may include the target project completion progress, cost control, quality compliance rate, resource utilization efficiency, etc. Then, the server will extract the raw data related to these KPIs from the target project progress data. For directly available data, the server will perform data cleaning and standardization. For composite target performance indicators that need to be calculated, the server will apply predefined calculation formulas, which may involve mathematical operations such as weighted average and normalization. During the calculation process, the server will consider the time dimension of the data and may calculate the trend or rate of change of certain indicators. For indicators with uncertainty, the server may use statistical methods to calculate confidence intervals or risk values. The server will also perform correlation analysis between indicators to identify possible internal correlations. Finally, the server will generate a report containing the target performance indicator values, which may include numerical values, charts, and brief explanations.

[0064] S212, comparing the target performance indicator value with a preset performance indicator threshold; After determining the target performance indicator value, the server immediately performs this step to evaluate whether the improvement plan has achieved the expected effect. Specifically, first, the server retrieves the preset performance indicator threshold, where the preset performance indicator threshold refers to the minimum acceptable value of the preset target performance indicator. These preset performance indicator thresholds may come from industry standards, expert experience, or historical data analysis. Then, the server compares each set of target performance indicator values ​​with the corresponding preset performance indicator threshold. For a single numerical indicator, the server may directly perform a numerical comparison. For a range-type indicator, the server may check whether the target performance indicator value falls within the acceptable range. For a trend-type indicator, the server may analyze whether its change direction is in line with expectations. During the comparison process, the server considers the importance weight of the target performance indicator and may use a weighted scoring method. For interrelated indicators, the server may perform a comprehensive evaluation to consider the trade-off relationship between indicators. The server also calculates the degree of deviation of each target performance indicator value from the preset performance indicator threshold, which may be expressed as a percentage or standard deviation. For target performance indicator values ​​close to the threshold boundary, the server may perform a sensitivity analysis to evaluate the impact of small changes on the overall evaluation. During the comparison process, the server will also consider the special circumstances and external factors of the target project, and may dynamically adjust the evaluation criteria of certain target performance indicators. Finally, the server will generate a detailed comparison report, including the compliance status, degree of deviation and potential improvement space of each target performance indicator value.

[0065] S213. If the target performance indicator value is lower than the preset performance indicator threshold, an improvement plan error prompt is returned; After the server has completed the comparison of the target performance indicator value with the preset performance indicator threshold, if it is found that the target performance indicator is not met, it will immediately perform this step to provide feedback. Specifically, first, the server will determine which target performance indicators have not reached the preset performance indicator threshold and calculate the degree of their gap. Then, the server will analyze the relationship between these target performance indicators that have not been met and identify possible root causes. For each target performance indicator that has not been met, the server will generate a detailed analysis report, including the expected value, actual value, gap size, and possible impact. The server will also try to trace the specific improvement measures that led to the failure to meet the target performance indicator, which may involve decision trees or cause-and-effect analysis techniques. When generating error prompts for improvement plans, the server will select appropriate expressions and professional terms.

[0066] S214, converting the target project progress data into the improvement result; After confirming that the improvement plan has achieved the expected effect, the server immediately executes this step to generate the improvement results. Specifically, first, the server will perform comprehensive data cleaning and normalization on the target project progress data to ensure data consistency and comparability. Then, the server will select appropriate analysis methods according to different data types. For time series data, the server may perform trend analysis and prediction. For categorical data, the server may calculate the distribution and changes of each category. For numerical data, the server may calculate statistical indicators such as mean and standard deviation. The server will also perform multi-dimensional data cross-analysis to explore the correlation and potential causal relationship between indicators. During the data processing process, the server may use data visualization techniques such as line charts, bar charts, heat maps, etc. to convert complex data relationships into intuitive graphics. For key improvement effects, the server will calculate specific improvement percentages or absolute value growth. In the process of converting the target project progress data into improvement results, the server will consider the needs of different users and may generate multiple versions of reports, including technical details version, management summary version, etc. For important findings or abnormal situations, the server will generate special instructions or warning information. Finally, the server organizes all analysis results into a structured improvement results report, including sections such as executive summary, detailed analysis, chart descriptions, and future recommendations.

[0067] S215, returning the user consultation information, the improvement plan and the improvement result to the user; For details, please refer to step S106, which will not be described in detail here.

[0068] S216, receiving feedback information from the user on the improvement plan; After showing the improvement results to the user, the server enters a waiting state to receive user feedback information, where the feedback information represents the user's evaluation, opinions and suggestions on the implementation effect of the improvement plan. Specifically, the server will provide users with multiple feedback channels, such as displaying evaluation forms, online questionnaires or text input interfaces. The server will design a structured feedback template, which includes two parts: quantitative scoring and qualitative evaluation. Quantitative scoring may involve scoring the effectiveness, feasibility, and innovation of the improvement plan. Qualitative evaluation allows users to enter detailed text opinions, which may include comments on specific improvement measures, challenges that may be encountered during implementation, and predictions of long-term impacts. The server will monitor feedback input in real time to ensure the integrity and validity of the data. For incomplete or vague feedback, the server may guide users to provide more detailed information through intelligent prompts. During the reception process, the server will perform preliminary natural language processing on the feedback information to identify keywords and sentiment tendencies. For complex feedback information, the server may use machine learning algorithms for topic classification and intent recognition. The server will also record the timestamp and user information of the feedback information for subsequent data analysis and tracking.

[0069] S217: Based on the feedback information, the preset project solution database is updated.

[0070] After receiving and initially analyzing the feedback, the server immediately performs this step to optimize and improve the preset project solution database. Specifically, first, the server will conduct an in-depth analysis of the feedback, possibly using natural language processing and machine learning techniques to extract key points. Then, the server will compare these key points with the content of the existing preset project solution database. For new issues or new situations mentioned in the feedback, the server will create new entries in the preset project solution database. For existing ones, the server will modify or supplement them based on the feedback.

[0071] By adopting the above technical solutions, the server builds a digital twin model of the target project, realizing the virtual implementation and effect prediction of the improvement plan. This simulation method based on digital twin technology provides a powerful decision support for engineering consulting, which not only greatly reduces the implementation risk, but also significantly improves the efficiency of solution optimization.

[0072] By adopting the above technical solution, the server sets the target performance indicator value and preset performance indicator threshold, and realizes the automatic evaluation of the effect of the improvement plan. This performance indicator-based evaluation mechanism provides an objective and quantitative standard for the quality control of the improvement plan, so that it can automatically identify the improvement plan that does not meet the standard and give error prompts in time, which greatly improves the efficiency and reliability of the consulting process, avoids providing unqualified improvement plans to users, and ensures the quality of consulting services.

[0073] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0074] It should be noted that Figure 3 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0075] like Figure 3 As shown, the server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0076] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0077] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0078] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0080] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the full-process engineering consulting method provided by the above embodiment is implemented.

[0081] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the full-process engineering consulting method provided in the above embodiment.

[0082] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0083] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0084] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A full-process engineering consulting method, characterized in that: Applied to a server, the method comprises: Acquire engineering planning data, engineering working condition data, engineering acceptance data, and user consultation information of the target project, wherein the engineering planning data is used to represent the planning and design information of the target project before construction, the engineering working condition data is used to represent the real-time status information of the target project during construction, the engineering acceptance data is used to represent the safety and quality information of the target project after construction, and the user consultation information includes the user's concerns about the target project; Extracting, from the engineering planning data, the engineering condition data and / or the engineering acceptance data, the point of interest data whose relevance to the point of interest exceeds a preset relevance threshold; Determine possible problem points of the target project based on the focus point data and historical engineering data, wherein the historical engineering data includes historical engineering planning data, historical engineering working condition data, historical engineering acceptance data, and historical risk issues of historical projects; Find the improvement solutions corresponding to the possible problem points in the preset project solution database; Simulate and implement the improvement plan to obtain improvement results; The user consultation information, the improvement plan and the improvement result are returned to the user.

2. The method according to claim 1, characterized in that The extracting, from the engineering planning data, the engineering condition data and / or the engineering acceptance data, the point of interest data having a correlation with the point of interest exceeding a preset correlation threshold value specifically includes: Performing natural language processing on the engineering planning data, the engineering operating condition data and the engineering acceptance data to extract engineering keywords; Calculating the relevance between the engineering keywords and the focus points; The data corresponding to the engineering keywords whose relevance exceeds the preset relevance threshold are marked as the focus point data.

3. The method according to claim 1, characterized in that The possible problem points of the target project are determined based on the focus point data and historical engineering data, wherein the historical engineering data includes historical engineering planning data, historical engineering working condition data, historical engineering acceptance data and historical risk issues of historical projects, specifically including: Determine historical risk data in the historical engineering planning data, the historical engineering working condition data, and the historical engineering acceptance data according to the historical risk problem, wherein the historical risk problem corresponds to the historical risk data; Calculating a first similarity between the historical risk data and the focus point data; The historical risk issues corresponding to the historical risk data whose first similarity exceeds a preset first similarity threshold are determined as possible problem points of the target project.

4. The method according to claim 1, characterized in that: The simulation implements the improvement plan to obtain the improvement result, which specifically includes: Build a digital twin model of the target project; Converting the improved scheme into an executable instruction set; Applying the executable instruction set to the digital twin model of the target project to obtain progress data of the target project after the improvement plan is implemented; The target project progress data is converted into the improvement results.

5. The method according to claim 4, characterized in that After the step of applying the executable instruction set to the target project digital twin model to obtain the target project progress data after the improvement plan is implemented, the method further includes: Determining target performance indicator values ​​in the target project progress data; Comparing the target performance indicator value with a preset performance indicator threshold; If the target performance indicator value is lower than the preset performance indicator threshold, an improvement plan error prompt is returned.

6. The method according to claim 1, characterized in that Before the step of determining possible problem points of the target project based on the focus point data and historical engineering data, wherein the historical engineering data includes historical engineering planning data, historical engineering working condition data, historical engineering acceptance data, and historical risk issues of historical projects, the method further includes: Based on a preset completed project database, calculating a second similarity between the completed project and the target project; Determine the completed project whose second similarity exceeds a preset second similarity threshold as the historical project; The historical project planning data, historical project working condition data, historical project acceptance data and historical risk issues of the historical project are obtained from the completed project database.

7. The method according to claim 1, characterized in that After the step of returning the user consultation information, the improvement plan and the improvement result to the user, the method further includes: receiving feedback information from the user on the improvement plan; Based on the feedback information, the preset project solution database is updated.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

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