Analogue simulation method based on complex building crowd evacuation model

Abstract

The invention relates to the technical field of emergency evacuation, in particular to an analogue simulation method based on a complex building crowd evacuation model. The method comprises the following steps: dividing personnel in a target building into an evacuation group, and combining a planned evacuation route of the personnel in the evacuation group with actual staying time of staying nodes on the evacuation group to obtain a theoretical evacuation route; determining an evacuation confusion degree based on the number of evacuated groups accommodated by the example node in the sub-period and the theoretical residence time; determining an evacuation risk degree based on the evacuation confusion degree and the volatility trend of the example node in each sub-time period in the simulation time period; and based on the evacuation risk degree difference between the example node and the adjacent node on the theoretical evacuation route, adjusting the theoretical staying time of the example node, determining the optimized staying time, and determining the optimized evacuation route of the example group. According to the method, the personnel in the building are divided into groups, the residence time of the nodes on the evacuation route is adjusted by considering path crossing and congestion, and the evacuation accuracy of the super high-rise building in a complex environment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency evacuation, in particular to a simulation method based on a complex building crowd evacuation model. BACKGROUND

[0002] Super high-rise building generally refers to a building with 40 floors or a building height of 100 meters or more, which is a product of industrialization and modernization development. Super high-rise building covers multiple functional areas such as residence, business and office according to height and use function. However, super high-rise building has the characteristics of high building height, large total number of personnel, long vertical evacuation distance, etc., which leads to serious challenges such as great difficulty in evacuation, difficulty in rescue, time-consuming and long in fire and other emergencies, and easy to cause major loss of life and property.

[0003] The existing evacuation model usually plans the route for personnel as independent individuals, and generally assumes that personnel are uniformly distributed or move according to simple rules. However, in actual evacuation situations, personnel on different floors will continuously converge at nodes such as staircases during downward evacuation, forming a dynamically changing crowd flow; the decision of an individual is not completely independent and is deeply influenced by the surrounding crowd, such as herd mentality, leadership behavior or cooperation, and group behavior will change the path selection of individuals. Therefore, the model based on the assumption of independent individuals has deficiencies in accuracy and reliability when simulating real and complex super high-rise building evacuation scenarios. SUMMARY

[0004] In order to solve the technical problem that the evacuation model based on the assumption of individual independence fails to consider the dynamic influence of group behavior in the evacuation process, resulting in insufficient simulation accuracy of super high-rise building crowd evacuation, the purpose of the present application is to provide a simulation method based on a complex building crowd evacuation model, and the technical solution adopted is as follows: The present application provides a simulation method based on a complex building crowd evacuation model, which comprises: obtaining the planned evacuation route of each personnel in the target building and the actual residence time of each personnel at each residence node on the planned evacuation route; dividing the personnel in the target building into different evacuation groups based on the difference in the number of floors where different personnel are located and the closeness of social relationship; merging the planned evacuation route of the personnel in the same evacuation group and the actual residence time of the personnel at the residence node, to obtain the theoretical evacuation route of each evacuation group and the theoretical residence time of each residence node thereon; optionally taking one evacuation group as an example group, and taking one node on the theoretical evacuation route of the example group as an example node; determining the evacuation confusion degree of the example node in each sub-period based on the number of evacuation groups accommodated by the example node in each sub-period within the simulation period and the theoretical residence time of the evacuation groups at the example node; determine the evacuation risk degree of the example node based on the evacuation chaos degree of the example node in each sub-period of the simulation period and the fluctuation trend thereof; adjust the theoretical residence time of the example node based on the difference between the evacuation risk degrees of the example node and adjacent nodes on the theoretical evacuation route of the example group, determine the optimized residence time of the example node, and determine the optimized evacuation route of the example group.

[0005] Further, the method for obtaining the evacuation group comprises: set the social distance between each two persons in the target building, and the smaller the social distance between two persons is, the closer the social relationship between the two persons is; take the absolute value of the difference between the floors where each two persons in the target building are located when starting to evacuate as the floor distance; take the product of the social distance and the floor distance as the distance index between each two persons; cluster all persons in the target building based on the distance index to obtain a plurality of clustering clusters; and form an evacuation group by the persons in the same clustering cluster.

[0006] Further, the method for obtaining the theoretical evacuation route of each evacuation group and the theoretical residence time of each residence node thereon comprises: for each evacuation group, form a residence set by the residence nodes contained in the planned evacuation route of all persons in the evacuation group; obtain the arrival time of each person in the evacuation group to each residence node on the planned evacuation route thereof; average the arrival time of all persons in the evacuation group to each residence node in the residence set to obtain the group arrival time of each residence node in the residence set; connect the residence nodes in the residence set in the order of the group arrival time to generate the theoretical evacuation route of the evacuation group; select an arbitrary residence node from the theoretical evacuation route as an analysis node, and take the maximum value of the actual residence time of all persons in the evacuation group at the analysis node as the theoretical residence time of the analysis node on the theoretical evacuation route.

[0007] Further, the method for determining the evacuation chaos degree of the example node in the corresponding sub-period comprises: obtain the number of evacuation groups accommodated by the example node at each time point in the simulation period; calculate the mean value of the number of evacuation groups accommodated by the example node at all time points in each sub-period, and take the ratio of the mean value to the area of the corresponding residence position of the example node as the group aggregation density of the example node in each sub-period; Optionally, one of the sub-periods is denoted as an example period, and a high-density gathering node in the example period is selected from the stay nodes on the theoretical evacuation route of the example group based on the group gathering density of the example period; The overall stay time of each high-density gathering node in the example period is obtained by averaging the evacuation groups accommodated by each high-density gathering node at all times in the example period and the theoretical stay time of the corresponding high-density gathering node; The evacuation chaos degree is obtained according to the group gathering density and the overall stay time of each high-density gathering node in the example period; The evacuation risk degree of the remaining stay nodes on the theoretical evacuation route of the example group except for the high-density gathering nodes in the example period is set to zero in the example period.

[0008] Further, the determination of the evacuation risk degree of the example node comprises: The evacuation chaos degrees of the example node in all sub-periods in the simulation period are arranged in time sequence to obtain a chaos degree sequence; The first-order difference sequence of the chaos degree sequence is obtained, and the number of changes in the signs of adjacent two elements in the first-order difference sequence is taken as the fluctuation trend degree of the example node; The overall chaos degree is obtained by averaging the evacuation chaos degrees of the example node in all sub-periods in the simulation period; The evacuation risk degree of the example node is obtained according to the fluctuation trend degree and the overall chaos degree.

[0009] Further, the determination of the optimized stay time of the example node comprises: The difference between the evacuation risk degrees of the example node and the adjacent next node on the theoretical evacuation route of the example group is normalized to obtain the burst coping degree of the example node; The product of the mapping result of the negative correlation mapping of the evacuation risk degree of the example node and the burst coping degree is normalized to obtain the planning rationality degree of the example node; The theoretical stay time of the example node is weighted by the planning rationality degree to obtain the optimized stay time of the example node.

[0010] Further, the determination of the optimized evacuation route of the example group comprises: The theoretical stay time of each stay node on the theoretical evacuation route of the example group is updated by using the optimized stay time of each stay node, and the updated theoretical evacuation route is taken as the optimized evacuation route of the example group.

[0011] Further, the selection of the high-density gathering node in the example period comprises: The mean value of the group gathering density of the example team at all stay nodes on the theoretical evacuation route in the example period is taken as the reference gathering density of the example period. The group gathering density of the high-density gathering node in the example period is greater than the reference gathering density of the example period.

[0012] Further, at least one person in the evacuation group accommodated by the example node at each time in the simulation period is at the example node at the corresponding time.

[0013] Further, the clustering method for all persons in the target building is a K-means clustering algorithm.

[0014] The present application has the following beneficial effects: First aspect: based on the number of layers difference of different persons and the close degree of social relationship, the evacuation group is divided, and the individual route is combined, the group dynamic behavior in real evacuation is fully simulated, the simulation is closer to reality, and the evacuation accuracy in complex environment of super high-rise building is significantly improved.

[0015] Second aspect: based on the number of groups accommodated by the example node in the sub-period and the stay time, the evacuation confusion degree is determined, and the fluctuation trend of the evacuation confusion degree in the sub-period is combined to evaluate the danger degree of the example node in the evacuation process, so that the path crossing and congestion changes caused by group behavior can be captured in real time, and the accuracy of risk prediction is improved.

[0016] Third aspect: the difference between the evacuation danger degrees of the example node and the adjacent node reflects the planning rationality of the theoretical evacuation route at the example node, the theoretical stay time of the stay node on the theoretical evacuation route of the evacuation group is dynamically adjusted, and the stay time of the group in the high-risk area is reduced. Considering the dynamic influence of group behavior, the efficiency and safety of evacuation in super high-rise building are ensured, and the overall evacuation time is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0018] Figure 1 A step flow chart of a simulation simulation method based on a complex building crowd evacuation model provided by an embodiment of the present application; Figure 2A computer device schematic diagram of a simulation simulation device based on a complex building crowd evacuation model according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object, the following describes in detail the specific implementation, structure, features and effects of a simulation simulation method based on a complex building crowd evacuation model according to the present application, with reference to the accompanying drawings and preferred embodiments. Different "one embodiment" or "another embodiment" in the following description do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0021] The specific scheme of the simulation simulation method based on a complex building crowd evacuation model according to the present application is described in detail below with reference to the accompanying drawings.

[0022] Embodiment 1 The present application proposes a simulation simulation method based on a complex building crowd evacuation model, please refer to Figure 1 which shows the step flow chart of a simulation simulation method based on a complex building crowd evacuation model according to an embodiment of the present application, the method comprises: Step S1: obtaining the planned evacuation route of each person in the target building and the actual stay time of each person at each stay node on the planned evacuation route.

[0023] Use Pathfinder and other evacuation simulation software for dynamic simulation, the parameters input into the software include: target evacuation scene such as fire or earthquake; attributes of each person participating in the evacuation test, including: gender, age and behavior mode, behavior mode refers to calm and panic, etc.; import building model, i.e. floor plan of target building, including stairwell, corridor, refuge layer and other rest points. Among them, personnel can stay at rest points. Each person starts to evacuate from the floor where they are located, and the evacuation simulation software can simulate according to the building layout and rest point location. After the simulation is completed, the software outputs the planned evacuation route of each person. Among them, the nodes on the planned evacuation route represent the personnel's stay position during the evacuation process, and each stay node corresponds to an actual stay time, which represents the time from the personnel's arrival to the departure of the rest point corresponding to the node.

[0024] Step S2: Based on the number difference of floors where different persons are located and the close degree of social relationship, the persons in the target building are divided into different evacuation groups; the planned evacuation route of the persons in the same evacuation group is combined with the actual stay time of the stay nodes thereon to obtain the theoretical evacuation route of each evacuation group and the theoretical stay time of each stay node thereon.

[0025] The existing evacuation model regards the persons as independent individuals, and assumes that the persons regularly travel along the planned evacuation route in the evacuation process, but the complex building can be divided into different functional areas, including shopping malls, offices and residences, etc., and the social relationship between the persons in different functional areas affects the path selection of the persons, resulting in the gathering characteristics of the persons in the evacuation process. Therefore, the persons in the target building are divided into different evacuation groups based on the number difference of floors where different persons are located and the close degree of social relationship. Then, the planned evacuation route of the persons in the same evacuation group is re-planned to determine the theoretical evacuation route of the evacuation group, which avoids the defect that the traditional model regards the persons as independent individuals and ignores the group dynamics, considers individual differences and ensures the internal coordination of the group.

[0026] Step S3: Optionally, one evacuation group is recorded as an example group, and one node on the theoretical evacuation route of the example group is recorded as an example node; based on the number of evacuation groups accommodated by the example node in each sub-period within the simulation period and the theoretical stay time of the evacuation groups at the example node, the evacuation confusion degree of the example node in each sub-period is determined.

[0027] The persons between different evacuation groups may choose to stay at the same location, resulting in the phenomenon that multiple evacuation groups converge at the same evacuation location in the same time period, causing the intersection of group evacuation trajectories, thereby making the evacuation process more chaotic. The theoretical stay time of the evacuation groups accommodated by the example node in the sub-period reflects the congestion severity of the example node, and serious congestion will increase the chaos degree of the evacuation process. Therefore, by analyzing the number of evacuation groups accommodated in the sub-period and the theoretical stay time, the spatial congestion and time delay of the example node are dynamically evaluated to obtain the evacuation confusion degree, which helps to prevent congestion and chaos and improve the controllability of the evacuation process.

[0028] In one implementation manner of the embodiment of the present application, the time period from the start of the simulation to the end of the simulation is recorded as a simulation period, the simulation period is evenly divided into a plurality of sub-periods with equal length, and the number of sub-periods needs to be greater than 10, which can be set by the implementer according to the specific circumstances.

[0029] In the embodiment of the present application, at least one person in the evacuation groups accommodated by the example node at each time within the simulation period is at the example node at the corresponding time.

[0030] Step S4: determining the evacuation danger degree of the example node based on the evacuation chaos degree of the example node in each sub-period of the simulation period and the volatility trend thereof.

[0031] The evacuation chaos degree reflects the possibility that the irregular movement of the evacuation groups in the example node causes path intersection, which intensifies the congestion of the example node and increases the time required for the evacuation groups accommodated by the example node to evacuate. In the case that the number of groups accommodated by the example node exceeds the accommodation capacity of the region, the evacuation chaos degree of the example node will rapidly increase or decrease with the entry or exit of the accommodated groups, and the volatility trend of the evacuation chaos degree of the example node in each sub-period can indicate whether the number of groups accommodated by the example node exceeds the accommodation capacity of the region, and the example node is more dangerous in the case that the number of groups accommodated by the example node exceeds the accommodation capacity of the region. Therefore, the two factors are combined to analyze the danger degree of the example node in the evacuation process, and the evacuation danger degree is obtained.

[0032] Step S5: adjusting the theoretical stay time of the example node based on the difference between the evacuation danger degrees of the example node and the adjacent node on the theoretical evacuation route of the example group, determining the optimized stay time of the example node, and determining the optimized evacuation route of the example group.

[0033] In the case of an emergency, the evacuation groups accommodated by the example node can evacuate to the adjacent next node with a lower danger degree in the evacuation process, so that the example node has the ability to cope with the emergency; and the evacuation danger degree of the example node directly indicates the planning rationality of the theoretical evacuation route at the example node. The theoretical stay time of the example node is adjusted by using the two factors, the evacuation time of the example node is adjusted by considering the actual evacuation capacity of the group, the optimized stay time is obtained, the theoretical stay time of the nodes on the theoretical evacuation route is updated, the evacuation route of the example group is re-planned, and the optimized evacuation route is obtained, so as to ensure that the personnel are safely evacuated in the shortest time in the complex building environment.

[0034] The scheme considers the dynamic influence of group behavior, dynamically adjusts the theoretical stay time of the stay node, realizes real-time optimization of the evacuation route, ensures the rapid and safe evacuation of the evacuation groups in the complex environment, makes the evacuation model more flexible and adaptive, and improves the simulation accuracy of the evacuation of the people in the super high-rise building.

[0035] Preferably, in some possible implementation manners of the embodiment of the present application, the division method of the groups comprises: setting a social distance between each two persons in the target building, and the smaller the social distance between each two persons, the closer the social relationship between the two persons; taking the absolute value of the difference in floors between each two persons in the target building at the start of evacuation as a floor distance; taking the product of the social distance and the floor distance as a distance index between each two persons; clustering all the persons in the target building based on the distance index to obtain a plurality of clustering clusters; and forming a group by the persons in the same clustering cluster.

[0036] If the social relationship of the personnel in the target building can be divided into: family members, company employees and shop employees. The personnel in the residential area is usually in the form of family, and the evacuation speed is affected by the mutual care between family members; the personnel in the office area is usually in the form of company, and most of them are young groups; the personnel in the shop area is usually in the form of shop, and the social relationship of the personnel in the form of family, company and shop is decreasing in turn. In this embodiment, the social distance between two family members in the same family is set to 0.1, the social distance between two company employees in the same company is set to 0.5, and the social distance between two shop employees in the same shop is set to 0.8, and the social distance between two personnel in other cases is set to 1.

[0037] It should be noted that if the social distance and the floor distance are both smaller, it indicates that the two personnel are closer in the floor where they start to evacuate and the social relationship is closer, and the possibility of the two personnel gathering in the evacuation process is greater.

[0038] In one implementation manner of the embodiment of the present application, the K-means clustering algorithm is selected to cluster the personnel, and the K value is determined by the elbow method.

[0039] Preferably, in some possible implementation manners of the embodiment of the present application, the method for obtaining the theoretical evacuation route and the theoretical residence time of the residence node comprises: for each evacuation group, a residence set is formed by the residence nodes contained in the planned evacuation route of all personnel in the evacuation group; the arrival time of each personnel in the evacuation group to each residence node on the planned evacuation route is obtained; the group arrival time of each residence node in the residence set is obtained by averaging the arrival time of all personnel in the evacuation group to each residence node in the residence set; the theoretical evacuation route of the evacuation group is generated by connecting the residence nodes in the residence set in the order of the group arrival time; and the maximum value of the actual residence time of all personnel in the evacuation group at an example node is taken as the theoretical residence time of the example node on the theoretical evacuation route.

[0040] It should be noted that the residence set can determine the key positions that the theoretical evacuation route may pass through all members in the evacuation group; the group arrival time provides a time reference standard for the coordinated action of the group, and is the time characteristic of the entire evacuation group arriving at the residence node. The evacuation of the evacuation group is restricted by the personnel with the longest residence time, and the theoretical residence time ensures enough space for all personnel. The theoretical evacuation route not only considers the individual differences, but also ensures the group coordination, and a good balance between safety and feasibility is achieved.

[0041] Preferably, in some possible implementation manners of the embodiment of the present application, the method for obtaining the evacuation confusion degree comprises: taking the number of evacuation groups accommodated by the example node at each time in the simulation period; calculating the mean value of the number of evacuation groups accommodated by the example node at all times in each sub-period, and taking the ratio of the mean value to the area of the corresponding stay location of the example node as the group gathering density of the example node in each sub-period; taking an optional sub-period as the example period, and selecting the high-density gathering nodes of the example period from the stay nodes on the theoretical evacuation route of the example group based on the group gathering density; averaging the evacuation groups accommodated by each high-density gathering node at all times in the example period to obtain the overall stay time of each high-density gathering node in the example period; and obtaining the evacuation confusion degree according to the group gathering density and the overall stay time of each high-density gathering node in the example period; and setting the evacuation danger degree of the remaining stay nodes on the theoretical evacuation route of the example group in the example period to zero, except for the high-density gathering nodes in the example period.

[0042] In the embodiment of the present application, the mean value of the group gathering density of all stay nodes on the theoretical evacuation route of the example team in the example period is taken as the reference gathering density of the example period; and the group gathering density of the high-density gathering node in the example period is greater than the reference gathering density of the example period. It should be noted that the reference gathering density reflects the overall level of congestion of the theoretical evacuation route in the sub-period; and the high-density gathering node is a congestion hotspot on the theoretical evacuation route, and needs to be targeted for dredging measures.

[0043] If the group gathering density is greater, the number of evacuation groups accommodated by the example node in the example period is greater, the degree of intersection of the group evacuation trajectories of the example node in the example period is more serious, and the area of the corresponding stay location of the example node is smaller, then the evacuation process of the example node in the example period is more chaotic. If the overall stay time is greater, it indicates that the stay time of the evacuation groups accommodated by the high-density gathering node in the example period is longer, and the longer the stay time means the more serious the congestion, then the evacuation process of the example node in the example period is more chaotic. Therefore, the group gathering density and the overall stay time are positively correlated with the evacuation confusion degree. In the embodiment of the present application, the product of the group gathering density and the overall stay time of the high-density gathering node in the example period is normalized to obtain the evacuation confusion degree of the high-density gathering node in the example period.

[0044] In the embodiment of the present application, based on the product of the group gathering density and the overall stay time of the high-density gathering node in all sub-periods, the maximum-minimum normalization is used for normalization processing, and other normalization methods such as function transformation and Sigmoid function can also be selected, which are not limited herein.

[0045] It should be noted that the theoretical evacuation route of the example group is obtained in the same way as the high-density aggregation node in the example period and the rest of the sub-periods; the evacuation chaos degree of the high-density aggregation node is obtained in the same way in the example period and the rest of the sub-periods.

[0046] Preferably, in some possible implementation manners of the embodiment of the present application, the obtaining method of the evacuation danger degree comprises: arranging the evacuation chaos degrees of the example node in all sub-periods in the simulation period in time sequence to obtain a chaos degree sequence; obtaining a first-order difference sequence of the chaos degree sequence, and taking the number of changes in the signs of two adjacent elements in the first-order difference sequence as the fluctuation trend degree of the example node; averaging the evacuation chaos degrees of the example node in all sub-periods in the simulation period to obtain an overall chaos degree; and obtaining the evacuation danger degree of the example node according to the fluctuation trend degree and the overall chaos degree.

[0047] It should be noted that, the greater the overall chaos degree is, the greater the possibility that the irregular movement of the evacuation group of the example node causes the intersection of the paths is, the congestion of the stay position corresponding to the example node is continuously intensified, the evacuation paths of different groups are more chaotic, and the longer the time required for the evacuation group accommodated by the example node to evacuate is, and the greater the danger degree of the evacuation process is. The greater the fluctuation trend degree is, the faster the evacuation chaos degree of the high-density aggregation node in adjacent sub-periods in the simulation period increases and decreases, which indicates that the number of groups accommodated by the stay position corresponding to the high-density aggregation node has far exceeded the regional accommodation capacity, and further indicates that the theoretical evacuation route planning is unreasonable, and the stay process of the node in the evacuation process is more dangerous. Therefore, the fluctuation trend degree and the overall chaos degree are positively correlated with the evacuation danger degree. In the embodiment of the present application, the product of the fluctuation trend degree and the overall chaos degree of the high-density aggregation node is taken as the evacuation danger degree.

[0048] It should be noted that the evacuation danger degree of the last stay node, i.e., the exit, on the theoretical evacuation route of the example group is set to zero.

[0049] Preferably, in some possible implementation manners of the embodiment of the present application, the obtaining method of the optimized evacuation time comprises: performing normalization processing on the difference between the evacuation danger degrees of the example node and the adjacent next node on the theoretical evacuation route of the example group to obtain a burst response degree of the example node; performing negative correlation mapping on the evacuation danger degree of the example node, performing normalization processing on the product of the mapping result and the burst response degree to obtain a planning rationality degree of the example node; and weighting the theoretical stay time of the example node by using the planning rationality degree to obtain an optimized stay time of the example node.

[0050] It should be noted that the greater the evacuation danger degree of the example node, the greater the influence of the group behavior mode on the evacuation process of the example node and the greater the danger degree, and the less reasonable the planning of the theoretical evacuation route at the example node. The greater the emergency response degree, the greater the possibility that the evacuation group accommodated by the example node can adjust to the adjacent next node with a lower danger degree in the evacuation process, and the greater the possibility that the emergency can be fully responded to, and the more reasonable the planning of the theoretical evacuation route at the example node. Therefore, the evacuation danger degree is negatively correlated with the planning rationality, and the emergency response degree is positively correlated with the planning rationality. The greater the planning rationality, the more reasonable the planning of the theoretical residence time of the example node, and the closer the modified optimal residence time should be to the theoretical residence time. Conversely, the less reasonable the planning of the theoretical residence time of the example node, and the greater the danger degree of the example node can be reduced by reducing the residence time, and the less the modified optimal residence time should be than the theoretical residence time.

[0051] In the embodiment of the application, the normalization processing is performed using the maximum-minimum normalization. Alternatively, a function transformation, a Sigmoid function, or the like can be selected for the normalization method, which is not limited herein.

[0052] It should be noted that the example group obtains the optimal residence time of the example node and the remaining residence nodes in the same way.

[0053] Preferably, in some possible implementation manners of the embodiment of the application, the method for obtaining the optimal evacuation route comprises: updating the theoretical residence time of each residence node on the theoretical evacuation route of the example group by using the optimal residence time of each residence node, and taking the updated theoretical evacuation route as the optimal evacuation route of the example group. It should be noted that all the evacuation groups obtain the optimal evacuation route in the same way as the example group.

[0054] Thus, the application is completed.

[0055] Embodiment 2 The application further provides a computer device schematic diagram of the simulation and emulation device based on the complex building crowd evacuation model, please refer to Figure 2 The computer device comprises a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the computer device can execute any one of the simulation and emulation methods based on the complex building crowd evacuation model.

[0056] In addition, the embodiment of the application also protects a device, which can comprise a memory and a processor, wherein the memory stores executable program code, and the processor is configured to call and execute the executable program code to execute the simulation and emulation method based on the complex building crowd evacuation model provided by the embodiment of the application.

[0057] The embodiment can divide the device into functional modules according to the method examples described above. For example, each functional module can be provided, or two or more functions can be integrated into one processing module. The integrated module can be implemented in the form of hardware. It should be noted that the division of the modules in the embodiment is illustrative, and is only a logical function division. In actual implementation, another division mode can be used.

[0058] In the case of dividing each module according to each function, the device can further include a communication module, a signal analysis module, a complexity analysis module, a positioning module, and the like. It should be noted that all related contents of each step involved in the method embodiments can be referred to the function description of the corresponding functional module, and will not be described here.

[0059] It should be understood that the device provided by the embodiment is used to execute the simulation method based on the complex building crowd evacuation model, and thus the same effect as the implementation method can be achieved.

[0060] In the case of using an integrated unit, the device can include a processing module and a storage module. When the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute program codes and the like.

[0061] The processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules and circuits included in the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, a combination of digital signal processing (DSP) and microprocessor, and the like. The storage module can be a memory.

[0062] Embodiment 3: The embodiment also provides a computer readable storage medium, which stores computer program codes. When the computer program codes run on a computer, the computer is caused to execute the related method steps to implement the simulation method based on the complex building crowd evacuation model provided in the above embodiment.

[0063] The device or computer readable storage medium provided by the embodiment is used to execute the corresponding method provided above, and thus the beneficial effects achieved thereby can be referred to the beneficial effects of the corresponding method provided above, which will not be described here.

[0064] In the embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the embodiments of the apparatus described above are merely schematic, and the division of the modules or units is merely logical function division, and there can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0065] It should be noted that the sequence of the above-mentioned embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0066] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be mutually referred to. Each embodiment focuses on the differences from other embodiments.

Claims

1. A simulation method based on a complex building crowd evacuation model, characterized in that, The method comprises: obtaining the planned evacuation route of each person in the target building and the actual stay time of each person at each stay node on the planned evacuation route; dividing the persons in the target building into different evacuation groups based on the difference in the number of floors of the floors where the different persons are located and the closeness of social relations; merging the planned evacuation route of the persons in the same evacuation group and the actual stay time of the persons at the stay nodes on the planned evacuation route to obtain the theoretical evacuation route of each evacuation group and the theoretical stay time of each stay node on the theoretical evacuation route; optionally taking one evacuation group as an example group, and taking one node on the theoretical evacuation route of the example group as an example node; determining the evacuation confusion degree of the example node in each sub-period based on the number of evacuation groups accommodated by the example node in each sub-period within the simulation period and the theoretical stay time of the evacuation groups at the example node; determining the evacuation risk degree of the example node based on the evacuation confusion degree of the example node in each sub-period within the simulation period and the fluctuation trend thereof; adjusting the theoretical stay time of the example node based on the difference between the evacuation risk degrees of the example node and adjacent nodes on the theoretical evacuation route of the example group, determining the optimized stay time of the example node, and determining the optimized evacuation route of the example group.

2. The simulation method based on the complex building crowd evacuation model according to claim 1, characterized in that, The method for obtaining the evacuation group comprises: setting the social distance between each two persons in the target building, and the closer the social distance between each two persons is, the closer the social relation between the two persons is; taking the absolute value of the difference between the floors where each two persons in the target building are located when starting to evacuate as the floor distance; taking the product of the social distance and the floor distance as the distance index between each two persons; performing clustering on all persons in the target building based on the distance index to obtain a plurality of clustering clusters; and taking the persons in the same clustering cluster to form an evacuation group.

3. The simulation method based on the complex building crowd evacuation model according to claim 1, characterized in that, The method for obtaining the theoretical evacuation route of each evacuation group and the theoretical stay time of each stay node on the theoretical evacuation route comprises: for each evacuation group, taking the stay nodes included in the planned evacuation route of all persons in the evacuation group to form a stay set; obtaining the arrival time of each person in the evacuation group to each stay node on the planned evacuation route of the person; averaging the arrival time of all persons in the evacuation group to each stay node in the stay set to obtain the group arrival time of each stay node in the stay set; connecting the stay nodes in the stay set in the order of the group arrival time to generate the theoretical evacuation route of the evacuation group; taking one stay node on the theoretical evacuation route as an analysis node, and taking the maximum value of the actual stay time of all persons in the evacuation group at the analysis node as the theoretical stay time of the analysis node on the theoretical evacuation route.

4. The simulation method based on the complex building crowd evacuation model according to claim 1, characterized in that, The method for determining the evacuation confusion degree of the example node in the corresponding sub-period comprises: obtaining the number of evacuation groups accommodated by the example node at each time within the simulation period; calculating the mean value of the number of evacuation groups accommodated by the example node at all times within each sub-period, and taking the ratio of the mean value to the area of the corresponding stay position of the example node as the group aggregation density of the example node in each sub-period. Optionally, one of the sub-periods is taken as an example period, and a high-density gathering node in the example period is selected from the stay nodes on the theoretical evacuation route of the example group based on the group gathering density in the example period; The overall stay time of each high-density gathering node in the example period is obtained by averaging the stay time of the evacuation group accommodated by each high-density gathering node at all times in the example period. The evacuation confusion degree is obtained according to the group gathering density and the overall stay time of each high-density gathering node in the example period. The evacuation risk degree of the remaining stay nodes on the theoretical evacuation route of the example group in the example period, except for the high-density gathering nodes in the example period, is set to zero.

5. The simulation method based on the complex building crowd evacuation model according to claim 1, characterized in that, The method for determining the evacuation risk degree of the example node comprises: The evacuation confusion degrees of the example node in all sub-periods in the simulation period are arranged in time sequence to obtain a confusion degree sequence; The number of times of sign change of adjacent two elements in the first-order difference sequence of the confusion degree sequence is taken as the fluctuation trend degree of the example node. The overall confusion degree is obtained by averaging the evacuation confusion degrees of the example node in all sub-periods in the simulation period. The evacuation risk degree of the example node is obtained according to the fluctuation trend degree and the overall confusion degree.

6. The simulation method based on the complex building crowd evacuation model according to claim 1, characterized in that, The method for determining the optimized stay time of the example node comprises: The burst coping degree of the example node is obtained by normalizing the difference between the evacuation risk degrees of the example node and the adjacent next node on the theoretical evacuation route of the example group. The planning rationality degree of the example node is obtained by normalizing the product of the mapping result of the negative correlation mapping of the evacuation risk degree of the example node and the burst coping degree. The optimized stay time of the example node is obtained by weighting the theoretical stay time of the example node by using the planning rationality degree.

7. The simulation method based on the complex building crowd evacuation model according to claim 1, characterized in that, The method for determining the optimized evacuation route of the example group comprises: The optimized stay time of each stay node on the theoretical evacuation route of the example group is used to update the theoretical stay time of the corresponding stay node, and the updated theoretical evacuation route is taken as the optimized evacuation route of the example group.

8. The simulation method based on the complex building crowd evacuation model according to claim 4, characterized in that, The method for selecting the high-density gathering node in the example period comprises: The mean value of the group gathering density of all stay nodes on the theoretical evacuation route of the example group in the example period is taken as the reference gathering density in the example period. The group gathering density of the high-density gathering node in the example period is greater than the reference gathering density in the example period.

9. The simulation method based on the complex building crowd evacuation model according to claim 4, characterized in that, At least one person in the evacuation group accommodated by the example node at each time in the simulation period is at the example node at the corresponding time.

10. The simulation method based on the complex building crowd evacuation model according to claim 2, characterized in that, The method for clustering all persons in the target building is the K-means clustering algorithm.

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