Emergency plan simulation method and system based on digital twinborn technology

Abstract

The invention discloses an emergency plan simulation method and system based on a digital twinborn technology, and the method comprises the steps: obtaining the physical parameters of a culture exhibition hall, the monitoring data of human traffic, and the environmental data; constructing a digital twinborn model of the cultural exhibition hall based on the physical parameters, and mapping the human traffic monitoring data into the digital twinborn model to obtain a target model; inputting the environment data into the target model to obtain a first parameter and a second parameter; according to the first parameter and the second parameter, determining a human traffic safety threshold value corresponding to each area in the cultural exhibition hall; when the human traffic of the target area exceeds a human traffic safety threshold corresponding to the target area, simulating an emergency scene through an artificial intelligence algorithm according to the human traffic of the target area and the target model to obtain a simulation result; and generating an emergency plan according to a simulation result. The emergency management efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of digital twin technology, and specifically relates to an emergency plan simulation method and system based on digital twin technology. Background Art

[0002] With the continuous expansion of the scale of cultural exhibition halls and the enrichment of exhibition content, cultural exhibition halls have gradually become important places for people to relax, entertain and acquire knowledge. However, the density and distribution of the number of people in cultural exhibition halls not only affect the experience of visitors, but also pose higher requirements for the safety management of the exhibition halls. Especially during holidays or specific events, the gathering of a large number of people may trigger emergencies such as congestion, stampedes, and fires. In order to effectively respond to emergencies and ensure public safety, many institutions and organizations have begun to adopt digital twin technology to monitor, predict and respond to various emergencies in real time. Especially in emergency plans, digital twin technology can help obtain real-time pedestrian flow and environmental data of the venue, and perform dynamic adjustment and early warning.

[0003] In the prior art, the management systems of some cultural exhibition halls, museums and other places have introduced digital twin technology to improve the efficiency and safety of venue management. Among them, digital twin technology mainly presents the operating state of the venue in a digital form by virtualizing the modeling of physical entities, environmental conditions and pedestrian flow distribution in the venue, and realizes real-time monitoring and analysis of the dynamic changes inside the venue. It can judge the degree of congestion in the venue to a certain extent, and build emergency scenarios based on historical data or simulation data to provide decision-making support for venue managers.

[0004] However, when the prior art conducts emergency simulation, it relies on a rule base or model of known event types to directly generate an emergency plan, and lacks effective data support for event scenarios that have not occurred or have not been fully recorded (such as environmental anomalies). Therefore, the system cannot simulate the development process of emergencies, resulting in the inability to provide corresponding emergency plans in a timely manner, which affects the efficiency of emergency management. Summary of the Invention

[0005] This application provides an emergency plan simulation method and system based on digital twin technology, which is used to solve the problem that the prior art cannot simulate the development process of emergencies, resulting in the inability to provide corresponding emergency plans in a timely manner, and improves the efficiency of emergency management.

[0006] In the first aspect of this application, an emergency plan simulation method based on digital twin technology is provided, which is applied to a server. The method includes: Obtain the physical parameters, pedestrian flow monitoring data, and environmental data of the cultural exhibition hall; construct a digital twin model of the cultural exhibition hall based on the physical parameters, and map the pedestrian flow monitoring data into the digital twin model to obtain the target model; input the environmental data into the target model to obtain the first parameter and the second parameter. The first parameter is the influence parameter of the environmental data on the pedestrian flow distribution in the pedestrian flow monitoring data, and the second parameter is the feedback influence parameter of the pedestrian flow distribution on the environmental data; calculate the pedestrian flow safety threshold corresponding to each area in the cultural exhibition hall according to the first parameter and the second parameter. The pedestrian flow safety threshold is the upper limit of the pedestrian flow that can be carried under the current environment; when the pedestrian flow in the target area exceeds the pedestrian flow safety threshold corresponding to the target area, according to the pedestrian flow in the target area and the target model, simulate the emergency scenario through the artificial intelligence algorithm to obtain the simulation result. The target area is any area in the cultural exhibition hall; generate an emergency plan according to the simulation result.

[0007] Optionally, calculating the pedestrian flow safety threshold of the pedestrian flow data in the current environment according to the first parameter and the second parameter specifically includes: Calculate the pedestrian flow safety threshold of the pedestrian flow data in the current environment through the first calculation formula according to the first parameter and the second parameter; the first calculation formula is: Among them, N max is the pedestrian flow safety threshold, is the pedestrian flow carrying capacity of the target area, A is the area of the target area, P is the safety occupancy area required per person in the target area, [1-(ω 1 ×E + ω 2 ×D)] is the dynamic correction factor, ω 1 is the influence parameter of the environmental data on the pedestrian flow distribution in the pedestrian flow monitoring data, E is the environmental pressure parameter, ω 2 is the feedback influence parameter of the pedestrian flow distribution on the environmental data, D is the pedestrian flow density parameter, is the evacuation ability correction factor, R is the safe evacuation ability per unit time, T is the safe evacuation time, N is the pedestrian flow of the area, g(C,L) is the evacuation passage correction factor, C is the width of the evacuation passage, and L is the length of the evacuation passage.

[0008] Optionally, when the pedestrian flow in the target area in the pedestrian flow monitoring data exceeds the pedestrian flow safety threshold corresponding to the target area, according to the pedestrian flow in the target area and the target model, simulate the emergency scenario through the artificial intelligence algorithm to obtain the simulation result, specifically including: Obtain a historical emergency database. Based on the historical emergency database, the change in the number of people flow, and the environmental data of the target area, calculate the correlation degree between the current emergency and the historical emergencies through a preset similarity matching algorithm; determine the type of the emergency according to the correlation degree; according to the type of the emergency and the target model, simulate the dynamic scenario in the target area after the occurrence of the emergency through an artificial intelligence algorithm; obtain the change data of the target area through the dynamic scenario and the target model, where the change data includes the change data of the number of people flow and the change data of the environment; perform predictive analysis on the change data to obtain the simulation result.

[0009] Optionally, calculating the correlation degree between the current emergency and the historical emergencies through a preset similarity matching algorithm specifically includes: Extract the current emergency features of the current emergency; set the similarity measurement criteria of the similarity matching algorithm according to the current emergency features; based on the similarity measurement criteria, calculate the similarity between the current emergency features and the historical emergency features in the historical emergency database to obtain the correlation degree.

[0010] Optionally, performing predictive analysis on the change data to obtain the simulation result specifically includes: Establish a time series model based on the number of people flow monitoring data and the environmental data; input the change data into the time series model for predictive analysis to obtain the simulation result.

[0011] Optionally, after generating an emergency plan according to the simulation result, the method further includes: Construct an API interface system through preset API definition rules; configure a permission authentication mechanism in the API interface system; in response to an access request from a target system, perform permission authentication on the target system; if the permission authentication is passed, allow the target system to access the API interface system and send the emergency plan to the target system.

[0012] Optionally, after generating an emergency plan according to the simulation result, the method further includes: Perform simulation verification on the emergency plan to obtain a simulation verification result; analyze the simulation verification result to obtain an analysis result, and adjust the emergency plan according to the analysis result; re-enter the adjusted emergency plan into the target model for cyclic verification until the simulation verification result meets the preset requirements and then stop the verification, and output the target emergency plan; use the target emergency plan as the final emergency plan.

[0013] In the second aspect of the present application, an emergency plan simulation system based on digital twin technology is provided, including: An acquisition module, configured to acquire the physical parameters, the number of people flow monitoring data, and the environmental data of the cultural exhibition hall; A building module for constructing a digital twin model of a cultural exhibition hall based on physical parameters and mapping the pedestrian flow monitoring data into the digital twin model to obtain a target model; An input module for inputting environmental data into the target model to obtain a first parameter and a second parameter. The first parameter is the influence parameter of the environmental data on the pedestrian flow distribution in the pedestrian flow monitoring data, and the second parameter is the feedback influence parameter of the pedestrian flow distribution on the environmental data; A calculation module for calculating the pedestrian flow safety threshold corresponding to each area in the cultural exhibition hall according to the first parameter and the second parameter. The pedestrian flow safety threshold is the upper limit of the pedestrian flow that can be carried in the current environment; A simulation module for, when the pedestrian flow in the target area exceeds the pedestrian flow safety threshold corresponding to the target area, simulating an emergency scenario through an artificial intelligence algorithm according to the pedestrian flow in the target area and the target model to obtain a simulation result. The target area is any area in the cultural exhibition hall; A generation module for generating an emergency plan according to the simulation result.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of the above.

[0015] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By combining the physical parameters of the cultural exhibition hall, the pedestrian flow monitoring data, and the environmental data, the system establishes a real-time dynamic association model in the digital twin model, which can accurately reflect the mutual relationship between the environmental conditions and the pedestrian flow distribution in the exhibition hall. By calculating the "pedestrian flow safety threshold" for each area through this model, that is, the maximum allowable pedestrian flow in each area under the current environment. When the actual pedestrian flow exceeds this threshold, the system can promptly identify and predict potential risk areas in advance. At this time, the system will simulate an emergency scenario through an artificial intelligence algorithm based on the digital twin model and analyze possible emergencies. By pre-simulating the emergency, an emergency plan is generated, so as to ensure that in the event of an emergency, an emergency response can be implemented quickly and efficiently, improving the emergency management efficiency.

[0017] 2. By comprehensively considering multiple factors such as the pedestrian flow carrying capacity of the target area, the impact of environmental data, and evacuation conditions through the first calculation formula, the safe threshold of pedestrian flow under the current environment is derived. Specifically, this calculation model combines dynamic variables such as the target area size, the safe occupancy area required per person, pedestrian flow density, environmental pressure parameters, and the correction factor of evacuation channels, and further corrects the results through elements such as evacuation time and safe evacuation capacity, effectively solving the problems in traditional methods that it is difficult to adjust the safe threshold of pedestrian flow in real time and accurately evaluate the safe capacity under different environmental conditions, and then realizing the accurate dynamic calculation of the safe threshold of pedestrian flow, ensuring the safety and reliability of the pedestrian flow management in each area under complex environments.

[0018] 3. Through the similarity matching algorithm, the correlation degree between the current emergency event and historical emergency events is quickly calculated, the type of the emergency event is accurately identified, and the dynamic scenario after the occurrence of the emergency event is simulated in combination with the target model to obtain the pedestrian flow and environmental change data of the target area. At the same time, through the time series model, the change data is predicted and analyzed to further accurately deduce the dynamic trend of the development of the emergency event. It effectively solves the problems in traditional technologies that it is impossible to quickly identify emergency events, difficult to simulate complex dynamic scenarios, and lack of trend prediction ability, significantly improves the timeliness and accuracy of emergency event response, provides a reliable basis for formulating efficient and scientific emergency plans, and greatly enhances the intelligent level of emergency event response.

[0019] 4. By extracting the characteristics of the current emergency event and setting the similarity measurement standard, the similarity between the characteristics of the current emergency event and historical emergency events can be efficiently calculated, and thus the correlation degree can be obtained. By accurately measuring the similarity of emergency event characteristics, it effectively solves the problem in traditional methods that it is impossible to quickly and accurately match the types of emergency events, resulting in event response delays, and then realizes the rapid identification of the types of emergency events, providing accurate basic data support for subsequent dynamic scenario simulation and emergency plan formulation.

[0020] 5. By introducing the preset API interface system and permission authentication mechanism, the safe and efficient distribution of emergency plans is realized. Specifically, by constructing the API interface system and configuring the permission authentication rules, when receiving the access request of the target system, its identity can be verified, and after the permission authentication is passed, the emergency plan is safely sent to the target system. It effectively solves the problems such as potential safety hazards, chaotic permission management, and low distribution efficiency that may exist in the traditional emergency plan distribution process, ensuring the data security and transmission reliability of the emergency plan during the distribution process. Further, this system also has high scalability, can adapt to the access requirements of various target systems, realize the rapid sharing and accurate distribution of emergency plans, thus significantly improving the collaborative efficiency and overall management level of emergency event response.

[0021] 6. The generated emergency plan is verified through simulation, the simulation verification results are analyzed, and the emergency plan is adjusted and optimized according to the analysis results. Then, the adjusted plan is re-input into the target model for loop verification until the simulation verification results meet the preset requirements, thereby outputting the final target emergency plan. This effectively solves the problems of inapplicability in actual execution and insufficient reliability that may be caused by the lack of verification and optimization links in traditional emergency plans. Through continuous optimization and dynamic adjustment, the emergency plan can more accurately adapt to the development scenarios of emergencies, improve the applicability and implementation effect of the plan, ensure the scientificity and efficiency of emergency handling, and provide more perfect and accurate support for emergency response. Description of the Drawings

[0022] Figure 1 is a flowchart of a method for simulating an emergency plan based on digital twin technology in an embodiment of the present application; Figure 2 is a schematic structural diagram of a system for simulating an emergency plan based on digital twin technology in an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.

[0023] Description of the reference numerals: 201, acquisition module; 202, construction module; 203, input module; 204, calculation module; 205, simulation module; 206, generation module; 207, sharing module; 208, verification module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0024] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0025] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for illustration" is intended to present related concepts in a specific manner.

[0026] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0027] Figure 1 It is a schematic flowchart of a process of an emergency plan simulation method based on digital twin technology in an embodiment of the present application.

[0028] Please refer to Figure 1 , an emergency plan simulation method based on digital twin technology in an embodiment of the present application, which is applied to a server, and the method includes: S101. Obtain the physical parameters, pedestrian flow monitoring data, and environmental data of the cultural exhibition hall; The physical parameters of the cultural exhibition hall mainly include the spatial structure parameters of the exhibition hall, building hardware parameters, and sensor deployment information. The spatial structure parameters refer to the overall area of the exhibition hall and the specific areas of each region, and the layout information of the internal space of the exhibition hall, including the specific distribution of exhibition halls, corridors, passages, exits, and evacuation passages, as well as the connection relationships between regions. At the same time, it is also necessary to obtain the layout information of the internal facilities of the exhibition hall, such as the specific locations of cameras, sensors, fire-fighting equipment, lighting facilities, etc., and their monitoring or service scopes. The building hardware parameters include the load-bearing capacity of each region of the cultural exhibition hall, the designed maximum pedestrian flow carrying capacity, and the coverage range and operating capacity of the ventilation system or air-conditioning system. The sensor deployment information is the specific deployment locations and monitoring coverage ranges of the personnel monitoring sensors (such as thermal imaging cameras, infrared sensors) and environmental monitoring sensors (such as temperature and humidity sensors, air quality sensors, noise sensors) installed inside the exhibition hall. When obtaining these physical parameters, they can be obtained by accessing the infrastructure management system of the exhibition hall, or extracting the spatial structure and load-bearing capacity information of the exhibition hall from the architectural design drawings, and combining the equipment installation records to obtain the specific facility layout information. For the missing data parts in the drawings or records, field measurement means can be used for supplementation to ensure the comprehensiveness and accuracy of the data.

[0029] The collection of pedestrian flow monitoring data is divided into two parts: real-time data and historical data. Real-time monitoring data can be collected through devices such as surveillance cameras, infrared sensors, and Wi-Fi probes deployed inside the exhibition hall. These devices can dynamically capture the real-time number of people in each area, as well as the number of people entering, leaving, and the total number of people changing in each area at each time point, forming real-time pedestrian flow data. Historical pedestrian flow data is obtained through long-term accumulation during the operation of the exhibition hall, including pedestrian flow statistics information at different time periods (such as day and night), different activities (such as regular exhibitions and special exhibitions), especially the personnel distribution and change rules during holidays, peak hours, and large-scale exhibition activities. The obtained real-time data and historical data need to be cleaned, de-duplicated, and formatted, removing abnormal data and uniformly storing them in the pedestrian flow monitoring database.

[0030] The process of obtaining environmental data is to accurately describe the internal and external environmental states of the cultural exhibition hall, analyze the impact of environmental factors on the pedestrian flow distribution, and the feedback of pedestrian flow activities on the environment. It is collected in real-time through various environmental monitoring sensors deployed inside, specifically including temperature and humidity data (obtained through temperature and humidity sensors), air quality data (including carbon dioxide concentration, PM2.5, formaldehyde content, etc., obtained through air quality sensors), noise data (monitored through noise sensors), and light intensity data (monitored through light sensors), etc.

[0031] S102. Build a digital twin model of the cultural exhibition hall based on physical parameters, and map the pedestrian flow monitoring data into the digital twin model to obtain the target model; According to the physical parameters of the cultural exhibition hall, such as the spatial structure of the cultural exhibition hall, building hardware characteristics, and sensor deployment information, build a digital twin model of the cultural exhibition hall. During the process of building the digital twin model of the exhibition hall, it can be built through IoT (Internet of Things) devices or based on BIM (Building Information Modeling) models. The built digital twin model can accurately reflect the actual spatial layout of the exhibition hall, including the spatial positions and connection relationships of exhibition halls, corridors, passages, exits, and evacuation passages. At the same time, the design parameters of building hardware, such as load-bearing capacity, maximum pedestrian flow carrying capacity, coverage range of ventilation systems and air conditioning systems, etc., need to be embedded in the model.

[0032] After the construction of the digital twin model is completed, the obtained pedestrian flow monitoring data is mapped into the digital twin model. Specifically, the pedestrian flow monitoring data is matched with the exhibition hall space structure, and the number of people and the pedestrian flow data at different time points are allocated to each area of the exhibition hall model, thereby forming a digital twin model reflecting the dynamic operation state of the exhibition hall. This digital twin model reflecting the dynamic operation state of the exhibition hall is the target model. During the mapping process, it is necessary to ensure the time synchronization of the data, that is, the pedestrian flow data is sorted through timestamps to make it consistent with the time benchmark of the digital twin model. During the process of mapping the pedestrian flow monitoring data into the model, it is necessary to further fuse and process the data. Specifically, the pedestrian flow monitoring data is cleaned to remove outliers and missing values. For some missing data, it can be supplemented by interpolation or other statistical methods.

[0033] S103. Input the environmental data into the target model to obtain the first parameter and the second parameter; In the target model, to analyze the mutual relationship between the environmental data and the pedestrian flow distribution, it is necessary to synchronize the environmental data and the pedestrian flow monitoring data in the time and space dimensions to ensure the correlation analysis of the two types of data at the same time and in the same area. By inputting the environmental data (such as temperature and humidity, air quality, noise, and light intensity, etc.) into the target model and combining with the pedestrian flow monitoring data, the first parameter and the second parameter are calculated. Among them, the first parameter is the influence parameter of the environmental data on the pedestrian flow distribution in the pedestrian flow monitoring data, and the second parameter is the feedback influence parameter of the pedestrian flow distribution on the environmental data.

[0034] Specifically, when inputting the environmental data into the target model, the environmental data needs to be synchronized with the pedestrian flow monitoring data in the time and space dimensions to ensure the analysis of the relationship between the two types of data at the same time and in the same area. Therefore, environmental data such as temperature and humidity data, air quality data, noise data, and light intensity data are input into the target model according to the regions divided by the cultural exhibition hall based on timestamps.

[0035] In the target model, through the correlation analysis of the environmental data and the pedestrian flow monitoring data, the influence of different environmental variables on the pedestrian flow distribution is studied. For example: analyze the influence of temperature and humidity on the pedestrian flow density, and study whether too high temperature or too low humidity will lead to a decrease in the number of pedestrians; analyze the air quality, for example, when the carbon dioxide concentration exceeds a certain threshold, whether it will reduce the pedestrian residence time; analyze whether the light intensity and noise level will affect the regional attractiveness, thereby leading to a decrease in the number of pedestrians, etc. The first parameter can be calculated through the calculation formula: Among them, is the first parameter of region i, T i ,H i ,AQ i ,Ni , L i are the temperature, humidity, air quality, noise level, and light intensity in area i respectively. f is the correlation function, representing the comprehensive impact of environmental data on the pedestrian flow distribution, usually obtained through regression analysis or machine learning methods.

[0036] The first parameter is calculated, which reflects the dynamic impact of environmental data on the pedestrian flow distribution. For example: when the temperature T ≥ 30°C, the pedestrian flow density in a certain exhibition hall decreases by 15%; when the carbon dioxide concentration CO 2 ≥ 1000 ppm, the pedestrian residence time decreases by 20%; when the light intensity L ≤ 300 lux, the pedestrian flow in the corridor decreases by 10%.

[0037] After obtaining the first parameter, in the target model, through the correlation analysis of environmental data and pedestrian flow monitoring data, the feedback impact of pedestrian flow distribution on environmental data is studied. For example: analyze whether an increase in pedestrian flow density will lead to an increase in carbon dioxide concentration, and calculate the change trend of carbon dioxide concentration; analyze whether the intensity of pedestrian activities will cause local temperature and humidity to rise; analyze whether pedestrian movement will increase the regional noise level, etc. The second parameter can be calculated through the following formula: Among them, is the second parameter of area i, D i is the pedestrian flow density in area i, M i is the intensity of pedestrian activities in area i. g is the feedback function, representing the comprehensive impact of pedestrian flow distribution on environmental data, usually obtained through time series analysis or modeling. Among them, the intensity of pedestrian activities is a comprehensive measure of the mobility and activity level of people in a certain space, used to describe the activity intensity of people in this area, reflecting factors such as the movement speed, direction change, and dynamic change of the number of people per unit time of the crowd, and used to measure whether the crowd is in a static state (such as staying, pausing) or a high-mobility state (such as passing quickly, flowing frequently) in a certain area. The calculation formula for the intensity of pedestrian activities is: Among them, M i is the intensity of pedestrian activities in area i, is the rate of change of the number of people in area i, F i is the total amount of personnel flow per unit time in area i, D i is the pedestrian flow density in area i, α 1 , α 2 , α 3 , α 4 are weight coefficients, used to adjust the importance of each variable according to the actual scenario.

[0038] The second parameter is calculated, which reflects the dynamic feedback of pedestrian flow distribution on environmental data. For example: when the pedestrian flow density D > 20 people / m 2When the carbon dioxide concentration in a certain exhibition hall increases by 50 ppm per hour; when the intensity of human flow activity M is relatively high (such as in a particularly popular exhibition area where the crowd is densely distributed and the activity intensity M is relatively high), the noise level increases by 15 dB; in a certain corridor, when the human flow density D = 30 people / m 2 the local temperature rises by 2 °C.

[0039] The first parameter quantifies the impact of environmental data on the distribution of human flow, providing a basis for optimizing the environmental conditions of the exhibition hall; the second parameter quantifies the dynamic feedback of the distribution of human flow on environmental data, providing support for adjusting the environmental equipment in the exhibition hall and formulating safety management measures.

[0040] S104. Determine the safety threshold of the human flow volume corresponding to each area in the cultural exhibition hall according to the first parameter and the second parameter; wherein, the safety threshold of the human flow volume is the upper limit of the human flow volume that can be borne under the current environment.

[0041] Specifically, according to the first parameter and the second parameter, calculate the safety threshold of the human flow volume in the current environment through the first calculation formula; the first calculation formula is: wherein, N max is the safety threshold of the human flow volume, is the carrying capacity of the human flow volume in the target area, A is the area of the target area, P is the safety occupancy area required per person in the target area, [1 - (ω 1 ×E + ω 2 ×D)] is the dynamic correction factor, ω 1 is the influence parameter of environmental data on the distribution of human flow in the human flow monitoring data, E is the environmental pressure parameter, ω 2 is the feedback influence parameter of the distribution of human flow on environmental data, D is the human flow density parameter, is the evacuation capacity correction factor, R is the safe evacuation capacity per unit time, T is the safe evacuation time, N is the human flow volume in the area, g(C, L) is the evacuation passage correction factor, C is the width of the evacuation passage, and L is the length of the evacuation passage.

[0042] The basic human flow carrying capacity is the upper limit of the maximum human flow volume in the target area under ideal conditions, which is mainly determined by the area of the target area and the minimum occupancy area per person. For example, if the area of a certain exhibition hall is 2000 square meters and the minimum occupancy area per person is set to 2 square meters / person, then the basic human flow carrying capacity of this exhibition hall is 1000 people. This calculation result reflects the maximum number of people that the area can accommodate under good environmental conditions and uniform distribution of human flow, providing an initial value for subsequent dynamic correction. And the dynamic correction factor [1 - (ω 1 ×E + ω 2×D)] is used for dynamically adjusting the pedestrian flow carrying capacity, comprehensively reflecting the influence of environmental pressure and pedestrian flow density feedback on the safety threshold. Among them, ω 1 ×E is the environmental impact correction term, indicating the impact of environmental pressure on the pedestrian flow carrying capacity. For example, when the PM2.5 concentration is close to the critical value, the environmental pressure parameter E may reach 0.8. If the first weight W1 is 0.4, then the environmental correction term is 0.32; ω 2 ×D is the pedestrian flow density correction term, indicating the feedback of pedestrian flow density on the environmental state. For example, when the current pedestrian flow density is 3 people per square meter and the safe density is 4 people per square meter, the pedestrian flow density parameter D is 0.75. If the second weight W2 is 0.5, then the density correction term is 0.375. Generally speaking, when the environment deteriorates (E is larger) or the pedestrian flow density is higher (D is larger), the value of the correction factor [1 - (ω 1 ×E + ω 2 ×D)] will decrease, thereby reducing the safety threshold.

[0043] Evacuation capacity correction factor Indicates the dynamic constraint of the current evacuation conditions on the safety threshold. When the real-time pedestrian flow is close to the upper limit of the evacuation capacity, this correction factor will decrease, thereby reducing the safety threshold. For example, if the channel width is 5 meters and the designed flow velocity is 1.5 meters per second, then the maximum evacuation capacity per unit time is 7.5 people per second. If the evacuation time is 60 seconds, then the maximum number of evacuated people is 450. For the evacuation channel correction factor g(C, L), it indicates the influence of channel characteristics (width and length) on the safety threshold. When the channel width is larger and the length is shorter, the evacuation efficiency is higher, the correction factor increases, and the safety threshold increases; on the contrary, when the channel is narrower or longer, the evacuation efficiency is lower, the correction factor decreases, and the safety threshold decreases.

[0044] The dynamic adjustment of the pedestrian flow safety threshold can reflect the changes in environmental pressure, pedestrian flow density, evacuation capacity, and channel characteristics in real time. When the environmental pressure increases (such as the increase in PM2.5 concentration or CO2 concentration), the environmental pressure parameter E increases accordingly, and the safety threshold will decrease due to the decrease in the correction factor; when the pedestrian flow density gradually approaches the safe density, the density correction term increases, further reducing the safety threshold to ensure that the pedestrian flow in the area is within the safe range; when the real-time pedestrian flow is close to the evacuation capacity limit, the evacuation capacity correction factor decreases, dynamically lowering the safety threshold to ensure the evacuation efficiency; and when the channel width increases or the length shortens, the channel correction factor increases, appropriately increasing the safety threshold to reflect the improvement of the evacuation conditions.

[0045] S105. When the pedestrian flow in the target area exceeds the pedestrian flow safety threshold corresponding to the target area, obtain the historical emergency database, and calculate the correlation degree between the current emergency and the historical emergencies based on the historical emergency database, the change amount of the pedestrian flow, and the environmental data of the target area through a preset similarity matching algorithm; Specifically, extract the current emergency event characteristics of the current emergency event; set the similarity measurement criteria of the similarity matching algorithm according to the current emergency event characteristics; based on the similarity measurement criteria, calculate the similarity between the current emergency event characteristics and the historical emergency event characteristics in the historical emergency event database to obtain the correlation degree.

[0046] When an emergency event occurs, the system first extracts the characteristic data of the current event from the target model. These data at least include the change in the number of people in the target area, the fluctuation of environmental parameters, and other key factors affecting the development of the event. For example, when the number of people in the target area suddenly exceeds the safety threshold, the system will record the change rate, change amplitude, and spatial distribution of the number of people. To accurately analyze the correlation between the current emergency event and historical emergency events, the system needs to set the similarity measurement criteria of the similarity matching algorithm according to the type and data structure of the emergency event characteristics. The selection of the similarity measurement criteria depends on the specific performance of the current event characteristics. For example, for the data of the change in the number of people, the system can use the Euclidean distance or dynamic time warping (DTW) algorithm of the time series for comparison; for environmental data (such as temperature, humidity, and air quality fluctuations), the vector cosine similarity or the normalized Manhattan distance may be used to measure the difference between features.

[0047] After setting the similarity measurement criteria, the system extracts the stored historical event characteristics from the historical emergency event database and compares them with the current emergency event characteristics one by one. The system can calculate the similarity between each historical emergency event and the current emergency event through the similarity matching algorithm. For example, for the characteristic of the change in the number of people, the system will analyze whether the trend, rate, and distribution of the change in the number of people in the current emergency event are similar to a certain event in the historical events; for environmental data, the system will compare the similarity between the current environmental fluctuations and the environmental change patterns in the historical records. Finally, the system obtains a correlation degree score based on the comprehensive similarity of all features, usually represented in numerical form (such as a percentage or a score value), to quantify the correlation degree between the current emergency event and each historical event.

[0048] S106. Determine the type of the emergency event according to the correlation degree; The correlation score obtained in step S105 reflects the degree of match in characteristics between the current emergency event and a historical emergency event. The system sorts the correlation scores in descending order and analyzes the historical emergency events with correlation scores higher than a preset value (such as 70 points). For these high-correlation events, the system extracts their corresponding event type tags one by one. For example, if the type tag of a historical emergency event is "crowded event" and the correlation score between the historical emergency event and the current emergency event is higher than 70 points, then "crowded event" is extracted as a potential event type; if a historical event is an "environmental anomaly event" and the correlation score between the historical emergency event and the current emergency event is also higher than 70 points, then "environmental anomaly event" is extracted as another potential type.

[0049] It should be noted that the current emergency event may not exactly match a single historical event type. Since complex events may exhibit multiple characteristics, the system may screen out multiple high-correlation historical event types. For example, the system may find that the match degree between the current emergency event and the "crowded event" is 85 points, and the match degree with the "environmental anomaly event" is 78 points, both higher than the set threshold. In this case, the system will use both event types as potential type tags for the current emergency event.

[0050] To further handle the situation of multiple event types, the system will conduct a comprehensive analysis of these types. For example, if the correlation score of the "crowded event" is significantly higher than other types (such as 85 points and 68 points), the system may give priority to determining the current event as a "crowded event". If the correlation scores of multiple event types are close (such as 85 points and 82 points), the system will combine other auxiliary information (such as the time characteristics of the current event, the flow distribution pattern of people, the fluctuation of environmental data, etc.) for refined analysis, or further evaluate the possibility of event types through artificial intelligence classification algorithms (such as weighted decision trees, Bayesian classifiers, etc.).

[0051] Finally, the type tags of the current emergency event are output according to the comprehensive analysis results. These tags may be a single type (such as "crowded event") or a combination of multiple types (such as "crowded event + environmental anomaly event"), depending on the distribution of the correlation scores and the type analysis results.

[0052] S107. According to the emergency event type and the target model, simulate the dynamic scenario in the target area after the occurrence of the emergency event through an artificial intelligence algorithm; In step S106, the type or combination of types of the current emergency event has been determined. The type or combination of types of the current emergency event is combined with the dynamic data (such as pedestrian flow monitoring data, environmental data, etc.) and static data (physical parameters of the cultural exhibition hall, etc.) in the target model to form the data to be simulated. The system dynamically simulates the scenario after the occurrence of an emergency event in the target area through artificial intelligence algorithms. Different types of emergency events will trigger different simulation logics. For example, for a crowded event, the system will simulate the dynamic changes of the pedestrian flow in the target area, including the moving direction, speed, density distribution of the crowd, etc. (such as when a crowded event occurs in a certain exhibition hall, the system will predict the situation of people gathering towards the exit or other evacuation channels according to the changing trend of the pedestrian flow, and simulate the real-time distribution and congestion changes of the crowd in the exhibition hall space); for an environmental anomaly event, the system will simulate the dynamic changes of environmental parameters (such as temperature and humidity, air quality, noise) due to the impact of the emergency event (such as when an air quality anomaly (such as leakage of polluting gas or spread of thick smoke) occurs in a certain area, the system will predict the diffusion speed, direction and coverage range of the pollutant, and combine with the air circulation model of the exhibition hall to simulate the changing trend of environmental data in different spaces); for a comprehensive event, the system will simultaneously simulate the dynamic changes of the pedestrian flow and environmental parameters (such as when a fire occurs in an exhibition hall, the system will simulate the dynamic process of people evacuating towards the exit, and at the same time simulate the diffusion range of the increasing temperature near the fire source and the gradual deterioration of the air quality). During the simulation process, the system relies on artificial intelligence algorithms to achieve accurate modeling of the dynamic scenario. These algorithms may include: time series prediction algorithms, multi-agent system (Multi-Agent System) simulation, computational fluid dynamics (CFD) models, and reinforcement learning algorithms.

[0053] In addition, the system will dynamically adjust the state of the digital twin model to make the simulation scenario consistent with the characteristics of the actual event. For example, if a trend of slow evacuation of people or an accelerated deterioration rate of environmental parameters is simulated during the simulation process, the system will automatically adjust the simulation logic to further predict the long-term impact of the emergency event on the target area.

[0054] Finally, the simulation results are output in the form of a dynamic scenario. These scenarios can be displayed in an intuitive data and visualization way, such as: pedestrian flow density distribution map, environmental parameter change curve, event diffusion range map, etc.

[0055] S108. Obtain the change data of the target area through the dynamic scenario and the target model. The change data includes the change data of the pedestrian flow and the change data of the environment. In step S107, the dynamic scenario simulates the change process of the flow of people and the environment in the target area after an emergency through an artificial intelligence algorithm, and presents it in an intuitive data and visual way. The target model is a digital twin model, which is a complete virtual mapping of the cultural exhibition hall in the digital space, including the physical layout in the exhibition hall (such as exhibition halls, entrances and exits, evacuation channels, etc.), real-time monitoring data (such as the current distribution of the flow of people, environmental parameters), and historical data (such as the law of the flow of people, equipment configuration, etc.). In step S108, the system can superimpose the simulation results represented by intuitive data in the dynamic scenario onto the target model to obtain the change data of the target area. For example, the dynamically simulated change in the flow of people will be superimposed on the specific locations of each exhibition hall and passage in the exhibition hall to generate the change in the regional flow density distribution. The simulation results of the change in environmental parameters will be combined with the spatial division of the exhibition hall, the air circulation system, etc. to generate the specific environmental change data such as temperature, humidity, and air quality in each area.

[0056] S109. Perform predictive analysis on the change data to obtain the simulation results; Specifically, establish a time series model based on the flow of people monitoring data and environmental data; input the change data into the time series model for predictive analysis to obtain the simulation results.

[0057] Based on the flow of people monitoring data and environmental data, the system first establishes a time series model to capture the laws of the dynamics of the crowd and the changes in environmental parameters in the target area. During the model establishment process, the data is first preprocessed, including removing outliers, filling in missing values, normalizing, and detecting time stationarity to ensure that the data is suitable for time series modeling; then key features are extracted, such as peak flow time periods, change trends and outliers of environmental parameters, and correlations between multiple variables. Select and construct a suitable time series model according to the characteristics of the data. For example, the traditional ARIMA model can be used to process linear change trends, the LSTM model based on deep learning can be used to capture complex non-linear dynamics, or a multivariate time series model (such as VAR or multivariate LSTM) can be used to analyze the mutual influence between the flow of people and environmental parameters at the same time. After the model is constructed, the change data (including real-time flow of people change data and environmental parameter dynamic data) is input into the time series model for predictive analysis. Through analysis, the model can generate simulation results for a period of time in the future, such as predicting the trend of the flow density distribution in each area, identifying possible congestion points and the completion time of crowd evacuation; at the same time, predicting the change trend of environmental parameters, such as temperature increase, smoke diffusion range, pollutant concentration change, and the time required to return to normal. The simulation results are displayed in a visual way, such as heat maps, time series curves, spatial diffusion maps, etc.

[0058] S110. Generate an emergency plan according to the simulation results.

[0059] The system analyzes the simulation results output by step S109, including the changing trends of the crowd flow density in each region in the future, the locations of potential congestion points, the time when the crowd evacuation is completed, and the dynamic changing trends of environmental parameters (such as temperature, smoke, pollutant concentration, etc.). Based on the simulation results, the system further assesses the potential risks of the event. According to the abnormal degrees of the crowd flow density and environmental parameters and the event diffusion trend, the system classifies the target area into high, medium, and low risk levels, and predicts the possible scope affected by the event, the number of people affected, and the priority of personnel evacuation. At the same time, according to the simulation results, the system analyzes the possible secondary risks caused by the event (such as the smoke diffusion caused by a fire or the congestion intensification affecting the evacuation efficiency), so as to obtain the risk assessment results of each region, providing a basis for resource allocation and the priority ranking of emergency measures.

[0060] Based on the risk assessment results, the system formulates a detailed resource allocation plan to ensure the priority response to high-risk areas. The resource allocation plan includes the distribution plan of emergency personnel (such as security guards, evacuation guides, firefighters, medical rescue teams), and the distribution list of emergency supplies (such as fire extinguishing equipment, smoke masks, emergency lighting equipment). At the same time, the system will optimize the execution path of resource allocation in combination with the on-site conditions. For example, by dynamically adjusting the use of evacuation channels and the material transportation routes, it ensures that resources can reach the target area quickly. After completing the resource allocation, the system formulates specific emergency measures according to the simulation results and the allocation plan. First, it plans a detailed crowd evacuation route, giving priority to ensuring the safe evacuation of people in high-risk areas, and dynamically adjusting the evacuation plan according to the real-time environmental changes (such as smoke diffusion or exit blockage). Second, in response to environmental anomalies, the system recommends measures such as isolating dangerous areas, starting ventilation equipment, and deploying external expert support to control the impact of the event. In addition, the system starts the emergency broadcast and communication mechanism, transmits the evacuation instructions and progress information in real time, and maintains close coordination with the command center, the fire department, and medical institutions to ensure the efficient execution of emergency measures.

[0061] During the execution of emergency measures, the system continuously monitors real-time data (including the number of people and environmental parameters), dynamically tracks the development of the event, and updates the simulation results in a timely manner. If it is found that the event development exceeds expectations (such as the acceleration of smoke diffusion or the overcrowding of people beyond the channel capacity), the system will re-evaluate the risk level and adjust the current emergency measures, such as reallocating resources, optimizing evacuation routes, or changing environmental control strategies. At the same time, the system will issue an intelligent warning, providing suggestions for rapid response to management personnel to ensure that the emergency plan can flexibly adapt to the changes of emergencies. After completing the dynamic adjustment, the system will finally generate a complete emergency plan and output it in a structured and visual form. The content of the plan includes a risk area division map, an optimized crowd evacuation route, a resource allocation list, specific emergency task instructions, and a dynamic scenario prediction of the event impact. At the same time, the system intuitively presents the content of the plan through visualization means such as heat maps, time series curves, and dynamic diffusion maps to ensure that management personnel can quickly understand and execute it.

[0062] Optionally, after Figure 1 step S110 of the illustrated embodiment, the following steps may be executed: Specifically, an API interface system is constructed by defining rules through a preset API (Application Programming Interface); a permission authentication mechanism is configured in the API interface system; in response to an access request from a target system, the target system is authenticated for permissions; if the permission authentication is passed, the target system is allowed to access the API interface system, and the emergency plan is sent to the target system.

[0063] To break the information silo effect and promote cross-departmental collaboration and resource integration, the cultural exhibition hall has constructed an API interface system to support the access of third-party services and data sharing. The API interface system can be designed based on the RESTful API standard. Through the API interface system, data can be efficiently shared with various third-party systems, such as the relevant systems of social organizations, enterprises, etc., to achieve multi-party linkage. To ensure the security and controllability of data access, the cultural exhibition hall has designed a perfect permission authentication mechanism for the API interface system. The permission authentication mechanism can be based on the OAuth 2.0 protocol. All accesses must pass the permission authentication and obtain an access token, which contains a clear permission scope and expiration date. After the permission authentication is passed, the emergency plan will be sent to all types of requesting systems. At the same time, different types of requestors are assigned different access permissions. For example, the fire department can access all data and models, while social organizations can only obtain data related to their cooperation scope. For example, after a social organization passes the authentication, the emergency plan will be sent to the relevant system of the social organization. At the same time, the social organization can access the real-time number of people data in the exhibition hall area it is responsible for, but cannot obtain other sensitive information.

[0064] The API interface system realizes the seamless connection between cultural exhibition halls and multiple systems such as enterprises and social organizations, and opens up the data and information flow between multiple entities. At the same time, the exhibition hall's emergency plan and crowd flow monitoring data can also be shared to the city emergency command center through the interface for global emergency dispatch and resource allocation.

[0065] Optional, in Figure 1 The following steps may also be performed after step S110 of the illustrated embodiment: Specifically, the emergency plan is simulated and verified to obtain simulation verification results; the simulation verification results are analyzed to obtain analysis results, and the emergency plan is adjusted according to the analysis results; the adjusted emergency plan is re-input into the target model for cyclic verification until the simulation verification results meet the preset requirements, then the verification is stopped and the target emergency plan is output; the target emergency plan is used as the final emergency plan.

[0066] Among them, a simulation system can be built based on digital twin technology, computer modeling and simulation technology, and the emergency plan can also be simulated and verified through artificial intelligence algorithms. Here, the simulation system built based on digital twin technology, computer modeling and simulation technology is used as an example to illustrate the simulation verification of the emergency plan.

[0067] The generated preliminary emergency plan is input into a simulation system, and the digital twin model is used to simulate the feasibility and effectiveness of the plan. The test process includes simulating the dynamic development process of the emergency, the evacuation path of personnel, the distribution changes of people flow in the area, and the environmental parameter response at the time node. Through the operation of the simulation system, performance indicators and key data related to the plan can be obtained, such as evacuation time, evacuation efficiency, and pressure changes in high-density areas.

[0068] Analyze the data generated during the simulation verification process to evaluate the performance of the emergency plan in different emergency scenarios. The analysis content includes the timeliness of emergency response (such as whether the evacuation start time meets the requirements), the rationality of the evacuation routes for personnel (such as whether there are congestion bottlenecks), the pressure distribution in each area (such as whether a specific area exceeds the safe pedestrian flow threshold), the allocation efficiency of emergency resources (such as the coordination of the use of evacuation personnel and equipment), and the changes in environmental parameters (such as the impact of significant changes in air quality or temperature and humidity on the behavior of the crowd). According to the analysis results, deficiencies in the emergency plan can be identified, such as response delays, unreasonable evacuation route design, or uneven resource allocation. For the problems in the analysis results, optimize and adjust the emergency plan. The adjustment content may include modifying the evacuation route or adding alternative evacuation exits, optimizing the crowd guidance strategy (such as the dynamic diversion strategy), improving the allocation efficiency of emergency resources (such as zonal management or increasing support personnel in key areas), adjusting the response time or action nodes in some scenarios (such as starting the evacuation in advance or extending the operation time of certain equipment), and optimizing the response measures for specific environmental parameters (such as increasing the deployment of fire extinguishing equipment in case of a sudden fire or increasing the operation of ventilation equipment in case of high temperature). The adjusted emergency plan will be re - input into the simulation system for cyclic verification, and further simulate and test its performance in different emergency scenarios to ensure that the optimization measures can effectively solve the previously discovered problems.

[0069] After each round of verification, record the simulation verification results and conduct a comparative analysis with the preset requirements. If the results still do not meet the requirements, continue to adjust the plan and conduct the next round of cyclic testing. Stop the cyclic verification and output the target emergency plan until the simulation verification results meet all the preset requirements. The preset requirements may include that the evacuation time is within the safe range (such as all personnel evacuate to the safe area within the specified time), the pedestrian flow in each area does not exceed the safe threshold, there is no obvious congestion or over - pressure area, the changes in environmental parameters are within the controllable range, and the deployment and response efficiency of emergency resources reach the expected goals. When the optimized target emergency plan meets all the preset requirements, use the target emergency plan as the final emergency plan, and the final emergency plan will be used as the standard guidance plan for the cultural exhibition hall to respond to emergencies in actual operations.

[0070] Please refer to Figure 2 , which is a schematic structural diagram of an emergency plan simulation system based on digital twin technology provided by an embodiment of the present application. An emergency plan simulation system 200 based on digital twin technology specifically includes: An acquisition module 201, configured to acquire the physical parameters, pedestrian flow monitoring data, and environmental data of the cultural exhibition hall; A construction module 202, configured to construct a digital twin model of the cultural exhibition hall based on the physical parameters, and map the pedestrian flow monitoring data into the digital twin model to obtain a target model; An input module 203 for inputting environmental data into a target model to obtain a first parameter and a second parameter, where the first parameter is an influence parameter of the environmental data on the flow distribution in the pedestrian flow monitoring data, and the second parameter is a feedback influence parameter of the flow distribution on the environmental data; A calculation module 204 for calculating a pedestrian flow safety threshold corresponding to each area in the cultural exhibition hall according to the first parameter and the second parameter, where the pedestrian flow safety threshold is the upper limit of the pedestrian flow that can be carried under the current environment; A simulation module 205 for, when the pedestrian flow in a target area exceeds the pedestrian flow safety threshold corresponding to the target area, simulating an emergency scenario through an artificial intelligence algorithm according to the pedestrian flow in the target area and the target model to obtain a simulation result, where the target area is any area in the cultural exhibition hall; A generation module 206 for generating an emergency plan according to the simulation result.

[0071] Optionally, the calculation module 204 is specifically configured to: Calculate the pedestrian flow safety threshold of the pedestrian flow data in the current environment according to the first parameter and the second parameter through a first calculation formula; the first calculation formula is: where, N max is the pedestrian flow safety threshold, is the pedestrian flow carrying capacity of the target area, A is the area of the target area, P is the safety occupancy area required per person in the target area, [1 - (ω 1 ×E + ω 2 ×D)] is a dynamic correction factor, ω 1 is the influence parameter of the environmental data on the flow distribution in the pedestrian flow monitoring data, E is the environmental pressure parameter, ω 2 is the feedback influence parameter of the flow distribution on the environmental data, D is the pedestrian flow density parameter, is the evacuation ability correction factor, R is the safe evacuation ability per unit time, T is the safe evacuation time, N is the pedestrian flow in the area, g(C, L) is the evacuation passage correction factor, C is the width of the evacuation passage, and L is the length of the evacuation passage.

[0072] Optionally, the simulation module 205 is specifically configured to: Obtain a historical emergency database, calculate the correlation degree between the current emergency and historical emergencies based on the historical emergency database, the change in the number of people flow, and the environmental data of the target area through a preset similarity matching algorithm; determine the type of emergency according to the correlation degree; according to the type of emergency and the target model, simulate the dynamic scenario in the target area after the occurrence of the emergency through an artificial intelligence algorithm; obtain the change data of the target area through the dynamic scenario and the target model, where the change data includes the change data of the number of people flow and the change data of the environment; perform predictive analysis on the change data to obtain the simulation result.

[0073] Optionally, the simulation module 205 is further specifically configured to: Extract the current emergency characteristics of the current emergency; set the similarity measurement criteria of the similarity matching algorithm according to the current emergency characteristics; calculate the similarity between the current emergency characteristics and the historical emergency characteristics in the historical emergency database based on the similarity measurement criteria to obtain the correlation degree.

[0074] Optionally, the simulation module 205 is further specifically configured to: Establish a time series model based on the people flow monitoring data and the environmental data; input the change data into the time series model for predictive analysis to obtain the simulation result.

[0075] Optionally, the system further includes a sharing module 207, which is specifically configured to: Construct an API interface system through preset API definition rules; configure a permission authentication mechanism in the API interface system; respond to the access request of the target system, perform permission authentication on the target system; if the permission authentication is passed, allow the target system to access the API interface system and send the emergency plan to the target system.

[0076] Optionally, the system further includes a verification module 208, which is specifically configured to: Perform simulation verification on the emergency plan to obtain the simulation verification result; analyze the simulation verification result to obtain the analysis result, and adjust the emergency plan according to the analysis result; re-enter the adjusted emergency plan into the target model for loop verification until the simulation verification result meets the preset requirements, then stop the verification and output the target emergency plan; use the target emergency plan as the final emergency plan.

[0077] It should be noted that: when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be repeated here.

[0078] This embodiment also discloses an electronic device. Referring to Figure 3 , the electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0079] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0080] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0081] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0082] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, as well as calling data stored in the memory 305, it executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. in a combination of one or several. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0083] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As Figure 3 shown, in the memory 305 as a computer storage medium, there may be included an operating system, a network communication module, a user interface module, and an application program of an emergency plan simulation method based on digital twin technology.

[0084] In Figure 3 the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user to obtain the data input by the user; and the processor 301 can be used to call the application program of an emergency plan simulation method based on digital twin technology stored in the memory 305. When executed by one or more processors 301, the electronic device is caused to execute the method of one or more of the above-mentioned embodiments.

[0085] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0086] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect couplings or communication connections of devices or units can be in electrical or other forms.

[0088] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. And the aforementioned memory 305 includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0091] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the disclosure of the specification. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An emergency plan simulation method based on digital twin technology, characterized in that: Applicable to servers, including: Obtain physical parameters, crowd flow monitoring data and environmental data of cultural exhibition halls; Constructing a digital twin model of the cultural pavilion based on the physical parameters, and mapping the crowd flow monitoring data into the digital twin model to obtain a target model; Inputting the environmental data into the target model to obtain a first parameter and a second parameter, wherein the first parameter is an influence parameter of the environmental data on the crowd flow distribution in the crowd flow monitoring data, and the second parameter is a feedback influence parameter of the crowd flow distribution on the environmental data; Calculate the crowd flow safety threshold corresponding to each area in the cultural exhibition hall according to the first parameter and the second parameter, where the crowd flow safety threshold is the upper limit of the crowd flow that can be carried in the current environment; When the flow of people in the target area exceeds the flow safety threshold corresponding to the target area, an artificial intelligence algorithm is used to simulate the emergency scenario according to the flow of people in the target area and the target model to obtain a simulation result, and the target area is any area in the cultural exhibition hall; An emergency plan is generated according to the simulation results.

2. The method according to claim 1, characterized in that The calculating, according to the first parameter and the second parameter, a pedestrian flow safety threshold of the pedestrian flow data in the current environment specifically includes: Calculate the pedestrian flow safety threshold of the pedestrian flow data in the current environment by using a first calculation formula according to the first parameter and the second parameter; The first calculation formula is: Among them, N max is the safety threshold for human traffic. is the crowd flow carrying capacity of the target area, A is the area of ​​the target area, P is the safe occupied area required for each person in the target area, [1-(ω1×E+ω2×D)] is the dynamic correction factor, ω1 is the influence parameter of the environmental data on the crowd flow distribution in the crowd flow monitoring data, E is the environmental pressure parameter, ω2 is the feedback influence parameter of the crowd flow distribution on the environmental data, and D is the crowd flow density parameter. is the evacuation capacity correction factor, R is the safe evacuation capacity per unit time, T is the safe evacuation time, N is the flow of people in the area, g(C,L) is the evacuation channel correction factor, C is the evacuation channel width, and L is the evacuation channel length.

3. The method according to claim 1, characterized in that When the flow of people in the target area in the crowd flow monitoring data exceeds the crowd flow safety threshold corresponding to the target area, the emergency scenario is simulated by an artificial intelligence algorithm according to the flow of people in the target area and the target model to obtain a simulation result, which specifically includes: Obtain a historical emergency event database, and calculate the correlation between the current emergency event and the historical emergency event through a preset similarity matching algorithm based on the historical emergency event database, the change in the flow of people and the environmental data of the target area; Determining the type of emergency event according to the correlation degree; According to the emergency event type and the target model, the artificial intelligence algorithm is used to simulate the dynamic scenario in the target area after the emergency event occurs; Obtaining change data of the target area through the dynamic scenario and the target model, wherein the change data includes human flow change data and environmental change data; The simulation result is obtained by performing a predictive analysis on the change data.

4. The method according to claim 3, characterized in that The calculation of the correlation between the current emergency event and the historical emergency event by using a preset similarity matching algorithm specifically includes: Extracting current emergency event features of the current emergency event; Setting a similarity metric standard of a similarity matching algorithm according to the current emergency feature; Based on the similarity metric, the similarity between the current emergency feature and the historical emergency feature in the historical emergency database is calculated to obtain the correlation degree.

5. The method according to claim 3, characterized in that: The performing predictive analysis on the change data to obtain the simulation result specifically includes: Establishing a time series model based on the human flow monitoring data and the environmental data; The change data is input into the time series model for forecasting analysis to obtain the simulation result.

6. The method according to claim 1, characterized in that After generating the emergency plan according to the simulation result, the method further includes: Build an API interface system by presetting API definition rules; Configure an authorization authentication mechanism in the API interface system; In response to an access request from a target system, performing authority authentication on the target system; If the permission authentication is passed, the target system is allowed to access the API interface system, and the emergency plan is sent to the target system.

7. The method according to claim 1, characterized in that After generating the emergency plan according to the simulation result, the method further includes: Performing simulation verification on the emergency plan to obtain simulation verification results; Analyze the simulation verification results to obtain analysis results, and adjust the emergency plan according to the analysis results; Re-inputting the adjusted emergency plan into the target model for cyclic verification, stopping the verification when the simulation verification result meets the preset requirements, and outputting the target emergency plan; The target emergency plan shall be taken as the final emergency plan.

8. An emergency plan simulation system based on digital twin technology, characterized in that: include: The acquisition module is used to obtain the physical parameters, crowd flow monitoring data and environmental data of the cultural exhibition hall; A construction module, used to construct a digital twin model of the cultural pavilion based on the physical parameters, and map the crowd flow monitoring data into the digital twin model to obtain a target model; An input module, used for inputting the environmental data into the target model to obtain a first parameter and a second parameter, wherein the first parameter is an influence parameter of the environmental data on the crowd flow distribution in the crowd flow monitoring data, and the second parameter is a feedback influence parameter of the crowd flow distribution on the environmental data; A calculation module, used for calculating a crowd flow safety threshold corresponding to each area in the cultural exhibition hall according to the first parameter and the second parameter, wherein the crowd flow safety threshold is an upper limit of the crowd flow that can be carried in the current environment; A simulation module, used to simulate the emergency scenario through an artificial intelligence algorithm according to the flow of people in the target area and the target model to obtain a simulation result when the flow of people in the target area exceeds the flow safety threshold corresponding to the target area, and the target area is any area in the cultural exhibition hall; A generation module is used to generate an emergency plan according to the simulation results.

9. An emergency plan simulation device based on digital twin technology, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, wherein the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the emergency plan simulation device based on digital twin technology to execute the method described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on an emergency plan simulation device based on digital twin technology, the emergency plan simulation device based on digital twin technology executes the method as described in any one of claims 1 to 7.

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