Municipal road construction machinery AI control system

Abstract

The invention provides an AI control system for municipal road construction machinery, and the system comprises a multifunctional sensor network unit which collects environment data, mechanical operation states and construction quality parameters of each stage of road construction in real time, and integrates the collected data parameters to form a comprehensive engineering parameter table. The central processing unit calls a comprehensive engineering parameter table, historical construction data and current construction data are analyzed according to parameter data in the comprehensive engineering parameter table through a deep learning algorithm of an artificial intelligence decision module, an optimal construction scheme is generated, and real-time changes of a construction site are adjusted with the optimal construction scheme as a standard parameter scheme; and after adjustment, the construction process coordination unit optimizes connection of milling, cleaning, paving, rolling and maintenance stages, and a scheme formed by an artificial intelligence decision module in the central processing unit and optimization steps are imported into the intelligent mechanical control interface unit.

Description

Technical Field

[0001] The present invention belongs to the field of road construction control, and in particular relates to an AI control system for municipal road construction machinery. Background Art

[0002] With the acceleration of urbanization and the continuous advancement of infrastructure construction, the quality and efficiency of municipal road construction are receiving more and more attention. Traditional road construction methods mainly rely on manual experience and fixed construction processes, which are difficult to adapt to the complex and changing construction environment and the increasing quality requirements. In recent years, with the rapid development of artificial intelligence, the Internet of Things and big data technologies, applying these advanced technologies to municipal road construction has become a new research direction. However, the existing municipal road construction machinery control system still has many shortcomings.

[0003] Traditional systems lack real-time data collection and analysis capabilities and cannot respond to changes on the construction site in a timely manner. Most systems rely only on preset parameters for operation and cannot be dynamically adjusted according to actual conditions, resulting in unstable construction quality and low efficiency. Existing systems are often isolated, and there is a lack of effective coordination and connection between the various construction stages. This fragmented management method is prone to waste of resources and delays in construction. Furthermore, traditional systems lack intelligent decision-making capabilities and cannot use historical data and empirical knowledge to optimize construction plans. This results in the system being unable to provide effective solutions when faced with complex or abnormal situations, and still requiring a lot of reliance on manual intervention. Existing quality control methods are usually post-detection, which makes it difficult to achieve real-time monitoring and timely correction. This not only increases the probability of rework, but also increases construction costs. The human-computer interaction interface of traditional systems is often not friendly enough, the operation is complicated, and the workload and error risk of operators are increased. In contrast, the AI ​​control system for municipal road construction machinery proposed in the present invention has significant innovation and advantages.

[0004] The system realizes all-round and real-time data collection through a multifunctional sensor network, covering multiple dimensions such as environmental data, mechanical operation status and construction quality parameters. This provides a comprehensive and accurate data basis for subsequent intelligent analysis and decision-making. The system introduces an artificial intelligence decision-making module, which uses a deep learning algorithm to analyze historical and current data to generate the optimal construction plan. This data-driven decision-making method greatly improves the scientificity and adaptability of the construction plan. The construction process coordination unit of this system realizes the intelligent connection of various construction stages, optimizes the entire construction process, and improves resource utilization efficiency. In addition, the system's intelligent mechanical control interface unit can automatically adjust the mechanical operation parameters according to the AI ​​decision results, realizing the intelligence and precision of the construction process. The system is equipped with a real-time quality inspection and monitoring unit, which can promptly detect and correct construction quality problems, ensuring the stability and consistency of construction quality.

[0005] This AI-based municipal road construction machinery control system represents a new direction in road construction technology. It integrates advanced technologies such as artificial intelligence, the Internet of Things, and big data to realize the intelligence, precision, and efficiency of the construction process, and is expected to significantly improve the quality, efficiency, and sustainability of municipal road construction. Summary of the invention

[0006] The present invention proposes an AI control system for municipal road construction machinery, which solves the following technical problems: it realizes real-time data collection and analysis, intelligent decision-making and dynamic adjustment, optimized connection of construction stages, automatic control of mechanical operation, and real-time quality monitoring and feedback. These functions jointly improve construction quality and efficiency, reduce human errors, optimize resource utilization, and realize intelligent and precise management of the construction process.

[0007] The technical solution of the present invention is implemented as follows: an AI control system for municipal road construction machinery, including real-time collection of environmental data, machinery operation status and construction quality parameters at each stage of road construction through a multifunctional sensor network unit, integrating the collected data parameters to form a comprehensive engineering parameter table, and the central processing unit calls the comprehensive engineering parameter table, and analyzes the parameter data therein through the deep learning algorithm of the artificial intelligence decision module. Historical construction data and current construction data are analyzed to generate the optimal construction plan, and the optimal construction plan is used as the standard parameter plan to adjust the real-time changes of the construction site; and after the adjustment, the construction process coordination unit optimizes milling, cleaning, The paving, rolling and maintenance stages are connected, and the plan and optimization steps formed by the artificial intelligence decision-making module in the central processing unit are imported into the intelligent machinery control interface unit. The instructions generated by the artificial intelligence decision-making module are transmitted to the construction machinery through the intelligent machinery control interface unit. The construction machinery automatically adjusts the machinery operating parameters according to the instructions received in combination with the pavement material characteristics and on-site environmental conditions, and conducts real-time quality inspection of the construction site in different areas through the quality inspection and monitoring unit. The standard quality parameter range for each construction stage is pre-stored in the quality inspection and monitoring unit. The quality inspection and monitoring unit calls the parameter data of the construction machinery in real time, and provides warning feedback when the parameter data exceeds the standard quality parameter range.

[0008] The AI ​​control system for municipal road construction machinery aims to improve the efficiency and quality of road construction through intelligent means. The core functions of the system can be divided into several main modules:

[0009] Data collection: Through the multifunctional sensor network unit, the environmental data, mechanical operation status and construction quality parameters of the construction site are collected in real time. These data provide the basis for subsequent analysis.

[0010] Data integration and analysis: The central processing unit integrates the collected data into a comprehensive engineering parameter table. Then, the artificial intelligence decision-making module uses deep learning algorithms to analyze historical construction data and current data to generate the optimal construction plan. This process can identify the best operation method under specific circumstances and conditions.

[0011] Real-time adjustment: Based on the generated optimal construction plan, the system can adjust to the real-time changes on the construction site. This dynamic adjustment ensures that the construction process always meets the optimization standards.

[0012] Construction process coordination: The role of the construction process coordination unit is to optimize the connection between various construction stages (such as milling, sweeping, paving, rolling and maintenance), ensure the smooth connection of each link, and improve the overall construction efficiency.

[0013] Intelligent machinery control: The instructions generated by the decision-making module are transmitted to the construction machinery through the intelligent machinery control interface unit. The machinery automatically adjusts the operating parameters according to the instructions and site conditions to adapt to different pavement material characteristics and environmental changes.

[0014] Quality monitoring: The quality inspection and monitoring unit is responsible for real-time quality inspection of the construction site in different areas. The pre-stored standard quality parameter range can provide a basis for real-time monitoring. When the parameter data exceeds the standard range, the system will issue a warning feedback so that corrective measures can be taken in time.

[0015] Through advanced sensing technology, data analysis and intelligent control, the system realizes intelligent management of municipal road construction, thereby improving the safety, efficiency and quality of construction.

[0016] The system uses a multifunctional sensor network unit to achieve real-time and comprehensive data collection of the entire road construction process, including environmental data, mechanical operating status and construction quality parameters. This all-round data collection method transcends the limitations of traditional systems that only focus on one aspect, and provides a more comprehensive and accurate data foundation for subsequent intelligent decision-making.

[0017] The system introduces the concept of a comprehensive engineering parameter table to integrate various types of collected data. This data integration method enables the system to analyze the construction situation from a holistic perspective, rather than looking at each parameter in isolation. The system uses artificial intelligence decision-making modules, especially deep learning algorithms, to analyze historical and current construction data and generate the optimal construction plan. This method significantly improves the accuracy and efficiency of decision-making, far exceeding traditional manual decision-making or simple programmatic decision-making. The system has real-time adjustment capabilities and can dynamically adjust the optimal plan based on real-time changes at the construction site. This flexibility is not available in traditional fixed plans.

[0018] The system optimizes the connection between milling, cleaning, paving, rolling and maintenance stages through the construction process coordination unit. This full-process optimization perspective ensures the consistency and efficiency of the entire construction process. The system adopts an intelligent mechanical control interface unit to achieve seamless connection from decision-making to execution, greatly improving the accuracy and efficiency of construction. The system allows construction machinery to automatically adjust operating parameters based on the received instructions, combined with the characteristics of the pavement material and the on-site environmental conditions. This adaptive ability makes construction more accurate and efficient. The system introduces a regional real-time quality inspection function and sets up an early warning mechanism, which not only improves the accuracy of quality control, but also can promptly detect and correct problems and prevent the expansion of quality defects.

[0019] This AI control system builds a closed-loop intelligent construction management system through comprehensive data collection, intelligent decision-making analysis, real-time dynamic adjustment, full-process optimization and coordination, precise execution control, and strict quality supervision.

[0020] As a preferred embodiment, the multifunctional sensor network unit package includes environmental sensors, mechanical state sensors and construction quality sensors; wherein the environmental sensors include temperature sensors, humidity sensors, wind speed sensors and light sensors for real-time monitoring of environmental conditions at the construction site; the mechanical state sensors include vibration sensors, pressure sensors, speed sensors and oil temperature sensors for monitoring the operating status of construction machinery; the construction quality sensors include flatness sensors, density sensors and thickness sensors for real-time detection of pavement construction quality parameters.

[0021] As a preferred embodiment, the multifunctional sensor network unit also includes a data preprocessing module, which is used to filter and calibrate the collected raw data. The multifunctional sensor network unit adopts wireless transmission technology to form a distributed sensor network, covers the construction site with transmission channel signals, and performs real-time wireless data transmission through the transmission channel.

[0022] As a preferred embodiment, the construction process coordination unit optimizes the connection between milling, cleaning, paving, rolling and maintenance stages, including the following steps:

[0023] Establish a digital model of the construction phase, and digitize the processes, equipment requirements, staffing, and time requirements of each phase of milling, sweeping, paving, rolling, and maintenance;

[0024] Using intelligent scheduling algorithms, dynamically calculate the optimal start time and duration of each stage based on real-time construction progress and optimal construction plan;

[0025] Through the resource allocation module, the equipment and personnel deployment required for each construction stage is optimized, buffer time is set, transition time is reserved between each construction stage, the construction quality of each construction stage is monitored in real time, and when quality abnormalities are detected, the parameter settings of the subsequent stages are automatically adjusted.

[0026] As a preferred implementation method, a material property database is established in the construction machinery to store the physical and chemical properties, construction parameters and environmental sensitivity data of standard pavement materials. The temperature, humidity and wind speed are monitored in real time through the environmental perception module equipped on the construction machinery. A parameter mapping model is simultaneously established to map the material properties, environmental conditions and construction quality requirements to specific mechanical operating parameters. The mechanical operating parameters are dynamically adjusted according to the real-time monitored road conditions and environmental changes using an adaptive control algorithm.

[0027] After adopting the above technical solution, the beneficial effects of the present invention are: the whole system significantly improves the construction efficiency. Through real-time data collection and intelligent decision-making, the system can quickly respond to changes in the construction site, optimize the connection between various construction stages, and reduce unnecessary waiting time and resource waste. The system greatly improves the construction quality. With the help of deep learning algorithms and real-time quality inspection functions, it can accurately control construction parameters, timely detect and correct quality deviations, and ensure high standards and consistency in road construction.

[0028] The system reduces the risk of human error, reduces the uncertainty of manual operation through automated and intelligent control, and improves the reliability and safety of construction. It achieves optimal utilization of resources, maximizes the efficiency of mechanical equipment through intelligent scheduling and parameter adjustment, and saves energy and materials. It improves the adaptability and flexibility of construction, and can automatically adjust construction strategies according to different road conditions, weather conditions and material characteristics to adapt to various complex construction environments.

[0029] The system strengthens the quality control of the entire process. There are real-time monitoring and early warning mechanisms at each stage from milling to maintenance, ensuring the quality of the entire construction process is controllable. It provides more scientific decision-making support, and provides managers with more accurate decision-making basis through historical data analysis and real-time data processing. It promotes the standardization and normalization of construction technology. Through AI control, best practices can be quickly promoted and applied. It improves the working environment on the construction site and reduces the labor intensity of workers and the time they are exposed to dangerous environments.

[0030] This system lays the foundation for the construction of future smart cities, and its data collection and analysis capabilities can provide information support for urban planning and management. In general, this AI control system not only improves the efficiency and quality of municipal road construction, but also promotes the development of the entire industry towards intelligence and refinement, with significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] Example:

[0035] like Figure 1 As shown, an AI control system for municipal road construction machinery includes real-time collection of environmental data, machinery operation status and construction quality parameters of each stage of road construction through a multifunctional sensor network unit, integration of the collected data parameters to form a comprehensive engineering parameter table, a central processing unit to retrieve the comprehensive engineering parameter table, and analyze historical construction data and current construction data through the deep learning algorithm of the artificial intelligence decision module, generate an optimal construction plan, and use the optimal construction plan as the standard parameter plan to adjust the real-time changes of the construction site; and after the adjustment, optimize the connection of milling, cleaning, paving, rolling and maintenance stages through the construction process coordination unit, import the plan and optimization steps formed by the artificial intelligence decision module in the central processing unit into the intelligent machinery control interface unit, and transmit the instructions generated by the artificial intelligence decision module to the construction machinery through the intelligent machinery control interface unit. The construction machinery automatically adjusts the machinery operation parameters according to the received instructions combined with the characteristics of the road surface material and the on-site environmental conditions, and performs real-time quality inspection of the construction site in different areas through the quality inspection and monitoring unit, and pre-stores the standard quality parameter range of each construction stage in the quality inspection and monitoring unit. The quality inspection and monitoring unit calls the parameter data of the construction machinery in real time and gives a warning feedback after the parameter data exceeds the standard quality parameter range.

[0036] The multifunctional sensor network unit collects environmental data, mechanical operating status and construction quality parameters at each stage of road construction in real time. These data cover key information in the construction process and provide comprehensive data support for subsequent intelligent decision-making. The collected data is integrated to form a comprehensive engineering parameter table. This step centralizes the scattered data to facilitate unified management and analysis by the central processing unit. After the central processing unit retrieves the comprehensive engineering parameter table, it analyzes historical construction data and current construction data through the deep learning algorithm of the artificial intelligence decision-making module. This process takes advantage of machine learning and can discover patterns and trends from large amounts of data to generate the optimal construction plan.

[0037] The system uses the optimal construction plan as the standard parameter plan and dynamically adjusts the real-time changes on the construction site. This real-time adjustment can adapt to various changes in the construction process and ensure the stability of construction quality. After the adjustment, the system optimizes the connection between the milling, cleaning, paving, rolling and maintenance stages through the construction process coordination unit. This step ensures the continuity and efficiency of the entire construction process. The plan and optimization steps formed by the artificial intelligence decision-making module are imported into the intelligent mechanical control interface unit, through which the instructions are transmitted to the construction machinery, so that the machinery can automatically adjust the operating parameters according to the received instructions combined with the characteristics of the pavement material and the on-site environmental conditions.

[0038] The quality inspection and monitoring unit conducts real-time quality inspection on the construction site in different areas, compares the pre-stored standard quality parameter range with the real-time construction machinery parameter data, and provides warning feedback when the parameters exceed the standard range.

[0039] This setup and operation method is effective for several reasons: It realizes the comprehensive digitization and intelligence of the construction process, forming a closed-loop intelligent control system from data collection to decision-making to mechanical control, which greatly improves the accuracy and efficiency of construction. The use of artificial intelligence and deep learning algorithms to analyze historical and current data can discover patterns and trends that are difficult to detect manually, providing a scientific basis for formulating the optimal construction plan. The real-time adjustment and optimization functions enable the system to respond quickly to changes in the construction site and ensure the stability of construction quality. The seamless connection between artificial intelligence decision-making and mechanical operation is achieved through the intelligent mechanical control interface unit, reducing the errors that may be caused by human intervention. The real-time quality inspection and warning feedback mechanism ensures the continuous monitoring and timely correction of construction quality. This system combines advanced artificial intelligence technology with traditional road construction technology to realize the intelligence, precision and efficiency of the construction process, providing an innovative solution for improving the quality and efficiency of municipal road construction.

[0040] The multifunctional sensor network unit package includes environmental sensors, mechanical state sensors and construction quality sensors; wherein the environmental sensors include temperature sensors, humidity sensors, wind speed sensors and light sensors, which are used to monitor the environmental conditions of the construction site in real time; the mechanical state sensors include vibration sensors, pressure sensors, speed sensors and oil temperature sensors, which are used to monitor the operating status of construction machinery; the construction quality sensors include flatness sensors, density sensors and thickness sensors, which are used to detect road construction quality parameters in real time.

[0041] From the overall architecture point of view, the system integrates three types of sensors: environmental sensors, mechanical status sensors, and construction quality sensors, forming a comprehensive data collection network. This multi-dimensional data collection method far exceeds the traditional single or local monitoring method and can provide more comprehensive and accurate construction site information.

[0042] In terms of environmental sensors, it includes monitoring of multiple parameters such as temperature, humidity, wind speed and light. This comprehensive environmental parameter collection can more accurately evaluate the impact of construction conditions on construction quality, while existing technologies often only focus on single factors such as temperature or humidity. The setting of mechanical status sensors also reflects innovation. In addition to common vibration and pressure sensors, speed and oil temperature sensors are also added, which enables the system to more comprehensively monitor the operating status of mechanical equipment, helping to promptly detect potential problems, prevent equipment failures, and improve construction efficiency. In particular, the addition of oil temperature sensors can effectively monitor the thermal load of mechanical equipment, which is particularly important in road construction with long-term continuous operations, and existing technologies often ignore this point.

[0043] In terms of construction quality sensors, the system uses flatness, density and thickness sensors at the same time. This combination can comprehensively evaluate the key quality indicators of road construction. In contrast, existing technologies may only focus on one or two parameters, making it difficult to fully grasp the construction quality. The real-time nature of this multifunctional sensor network is also a highlight. Traditional construction quality inspections often rely on post-sampling inspections, while this system can monitor various parameters during the construction process in real time, turning quality control from passive to active, greatly improving the controllability and stability of construction quality. Another important innovation is the integrated application of these sensor data. By combining and analyzing the environment, mechanical status and construction quality data, the system can establish a more comprehensive and accurate construction model. This data fusion method is relatively rare in existing technologies.

[0044] For example, the system can analyze the impact of environmental conditions on mechanical performance and construction quality, thereby making more intelligent construction decisions. The design of this multifunctional sensor network unit breaks through the limitations of traditional construction monitoring, and provides a solid data foundation for intelligent construction control through comprehensive, real-time, and integrated data collection and analysis, greatly improving the accuracy, efficiency, and quality control level of road construction, and represents an important trend in the development of road construction technology towards intelligence and refinement.

[0045] The multifunctional sensor network unit also includes a data preprocessing module, which is used to filter and calibrate the collected raw data. The multifunctional sensor network unit adopts wireless transmission technology to form a distributed sensor network, covers the construction site with transmission channel signals, and performs real-time wireless data transmission through the transmission channel.

[0046] The introduction of the data preprocessing module is a significant improvement. In traditional sensor systems, raw data is often transmitted directly to the central processing unit, which may contain noise and errors. This system uses the data preprocessing module to filter and calibrate the collected raw data, which can significantly improve the data quality. The filtering process can remove noise and interference in the signal, while the calibration can correct the systematic errors of the sensor to provide more accurate and reliable data. This preprocessing not only improves the accuracy of the data, but also reduces the burden on the central processing unit, making the entire system run more efficiently. The use of wireless transmission technology to form a distributed sensor network is another important innovation.

[0047] Traditional sensor systems usually rely on wired connections, which may face installation difficulties and line damage in complex construction environments. Wireless transmission technology overcomes these limitations, making the layout of sensors more flexible, while also greatly reducing installation and maintenance costs. The design of a distributed sensor network improves the reliability and coverage of the system. Even if a node fails, the entire network can still operate normally. This redundant design enhances the robustness of the system. Covering the transmission channel signal at the construction site is a meticulous and important consideration. This means that the system fully considers the signal transmission problem in a complex construction environment when it is designed, ensuring the stability and reliability of data transmission. This comprehensive signal coverage design is not common in existing technologies, and it can effectively solve the problem of signal blind spots or weak areas that may occur at the construction site. The realization of real-time wireless data transmission is another advantage of the system. Traditional systems may rely on periodic data collection or batch data transmission, while real-time transmission can provide instant construction status information, making decision-making and control more timely and accurate. This is particularly important for road construction that requires rapid response, which can greatly improve construction efficiency and the timeliness of quality control. In general, this design significantly improves the accuracy of data collection, the reliability of the system and the real-time performance of data transmission by introducing technologies such as data preprocessing, wireless transmission, distributed networks and comprehensive signal coverage.

[0048] As a preferred embodiment, the construction process coordination unit optimizes the connection between milling, cleaning, paving, rolling and maintenance stages, including the following steps:

[0049] Establish a digital model of the construction phase, and digitize the processes, equipment requirements, staffing, and time requirements of each phase of milling, sweeping, paving, rolling, and maintenance;

[0050] Using intelligent scheduling algorithms, dynamically calculate the optimal start time and duration of each stage based on real-time construction progress and optimal construction plan;

[0051] Through the resource allocation module, the equipment and personnel deployment required for each construction stage is optimized, buffer time is set, transition time is reserved between each construction stage, the construction quality of each construction stage is monitored in real time, and when quality abnormalities are detected, the parameter settings of the subsequent stages are automatically adjusted.

[0052] Establishing a digital model of the construction phase is an innovative approach. Traditional construction management usually relies on experience and static schedules, while this digital model fully digitizes the processes, equipment requirements, staffing, and time requirements of each stage of milling, cleaning, paving, rolling, and maintenance, creating a dynamic and quantifiable construction process model. This digitization not only improves the accuracy of construction management, but also lays the foundation for subsequent intelligent scheduling and optimization.

[0053] The use of intelligent scheduling algorithms for dynamic calculations is another major breakthrough. Traditional construction scheduling is often fixed or semi-dynamic, and it is difficult to respond quickly to changes on site. This dynamic calculation method based on real-time construction progress and optimal construction plans can adjust the optimal start time and duration of each stage in real time, greatly improving the flexibility and efficiency of construction. This intelligent scheduling algorithm may involve machine learning or artificial intelligence technology, which can continuously learn and optimize, and become more accurate as the project progresses. The introduction of the resource allocation module is the third important innovation. Traditional resource allocation is usually fixed in advance and difficult to cope with complex and changing construction environments. This dynamic resource allocation module can optimize the deployment of equipment and personnel according to real-time conditions, ensuring that each stage has the most suitable resource configuration, thereby improving overall construction efficiency. Setting buffer time is a meticulous and important consideration.

[0054] Traditional construction plans often ignore the transition needs between stages, resulting in poor connection. This design effectively reduces the delays and problems that may occur when switching between stages by reserving transition time between each construction stage, thereby improving the smoothness of the entire construction process. One of the most advanced features of the system is real-time monitoring of the quality of each construction stage and automatic adjustment of the parameter settings of the subsequent stages. Traditional quality control is usually a post-inspection or regular spot check, which makes it difficult to detect and solve problems in a timely manner. This real-time monitoring and automatic adjustment method can respond immediately when quality anomalies are detected, and automatically adjust the parameter settings of the subsequent stages, thereby ensuring the quality stability of the entire construction process. This closed-loop quality control system is very rare in the existing technology.

[0055] A material property database is established in the construction machinery to store the physical and chemical properties, construction parameters and environmental sensitivity data of standard pavement materials. The temperature, humidity and wind speed are monitored in real time through the environmental perception module equipped on the construction machinery. A parameter mapping model is simultaneously established to map material properties, environmental conditions and construction quality requirements to specific mechanical operating parameters. The mechanical operating parameters are dynamically adjusted according to the real-time monitored road conditions and environmental changes using an adaptive control algorithm.

[0056] Establishing a material property database in construction machinery is a major innovation. Traditional construction machinery usually has only fixed operating parameters and cannot be flexibly adjusted according to the characteristics of different materials. This built-in database not only contains the physical and chemical properties of standard pavement materials, but also includes construction parameters and environmental sensitivity data, which provides comprehensive data support for the intelligent operation of the machinery. This design enables the machinery to automatically adjust according to the specific material characteristics used, greatly improving the accuracy and adaptability of construction. Secondly, equipping with an environmental perception module for real-time monitoring is another important breakthrough.

[0057] Traditional construction machinery often ignores the impact of environmental factors on construction quality, or relies solely on manual observation to adjust parameters. This design enables the machinery to respond to environmental changes in a timely manner by real-time monitoring of environmental parameters such as temperature, humidity and wind speed, which is crucial to ensuring the stability of construction quality.

[0058] The third innovation of the design is to establish a parameter mapping model. This model directly maps material properties, environmental conditions and construction quality requirements to specific mechanical operating parameters, creating a dynamic, multi-dimensional parameter adjustment system. This mapping model is very rare in the existing technology. It can achieve accurate correspondence between materials, environment and quality requirements and mechanical operation, greatly improving the accuracy and intelligence level of construction. The most advanced feature of the design is the dynamic adjustment using adaptive control algorithms. Traditional construction machinery usually uses fixed operating parameters or requires manual intervention to adjust parameters.

[0059] This adaptive control algorithm can automatically adjust the mechanical operating parameters according to the real-time monitored road conditions and environmental changes. This dynamic adjustment not only improves construction efficiency, but also better adapts to the complex and changing construction environment and ensures the consistency of construction quality. In addition, this design also embodies the idea of ​​an overall system, organically combining multiple aspects such as material properties, environmental monitoring, parameter mapping and adaptive control. This systematic design is very rare in the existing technology, and it can achieve comprehensive intelligent control of the construction process.

[0060] 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A municipal road construction machinery AI control system, characterized in that: The method includes collecting environmental data, machine operation status and construction quality parameters of each stage of road construction in real time through a multifunctional sensor network unit, integrating the collected data parameters to form a comprehensive engineering parameter table, calling the comprehensive engineering parameter table by a central processing unit, analyzing historical construction data and current construction data through the deep learning algorithm of an artificial intelligence decision module, generating an optimal construction plan, and adjusting the real-time changes of the construction site with the optimal construction plan as the standard parameter plan; and after the adjustment, optimizing the connection between the milling, cleaning, paving, rolling and maintenance stages through a construction process coordination unit, importing the plan and optimization steps formed by the artificial intelligence decision module in the central processing unit into an intelligent machine control interface unit, transmitting the instructions generated by the artificial intelligence decision module to the construction machinery through the intelligent machine control interface unit, and the construction machinery automatically adjusts the machine operation parameters according to the received instructions combined with the characteristics of the road surface material and the on-site environmental conditions, and conducting real-time quality inspection of the construction site in different areas through a quality inspection and monitoring unit, pre-storing the standard quality parameter range for each construction stage in the quality inspection and monitoring unit, and calling the parameter data of the construction machinery in real time, and providing warning feedback after the parameter data exceeds the standard quality parameter range.

2. The AI ​​control system for municipal road construction machinery according to claim 1, characterized in that: The multifunctional sensor network unit package includes environmental sensors, mechanical state sensors and construction quality sensors; wherein the environmental sensors include temperature sensors, humidity sensors, wind speed sensors and light sensors, which are used to monitor the environmental conditions of the construction site in real time; the mechanical state sensors include vibration sensors, pressure sensors, speed sensors and oil temperature sensors, which are used to monitor the operating status of construction machinery; the construction quality sensors include flatness sensors, density sensors and thickness sensors, which are used to detect road construction quality parameters in real time.

3. The AI ​​control system for municipal road construction machinery according to claim 1, characterized in that: The multifunctional sensor network unit also includes a data preprocessing module, which is used to filter and calibrate the collected raw data. The multifunctional sensor network unit adopts wireless transmission technology to form a distributed sensor network, covers the construction site with transmission channel signals, and performs real-time wireless data transmission through the transmission channel.

4. The AI ​​control system for municipal road construction machinery according to claim 1, characterized in that: The construction process coordination unit optimizes the connection between milling, cleaning, paving, rolling and maintenance stages and includes the following steps: Establish a digital model of the construction phase, and digitize the processes, equipment requirements, staffing, and time requirements of each phase of milling, sweeping, paving, rolling, and maintenance; Using intelligent scheduling algorithms, dynamically calculate the optimal start time and duration of each stage based on real-time construction progress and optimal construction plan; Through the resource allocation module, the equipment and personnel deployment required for each construction stage is optimized, buffer time is set, transition time is reserved between each construction stage, the construction quality of each construction stage is monitored in real time, and when quality abnormalities are detected, the parameter settings of the subsequent stages are automatically adjusted.

5. The AI ​​control system for municipal road construction machinery according to claim 1, characterized in that: A material property database is established in the construction machinery to store the physical and chemical properties, construction parameters and environmental sensitivity data of standard pavement materials. The temperature, humidity and wind speed are monitored in real time through the environmental perception module equipped on the construction machinery. A parameter mapping model is simultaneously established to map material properties, environmental conditions and construction quality requirements to specific mechanical operating parameters. The mechanical operating parameters are dynamically adjusted according to the real-time monitored road conditions and environmental changes using an adaptive control algorithm.

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