Intelligent management system based on digital twinning technology

Abstract

The invention provides an intelligent management system based on a digital twinborn technology, and the system comprises a data collection unit, a digital twinborn construction unit, a pollution source detection unit, a diffusion simulation unit, and a risk assessment unit. The digital twinborn construction unit can construct a digital twinborn model corresponding to the monitoring space, so that the real-time change of the monitoring space can be simulated in the digital twinborn model, the pollution source detected by the pollution source detection unit is updated to the digital twinborn model, and pollution source diffusion simulation is performed in the digital twinborn model through the diffusion simulation unit. Finally, the pollution risk of the personnel is judged according to the distribution, the moving track and the like of the personnel in the monitoring space, so that early warning and risk avoidance are carried out in time, the personnel are prevented from being exposed to a pollution source for a long time, the safety and health of the personnel are guaranteed, and intelligent management is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart environment management, and particularly relates to a smart management system based on digital twin technology. BACKGROUND

[0002] In closed or semi-closed spaces such as factories, chemical plants, and storage areas, environmental safety management faces key problems such as strong concealment of pollution sources, complex diffusion paths, and lagging risk early warning. Current environmental management of such spaces relies on traditional sensors to collect instantaneous environmental parameters and combines manual inspection to identify pollution sources. However, traditional sensors can only capture quantitative instantaneous data and are difficult to monitor the long-term cumulative impact of low-concentration pollutants on the environment. Manual inspection has the defects of coverage blind spots and low response efficiency, which can easily lead to the neglect of environmental change trends and the difficulty in identifying pollution source leaks in the early stage, thereby increasing the risk of personnel exposure and subsequent disposal difficulty.

[0003] With the application of digital twin technology in the field of environmental management, some systems have been able to build a space twin model based on sensor and camera data, trying to achieve pollution source diffusion simulation and risk assessment. However, existing systems still have significant limitations. In the pollution diffusion simulation stage, a fixed parameter fluid mechanics model is used, which does not adapt to the dynamic changes of environmental conditions in the monitoring space in real time, resulting in a large deviation between the simulated pollution diffusion path and the actual trajectory, and making it impossible to accurately predict the pollution impact range. In terms of personnel risk assessment, only the real-time position of personnel is simply compared with the pollution area, without considering the personnel movement trajectory, the decay law of pollution concentration over time, and the toxicity threshold differences of different pollutants, making the risk assessment result rough and difficult to push accurate early warning information to different personnel. Ultimately, the personnel risk response is lagging, and there is still a high exposure safety risk. SUMMARY

[0004] In view of this, the present application proposes a smart management system based on digital twin technology, which can combine digital twin models to simulate pollution source diffusion and assess risks, thereby prompting personnel to avoid exposure to pollution sources and avoid safety hazards.

[0005] The technical solution of the present application is as follows: The smart management system based on digital twin technology comprises: a data acquisition unit for acquiring environmental parameter data and image data in a monitoring space; a digital twin construction unit for constructing a digital twin model that is a twin of the monitoring space according to the environmental parameter data and the image data; a pollution source detection unit for determining whether a pollution source exists according to the environmental parameter data and the image data, and determining the position coordinates, concentration, and type of the pollution source; a diffusion simulation unit configured to simulate a diffusion path of the pollution source in the digital twin model; a risk assessment unit configured to assess a pollution risk faced by the personnel on the diffusion path of the pollution source; The digital twin construction unit is in data connection with the data acquisition unit, the diffusion simulation unit, and the risk assessment unit, respectively, and the pollution source detection unit is in data connection with the data acquisition unit and the diffusion simulation unit, respectively.

[0006] Preferably, the execution steps of the data acquisition unit include: Step S11, collecting environmental parameter data through a network of temperature and humidity sensors, air pressure sensors, and gas sensors deployed in the monitoring space; Step S12, filtering, denoising, and unifying the collected environmental parameter data; Step S13, collecting continuous video streams as image data through panoramic cameras deployed in the monitoring space; Step S14, compressing and encoding the image data and labeling it with a uniform timestamp and spatial location tag together with the environmental parameter data.

[0007] Preferably, the execution steps of the digital twin construction unit include: Step S21, analyzing the image data, generating a geometric model of the monitoring space through a three-dimensional reconstruction algorithm, and identifying and locating internal facilities, obstacles, and ventilation openings in the monitoring space; Step S22, obtaining and determining the spatial positions of the surface materials and environmental parameter data of the inside and outside of the building in the monitoring space in the geometric model, and mapping them; Step S23, performing computational fluid dynamics grid division on the geometric model, grid encryption in the potential pollution source area and personnel activity area, and instantiating the geometric model into a real-time simulated digital twin model.

[0008] Preferably, the specific steps of Step S22 for obtaining and determining the surface materials of the inside and outside of the building in the monitoring space are as follows: Step S221, obtaining construction planning maps of each building in the monitoring space, and obtaining the surface materials of the inside and outside of the building through the construction planning maps; Step S222, extracting building damage locations from the management log of the monitoring space, and determining whether the building damage locations have repair records; Step S223, when there are repair records, updating the surface materials of the corresponding positions of the inside and outside of the building based on the repair materials recorded in the repair records; Step S224, when there are no repair records, extracting the building materials used in the building damage locations based on the construction planning maps, and updating the surface materials based on the building materials.

[0009] Preferably, the execution step of the pollution source detection unit comprises: Step S31, setting a threshold range for each type of pollutant, comparing the environmental parameter data with the threshold range, and judging the position, concentration and type of the suspected pollution source based on the comparison result; Step S32, reading image data near the position of the suspected pollution source, and identifying whether there is smoke, steam or leakage trace through a target detection algorithm; Step S33, if the identification result is consistent with the suspected pollution source in spatial position and time, it is judged that a pollution source has occurred, and the position coordinates, concentration and type of the pollution source are recorded.

[0010] Preferably, the specific steps of the diffusion simulation unit comprise: Step S41, setting the pollution source as a release source term in the digital twin model, and mapping the position coordinates of the pollution source to the digital twin model; Step S42, taking the environmental parameter data as the initial dynamic boundary condition, and simulating the diffusion process of the pollution source in the digital twin model based on fluid mechanics; Step S43, constructing the position change of the pollution cloud cluster during the diffusion process into a diffusion path, and marking different colors based on the concentration during the diffusion process.

[0011] Preferably, the specific steps of simulating the diffusion process of the pollutant in the digital twin model based on fluid mechanics in step S42 are: Extracting wind speed, temperature and air pressure data from the environmental parameter data as initial conditions, and constructing a three-dimensional convection diffusion equation:

[0012] wherein is the concentration of the pollutant, t is the time, (u, v, w) is the wind speed component, D is the diffusion coefficient, S is the source term intensity, and x, y, z are spatial coordinates; Solving the three-dimensional convection diffusion equation using a numerical algorithm, converting the continuous space into discrete point calculation, and iteratively calculating according to the preset time step, and updating the concentration distribution at intervals; When the calculation result area is stable, output the dynamic diffusion process.

[0013] Preferably, the specific steps of the risk assessment unit comprise: Step S51, obtaining the real-time coordinate position of the personnel in the monitoring space through the UWB positioning tag or the wifi connection strength, and constructing the personnel movement trajectory according to the real-time coordinate position; Step S52, superimposing the personnel movement trajectory into the digital twin model for simulation, and obtaining the exposure time and exposure concentration of the personnel in the diffusion path; Step S53: Calculate the cumulative inhalation dose of personnel based on exposure time and exposure concentration, and assess the pollution risk.

[0014] Preferably, it also includes an early warning unit, which is used to issue early warnings to relevant personnel based on the degree of pollution risk, and at the same time generate avoidance or evacuation routes and push them to the relevant personnel. The early warning unit is connected to the risk assessment unit for data.

[0015] Preferably, it also includes a biomarker assessment unit for assessing environmental changes within the monitoring space based on the growth status data of mosses or lichens, and the specific steps include: Step S61: Acquire remote sensing image data of the monitored space over a period of time, and identify and extract moss or lichen areas based on the remote sensing image data; Step S62: Calculate the biosphere area of ​​the moss or lichen area using the grid method, and compare the biosphere area to obtain the area change trend; Step S63: Compare the color changes of the moss or lichen area before and after, and obtain the color change trend based on the color changes before and after. Step S64: Input the area change trend and color change trend into the digital twin model; The biometrics assessment unit is connected to the digital twin construction unit via data.

[0016] Compared with the prior art, the beneficial effects of the present invention are: The intelligent management system based on digital twin technology of the present invention is used for monitoring and managing spatial pollution. After collecting corresponding environmental parameter data and image data, a digital twin model corresponding to the monitoring space can be constructed to simulate the actual monitoring space. Then, pollution sources are detected in the monitoring space. When a pollution source is detected, it is updated in real time to the digital twin model, and the diffusion path of the pollution source is simulated in the digital twin model to determine the specific change path of the pollution source in the monitoring space and the corresponding concentration changes. Then, based on the specific location and movement trend of personnel in the monitoring space, the risk of their exposure to pollution sources is assessed, thereby providing risk warnings, predicting and responding in advance, avoiding personnel from being harmed by toxic gases, and realizing intelligent management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1A schematic diagram of the intelligent management system based on digital twin technology of the present application; Figure 2 An execution step diagram of the data acquisition unit of the intelligent management system based on digital twin technology of the present application; Figure 3 An execution step diagram of the digital twin construction unit of the intelligent management system based on digital twin technology of the present application; Figure 4 A specific step diagram of step S22 of the intelligent management system based on digital twin technology of the present application; Figure 5 An execution step diagram of the pollution source detection unit of the intelligent management system based on digital twin technology of the present application; Figure 6 An execution step diagram of the diffusion simulation unit of the intelligent management system based on digital twin technology of the present application; Figure 7 An execution step diagram of the risk assessment unit of the intelligent management system based on digital twin technology of the present application; Figure 8 An execution step diagram of the biological indicator assessment unit of the intelligent management system based on digital twin technology of the present application; In the figure, 1, data acquisition unit; 2, digital twin construction unit; 3, pollution source detection unit; 4, diffusion simulation unit; 5, risk assessment unit; 6, early warning unit; 7, biological indicator assessment unit. DETAILED DESCRIPTION

[0019] In order to better understand the technical content of the present application, a specific embodiment is provided below, and the present application is further described in conjunction with the accompanying drawings.

[0020] Referring to Figures 1 to 8 The intelligent management system based on digital twin technology provided by the present application comprises: A data acquisition unit for acquiring environmental parameter data and image data in a monitoring space; A digital twin construction unit for constructing a digital twin model of the monitoring space according to the environmental parameter data and the image data; A pollution source detection unit for determining whether a pollution source exists and determining the position coordinates, concentration and type of the pollution source according to the environmental parameter data and the image data; A diffusion simulation unit for simulating the diffusion path of the pollution source in the digital twin model; A risk assessment unit for assessing the pollution risk faced by personnel on the diffusion path of the pollution source; The digital twin construction unit is respectively connected with the data acquisition unit, the diffusion simulation unit and the risk assessment unit, and the pollution source detection unit is respectively connected with the data acquisition unit and the diffusion simulation unit.

[0021] The intelligent management system based on the digital twin technology is used for environmental monitoring management of special places such as factories and laboratories, specifically dynamic monitoring of pollution sources. Traditional monitoring mostly detects real-time pollution sources in the monitoring space, but does not simulate the diffusion of pollution sources, and cannot evaluate the diffusion process of pollution sources, so as to timely regulate the corresponding personnel to avoid risks. The present application introduces a digital twin model for simulation. Firstly, the data acquisition unit collects environmental parameter data and image data in the monitoring space. The environmental parameter data includes atmospheric data such as air temperature and humidity, wind speed, air pressure and other data. The image data can be directly collected by the monitoring camera in the monitoring space, and can collect personnel data and building structure data in the monitoring space. On the other hand, it can also monitor the changes of gas, such as the flow of colored gas or smoke. After collecting the environmental parameter data and image data, the digital twin construction unit can construct a digital twin model. The digital twin model is a consistent model with the monitoring space, including the corresponding building structure, corresponding channels and equipment, etc. The digital twin model can be updated according to the real-time collected environmental parameter data and image data, that is, a real-time simulation model is constructed.

[0022] The pollution source detection unit is used to detect whether there is a pollution source in the monitoring space. It is mostly installed in the pollution accumulation area of the monitoring space. If pollution leakage occurs, the pollution source detection unit can determine the specific position, concentration and type of the pollution source. Then the diffusion simulation unit can simulate the diffusion in the digital twin model according to the position coordinates, concentration and type of the pollution source, judge and obtain the diffusion path of the pollution source. If there are personnel working on the diffusion path, they will be affected by the pollution source. Therefore, the risk assessment unit can evaluate the pollution risk faced by the personnel in the digital twin model. Based on the pollution risk, a warning signal can be sent to the corresponding personnel to evacuate in time to avoid risks and avoid long-term exposure to pollution sources that may cause safety hazards. By introducing the digital twin model for simulation, the risk can be avoided in advance after the pollution source leaks, and the intelligent management of the monitoring space can be realized.

[0023] Preferably, the execution steps of the data acquisition unit include: Step S11, collecting environmental parameter data by deploying a network of temperature and humidity sensors, air pressure sensors and gas sensors in the monitoring space; Step S12, filtering, denoising and unifying the collected environmental parameter data; Step S13, a continuous video stream is collected by a panoramic camera deployed in the monitoring space as image data; Step S14, the image data is compressed and encoded, and is labeled with a uniform timestamp and spatial position with the environmental parameter data.

[0024] The sensor network is used to collect atmospheric data, and the building structure design of the monitoring space can also be collected through building planning design, and data such as overhead layers, passages and equipment in the building are obtained, and image data is collected through the panoramic camera erected in the monitoring space, and the environmental parameter data and the image data are labeled after preprocessing to ensure the time alignment of the two data.

[0025] Preferably, the execution steps of the digital twin construction unit include: Step S21, the image data is parsed, a geometric model of the monitoring space is generated by a three-dimensional reconstruction algorithm, and internal facilities, obstacles and air vents in the monitoring space are identified and positioned; Step S22, the surface material and environmental parameter data of the inside and outside of the building in the monitoring space are obtained and determined in the corresponding space position in the geometric model, and are mapped; Step S23, the geometric model is meshed by computational fluid dynamics, and the grid is encrypted in the potential area of the pollution source and the personnel activity area, and the geometric model is instantiated as a real-time simulated digital twin model.

[0026] The image data will contain building parameters and other data in the monitoring space. After the image data is parsed in combination with the construction planning diagram, a geometric model can be constructed using a three-dimensional reconstruction algorithm. The geometric model not only ensures the basic building structure, but also adds internal facilities, obstacles and air vents in the monitoring space to ensure that the diffusion path of the pollution source can be accurately obtained subsequently. When the pollutant diffuses, the different materials of the building surface will cause different degrees of adsorption of the pollution source. Therefore, the surface material of the inside and outside of the building in the monitoring space needs to be determined and mapped to the geometric space. The subsequent geometric space needs to be studied for pollutant diffusion, so the geometric model is meshed by computational fluid dynamics to discretize the continuous three-dimensional space into a large number of small, non-overlapping units, and the grid in the potential area of the pollution source and the personnel activity area is encrypted to maximize the accuracy and reliability of the simulation results. Finally, the geometric model is converted into a digital twin model corresponding to the monitoring space for real-time simulation analysis.

[0027] Preferably, the specific steps of step S22 for obtaining and determining the surface material of the inside and outside of the building in the monitoring space are as follows: Step S221, obtain the construction planning diagram of each building in the monitoring space, and obtain the surface material of the inside and outside of the building through the construction planning diagram; Step S222, extracting the building damage position from the management log of the monitoring space, and judging whether the building damage position has a repair record; Step S223, when there is a repair record, updating the surface material of the corresponding position inside and outside the building based on the repair material recorded in the repair record; Step S224, when there is no repair record, extracting the building material used by the building damage position based on the construction planning map, and updating the surface material based on the building material.

[0028] The surface materials inside and outside the building when the building is initially built can be determined through the construction planning map. In the long-term use, the inside and outside will be damaged or peeled off to different degrees. The staff in the monitoring space will repair it. The repair causes the material to change, so the surface material needs to be updated. First, the building damage position is queried from the management log of the monitoring space. It is judged whether the building damage position has a repair record. If there is a repair record, the digital twin model can be updated according to the specific material recorded in the repair record. If there is no repair record, the building material is directly exposed at the damage position. The building material also has an adsorption effect on the polluted gas. Therefore, according to the construction planning map, the building material can be updated to the surface material.

[0029] Preferably, the execution step of the pollution source detection unit comprises: Step S31, setting the threshold range of each type of pollutant, comparing the environmental parameter data with the threshold range, and judging the position, concentration and type of the suspected pollution source based on the comparison result; Step S32, reading the image data near the position of the suspected pollution source, and identifying whether there is smoke, steam or leakage trace through a target detection algorithm; Step S33, if the identification result coincides with the suspected pollution source in space position and time, it is judged that the pollution source appears, and the position coordinates, concentration and type of the pollution source are recorded.

[0030] The types and concentrations of various gases are included in the environmental parameter data. When compared with the preset threshold range, it can be judged whether the concentration of the gas exceeds the threshold range. If it exceeds, it can be judged as a suspected pollution leak, and the initial position, concentration and type are recorded. Then the image data collected by the camera near the position of the suspected pollution source is called to judge whether there is image data of gas leakage, such as smoke, color, steam or leakage trace, etc. According to the comparison result of the environmental parameter data and the image recognition result, the space-time judgment is made to further determine whether the pollution source appears, and the specific position coordinates, concentration and type of the pollution source are recorded.

[0031] Preferably, the specific steps of the diffusion simulation unit comprise: Step S41, set the pollution source as a release source item in the digital twin model, and map the pollution source to the digital twin model according to the position coordinates of the pollution source; Step S42, simulate the diffusion process of the pollution source in the digital twin model based on the environmental parameter data as initial dynamic boundary conditions; Step S43, construct the diffusion path of the position change of the pollution cloud in the diffusion process, and mark different colors based on the different concentrations in the diffusion process.

[0032] After the diffusion simulation unit sets the pollution source as a release source item in the digital twin model, the specific position coordinates of the pollution source are mapped to the digital twin model, and then the initial dynamic boundary conditions are constructed, including the specific structure of the building, the channel, the equipment, the surface material inside and outside the building, etc., and then the fluid mechanics is introduced to simulate the diffusion process of the pollution source, wherein the range of the pollution source can be simulated as a pollution cloud, and the positions of different colors in the pollution cloud represent different concentrations. After the diffusion path is visualized, the range of the pollution source and the concentration of the corresponding position can be quickly confirmed by the staff.

[0033] Preferably, the specific steps of simulating the diffusion process of the pollution source in the digital twin model based on fluid mechanics in step S42 are as follows: Extract wind speed, temperature and air pressure data from the environmental parameter data as initial conditions to construct a three-dimensional convection diffusion equation:

[0034] Wherein is the concentration of the pollutant, t is the time, (u, v, w) is the wind speed component, D is the diffusion coefficient, S is the source item intensity, and x, y, z are spatial coordinates; Solve the three-dimensional convection diffusion equation by using a numerical algorithm, convert the continuous space into discrete point calculation, and update the concentration distribution at intervals according to the preset time step; When the calculation result area is stable, output the dynamic diffusion process.

[0035] In order to accurately obtain the diffusion path of the pollution source, the three-dimensional convection diffusion equation is introduced, the concentration of the pollutant, the wind speed and the diffusion coefficient are calculated, then the continuous space of the three-dimensional convection diffusion equation is converted into discrete points for calculation by using a numerical algorithm, and the concentration distribution is updated every 0.5-2 seconds according to the preset time step, and finally when the calculation result tends to be stable, for example, the concentration difference between adjacent two steps is less than 3%, the dynamic diffusion process is output.

[0036] Preferably, the specific steps of the risk assessment unit include: Step S51, obtain the real-time coordinate position of the personnel in the monitoring space through the UWB positioning tag or the wifi connection strength, and construct the personnel movement trajectory according to the real-time coordinate position; Step S52, superimpose the personnel movement trajectory into the digital twin model for simulation, and obtain the exposure time and exposure concentration of the personnel in the diffusion path; Step S53, calculate the cumulative inhalation dose of the personnel according to the exposure time and exposure concentration, and evaluate the pollution risk.

[0037] When performing risk assessment, first, the positions of all personnel in the monitoring space need to be determined, the real-time coordinate position of the personnel can be determined through the set UWB positioning tag or wifi connection strength, then the personnel movement trajectory can be constructed according to the change of the real-time coordinate position, the personnel movement trajectory is superimposed into the digital twin model for simulation, when the personnel movement trajectory overlaps with the diffusion path, it means that the corresponding personnel will be exposed to the pollution source as time changes, the exposure time and exposure concentration of the personnel are extracted, and the cumulative inhalation dose of the personnel is evaluated, and finally the pollution risk can be evaluated according to the cumulative inhalation dose, the pollution risk can be divided into low, medium and high, different early warnings and risk avoidance can be performed according to different pollution risks.

[0038] Preferably, it further comprises a warning unit for warning the corresponding personnel according to the degree of pollution risk, and generating a risk avoidance or evacuation path and pushing it to the corresponding personnel, the warning unit is in data connection with the risk evaluation unit.

[0039] The warning unit can perform different degrees of warning according to the pollution risk, thereby prompting the corresponding personnel, and a risk avoidance or evacuation path can also be generated to guide the corresponding personnel to avoid risks.

[0040] Preferably, it further comprises a biological index evaluation unit for evaluating the environmental change in the monitoring space based on the growth state data of moss or lichen, and the specific steps comprise: Step S61, obtain remote sensing image data of the monitoring space in a period of time, and identify and extract the moss or lichen area based on the remote sensing image data; Step S62, calculate the biological area by using the grid method on the moss or lichen area, and obtain the area change trend by comparing the biological area; Step S63, compare the color change of the moss or lichen area before and after, and obtain the color change trend based on the color change before and after; Step S64, input the area change trend and the color change trend into the digital twin model; The biological index evaluation unit is in data connection with the digital twin construction unit.

[0041] In order to ensure that the digital twin model can accurately simulate the environment in the monitoring space and manage, a biological index evaluation unit is also set, which evaluates the environmental change trend in the monitoring space by introducing the change of moss or lichen, and superimposes the environmental change trend into the digital twin model, dynamically calibrates the basic parameters of the digital twin model, corrects the deviation caused by time elapse, ensures the accuracy of diffusion simulation, at the same time, supplements the long-term environmental change information that the sensor is difficult to capture, makes the digital twin model not only reflect the instantaneous state, but also embodies the cumulative characteristics, provides evolution logic for the digital twin model, supports predictive simulation of the future state of the environment, makes the risk assessment more forward-looking, and finally improves the accuracy of management decision.

[0042] And the environmental change is determined by the change of moss or lichen, moss and lichen have high requirements for the environment, if the pollution degree is large, moss and lichen will gradually decrease, and the color will also appear difference, therefore, the area change and color change of moss or lichen can be used for calibration of the digital twin model, after collecting remote sensing images in a period of time, the moss or lichen area can be analyzed and extracted, and then the area change and color change of the biological area before and after the period of time can be compared to obtain the area change trend and color change trend respectively, the area change trend and color change trend can be used as the input of the digital twin model to realize calibration and supplement.

[0043] The above only describes the preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A smart management system based on digital twin technology, characterized in that, The method comprises the following steps: A data acquisition unit is used to acquire environmental parameter data and image data in a monitoring space; A digital twin construction unit is used to construct a digital twin model of the monitoring space according to the environmental parameter data and the image data; A pollution source detection unit is used to determine whether a pollution source exists according to the environmental parameter data and the image data, and to determine the position coordinates, concentration and type of the pollution source; A diffusion simulation unit is used to simulate the diffusion path of the pollution source in the digital twin model; A risk assessment unit is used to assess the pollution risk faced by personnel on the diffusion path of the pollution source. The digital twin construction unit is connected to the data acquisition unit, the diffusion simulation unit and the risk assessment unit, and the pollution source detection unit is connected to the data acquisition unit and the diffusion simulation unit. 2.The smart management system based on digital twin technology according to claim 1, characterized in that, The execution steps of the data acquisition unit include: Step S11: Collecting environmental parameter data through a network of temperature and humidity sensors, air pressure sensors and gas sensors deployed in the monitoring space; Step S12: Filtering, denoising and unifying the collected environmental parameter data; Step S13: Collecting continuous video streams as image data through panoramic cameras deployed in the monitoring space; Step S14: Compressing and encoding the image data, and labeling it with a uniform timestamp and spatial position label together with the environmental parameter data. 3.The smart management system based on digital twin technology of claim 1, wherein, The execution steps of the digital twin construction unit include: Step S21: Analyzing the image data, generating a geometric model of the monitoring space through a three-dimensional reconstruction algorithm, and identifying and locating internal facilities, obstacles and ventilation openings in the monitoring space; Step S22: Obtaining and determining the spatial positions of the surface materials and environmental parameter data of the inside and outside of the buildings in the monitoring space in the geometric model, and mapping them; Step S23: Performing computational fluid dynamics grid division on the geometric model, encrypting the grid in the potential area of the pollution source and the personnel activity area, and instantiating the geometric model into a real-time simulated digital twin model. 4.The smart management system based on digital twin technology of claim 3, wherein, The specific steps for obtaining and determining the surface materials of the inside and outside of the buildings in the monitoring space in step S22 are as follows: Step S221: Obtaining the construction planning drawings of each building in the monitoring space, and obtaining the surface materials of the inside and outside of the buildings through the construction planning drawings; Step S222: Extracting the building damage locations from the management log of the monitoring space, and determining whether there are repair records for the building damage locations; Step S223: When there are repair records, updating the surface materials of the corresponding positions of the inside and outside of the buildings based on the repair materials recorded in the repair records; Step S224: When there are no repair records, extracting the building materials used at the building damage locations based on the construction planning drawings, and updating the surface materials based on the building materials. 5.The smart management system based on digital twin technology of claim 1, wherein, The execution steps of the pollution source detection unit include: Step S31: Setting threshold ranges for various types of pollutants, comparing the environmental parameter data with the threshold ranges, and determining the position, concentration and type of the suspected pollution source based on the comparison results; Step S32: Reading the image data near the position of the suspected pollution source, and identifying whether there are smoke, steam or leakage traces through a target detection algorithm. Step S33, if the identification result matches the suspected pollution source in spatial position and time, it is judged that a pollution source appears, and the position coordinates, concentration and type of the pollution source are recorded. 6.The smart management system based on digital twin technology of claim 1, wherein, The specific steps of the diffusion simulation unit include: Step S41, setting the pollution source as a release source item in the digital twin model, and mapping the pollution source to the digital twin model according to the position coordinates of the pollution source; Step S42, taking the environmental parameter data as the initial dynamic boundary condition, simulating the diffusion process of the pollution source in the digital twin model based on fluid mechanics; Step S43, constructing the position change of the pollution cloud in the diffusion process into a diffusion path, and marking different colors based on the concentration in the diffusion process. 7.The smart management system based on digital twin technology of claim 6, wherein, The specific steps of simulating the diffusion process of the pollutant in the digital twin model based on fluid mechanics in step S42 are: Extracting wind speed, temperature and pressure data from the environmental parameter data as initial conditions to construct a three-dimensional convection diffusion equation: wherein C is the pollutant concentration, t is time, (u, v, w) are wind velocity components, D is the diffusion coefficient, S is the source term strength, and x, y, z are spatial coordinates. Solving the three-dimensional convection diffusion equation by numerical algorithm, converting the continuous space into discrete point calculation, and iterating calculation according to the preset time step, and updating the concentration distribution at intervals; When the calculation result area is stable, output the dynamic diffusion process. 8.The smart management system based on digital twin technology of claim 1, wherein, The specific steps of the risk assessment unit include: Step S51, obtaining the real-time coordinate position of the personnel in the monitoring space through the UWB positioning tag or the wifi connection strength, and constructing the personnel movement trajectory according to the real-time coordinate position; Step S52, superimposing the personnel movement trajectory into the digital twin model for simulation, obtaining the exposure time and exposure concentration of the personnel in the diffusion path; Step S53, calculating the cumulative inhalation dose of the personnel according to the exposure time and exposure concentration, and evaluating the pollution risk. 9.The smart management system based on digital twin technology of claim 1, wherein, It also includes a warning unit for warning the corresponding personnel according to the degree of pollution risk, and generating a risk avoidance or evacuation path to push to the corresponding personnel, the warning unit is data connected with the risk assessment unit. 10.The smart management system based on digital twin technology of claim 1, wherein, It also includes a biological indicator evaluation unit for evaluating the environmental change in the monitoring space based on the growth state data of moss or lichen, and the specific steps include: Step S61, obtaining remote sensing image data of the monitoring space within a period of time, identifying and extracting the moss or lichen area based on the remote sensing image data; Step S62, calculating the biological area by grid method on the moss or lichen area, and comparing the biological area to obtain the area change trend; Step S63, comparing the color change of the moss or lichen area before and after, and obtaining the color change trend based on the color change before and after; Step S64, inputting the area change trend and color change trend into the digital twin model; The biological indicator evaluation unit is data connected with the digital twin construction unit.

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