Digital workshop construction method and system based on multi-source data

Abstract

The invention relates to the technical field of digital construction, in particular to a digital workshop construction method and system based on multi-source data. The method comprises the following steps: acquiring air quality, temperature, humidity and noise environment monitoring data in a workshop by using an intelligent environment sensing measurer, and recording initial environment parameters; according to the initial environment parameters, building a workshop preliminary digital three-dimensional model by using a building information modeling technology; therefore, by integrating the environment monitoring and equipment management system, the dynamic feedback optimization is realized, the problems of resource waste and environmental instability in the traditional construction and management process of the workshop are solved, and the energy utilization efficiency, the production efficiency and the environmental comfort are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital construction, and particularly to a method and system for constructing a digital workshop based on multi-source data. Background Art

[0002] The existing technologies are mostly limited to the isolated management of various types of data, and fail to effectively integrate and exploit the potential of multi-source data. For example, traditional environmental monitoring systems usually only focus on environmental factors such as air quality, temperature and humidity, while equipment operation monitoring mainly focuses on the health status and load conditions of a single device, lacking in-depth exploration of the relationship between environmental changes and device states. In addition, many systems are still unable to achieve dynamic adjustment in real-time monitoring and data feedback, and the feedback mechanism lacks sufficient intelligence to comprehensively analyze multiple complex factors, resulting in a lag in response and low efficiency when facing sudden changes. There are generally data sharing barriers in the existing technologies, poor interoperability between systems, and inability to make full use of cross-domain data resources for joint analysis and prediction. These systems usually rely on simple preset thresholds and rules, are difficult to cope with complex and dynamically changing production environments, and lack flexibility and adaptability. At the same time, traditional optimization methods mostly use artificial experience or rule-based quantitative models, and it is difficult to perform precise dynamic feedback optimization based on real-time monitoring data. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for constructing a digital workshop based on multi-source data to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing a digital workshop based on multi-source data, the method includes the following steps: Step S1: Use an intelligent environmental sensor to collect air quality, temperature and humidity, and noise environment monitoring data in the workshop, and record the initial environmental parameters; according to the initial environmental parameters, use building information modeling technology to establish a preliminary digital three-dimensional model of the workshop; Step S2: Collect the operation status data of each device in the workshop and input the operation status data of the workshop devices into the preliminary digital three-dimensional model of the workshop, use simulation software to perform dynamic simulation of the interaction between the device and the environment, evaluate the impact of workshop resource consumption and environmental changes, and obtain preliminary evaluation data; Step S3: Optimize the preliminary digital three-dimensional model of the workshop according to the preliminary evaluation data. If the evaluation result shows that the energy consumption is too high or the device operation is unstable, adjust the spatial layout, device configuration and energy use of the workshop to generate an optimized digital model for workshop construction; Step S4: Integrate the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, and dynamically optimize through feedback of the actual operation data of the workshop to generate a workshop construction digital optimization report.

[0005] The beneficial effects of the present invention are achieved by integrating intelligent environmental sensors and building information modeling (BIM) technology to realize real-time monitoring and dynamic optimization of the workshop environment and equipment operation status, which has significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature, humidity, and noise, and the preliminary digital three-dimensional model of the workshop is established in combination with the building information modeling technology, which provides accurate basic data and model framework for the spatial layout and equipment configuration of the workshop. Through this process, the environmental changes in the workshop can be grasped in time, providing reliable data support for subsequent optimization. Secondly, the collected equipment operation status data is combined with the environmental parameters, input into the digital three-dimensional model, and dynamic simulation is performed using simulation software to evaluate the mutual influence of workshop resource consumption and environmental changes. This dynamic simulation process can reveal the complex relationship between equipment and environment, thereby providing a basis for improving energy efficiency, reducing resource waste, and improving environmental quality. Through further optimization model adjustment, combined with preliminary evaluation data, the spatial layout, equipment configuration and energy use of the workshop are optimized in a targeted manner, thereby improving the energy efficiency and environmental comfort of the overall system. Finally, the optimized digital model of the workshop is integrated into an intelligent digital control device for real-time monitoring and dynamic adjustment, so that the resource consumption and environmental control of the workshop are always kept in the optimal state, and can be adjusted according to real-time feedback data to ensure the sustainable development and efficient operation of the workshop. In addition, the optimization results are output in the form of a digital optimization report to provide decision support for management, thereby realizing refined management and continuous improvement of the workshop. Therefore, the present invention realizes dynamic feedback optimization by integrating environmental monitoring and equipment management systems, solves the problems of resource waste and environmental instability in the construction and management of traditional workshops, and improves energy utilization efficiency, production efficiency and environmental comfort.

[0006] Preferably, step S1 comprises the following steps: Step S11: using a temperature and humidity sensor to obtain workshop temperature and humidity data; Step S12: using an air quality sensor to analyze PM2.5 and measure small air particles in the workshop to generate workshop air quality data; Step S13: using sound wave propagation vibration to perform sound wave signal vibration analysis on the workshop to generate workshop noise environment monitoring data; Step S14: integrating the workshop temperature and humidity data, the workshop air quality data and the workshop noise environment monitoring data into workshop parameters to generate initial environment parameters; Step S15: Build a model for the initial environmental parameters based on building information modeling technology to generate a preliminary digital 3D model of the work area.

[0007] The present invention realizes the construction of accurate initial environmental parameters and the generation of a digital 3D model through systematic collection and integration of work area environmental data, and has significant beneficial effects. First, the application of temperature and humidity sensors, air quality sensors, and acoustic wave propagation vibration analysis technology enables comprehensive and efficient collection of environmental monitoring data in the work area. The temperature and humidity data accurately obtained by the temperature and humidity sensors provide a key basis for subsequent environmental control and comfort optimization, ensuring that the temperature and humidity distribution inside the work area meets the production requirements and the standards of employee work comfort. The real-time monitoring of PM2.5 concentration and air microparticles by the air quality sensors can reveal the changing trend of the air quality in the work area, timely reflect the diffusion of pollution sources, and provide data support for air purification and ventilation system optimization in the work area. Acoustic wave propagation vibration analysis, by accurately measuring the noise environment in the work area, identifies noise sources and their propagation paths, providing a data basis for noise control and ensuring that the noise level in the work area is controlled within the range that meets production requirements. By integrating the above various types of environmental data, the generated initial environmental parameters provide comprehensive data support for the establishment of the work area digital model. This data integration process can eliminate the one-sided influence of a single environmental factor on the work area environment, comprehensively consider multi-dimensional factors such as temperature and humidity, air quality, and noise, provide comprehensive and accurate environmental parameter information, and thus help accurately depict the environmental characteristics of the work area. On this basis, using building information modeling technology (BIM) to build a model for the initial environmental parameters, the generated preliminary digital 3D model not only accurately reflects the spatial layout of the work area, but also can simulate and optimize the impact of environmental conditions on the work area functions, promoting the digitization, refinement, and intelligence of work area management. Through this process, all aspects of work area environmental control, such as temperature and humidity regulation, air quality monitoring, and noise control, can be effectively integrated and optimized, thus realizing the organic coordination and intelligent management between the environment and equipment.

[0008] Preferably, the steps of using building information modeling technology to establish a preliminary digital 3D model of the work area are as follows: Perform time synchronization processing on the work area temperature and humidity data to generate work area temperature and humidity time synchronization data; perform interpolation method to fill in the missing data for the work area temperature and humidity time synchronization data to generate work area missing value supplementary data; Perform filtering and removal of periodic fluctuations on the work area air quality data to generate work area long-term trend data; perform normalization and standardization processing on the work area long-term trend data to generate work area air quality standardized data; Perform wavelet transform time-domain analysis on the in-work noise environment monitoring data to generate in-work noise time-domain analysis data; classify the sound sources of the in-work noise time-domain analysis data and remove background noise to generate in-work noise environment standard data; Perform principal component analysis on the in-work missing value supplementary data, in-work air quality standardized data, and in-work noise environment standard data to generate initial principal component analysis data; Use a preset thermal analysis and computational fluid dynamics simulation model to perform heat island effect and humidity concentration identification analysis on the initial principal component analysis data to generate in-work heat island effect analysis data and in-work humidity concentration area data; Based on the in-work noise environment standard data, perform noise propagation modeling to generate in-work noise distribution data; determine the control points of the sound source transmission path according to the in-work noise distribution data to generate in-work noise transmission path data; Use BIM technology to perform digital 3D modeling on the in-work heat island effect analysis data and in-work humidity concentration area data, and use the in-work noise transmission path data to divide the model attribute areas to obtain a preliminary digital 3D model of the workplace.

[0009] Through multi-level data processing and integration, the present invention provides precise data support for the optimization of the in-workspace environment, with remarkable beneficial effects. By performing time synchronization processing on the in-workspace temperature and humidity data and supplementing the missing data by the interpolation method, the integrity and accuracy of the temperature and humidity data are ensured, providing a reliable data basis for subsequent environmental control and comfort evaluation. The use of the interpolation method effectively fills the gaps in the data, avoiding analysis biases caused by data missing, thus ensuring the continuity of environmental monitoring and the representativeness of the data. For the in-workspace air quality data, by filtering out the periodic fluctuations, the long-term trend of the air quality is further extracted, providing a scientific basis for the optimization of the air purification system. After normalization and standardization processing, the scales of the air quality data are unified, eliminating the biases between different sensor data and ensuring the comparability and accuracy of subsequent analysis. For the in-workspace noise environment monitoring data, by separating the noise signal from the complex background noise through wavelet transform time-domain analysis, and further classifying the sound sources and removing the noise, the clarity and accuracy of the noise data are ensured, thus providing a reliable basis for the noise control and improvement plan. By performing dimensionality reduction on the processed data through principal component analysis, the correlation between various environmental parameters is further revealed, simplifying the data dimension and extracting the key features, making the subsequent analysis and modeling more efficient and accurate. Using the thermal analysis and fluid dynamics simulation model to analyze the initial principal component analysis data, the in-workspace heat island effect and humidity concentration areas are accurately identified, providing targeted data support for the design optimization of the temperature control and ventilation systems. In addition, by determining the noise propagation model and the sound source path control points, the laws of noise distribution and noise propagation are further clarified, providing quantitative data support for the formulation of noise management and emission reduction strategies. Finally, based on the BIM technology, digital three-dimensional modeling is carried out on the data such as the heat island effect, humidity concentration areas and noise transmission paths, enabling the optimization of the in-workspace environment not only to have high-precision data support, but also to achieve visual management and optimization. Through the above data processing and modeling methods, the present invention realizes the comprehensive analysis and optimization of the in-workspace environment, ensuring the coordinated management of multiple dimensions such as temperature and humidity, air quality and noise, thus laying a solid foundation for the intelligent and refined operation of the in-workspace.

[0010] Preferably, step S2 includes the following steps: Step S21: Obtain the operation status data of the in-workspace equipment; Step S22: Input the operation status data of the in-workspace equipment into the preliminary digital three-dimensional model of the in-workspace for numerical in-workspace construction analysis, and generate the output data of the in-workspace digital construction model; Step S23: Perform dynamic simulation of the interaction between the equipment and the environment on the output data of the in-workspace digital construction model, and evaluate the in-workspace resource consumption and environmental changes to obtain preliminary evaluation data.

[0011] The present invention combines the operating state data of in-workshop equipment with a digital three-dimensional model for accurate modeling and dynamic simulation, having significant beneficial effects. First, by obtaining the operating state data of in-workshop equipment, important parameters such as the load, energy consumption, and operating cycle of the equipment can be reflected in real time, providing an accurate real-time data basis for subsequent optimization decisions. This data includes the working state and performance of the equipment, which can help analyze the efficiency and problems of equipment operation, ensuring the stability and high efficiency of equipment operation. Second, inputting these equipment operating state data into the preliminary digital three-dimensional model of the in-workshop further realizes the numerical analysis of in-workshop construction, generating the output data of the in-workshop digital construction model. This process integrates the spatial layout, equipment configuration, and environmental factors of the in-workshop into a unified digital platform, realizing the virtual modeling and digital expression of the in-workshop, and helping decision-makers quickly identify potential problems in space utilization and resource allocation. This digital modeling provides an intuitive and comprehensive platform for subsequent analysis and decision-making. Finally, performing dynamic simulation of the interaction between equipment and environment on the output data of the digital construction model can accurately evaluate the impact of in-workshop resource consumption and environmental changes. This dynamic simulation process considers the complex interaction between equipment operation and environmental factors, thus comprehensively evaluating aspects such as energy consumption, resource utilization efficiency, and environmental comfort in the in-workshop, generating preliminary evaluation data. These evaluation data not only help optimize the spatial layout and equipment configuration of the in-workshop, but also provide a scientific basis for aspects such as energy management and environmental control in the in-workshop.

[0012] Preferably, step S23 includes the following steps: Step S231: Perform equipment operation efficiency simulation based on the output data of the in-workshop digital construction model, generating in-workshop simulation data; Step S232: Perform calculations on the changes in temperature, humidity, pollutant diffusion path, and noise source propagation through fluid dynamics simulation on the in-workshop simulation data, generating multi-dimensional equipment and environment simulation data; Step S233: Calculate the energy consumption under working conditions based on the multi-dimensional equipment and environment simulation data, generating energy consumption evaluation data; conduct an in-workshop environmental impact assessment based on the multi-dimensional equipment and environment simulation data, generating environmental quality evaluation data; perform resource calculation and evaluation of the utilization efficiency of space resources based on the multi-dimensional equipment and environment simulation data, generating in-workshop comprehensive resource optimization data.

[0013] Through multi-dimensional simulation and evaluation of the output data of the in-work digital construction model, the present invention provides a comprehensive and accurate in-work optimization solution, which has significant beneficial effects. First, based on the output data of the in-work digital construction model, equipment operation efficiency simulation is carried out, which can simulate the operation performance of equipment under different working conditions. The generated in-work simulation data provides a preliminary basis for subsequent resource evaluation and optimization. These simulation data reflect the energy efficiency, load distribution and operation stability of the equipment in actual operation, and can help identify the energy efficiency bottlenecks and performance deficiencies of the equipment in different working states. Second, through computational fluid dynamics (CFD) simulation of the in-work simulation data, the temperature and humidity changes, pollutant diffusion paths and noise source propagation are accurately analyzed. This process generates multi-dimensional simulation data of equipment and environment. These data not only consider the interaction effect between equipment and environment, but also reveal potential problems in environmental control, such as areas with too high temperature and humidity, sources of air pollution and propagation paths of noise pollution, thus providing a scientific basis for optimizing the environmental control system and equipment layout. Further, based on these multi-dimensional simulation data, the energy consumption calculation under working conditions is carried out, and energy consumption evaluation data is generated, providing a quantitative basis for energy management. At the same time, the environmental quality evaluation data provides a reference for environmental optimization by evaluating environmental parameters such as air quality, temperature and humidity in the in-work area. Finally, through the evaluation of the use efficiency of computing resources and space resources, in-work comprehensive resource optimization data is generated, which helps to identify waste points in resource use and provides data support for the reasonable allocation of in-work resources and the optimization of space layout.

[0014] Preferably, step S3 includes the following steps: Step S31: Optimize the preliminary in-work digital three-dimensional model according to the preliminary evaluation data to generate in-work comprehensive evaluation data; Step S32: Use the preset in-work evaluation criteria to evaluate the in-work comprehensive evaluation data. If the evaluation result shows that the energy consumption is too high or the equipment operation is unstable, adjust the space layout, equipment configuration and energy use of the in-work area to generate in-work optimization adjustment suggestions; Step S33: Based on the in-work optimization adjustment suggestions, perform final digital construction optimization on the preliminary in-work digital three-dimensional model to generate an in-work construction optimization digital model.

[0015] Through comprehensive evaluation and optimization adjustment of the in-work digital model, the present invention significantly improves the efficiency of in-work design and operation, and has important beneficial effects. First, by optimizing the preliminary in-work digital model according to the preliminary optimization suggestions, comprehensive in-work evaluation data is generated, providing multi-dimensional data support for subsequent optimization. These comprehensive evaluation data include data at multiple levels such as equipment operation, environmental quality, and energy consumption, which can comprehensively reflect the operation status of the in-work area, providing a multi-angle data view for analysis and optimization. Second, using the preset in-work evaluation criteria to evaluate the comprehensive in-work evaluation data ensures the scientificity and standardization of the evaluation results. Through the analysis of the comprehensive evaluation data, potential problems such as excessive energy consumption and unstable equipment operation can be identified in a timely manner. This process not only provides a basis for specific adjustments in equipment configuration and energy use, but also reveals the optimization space in the in-work space layout and resource allocation. According to the evaluation results, specific in-work optimization adjustment suggestions are generated, covering adjustment strategies in aspects such as space layout, equipment configuration, and energy use, thus ensuring that the in-work area can achieve the maximum benefits in terms of resource optimization and energy conservation. Finally, based on the optimization adjustment suggestions, the preliminary in-work digital three-dimensional model is finally optimized to generate an optimized digital model for in-work construction. This optimized model not only improves the energy efficiency, comfort, and safety of the in-work area, but also provides an efficient reference basis for actual construction and operation.

[0016] Preferably, the adjustment of the in-work space layout, equipment configuration, and energy use includes the following: Mark the equipment with overloaded operation or ineffective operation according to the energy consumption evaluation data, and generate load adjustment or shutdown maintenance instructions to obtain preliminary optimized energy suggestions; Optimize the overall layout of the ventilation facilities according to the environmental quality evaluation data, and at the same time adopt a strategy of adding sound insulation materials to the areas with high noise pollution, and generate equipment area optimization suggestions; Conduct an analysis of peak call for optimized power usage periods according to the in-work comprehensive resource optimization data to generate peak power usage analysis data; conduct an analysis of the layout of the cooling water pipes according to the in-work comprehensive resource optimization data to generate cooling water pipe analysis data; based on the peak power usage analysis data and the cooling water pipe analysis data, conduct an analysis and processing of the optimal call for resource allocation to generate the optimal in-work resource allocation suggestions.

[0017] Through in-depth analysis and optimization of multi-dimensional data such as energy consumption, environmental quality, and resource utilization, the present invention significantly improves the operating efficiency, energy efficiency, and environmental quality in the workshop. First, by marking equipment with overloaded operation or ineffective operation based on energy consumption assessment data, it is possible to accurately identify equipment with low efficiency or unnecessary operation, thereby avoiding the negative impact of improper equipment operation on overall energy efficiency. Combining the generated load adjustment or shutdown maintenance instructions provides clear directions for optimizing equipment operation, reduces ineffective operation, lowers energy consumption, and ensures the efficient use of workshop resources. This process provides specific operation suggestions for subsequent energy conservation and consumption reduction measures and helps reduce the risk of equipment failures caused by overloaded operation. Secondly, based on environmental quality assessment data, the overall layout of ventilation facilities is optimized, and a strategy for adding sound insulation materials is proposed for areas with high noise pollution, thereby effectively improving the air quality and noise environment in the workshop and enhancing the comfort of the working environment for workers. Through these measures, it is possible to optimize air circulation, avoid uneven temperature and humidity in local areas, and reduce the impact of noise pollution on production and employees, improving the overall environmental quality in the workshop. Further, through peak call analysis of electricity usage periods based on comprehensive resource optimization data in the workshop, it is possible to identify peak periods of energy consumption in the workshop, providing a basis for power management. Combining with the analysis of the layout of cooling water pipes can further optimize resource utilization efficiency. By reasonably arranging the call periods of energy and the allocation of resources, the load pressure during peak periods is reduced, and energy consumption is lowered. This optimization not only helps balance energy consumption but also reduces the burden on equipment and extends its service life. Finally, by combining peak electricity usage analysis data and cooling water pipe analysis data, an optimal call analysis for resource allocation is carried out, generating optimal suggestions for resource allocation for the workshop. These data supports provide an efficient and operable optimization plan for decision-making, thereby ensuring the precise allocation and continuous optimization of workshop resources.

[0018] Preferably, step S4 includes the following steps: Step S41: Integrate the digital model for optimizing workshop construction into an intelligent digital control device for real-time monitoring and adjustment, generating real-time monitoring data for workshop construction; Step S42: Perform dynamic feedback optimization based on the real-time monitoring data for workshop construction, generating dynamic feedback data for real-time monitoring of the workshop; Step S43: Construct a visual report based on the dynamic feedback data for real-time monitoring of the workshop, generating a digital optimization report for workshop construction.

[0019] Through the integration of the digital model for optimizing workplace construction and the intelligent digital control system, the present invention realizes real-time monitoring, dynamic optimization, and report generation of workplace construction, with remarkable beneficial effects. From the data perspective, firstly, integrating the digital model for optimizing workplace construction into the intelligent digital control device enables real-time monitoring and feedback of each link and data in the workplace. Through this integration, all real-time data of workplace operation, including energy consumption, environmental quality, equipment operation status, etc., can be continuously collected and analyzed to generate real-time monitoring data for workplace construction. These real-time monitoring data provide data support for timely identifying potential problems and optimizing equipment operation and management strategies, ensuring that the workplace can be adjusted in real time during operation and avoiding problems caused by data lag or untimely processing. Secondly, using these real-time monitoring data, the operation of the workplace is further improved through dynamic feedback optimization. The real-time monitoring and dynamic feedback data of the workplace can promptly reflect the deficiencies in operation and be adjusted and optimized through intelligent algorithms. Finally, based on the real-time monitoring and dynamic feedback data, the generated visual report can not only intuitively display the operation status of the workplace but also provide a clear optimization direction to help managers make scientific decisions. This report can provide detailed basis for the continuous optimization of the workplace, ensuring that all aspects such as environment, equipment, and energy can work in coordination to maximize the comprehensive benefits.

[0020] Preferably, step S42 includes the following steps: Step S421: Conduct environmental monitoring based on the real-time monitoring data of workplace construction to generate monitoring data for the environmental dimension of the workplace; conduct equipment operation monitoring based on the real-time monitoring data of workplace construction to generate monitoring data for the equipment dimension of the workplace; Step S422: Conduct clustering analysis and trend prediction on the monitoring data for the environmental dimension of the workplace and the monitoring data for the equipment dimension of the workplace to generate multi-dimensional mining data for workplace construction; Step S423: Perform multi-objective optimization of the genetic algorithm on the multi-dimensional mining data for workplace construction to generate real-time monitoring and dynamic feedback data for the workplace.

[0021] The present invention realizes the intelligent and precise management of the work area by using the real-time monitoring data of the work area construction for environment and equipment dimension monitoring, and combining clustering analysis, trend prediction, and genetic algorithm multi-objective optimization, with significant beneficial effects. From the data level, first of all, by obtaining the real-time monitoring data of the work area construction, the system can comprehensively track the environmental changes and equipment operation status in the work area. This process generates the work area environmental dimension monitoring data and equipment dimension monitoring data through the separate monitoring of the environmental dimension and equipment dimension. These data provide rich inputs for subsequent analysis, covering environmental factors such as temperature, humidity, air quality, and noise in the work area, as well as performance indicators such as equipment operation status, load, and efficiency, providing a multi-dimensional data basis for comprehensive optimization. Next, the system performs clustering analysis and trend prediction on the monitoring data of these environmental and equipment dimensions, thereby generating multi-dimensional mining data of the work area construction. Through clustering analysis, the system can identify the operation modes, change trends, and potential risk points of different equipment and environments, which provides data support for predicting future operation conditions, optimizing control strategies, and responding to emergencies. Finally, based on the multi-dimensional mining data, genetic algorithm is used for multi-objective optimization processing to further generate the dynamic feedback data of the work area real-time monitoring. The genetic algorithm can efficiently solve multiple optimization objectives and find the optimal adjustment plan, such as reasonably allocating equipment resources, adjusting environmental parameters, reducing energy consumption, etc., to ensure that the work area system can achieve the best balance and performance improvement in each dimension.

[0022] In this specification, a digital work area construction system based on multi-source data is provided for implementing the above-mentioned digital work area construction method based on multi-source data. The digital work area construction system based on multi-source data includes: An environmental data acquisition and digital modeling module, which is used to collect the air quality, temperature, humidity, and noise environment monitoring data in the work area by using an intelligent environmental sensor, and record the initial environmental parameters; according to the initial environmental parameters, use building information modeling technology to establish a preliminary digital three-dimensional model of the work area; A device status monitoring and dynamic simulation module, which is used to collect the operation status data of each device in the work area and input the operation status data of the work area devices into the preliminary digital three-dimensional model of the work area, and use simulation software to perform dynamic simulation of the interaction between the device and the environment, evaluate the impact of work area resource consumption and environmental changes, and obtain preliminary evaluation data; An optimization algorithm and model adjustment module, which is used to optimize the preliminary digital three-dimensional model of the work area according to the preliminary evaluation data. If the evaluation result shows that the energy consumption is too high or the device operation is unstable, adjust the spatial layout, device configuration, and energy use of the work area to generate an optimized digital model of the work area construction; The real-time monitoring and dynamic feedback optimization module is used to integrate the digital model for optimizing the construction of the work area into an intelligent digital control device for real-time monitoring and adjustment. Through the feedback of the actual operation data of the work area, dynamic optimization is carried out to generate a digital optimization report for the construction of the work area.

[0023] The beneficial effects of the present invention are as follows. By integrating intelligent environment sensors and Building Information Modeling (BIM) technology, real-time monitoring and dynamic optimization of the work area environment and equipment operation status are achieved, with significant beneficial effects. First, the intelligent environment sensors are used to accurately collect environmental parameters such as air quality, temperature and humidity, and noise. Combined with the building information modeling technology, a preliminary digital three-dimensional model of the work area is established, providing accurate basic data and model frameworks for the spatial layout and equipment configuration of the work area. Through this process, the environmental changes in the work area can be grasped in a timely manner, providing reliable data support for subsequent optimization. Second, the collected equipment operation status data is combined with environmental parameters and input into the digital three-dimensional model. Using simulation software for dynamic simulation, the mutual influence between the resource consumption and environmental changes in the work area is evaluated. This dynamic simulation process can reveal the complex relationship between equipment and the environment, providing a basis for improving energy efficiency, reducing resource waste, and improving environmental quality. Through further optimization of the model adjustment, combined with the preliminary evaluation data, the spatial layout, equipment configuration, and energy use of the work area are optimized specifically, thereby improving the energy efficiency and environmental comfort of the overall system. Finally, the optimized digital model of the work area is integrated into the intelligent digital control device for real-time monitoring and dynamic adjustment, enabling the resource consumption and environmental control of the work area to always be in the optimal state and capable of being adjusted according to real-time feedback data, ensuring the sustainable development and efficient operation of the work area. In addition, the optimization results are output in the form of a digital optimization report, providing decision-making support for management, thereby realizing the refined management and continuous improvement of the work area. Therefore, the present invention realizes dynamic feedback optimization by integrating the environment monitoring and equipment management system, solves the problems of resource waste and environmental instability existing in the traditional construction and management process of the work area, and improves the energy utilization efficiency, production efficiency, and environmental comfort. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the step flow of a digital work area construction method based on multi-source data; Figure 2 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in Figure 3 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in Figure 4 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S4 in The realization, functional features and advantages of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0025] The technical method of the present invention for patent will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work fall within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although terms such as "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0028] To achieve the above object, please refer to Figures 1 to 4 , a digital workshop construction method based on multi-source data, the method comprising the following steps: Step S1: Use an intelligent environmental sensor to collect air quality, temperature and humidity, and noise environment monitoring data in the workshop, and record the initial environmental parameters; according to the initial environmental parameters, use building information modeling technology to establish a preliminary digital three-dimensional model of the workshop; Step S2: Collect the operation status data of each device in the workshop and input the operation status data of the workshop devices into the preliminary digital three-dimensional model of the workshop, use simulation software to perform a dynamic simulation of the interaction between the devices and the environment, evaluate the impact of the resource consumption and environmental changes in the workshop, and obtain preliminary evaluation data; Step S3: Optimize the preliminary digital three-dimensional model of the workshop according to the preliminary evaluation data. If the evaluation result shows that the energy consumption is too high or the device operation is unstable, adjust the spatial layout, device configuration and energy use of the workshop to generate an optimized digital model for workshop construction; Step S4: Integrate the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, and dynamically optimize through feedback of the actual operation data of the workshop to generate a workshop construction digital optimization report.

[0029] The beneficial effects of the present invention are achieved by integrating intelligent environmental sensors and building information modeling (BIM) technology to realize real-time monitoring and dynamic optimization of the workshop environment and equipment operation status, which has significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature, humidity, and noise, and the preliminary digital three-dimensional model of the workshop is established in combination with the building information modeling technology, which provides accurate basic data and model framework for the spatial layout and equipment configuration of the workshop. Through this process, the environmental changes in the workshop can be grasped in time, providing reliable data support for subsequent optimization. Secondly, the collected equipment operation status data is combined with the environmental parameters, input into the digital three-dimensional model, and dynamic simulation is performed using simulation software to evaluate the mutual influence of workshop resource consumption and environmental changes. This dynamic simulation process can reveal the complex relationship between equipment and environment, thereby providing a basis for improving energy efficiency, reducing resource waste, and improving environmental quality. Through further optimization model adjustment, combined with preliminary evaluation data, the spatial layout, equipment configuration and energy use of the workshop are optimized in a targeted manner, thereby improving the energy efficiency and environmental comfort of the overall system. Finally, the optimized digital model of the workshop is integrated into an intelligent digital control device for real-time monitoring and dynamic adjustment, so that the resource consumption and environmental control of the workshop are always kept in the optimal state, and can be adjusted according to real-time feedback data to ensure the sustainable development and efficient operation of the workshop. In addition, the optimization results are output in the form of a digital optimization report to provide decision support for management, thereby realizing refined management and continuous improvement of the workshop. Therefore, the present invention realizes dynamic feedback optimization by integrating environmental monitoring and equipment management systems, solves the problems of resource waste and environmental instability in the construction and management of traditional workshops, and improves energy utilization efficiency, production efficiency and environmental comfort.

[0030] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the steps of a digital workshop construction method based on multi-source data of the present invention. In this example, the digital workshop construction method based on multi-source data includes the following steps: Step S1: using an intelligent environmental sensor to collect air quality, temperature, humidity, and noise environment monitoring data in the workshop, and recording initial environmental parameters; using building information modeling technology to establish a preliminary digital three-dimensional model of the workshop based on the initial environmental parameters; In the embodiments of the present invention, an intelligent environmental sensor is used to perform precise data collection. These sensors include, but are not limited to, air quality sensors, temperature and humidity sensors, and noise environment monitoring devices. Through these devices, the system can collect key environmental data in the workplace in real time, such as the PM2.5 concentration in the air, the temperature and humidity levels, and the intensity of noise pollution. These data not only reflect the current environmental state of the workplace but also provide a basis for subsequent modeling and optimization. For the collected data, preliminary data processing is first performed. For example, time synchronization is performed on the temperature and humidity data to ensure consistency in the time series; time domain analysis is performed on the noise data to extract key features of the noise pollution; filtering and denoising are performed on the air quality data to eliminate external interference and ensure the accuracy of the data. All these environmental parameters are summarized as the initial environmental parameters of the workplace, forming a high-quality original data set. Based on these initial environmental parameters, Building Information Modeling (BIM) technology is used to perform digital modeling of the workplace. BIM technology integrates environmental parameters with the geometric information of the workplace building space, converting these sensor data into a digital model with spatial attributes. This process includes combining environmental data with the three-dimensional geometric structure of the workplace to form an accurate digital three-dimensional model that can reflect the environmental characteristics and spatial layout of the workplace in the actual operating state. Through BIM technology, environmental data can be effectively combined with building design information, providing a scientific basis and technical support for subsequent dynamic simulation, optimized design, and operation management.

[0031] Step S2: Collect the operating state data of each device in the workplace and input the operating state data of the workplace devices into the preliminary digital three-dimensional model of the workplace, and use simulation software to perform dynamic simulation of the interaction between the devices and the environment, evaluate the impact of resource consumption and environmental changes in the workplace, and obtain preliminary evaluation data; In the embodiments of the present invention, a variety of advanced technical means are adopted to ensure the accurate collection and analysis of the impact of in-workspace equipment operation status on the environment. First, the operation status data of the equipment in the workspace is collected in real time through Internet of Things (IoT) technology. Each device is equipped with sensors, such as temperature sensors, pressure sensors, vibration sensors, and energy efficiency monitoring modules, for collecting real-time operation parameters of the device, such as power consumption, operating temperature, vibration frequency, etc. These device operation data are transmitted to the central data processing platform through a wireless network to ensure real-time and data integrity. Secondly, in order to effectively integrate these device operation data into the environmental monitoring system of the workspace, these data are input into the preliminary digital three-dimensional model of the workspace, which is constructed based on Building Information Modeling (BIM) technology, closely integrating the device data with the spatial layout and environmental parameters of the workspace. Through the correlation analysis with the workspace environmental data (such as temperature and humidity, air quality, noise level, etc.), a multi-dimensional interactive data set is obtained. Then, using simulation software (such as CFD simulation, thermodynamic analysis, fluid dynamics simulation, etc. tools), the dynamic simulation of the interaction between the device and the environment is carried out based on the digital three-dimensional model. During the simulation process, the impact of the device operation status on the environment, such as factors like energy consumption, heat release, pollutant emissions, and noise propagation, are obtained as accurate dynamic data through the simulation model. These simulation results not only help evaluate the direct impact of device operation on resource consumption in the workspace, but also quantify its contribution to environmental changes. For example, the simulation can predict the temperature fluctuations, air pollutant diffusion paths, and noise propagation ranges caused during the device operation, thus forming a complete assessment system for resource consumption and environmental change impacts.

[0032] Step S3: Optimize the preliminary digital three-dimensional model of the workspace according to the preliminary evaluation data. If the evaluation results show excessive energy consumption or unstable device operation, adjust the spatial layout, device configuration, and energy use of the workspace to generate an optimized digital model for workspace construction; In the embodiments of the present invention, based on the obtained preliminary evaluation data, the system identifies potential problems of excessive energy consumption or unstable equipment operation. These evaluation data include the operation efficiency of various types of equipment in the workshop, the resource consumption patterns, and the simulation results of environmental changes. Based on these data, the preliminary digital model of the workshop is adjusted by using multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.). The optimization objectives include reducing energy consumption, improving the operation stability of equipment, and maximizing space utilization. In this process, first, the space layout of the workshop is re-evaluated. Based on the analysis of equipment operation data, the energy load and equipment operation status in different regions are analyzed, and the equipment layout is optimized through the space layout optimization algorithm to reduce unnecessary energy losses and improve equipment operation efficiency. Secondly, based on the analysis of equipment configuration data, the system dynamically adjusts the equipment. For those equipment with high energy consumption or unstable performance, the optimization algorithm will consider replacing the equipment, adjusting the operation mode, or reconfiguring the equipment parameters, so as to reduce its energy consumption while ensuring the function and improve the overall stability of the system. Finally, the adjustment of energy use is achieved by collecting energy consumption data and applying dynamic load scheduling and energy efficiency management algorithms to redistribute the energy consumption of the entire workshop to achieve more efficient energy utilization. In this process, by continuously analyzing historical data and real-time data, the system can provide accurate optimization adjustment suggestions. The generated digital optimization model for workshop construction, based on comprehensive consideration of factors such as equipment performance, space layout, and energy utilization rate, adjusts and optimizes the operation status of the entire workshop through precise data-driven decision-making, thereby improving the overall operation efficiency and energy utilization efficiency of the workshop.

[0033] Step S4: Integrate the digital optimization model for workshop construction into the intelligent digital control device for real-time monitoring and adjustment, and perform dynamic optimization through the feedback of the actual operation data of the workshop to generate a digital optimization report for workshop construction.

[0034] In the embodiments of the present invention, the optimized digital model of the workplace construction is converted into a model format that can be applied in real time in the intelligent digital control system. This process is achieved through model data standardization and integration technologies, which convert the information such as spatial layout, equipment configuration, and energy usage obtained in the previous optimization process into dynamically adjustable parameters. The intelligent digital control device continuously monitors various operating indicators of the workplace by collecting real-time operating data of the workplace, including equipment operating status, energy consumption, environmental parameters (such as temperature, humidity, air quality, noise, etc.), and other key operating condition data. Through the Internet of Things technology (IoT), these sensor data will be efficiently transmitted to the control system to form a real-time data stream. Based on the data stream, the system uses big data analysis algorithms to compare the actual operating data with the preset optimization model and perform deviation analysis, and determines whether there are abnormalities or optimization spaces through the set thresholds. If it is found that the energy consumption is too high, the equipment is operating abnormally, or the environmental parameters do not meet the expectations, the system will immediately generate adjustment instructions to guide the intelligent control device to make automatic adjustments to ensure that the workplace operation is always in the optimal state. At the same time, the system will generate dynamic optimization reports based on the feedback of the operating data. These reports propose further optimization suggestions by comparing the gap between the actual operating data and the optimization goals, and provide visual decision-making support. These reports not only help the operators to timely discover potential problems, but also provide a scientific basis for subsequent optimization and scheduling. In addition, the system can perform model self-learning using historical data and real-time data through machine learning algorithms, gradually improving the prediction accuracy and decision-making ability of the model to form a closed-loop optimization mechanism.

[0035] Preferably, step S1 includes the following steps: Step S11: Use a temperature and humidity sensor to obtain the temperature and humidity data of the workplace; Step S12: Use an air quality sensor to analyze the PM2.5 in the air and measure the small particles in the air of the workplace to generate the air quality data of the workplace; Step S13: Use the vibration of sound wave propagation to analyze the sound wave signal vibration of the workplace to generate the noise environment monitoring data of the workplace; Step S14: Integrate the temperature and humidity data of the workplace, the air quality data of the workplace, and the noise environment monitoring data of the workplace to generate initial environmental parameters; Step S15: Construct a model for the initial environmental parameters according to the building information modeling technology to generate a preliminary digital three-dimensional model of the workplace.

[0036] In the embodiments of the present invention, temperature and humidity sensors are used to obtain temperature and humidity data in the workplace in real time. These sensors usually measure data based on the principles of resistance, capacitance or thermal conductivity, and can provide high-precision temperature and humidity values in a dynamic environment. These data will be transmitted to the data acquisition system and preliminarily processed, such as outlier removal and noise filtering, to ensure the validity and accuracy of the data. Air quality sensors, which generally use light scattering or light absorption technology to measure PM2.5 and small particle concentrations. The PM2.5 data acquisition system reflects the pollution level of the air in the workplace by measuring the concentration of fine particulate matter in the air. The sensor can efficiently capture suspended particulate matter in the air and generate continuous air quality data. These data will also be filtered to remove environmental interference and periodic fluctuations in the data, thus ensuring the accuracy and timeliness of the air quality data. Acoustic wave propagation vibration analysis uses acoustic sensors or accelerometers to analyze the spectral characteristics of the noise environment in the workplace. The noise environment data reveals the distribution and propagation characteristics of the noise sources by capturing vibration signals and performing frequency-domain analysis. These data will be used for subsequent noise control and optimization analysis. Integrate the above-collected temperature and humidity data, air quality data and noise environment data. Through data fusion algorithms, such as weighted average method or Kalman filtering method, various environmental parameters can be integrated, the deviation between different sensor data can be eliminated, and a representative set of initial environmental parameters can be generated to provide reliable input for subsequent modeling. Finally, Building Information Modeling (BIM) technology is applied to digitally model the initial environmental parameters. BIM technology generates an accurate three-dimensional digital model by integrating different data sources (such as spatial data, environmental data and equipment operation data). The model not only reflects the physical space layout of the workplace, but also dynamically integrates environmental changes, providing a scientific basis for subsequent environmental optimization, resource allocation and other decisions. With the support of BIM technology, the preliminary digital three-dimensional model of the workplace can intuitively display the environmental state of the workplace and provide a basic framework for further simulation and optimization analysis.

[0037] Preferably, the use of building information modeling technology to establish a preliminary digital three-dimensional model of the workplace is as follows: Perform time synchronization processing on the temperature and humidity data in the workplace to generate time-synchronized temperature and humidity data in the workplace; perform interpolation to fill in the missing data on the time-synchronized temperature and humidity data in the workplace to generate supplementary data for missing values in the workplace; Perform filtering and remove periodic fluctuations on the air quality data in the workplace to generate long-term trend data of the air quality in the workplace; perform normalization and standardization processing on the long-term trend data of the air quality in the workplace to generate standardized air quality data in the workplace; Perform wavelet transform time-domain analysis on the noise environment monitoring data in the workplace to generate time-domain analysis data of the noise in the workplace; perform sound source classification and remove background noise processing on the time-domain analysis data of the noise of the workpiece to generate standard data of the noise environment in the workplace; Perform principal component analysis on the in-work missing value supplementary data, in-work air quality standardized data, and in-work noise environment standard data to generate initial principal component analysis data; Use a preset thermal analysis and fluid dynamics simulation model to conduct heat island effect and humidity concentration identification analysis on the initial principal component analysis data, generating in-work heat island effect analysis data and in-work humidity concentration area data; Based on the in-work noise environment standard data, conduct noise propagation modeling to generate in-work noise distribution data; determine the control points of the sound source transmission path according to the in-work noise distribution data to generate in-work noise transmission path data; Use BIM technology to perform digital three-dimensional modeling on the in-work heat island effect analysis data and in-work humidity concentration area data, and use the in-work noise transmission path data to divide the model attribute area to obtain a preliminary digital three-dimensional model of the work area.

[0038] In the embodiments of the present invention, through in-depth mining and refined processing of in-workspace environmental data, a precise digital model is constructed to support intelligent optimization of the in-workspace environment. First, in the processing of in-workspace temperature and humidity data, time alignment of multi-source data is performed through time synchronization processing to ensure the temporal consistency of different sensors. For the parts with missing data, interpolation methods such as linear interpolation or spline interpolation are used to fill in the gaps, ensuring the continuity and integrity of the data, generating the supplemented temperature and humidity data, and further improving the data quality. Then, for the in-workspace air quality data, filtering techniques such as Kalman filtering or mean filtering are adopted to remove periodic fluctuations and noise effects, and extract long-term trend data. In addition, normalization and standardization processing means are used to standardize the air quality data to make it conform to a unified scale, eliminating the influence of data dimensions and facilitating subsequent analysis. The noise environment data is analyzed in the time domain through wavelet transform methods. Utilizing the multi-scale characteristics of wavelets, the characteristics of the noise signal are extracted, and then the time-domain distribution of the noise signal is analyzed. To further improve the data quality, sound source classification technology is used to distinguish the noise signal from other interference signals, remove the background noise, and obtain more accurate noise environment standard data. Subsequently, principal component analysis (PCA) is used to perform dimensionality reduction on the in-workspace temperature and humidity, air quality, and noise data. The most representative features are extracted through the principal components to generate the initial principal component analysis data, providing strong support for subsequent modeling. Based on the thermal analysis and fluid dynamics simulation model, identification and analysis of the heat island effect and humidity concentration areas in the in-workspace are carried out. This process calculates the heat distribution and the location of humidity concentration in the in-workspace by simulating physical processes such as air flow and heat conduction, generating heat island effect and humidity concentration area data. The noise propagation modeling relies on the theory of sound wave propagation. By constructing an acoustic model, the propagation process of noise in the in-workspace is simulated to obtain noise distribution data, and the influence range of the noise source is further refined by determining the control points of the sound source transmission path. Finally, based on BIM (Building Information Modeling) technology, three-dimensional modeling of the heat island effect analysis data and humidity concentration area data in the in-workspace is carried out. Combining the noise transmission path data, attribute area division is performed in the digital three-dimensional model, thereby generating a preliminary digital three-dimensional model of the in-workspace.

[0039] As an example of the present invention, refer to Figure 2 shown. In this example, step S2 includes: Step S21: Obtain the operation status data of in-workspace equipment; Step S22: Input the operation status data of in-workspace equipment into the preliminary digital three-dimensional model of the in-workspace for numerical in-workspace construction analysis, and generate the output data of the in-workspace digital construction model; Step S23: Perform dynamic simulation of the interaction between equipment and environment on the output data of the in-workspace digital construction model, and evaluate the resource consumption and environmental changes in the in-workspace to obtain preliminary evaluation data.

[0040] In the embodiments of the present invention, the operation status data of the equipment is obtained through the monitoring system of the work area equipment. These data may include the running speed, load, power consumption, temperature change, etc. of the equipment. Specific technical means include using various sensors and data acquisition modules (such as PLC, SCADA systems, etc.) to monitor the equipment status in real time and transmit the data to the central database. After obtaining the operation status data of the equipment, these data will be input into the preliminary digital three-dimensional model of the work area. By using Building Information Modeling (BIM) technology, these equipment operation data are combined with environmental parameters to form a complete digital model. The construction of the digital three-dimensional model involves parametric modeling of the spatial layout, equipment configuration, and environmental factors, and converts the equipment status into model-operable data input through numerical methods, so as to generate the output data of the work area construction model. In order to achieve the dynamic simulation of the interaction between the work area equipment and the environment, technical means include using multi-physics field simulation software (such as ANSYS, COMSOL, etc.) to conduct simulation analysis of the interaction between the equipment and the environment. Through numerical calculations, the impact of equipment operation on the work area environment is simulated, and then the relationship between resource consumption (such as electricity, water resources, air conditioning load, etc.) and environmental changes (such as temperature and humidity changes, noise pollution, etc.) is evaluated. Technologies such as Computational Fluid Dynamics (CFD) are used to evaluate the distribution of heat and humidity, as well as the efficiency and stability of the equipment under different working conditions. These simulation results provide preliminary evaluation data, which can reveal the operation efficiency, energy consumption, and environmental impact of the equipment, and provide a basis for formulating subsequent optimization measures.

[0041] Preferably, step S23 includes the following steps: Step S231: Perform equipment operation efficiency simulation based on the output data of the work area digital construction model to generate work area simulation data; Step S232: Conduct fluid dynamics simulation of temperature and humidity changes, pollutant diffusion paths, and noise source propagation on the work area simulation data to generate multi-dimensional equipment and environment simulation data; Step S233: Calculate the energy consumption under working conditions based on the multi-dimensional equipment and environment simulation data to generate energy consumption assessment data; conduct work area environmental impact assessment based on the multi-dimensional equipment and environment simulation data to generate environmental quality assessment data; conduct resource calculation and evaluation of the use efficiency of space resources based on the multi-dimensional equipment and environment simulation data to generate work area comprehensive resource optimization data.

[0042] In the embodiments of the present invention, based on the output data of the in-work digital construction model, simulation software is used to simulate the operation efficiency of equipment, generating in-work simulation data. This process mainly relies on simulation modeling tools such as MATLAB, Simulink, ANSYS, etc. to model the dynamic behavior of equipment and couple it with the in-work environment data. By simulating the operation efficiency of equipment, the performance of equipment under different operating conditions can be evaluated, including key parameters such as efficiency, load changes, and energy consumption. Then, in step S232, computational fluid dynamics (CFD) technology is used to simulate and analyze the temperature and humidity changes, pollutant diffusion paths, and noise source propagation of the in-work simulation data, generating multi-dimensional equipment and environment simulation data. CFD software (such as ANSYS Fluent or COMSOL Multiphysics) can accurately simulate environmental factors such as air flow, temperature and humidity distribution, pollutant propagation paths, and noise diffusion by solving fluid dynamics equations. These factors are crucial for the performance of equipment and the in-work environment. Through these simulation data, the spatio-temporal changes of temperature and humidity in the in-work environment, the paths of pollutant diffusion, and the propagation ranges of noise sources can be obtained. Further, in step S233, based on the multi-dimensional data obtained from the equipment and environment simulation, evaluations of energy consumption, environmental impact, and resource use efficiency are carried out. By calculating the energy consumption under working conditions and combining the working status of the equipment and environmental changes, energy consumption assessment data can be generated. Using statistical analysis and optimization algorithms, the use efficiency of in-work resources can be quantitatively evaluated, high-energy-consuming and inefficient operation links can be identified, and further improvement measures can be proposed. In addition, by analyzing the temperature and humidity changes, pollutant concentrations, and noise levels, the environmental quality assessment data can evaluate the air quality, noise pollution, and temperature and humidity comfort in the in-work area, thus providing decision support for environmental optimization. Through the evaluation of the use efficiency of computing resources and space resources, the links of resource waste can be identified, the equipment layout and space use can be optimized, and finally the in-work comprehensive resource optimization data can be generated, providing a basis for the resource management and scheduling of the in-work area.

[0043] As an example of the present invention, refer to Figure 3 shown, in this example, step S3 includes: Step S31: Optimize the preliminary digital three-dimensional model of the in-work area according to the preliminary evaluation data to generate in-work comprehensive evaluation data; Step S32: Evaluate the in-work comprehensive evaluation data using the preset in-work evaluation criteria. If the evaluation result shows that the energy consumption is too high or the equipment operation is unstable, adjust the space layout, equipment configuration, and energy use of the in-work area to generate in-work optimization adjustment suggestions; Step S33: Based on the in-work optimization adjustment suggestions, perform final digital construction optimization on the preliminary digital three-dimensional model of the in-work area to generate an optimized digital model for in-work area construction.

[0044] In the embodiments of the present invention, by collecting and analyzing preliminary data related to the work area, such as spatial layout, equipment configuration, energy consumption, and equipment operation efficiency, etc., the preliminary digital model of the work area is optimized based on these data. The optimization at the data level adjusts the model parameters to make them more in line with the requirements of actual production. For example, the equipment position, capacity, operation mode, etc. are adjusted in a parametric way. The optimized preliminary digital model of the work area will contain more accurate spatial distribution and resource configuration information, forming the comprehensive evaluation data of the work area. The comprehensive evaluation data of the work area will be evaluated through preset work area evaluation criteria, and these criteria include indicators such as energy consumption, equipment operation stability, and space utilization efficiency. Through data mining and analysis, the evaluation results can reflect potential problems such as excessive energy consumption or unstable equipment operation. On this basis, for the discovered deficiencies, the spatial layout, equipment configuration, and energy use of the work area will be adjusted, and optimization adjustment suggestions for the work area will be generated through an optimization algorithm. During the adjustment process, optimization models, such as intelligent algorithms like genetic algorithms and particle swarm optimization, are used to select the optimal adjustment plan by simulating the effects of different configurations. Finally, the preliminary digital three-dimensional model is finally optimized according to the optimization adjustment suggestions for the work area. At this time, the optimized model will comprehensively consider various data to achieve the optimal combination of spatial layout and resource configuration, thereby generating the final optimized digital model for work area construction.

[0045] Preferably, the adjustment of the spatial layout, equipment configuration, and energy use of the work area includes the following: Mark the equipment with overloaded operation or ineffective operation according to the energy consumption evaluation data, and generate load adjustment or shutdown maintenance instructions to obtain preliminary optimized energy suggestions; Optimize the overall layout of the ventilation facilities according to the environmental quality evaluation data, and at the same time add sound insulation materials to the areas with high noise pollution, and generate equipment area optimization suggestions; Conduct peak call analysis of optimized power usage periods according to the comprehensive resource optimization data of the work area to generate peak power usage analysis data; conduct cooling water pipeline layout analysis according to the comprehensive resource optimization data of the work area to generate cooling water pipeline analysis data; conduct optimal call analysis and processing of resource allocation based on the peak power usage analysis data and the cooling water pipeline analysis data to generate the optimal suggestion for work area resource allocation.

[0046] In the embodiments of the present invention, the operating status of equipment is analyzed in detail through energy consumption assessment data to identify equipment with overload operation or ineffective operation. At this time, by combining the load analysis model and equipment performance data, the system can establish a threshold judgment rule to mark equipment with low energy efficiency or unreasonable operation, and then generate load adjustment or shutdown maintenance instructions. Based on these data, the operation mode of the equipment can be adjusted through an optimization algorithm to reduce energy waste and form preliminary optimized energy suggestions. In the analysis of environmental quality assessment data, key indicators such as air quality, temperature and humidity, ventilation effect, and noise pollution are focused on. By constructing an environmental quality simulation model and using numerical optimization methods to analyze the overall layout of ventilation facilities, a layout optimization plan is proposed to ensure that the air flow distribution and air quality in the working environment reach the best state. At the same time, areas with high noise pollution are identified based on noise level data, and through the simulation analysis of material properties and sound insulation effects, an additional strategy for noise insulation materials is proposed, and finally an optimized suggestion for the equipment area is generated. For the optimization of power use in the work area, first, based on the comprehensive resource optimization data in the work area, through the peak call analysis of the power use period, the peak load period and the problem of unbalanced power use are identified. Through big data mining technology, the correlation between power demand and production process is analyzed to generate power use peak analysis data, and this data is further used to optimize the arrangement of the power use period. In addition, the layout analysis of the cooling water pipeline is based on the hydrodynamic model to simulate and evaluate the layout of the existing cooling water pipeline and generate cooling water pipeline analysis data. These analysis data, combined with the power use peak analysis results, are used to perform an optimal call analysis on the resource allocation in the work area through a comprehensive optimization algorithm to determine the most appropriate resource allocation method.

[0047] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: Integrate the digital model for optimizing the construction of the work area into the intelligent digital control device for real-time monitoring and adjustment to generate real-time monitoring data for the construction of the work area; Step S42: Perform dynamic feedback optimization based on the real-time monitoring data for the construction of the work area to generate dynamic feedback data for real-time monitoring of the work area; Step S43: Construct a visual report based on the dynamic feedback data for real-time monitoring of the work area to generate a digital optimization report for the construction of the work area.

[0048] In the embodiments of the present invention, various types of digital information in the workshop (such as equipment operation status, energy consumption, space utilization rate, etc.) are transmitted to the intelligent control system, combined with sensors and real-time data acquisition modules to ensure real-time monitoring and adjustment of all dimensions of the workshop. Through the interconnection of the data interface with the control device, the intelligent digital control device can dynamically adjust the model according to real-time data, obtain the key data of the workshop construction in real time, and form the real-time monitoring data of the workshop construction. These data include but are not limited to equipment operation parameters, environmental quality indicators, energy consumption, production efficiency, etc., involving a large amount of multi-source data interaction and real-time update. Based on the collected real-time monitoring data, the workshop construction is optimized through a data-driven dynamic feedback mechanism. Specifically, based on historical data, real-time data, and preset optimization rules, the system operation parameters are continuously adjusted through a feedback loop, and multiple simulations and predictions are carried out to generate the real-time monitoring dynamic feedback data of the workshop. This process relies on real-time data analysis, data mining, and intelligent algorithms (such as adaptive control algorithms, machine learning, etc.) to achieve immediate optimization and feedback in aspects such as equipment operation, energy distribution, and environmental regulation. Finally, based on the real-time monitoring dynamic feedback data of the workshop, the system converts these data into visual reports to construct a digital optimization report for the workshop construction. Through data visualization techniques, such as charts, heat maps, 3D displays, etc., the optimized data are presented in an easy-to-understand form to help decision-makers accurately evaluate the optimization effect and guide subsequent adjustments.

[0049] Preferably, step S42 includes the following steps: Step S421: Perform environmental monitoring based on the real-time monitoring data of the workshop construction to generate the monitoring data of the workshop environmental dimension; perform equipment operation monitoring based on the real-time monitoring data of the workshop construction to generate the monitoring data of the workshop equipment dimension; Step S422: Perform clustering analysis and trend prediction on the monitoring data of the workshop environmental dimension and the monitoring data of the workshop equipment dimension to generate the multi-dimensional mining data of the workshop construction; Step S423: Perform multi-objective optimization of the genetic algorithm on the multi-dimensional mining data of the workshop construction to generate the real-time monitoring dynamic feedback data of the workshop.

[0050] In the embodiments of the present invention, the workshop environment and equipment are monitored separately through real-time monitoring data of workshop construction. The environmental monitoring data mainly includes environmental factors such as temperature and humidity, air quality, light intensity, and noise, while the equipment operation monitoring data includes parameters such as the operation status, load, power consumption, and failure rate of the equipment. These monitoring data are collected and transmitted to the control system in real time through sensors and Internet of Things technologies to ensure that the data in each dimension can accurately reflect the actual operation of the workshop. Through the data acquisition module, the data in these environmental dimensions and equipment dimensions are respectively generated into workshop environmental dimension monitoring data and equipment dimension monitoring data, providing a basis for subsequent analysis. Cluster analysis and trend prediction are performed on the workshop environmental dimension monitoring data and equipment dimension monitoring data. Cluster analysis uses unsupervised learning algorithms (such as K-means or DBSCAN) to group the data, thereby identifying potential patterns and problems existing in the workshop operation, especially the correlation between environmental factors and equipment performance. Through a trend prediction model (such as time series analysis or regression models in machine learning), the future environmental changes or changes in equipment operation status are predicted. The core of this process lies in the mining of multi-dimensional correlations of data, which can provide valuable prediction information for the optimization of the workshop and generate multi-dimensional mining data for workshop construction, providing data support for subsequent decision-making. Based on the multi-dimensional mining data of workshop construction, a genetic algorithm is used for multi-objective optimization. The genetic algorithm comprehensively adjusts the environmental control and equipment operation of the workshop under the multi-objective optimization framework by simulating the natural selection mechanism. The optimization objectives include energy conservation, improvement of production efficiency, reduction of equipment failures, and other aspects.

[0051] In this specification, a digital workshop construction system based on multi-source data is provided for implementing the above-mentioned digital workshop construction method based on multi-source data. The digital workshop construction system based on multi-source data includes: An environmental data acquisition and digital modeling module, which is used to collect air quality, temperature and humidity, and noise environment monitoring data in the workshop by using an intelligent environmental sensor, and record the initial environmental parameters; according to the initial environmental parameters, use building information modeling technology to establish a preliminary digital three-dimensional model of the workshop; A device status monitoring and dynamic simulation module, which is used to collect the operation status data of each device in the workshop and input the operation status data of the workshop equipment into the preliminary digital three-dimensional model of the workshop, and use simulation software to perform dynamic simulation of the interaction between the device and the environment, evaluate the impact of workshop resource consumption and environmental changes, and obtain preliminary evaluation data; An optimization algorithm and model adjustment module, which is used to optimize the preliminary digital three-dimensional model of the workshop according to the preliminary evaluation data. If the evaluation result shows that the energy consumption is too high or the equipment operation is unstable, the spatial layout, equipment configuration, and energy use of the workshop are adjusted to generate an optimized digital model for workshop construction; The real-time monitoring and dynamic feedback optimization module is used to integrate the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, and to generate a digital optimization report for workshop construction by dynamically optimizing the feedback of the actual operation data of the workshop.

[0052] The beneficial effects of the present invention are achieved by integrating intelligent environmental sensors and building information modeling (BIM) technology to realize real-time monitoring and dynamic optimization of the workshop environment and equipment operation status, which has significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature, humidity, and noise, and the preliminary digital three-dimensional model of the workshop is established in combination with the building information modeling technology, which provides accurate basic data and model framework for the spatial layout and equipment configuration of the workshop. Through this process, the environmental changes in the workshop can be grasped in time, providing reliable data support for subsequent optimization. Secondly, the collected equipment operation status data is combined with the environmental parameters, input into the digital three-dimensional model, and dynamic simulation is performed using simulation software to evaluate the mutual influence of workshop resource consumption and environmental changes. This dynamic simulation process can reveal the complex relationship between equipment and environment, thereby providing a basis for improving energy efficiency, reducing resource waste, and improving environmental quality. Through further optimization model adjustment, combined with preliminary evaluation data, the spatial layout, equipment configuration and energy use of the workshop are optimized in a targeted manner, thereby improving the energy efficiency and environmental comfort of the overall system. Finally, the optimized digital model of the workshop is integrated into an intelligent digital control device for real-time monitoring and dynamic adjustment, so that the resource consumption and environmental control of the workshop are always kept in the optimal state, and can be adjusted according to real-time feedback data to ensure the sustainable development and efficient operation of the workshop. In addition, the optimization results are output in the form of a digital optimization report to provide decision support for management, thereby realizing refined management and continuous improvement of the workshop. Therefore, the present invention realizes dynamic feedback optimization by integrating environmental monitoring and equipment management systems, solves the problems of resource waste and environmental instability in the construction and management of traditional workshops, and improves energy utilization efficiency, production efficiency and environmental comfort.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a digital workshop based on multi-source data, characterized in that, During the construction process of the digital workshop, intelligent environmental sensors and intelligent digital control devices are integrated. The method for constructing a digital workshop based on multi-source data includes the following steps: Step S1: Use intelligent environmental sensors to collect air quality, temperature and humidity, and noise environment monitoring data in the workshop, and record the initial environmental parameters; according to the initial environmental parameters, use building information modeling technology to establish a preliminary digital three-dimensional model of the workshop; Step S2: Collect the operating status data of each device in the workshop and input the operating status data of the workshop devices into the preliminary digital three-dimensional model of the workshop. Use simulation software to perform dynamic simulation of the interaction between the devices and the environment, evaluate the impact of resource consumption and environmental changes in the workshop, and obtain preliminary evaluation data; Step S3: Optimize the preliminary digital three-dimensional model of the workshop according to the preliminary evaluation data. If the evaluation result shows that the energy consumption is too high or the device operation is unstable, adjust the spatial layout, device configuration, and energy use of the workshop to generate an optimized digital model for workshop construction; Step S4: Integrate the optimized digital model for workshop construction into the intelligent digital control device for real-time monitoring and adjustment, and perform dynamic optimization through the feedback of the actual operation data of the workshop to generate a digital optimization report for workshop construction.

2. The digital workshop construction method based on multi-source data according to claim 1, wherein The intelligent environmental sensor includes a temperature and humidity sensor, an air quality sensor, and a light intensity sensor. Step S1 includes the following steps: Step S11: Use the temperature and humidity sensor to obtain the temperature and humidity data in the workshop; Step S12: Use the air quality sensor to analyze the PM2.5 in the air and measure the small particles in the air in the workshop to generate air quality data for the workshop; Step S13: Use the vibration of sound wave propagation to analyze the sound wave signal vibration in the workshop to generate noise environment monitoring data for the workshop; Step S14: Integrate the temperature and humidity data, air quality data, and noise environment monitoring data in the workshop to generate initial environmental parameters; Step S15: According to the building information modeling technology, construct a model for the initial environmental parameters to generate a preliminary digital three-dimensional model of the workshop.

3. The digital workshop construction method based on multi-source data according to claim 1, characterized in that The establishment of the preliminary digital three-dimensional model of the workshop using building information modeling technology includes the following: Perform time synchronization processing on the temperature and humidity data in the workshop to generate time-synchronized temperature and humidity data for the workshop; Use the interpolation method to fill in the missing data in the time-synchronized temperature and humidity data in the workshop to generate supplementary data for the missing values in the workshop; Filter and remove the periodic fluctuations of the air quality data in the workshop to generate long-term trend data for the workshop; perform normalization and standardization processing on the long-term trend data of the workshop air quality to generate standardized air quality data for the workshop; Perform wavelet transform time-domain analysis on the noise environment monitoring data in the workshop to generate time-domain analysis data for the workshop noise; Classify the sound sources of the time-domain analysis data of the workpiece noise and remove the noise to generate standard data for the noise environment in the workshop; Perform principal component analysis on the supplementary data for the missing values in the workshop, the standardized air quality data in the workshop, and the standard data for the noise environment in the workshop to generate initial principal component analysis data; Using a preset thermal analysis and fluid dynamics simulation model to conduct heat island effect and humidity concentration identification analysis on the initial principal component analysis data, generating in-work heat island effect analysis data and in-work humidity concentration area data; Based on the in-work noise environment standard data, conduct noise propagation modeling to generate in-work noise distribution data; determine the control points of the sound source transmission path according to the in-work noise distribution data to generate in-work noise transmission path data; Use BIM technology to perform digital three-dimensional modeling on the in-work heat island effect analysis data and in-work humidity concentration area data, and use the in-work noise transmission path data to divide the model attribute area to obtain a preliminary digital three-dimensional model of the workplace.

4. The digital workshop construction method based on multi-source data according to claim 1, wherein, Step S2 includes the following steps: Step S21: Obtain the in-work equipment operation status data; Step S22: Input the in-work equipment operation status data into the preliminary digital three-dimensional model of the workplace for numerical in-work construction analysis, generating output data of the in-work digital construction model; Step S23: Perform dynamic simulation of the interaction between equipment and the environment on the output data of the in-work digital construction model, and evaluate the in-work resource consumption and environmental changes to obtain preliminary evaluation data.

5. The digital workshop construction method based on multi-source data according to claim 4, characterized in that, Step S23 includes the following steps: Step S231: Based on the output data of the in-work digital construction model, conduct equipment operation efficiency simulation to generate in-work simulation data; Step S232: Perform calculations on the changes in temperature, humidity, pollutant diffusion paths, and noise source propagation through fluid dynamics simulation on the in-work simulation data to generate multi-dimensional equipment and environment simulation data; Step S233: Based on the multi-dimensional equipment and environment simulation data, conduct calculation of working condition energy consumption to generate energy consumption evaluation data; conduct in-work environmental impact assessment based on the multi-dimensional equipment and environment simulation data to generate environmental quality assessment data; conduct resource calculation and evaluation of the use efficiency of space resources based on the multi-dimensional equipment and environment simulation data to generate in-work comprehensive resource optimization data.

6. The digital workshop construction method based on multi-source data according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Optimize the preliminary digital three-dimensional model of the workplace according to the preliminary evaluation data to generate in-work comprehensive evaluation data; Step S32: Use the preset in-work evaluation criteria to evaluate the in-work comprehensive evaluation data. If the evaluation result shows excessive energy consumption or unstable equipment operation, adjust the space layout, equipment configuration, and energy use of the workplace to generate in-work optimization and adjustment suggestions; Step S33: Based on the in-work optimization and adjustment suggestions, perform final digital construction optimization on the preliminary digital three-dimensional model of the workplace to generate an optimized digital construction model of the workplace.

7. The digital workshop construction method based on multi-source data according to claim 1, characterized in that The adjustment of the space layout, equipment configuration, and energy use of the workplace includes the following: Mark the equipment with overloaded operation or ineffective operation according to the energy consumption evaluation data, and generate load adjustment or shutdown maintenance instructions to obtain preliminary optimized energy suggestions; Optimize the overall layout of the ventilation facilities according to the environmental quality evaluation data, and at the same time implement a strategy of adding sound insulation materials to areas with high noise pollution to generate equipment area optimization suggestions; Conduct analysis on the peak call of the optimized electricity usage period according to the in-work comprehensive resource optimization data to generate peak electricity usage analysis data; Analyze the layout of the cooling water pipes based on the optimized data of the comprehensive in-work resources, and generate the analysis data of the cooling water pipes; perform the optimal call analysis and processing of resource allocation based on the analysis data of the peak power consumption and the analysis data of the cooling water pipes, and generate the optimal suggestions for in-work resource allocation.

8. The digital workshop construction method based on multi-source data according to claim 1, wherein Step S4 includes the following steps: Step S41: Integrate the optimized digital model of in-work construction into the intelligent digital control device for real-time monitoring and adjustment, and generate the real-time monitoring data of in-work construction; Step S42: Perform dynamic feedback optimization based on the real-time monitoring data of in-work construction, and generate the dynamic feedback data of real-time monitoring of in-work; Step S43: Construct a visual report based on the dynamic feedback data of real-time monitoring of in-work, and generate the digital optimization report of in-work construction.

9. The digital workshop construction method based on multi-source data according to claim 8, characterized in that, Step S42 includes the following steps: Step S421: Perform environmental monitoring based on the real-time monitoring data of in-work construction to generate the monitoring data of the environmental dimension of in-work; perform equipment operation monitoring based on the real-time monitoring data of in-work construction to generate the monitoring data of the equipment dimension of in-work; Step S422: Conduct clustering analysis and trend prediction on the monitoring data of the environmental dimension of in-work and the monitoring data of the equipment dimension of in-work to generate the multi-dimensional mining data of in-work construction; Step S423: Perform multi-objective optimization of the genetic algorithm on the multi-dimensional mining data of in-work construction to generate the dynamic feedback data of real-time monitoring of in-work.

10. A digital workshop construction system based on multi-source data, characterized in that, For executing the digital in-work construction method based on multi-source data as described in claim 1, the digital in-work construction system based on multi-source data includes: The environmental data collection and digital modeling module is used to collect the monitoring data of air quality, temperature and humidity, and noise environment in the in-work using an intelligent environmental sensor and measure, and record the initial environmental parameters; according to the initial environmental parameters, use building information modeling technology to establish a preliminary digital three-dimensional model of the in-work; The equipment status monitoring and dynamic simulation module is used to collect the operation status data of each equipment in the in-work and input the operation status data of the in-work equipment into the preliminary digital three-dimensional model of the in-work, and use simulation software to perform dynamic simulation of the interaction between the equipment and the environment, evaluate the impact of in-work resource consumption and environmental changes, and obtain preliminary evaluation data; The optimization algorithm and model adjustment module is used to optimize the preliminary digital three-dimensional model of the in-work according to the preliminary evaluation data. If the evaluation result shows that the energy consumption is too high or the equipment operation is unstable, adjust the spatial layout, equipment configuration and energy use of the in-work to generate the optimized digital model of in-work construction; The real-time monitoring and dynamic feedback optimization module is used to integrate the optimized digital model of in-work construction into the intelligent digital control device for real-time monitoring and adjustment, and perform dynamic optimization through the feedback of the actual operation data of the in-work to generate the digital optimization report of in-work construction.

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