A digital chemical workshop construction method and system based on multi-source data

Abstract

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

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital construction, and in particular to a digital workshop construction method and system based on multi-source data. BACKGROUND

[0002] The prior art is limited to isolated management of various types of data, and fails to effectively integrate and tap 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 of a single device, lacking deep mining of the relationship between environmental changes and device status. In addition, many systems still cannot achieve dynamic adjustment in real-time monitoring and data feedback, and the feedback mechanism lacks sufficient intelligence, and cannot comprehensively analyze multiple complex factors, resulting in a lag in response to sudden changes and low efficiency. The prior art generally has data sharing obstacles, poor interoperability between systems, and cannot fully utilize cross-domain data resources for joint analysis and prediction. These systems usually rely on simple preset thresholds and rules, and are difficult to cope with complex and dynamic production environments, lacking flexibility and adaptability. At the same time, traditional optimization methods mostly use artificial experience or rule-based quantitative models, which are difficult to accurately dynamically feedback and optimize according to real-time monitoring data. SUMMARY

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

[0004] To achieve the above purpose, a digital workshop construction method based on multi-source data, the method comprising the following steps:

[0005] Step S1: collecting air quality, temperature and humidity, noise environment monitoring data in the workshop by using an intelligent environment sensing measurer, and recording initial environmental parameters; establishing a preliminary digital three-dimensional model of the workshop using building information modeling technology according to the initial environmental parameters;

[0006] Step S2: collecting the running state data of each device in the workshop and inputting the workshop device running state data into the preliminary digital three-dimensional model of the workshop, using simulation software to dynamically simulate the interaction between the device and the environment, evaluating the influence of workshop resource consumption and environmental change, and obtaining preliminary evaluation data;

[0007] Step S3: optimizing 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, adjusting the space layout, device configuration and energy use of the workshop, and generating a workshop construction optimization digital model;

[0008] Step S4: integrating the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, dynamically optimizing through feedback on actual workshop operation data to generate a workshop construction digital optimization report.

[0009] The beneficial effects of the present application are achieved by integrating intelligent environmental sensors and building information modeling (BIM) technology to realize real-time monitoring and dynamic optimization of workshop environment and equipment operation status, with significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature and humidity, and noise, and combined with the building information modeling technology to establish a preliminary digital three-dimensional model of the workshop, providing 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 timely grasped, providing reliable data support for subsequent optimization. Second, the collected equipment operation status data and environmental parameters are combined and 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, thereby improving the overall system energy efficiency and environmental comfort. Finally, the optimized workshop digital model is integrated into the intelligent digital control device for real-time monitoring and dynamic adjustment, so that the resource consumption and environmental control of the workshop are always maintained in an 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 achieving fine management and continuous improvement of the workshop. Therefore, by integrating environmental monitoring and equipment management systems, the present application realizes dynamic feedback optimization, solves the problems of resource waste and environmental instability in traditional workshop construction and management processes, and improves energy utilization efficiency, production efficiency, and environmental comfort.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: obtaining workshop temperature and humidity data using a temperature and humidity sensor;

[0012] Step S12: using an air quality sensor to analyze air PM2.5 and measure small air particles in the workshop to generate air quality data;

[0013] Step S13: using sound wave propagation vibration to analyze sound wave signal vibration in the workshop to generate noise environment monitoring data;

[0014] Step S14: integrating the inter-shift temperature and humidity data, inter-shift air quality data and inter-shift noise environment monitoring data to generate initial environment parameters;

[0015] Step S15: constructing a model according to the building information modeling technology to generate an initial digital three-dimensional model of the workshop.

[0016] The present application realizes accurate initial environment parameter construction and digital three-dimensional model generation by systematically collecting and integrating workshop environment data, which has significant beneficial effects. First, the application of temperature and humidity sensors, air quality sensors and sound wave propagation vibration analysis technology enables comprehensive and efficient collection of environment monitoring data in the workshop. The temperature and humidity data accurately obtained by the temperature and humidity sensor provide a key basis for subsequent environment regulation and comfort optimization, ensuring that the temperature and humidity distribution inside the workshop meets the production requirements and the standard of employee work comfort. The real-time monitoring of PM2.5 concentration and air particles by the air quality sensor can reveal the trend of air quality changes in the workshop and timely reflect the diffusion of pollution sources, providing data support for air purification and ventilation system optimization in the workshop. Sound wave propagation vibration analysis accurately measures the noise environment in the workshop, identifies noise sources and their propagation paths, and provides a data basis for noise control, ensuring that the noise level in the workshop is controlled within the range that meets the production requirements. By integrating the above various types of environment data, the generated initial environment parameters provide comprehensive data support for the establishment of a digital model of the workshop. This data integration process can eliminate the one-sided influence of single environmental factors on the workshop environment, consider multiple factors such as temperature and humidity, air quality, noise, etc., and provide comprehensive and accurate environment parameter information, which helps to accurately depict the environmental characteristics of the workshop. On this basis, the building information modeling technology (BIM) is used to construct a model of the initial environment parameters, and the generated preliminary digital three-dimensional model not only accurately reflects the spatial layout of the workshop, but also simulates and optimizes the influence of environmental conditions on the function of the workshop, promoting the digitization, refinement and intelligentization of workshop management. Through this process, various aspects of workshop environment control, such as temperature and humidity regulation, air quality monitoring and noise control, can be effectively integrated and optimized, thereby realizing the organic coordination and intelligent management between the environment and the equipment.

[0017] Preferably, the use of building information modeling technology to establish a preliminary digital three-dimensional model of the workshop includes:

[0018] The inter-shift temperature and humidity data is time-synchronized to generate inter-shift temperature and humidity time-synchronized data, and the inter-shift temperature and humidity time-synchronized data is interpolated to fill in missing data to generate inter-shift missing value supplement data.

[0019] Filtering and removing periodic fluctuations of the intermission air quality data to generate intermission long-term trend data; normalizing and standardizing the intermission long-term trend data to generate intermission air quality standardized data;

[0020] Performing wavelet transform time domain analysis on the intermission noise environment monitoring data to generate intermission noise time domain analysis data; performing sound source classification and removing noise processing on the intermission noise time domain analysis data to generate intermission noise environment standard data;

[0021] Performing principal component analysis on the intermission missing value supplement data, the intermission air quality standardized data and the intermission noise environment standard data to generate initial principal component analysis data;

[0022] Using a preset thermal analysis and fluid dynamics simulation model to perform heat island effect and humidity concentration identification analysis on the initial principal component analysis data to generate intermission heat island effect analysis data and intermission humidity concentration area data;

[0023] Based on the intermission noise environment standard data, noise propagation modeling is performed to generate intermission noise distribution data; according to the intermission noise distribution data, sound source transmission path control point determination is performed to generate intermission noise transmission path data;

[0024] Using BIM technology to perform digital three-dimensional modeling on the intermission heat island effect analysis data and the intermission humidity concentration area data, and using the intermission noise transmission path data to divide the model attribute area to obtain an intermission preliminary digital three-dimensional model.

[0025] The present application provides precise data support for workshop environment optimization through multi-level data processing and integration, which has significant beneficial effects. Through time synchronization processing and interpolation method to supplement missing data for workshop temperature and humidity data, the integrity and accuracy of temperature and humidity data are ensured, providing a reliable data basis for subsequent environmental control and comfort evaluation. The use of interpolation method effectively fills the gaps in the data, avoiding analysis bias caused by data missing, thereby ensuring the continuity of environmental monitoring and the representativeness of data. For workshop air quality data, periodic fluctuations are removed by filtering, further extracting the long-term trend of air quality, providing a scientific basis for the optimization of air purification system. After normalization and standardization processing, the scale of air quality data is unified, eliminating the deviation between different sensor data, ensuring the comparability and accuracy of subsequent analysis. For workshop noise environment monitoring data, noise signals are separated from complex background noise through wavelet transform time domain analysis, further classified and removed, ensuring the clarity and accuracy of noise data, thereby providing reliable basis for noise control and improvement scheme. Through principal component analysis for processed data, the correlation between environmental parameters is further revealed, simplifying the data dimension and extracting key features, making subsequent analysis and modeling more efficient and accurate. Using thermal analysis and fluid dynamics simulation model to analyze the initial principal component analysis data, accurately identifying the workshop heat island effect and humidity concentration area, providing targeted data support for the design and optimization of temperature control and ventilation system. In addition, through noise propagation modeling and sound source path control point determination, the noise distribution and noise propagation rules are further clarified, providing quantitative data support for noise management and emission reduction strategy. Finally, based on BIM technology, the heat island effect, humidity concentration area and noise transmission path are digitally modeled in three dimensions, so that the workshop environment optimization not only has high-precision data support, but also can realize visual management and optimization. Through the above data processing and modeling methods, the present application realizes the comprehensive analysis and optimization of workshop environment, ensuring the coordinated management of temperature, humidity, air quality and noise in multiple dimensions, thereby laying a solid foundation for the intelligent and fine operation of the workshop.

[0026] Preferably, step S2 comprises the following steps:

[0027] Step S21: acquiring workshop equipment operation state data;

[0028] Step S22: inputting the workshop equipment operation state data into the workshop preliminary digital three-dimensional model for numerical workshop construction analysis, and generating workshop digital construction model output data;

[0029] Step S23: Perform dynamic simulation of the equipment and environment interaction based on the workshop digital construction model output data, and evaluate the workshop resource consumption and environmental changes to obtain preliminary evaluation data.

[0030] The present application has significant beneficial effects by combining workshop equipment operating state data with digital three-dimensional models for accurate modeling and dynamic simulation. First, by obtaining the operating state data of the equipment in the workshop, the important parameters such as the load, energy consumption, and operating cycle of the equipment can be reflected in real time, providing 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, and ensure the stability and efficiency of equipment operation. Second, inputting these equipment operating state data into the preliminary digital three-dimensional model of the workshop further realizes the numerical analysis of the workshop construction, and generates the workshop digital construction model output data. This process integrates the spatial layout, equipment configuration and environmental factors of the workshop into a unified digital platform, realizes the virtual modeling and digital expression of the workshop, and helps decision-makers quickly identify potential space utilization and resource allocation problems. This digital modeling provides an intuitive and comprehensive platform for subsequent analysis and decision-making. Finally, dynamic simulation of the interaction between equipment and environment based on the digital construction model output data can accurately evaluate the impact of workshop resource consumption and environmental changes. This dynamic simulation process considers the complex interaction between equipment operation and environmental factors, thereby comprehensively evaluating the energy consumption, resource utilization efficiency and environmental comfort of the workshop, and generating preliminary evaluation data. These evaluation data not only help optimize the spatial layout and equipment configuration of the workshop, but also provide scientific basis for energy management, environmental control and other aspects of the workshop.

[0031] Preferably, step S23 includes the following steps:

[0032] Step S231: Perform equipment operation efficiency simulation based on the workshop digital construction model output data to generate workshop simulation data;

[0033] Step S232: Perform fluid dynamics simulation temperature and humidity change calculation, pollutant diffusion path and noise source propagation on the workshop simulation data to generate multi-dimensional equipment and environment simulation data;

[0034] Step S233: Perform working condition energy consumption calculation based on the multi-dimensional equipment and environment simulation data to generate energy consumption evaluation data; perform workshop environmental impact evaluation based on the multi-dimensional equipment and environment simulation data to generate environmental quality evaluation data; perform resource calculation and spatial resource utilization efficiency evaluation based on the multi-dimensional equipment and environment simulation data to generate comprehensive resource optimization data of the workshop.

[0035] This invention provides a comprehensive and accurate workshop optimization solution by performing multi-dimensional simulation and evaluation on the output data of a digital workshop construction model, demonstrating significant benefits. First, equipment performance simulation based on the output data of the digital workshop construction model simulates equipment performance under different operating conditions. The generated workshop simulation data provides a preliminary basis for subsequent resource assessment and optimization. This simulation data reflects the energy efficiency, load distribution, and operational stability of the equipment in actual operation, helping to identify energy efficiency bottlenecks and performance deficiencies in different operating conditions. Second, computational fluid dynamics (CFD) simulations of the workshop simulation data accurately analyze temperature and humidity variations, pollutant diffusion paths, and noise source propagation. This process generates multi-dimensional equipment and environment simulation data. This data not only considers the interaction between equipment and the environment but also reveals potential issues in environmental control, such as areas of excessively high temperature and humidity, sources of air pollution, and the propagation paths of noise pollution. This provides a scientific basis for optimizing environmental control systems and equipment layout. Furthermore, based on this multi-dimensional simulation data, energy consumption under operating conditions is calculated, generating energy consumption assessment data, providing a quantitative basis for energy management. Furthermore, environmental quality assessment data provides a reference for environmental optimization by evaluating workplace air quality, temperature, humidity, and other environmental parameters. Finally, by evaluating the efficiency of computing and spatial resource utilization, comprehensive workplace resource optimization data is generated, helping to identify areas of resource waste and providing data support for the rational allocation of workplace resources and optimization of spatial layout.

[0036] Preferably, step S3 includes the following steps:

[0037] Step S31: Optimizing the preliminary digital three-dimensional model of the workshop based on the preliminary evaluation data to generate comprehensive workshop evaluation data;

[0038] Step S32: Evaluate the comprehensive evaluation data of the workroom using the preset workroom evaluation criteria. If the evaluation results show that the energy consumption is too high or the equipment operation is unstable, adjust the spatial layout, equipment configuration, and energy usage of the workroom to generate optimization adjustment suggestions for the workroom;

[0039] Step S33: Based on the workshop optimization adjustment suggestions, the preliminary digital three-dimensional model of the workshop is finally optimized to generate an optimized digital model of the workshop construction.

[0040] The application significantly improves the efficiency of workshop design and operation through comprehensive evaluation and optimization adjustment of the preliminary digital model. First, the preliminary digital model of the workshop is optimized according to the preliminary optimization suggestions, and comprehensive evaluation data of the workshop is generated, providing multi-dimensional data support for subsequent optimization. These comprehensive evaluation data include equipment operation, environmental quality, energy consumption and other aspects of data, which can fully reflect the operation status of the workshop and provide multi-angle data view for analysis and optimization. Second, the comprehensive evaluation data of the workshop is evaluated by using the preset workshop evaluation standard, which ensures the scientificity and normativity of the evaluation results. Through the analysis of the comprehensive evaluation data, potential problems such as high energy consumption and unstable equipment operation can be identified in time. This process not only provides specific adjustment basis for equipment configuration and energy use, but also reveals the optimization space of workshop space layout and resource allocation. According to the evaluation results, specific optimization adjustment suggestions for the workshop are generated, covering adjustment strategies for space layout, equipment configuration and energy use, so as to ensure that the workshop can achieve maximum benefit in resource optimization and energy saving. Finally, based on the optimization adjustment suggestions, the preliminary digital three-dimensional model of the workshop is optimized to generate the optimized digital model of the workshop construction. This optimized model not only improves the energy efficiency, comfort and safety of the workshop, but also provides efficient reference for actual construction and operation.

[0041] Preferably, the adjustment of the space layout, equipment configuration and energy use of the workshop includes the following:

[0042] According to the energy consumption evaluation data, mark the equipment overload operation or invalid operation equipment, and generate load adjustment or shutdown maintenance instructions to obtain preliminary optimization energy suggestions;

[0043] According to the environmental quality evaluation data, optimize the overall layout of the ventilation facilities, and at the same time, add soundproofing materials in the high noise pollution area, and generate equipment area optimization suggestions;

[0044] According to the comprehensive resource optimization data of the workshop, analyze the peak calling of power use period, generate power use peak analysis data; according to the comprehensive resource optimization data of the workshop, analyze the layout of cooling water pipes, generate cooling water pipe analysis data; based on the power use peak analysis data and the cooling water pipe analysis data, perform resource allocation optimal calling analysis processing, and generate resource allocation optimal suggestions of the workshop.

[0045] The present application significantly improves the operation efficiency, energy efficiency and environmental quality of the workshop by in-depth analysis and optimization of multi-dimensional data such as energy consumption, environmental quality and resource use. First, by marking the overloading or inefficient operation of the equipment according to the energy consumption evaluation data, it can accurately identify those inefficient or unnecessary running equipment, thereby avoiding the negative impact of improper operation of the equipment on the overall energy efficiency. Combined with the generated load adjustment or shutdown maintenance instructions, the equipment is provided with a clear operation optimization direction, reducing invalid operation and energy consumption, and ensuring efficient use of workshop resources. This process provides specific operation recommendations for subsequent energy-saving measures and helps reduce the risk of equipment failure due to overloading. Second, based on environmental quality evaluation data, the overall layout of the ventilation facilities is optimized, and additional soundproofing materials are proposed for areas with high noise pollution, effectively improving the air quality and noise environment of the workshop and improving the comfort of the workers' working environment. Through these measures, the air circulation can be optimized, avoiding local temperature and humidity imbalance, and reducing the impact of noise pollution on production and employees, improving the overall environmental quality of the workshop. Further, by analyzing the peak calling of power usage period based on comprehensive resource optimization data of the workshop, the peak period of energy consumption in the workshop can be identified, providing a basis for power management. Combined with cooling water pipeline layout analysis, resource use efficiency can be further optimized by reasonably arranging the calling period of energy and the allocation of resources to reduce the load pressure in the peak period and reduce energy consumption. This optimization not only helps to balance energy consumption, but also reduces the burden on equipment and prolongs its service life. Finally, by combining the peak power usage analysis data and cooling water pipeline analysis data, resource allocation optimal calling analysis is performed to generate resource allocation optimal recommendations for the workshop. These data support provides efficient and operable optimization solutions for decision-making, ensuring accurate allocation and continuous optimization of workshop resources.

[0046] Preferably, step S4 comprises the following steps:

[0047] Step S41: integrate the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, and generate workshop construction real-time monitoring data;

[0048] Step S42: dynamically feedback optimization according to the workshop construction real-time monitoring data, and generate workshop real-time monitoring dynamic feedback data;

[0049] Step S43: build a visual report based on the workshop real-time monitoring dynamic feedback data, and generate a workshop construction digital optimization report.

[0050] The present application realizes real-time monitoring, dynamic optimization and report generation of workshop construction through the integration of workshop construction optimization digital model and intelligent digital control system, which has significant beneficial effects. From the data level, first, the workshop construction optimization digital model is integrated into the intelligent digital control device, so that each link and data of the workshop can be monitored and fed back in real time. Through this integration, all real-time data of the workshop operation, including energy consumption, environmental quality, equipment operation status, etc., can be continuously collected and analyzed to generate real-time monitoring data of workshop construction. These real-time monitoring data provide data support for timely identification of potential problems, optimization of equipment operation and management strategies, ensuring that the workshop 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 workshop is further improved through dynamic feedback optimization. The real-time monitoring dynamic feedback data of the workshop can reflect the deficiencies in the operation in a timely manner and be adjusted and optimized through intelligent algorithms. Finally, based on the real-time monitoring dynamic feedback data, the generated visual report can not only visually display the operation of the workshop, but also provide clear optimization direction to help management personnel make scientific decisions. The report can provide detailed basis for the continuous optimization of the workshop, ensuring that the environment, equipment, energy and other aspects can work in coordination to maximize the comprehensive benefits.

[0051] Preferably, step S42 comprises the following steps:

[0052] Step S421: monitoring the environment according to the real-time monitoring data of the workshop construction to generate workshop environment dimension monitoring data; monitoring the equipment operation according to the real-time monitoring data of the workshop construction to generate workshop equipment dimension monitoring data;

[0053] Step S422: clustering analysis trend prediction is performed on the workshop environment dimension monitoring data and the workshop equipment dimension monitoring data to generate multi-dimensional mining data of the workshop construction;

[0054] Step S423: genetic algorithm multi-objective optimization is performed on the multi-dimensional mining data of the workshop construction to generate real-time monitoring dynamic feedback data of the workshop.

[0055] This invention utilizes real-time monitoring data from workshop construction to monitor both the environment and equipment dimensions, combining it with cluster analysis, trend prediction, and genetic algorithm multi-objective optimization to achieve intelligent and precise management of workshops, with significant benefits. From a data perspective, first, by acquiring real-time monitoring data from workshop construction, the system can comprehensively track environmental changes and equipment operating conditions within the workshop. This process generates workshop environment- and equipment-dimensional monitoring data by separately monitoring the environment and equipment dimensions. This data provides rich input for subsequent analysis, covering environmental factors such as workshop temperature, humidity, air quality, and noise, as well as performance indicators such as equipment operating status, load, and efficiency, providing a multi-dimensional data foundation for comprehensive optimization. Next, the system performs cluster analysis and trend prediction on this environmental and equipment-dimensional monitoring data, generating multi-dimensional mining data on workshop construction. Through cluster analysis, the system can identify the operating modes, changing trends, and potential risk points of different equipment and environments, providing data support for predicting future operating conditions, optimizing control strategies, and responding to emergencies. Finally, based on multi-dimensional data mining, a genetic algorithm is used for multi-objective optimization, further generating dynamic feedback data for real-time monitoring of the workstations. Genetic algorithms can efficiently solve multiple optimization objectives and find the optimal adjustment plan, such as properly allocating equipment resources, adjusting environmental parameters, and reducing energy consumption, ensuring that the workstation system achieves optimal balance and performance improvement across all dimensions.

[0056] In this specification, a digital workshop construction system based on multi-source data is provided, which is used to execute the above-mentioned digital workshop construction method based on multi-source data. The digital workshop construction system based on multi-source data includes:

[0057] The environmental data acquisition and digital modeling module is used to collect air quality, temperature, humidity, and noise environmental monitoring data in the workshop using intelligent environmental sensors and record initial environmental parameters. Based on the initial environmental parameters, a preliminary digital 3D model of the workshop is created using building information modeling technology.

[0058] The equipment status monitoring and dynamic simulation module is used to collect the operating status data of each device in the workshop and input the operating status data of the equipment in the workshop into the preliminary digital 3D model of the workshop. The simulation software is used to dynamically simulate the interaction between the equipment and the environment, evaluate the impact of workshop resource consumption and environmental changes, and obtain preliminary evaluation data;

[0059] The optimization algorithm and model adjustment module is used to optimize the preliminary digital 3D model of the workshop based on the preliminary assessment data. If the assessment results show that the energy consumption is too high or the equipment operation is unstable, the spatial layout, equipment configuration and energy use of the workshop will be adjusted to generate an optimized digital model of the workshop construction;

[0060] A 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 through the feedback of the actual operation data of the workshop, dynamic optimization is carried out to generate a workshop construction digital optimization report.

[0061] The present application has the beneficial effect of 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 state, which has significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature and humidity, noise, etc., and combined with the building information modeling technology to establish a preliminary digital three-dimensional model of the workshop, providing 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 state data and environmental parameters are combined and input into the digital three-dimensional model, and dynamic simulation is carried out 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, thereby improving the overall system energy efficiency and environmental comfort. Finally, the optimized workshop digital model is integrated into the 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 fine management and continuous improvement of the workshop. Therefore, the present application integrates environmental monitoring and equipment management systems to realize dynamic feedback optimization, solves the problems of resource waste and environmental instability in the traditional workshop construction and management process, and improves energy utilization efficiency, production efficiency and environmental comfort. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 It is a schematic diagram of the step flow of the digital workshop construction method based on multi-source data;

[0063] Figure 2 It is a schematic diagram of the step flow of the digital workshop construction method based on multi-source data; Figure 1 It is a schematic diagram of the detailed implementation step flow of step S2 in the method;

[0064] Figure 3 It is a schematic diagram of the detailed implementation step flow of step S3 in the method; Figure 1 It is a schematic diagram of the detailed implementation step flow of step S3 in the method;

[0065] Figure 4To Figure 1 Detailed implementation step flow diagram of step S4 in the method;

[0066] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0069] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0070] To achieve the above-mentioned purpose, please refer to Figures 1 to 4 A digital chemical plant construction method based on multi-source data, the method comprising the following steps:

[0071] Step S1: Collecting air quality, temperature and humidity, noise environment monitoring data in the plant by using intelligent environment sensing measurer, and recording initial environmental parameters; according to the initial environmental parameters, using building information modeling technology to establish a preliminary digital three-dimensional model of the plant;

[0072] Step S2: Collecting the running state data of each equipment in the plant and inputting the plant equipment running state data into the preliminary digital three-dimensional model of the plant, using simulation software to simulate the dynamic interaction between equipment and environment, evaluating the influence of plant resource consumption and environmental change, and obtaining preliminary evaluation data;

[0073] 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 equipment operation is unstable, adjust the spatial layout, equipment configuration and energy use of the workshop to generate an optimized digital model of the workshop construction;

[0074] Step S4: Integrate the optimized digital model of the workshop construction into the intelligent digital control device for real-time monitoring and adjustment. Through dynamic optimization based on feedback of actual operation data of the workshop, a digital optimization report of the workshop construction is generated.

[0075] The present application has the beneficial effect of 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 state, which has significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature and humidity, noise, etc., and combined with the building information modeling technology to establish a preliminary digital three-dimensional model of the workshop, 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 state data and environmental parameters are combined and 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 of the model, combined with preliminary evaluation data, the spatial layout, equipment configuration and energy use of the workshop are optimized, thereby improving the overall system energy efficiency and environmental comfort. Finally, the optimized digital model of the workshop is integrated into the 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 fine management and continuous improvement of the workshop. Therefore, by integrating environmental monitoring and equipment management systems, the present application realizes dynamic feedback optimization, solves the problems of resource waste and environmental instability in the traditional workshop construction and management process, and improves energy utilization efficiency, production efficiency and environmental comfort.

[0076] In the embodiment of the present application, as shown in the reference Figure 1 The method comprises the following steps:

[0077] Step S1: Collecting air quality, temperature and humidity, and noise environment monitoring data in the workshop using intelligent environmental sensing measurers, and recording initial environmental parameters; establishing a preliminary digital three-dimensional model of the workshop using building information modeling technology according to the initial environmental parameters;

[0078] In the embodiment of the present application, precise data collection is performed using intelligent environmental sensing measurers, which include but are not limited to air quality sensors, temperature and humidity sensors, and noise environment monitoring equipment. Through these devices, the system can collect key environmental data in the workshop in real time, such as PM2.5 concentration in the air, temperature and humidity levels, and the intensity of noise pollution, etc. These data not only reflect the existing environmental state of the workshop, but also provide a basis for subsequent modeling and optimization. For the collected data, preliminary data processing is first performed, such as time synchronization of temperature and humidity data to ensure consistency in time series, time domain analysis of noise data to extract key features of noise pollution, and filtering and denoising of air quality data to eliminate external interference and ensure data accuracy. All these environmental parameters are summarized as initial environmental parameters of the workshop, forming a high-quality raw data set. Based on these initial environmental parameters, building information modeling (BIM) technology is used for digital modeling of the workshop. BIM technology integrates environmental parameters and geometric information of the workshop building space to convert sensor data into a digital model with spatial attributes. This process includes combining environmental data with three-dimensional geometric structure of the workshop to form an accurate digital three-dimensional model that can reflect the environmental characteristics and spatial layout of the workshop in actual operating state. Through BIM technology, environmental data and building design information can be effectively combined to provide scientific basis and technical support for subsequent dynamic simulation, optimization design, and operation management.

[0079] Step S2: Collecting the running state data of each device in the workshop and inputting the workshop equipment running state data into the preliminary digital three-dimensional model of the workshop, using simulation software to dynamically simulate the interaction between equipment and environment, evaluating the influence of workshop resource consumption and environmental change, and obtaining preliminary evaluation data;

[0080] In the embodiments of the present application, a variety of advanced technical means are adopted to ensure accurate collection and analysis of the influence of workshop equipment running state on the environment. First, the equipment running state data in the workshop 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, to collect real-time running parameters of the equipment, such as power consumption, operating temperature, vibration frequency, etc. These equipment running data are transmitted to the central data processing platform through wireless network to ensure real-time and data integrity. Secondly, in order to effectively integrate these equipment running data into the environment monitoring system of the workshop, these data are input into the preliminary digital three-dimensional model of the workshop, which is based on Building Information Modeling (BIM) technology to build a model that combines equipment data with the spatial layout of the workshop and environmental parameters. Through correlation analysis with workshop 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 tools, etc.), dynamic simulation of the interaction between equipment and environment is carried out based on the digital three-dimensional model. During the simulation process, the influence of the equipment running state on the environment, such as energy consumption, heat release, pollutant emission, and noise propagation, etc. factors, are accurately simulated through the simulation model. These simulation results not only help to evaluate the direct impact of equipment operation on resource consumption in the workshop, but also can quantify its contribution to environmental changes. For example, simulation can predict the temperature fluctuations caused by equipment operation, air pollutant diffusion path, noise propagation range, etc., thereby forming a complete resource consumption and environmental change impact evaluation system.

[0081] Step S3: optimizing the preliminary digital three-dimensional model of the workshop according to the preliminary evaluation data, if the evaluation results show that the energy consumption is too high or the equipment operation is unstable, adjusting the spatial layout, equipment configuration and energy use of the workshop to generate an optimized digital model of the workshop construction;

[0082] In the embodiments of the present application, through the obtained preliminary evaluation data, the system identifies potential problems of excessive energy consumption or unstable operation of equipment. These evaluation data include the operation efficiency of various equipment in the workshop, resource consumption patterns, and simulation results of environmental changes. Based on these data, the preliminary digital model of the workshop is adjusted using multi-objective optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm, etc.). The optimization goals include reducing energy consumption, improving equipment operation stability, and maximizing space utilization. In this process, first, the spatial layout of the workshop is re-evaluated, and based on the equipment operation data, the energy load and equipment operation state of different areas are analyzed, and the equipment layout is optimized through the spatial layout optimization algorithm to reduce unnecessary energy loss and improve equipment operation efficiency. Secondly, based on the data analysis of equipment configuration, the system will dynamically adjust the equipment. For those equipment with high energy consumption or unstable performance, the optimization algorithm will consider replacing, adjusting the operation mode or reconfiguring the equipment parameters, so as to reduce the energy consumption of the equipment under the premise of ensuring the function, and improve the stability of the whole system. Finally, the adjustment of energy use is through the collected energy consumption data, and the dynamic load scheduling and energy efficiency management algorithm is applied to redistribute the energy consumption of the whole workshop, so as to achieve more efficient energy utilization. In this process, the system can provide accurate optimization adjustment suggestions by continuously analyzing historical data and real-time data. The generated workshop construction optimization digital model is based on the comprehensive consideration of equipment performance, spatial layout, energy utilization rate and other factors, through precise data-driven decision-making, the operation state of the whole workshop is adjusted and optimized, so as to improve the overall operation efficiency and energy utilization efficiency of the workshop.

[0083] 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 the feedback of the actual operation data of the workshop to generate a workshop construction digital optimization report.

[0084] In the embodiments of the present application, the workshop construction optimization digital model is converted into a model format that can be applied in real-time in an intelligent digital control system. This process is achieved through model data standardization and integration technology, which converts the information obtained during the early optimization process, such as spatial layout, equipment configuration, and energy use, into dynamically adjustable parameters. The intelligent digital control device continuously monitors various operation indicators of the workshop by real-time collection of operation data, including equipment running status, energy consumption, environmental parameters (such as temperature and humidity, air quality, noise, etc.), and other key working condition data. Through Internet of Things (IoT) technology, these sensor data are efficiently transmitted to the control system to form real-time data streams. Based on the data streams, the system uses big data analysis algorithms to compare and analyze the deviation between actual operation data and the preset optimization model, and determines whether there are abnormalities or optimization spaces through a set threshold. If it is found that energy consumption is high, equipment operation is abnormal, or environmental parameters do not meet expectations, the system will immediately generate adjustment instructions to guide the intelligent control device to automatically adjust, ensuring that the workshop operation is always in the optimal state. At the same time, the system generates dynamic optimization reports based on operation data feedback, which compare the gap between actual operation data and optimization targets, propose further optimization suggestions, and provide visual decision support. These reports not only help operators to discover potential problems in a timely manner, but also provide a scientific basis for subsequent optimization and scheduling. In addition, the system can use machine learning algorithms to perform model self-learning using historical data and real-time data, gradually improving the prediction accuracy and decision-making ability of the model, and forming a closed-loop optimization mechanism.

[0085] Preferably, step S1 comprises the following steps:

[0086] Step S11: Obtain workshop temperature and humidity data using a temperature and humidity sensor;

[0087] Step S12: Perform air PM2.5 analysis and air particle measurement on the workshop using an air quality sensor to generate workshop air quality data;

[0088] Step S13: Perform sound wave signal vibration analysis on the workshop using sound wave propagation vibration to generate workshop noise environment monitoring data;

[0089] Step S14: Integrate the workshop temperature and humidity data, workshop air quality data, and workshop noise environment monitoring data to generate initial environmental parameters;

[0090] Step S15: Construct a model based on the initial environmental parameters according to the building information modeling technology to generate a preliminary digital three-dimensional model of the workshop.

[0091] In the embodiments of the present application, temperature and humidity sensors are used to obtain real-time temperature and humidity data in the workshop. These sensors generally measure data based on resistance, capacitance or thermal conductivity principles, and can provide high-precision temperature and humidity values in a dynamic environment. These data are transmitted to the data acquisition system and subjected to preliminary processing, such as outlier rejection 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 concentration, are used to measure the concentration of fine particulate matter in the air, reflecting the pollution level of the workshop air. The sensor can efficiently capture suspended particles in the air and generate continuous air quality data. These data are also subjected to filtering to remove environmental interference and periodic fluctuations in the data, thereby ensuring the accuracy and timeliness of the air quality data. Sound wave propagation vibration analysis uses acoustic sensors or accelerometers to analyze the frequency spectrum characteristics of the noise environment in the workshop. Noise environment data are captured by capturing vibration signals and performing frequency domain analysis to reveal the distribution and propagation characteristics of noise sources. These data will be used for subsequent noise control and optimization analysis. The temperature and humidity data, air quality data and noise environment data collected above are integrated. Through data fusion algorithms such as weighted average method or Kalman filtering method, various environmental parameters can be integrated to eliminate the deviation between different sensor data and generate a representative initial environmental parameter set to provide reliable input for subsequent modeling. Finally, building information modeling (BIM) technology is applied to digitally model the initial environmental parameters. BIM technology integrates different data sources (such as spatial data, environmental data and equipment operation data) to generate an accurate three-dimensional digital model. This model not only reflects the physical space layout of the workshop, but also dynamically integrates environmental changes to provide 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 workshop can visually display the environmental state of the workshop and provide a basic framework for further simulation and optimization analysis.

[0092] Preferably, the use of building information modeling technology to establish a preliminary digital three-dimensional model of the workshop includes the following:

[0093] The temperature and humidity data of the workshop are subjected to time synchronization processing to generate temperature and humidity time synchronization data of the workshop. The temperature and humidity time synchronization data of the workshop are subjected to interpolation method to fill in data gaps to generate missing value supplement data of the workshop.

[0094] The air quality data of the workshop are subjected to filtering and removal of periodic fluctuations to generate long-term trend data of the workshop. The long-term trend data of the workshop are subjected to normalization standardization processing to generate air quality standardized data of the workshop.

[0095] The interworking noise environment monitoring data is subjected to wavelet transform time domain analysis to generate interworking noise time domain analysis data; the interworking noise time domain analysis data is subjected to sound source classification and noise removal processing to generate interworking noise environment standard data;

[0096] The interworking missing value supplement data, the interworking air quality standardized data and the interworking noise environment standard data are subjected to principal component analysis to generate initial principal component analysis data;

[0097] The initial principal component analysis data is subjected to heat island effect and humidity concentration identification analysis by using a preset thermal analysis and fluid dynamics simulation model to generate interworking heat island effect analysis data and interworking humidity concentration area data;

[0098] Noise propagation modeling is performed based on the interworking noise environment standard data to generate interworking noise distribution data; sound source transmission path control point determination is performed according to the interworking noise distribution data to generate interworking noise transmission path data;

[0099] The interworking heat island effect analysis data and the interworking humidity concentration area data are subjected to digital three-dimensional modeling by using BIM technology, and the interworking noise transmission path data is used for model attribute area division to obtain an interworking preliminary digital three-dimensional model.

[0100] In the embodiment of the present application, through deep mining and fine processing of workshop environment data, a precise digital model is constructed to support intelligent optimization of the workshop. First, in the processing of workshop temperature and humidity data, time synchronization processing is used to align the time of multi-source data, ensuring the consistency of different sensors in time. For the part with data missing, interpolation method is used for filling, such as linear interpolation or spline interpolation method, to ensure the continuity and integrity of the data, generate the supplemented temperature and humidity data, and further improve the data quality. Then, for the workshop air quality data, filtering technology such as Kalman filtering or mean filtering is used 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, so that it conforms to the unified scale, eliminates the dimension effect of the data, and facilitates subsequent analysis. Noise environment data is analyzed in time domain by wavelet transform method, using the multi-scale characteristics of wavelet to extract the characteristics of noise signal, and then analyze the time domain distribution of noise signal. In order to further improve the data quality, sound source classification technology is used to distinguish noise signal from other interference signals, remove noise, and obtain more accurate noise environment standard data. Then, the principal component analysis (PCA) is used to reduce the dimension of the temperature and humidity, air quality and noise data of the workshop, and the most representative features are extracted by principal component to generate the initial principal component analysis data, which provides strong support for subsequent modeling. Based on the thermal analysis and fluid dynamics simulation model, the heat island effect and humidity concentration area of the workshop are identified and analyzed. This process calculates the heat distribution and humidity concentration position of the workshop by simulating the physical processes such as air flow and heat conduction, and generates heat island effect and humidity concentration area data. Noise propagation modeling relies on sound wave propagation theory, and by constructing an acoustic model, the propagation process of noise in the workshop is simulated to obtain noise distribution data, and by determining the control points of the sound source transmission path, the influence range of the noise source is further refined. Finally, based on BIM (Building Information Modeling) technology, the heat island effect analysis data and humidity concentration area data of the workshop are modeled in three dimensions, and combined with the noise transmission path data, the attribute area is divided in the digital three-dimensional model, thereby generating the preliminary digital three-dimensional model of the workshop.

[0101] As an example of the present application, reference is made to Figure 2 In this example, the step S2 comprises:

[0102] Step S21: acquiring workshop equipment operation state data;

[0103] Step S22: inputting the workshop equipment operation state data into the preliminary digital three-dimensional model of the workshop for numerical workshop construction analysis, and generating workshop digital construction model output data;

[0104] Step S23: Perform dynamic simulation of the equipment and environment interaction based on the workshop digital construction model output data, and evaluate the workshop resource consumption and environmental changes to obtain preliminary evaluation data.

[0105] In the embodiments of the present application, the equipment operation state data is obtained through the monitoring system of the workshop equipment, which can include the running speed, load, power consumption, temperature change, etc. of the equipment. The specific technical means include using various sensors and data acquisition modules (such as PLC, SCADA system, etc.) to monitor the equipment state in real time and transmit the data to the central database. After obtaining the equipment operation state data, these data will be input into the preliminary digital three-dimensional model of the workshop, and through the building information modeling (BIM) technology, the equipment operation data and environmental parameters are combined to form a complete digital model. The construction of the digital three-dimensional model parameterizes the space layout, equipment configuration and environmental factors, and through the numerical method, the equipment state is converted into model operable data input, thereby generating the output data of the workshop construction model. In order to realize the dynamic simulation of the interaction between the equipment and the environment in the workshop, the technical means include using multi-physical field simulation software (such as ANSYS, COMSOL, etc.) to simulate and analyze the interaction between the equipment and the environment. Through numerical calculation, the influence of equipment operation on the workshop environment is simulated, and then the relationship between resource consumption (such as power, water resources, air conditioning load, etc.) and environmental changes (such as temperature and humidity changes, noise pollution, etc.) is evaluated, and the heat and humidity distribution is evaluated using computational fluid dynamics (CFD) and other technologies, and the efficiency and stability of the equipment under different working conditions are simulated. 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 the development of subsequent optimization measures.

[0106] Preferably, step S23 includes the following steps:

[0107] Step S231: Perform equipment operation efficiency simulation based on the workshop digital construction model output data to generate workshop simulation data;

[0108] Step S232: Perform fluid dynamics simulation temperature and humidity change calculation, pollutant diffusion path and noise source propagation on the workshop simulation data to generate multi-dimensional equipment and environment simulation data;

[0109] Step S233: Perform working condition energy consumption calculation based on the multi-dimensional equipment and environment simulation data to generate energy consumption evaluation data; perform workshop environment impact evaluation based on the multi-dimensional equipment and environment simulation data to generate environment quality evaluation data; perform resource calculation and space resource use efficiency evaluation based on the multi-dimensional equipment and environment simulation data to generate comprehensive resource optimization data of the workshop.

[0110] In the embodiment of the present application, based on the inter-department digital construction model output data, the simulation software is used to simulate the equipment operation efficiency, and inter-department simulation simulation data is generated. This process mainly relies on simulation modeling tools such as MATLAB, Simulink, ANSYS, etc. to model the dynamic behavior of the equipment, and to couple it with the inter-department environment data. By simulating the equipment operation efficiency, the performance of the equipment under different operating conditions can be evaluated, including efficiency, load variation, and energy consumption, etc. key parameters. Then, in step S232, the computational fluid dynamics (CFD) technology is used to simulate and analyze the inter-department simulation simulation data for temperature and humidity changes, pollutant diffusion path and noise source propagation, generating multi-dimensional equipment and environment simulation data. CFD software (such as ANSYS Fluent or COMSOL Multiphysics) can accurately simulate air flow, temperature and humidity distribution, pollutant propagation path and noise diffusion, etc. environmental factors, which are crucial to the performance of the equipment and the inter-department environment. Through these simulation data, the spatio-temporal variation of temperature and humidity, the path of pollutant diffusion, and the propagation range of noise sources in the inter-department environment can be obtained. Further, in step S233, based on the multi-dimensional data obtained from the equipment and environment simulation, the energy consumption, environmental impact, and resource use efficiency are evaluated. By calculating the energy consumption under the working condition, combined with the working state of the equipment and the environmental change, energy consumption evaluation data can be generated. Using statistical analysis and optimization algorithms, the efficiency of resource use in the inter-department can be quantitatively evaluated, identifying high energy consumption and low efficiency operation links, and further proposing improvement measures. In addition, environmental quality evaluation data can evaluate the air quality, noise pollution and temperature and humidity comfort in the inter-department by analyzing temperature and humidity changes, pollutant concentration and noise level, thereby providing decision support for environmental optimization. Through the evaluation of resource and space resource use efficiency, resource waste can be identified, and equipment layout and space use can be optimized, finally generating inter-department comprehensive resource optimization data to provide a basis for inter-department resource management and scheduling.

[0111] As an example of the present application, reference is made to Fig. 1, which shows the steps S3 in this example comprising: Figure 3

[0112] Step S31: optimizing the inter-department preliminary digital three-dimensional model according to the preliminary evaluation data, and generating inter-department comprehensive evaluation data;

[0113] Step S32: evaluating the inter-department comprehensive evaluation data using preset inter-department evaluation standards, and if the evaluation result shows that the energy consumption is too high or the equipment operation is unstable, adjusting the space layout, equipment configuration and energy use of the inter-department, and generating inter-department optimization adjustment suggestions;

[0114] ​Step S33: based on the workshop optimization adjustment suggestion, the preliminary digital three-dimensional model of the workshop is optimized for final digital construction optimization, and a workshop construction optimization digital model is generated.

[0115] In the embodiment of the present application, by collecting and analyzing preliminary data related to the workshop, such as space layout, equipment configuration, energy consumption and equipment operation efficiency, the preliminary digital model of the workshop 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, by adjusting the equipment position, capacity, operation mode, etc. in a parameterized manner. The optimized preliminary digital model of the workshop will contain more accurate spatial distribution and resource allocation information, forming comprehensive evaluation data of the workshop. The comprehensive evaluation data of the workshop will be evaluated by pre-set workshop evaluation standards, which include energy consumption, equipment operation stability, space utilization efficiency and other indicators. Through data mining and analysis, the evaluation results can reflect potential problems such as high energy consumption or unstable equipment operation. On this basis, the space layout, equipment configuration and energy use of the workshop will be adjusted according to the deficiencies found, and the workshop optimization adjustment suggestion will be generated through optimization algorithm. During the adjustment process, optimization models such as genetic algorithm, particle swarm optimization and other intelligent algorithms are used to simulate the effects of different configurations and select the optimal adjustment scheme. Finally, the preliminary digital three-dimensional model is optimized according to the workshop optimization adjustment suggestion. At this time, the optimized model will consider all data comprehensively to achieve the optimal combination of space layout and resource allocation, thereby generating the final workshop construction optimization digital model.

[0116] Preferably, the adjustment of the space layout, equipment configuration and energy use of the workshop includes the following:

[0117] According to the energy consumption evaluation data, mark the equipment overload operation or invalid operation equipment, and generate load adjustment or shutdown maintenance instructions to obtain preliminary optimization energy suggestion;

[0118] According to the environmental quality evaluation data, the overall layout of the ventilation facilities is optimized, and the additional strategy of soundproofing materials is carried out in the high noise pollution area, and the equipment area optimization suggestion is generated;

[0119] According to the comprehensive resource optimization data of the workshop, the peak calling analysis of the optimized power use period is carried out, and the power use peak analysis data is generated; according to the comprehensive resource optimization data of the workshop, the cooling water pipe layout analysis is carried out, and the cooling water pipe analysis data is generated; based on the power use peak analysis data and the cooling water pipe analysis data, the resource allocation optimal calling analysis processing is carried out, and the workshop resource allocation optimal suggestion is generated.

[0120] In the embodiment of the present application, the energy consumption evaluation data is used to analyze the running state of the equipment in detail, and the equipment in overload operation or invalid operation is identified. At this time, combined with the load analysis model and the equipment performance data, the system can mark the equipment with low energy efficiency or unreasonable operation by establishing threshold judgment rules, and then generate load adjustment or shutdown maintenance instructions. Based on these data, the running mode of the equipment can be adjusted through optimization algorithm to reduce energy waste and form preliminary optimization energy suggestions. In the analysis of environmental quality evaluation data, the indicators such as air quality, temperature and humidity, ventilation effect and noise pollution are focused on. By constructing an environmental quality simulation model, the overall layout of the ventilation facilities is analyzed by using numerical optimization method, so as to put forward the layout optimization scheme and ensure that the airflow distribution and air quality in the working environment reach the best state. At the same time, the high noise pollution area is identified according to the noise level data, and the additional strategy of noise insulation material is put forward through the simulation analysis of material characteristics and sound insulation effect, and finally the equipment area optimization suggestion is generated. For the optimization of workshop power use, first of all, based on the comprehensive resource optimization data of the workshop, the peak calling analysis of power use period is carried out to identify the load peak period and the problem of unbalanced power use. Through big data mining technology, the correlation between power demand and production process is analyzed to generate power use peak analysis data, and further use this data to optimize the arrangement of power use period. In addition, the cooling water pipeline layout analysis is based on the water flow dynamics model to simulate and evaluate the layout of the existing cooling water pipeline to generate cooling water pipeline analysis data. These analysis data combined with the power use peak analysis result are analyzed by comprehensive optimization algorithm to determine the most suitable resource allocation mode.

[0121] As an example of the present application, reference is made to Figure 4 In this example, the step S4 comprises:

[0122] Step S41: integrate the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, and generate workshop construction real-time monitoring data;

[0123] Step S42: dynamically feedback optimization according to the workshop construction real-time monitoring data, and generate workshop real-time monitoring dynamic feedback data;

[0124] Step S43: construct a visual report based on the workshop real-time monitoring dynamic feedback data, and generate a digital optimization report for workshop construction.

[0125] In the embodiments of the present application, by transmitting various types of digital information (such as equipment running state, energy consumption, space utilization rate, etc.) of the workshop to the intelligent control system, combined with sensors and real-time data acquisition modules, real-time monitoring and adjustment of each dimension of the workshop are ensured. Through the interconnection of data interface and control device, the intelligent digital control device can dynamically adjust the model according to real-time data, real-time acquire key data of workshop construction, and form real-time monitoring data of workshop construction. These data include but are not limited to equipment running parameters, environmental quality indicators, energy consumption, production efficiency, etc., and a large amount of multi-source data interaction and real-time update. According to the collected real-time monitoring data, the workshop construction is optimized through the dynamic feedback mechanism driven by data. Specifically, based on historical data, real-time data and preset optimization rules, the system running parameters are continuously adjusted through the feedback loop, and multiple simulations and predictions are performed to generate 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 algorithm, machine learning, etc.), which can realize immediate optimization and feedback of equipment operation, energy distribution, environmental regulation, etc. Finally, based on the real-time monitoring dynamic feedback data of the workshop, the system converts these data into visual reports to build digital optimization reports of the workshop construction. Through data visualization techniques such as charts, heat maps, three-dimensional displays, etc., each item of optimization data is presented in an easy-to-understand form to help decision-makers accurately assess the optimization effect and guide subsequent adjustments.

[0126] Preferably, step S42 comprises the following steps:

[0127] Step S421: performing environment monitoring according to the real-time monitoring data of the workshop construction to generate workshop environment dimension monitoring data; performing equipment operation monitoring according to the real-time monitoring data of the workshop construction to generate workshop equipment dimension monitoring data;

[0128] Step S422: performing cluster analysis trend prediction on the workshop environment dimension monitoring data and the workshop equipment dimension monitoring data to generate multi-dimensional mining data of the workshop construction;

[0129] Step S423: performing genetic algorithm multi-objective optimization on the multi-dimensional mining data of the workshop construction to generate real-time monitoring dynamic feedback data of the workshop.

[0130] In the embodiments of the present application, the inter-department construction real-time monitoring data is used to monitor the inter-department environment and equipment respectively. The environmental monitoring data mainly includes environmental factors such as temperature and humidity, air quality, light intensity, noise, etc., while the equipment operation monitoring data includes parameters such as the running state, load, power consumption, 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 technology, ensuring that the data in each dimension can accurately reflect the actual operation of the inter-department. Through the data acquisition module, the data of these environmental dimensions and equipment dimensions are respectively generated into inter-department environmental dimension monitoring data and equipment dimension monitoring data, providing a basis for subsequent analysis. Cluster analysis and trend prediction are performed on the inter-department environmental dimension monitoring data and equipment dimension monitoring data. Cluster analysis uses unsupervised learning algorithms (such as K-means or DBSCAN) to group data, thereby identifying potential patterns and problems in the operation of the inter-department, especially the correlation between environmental factors and equipment performance. Through the trend prediction model (such as time series analysis or regression model in machine learning), the future changes in the environment or the running state of the equipment are predicted. The core of this process is the multi-dimensional correlation mining of data, which can provide valuable prediction information for the optimization of the inter-department, and generate multi-dimensional mining data of the inter-department construction, providing data support for subsequent decision-making. Based on the multi-dimensional mining data of the inter-department construction, a multi-objective optimization is performed using a genetic algorithm. Genetic algorithm simulates natural selection mechanism to comprehensively adjust the environmental control and equipment operation of the inter-department in the multi-objective optimization framework, and the optimization objectives include energy saving, improving production efficiency, reducing equipment failure, etc.

[0131] In the present specification, a multi-source data-based digital inter-department construction system is provided for performing the multi-source data-based digital inter-department construction method described above, which comprises:

[0132] An environmental data acquisition and digital modeling module is used to collect air quality, temperature and humidity, noise environmental monitoring data in the inter-department using intelligent environmental sensing instruments, and record initial environmental parameters; according to the initial environmental parameters, a preliminary digital three-dimensional model of the inter-department is established using building information modeling technology;

[0133] An equipment state monitoring and dynamic simulation module is used to collect the running state data of each equipment in the inter-department and input the inter-department equipment running state data into the preliminary digital three-dimensional model of the inter-department, and use simulation software to dynamically simulate the interaction between equipment and environment, evaluate the influence of inter-department resource consumption and environmental change, and obtain preliminary evaluation data;

[0134] An optimization algorithm and model adjustment module 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 of the workshop construction.

[0135] A real-time monitoring and dynamic feedback optimization module is used to integrate the optimized digital model of the workshop construction into an intelligent digital control device for real-time monitoring and adjustment. Through dynamic optimization based on feedback of actual operation data of the workshop, a digital optimization report of the workshop construction is generated.

[0136] The present application has the beneficial effect of 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 state, which has significant beneficial effects. First, the intelligent environmental sensors are used to accurately collect environmental parameters such as air quality, temperature and humidity, noise, etc., and the building information modeling technology is used to establish a preliminary digital three-dimensional model of the workshop, 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. Second, the collected equipment operation state data and environmental parameters are combined and 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, thereby improving the overall system energy efficiency and environmental comfort. 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 an 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 fine management and continuous improvement of the workshop. Therefore, by integrating environmental monitoring and equipment management systems, the present application realizes dynamic feedback optimization, solves the problems of resource waste and environmental instability in the traditional workshop construction and management process, and improves energy utilization efficiency, production efficiency and environmental comfort.

[0137] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims rather than the above description, and it is therefore intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file.

[0138] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A digital workshop construction method based on multi-source data, characterized in that: Using a digital workshop construction process that integrates intelligent environmental sensor measurement devices and intelligent digital control devices, the digital workshop construction method based on multi-source data includes the following steps: Step S1: Using intelligent environmental sensors to collect air quality, temperature, humidity, and noise monitoring data within the workshop, and recording initial environmental parameters; using building information modeling technology to establish a preliminary digital 3D model of the workshop based on the initial environmental parameters; establishing the preliminary digital 3D model of the workshop specifically includes: Perform time synchronization processing on the temperature and humidity data of the workshop to generate the time synchronization data of the temperature and humidity of the workshop; perform interpolation on the time synchronization data of the temperature and humidity of the workshop to fill in the missing data and generate the supplementary data of the missing values ​​of the workshop; Filter the workshop air quality data and remove periodic fluctuations to generate long-term trend data; normalize the long-term trend data to generate standardized workshop air quality data; Perform wavelet transform time domain analysis on the workshop noise environment monitoring data to generate workshop noise time domain analysis data; perform sound source classification and noise removal on the workshop noise time domain analysis data to generate workshop noise environment standard data; Perform principal component analysis on the supplementary data of missing values ​​in the workshop, the standardized data of air quality in the workshop, and the standardized data of noise environment in the workshop to generate initial principal component analysis data; Use the preset thermal analysis and fluid dynamics simulation model to perform heat island effect and humidity concentration identification analysis on the initial principal component analysis data, and generate workshop heat island effect analysis data and workshop humidity concentration area data; Based on the standard data of the workshop noise environment, noise propagation modeling is carried out to generate workshop noise distribution data; based on the workshop noise distribution data, the control points of the sound source transmission path are determined to generate workshop noise transmission path data; The heat island effect analysis data and humidity concentration area data of the workshop were used to conduct digital 3D modeling using BIM technology. The noise transmission path data of the workshop was used to divide the model attribute areas, and a preliminary digital 3D model of the workshop was obtained. Step S2: Collecting the operating status data of each device in the workshop and inputting the operating status data of the equipment in the workshop into a preliminary digital three-dimensional model of the workshop, using simulation software to perform a dynamic simulation of the interaction between the equipment and the environment, evaluate the impact of the workshop resource consumption and environmental changes, and obtain preliminary evaluation data; Step S3: Optimize the preliminary digital 3D model of the workshop based on the preliminary assessment data. If the assessment results show that the energy consumption is too high or the equipment operation is unstable, adjust the spatial layout, equipment configuration, and energy usage of the workshop to generate an optimized digital model of the workshop construction; Step S4 is specifically as follows: Step S41: Integrate the workshop construction optimization digital model into the intelligent digital control device for real-time monitoring and adjustment, and generate real-time monitoring data of the workshop construction; Step S42: Perform dynamic feedback optimization based on the real-time monitoring data of the workshop construction to generate real-time monitoring dynamic feedback data of the workshop; Step S42 is specifically as follows: Step S421: Perform environmental monitoring based on the real-time monitoring data of the workshop construction to generate workshop environment dimension monitoring data; perform equipment operation monitoring based on the real-time monitoring data of the workshop construction to generate workshop equipment dimension monitoring data; Step S422: performing cluster analysis and trend prediction on the workshop environment dimension monitoring data and the workshop equipment dimension monitoring data to generate multi-dimensional mining data of workshop construction; Step S423: performing a genetic algorithm multi-objective optimization on the multi-dimensional mining data of the workshop construction to generate dynamic feedback data for real-time monitoring of the workshop; Step S43: Construct a visual report based on the dynamic feedback data of the real-time monitoring of the workshop and generate a digital optimization report for the workshop construction.

2. The method for constructing a digital workshop based on multi-source data according to claim 1, characterized in that: The intelligent environmental sensor measurement device includes a temperature and humidity sensor, an air quality sensor, and a noise environment monitoring device. Step S1 includes the following steps: Step S11: using a temperature and humidity sensor to obtain temperature and humidity data in the workshop; 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 noise environment monitoring equipment to perform acoustic 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: constructing a model of the initial environmental parameters according to the building information modeling technology to generate a preliminary digital three-dimensional model of the workshop.

3. The method for constructing a digital workshop based on multi-source data according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire the operating status data of the workshop equipment; Step S22: Inputting the workshop equipment operation status data into the preliminary digital three-dimensional model of the workshop to perform numerical workshop construction analysis and generate output data of the digital construction model of the workshop; Step S23: The output data of the digital construction model of the workshop is used to perform a dynamic simulation of the interaction between equipment and the environment, and the resource consumption and environmental changes of the workshop are evaluated to obtain preliminary evaluation data.

4. The method for constructing a digital workshop based on multi-source data according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: simulating equipment operation efficiency based on the output data of the workshop digital construction model to generate workshop simulation data; Step S232: Perform computational fluid dynamics simulation on the workshop simulation data to simulate temperature and humidity changes, pollutant diffusion paths, and noise source propagation, generating multi-dimensional equipment and environment simulation data; Step S233: Calculate the working condition energy consumption based on the multi-dimensional equipment and environment simulation data to generate energy consumption assessment data; perform a workshop environmental impact assessment based on the multi-dimensional equipment and environment simulation data to generate environmental quality assessment data; perform a computing resource and space resource utilization efficiency assessment based on the multi-dimensional equipment and environment simulation data to generate comprehensive workshop resource optimization data.

5. The method for constructing a digital workshop based on multi-source data according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Optimizing the preliminary digital three-dimensional model of the workshop based on the preliminary evaluation data to generate comprehensive workshop evaluation data; Step S32: Evaluate the comprehensive evaluation data of the workroom using the preset workroom evaluation criteria. If the evaluation results show that the energy consumption is too high or the equipment operation is unstable, adjust the spatial layout, equipment configuration, and energy usage of the workroom to generate optimization adjustment suggestions for the workroom; Step S33: Based on the workshop optimization adjustment suggestions, the preliminary digital three-dimensional model of the workshop is finally optimized to generate an optimized digital model of the workshop construction.

6. The method for constructing a digital workshop based on multi-source data according to claim 1, characterized in that: The adjustments to the workshop's spatial layout, equipment configuration, and energy usage include the following: Based on energy consumption assessment data, equipment that is overloaded or inefficiently operating is marked, and load adjustment or shutdown maintenance instructions are generated to obtain preliminary energy optimization suggestions; Optimize the overall layout of ventilation facilities based on environmental quality assessment data, implement additional sound insulation materials in areas with high noise pollution, and generate equipment area optimization recommendations; Optimize peak call analysis of power usage period based on comprehensive resource optimization data during work periods and generate peak power usage analysis data; Based on the comprehensive resource optimization data of the workshop, the cooling water pipeline layout is analyzed to generate cooling water pipeline analysis data; based on the peak power usage analysis data and the cooling water pipeline analysis data, the optimal resource allocation call analysis and processing are performed to generate the optimal recommendations for workshop resource allocation.

7. A digital workshop construction system based on multi-source data, characterized in that: For executing the method for constructing a digital workshop based on multi-source data according to claim 1, the digital workshop construction system based on multi-source data comprises: The environmental data acquisition and digital modeling module is used to collect air quality, temperature, humidity, and noise environmental monitoring data in the workshop using intelligent environmental sensors and record initial environmental parameters. Based on the initial environmental parameters, a preliminary digital 3D model of the workshop is created using building information modeling technology. The equipment status monitoring and dynamic simulation module is used to collect the operating status data of each device in the workshop and input the operating status data of the equipment in the workshop into the preliminary digital 3D model of the workshop. The simulation software is used to dynamically simulate the interaction between the equipment and the environment, evaluate the impact of workshop resource consumption and environmental changes, and obtain preliminary evaluation data; The optimization algorithm and model adjustment module is used to optimize the preliminary digital 3D model of the workshop based on the preliminary assessment data. If the assessment results show that the energy consumption is too high or the equipment operation is unstable, the spatial layout, equipment configuration and energy use of the workshop will be adjusted to generate an optimized digital model of the workshop construction; The real-time monitoring and dynamic feedback optimization module is used to integrate the digital model of workshop construction optimization 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.

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