Construction industry operation condition risk evaluation system based on cloud platform

Abstract

The invention relates to the technical field of construction safety management, in particular to a cloud platform-based construction industry operation condition risk evaluation system, which integrates cloud computing, Internet of Things and AI (artificial intelligence) technologies, and constructs a whole-process safety management system. Risk indexes are intelligently calculated in combination with an improved LEC-M model, four levels of risk levels are divided, and parameter customization is supported; multi-mode early warning is automatically triggered based on a risk threshold value, and rectification closed-loop management is realized through dynamic recheck; cloud architecture deployment is adopted, multi-terminal access and data interaction are supported, and data security is guaranteed through HTTPS encryption and an AES-256 technology; and the supervision docking module realizes real-time sharing of risk data, provides risk map visualization, historical data mining and AI auxiliary analysis, and supports risk trend prediction and intelligent recommendation of rectification schemes. According to the system, the accuracy and efficiency of building safety management are improved, and the intelligence from risk identification to supervision decision making is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of building safety management, and in particular to a cloud platform-based construction industry operating condition hazard assessment system. Background Art

[0002] As a key pillar of the national economy, the construction industry faces complex and ever-changing construction environments, encompassing a wide range of hazardous operations, including height work, electrical work, and hot work. These operations present numerous safety risks, including falls from height, electric shock, fire, and collapse. Traditional construction safety risk assessment methods rely primarily on manual inspections, empirical judgment, and regular safety reviews, but these approaches have numerous limitations.

[0003] Manual inspections are subjective and biased. Different inspectors have varying levels of expertise and experience, and their risk assessments for the same work scenario can differ significantly. When inspecting work at height, some inspectors may focus solely on obvious missing protective measures while overlooking potential risk factors, such as the stability of climbing equipment and the placement of objects at height. This can lead to incomplete and inaccurate risk assessments.

[0004] Experience-based judgments lack real-time data support. With the continuous development of construction technology, new building materials and construction techniques are constantly emerging, and previous experience is unable to cope with these new changes. When using new lightweight building materials, due to their different properties from traditional materials, relying on past experience may not accurately assess the spread and severity of fire after it occurs, making it difficult to promptly identify emerging safety hazards.

[0005] Regular safety reviews are time-consuming. During construction, operating conditions can change at any time, such as inclement weather causing sudden changes in the construction site environment or adjustments to the construction schedule leading to a change in the construction sequence. Regular reviews are unable to capture these changes in a timely manner, often ignoring potential hazards only after they have already existed for some time, missing the optimal opportunity for prevention and resolution.

[0006] The construction industry urgently needs a safety risk management system that leverages cloud platform technology and integrates real-time data collection, intelligent analysis, and automated assessment capabilities. This approach aims to improve the accuracy, timeliness, and comprehensiveness of risk assessments, safeguarding the safety of construction workers and the smooth progress of projects. Conventional LEC models fail to account for geological conditions and structural complexity, resulting in assessment results that deviate from actual construction scenarios. Existing cloud platform systems are often single-function (e.g., data collection or early warning only) and lack intelligent, closed-loop management of the entire process, from risk identification to regulatory decision-making. This invention, through the improved LEC-M model and multi-module integration, fills this gap in technology. Summary of the Invention

[0007] This invention aims to address the shortcomings of existing building safety risk assessment methods by providing a cloud-based construction industry operating condition hazard assessment system. This system utilizes a cloud computing architecture, cloud servers for computational processing, and a cloud database for storing risk assessment data. Advanced database management technologies are used for data management, ensuring efficient operation and scalability.

[0008] The system, based on the Improved LEC-M model, automatically calculates operational risk values and categorizes risk levels by setting appropriate thresholds. Building on the traditional LEC model, this model incorporates more parameters relevant to the construction industry, such as construction site geology and building structural complexity, making risk assessments more realistic.

[0009] The system integrates real-time data collection, risk warnings, rectification tracking, and government regulatory integration. It enables multi-terminal access through a RESTful API architecture and employs AI algorithms for historical data analysis and risk prediction, further enhancing the scientific and intelligent nature of building safety management. This invention effectively addresses the subjectivity, lag, and partiality of traditional building safety assessment methods, providing construction companies with a more accurate, efficient, and intelligent safety management solution.

[0010] The technical solution adopted by the present invention to solve the technical problem is: a cloud platform-based construction industry operating condition hazard assessment system, including the following modules: The system architecture module is used to select the server-side architecture, design the client-side architecture based on the selected server-side architecture, design the data interaction and calculation framework based on the client-side architecture design, manage user rights based on the data interaction and calculation framework design, and provide the system operating environment and underlying support for the risk assessment module, data collection module, early warning and rectification module, and supervision docking module; The risk assessment module is used to configure the risk assessment model and calculate the D value. Based on the D value calculation result, the risk level is divided and the assessment result is obtained. Based on the assessment result, the risk assessment module database is created and the assessment result is passed as input information to the early warning and rectification module and the supervision docking module to support the subsequent risk response and supervision processing process; The data acquisition module is used to configure data acquisition equipment through the backend management terminal, obtain comprehensive data through the data acquisition equipment, conduct classification assessment and initial analysis based on the obtained comprehensive data, obtain preliminary analysis results, adjust risk point parameters based on the preliminary analysis results, perform final risk assessment and report generation based on the adjusted risk point parameters, and transmit the collected data in real time to the risk assessment module as the basis for risk index calculation; The early warning and rectification module is used to set up a risk early warning mechanism, conduct dynamic assessment and closed-loop rectification based on the risk early warning mechanism, carry out safety management based on the dynamic assessment and closed-loop rectification, receive risk level information provided by the risk assessment module, automatically trigger early warnings based on the assessment results, and feed back the assessment results after rectification to the supervision docking module; The supervision docking module is used to configure the data reporting mechanism, conduct real-time supervision and review based on the configured data reporting mechanism, conduct historical data comparison and trend analysis based on real-time supervision and review, introduce artificial intelligence to assist in analysis, and connect with the risk assessment module and the early warning and rectification module to realize the automatic uploading and synchronous sharing of assessment data and rectification information, which is used for online monitoring and security decision-making by government regulatory departments.

[0011] The data acquisition module and the risk assessment module implement data interaction through a RESTful API interface, and the data acquisition module transmits the real-time monitored environment, equipment, personnel and geological data to the risk assessment module through the interface; The D value result calculated by the risk assessment module is pushed to the early warning and rectification module and the supervision docking module through the message queue (MQ), triggering the early warning mechanism or supervision data synchronization.

[0012] The system architecture module is specifically: A server-side architecture was selected, cloud servers were used as computing resources, cloud databases were used to store construction operation risk assessment data, and cloud storage was used to store unstructured data such as historical assessment reports, construction drawings, on-site photos and videos. Load balancing technology was used to balance the load across multiple servers, and Nginx was deployed as a reverse proxy server. Use Oracle or MySQL database as the back-end database and design the data table structure, which includes user management table, construction operation condition table, risk assessment table, rectification management table and construction schedule; The client-side architecture was designed based on the server-side architecture. The front-end interface was developed using Vue.js, combined with the Element-UI component library and the RESTful API architecture. The client accessed the server-side data through HTTP requests. The front-end development included the login page, construction operation data management page, risk assessment page, rectification management page, report generation and export page, and construction progress monitoring page. Based on the client architecture design, a data interaction and computing framework is developed. Spring Boot, MyBatis, and the selected database are used to build the backend data interaction layer, and a RESTful API interface is configured. The interface includes user management, operation condition management, risk assessment management, corrective measures management, and construction progress management. User rights management is carried out based on the design of the data interaction and computing framework. User rights management includes system administrators, enterprise administrators, project leaders, security specialists and government regulators. The RBAC mechanism is adopted to manage the access rights of different users to different modules, and OAuth2.0 and JWT authentication are used to verify user identities. Construction personnel behavior records and location data are only used for real-time security monitoring and not for other purposes. They are automatically deleted within 30 days after project completion. Sensitive information such as construction drawings and budget data uploaded by enterprises is restricted to authorized users (such as enterprise administrators and government regulators) through access control policies.

[0013] The risk assessment module is specifically: Configure the risk assessment model according to the designed data table structure, define the calculation method of the operating condition hazard index, and adopt the improved operating condition hazard evaluation method: , where: D is the hazardousness index of working conditions, L is the probability of accident occurrence, ranging from 0.1 to 10, E is the degree of exposure, ranging from 0.5 to 10, C is the consequence of the accident, ranging from 1 to 100, G is the geological condition coefficient of the construction site, ranging from 0.5 to 2, and S is the building structure complexity coefficient, ranging from 0.8 to 1.5; Based on the calculation of the operating condition hazard index, the D value calculation result is obtained, and the risk level is divided and the risk level threshold is set. When the D value is greater than or equal to 320, the assessment result is a major risk and the system is marked as a red warning. When the D value is between 160 and 320, the assessment result is a large risk and the system is marked as an orange warning. When the D value is between 40 and 160, the assessment result is a general risk and the system is marked as a yellow warning. When the D value is less than 40, the assessment result is a low risk and the system is marked as a blue warning. Based on the risk level threshold, the assessment results are obtained, and based on the assessment results, a risk assessment module database is created. The selected database is used as the back-end database. The risk assessment module database includes the creation of risk point data tables, the creation of operation risk assessment data tables, and the creation of rectification management data tables; Preset work categories based on assessment results, including foundation engineering, main structure construction, height work, electrical work, hot work, waterproofing, and decoration and renovation. Standard risk points are stored in the risk point database, including typical risk points under each work category and their default L, E, C, G, and S scoring standards. Enterprise administrators and project leaders are supported to manually adjust the various parameter values of risk points. The risk assessment process is set up, the system administrator enters the standard risk point data, the enterprise administrator or project leader selects the operation category, the system automatically loads the risk point database, matches the risk points and default scores, calculates the D value and divides the risk level, and marks the warning. The project leader can manually adjust the score, the enterprise administrator generates an assessment report for filing, and re-evaluates after rectification. The government regulator reviews the rectification effect.

[0014] The data acquisition module is specifically: Configure data collection equipment through the backend management terminal, including environmental monitoring sensors, equipment status monitoring equipment, personnel behavior recording systems and geological monitoring equipment; Through enterprise administrators accessing the data collection system, project leaders and safety specialists manually enter data to supplement safety information that cannot be monitored by sensors. Manually entered data must be marked with the "manual supplement" logo. In the event of a conflict with sensor data, real-time monitoring data will prevail (for example, if the difference between dust concentration sensor data and manual records is greater than 10%, sensor data will take precedence). Manual data will be archived as notes. Comprehensive data is collected through various monitoring equipment, including construction site temperature and humidity, wind speed, dust concentration, equipment operating parameters, personnel behavior and location, geological conditions, etc. Based on the comprehensive data obtained, classification assessment and initial analysis are carried out to obtain preliminary analysis results. Log in to the system and select the operation category. The system automatically loads the risk assessment module database, matches the preset risk points, and automatically generates a primary assessment record. The system screens risk points according to the operation category, matches standard risk points, and calculates L, E, C, G, and S values; Based on the preliminary analysis results, risk point parameters are adjusted. The project leader or safety specialist manually adjusts parameter values to optimize the assessment results, recalculates the D value to update the risk level, and manually adds custom risk points. Custom risk points require the associated operation category (such as 'Foundation Engineering - Geological Exploration') to be selected. By default, the L / E / C basic score of that category is inherited (with a 20% fluctuation). The G / S value must be filled in based on the actual site conditions. After approval by the enterprise administrator, it is automatically synchronized to the risk point database for subsequent assessment. Based on the risk point parameter adjustments, a final risk assessment and report generation is performed. The D values of all assessed risk points are calculated and risk levels are divided. The overall risk level of the operation area is calculated: , and generate a risk assessment report based on the results. The report content includes the operation category, risk point name, scores of various parameters, D value calculation results, risk level, safety management suggestions and corrective measures suggestions.

[0015] The early warning and rectification module is specifically: Establish a risk warning mechanism, using color coding to intuitively display risk levels. The system administrator configures the risk level color coding, and the system automatically loads risk assessment data. Alarm rules and trigger conditions are set, including D value exceeding the threshold, abnormal equipment status, personnel violation operation, and geological displacement exceeding the range. Configure the alarm reception method, and the system triggers the warning and records the log; Based on the established risk early warning mechanism, dynamic assessment and closed-loop rectification are carried out. System administrators set risk assessment task scheduling rules. The system automatically performs risk review, checks risk status, and reminds users to recheck or recommends further rectification. If the rectification status is still marked as "in progress" after the preset 72-hour period, the system automatically triggers a secondary warning: a text message reminder is sent to the enterprise administrator, and the rectification record is synchronized with the government supervision docking module. Supervisors can intervene online to supervise the work. Enterprise administrators review the rectified work points and arrange for the project leader to re-evaluate. The D value of the re-evaluation is compared with the D value before the rectification to generate a rectification effect report. Safety management is carried out based on dynamic assessment and closed-loop rectification. Enterprise administrators adjust preset risk points and various parameters, and the system records the changes. Enterprises can customize risk assessment models and calculation rules. The system provides customized assessment rule management, automatically calculates D values based on customized rules, and divides risk levels: , the enterprise's customized risk assessment model must submit a "Model Change Application Form" through the system, which will be reviewed by the system administrator (with a focus on verifying the compliance of parameter logic with industry standards). It will take effect after passing the review. The default model and customized model can be switched in the system settings.

[0016] The regulatory docking module is specifically: Configure the data reporting mechanism. System administrators configure data reporting rules. Construction operation risk assessment data is automatically uploaded to the government supervision platform. Both automatic and manual reporting methods are available. The system uses the HTTPS protocol to transmit data and the AES-256 encryption algorithm to encrypt sensitive data. The database storage layer uses transparent data encryption (TDE) technology to fully encrypt core data tables such as risk assessment tables and rectification management tables. Sensitive fields such as personnel location and company contact information are additionally desensitized (for example, the middle four digits of a mobile phone number are replaced with '****'). Data reporting generates log records and stores them. Real-time supervision and review are carried out based on the configured data reporting mechanism. Government regulators log in to the regulatory system to obtain real-time risk data. The system provides a risk map visualization function, automatically marks high-risk enterprises and sends early warning notifications. Regulators can issue corrective instructions online. Based on real-time supervision and review, historical data comparison and trend analysis are conducted. System administrators configure data storage strategies. The system stores risk assessment data and provides query functions. Data mining algorithms are used to analyze historical data. Time series analysis and K-means clustering algorithms are used to identify risk trends and high-risk areas. Multi-terminal applications are developed to implement different functions. Introducing artificial intelligence-assisted analysis, the system uses random forests to train risk prediction models: , combined with AI models to analyze historical rectification records, and automatically recommend the optimal rectification plan. The training data are all aggregated data after anonymization (such as the average L / E / C value of a certain area's operation category), and do not contain specific company names, personnel names and other privacy information, which complies with the relevant provisions of the "Personal Information Protection Law".

[0017] Beneficial effects of the present invention: Improving the accuracy of risk assessment: By introducing advanced sensors and data acquisition equipment, various types of construction site data are acquired in real time. Risk assessment is conducted in combination with the improved LEC-M model, which comprehensively considers multiple factors, avoiding the subjectivity and one-sidedness of traditional assessment methods and greatly improving the accuracy of risk assessment.

[0018] Real-time early warning and rapid response: The system has set up a complete risk early warning mechanism. Once the risk value reaches the warning threshold, early warning information will be immediately sent to relevant personnel through various means (such as SMS, email, platform notification), ensuring that safety hazards can be discovered and handled in time, effectively reducing the possibility of accidents.

[0019] Promote closed-loop rectification management: From risk discovery, early warning, rectification, to reassessment, a complete closed-loop management process has been formed. The system tracks and records the rectification process to ensure the effective implementation of rectification measures, improving the efficiency and quality of safety management.

[0020] Improved Supervision Effectiveness: The supervisory integration module enables data sharing and real-time interaction with government regulators. Supervisors can view companies' construction safety status in real time, compare historical data, and analyze trends. Leveraging AI-assisted analysis, they can pinpoint high-risk areas and projects, improving the relevance and effectiveness of supervision.

[0021] Enhanced system scalability and compatibility: Using advanced technologies such as cloud computing architecture and RESTful API, the system has good scalability and compatibility, and can easily integrate new equipment and functional modules to adapt to the ever-evolving needs of the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further described below with reference to the accompanying drawings and examples.

[0023] Figure 1This is a system architecture diagram of the cloud platform-based construction industry operating condition hazard assessment system provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0025] like Figure 1 As shown, the cloud platform-based construction industry operating condition hazard assessment system of the present invention includes the following modules: The system architecture module is used to select the server-side architecture, design the client architecture based on the selected server-side architecture, design the data interaction and calculation framework based on the client architecture design, manage user rights based on the data interaction and calculation framework design, and provide the system operating environment and underlying support for the risk assessment module, data collection module, early warning and rectification module, and supervision docking module.

[0026] The risk assessment module is used to configure the risk assessment model and calculate the D value. Based on the D value calculation result, the risk level is divided and the assessment result is obtained. Based on the assessment result, a risk assessment module database is created. The assessment result is passed as input information to the early warning rectification module and the supervision docking module to support subsequent risk response and supervision processing procedures.

[0027] The data acquisition module is used to configure data acquisition equipment through the background management end, obtain comprehensive data through the data acquisition equipment, perform classification evaluation and initial analysis based on the obtained comprehensive data, obtain preliminary analysis results, adjust risk point parameters based on the preliminary analysis results, perform final risk assessment and report generation based on the risk point parameter adjustment, and transmit the collected data to the risk assessment module in real time as the basis for risk index calculation.

[0028] The early warning and rectification module is used to set up a risk early warning mechanism, conduct dynamic assessment and closed-loop rectification based on the set risk early warning mechanism, conduct safety management based on the dynamic assessment and closed-loop rectification, and receive risk level information provided by the risk assessment module. It automatically triggers early warnings based on the assessment results and feeds back the assessment results after rectification to the supervision docking module.

[0029] The supervision docking module is used to configure the data reporting mechanism, conduct real-time supervision and review based on the configured data reporting mechanism, conduct historical data comparison and trend analysis based on real-time supervision and review, introduce artificial intelligence to assist in analysis, and connect with the risk assessment module and the early warning and rectification module to realize the automatic uploading and synchronous sharing of assessment data and rectification information, which is used for online monitoring and security decision-making by government regulatory departments.

[0030] Example 1: Detailed design of system architecture module A server-side architecture is employed, using cloud servers (such as Alibaba Cloud ECS) as computing resources, cloud databases (such as Alibaba Cloud RDS) to store construction risk assessment data, and cloud storage (such as Alibaba Cloud OSS) to store unstructured data such as historical assessment reports, construction drawings, site photos, and videos. Load balancing technology is used to balance the load across multiple servers, ensuring stable operation under high concurrency. Nginx is deployed as a reverse proxy server to improve system security and access efficiency. A Web Application Firewall (WAF) and an Intrusion Detection System (IDS) are also deployed. Cloud servers are monitored 24 / 7, with regular (weekly) vulnerability scanning and patch updates to ensure secure data transmission and storage. The system utilizes a RESTful API architecture for inter-module data exchange. The data acquisition module pushes real-time monitoring data to the risk assessment module via standardized interfaces (such as / api / data / monitor). The risk assessment module asynchronously transmits D-value calculation results to the early warning and rectification module and the supervisory docking module via message queues (such as RabbitMQ or Kafka), ensuring reliable data transmission in high-concurrency scenarios.

[0031] A database such as Oracle or MySQL is used as the backend database, and a data table structure is designed. The table structure includes a user management table, a construction operation condition table, a risk assessment table, a rectification management table, and a construction schedule. The user management table stores basic user information, roles, and permissions; the construction operation condition table records information such as construction site environmental parameters, equipment status, and construction processes; the risk assessment table stores risk assessment results and related parameters; the rectification management table tracks the implementation of rectification measures; and the construction schedule records the project's planned and actual construction progress.

[0032] The client-side architecture was designed based on the selected server-side architecture. The front-end interface was developed using Vue.js, combined with the Element-UI component library to improve development efficiency and user experience. Using the RESTful API architecture, the client accesses server-side data through HTTP requests. Front-end development includes a login page, a construction data management page, a risk assessment page, a rectification management page, a report generation and export page, and a construction progress monitoring page. The login page uses JSON Web Token for authentication to ensure user login security; the construction data management page allows users to easily enter and view various types of construction site information; the risk assessment page displays the results and detailed information of the risk assessment; the rectification management page is used to submit rectification measures and track rectification progress; the report generation and export page supports exporting assessment reports to PDF and Excel formats; and the construction progress monitoring page displays the project's construction progress in real time.

[0033] Based on the client-side architecture design, a data interaction and computation framework was developed. The backend data interaction layer was constructed using Spring Boot, MyBatis, and the selected database. A RESTful API was configured, covering user management, operating conditions management, risk assessment management, corrective measures management, and construction progress management. To enable data interaction between the frontend and backend, the client sent requests to the backend API via Axios, retrieved data, and dynamically rendered the page. WebSocket technology was used to enable real-time data exchange between the enterprise and regulatory agencies, ensuring timely information delivery.

[0034] User rights management is implemented based on the design of a data interaction and computing framework. This user rights management encompasses system administrators, enterprise administrators, project leaders, security specialists, and government regulators. System administrators are responsible for the overall management and maintenance of the system and can manage all data and users. Enterprise administrators manage their own project data, risk assessment data, and rectification records. Project leaders are responsible for the day-to-day management and risk assessment of their projects. Security specialists focus on safety inspections and risk assessments at construction sites. Government regulators review risk assessment data for all enterprises and conduct audits and oversight. RBAC (Role-Based Access Control) is used to manage the access rights of different users to different modules in a granular manner. OAuth2.0 and JWT authentication are used to verify user identities and ensure system data security.

[0035] Example 2: Detailed design of risk assessment module Configure the risk assessment model according to the designed data table structure. Define the calculation method of the operating condition hazard index and adopt the improved operating condition hazard assessment method. . In the formula: D is the operating condition hazard index, which is used to measure the risk level of the operating environment. The larger the D value, the more dangerous the operating conditions are. L is the probability of an accident, which is used to evaluate the probability of an accident or dangerous event occurring under specific operating conditions. The L value range is 0.1 to 10. The larger the value, the higher the possibility of an accident. E is the exposure level, which is used to measure the frequency of workers' contact with a dangerous environment. The E value range is 0.5 to 10. The larger the value, the longer the workers are exposed to the dangerous environment. C is the consequence of the accident, which is used to evaluate the possible losses caused by the accident. The C value range is 1 to 100. The larger the value, the more serious the impact of the accident, including casualties and economic losses. G is the geological condition coefficient of the construction site, which is calculated based on the geological stability, soil The G value is determined by factors such as soil type, ranging from 0.5 to 2. The worse the geological conditions, the larger the G value. It is classified according to geological stability: stable geology (such as hard rock layer) takes 0.5-1.0, moderately stable geology (such as clay layer) takes 1.0-1.5, and unstable geology (such as soft soil layer, fault zone) takes 1.5-2.0. S is the building structure complexity coefficient, determined by factors such as building structure type and number of floors, ranging from 0.8 to 1.5. The more complex the structure, the larger the S value. It is divided according to the number of floors and complexity of the building structure: single-story / simple structure (such as bungalow) takes 0.8-1.0, multi-story / moderately complex structure (such as ordinary residence) takes 1.0-1.3, and super-high-rise / complex structure (such as commercial complex, special-shaped building) takes 1.3-1.5.

[0036] Based on the hazard index of the operating conditions, the D value is calculated and the risk level is classified. A risk level threshold is set, and the operating risk is automatically classified based on the calculated D value. When the D value is greater than or equal to 320, the assessment result is a major risk, and the system indicates a red alert. When the D value is between 160 and 320, the assessment result is a high risk, and the system indicates an orange alert. When the D value is between 40 and 160, the assessment result is a moderate risk, and the system indicates a yellow alert. When the D value is less than 40, the assessment result is a low risk, and the system indicates a blue alert.

[0037] Based on the risk level threshold, the assessment results are obtained, and based on the assessment results, a risk assessment module database is created, using the selected database as the backend database. The risk assessment module database includes the creation of a risk point data table, the creation of an operation risk assessment data table, and the creation of a rectification management data table. The risk point data table is created to store detailed information on operation risk points in the construction industry. The fields include risk point name, operation category, L value, E value, C value, G value, S value, calculated D value, and risk level information; the operation risk assessment data table is created to record the detailed information of each assessment. The fields include assessment record ID, assessor ID, construction company ID, project ID, assessment time, L value, E value, C value, G value, S value, D value, risk level, and rectification measures recommended information; the rectification management data table is created to track the rectification status after the risk assessment. The fields include rectification record ID, associated assessment record ID, rectification measures, rectification execution time, and rectification status information.

[0038] Based on the assessment results, pre-set work categories are defined, including foundation engineering, main structure construction, work at height, electrical work, hot work, waterproofing, and interior decoration. Standard risk points are stored in the risk point database, including typical risk points for each work category and their default L, E, C, G, and S scoring criteria. Enterprise administrators and project leaders can also manually adjust the various risk point parameters.

[0039] Set up the risk assessment process. When manually adjusting parameters, you need to fill in the reason for the adjustment (such as sudden geological changes on site, structural design changes), and it will take effect after being reviewed and approved by the enterprise administrator or safety specialist. The system automatically records the adjustment time, the person who made the adjustment, and the comparison log of the parameters before and after the adjustment. The system administrator enters the standard risk point data in the background management terminal and establishes default scoring standards for different job categories; the enterprise administrator or project leader logs in to the system and selects the job category in the risk assessment management page. The system automatically loads the risk point database and matches the corresponding risk points and default scores according to the job category. The system automatically calculates the D value and divides the risk level. According to the set risk level threshold, the corresponding color warning is automatically marked; the project leader can manually adjust the parameter scores according to the actual situation, and the system recalculates the D value and updates the risk level; the enterprise administrator generates an assessment report and submits it to the internal management of the enterprise or the government regulatory agency for filing.

[0040] The enterprise administrator views the risk assessment report on the rectification management page, determines the work points that need rectification, submits the rectification measures, and records the rectification execution time and the rectification responsible person information in the system; after the rectification measures are implemented, the project leader re-conducts the risk assessment, adjusts the scores of various parameters, and recalculates the D value. The system automatically updates the rectification management data table, records the changes in risk levels after rectification, and generates an updated assessment report; government regulators can view the risk assessment and rectification status of all construction companies and review the rectification results.

[0041] Example 3: Detailed design of data acquisition module Configure data collection equipment through the backend management terminal. Data collection equipment includes environmental monitoring sensors (such as temperature and humidity sensors, wind speed sensors, and dust sensors), equipment status monitoring equipment (such as tower crane monitors, construction elevator monitors, and electrical equipment monitoring devices), personnel behavior recording systems (such as cameras and helmet positioning devices), and geological monitoring equipment (such as geological radar and displacement monitors).

[0042] Through access to the data collection system by enterprise administrators, manual data entry is performed by project managers and safety specialists to supplement safety information that cannot be monitored by sensors, such as descriptions of construction workers' illegal operating behaviors and precautions for special construction processes.

[0043] Comprehensive data is collected through various monitoring devices. This includes: collecting data from temperature and humidity sensors to monitor the temperature and humidity at the construction site to ensure that the construction environment meets requirements; collecting data from wind speed sensors to monitor wind speed at the construction site to avoid dangerous operations in windy weather; collecting data from dust sensors to monitor dust concentration at the construction site to protect the health of construction workers; collecting data from tower crane monitors to obtain parameters such as the operating status, lifting capacity, and amplitude of the tower crane to prevent tower crane accidents; collecting data from construction elevator monitors to monitor the operating speed, load, and other information of the construction elevator to ensure the safety of personnel and material transportation; collecting data from electrical equipment monitoring devices to monitor the voltage, current, and leakage of electrical equipment to prevent electric shock accidents; collecting data from cameras to record the behavior of construction workers and promptly detect violations; collecting data from hard hat positioning devices to obtain real-time location information of construction workers; collecting data from geological radar to monitor changes in geological conditions at the construction site; and collecting data from displacement monitors to monitor the displacement of buildings to prevent collapse accidents.

[0044] Based on the comprehensive data obtained, a classification assessment and initial analysis are performed to obtain preliminary analysis results. The enterprise administrator or project leader logs in to the system and selects the current job category on the job assessment management page. The system automatically loads the risk assessment module database and matches the preset risk points for the current job category. The user can select "Automatically generate primary assessment records," and the system automatically imports risk points for the operating environment, equipment status, and personnel behavior from the database.

[0045] The system automatically screens risk points based on job categories and matches them to standard risk points in the database. The system then calculates L, E, C, G, and S values based on the collected data. The L value is calculated by calculating a likelihood score based on historical accident data, equipment failure rates, and the stability of the construction environment. The E value is calculated by calculating an exposure score based on personnel working hours and exposure frequency. The C value is calculated by determining an accident consequence score based on industry standards and historical data. The G value is determined based on data collected by geological monitoring equipment and geological survey reports. The S value is determined based on architectural design drawings and actual construction conditions.

[0046] Adjust risk point parameters based on preliminary analysis results. The project leader or safety specialist accesses the risk assessment page to view automatically generated parameter values and the calculated D value. Parameter values can be manually adjusted to optimize risk assessment results based on actual site conditions. The D value is recalculated based on the adjusted parameter values, and the system automatically updates the risk level.

[0047] Enterprise administrators or project leaders can manually add custom risk points to supplement special operation risks not covered in the database. They can fill in the risk point parameter entry interface provided by the system. The content to be filled in includes: risk point name, operation category, scores of various parameters, possible accident types and recommended risk management measures.

[0048] The final risk assessment and report generation are performed based on the risk point parameter adjustment. The system calculates the D value of all assessed risk points, classifies the risk levels, and calculates the overall risk level of the operation area, specifically: Where: It is the overall risk index of the operating area, which is used to assess the comprehensive risk level of a specific operating area. The larger it is, the higher the overall risk of the operation area; is the hazardousness index of the operating conditions of the i-th risk point, which is used to measure the hazardousness of a single risk point. pass Calculated; n is the total number of risk points in the operating area, which is used to represent the total number of risk points assessed in the operating area; This means that the D values of all risk points are accumulated to ensure that the D values of all risk points are taken into account when calculating the overall risk of the operating area.

[0049] A risk assessment report is generated based on the overall risk level of the calculated operation area. The report content includes: operation category, risk point name, scores of various parameters, D value calculation results, risk level, safety management suggestions and corrective measures suggestions.

[0050] Example 4: Detailed design of the early warning and rectification module Set up a risk early warning mechanism. Use color coding to intuitively display the risk level. After the system administrator configures the risk level color coding in the background management terminal, the system will automatically load the risk assessment data. , the system marks it as a red alert and sets it as a major risk; , the system marks it as orange warning and sets it as a high risk; , the system marks it as a yellow warning and sets it as a general risk; , the system marked it as a blue warning and set it as low risk.

[0051] The system administrator sets risk alarm rules in the background and configures alarm trigger conditions including: D value exceeding the specified threshold, abnormal equipment status (such as tower crane overload, construction elevator overspeed), personnel illegal operation (such as not wearing a safety belt, illegal fire) and geological displacement exceeding the safety range.

[0052] Log in to the system through the enterprise administrator or project leader to configure alarm reception methods, including SMS notifications, email notifications, and platform notifications. When the alarm conditions are met, the system automatically triggers an alarm, records the alarm log, and stores it in the risk warning database.

[0053] Dynamic assessment and closed-loop rectification are conducted based on the established risk early warning mechanism. System administrators can set up risk assessment task scheduling rules and configure the regular assessment frequency in the background. The system will automatically perform risk reviews, scanning all assessed work points and checking the risk status. If the D value does not decrease, the system will automatically remind the project leader to recheck. If the D value decreases but still does not fall into the safe range, the system will recommend further rectification.

[0054] The enterprise administrator can view the rectified work points on the rectification management page and arrange for the project leader to conduct a risk reassessment. The project leader recalculates the parameter values, the system automatically updates the D value, and reclassifies the risk level. The system compares the D value after reassessment with the D value before rectification and generates a rectification effect report. The report content includes: if the D value drops and reaches the safe range, the rectification is marked as completed and the operation risk database is updated. If the D value still does not meet the standard, the system automatically reminds the enterprise administrator to take further measures.

[0055] Safety management is carried out based on dynamic assessment and closed-loop rectification. Enterprise administrators can adjust preset risk points and modify various parameters according to the actual situation at the work site. The system will provide a risk point adjustment log to record all risk parameter changes.

[0056] Enterprises can customize risk assessment models and configure exclusive calculation rules. For example, the L value can be adjusted based on the complexity of the construction process; the E value can be weighted according to the personnel flow in different construction stages; the C value can be used to calculate the actual consequence score based on the project's economic budget and personnel input; the G value can be dynamically adjusted according to geological change trends; and the S value can be corrected according to the construction progress of the building structure.

[0057] The system provides custom assessment rule management. Enterprises can choose to use the default LEC-M model or enable enterprise-defined rules. The system will automatically calculate the D value based on the custom rules and divide the risk level into the following categories: Where: It is an enterprise-defined risk index, which is used to measure the risk index set by the enterprise according to its own safety management standards; This is an enterprise-defined risk assessment function, a specific formula used to calculate the values of various parameters. Enterprises can adjust the calculation method based on industry needs. The meanings of L, E, C, G, and S are the same as those mentioned above.

[0058] Example 5: Detailed design of the regulatory docking module Configure a data reporting mechanism. By configuring data reporting rules in the backend management, system administrators can automatically upload construction operation risk assessment data to the government supervision platform. After the enterprise administrator completes the risk assessment, the system automatically organizes the data and stores it in the specified format in the data reporting buffer. Two reporting methods are provided: automatic reporting and manual reporting.

[0059] Automatic reporting: The system automatically uploads risk assessment data at set intervals. Manual reporting: Enterprise administrators can manually submit assessment data on the data management page. The system uses the HTTPS protocol for data transmission and the AES-256 encryption algorithm to encrypt sensitive data. Data reports are logged and stored in the data reporting log table.

[0060] Real-time supervision and review are conducted based on the configured data reporting mechanism. Government regulators log into the supervision system through identity verification and obtain real-time risk data for construction companies nationwide or regionally. The system provides a risk map visualization function, allowing government regulators to view the risk level of each construction company through the map interface, including red, orange, yellow, and blue markers.

[0061] Based on the D-value assessment results, the system automatically flags high-risk companies and sends warning notices to government regulators. Regulators can then issue corrective actions online, including requiring companies to supplement risk assessment data, complete rectification within a specified timeframe, and submit a rectification report.

[0062] Historical data comparison and trend analysis are conducted based on real-time monitoring and review. System administrators configure data storage policies, and the system stores risk assessment data in chronological order. It also provides historical data query capabilities, allowing users to filter data by company name, project name, job category, and risk level, and output data in Excel and PDF formats.

[0063] The system uses data mining algorithms to analyze historical data and identify risk trends in the construction industry. It uses time series analysis to calculate the changes in average D values in different time periods and uses the K-means clustering algorithm to classify the risk levels of construction companies and identify high-risk areas.

[0064] A cross-platform architecture is used to develop PC applications, with functions including risk assessment management, risk warning management, and data statistics and export. Flutter or React Native frameworks are used to develop mobile applications, with functions including real-time viewing of risk assessment results, receiving warning notifications, and submitting corrective measures. Vue.js + Spring Boot architecture is used to develop web applications, with functions including data query, risk analysis chart display, and online management.

[0065] Introducing artificial intelligence-assisted analysis, the system uses random forests to train risk prediction models, inputs historical risk data, and outputs predictions of future high-risk operating areas, specifically: Where: It is the future risk probability of the operating area, which is used to predict the probability of accidents occurring in a certain operating area in the future; is a risk prediction function used to calculate future risk probabilities based on multiple input variables; L, E, C, G, and S have the same meanings as before; T is the time factor, used to reflect the degree of impact of time on risk, such as construction peak periods and severe weather seasons; M is equipment maintenance data, used to measure the maintenance status and frequency of equipment.

[0066] The system uses AI models to analyze historical rectification records and automatically recommends the optimal rectification plan, including: increasing the frequency of safety inspections in high-risk areas, increasing the number of training sessions for specific work categories, optimizing equipment maintenance plans, and improving construction processes.

[0067] Example 6: Example of actual application scenario of the system The system was fully operational in a large-scale construction project. During the foundation construction phase, the data acquisition module, using geological monitoring equipment, detected minor displacements in a localized area of the construction site. Simultaneously, environmental monitoring sensors detected dust concentrations approaching the warning threshold. This data was transmitted in real time to the risk assessment module. Combined with information on personnel activity and equipment operating status in the area, the LEC-M model calculated that the D value reached the yellow warning range, indicating a general risk.

[0068] The early warning and rectification module immediately issued a yellow alert, notifying the project manager and safety officer via text message and the platform. Upon receiving the alert, the project manager organized a detailed survey of the geological displacement area and strengthened dust reduction measures at the construction site. At the same time, they submitted corrective measures and an estimated timeframe on the rectification management page.

[0069] During the rectification process, the system regularly reviewed the risks in the work area according to the established risk assessment task scheduling rules. After a period of rectification, the D value decreased during the reassessment, but remained within the yellow warning range. The system recommended further optimization of dust suppression measures and continued monitoring of geological changes. The project leader adjusted the rectification plan based on the recommendations, increasing investment in dust suppression equipment and assigning personnel to closely monitor geological displacement data.

[0070] The supervisory docking module uploads the project's risk assessment data and rectification status to the government regulatory platform in real time. Government regulators use the system's risk map to visually visualize the project's risk status and review the rectification process. After further rectification, the system reassesses the project, and if the D value drops to the blue warning range, the rectification is complete. The system then updates the operational risk database, documenting the entire rectification process.

[0071] Through the continuous operation of the system throughout the entire project construction process, various safety hazards were discovered and dealt with in a timely manner, ensuring the safety of construction workers and the smooth progress of the project. At the same time, it also provided effective supervision means for government regulatory departments and improved the overall safety management level of the construction industry.

[0072] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above-described embodiments. The above-described embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The cloud platform-based construction industry operating conditions hazard assessment system is characterized by: Includes the following modules: The system architecture module is used to select the server-side architecture, design the client-side architecture based on the selected server-side architecture, design the data interaction and calculation framework based on the client-side architecture design, manage user rights based on the data interaction and calculation framework design, and provide the system operating environment and underlying support for the risk assessment module, data collection module, early warning and rectification module, and supervision docking module; The risk assessment module is used to configure the risk assessment model and calculate the D value. Based on the D value calculation result, the risk level is divided and the assessment result is obtained. Based on the assessment result, the risk assessment module database is created and the assessment result is passed as input information to the early warning and rectification module and the supervision docking module to support the subsequent risk response and supervision processing process; The data acquisition module is used to configure data acquisition equipment through the backend management terminal, obtain comprehensive data through the data acquisition equipment, conduct classification assessment and initial analysis based on the obtained comprehensive data, obtain preliminary analysis results, adjust risk point parameters based on the preliminary analysis results, perform final risk assessment and report generation based on the adjusted risk point parameters, and transmit the collected data in real time to the risk assessment module as the basis for risk index calculation; The early warning and rectification module is used to set up a risk early warning mechanism, conduct dynamic assessment and closed-loop rectification based on the risk early warning mechanism, carry out safety management based on the dynamic assessment and closed-loop rectification, receive risk level information provided by the risk assessment module, automatically trigger early warnings based on the assessment results, and feed back the assessment results after rectification to the supervision docking module; The supervision docking module is used to configure the data reporting mechanism, conduct real-time supervision and review based on the configured data reporting mechanism, conduct historical data comparison and trend analysis based on real-time supervision and review, introduce artificial intelligence to assist in analysis, and connect with the risk assessment module and the early warning and rectification module to realize the automatic uploading and synchronous sharing of assessment data and rectification information, which is used for online monitoring and security decision-making by government regulatory departments.

2. The cloud-based construction industry operating condition hazard assessment system according to claim 1 is characterized by: The system architecture module is specifically: A server-side architecture was selected, cloud servers were used as computing resources, cloud databases were used to store construction operation risk assessment data, and cloud storage was used to store unstructured data such as historical assessment reports, construction drawings, on-site photos and videos. Load balancing technology was used to balance the load across multiple servers, and Nginx was deployed as a reverse proxy server. Use Oracle or MySQL database as the back-end database and design the data table structure, which includes user management table, construction operation condition table, risk assessment table, rectification management table and construction schedule; The client-side architecture was designed based on the server-side architecture. The front-end interface was developed using Vue.js, combined with the Element-UI component library and the RESTful API architecture. The client accessed the server-side data through HTTP requests. The front-end development included the login page, construction operation data management page, risk assessment page, rectification management page, report generation and export page, and construction progress monitoring page. Based on the client architecture design, a data interaction and computing framework is developed. Spring Boot, MyBatis, and the selected database are used to build the backend data interaction layer, and a RESTful API interface is configured. The interface includes user management, operation condition management, risk assessment management, corrective measures management, and construction progress management. User authority management is carried out based on the design of data interaction and computing framework. User authority management includes system administrators, enterprise administrators, project leaders, security specialists and government regulators. The RBAC mechanism is used to manage the access rights of different users to different modules, and OAuth2.0 and JWT authentication are used to verify user identity.

3. The cloud-based construction industry operating condition hazard assessment system according to claim 1 is characterized by: The risk assessment module is specifically: Configure the risk assessment model according to the designed data table structure, define the calculation method of the operating condition hazard index, and adopt the improved operating condition hazard evaluation method: , where: D is the hazardousness index of working conditions, L is the probability of accident occurrence, ranging from 0.1 to 10, E is the degree of exposure, ranging from 0.5 to 10, C is the consequence of the accident, ranging from 1 to 100, G is the geological condition coefficient of the construction site, ranging from 0.5 to 2, and S is the complexity coefficient of the building structure, ranging from 0.8 to 1.5; Based on the calculation of the operating condition hazard index, the D value calculation result is obtained, and the risk level is divided and the risk level threshold is set. When the D value is greater than or equal to 320, the assessment result is a major risk and the system is marked as a red warning. When the D value is between 160 and 320, the assessment result is a large risk and the system is marked as an orange warning. When the D value is between 40 and 160, the assessment result is a general risk and the system is marked as a yellow warning. When the D value is less than 40, the assessment result is a low risk and the system is marked as a blue warning. Based on the risk level threshold, the assessment results are obtained, and based on the assessment results, a risk assessment module database is created. The selected database is used as the back-end database. The risk assessment module database includes the creation of risk point data tables, the creation of operation risk assessment data tables, and the creation of rectification management data tables; Preset work categories based on assessment results, including foundation engineering, main structure construction, height work, electrical work, hot work, waterproofing, and decoration and renovation. Standard risk points are stored in the risk point database, including typical risk points under each work category and their default L, E, C, G, and S scoring standards. Enterprise administrators and project leaders are supported to manually adjust the various parameter values of risk points. The risk assessment process is set up, the system administrator enters the standard risk point data, the enterprise administrator or project leader selects the operation category, the system automatically loads the risk point database, matches the risk points and default scores, calculates the D value and divides the risk level, and marks the warning. The project leader can manually adjust the score, the enterprise administrator generates an assessment report for filing, and re-evaluates after rectification. The government regulator reviews the rectification effect.

4. The cloud-based construction industry operating condition hazard assessment system according to claim 1 is characterized by: The data acquisition module is specifically: Configure data collection equipment through the backend management terminal, including environmental monitoring sensors, equipment status monitoring equipment, personnel behavior recording systems and geological monitoring equipment; Through enterprise administrators accessing the data collection system, project leaders and safety specialists manually enter data to supplement safety information that sensors cannot monitor. Manually entered data must be marked with the "manual supplement" symbol. In the event of a conflict with sensor data, real-time monitoring data will prevail, and manual data will be archived as notes. Comprehensive data is collected through various monitoring equipment, including construction site temperature and humidity, wind speed, dust concentration, equipment operating parameters, personnel behavior and location, and geological conditions; Based on the comprehensive data obtained, classification assessment and initial analysis are carried out to obtain preliminary analysis results. Log in to the system and select the operation category. The system automatically loads the risk assessment module database, matches the preset risk points, and automatically generates a primary assessment record. The system screens risk points according to the operation category, matches standard risk points, and calculates L, E, C, G, and S values; Based on the preliminary analysis results, risk point parameters are adjusted. The project leader or safety specialist manually adjusts the parameter values to optimize the assessment results, recalculates the D value to update the risk level, and manually adds custom risk points. Custom risk points require the selection of the associated operation category, which inherits the L / E / C basic score of the category by default. The G / S value must be filled in based on the actual situation on site. After approval by the enterprise administrator, it is automatically synchronized to the risk point database for subsequent assessment. Based on the risk point parameter adjustment, a final risk assessment and report generation are performed. The D values of all assessed risk points are calculated and risk levels are divided. The overall risk level of the operation area is calculated: , and generate a risk assessment report based on the results. The report content includes the operation category, risk point name, scores of various parameters, D value calculation results, risk level, safety management suggestions and corrective measures suggestions.

5. The cloud-based construction industry operating condition hazard assessment system according to claim 1 is characterized by: The early warning and rectification module is specifically: Establish a risk warning mechanism, using color coding to intuitively display risk levels. The system administrator configures the risk level color coding, and the system automatically loads risk assessment data. Alarm rules and trigger conditions are set, including D value exceeding the threshold, abnormal equipment status, personnel violation operation, and geological displacement exceeding the range. Configure the alarm reception method, and the system triggers the warning and records the log; Based on the established risk early warning mechanism, dynamic assessment and closed-loop rectification are carried out. System administrators set risk assessment task scheduling rules. The system automatically performs risk review, checks risk status, and reminds users to recheck or recommends further rectification. If the rectification status is still marked as "in progress" after the preset 72-hour period, the system automatically triggers a secondary warning: a text message reminder is sent to the enterprise administrator, and the rectification record is synchronized with the government supervision docking module. Supervisors can intervene online to supervise the work. Enterprise administrators review the rectified work points and arrange for the project leader to re-evaluate. The D value of the re-evaluation is compared with the D value before the rectification to generate a rectification effect report. Safety management is carried out based on dynamic assessment and closed-loop rectification. Enterprise administrators adjust preset risk points and various parameters, and the system records the changes. Enterprises can customize risk assessment models and calculation rules. The system provides customized assessment rule management, automatically calculates D values based on customized rules, and divides risk levels: .

6. The cloud-based construction industry operating condition hazard assessment system according to claim 1 is characterized by: The regulatory docking module is specifically: Configure the data reporting mechanism. System administrators configure data reporting rules. Construction operation risk assessment data is automatically uploaded to the government supervision platform. Both automatic and manual reporting methods are available. The system uses the HTTPS protocol to transmit data and the AES-256 encryption algorithm to encrypt sensitive data. The database storage layer uses transparent data encryption technology to fully encrypt core data tables such as risk assessment tables and rectification management tables. Sensitive fields such as personnel location and company contact information are additionally desensitized. Data reporting generates log records and stores them. Real-time supervision and review are carried out based on the configured data reporting mechanism. Government regulators log in to the regulatory system to obtain real-time risk data. The system provides a risk map visualization function, automatically marks high-risk enterprises and sends early warning notifications. Regulators can issue corrective instructions online. Based on real-time supervision and review, historical data comparison and trend analysis are conducted. System administrators configure data storage strategies. The system stores risk assessment data and provides query functions. Data mining algorithms are used to analyze historical data. Time series analysis and K-means clustering algorithms are used to identify risk trends and high-risk areas. Multi-terminal applications are developed to implement different functions. Introducing artificial intelligence-assisted analysis, the system uses random forests to train risk prediction models: , combined with AI models to analyze historical rectification records and automatically recommend the optimal rectification plan.

7. The cloud-based construction industry operating condition hazard assessment system according to any one of claims 1 to 6, characterized in that: The data acquisition module and the risk assessment module implement data interaction through a RESTful API interface, and the data acquisition module transmits the real-time monitored environment, equipment, personnel and geological data to the risk assessment module through the interface; The D value result calculated by the risk assessment module is pushed to the early warning rectification module and the supervision docking module through the message queue, triggering the early warning mechanism or supervision data synchronization.

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