Construction project approval method and system based on artificial intelligence

Abstract

The invention discloses a construction project examination and approval method and system based on artificial intelligence, and relates to the technical field of construction project examination and approval. The accuracy improvement rate of the construction project approval system compared with an old system is calculated according to the authenticity of the personnel registration information; for simple examination and approval items, relevant data information is obtained according to data docking and sharing, the simple items are examined and approved by comparing and verifying the data information in real time, and an examination and approval result is automatically sent to an applicant; formulating a unified enterprise credit information management mechanism, and evaluating the compliance of the enterprise; and the effect of the construction project approval system is evaluated based on fuzzy reasoning according to the accuracy improvement rate of the authenticity judgment of the personnel registration information and the compliance of the enterprise. The problems of're-approval and light supervision 'of some regional departments are solved, and the approval and supervision efficiency of the construction project approval system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction project approval, and more specifically, to a construction project approval method and system based on artificial intelligence. Background Art

[0002] The current construction project approval model is not convenient for information sharing and data query, which will lead to time differences in information exchange between industries and departments, resulting in barriers between data and difficulty in achieving intercommunication. This not only affects the integration and operability of data, but also is not conducive to timely and effective decision-making by management departments.

[0003] Deficiencies of existing technologies:

[0004] According to the previous housing and construction business systems, they are basically built and deployed independently according to a single enterprise type or business system. For example, each system such as the second-level construction engineer and the construction project approval system is in an independent operation state. No system can globally reflect the overall situation of various types of enterprises in the real estate industry and realize the unified management function of the management department. Therefore, the present invention proposes a construction project approval method and system based on artificial intelligence, the purpose of which is to solve the increasing approval workload with the reform of the approval system. The staff configuration needs to be strengthened, and it is urgently hoped that advanced technology can be introduced to improve the approval efficiency. Some local departments have the problem of "heavy approval and light supervision". The dynamic verification of construction projects has not established a normalized mechanism and early warning indicator management, resulting in weak supervision capabilities during and after the system. Enterprises need to fill in the declaration information repeatedly every time they apply, there is a lot of input work, and the window staff need to spend a lot of time to verify. With the reform of qualifications and the decentralization of qualifications, the number of approval matters will increase day by day. It is difficult for the system to carry the approval of too many matters, and the system scalability is poor.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a construction project approval method and system based on artificial intelligence, which solves the problems raised in the above-mentioned background technology through the construction project approval method and system based on artificial intelligence.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A construction project approval method and system based on artificial intelligence, comprising: obtaining personnel registration information data, and using data comparison analysis and intelligent recognition technology to determine the authenticity of personnel registration information, and calculating the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity determination of personnel registration information; for simple approval matters, obtaining relevant data information based on data docking and sharing, approving simple matters through real-time comparison and verification of data information, and automatically sending the approval results to the applicant; entering enterprise information data, uniformly publicizing and publishing enterprise credit information, and formulating a unified enterprise credit information management mechanism to supervise the industry behavior norms of enterprises in the entire real estate industry and evaluate the compliance of enterprises; building a dynamic supervision information platform for the real estate industry, realizing all-round supervision of each key link in the entire life cycle of the project, and evaluating the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of the authenticity determination of personnel registration information and the compliance of the enterprise.

[0009] In a preferred embodiment, the personnel registration information data includes personnel education information, professional and technical titles, business licenses, qualification certificates, three types of personnel, and special personnel registration information.

[0010] In a preferred embodiment, the steps of the comparison analysis and intelligent recognition technology are: using OCR technology to intelligently identify personnel registration information; comparing the identified content with a credible external database to verify whether the information provided by the personnel is true; setting rules to check whether the work experience and job title are consistent with the standards of the same industry; identifying abnormal data based on a support vector machine (SVM) according to the rules and the external database. The process of identifying abnormal data based on a support vector machine (SVM) is as follows: training a classification SVM, learning through a training set, finding a hyperplane, and dividing the data points into normal data points and abnormal data points by calculating the distance between the data points and the hyperplane to identify abnormal data.

[0011] In a preferred embodiment, the process of determining the authenticity of personnel registration information is as follows: comparing and analyzing the distance between the data point and the hyperplane with a preset distance threshold; if the distance between the data point and the hyperplane is less than the preset distance threshold, the personnel registration information is true; if the distance between the data point and the hyperplane is greater than the preset distance threshold, the personnel registration information is false, and the registration information is returned to the registered person.

[0012] In a preferred embodiment, the steps for approving simple matters by comparing and verifying data information in real time are as follows: obtaining data information and integrating data sources through data docking technology; establishing a unified data exchange platform to realize information sharing between different systems to ensure that the required information can be obtained in real time during the approval process; performing real-time comparison and verification of data, and automatically determining the approval result based on pre-set approval rules; for qualified applications, the system will automatically mark them as passed; for applications that do not meet the requirements, the system will mark them as rejected.

[0013] In a preferred embodiment, the specific implementation process of the real estate industry dynamic supervision information platform is as follows: based on the real estate industry dynamic supervision information platform, all-round supervision is achieved on each key link of the project's entire life cycle, and a communication bridge is built between the Housing and Urban-Rural Development Department and the competent departments of various cities, and a verification, supervision, processing and feedback mechanism for problem projects is implemented.

[0014] In a preferred embodiment, the process of evaluating the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance of the enterprise is as follows: the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance of the enterprise are defined as input variables, and they are divided into different fuzzy sets respectively; the effect of the construction project approval system is defined as an output variable, and it is divided into fuzzy sets; fuzzy rules are formulated to describe the impact of the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance definition of the enterprise on the effectiveness of the construction project approval system; fuzzy reasoning is performed according to the fuzzy rules to evaluate the effectiveness of the construction project approval system.

[0015] A construction project approval method and system based on artificial intelligence, comprising: an acquisition module, an approval module, a supervision module, and an evaluation module, wherein the modules are connected: the acquisition module is used to acquire personnel registration information data, and use data comparison analysis and intelligent recognition technology to determine the authenticity of personnel registration information, and calculate the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity determination of personnel registration information; the approval module is used to obtain relevant data information for simple approval matters based on data docking and sharing, approve simple matters through real-time comparison and verification of data information, and automatically send the approval results to the applicant; the supervision module is used to input enterprise information data, uniformly publicize and publish enterprise credit information, and formulate a unified enterprise credit information management mechanism to supervise the industry behavior norms of enterprises in the entire real estate industry and evaluate the compliance of enterprises; the evaluation module is used to build a dynamic supervision information platform for the real estate industry, realize all-round supervision of each key link in the entire life cycle of the project, and evaluate the effect of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of the authenticity determination of personnel registration information and the compliance of enterprises.

[0016] The technical effects and advantages of the construction project approval method and system based on artificial intelligence of the present invention are as follows:

[0017] Compared with the traditional approval function, the present invention can complete the approval matters on the same day. The intelligent approval system automatically reviews and issues certificates, without the need to submit for approval through the OA process and publish result announcements. This is 24 days shorter than the traditional approval (7 days + 18 days). If staff review is required, it can be completed the next day. The submission of materials can be reduced: corporate legal person commitment letter, purchase list of engineering test and inspection equipment. A fixed template is set in the declaration form of the intelligent approval system, and the commitment is reported by checking the consent option. The integrity and timeliness of credit information are greatly improved. The system automatically processes the collected administrative licensing information at a fixed time period every day, generates the public information required by the Guangxi Dual Credit Information Platform, and automatically uploads it. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a structural schematic diagram of a construction project approval method based on artificial intelligence.

[0019] Figure 2 The present invention is a schematic diagram of the system structure of a construction project approval method based on artificial intelligence. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 The present invention provides a construction project approval method based on artificial intelligence.

[0022] S10, obtaining personnel registration information data, and using data comparison analysis and intelligent recognition technology to determine the authenticity of the personnel registration information, and calculating the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity determination of the personnel registration information;

[0023] The personnel registration information data includes personnel education information, professional and technical titles, business licenses, qualification certificates, three types of personnel, and special personnel registration information;

[0024] Data can be collected from external systems through online forms, API interfaces, or manually entered;

[0025] First, clean the data to remove invalid or duplicate information and process missing data;

[0026] Standardize the data format to ensure that all data is stored and represented in a unified format;

[0027] Convert unstructured data from different sources (such as PDF files) into structured data;

[0028] It should be noted that data comparison and analysis is the key to determining the authenticity of personnel registration information. This process identifies potential forged or erroneous information by comparing it with external databases or detecting anomalies in information through artificial intelligence algorithms;

[0029] The steps of the comparison analysis and intelligent recognition technology are as follows:

[0030] Intelligently identify personnel registration information. For uploaded image files, OCR technology can be used to identify their content.

[0031] Compare the identified content with a credible external database to verify whether the information provided by the person is authentic, for example, verify whether the ID number exists in the database of the Ministry of Public Security, and verify whether the academic certificate is registered in the database of the Ministry of Education;

[0032] Set up a series of rules to check whether work experience and job titles are consistent with industry standards;

[0033] Identify abnormal data based on rules and external databases using support vector machines (SVM) in machine learning algorithms;

[0034] Train a classification SVM, learn through the training set, find a hyperplane, divide the data points into normal data points and abnormal data points, and identify abnormal data; the distance between the data point and the hyperplane can be calculated using the following formula: Where w is the normal vector of the hyperplane, b is the bias term, and x i is the distance between the data point, d and the hyperplane;

[0035] The process of determining the authenticity of personnel registration information is as follows:

[0036] If the distance between the data point and the hyperplane is less than the preset distance threshold, the person registration information is true;

[0037] If the distance between the data point and the hyperplane is greater than the preset distance threshold, the person registration information is false, and the registration information is returned to the registered person;

[0038] The accuracy improvement rate of the construction project approval system compared with the old system is calculated based on the authenticity of the personnel registration information. The specific calculation formula of the improvement rate is as follows: Where Z is the improvement rate, F j is the error rate of the old system, Fx is the error rate of the new system;

[0039] S20, for simple approval items, obtain relevant data information through data docking and sharing, approve simple items through real-time comparison and verification of data information, and automatically send the approval results to the applicant;

[0040] During the approval process, relevant data must first be obtained, and these data are usually distributed in different systems and platforms. To this end, relevant data sources can be integrated through data docking technology to ensure that the required information can be obtained in real time during the approval process. By establishing a unified data exchange platform (such as API interface, data service bus), information sharing between different systems can be achieved;

[0041] After obtaining the applicant's information, the approval system needs to compare and verify the data in real time to ensure that the information submitted by the applicant is accurate and complies with relevant policies;

[0042] After completing data comparison and verification, the system will automatically determine the approval result based on the pre-set approval rules. The automated approval here is based on a series of pre-defined rules and conditions, avoiding manual intervention and improving approval efficiency and accuracy;

[0043] Once the data verification is completed, the approval system will automatically determine whether to approve or reject the application based on the preset rules. For qualified applications, the system will automatically mark them as approved; for applications that do not meet the requirements, the system will mark them as rejected;

[0044] The approval results will be fed back to the applicant in an automated manner in a timely manner, avoiding manual intervention and delays;

[0045] In order to continuously improve approval efficiency and accuracy, the approval process can be optimized through data analysis and machine learning algorithms. By analyzing historical data, the system can identify common approval errors or data problems and issue early warnings or optimizations.

[0046] Data analysis: By analyzing the data of a large number of approval cases, bottlenecks or common problems in approval can be identified, so as to adjust the approval rules or processes.

[0047] Machine learning optimization: Through machine learning technology, the system can gradually learn the rules in approval, continuously optimize rule judgments, and improve the intelligence and accuracy of approval.

[0048] S30, input enterprise information data, make unified publicity and release of enterprise credit information, formulate a unified enterprise credit information management mechanism, supervise the industry behavior norms of enterprises in the entire real estate industry, and evaluate the compliance of enterprises;

[0049] With the development of the market economy, the management of corporate credit information has become an important link, especially for special industries such as the real estate industry, which involves the interests of all parties and has a greater impact on the market. In order to improve the integrity of enterprises and standardize industry behavior, the government and relevant departments will establish a unified corporate credit information management mechanism to supervise and optimize the market order;

[0050] The entry of corporate credit information refers to the process of uniformly organizing, storing and displaying the basic information, credit records, and behavioral performance of an enterprise through certain channels and platforms; the specific steps are as follows:

[0051] Enterprise information can be collected through a variety of channels, including:

[0052] Government departments collect information such as business registration, operating conditions, financial statements, etc. from departments such as industrial and commercial administration, taxation, and auditing;

[0053] Data from industry associations and rating agencies: Information is collected through corporate behavior reports of industry organizations, rating results, and activity records of industry associations;

[0054] Third-party credit assessment platform: credit data provided by banks, credit assessment companies, etc., mainly including default records, loan repayment status, litigation records, etc.;

[0055] The collected data is entered into a unified enterprise credit information platform, which usually includes the following modules:

[0056] Basic information of the enterprise: company name, registered capital, registered address, legal representative, business scope, etc.;

[0057] Credit records: including historical defaults, litigation records, administrative penalties, etc.;

[0058] Financial status: annual report, audit report, financial health, etc.;

[0059] Industry Code of Conduct: such as whether the industry standards are followed, whether false advertising is involved, whether unfair competition is involved, etc.;

[0060] Review the entered enterprise information to ensure the authenticity, completeness and consistency of the data. This can be done by combining manual review with automated tools to prevent erroneous information from entering the system.

[0061] The credit information of enterprises is released to the public through a unified platform. The information can be released through various channels such as government websites, portal websites of industry associations, and credit information platforms;

[0062] Update the company's credit information regularly to ensure the timeliness of the information. The published information should clearly indicate the company's credit rating, historical record, compliance with industry regulations, etc.

[0063] In order to effectively implement supervision of the real estate industry, there must be a unified credit information management mechanism, which mainly includes the following aspects:

[0064] Unified information platform: Build a provincial enterprise credit information sharing platform to realize the centralized storage and unified release of enterprise credit information across the province, and interconnect with the national credit information platform to ensure synchronous update and sharing of information;

[0065] Multi-party collaboration mechanism: government departments, industry associations, rating agencies, financial institutions and other parties cooperate to share information and coordinate supervision;

[0066] Credit classification and evaluation system: Based on the credit data of enterprises, establish an enterprise credit grading management system (for example, A, B, C, D, etc.), and provide reward and punishment measures according to the credit rating and behavioral norms of the enterprise;

[0067] Regular review and feedback mechanism: By regularly reviewing corporate credit records, we can identify any non-compliant behaviors and provide timely feedback and rectification.

[0068] The supervision of the real estate industry is mainly carried out through the formulation of industry codes of conduct to ensure that enterprises comply with laws and regulations in the market, protect the interests of consumers, and maintain market order. Common industry codes of conduct include:

[0069] 1. Corporate compliance review

[0070] Review the project approval, land acquisition, construction quality, environmental impact, etc. of real estate companies, and supervise whether the companies carry out construction, project promotion, and housing delivery in accordance with laws and regulations;

[0071] 2. Credit rating and risk warning

[0072] Credit rating of real estate companies, including liquidity, debt repayment ability, market demand, etc., to provide risk warning. This rating is often determined based on the company's credit record, historical behavior and other data;

[0073] 3. Industry behavior constraints

[0074] Fund management: supervise whether real estate enterprises use funds in accordance with regulations and prevent illegal financing.

[0075] Quality and safety: Enforce real estate companies to comply with quality standards in construction to prevent problematic projects.

[0076] Consumer protection: Review whether the company has engaged in unfair practices such as false advertising and improper after-sales service;

[0077] 4. Information disclosure and transparency

[0078] Strengthen the information disclosure efforts of real estate companies, and make real-time public disclosures of the companies' real estate project progress, investment trends, construction quality, and capital utilization to enhance market transparency.

[0079] Compliance assessment is the core of the regulatory system, which aims to ensure that real estate companies comply with government regulations and industry standards. The effectiveness of supervision can be evaluated by measuring the degree of corporate compliance.

[0080] The specific calculation formula for evaluating the compliance of an enterprise is as follows: H = u1I + u2C + u3R; where I is the degree of compliance with industry standards, u1 is the weight factor of the degree of compliance with industry standards, C is the internal governance structure of the enterprise, u2 is the weight factor of the internal governance structure of the enterprise, R is the risk management capability, u3 is the weight factor of the risk management capability, and H is the compliance of the enterprise;

[0081] S40, build a dynamic supervision information platform for the real estate industry, realize all-round supervision of each key link in the whole life cycle of the project, and evaluate the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of authenticity judgment of personnel registration information and corporate compliance.

[0082] Build a communication bridge between the Department of Housing and Urban-Rural Development and the competent departments of various cities and towns, and implement the verification, supervision, handling and feedback mechanism of problem projects.

[0083] The core goal of this information platform is to achieve all-round, real-time and dynamic supervision of the real estate industry through technical means. Specific goals include:

[0084] Full life cycle supervision: covers all aspects of real estate projects from project establishment, construction, sales to completion acceptance, ensuring compliance at every stage.

[0085] Cross-departmental collaboration: Build a bridge for information sharing and communication between the Department of Housing and Urban-Rural Development and the competent departments of various cities, and form a collaborative governance pattern for the entire industry and society.

[0086] Risk warning: Through data monitoring and analysis, timely warnings are issued for potential project risks, financial risks, engineering quality issues, etc. to prevent problems from expanding.

[0087] Problem handling and feedback mechanism: Verify and supervise the problem projects found to ensure that the problems are effectively handled, and feedback the handling results to the society and relevant departments.

[0088] To achieve the above goals, the dynamic supervision information platform needs to include the following main functional modules:

[0089] (1) Project filing and project approval supervision module

[0090] Project approval review: The approval information of real estate projects needs to be filed in real time on the platform, and relevant departments shall review the legality, planning, funding, etc. of the projects.

[0091] Multi-department collaborative approval: Housing and Urban-Rural Development, Planning, Environmental Protection, Fire Protection, Taxation and other departments share information through the platform to avoid information silos.

[0092] (2) Construction process supervision module

[0093] Project progress tracking: By connecting with the project management system, real-time information such as the project's construction progress, fund usage, and construction quality can be obtained to ensure that the project is implemented as planned.

[0094] On-site inspection records: The platform records the results of various on-site inspections, including quality supervision, hidden danger investigation, safety inspection, etc., and generates traceable inspection reports.

[0095] Fund supervision: Real-time monitoring of the project's fund flow and payment status to prevent misappropriation and non-compliant use of funds.

[0096] (3) Sales and filing management module

[0097] Dynamic sales monitoring: By connecting with the real estate sales system, the sales status of real estate projects can be monitored in real time, including sales price, sales progress, compliance, etc.

[0098] Contract filing and review: All real estate sales contracts and agreements are filed on the platform to ensure the legality and transparency of sales activities.

[0099] Homebuyer information protection and supervision: safeguard the rights and interests of homebuyers and prevent false propaganda and improper marketing.

[0100] (4) Quality and completion acceptance supervision module

[0101] Quality supervision: Monitor the quality of the project throughout the entire process to ensure that the construction quality meets national standards.

[0102] Completion acceptance filing: After the project is completed, the platform will automatically trigger the completion acceptance process, including acceptance application, expert group review, qualification determination, etc., to ensure that the project delivery meets relevant standards.

[0103] Acceptance certificate: The platform automatically generates and uploads the project's completion certificate and related files to facilitate subsequent supervision and inquiry.

[0104] (5) Problem feedback and supervision mechanism module

[0105] Problem tracking and feedback: When the platform detects problems with the project (such as progress delays, funding violations, quality issues, etc.), it will automatically generate a problem list and send a processing notice to the relevant departments.

[0106] Supervision mechanism: Each problem needs to have a responsible person and a processing time limit. The platform will automatically track the progress of problem handling to ensure that the problem is resolved in a timely manner.

[0107] Problem files and historical records: Each problem is recorded and archived to facilitate subsequent supervision and review, and also to facilitate statistical analysis and identify common problems.

[0108] In order to ensure the efficient operation of the platform and the accuracy of the data, the platform needs to rely on the following technical architecture:

[0109] (1) Data source integration

[0110] The platform needs to connect with various related systems to obtain the following data:

[0111] Real estate development company information: basic company information, credit records, historical projects, etc.

[0112] Construction project data: all documents and data from project approval, design, construction, and acceptance stages.

[0113] Data from regulatory departments: inspection records and approval information from housing and construction departments and local regulatory agencies.

[0114] Market sales data: sales and capital flow data of real estate trading platforms, financial institutions, and real estate developers.

[0115] (2) Big Data and Artificial Intelligence

[0116] Big data analysis: The platform should have big data processing capabilities and be able to process data from various departments and channels in real time to conduct risk identification, trend analysis, pattern recognition, etc.

[0117] Artificial Intelligence Early Warning System: Use AI technology to conduct risk assessment, automatically identify potential violations or risky projects, and issue early warnings in a timely manner.

[0118] (3) Cloud platform and blockchain technology

[0119] Cloud platform support: Cloud computing technology is used to ensure that the platform can be rapidly expanded to meet the regulatory needs of projects in different regions and of different sizes.

[0120] Blockchain technology: Ensure the transparency of project information and the immutability of data, especially in the recording of important information such as capital flow and project changes, using blockchain technology for data storage.

[0121] The construction of the platform is not just a technical system, but also needs to promote cross-departmental and cross-regional collaboration and information sharing. Specifically:

[0122] The Department of Housing and Urban-Rural Development connects with local competent departments: Through the platform, the Department of Housing and Urban-Rural Development can understand the progress of projects in various cities in real time, and promptly discover and resolve problems in local supervision.

[0123] Real-time information flow: The competent departments of various cities can upload the processing progress of problem projects through the platform, and the Housing and Urban-Rural Development Department can check it at any time and urge local departments to speed up the progress.

[0124] Shared database and reporting system: The platform can provide a unified database and reporting system for all relevant departments, reducing duplication of work and improving efficiency.

[0125] The process of evaluating the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of the authenticity judgment of personnel registration information and the compliance of the enterprise is as follows:

[0126] Step C1, define the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance of the enterprise as input variables, and divide them into different fuzzy sets respectively.

[0127] For example, "Low", "Medium", and "High" are used to improve the accuracy of authenticity judgment of personnel registration information, and "Low", "Medium", and "High" are used to improve the compliance of enterprises.

[0128] Step C2, the effect of the construction project approval system is defined as the output variable, which is divided into fuzzy sets, for example, "High", "Low", for the effect of the construction project approval system.

[0129] Step C3, formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0130] The accuracy improvement rate of personnel registration information authenticity judgment is marked as Z, the compliance of the enterprise is marked as H, and the effect of the construction project approval system is marked as P.

[0131] Rule 1:IF(Z is Low)AND(H is Low)THEN(P is Low)

[0132] Rule 2:IF(Z is High)AND(H is High)THEN(P is High) ...

[0134] Step C4, fuzzy reasoning is performed based on fuzzy rules to evaluate the effectiveness of the construction project approval system.

[0135] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment takes three fuzzy sets as examples, in fact, the accuracy improvement rate of authenticity judgment of personnel registration information, the compliance of the enterprise, and the effectiveness of the construction project approval system can be divided into more than three sets to facilitate better and more accurate identification.

[0136] Furthermore, for the accuracy improvement rate of determining the authenticity of personnel registration information and the judgment of the high, medium and low compliance of the enterprise, thresholds can be set according to actual conditions for judgment; when the accuracy improvement rate of determining the authenticity of personnel registration information is higher than 80%, it will be marked as "High", and when the compliance of the enterprise is higher than 0.7, it will be marked as "High", and so on. I will not go into details here.

[0137] Embodiment 2, Figure 2 The present invention provides a system for a construction project approval method based on artificial intelligence.

[0138] The acquisition module is used to obtain personnel registration information data, and use data comparison analysis and intelligent recognition technology to determine the authenticity of personnel registration information, and calculate the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity of personnel registration information;

[0139] The approval module is used to obtain relevant data information for simple approval matters based on data docking and sharing, approve simple matters through real-time comparison and verification of data information, and automatically send the approval results to the applicant;

[0140] The supervision module is used to input enterprise information data, uniformly publicize and publish enterprise credit information, and formulate a unified enterprise credit information management mechanism to supervise the industry behavior norms of enterprises in the entire real estate industry and evaluate the compliance of enterprises;

[0141] The evaluation module is used to build a dynamic supervision information platform for the real estate industry, realize all-round supervision of each key link in the whole life cycle of the project, and evaluate the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance of the enterprise.

[0142] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0143] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0144] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0145] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0146] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0147] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A construction project approval method and system based on artificial intelligence, characterized in that: include: Obtain personnel registration information data, and use data comparison analysis and intelligent recognition technology to determine the authenticity of personnel registration information, and calculate the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity of personnel registration information; For simple approval items, relevant data information is obtained through data connection and sharing, and simple items are approved through real-time comparison and verification of data information, and the approval results are automatically sent to the applicant; Enter enterprise information data, make unified public announcement and release of enterprise credit information, formulate a unified enterprise credit information management mechanism, supervise the industry behavior norms of enterprises in the entire real estate industry, and evaluate the compliance of enterprises; Build a dynamic supervision information platform for the real estate industry to achieve all-round supervision of all key links in the entire life cycle of the project, and evaluate the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance of the enterprise.

2. According to the artificial intelligence-based construction project approval method and system according to claim 1, it is characterized in that: The personnel registration information data includes personnel education information, professional and technical titles, business licenses, qualification certificates, three types of personnel, and special personnel registration information.

3. The construction project approval method and system based on artificial intelligence according to claim 2 is characterized in that: The steps of the comparison analysis and intelligent recognition technology are as follows: Use OCR technology to intelligently identify personnel registration information; Compare the identified content with a credible external database to verify whether the information provided by the person is authentic; Set rules to check whether work experience and job titles are consistent with industry standards; Identify abnormal data based on rules and external databases based on support vector machines (SVM); The process of identifying abnormal data based on support vector machine (SVM) is as follows: Train a classification SVM, learn through the training set, find a hyperplane, and divide the data points into normal data points and abnormal data points by calculating the distance between the data points and the hyperplane to identify abnormal data; the distance between the data points and the hyperplane can be calculated using the following formula: Where w is the normal vector of the hyperplane, b is the bias term, and x i is the distance between the data point and the hyperplane.

4. The construction project approval method and system based on artificial intelligence according to claim 3 is characterized in that: The process of determining the authenticity of personnel registration information is as follows: Compare and analyze the distance between the data point and the hyperplane with the preset distance threshold. If the distance between the data point and the hyperplane is less than the preset distance threshold, the person registration information is true; If the distance between the data point and the hyperplane is greater than the preset distance threshold, the person registration information is false and the registration information is returned to the registered person.

5. The construction project approval method and system based on artificial intelligence according to claim 4 is characterized in that: The specific calculation formula for calculating the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity judgment of personnel registration information is as follows: Where Z is the improvement rate, F j is the error rate of the old system, F x is the error rate of the new system.

6. The construction project approval method and system based on artificial intelligence according to claim 5 is characterized in that: The steps for approving simple matters by real-time comparison and verification of data information are as follows: Obtain data information and integrate data sources through data docking technology; A unified data exchange platform will be established to achieve information sharing among different systems and ensure that the required information can be obtained in real time during the approval process; Compare and verify data in real time, and automatically determine the approval result based on pre-set approval rules; For qualified applications, the system will automatically mark them as approved; for ineligible applications, the system will mark them as rejected.

7. The construction project approval method and system based on artificial intelligence according to claim 6 is characterized in that: The specific calculation formula for evaluating the compliance of an enterprise is as follows: H=u1I+u2C+u3R; wherein, I is the degree of compliance with industry standards, u1 is the weight factor of the degree of compliance with industry standards, C is the internal governance structure of the enterprise, u2 is the weight factor of the internal governance structure of the enterprise, R is the risk management capability, u3 is the weight factor of the risk management capability, and H is the compliance of the enterprise.

8. The construction project approval method and system based on artificial intelligence according to claim 7 is characterized in that: The specific implementation process of the real estate industry dynamic supervision information platform is as follows: Based on the dynamic supervision information platform of the real estate industry, all-round supervision is achieved on all key links in the entire life cycle of the project, and a communication bridge is built between the Housing and Urban-Rural Development Department and the competent departments of various cities to implement the verification, supervision, handling and feedback mechanism of problem projects.

9. The construction project approval method and system based on artificial intelligence according to claim 8 is characterized in that: The process of evaluating the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of the authenticity judgment of personnel registration information and the compliance of the enterprise is as follows: The accuracy improvement rate of the authenticity judgment of personnel registration information and the compliance of the enterprise are defined as input variables, and they are divided into different fuzzy sets respectively; The effect of the construction project approval system is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the accuracy improvement rate of personnel registration information authenticity judgment and the corporate compliance definition on the effectiveness of the construction project approval system; Fuzzy reasoning is performed based on fuzzy rules to evaluate the effectiveness of the construction project approval system.

10. A construction project approval method and system based on artificial intelligence, characterized in that: include: Acquisition module, approval module, supervision module, evaluation module, there are connections between modules: The acquisition module is used to obtain personnel registration information data, and use data comparison analysis and intelligent recognition technology to determine the authenticity of personnel registration information, and calculate the accuracy improvement rate of the construction project approval system compared with the old system based on the authenticity of personnel registration information; The approval module is used to obtain relevant data information for simple approval matters based on data docking and sharing, approve simple matters through real-time comparison and verification of data information, and automatically send the approval results to the applicant; The supervision module is used to input enterprise information data, uniformly publicize and publish enterprise credit information, and formulate a unified enterprise credit information management mechanism to supervise the industry behavior norms of enterprises in the entire real estate industry and evaluate the compliance of enterprises; The evaluation module is used to build a dynamic supervision information platform for the real estate industry, realize all-round supervision of each key link in the whole life cycle of the project, and evaluate the effectiveness of the construction project approval system based on fuzzy reasoning according to the accuracy improvement rate of authenticity judgment of personnel registration information and the compliance of the enterprise.

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