Layout optimization method and device for shops in the functional interweaving area of a commercial complex

Abstract

The present disclosure relates to the technical field of architectural space planning, and in particular, provides a method and device for optimizing the layout of shops in the functional interweaving area of a commercial complex. The method includes: obtaining a pre-built macro optimization model based on shop attributes and a micro optimization model based on pedestrian attributes; respectively determining the macro optimization objectives and constraints of the macro optimization model, and the micro optimization objectives of the micro optimization model; using the macro optimization model, the macro optimization objectives and the constraints to determine multiple candidate shop layouts; using the micro optimization model and the micro optimization objectives to determine a target shop layout from multiple candidate shop layouts, so as to use the target shop layout to layout shops in the functional interweaving area of the commercial complex. The present disclosure can not only increase the overall number of pedestrians entering the shops, but also can guide the flow of people, avoid internal traffic congestion, reduce cross-interference, and improve the operational efficiency of the commercial complex.

Description

Technical Field

[0001] The present disclosure relates to the technical field of building space planning, and in particular to a method and device for optimizing the layout of shops in a functionally interwoven area of a commercial complex. Background Art

[0002] Commercial complexes are designed to integrate various urban functions, such as commercial activities, office space, catering services, shopping, entertainment, and transportation. Within the functional interchange area (FIA) of a commercial complex, pedestrians with different travel purposes, such as commuting, shopping, and business, are accompanied by multi-attribute shops that meet various needs. However, this also leads to challenges such as crowded traffic, complex facility layout, and confusing spatial perception.

[0003] In related technologies, the utilization rate of shops in the functional interweaving areas of commercial complexes is low, and there are problems such as cross-congestion of pedestrian flow lines, high regional density, and low operating efficiency of commercial complexes. Summary of the Invention

[0004] The present disclosure is proposed in view of the above problems. The present disclosure provides a layout optimization method and device for shops in a functional interweaving area of a commercial complex.

[0005] According to one aspect of the present disclosure, a method for optimizing the layout of shops in a functionally interwoven area of a commercial complex is provided, comprising:

[0006] Obtain a pre-built macro-optimization model based on store attributes and a pre-built micro-optimization model based on pedestrian attributes; wherein the macro-optimization model is used to optimize the total attractiveness of stores and the penalty for unbalanced passenger flow in the functional interweaving area of the commercial complex, and the micro-optimization model is used to optimize the passenger flow density of the passages in the functional interweaving area of the commercial complex;

[0007] respectively determining the macro optimization objectives and constraints of the macro optimization model and the micro optimization objectives of the micro optimization model;

[0008] Determining a plurality of candidate store layouts using the macro optimization model, the macro optimization objective, and the constraint conditions;

[0009] The micro-optimization model and the micro-optimization target are used to determine a target shop layout from the multiple candidate shop layouts, so as to use the target shop layout to arrange shops in the functional interweaving area of the commercial complex.

[0010] According to another aspect of the present disclosure, a layout optimization device for shops in a functionally interwoven area of a commercial complex is provided, comprising:

[0011] An acquisition module is used to acquire a pre-built macro-optimization model based on store attributes and a pre-built micro-optimization model based on pedestrian attributes; wherein the macro-optimization model is used to optimize the total attractiveness of stores and the penalty for unbalanced passenger flow in the functional interweaving area of the commercial complex, and the micro-optimization model is used to optimize the passenger flow density of the passages in the functional interweaving area of the commercial complex;

[0012] a processing module, configured to respectively determine the macro optimization objective and constraints of the macro optimization model and the micro optimization objective of the micro optimization model;

[0013] The processing module is further configured to determine a plurality of candidate store layouts using the macro optimization model, the macro optimization objective, and the constraint conditions;

[0014] The processing module is also used to use the micro-optimization model and the micro-optimization target to determine a target shop layout from the multiple candidate shop layouts, so as to use the target shop layout to arrange shops in the functional interweaving area of the commercial complex.

[0015] According to another aspect of the exemplary embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in the exemplary embodiments of the present disclosure.

[0016] According to another aspect of the exemplary embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0017] According to another aspect of the exemplary embodiments of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0018] As will be described in detail below, according to the layout optimization method of shops in the functional interweaving area of a commercial complex according to the embodiment of the present disclosure, a pre-built macro optimization model based on shop attributes and a pre-built micro optimization model based on pedestrian attributes are obtained; wherein, the macro optimization model is used to optimize the total attractiveness and imbalanced passenger flow penalty of shops in the functional interweaving area of the commercial complex, and the micro optimization model is used to optimize the passenger flow density of the channels in the functional interweaving area of the commercial complex; the macro optimization objectives and constraints of the macro optimization model and the micro optimization objectives of the micro optimization model are determined respectively; a plurality of candidate shop layouts are determined by using the macro optimization model, the macro optimization objectives and the constraints; a target shop layout is determined from a plurality of candidate shop layouts by using the micro optimization model and the micro optimization objectives, so as to layout shops in the functional interweaving area of the commercial complex using the target shop layout, which can ensure the rationality of the regional division in terms of internal functional space, attract more non-shopping pedestrians into shops, and increase the overall number of pedestrians entering shops; in terms of traffic planning, it can induce pedestrian flow lines, avoid internal traffic congestion, reduce cross interference, and improve the operational efficiency of the commercial complex. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other purposes, features, and advantages of the present disclosure will become more apparent through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and are not intended to limit the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 A flow chart illustrating a method for optimizing the layout of shops in a functionally interwoven area of a commercial complex provided by an exemplary embodiment of the present disclosure is shown;

[0021] Figure 2 A graph showing changes in overall channel density under three layout schemes provided by an embodiment of the present disclosure is shown;

[0022] Figure 3 A comparison chart showing the total in-store traffic of various types of shops under the three layout solutions provided in the embodiments of the present disclosure is shown;

[0023] Figure 4 A schematic diagram of the structure of a layout optimization device for shops in a functionally interwoven area of a commercial complex provided by an exemplary embodiment of the present disclosure is shown;

[0024] Figure 5 A schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown;

[0025] Figure 6 A schematic diagram of the structure of a computer system provided by an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present disclosure more apparent, the following will describe in detail exemplary embodiments of the present disclosure with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0029] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0030] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0031] Space syntax theory and optimization models are two of the most commonly used approaches for macro-level layout strategies. However, these methods are too macroscopic to optimize the layout of shops in functionally interwoven areas, diminishing the behavioral diversity and randomness of heterogeneous pedestrians with varying travel attributes. Furthermore, these methods overlook the differentiated attraction of multi-attribute shops to heterogeneous pedestrians and the exchange of customer flow between shops. Consequently, the layout optimization strategies planned using these methods may reduce shop utilization.

[0032] At the same time, whether it's pedestrian behavior analysis, preference analysis, or trajectory analysis, it's actually all about the attractiveness of shops to pedestrians. Specifically, pedestrians in traditional shopping malls have only two attributes: purposeful shopping and seeking out interesting shops, as well as switching between the two. However, functionally interwoven areas gather pedestrians with different travel purposes, such as commuting, shopping, and business, and each type of pedestrian has different shopping needs. However, most existing methods ignore the inherent connection between multi-attribute shops and heterogeneous pedestrians, making it difficult to describe the attractiveness of shops to different types of pedestrians and unable to truly simulate the travel behavior and flow of pedestrians in commercial complexes. Therefore, the layout optimization strategies simulated by these models can lead to problems such as cross-congestion of pedestrian flow lines, high regional density, and low operational efficiency of commercial complexes.

[0033] In summary, the layout of interwoven functional areas currently faces two major challenges: Regarding internal functional space, how can the layout be designed based on commercial needs, ensuring the rationality of zoning, attracting more non-shoppers to shops, and increasing the overall number of pedestrians entering shops? Regarding traffic planning, how can the layout of the commercial complex be optimized to guide pedestrian flow, avoid internal traffic congestion, reduce cross-interference, and improve the operational efficiency of the commercial complex?

[0034] In order to solve these two problems, the embodiment of the present disclosure proposes a macro-to-micro functional interweaving area layout optimization framework based on the intrinsic connection between multi-attribute shops and heterogeneous pedestrians. At the macro level, taking the maximization of the total attractiveness of shops as the optimization goal, the complementarity between different types of shops is taken into consideration to obtain a preliminary layout plan; at the micro level, an Attraction-Potential Social Force Model (AP-SFM) based on attraction potential is proposed to simulate the attractive effects of different shops on heterogeneous pedestrians and to realistically simulate the movement process and behavior of heterogeneous pedestrians in the functional interweaving area. On the basis of the preliminary plan, the optimization goal at the micro level is to minimize the pedestrian density in the functional interweaving area channels. According to the simulation results of AP-SFM, the final layout plan of the functional interweaving area is obtained.

[0035] This disclosure combines research and analysis of the actual situation of typical functional intertwined areas (such as the underground level of a certain city's China World Trade Center) to study the macro-to-micro layout optimization method of the functional intertwined areas of commercial complexes. To better illustrate the purpose of this disclosure, the following assumptions are first made:

[0036] 1. Assumptions

[0037] This disclosed embodiment aims to incorporate the complementarity between multi-attribute shops into the layout plan of the functional interweaving area of a commercial complex, and considers the impact of multi-attribute shops on the different appeals to heterogeneous pedestrians. Layout optimization is a complex issue involving multiple factors such as construction, operation, management, economics, visuals, and transportation. Therefore, this disclosed embodiment makes the following assumptions:

[0038] Assumption 1: When optimizing the layout of shops within the functionally interwoven areas of a commercial complex, operators only need to allocate shops within the existing designated spaces without having to re-divide the mall's interior. In other words, the purpose of the disclosed embodiments is to optimize the layout of shops within the existing space without considering re-dividing the functionally interwoven areas.

[0039] Hypothesis 2: The macro-layout optimization model takes into account the complementarity between stores, thus eliminating the need to consider externalities between anchor stores and non-anchor stores separately. Furthermore, anchor stores are quantified based on factors such as store size, type, and brand, making them no longer fixed.

[0040] Assumption 3: Pedestrians in a group are calculated based on their individual attraction. When one person in the group is attracted, all of them enter the store simultaneously. However, when the group is a family, the store's influence on children is ignored, and only the influence on adults is considered.

[0041] Hypothesis 4: When shoppers complete their initial goal, the entrance farthest from their current location will be identified as a new goal, and they will move within the functional interweaving area as potential consumers.

[0042] Assumption 5: The influence of factors related to architecture and visual communication, such as building appearance and lighting design, is not considered.

[0043] 2. Constructing a macro optimization model based on store attributes

[0044] 1. Optimization objectives of the macro optimization model

[0045] Most existing studies use rent maximization as the objective function of layout optimization, but this ignores the needs and characteristics of shops. In the embodiment of the present disclosure, based on the diversity of functions of commercial complexes, heterogeneous pedestrians with various travel purposes will flow randomly inside. At the same time, according to hypothesis 4, pedestrians who originally had shopping purposes will also be converted into potential consumers of random shopping behavior after completing their shopping purposes. Therefore, the goal of the embodiment of the present disclosure is to attract more potential customers to convert into in-store customers through layout optimization, while avoiding the phenomenon of unbalanced customer flow between different shops. In short, the optimization goal is to maximize the overall attractiveness of shops while reducing the imbalance of customer flow.

[0046] Exemplarily, the optimization objective of the macro optimization model may include the objective function of the macro optimization model and the constraints of the macro optimization model; wherein the macro optimization model may be represented by the following formula (1), the objective function of the macro optimization model may be represented as maxZ, and the constraints of the macro optimization model may be represented by the following formulas (2) to (6). Wherein, formulas (1) to (6) are as follows:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] Among them, I={1,2...,n} represents the set of shops, J={1,2...,m} represents the location set of shops, and x ij ∈{0,1} indicates whether shop i is assigned to location j, represents the attractiveness of store i at location j, represents the penalty weight of unbalanced passenger flow, Indicates passenger flow ratio limit, represents the initial predicted attractiveness of store i at location j, represents the area of position j, represents the maximum passenger flow carrying capacity of shop i, Indicates the type of shop i, It represents the total attractiveness of shops in the functional interweaving area.

[0054] Here, st means being subject to constraints.

[0055] In the above formula (1), the first term is the attraction term, which represents the total attraction of the shops under the optimized layout, and the second term is the penalty term, which represents the penalty for unbalanced customer flow. The purpose of the constraint condition of the above formula (2) is to ensure that each shop has a location. According to the assumption 2 mentioned above, the main shops are no longer fixed, and each shop can be located in any open location. The constraint condition of the above formula (3) can ensure the full utilization of the rentable area in the functional interweaving area. The constraint condition of the above formula (4) can be used to indicate that the customer flow of each shop cannot exceed its maximum customer flow carrying capacity. The constraint condition of the above formula (5) is proposed based on the assumptions 1 and 5 mentioned above, indicating that the embodiment of the present disclosure does not consider the influence of relevant factors in the architectural field, and catering shops involve factors such as smoke ducts and fire protection. Therefore, during the optimization process, adjustments can only be made to the original location of the catering shops. The constraint condition of the above formula (6) can be used to ensure that the total customer flow in the functional interweaving area is fixed.

[0056] 2. The attractiveness of the shop

[0057] In the macro-layout optimization model, the disclosed embodiment ignores the impact of pedestrian attributes on the attractiveness of shops. That is, the disclosed embodiment neither considers the matching relationship between heterogeneous pedestrians and diverse shops nor the location of pedestrians, and only establishes an attractiveness function based on the shop's own attributes. Based on previous research and many empirical studies, the proposed attractiveness function depends on the shop's size, type, location, brand level, and complementarity:

[0058]

[0059] in, Indicates the attractiveness of the store. Indicates the size of the store. Indicates the type of store. Indicates the brand level of the store. Indicates the location of the store. Indicates the complementarity of shops.

[0060] Here, regarding the scale of a shop: it can be the area of the shop. When shop i is located at position j, the area of shop i is equal to the area of position j.

[0061] Regarding store types: According to the commercial industry standard "Shopping Center Tenant Mix Strategy Guide," shopping mall store types can be divided into eight categories: Fashion, Family & Children's, Accessories, Food & Beverage, Desserts & Beverages, Lifestyle, Beauty & Cosmetics, and Services. Food & Beverage refers to formal restaurants and must be located within designated, permitted areas. Dessert and Beverage stores, on the other hand, can be freely adjusted and are not restricted by location.

[0062] Regarding the brand level of a shop: it is determined by the scale and type of the shop. In the embodiment of the present disclosure, the brands of shops are divided into five levels, and the identification of brand levels is determined based on the type and scale of the shop.

[0063] Regarding the location of shops: it can be the spatial distribution of shops. In addition to the type and brand level of the shops, the relative location of the shops to the entrance and elevator will also lead to differences in the attractiveness of the shops. The location attractiveness parameter is introduced as the location attractiveness of shop i at location j. It can use the Euclidean distance to measure the distance between the center point of each location and the center point of the mall entrance or elevator, as shown in the following formula (8):

[0064]

[0065] in, represents the location attractiveness of shop i at location j, The set of entries e representing the functional interweaving area, represents the set of elevators l, ( , ) represents the coordinates of position j, ( , ) represents the coordinates of the entrance e, ( , ) represents the coordinates of elevator l.

[0066] Shop complementarity: The degree to which shop i is influenced by surrounding shops. During the shopping process, whether for the purpose of comparing product quality or price, customers will unconsciously shift their attention to other shops in the same area, which can easily convert potential customers into in-store customers.

[0067] Based on this pedestrian psychology, attempts are being made to improve the complementarity of shops through layout optimization, thereby effectively increasing their overall appeal. Specifically, the spatial range within which shops can achieve complementarity is determined. Only shops within a certain range are considered to exhibit complementarity. Furthermore, complementarity between shops exhibits a saturation effect. It is assumed that pedestrian interest in a particular type of shop changes as the number of similar shops in the area increases. This process can be divided into two stages. However, once the number of similar shops exceeds a certain threshold, the system enters a saturation stage, at which point pedestrian interest begins to decline.

[0068] Furthermore, field research has revealed that pedestrians' desire to purchase similar products decreases after a shopping experience. Therefore, similar stores not only exhibit positive complementarity but also negative mutual exclusion, meaning the appeal of similar stores decreases after a purchase. For example, after spending money at a restaurant or dessert and beverage store, the appeal of nearby similar stores drops to zero, leading to a complete lack of interest in visiting other similar stores nearby.

[0069] In summary, in order to eliminate the dimensional inconsistency and numerical incomparability between different parameters, the above parameters are normalized and the attractiveness of store i at location j is calculated. The definition is as follows:

[0070]

[0071] in, Indicates the size of shop i, which is equal to the area of location j , Indicates the type of shop i, represents the brand level of shop i, Represents a distance Boolean variable, used to characterize the complementary effect between shop i at location j and shop n. If > , =0, represents the distance between the center coordinates of location j and shop n, represents the influence radius of shop i, represents the complementary weight between shop i and shop n, represents the initial predicted attractiveness of store n at location m, , represents the saturation effect function of shop i, represents the number of shops of the same type within the influence radius of shop i at location j, α>0 represents the saturation rate parameter, represents the purchase desire adjustment parameter, ∈[0,1].

[0072] 3. Constructing a micro-optimization model based on pedestrian heterogeneity

[0073] 1. Optimization objectives of micro-optimization models

[0074] After optimizing the macro-optimization model based on store attributes, the second step of optimization is performed using the micro-optimization model based on pedestrian heterogeneity for two reasons:

[0075] On the one hand, due to its inherent structure, the objective function of the first-step macro-optimization model is non-convex. The attraction term introduces nonlinearity through the complementary effect and saturation effect functions. These factors generate dependencies between the store allocations, resulting in a complex non-convex state for the objective function of the macro-optimization model. In addition, the penalty term is an isolated quadratic convex term. However, when the penalty term is combined with the nonlinear attraction term, the entire objective function of the macro-optimization model becomes non-convex. Non-convex objective functions have multiple local optimal points, making it difficult to ensure that the solution obtained is the global optimal. The constraints such as the above formulas (2) to (6) further exacerbate this challenge because they bring additional complexity to the feasible region of the functional interweaving area. In this case, standard optimization techniques such as gradient or heuristic methods can easily converge to local optimal solutions. Therefore, the resulting solutions are usually multiple suboptimal solutions, representing local optimal solutions rather than global optimal solutions.

[0076] On the other hand, macro-optimization from the perspective of shops ignores the impact of pedestrian heterogeneity. As mentioned above, pedestrian behavior within shops is largely influenced by their attributes, and the ultimate goal of the disclosed embodiments is to increase the number of pedestrians within shops by optimizing shop layout. Therefore, it is necessary to introduce a simulation method and incorporate the impact of pedestrian heterogeneity in the second step of the optimization model to determine the final shop layout.

[0077] In summary, considering that the first step of optimization may produce multiple suboptimal solutions and the heterogeneity of pedestrians needs to be incorporated into the optimization model, the second step of optimization based on simulation is introduced.

[0078] The embodiment of the present disclosure proposes a social force model based on attraction potential (AP-SFM) to simulate the attraction effect of different shops on heterogeneous pedestrians under multiple suboptimal shop layouts (i.e., multiple candidate shop layouts) generated in the first step. The ultimate goal of the embodiment of the present disclosure is to maximize the number of pedestrians in the shops. However, due to the inherent differences in the number of customers and the length of stay of shops of different types and brand levels, it is challenging to comprehensively evaluate the overall rationality of the shop layout in the functional interweaving area. Therefore, in order to solve this problem, the optimization objective of the micro-optimization model is defined as minimizing the average pedestrian density in the channel. The micro-optimization model based on pedestrian attributes can be expressed by the following formulas (10) to (12):

[0079]

[0080]

[0081]

[0082] in, represents the passenger flow density of the channel in the functional interweaving area, C represents the range of the channel in the functional interweaving area, represents the vertex coordinates of the channel, T represents the total time step, P represents the total number of pedestrians in the functional interweaving area, represents the area of the channel, represents the pedestrian number indicator function of the channel, represents the position of pedestrian p at time t, represents the position of pedestrian p at time step t+1, represents the actual speed of pedestrian p at time t, Represents the pedestrian position update function.

[0083] Here, the pedestrian movement in the constraint condition of the above formula (12) is based on the proposed AP-SFM to determine the pedestrian trajectory in the next time step. The pedestrian position update function represented by can be the attraction potential based social force model (AP-SFM).

[0084] The shape of the channel in the functional interweaving area may be polygonal, which is determined according to the actual application scenario. The embodiment of the present disclosure does not specifically limit the shape of the channel.

[0085] 2. Social force model in related technologies

[0086] According to the original social force model (SFM), the movement of pedestrian p in a complex environment is represented by the driving force of pedestrian p, the interaction force between pedestrian p and other pedestrians q, and the repulsive force between pedestrian p and wall w. The social force model can be expressed by the following formulas (13) to (18):

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] in, represents the mass of pedestrian p, represents the driving force of pedestrian p, represents the interaction force between pedestrian p and other pedestrians q, represents the repulsive force between the pedestrian p and the wall w in the channel, represents the actual speed of pedestrian p, represents the actual speed of other pedestrian q, represents the expected speed of pedestrian p at time t, represents the unit vector pointing to the desired target at time t, τ represents a certain characteristic time, represents the strength of social interaction, represents the scope of social interaction forces, and are all constants, represents the sum of the radii of pedestrian p and other pedestrians q, represents the radius of pedestrian p, represents the distance between the center of mass of pedestrian p and other pedestrian q, represents the distance between the center of mass of pedestrian p and wall w, represents the normalized vector from other pedestrian q to pedestrian p, represents the normalized vector from the wall w to the pedestrian p, represents the body compressibility coefficient, represents the coefficient of sliding friction, represents the tangent direction of pedestrian p and other pedestrians q, represents the tangent direction of pedestrian p and wall w, Q represents the total number of pedestrians, W represents the total number of walls, Indicates whether pedestrians are not touching each other or touching the wall, represents the position of pedestrian p at time t, represents the position of pedestrian p at the initial time.

[0094] Here, is a step function (when >0, take ”, otherwise “0”), indicating that only when in contact ( < or < ) produces physical force, that is, take "0" for no contact, take " " is in contact (the same applies to pedestrians and walls).

[0095] Obviously, the SFM in related technologies cannot truly and effectively simulate the attraction effect of different shops on heterogeneous pedestrians. Therefore, the embodiments of this disclosure introduce attraction to improve the original social force model and propose a social force model based on attraction potential (AP-SFM).

[0096] 3. Pedestrian attraction

[0097] Similar to the attractiveness of shops, the attractiveness of shops to pedestrians is also affected by factors such as the size and type of the shops. When considering pedestrian-related factors, additional considerations must be given to the distance between shops and pedestrians, the shop's guiding qualities, and the visual distance of pedestrians.

[0098] Based on this, the embodiment of the present disclosure introduces the gravitational potential based on the law of universal gravitation, namely the attractive potential energy (also called attractive potential energy). The attractive potential energy between shops and pedestrians can be expressed by the following formulas (19) to (20):

[0099]

[0100]

[0101] in, Indicates a shop and pedestrians The attractive potential energy between Indicates a shop and pedestrians The attraction between Indicates pedestrians Point to the shop The normalized vector of Represents the weight parameter to ensure the consistency of unit magnitude, and its value is , which can be determined based on the strength of the social interaction force of the original SFM, Indicates a shop The scale, Indicates a shop The guidance coefficient, Indicates a shop With pedestrians The distance between Indicates a shop With pedestrians The matching degree between Represents a range constraint, used to determine whether a shop is within the pedestrian's attention range. Indicates a shop and pedestrians The angle between The angle representing the pedestrian's attention range, Indicates the depth of the pedestrian's attention range.

[0102] Here, regarding the scale of the shops: In the universal gravitational model, the scale of the shops should be the mass of the object, that is, the product of density and volume. Since all shops are on the same horizontal plane, the density and height of the environment of the shops are considered to be the same, so the mass of the shops can be converted into the area of the shops, which is consistent with the definition of the size factor of the attraction function of the shops in the previous article. In addition, for normalization, The value range should be between [0,1], which can be determined by the area of shop i. Calculated as a ratio to the total area of all shops, that is:

[0103]

[0104] in, represents the area of shop i.

[0105] About the matching matrix between shops and pedestrians: The matching degree between the types of shops and the attributes of pedestrians is one of the important factors affecting attraction. , where each element represents the matching degree between different types of shops and different types of pedestrians. All elements of are normalized to the minimum value in the range [0,1].

[0106] Regarding the guidance coefficient of shops: In functional interweaving areas, there are many different shops arranged to attract pedestrians, but pedestrians have limited perception. It is often challenging to rely solely on visual stimulation to attract pedestrians into shops. Therefore, various guidance measures are usually taken, but some measures may be counterproductive (for example, playing too loud music through speakers may irritate pedestrians). These guidance measures are divided into three categories: publicity, events, and hawking. Among them, The normalized value of is in the range [-1,1], which ensures that ( ) are normalized to the range [0,2].

[0107] Regarding the attention range of pedestrians:

[0108] When pedestrians move, the range they can perceive is farther than the range of movement. This perception range is defined as the pedestrian's attention range, which is determined by the angle ( ) and depth ( ) determined by the fan-shaped range. Among them, the attraction will only work when the shop is within the attention range of pedestrians. =120°, =10m.

[0109] Regarding range constraints: For simplicity, the entrance of shop i is defined as a solid line, so It can be calculated as the distance between the center of mass of pedestrian p and the near end of the solid line. At the same time, whether shop i is within the pedestrian's perception range can be determined by the angle formed by the line connecting the center of mass of pedestrian p and the near end of shop i and the direction of pedestrian p. To judge. The attraction effect will only occur when shop i is within the attention range of pedestrian p. Therefore, the range constraint can be calculated as:

[0110]

[0111] in, Represents an indicator function. It returns 1 if the input predicate is true, and 0 otherwise.

[0112] 4. Social force model based on attractive potential

[0113] The disclosed embodiments utilize a comprehensive force criterion, rather than simply adding or subtracting force, to integrate attraction into the original social force model. Furthermore, the process from pedestrians perceiving attraction to being successfully influenced by it is conceptualized as a cumulative process of attraction potential, divided into two stages: (a) Perceived attraction, in which pedestrians evaluate various attractions and select the one that most stimulates or attracts them; and (b) Successful attraction, in which when the attraction exceeds the pedestrian's original driving force, the pedestrian's temporary goal and trajectory are altered accordingly.

[0114] To simulate this situation, a comprehensive force criterion is proposed that combines attraction with the original SFM. That is, at each time step, the attraction and driving force of pedestrians are compared. If the attraction is smaller than the driving force, it is not considered. On the contrary, if the attraction is larger than the driving force, the latter is temporarily overwritten. In addition, once a store is visited, its attraction is set to zero to prevent further influence. Therefore, the social force model based on attraction potential can be used to characterize the above formula (12) , which can be expressed by the following formulas (23) to (30):

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] in, Indicates the effective force value.

[0124] Here, is the binary matching parameter, and its calculation formula is:

[0125]

[0126] On this basis, an embodiment of the present disclosure provides a method for optimizing the layout of shops in the functional interweaving area of a commercial complex, which can be executed by a terminal or by a chip applied to the terminal.

[0127] Exemplarily, the above-mentioned terminal may include one or more of a mobile phone, a tablet computer, a wearable device, an in-vehicle device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and a wearable device based on augmented reality (AR) and / or virtual reality (VR) technology, etc., and the exemplary embodiments of the present disclosure do not impose specific restrictions on this.

[0128] Figure 1 The following is a flow chart showing a method for optimizing the layout of shops in the functional interweaving area of a commercial complex provided by an exemplary embodiment of the present disclosure. Figure 1 As shown in the figure, the layout optimization method of shops in the functional interweaving area of the commercial complex includes:

[0129] S101, obtaining a pre-built macro optimization model based on store attributes and a pre-built micro optimization model based on pedestrian attributes; wherein the macro optimization model is used to optimize the total attractiveness of stores and the penalty for unbalanced passenger flow in the interwoven functional area of the commercial complex, and the micro optimization model is used to optimize the passenger flow density of passages in the interwoven functional area of the commercial complex;

[0130] S102, respectively determining the macro optimization objectives and constraints of the macro optimization model and the micro optimization objectives of the micro optimization model;

[0131] S103, using the macro optimization model, macro optimization objectives and constraints, determining multiple candidate store layouts;

[0132] S104, using the micro-optimization model and the micro-optimization goal, determining a target shop layout from multiple candidate shop layouts, and using the target shop layout to arrange shops in the functional interweaving area of the commercial complex.

[0133] Specifically, as can be seen from the foregoing, the embodiments of the present disclosure utilize the diverse functions of a commercial complex, where heterogeneous pedestrians with various travel purposes will randomly flow within it. Layout optimization is used to attract more potential customers to convert into in-store customers while avoiding imbalanced customer flow between different shops. Based on this, the embodiments of the present disclosure construct a macro-optimization model based on shop attributes and, subject to the constraints of the macro-optimization objectives and constraints, utilize the macro-optimization model to perform preliminary optimization of the shop layout, obtaining multiple candidate shop layouts.

[0134] This disclosed embodiment constructs a micro-optimization model based on pedestrian attributes to simulate the attractiveness of different shops to heterogeneous pedestrians, realistically simulating their movement and behavior within the functional interweaving area. Based on this, guided by the micro-optimization objectives, the micro-optimization model is used to further optimize the shop layout, determining a target shop layout from multiple candidate shop layouts. This target shop layout is then used to determine the shop layout within the functional interweaving area of the commercial complex.

[0135] According to the technical solution of the exemplary embodiment of the present disclosure, by obtaining a pre-built macro optimization model based on shop attributes and a micro optimization model based on pedestrian attributes; wherein, the macro optimization model is used to optimize the total attractiveness and unbalanced passenger flow penalty of shops in the functional interweaving area of the commercial complex, and the micro optimization model is used to optimize the passenger flow density of the channels in the functional interweaving area of the commercial complex; the macro optimization objectives and constraints of the macro optimization model, and the micro optimization objectives of the micro optimization model are determined respectively; a plurality of candidate shop layouts are determined by using the macro optimization model, the macro optimization objectives and the constraints; the micro optimization model and the micro optimization objectives are used to determine a target shop layout from a plurality of candidate shop layouts, so as to use the target shop layout to layout shops in the functional interweaving area of the commercial complex, which can ensure the rationality of the regional division in terms of internal functional space, attract more non-shopping pedestrians to enter the shops, and increase the overall number of pedestrians entering the shops; in terms of traffic planning, it can induce pedestrian flow lines, avoid internal traffic congestion, reduce cross interference, and improve the operational efficiency of the commercial complex.

[0136] In some embodiments, the method may further include:

[0137] Based on the attributes of the shops, determine the total attractiveness of the shops in the functional interweaving area of the commercial complex;

[0138] Obtaining the penalty weights for unbalanced passenger flow, passenger flow ratio limits, and initial predicted attractiveness of shops, and determining the unbalanced passenger flow penalties for shops in the functional interweaving area of the commercial complex based on the total attractiveness of the shops in the functional interweaving area of the commercial complex, the penalty weights for unbalanced passenger flow, passenger flow ratio limits, and initial predicted attractiveness of the shops;

[0139] Based on the total attractiveness of shops in the functional interweaving area of the commercial complex and the penalty for unbalanced customer flow, a macro optimization model is constructed;

[0140] Specifically, the macro optimization model is constructed based on the store attributes, which can include two parts: the total attractiveness of the store and the imbalanced customer flow penalty of the store.

[0141] Exemplarily, the macro optimization model is expressed by the following formula:

[0142]

[0143] Among them, I={1,2...,n} represents the set of shops, J={1,2...,m} represents the location set of shops, and x ij ∈{0,1} indicates whether shop i is assigned to location j, represents the attractiveness of store i at location j, represents the penalty weight of unbalanced passenger flow, Indicates passenger flow ratio limit, It represents the initial predicted attractiveness of store i at location j.

[0144] In the above formula (1), the first term is the attraction term, which is used to characterize the total attraction of shops in the functional interweaving area of the commercial complex under the optimized layout; the second term is the penalty term, which is used to characterize the unbalanced customer flow penalty of shops in the functional interweaving area of the commercial complex under the optimized layout.

[0145] For example, the above-mentioned store attributes may include the store's size, type, location, brand level, and complementarity; the attractiveness of store i at location j may be calculated using the following formula:

[0146]

[0147] in, Indicates the size of shop i, which is equal to the area of location j , Indicates the type of shop i, represents the brand level of shop i, represents the location attractiveness of shop i at location j, Represents a distance Boolean variable, used to characterize the complementary effect between shop i at location j and shop n. If > , =0, represents the distance between the center coordinates of location j and shop n, represents the influence radius of shop i, represents the complementary weight between shop i and shop n, represents the initial predicted attractiveness of store n at location m, , represents the saturation effect function of shop i, represents the number of shops of the same type within the influence radius of shop i at location j, α>0 represents the saturation rate parameter, indicates the purchase desire adjustment parameter, ∈[0,1];

[0148] The location attractiveness of store i at location j is expressed by the following formula:

[0149]

[0150] in, The set of entries e representing the functional interweaving area, represents the set of elevators l, ( , ) represents the coordinates of position j, ( , ) represents the coordinates of the entrance e, ( , ) represents the coordinates of elevator l.

[0151] In some embodiments, the macro optimization goal is to maximize the total attractiveness of shops in the functional interweaving area of the commercial complex; it can be represented by maxZ.

[0152] The above constraints can be expressed by the following formula:

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] in, represents the area of position j, represents the maximum passenger flow carrying capacity of shop i, Indicates the type of shop i, It represents the total attractiveness of shops in the functional interweaving area.

[0159] Here, for the relevant contents of the above formulas (2) to (6), please refer to the previous text and will not be repeated here.

[0160] In some embodiments, the micro-optimization goal is to minimize the passenger flow density of the passages in the functional interweaving area of the commercial complex;

[0161] The method may further include:

[0162] Obtain the range of the channel, the area of the channel, the pedestrian quantity indicator function, and the pedestrian position update function in the functional interweaving area of the commercial complex;

[0163] A micro-optimization model is constructed based on the scope of the channel, the area of the channel, the pedestrian quantity indicator function and the pedestrian position update function in the functional interweaving area of the commercial complex;

[0164] Specifically, the micro-optimization model is constructed based on pedestrian attributes, where the pedestrian attributes here can be the pedestrian heterogeneity mentioned above.

[0165] For example, the micro-optimization model can be expressed by the following formula:

[0166]

[0167]

[0168]

[0169] in, represents the passenger flow density of the channel in the functional interweaving area, C represents the range of the channel in the functional interweaving area, represents the vertex coordinates of the channel, T represents the total time step, P represents the total number of pedestrians in the functional interweaving area, represents the area of the channel, An indicator function representing the number of pedestrians in the channel, represents the position of pedestrian p at time t, represents the position of pedestrian p at time step t+1, represents the pedestrian position update function, represents the actual speed of pedestrian p at time t.

[0170] In some embodiments, the pedestrian position update function may be a social force model based on attractive potential energy;

[0171] The method may further include:

[0172] Obtain the driving force of pedestrians in the functional interweaving area of the commercial complex, the interaction force between pedestrians and other pedestrians, the repulsive force between pedestrians and the walls in the passage, and the attractive potential energy between shops and pedestrians;

[0173] Based on the driving force of pedestrians in the functional interweaving area of the commercial complex, the interaction force between pedestrians and other pedestrians, the repulsive force between pedestrians and the walls in the passage, and the attractive potential energy between shops and pedestrians, a social force model based on attractive potential energy is constructed.

[0174] Specifically, in the functional interweaving area of a commercial complex, the movement of pedestrians is affected by multiple forces, which can include: the driving force of pedestrians, the interaction force between pedestrians and other pedestrians, the repulsive force between pedestrians and the walls in the passage, and the attractive potential energy between shops and pedestrians.

[0175] After obtaining the driving force of pedestrians, the interaction force between pedestrians and other pedestrians, the repulsive force between pedestrians and the walls in the passage, and the attractive potential energy between shops and pedestrians, a social force model based on attractive potential energy can be constructed based on the driving force of pedestrians, the interaction force between pedestrians and other pedestrians, the repulsive force between pedestrians and the walls in the passage, and the attractive potential energy between shops and pedestrians in the functional interweaving area of the commercial complex.

[0176] For example, the social force model based on attractive potential energy is expressed by the following formula:

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185] in, represents the mass of pedestrian p, represents the driving force of pedestrian p, represents the interaction force between pedestrian p and other pedestrians q, represents the repulsive force between the pedestrian p and the wall w in the channel, Indicates a shop and pedestrians The attractive potential energy between represents the actual speed of pedestrian p, represents the actual speed of other pedestrian q, represents the expected speed of pedestrian p at time t, represents the unit vector pointing to the desired target at time t, τ represents a certain characteristic time, represents the strength of social interaction, represents the scope of social interaction forces, and are all constants, represents the sum of the radii of pedestrian p and other pedestrians q, represents the radius of pedestrian p, represents the distance between the center of mass of pedestrian p and other pedestrian q, represents the distance between the center of mass of pedestrian p and wall w, represents the normalized vector from other pedestrian q to pedestrian p, represents the normalized vector from the wall w to the pedestrian p, represents the body compressibility coefficient, represents the coefficient of sliding friction, represents the tangent direction of pedestrian p and other pedestrians q, represents the tangent direction of pedestrian p and wall w, Q represents the total number of pedestrians, W represents the total number of walls, Indicates whether pedestrians are not touching each other or touching the wall, represents the position of pedestrian p at time t, represents the position of pedestrian p at the initial time, Indicates the effective force value; Indicates a shop For pedestrians attractiveness; Indicates pedestrians Point to the shop The normalized vector of ; Represents the weight parameter to ensure the consistency of unit magnitude, and its value is , Indicates a shop The scale, Indicates a shop The guidance coefficient, Indicates a shop With pedestrians The distance between Indicates a shop With pedestrians The matching degree between Represents a range constraint, used to determine whether a shop is within the pedestrian's attention range. Indicates a shop and pedestrians The angle between The angle representing the pedestrian's attention range, Indicates the depth of the pedestrian's attention range.

[0186] Based on this, the exemplary embodiment of the present disclosure provides a specific application embodiment to specifically illustrate the method of the embodiment of the present disclosure.

[0187] The multi-objective optimization problem of store layout is essentially a two-stage optimization process. Based on the store attributes, various feasible store layout strategies are proposed. These strategies are then evaluated using pedestrian simulation results to determine the final layout solution. The optimization process involves numerous parameters that require calibration. Therefore, the implementation involves data collection, calibration of the optimization model parameters using the collected data, and optimization algorithms. The specific implementation steps are as follows:

[0188] Step 1: Data Collection

[0189] In order to analyze the travel behavior of heterogeneous pedestrians in FIA and quantify the parameters of the optimization model, the B1 floor of an international trade center was selected as the research object, and data were collected through video recording, pedestrian tracking and questionnaire survey.

[0190] Located in a city's central business district, the China World Mall covers an area of 230,000 square meters and includes a shopping mall and office buildings. In short, the China World Mall is a typical commercial complex, encompassing pedestrians for various purposes, including commuting, shopping, and business. Furthermore, the China World Mall Station, serving Lines 1 and 10 of the city's subway system, is located beneath the mall, with its entrances and exits connected to the mall's B1 level. The selected area represents a typical FIA, encompassing not only 122 stores of the eight aforementioned types but also multiple entrances and exits connecting the mall, office space, hotel area, subway lines, and the external environment.

[0191] Pedestrian activity is highly random and variable. The same pedestrian can exhibit different behaviors depending on whether they are traveling on a weekday or weekend, or during peak or off-peak hours. Therefore, to minimize the influence of external factors and explore the appeal of businesses to pedestrians, eight volunteers were recruited to collect data. From December 2, 2024 (Monday) to December 8, 2024 (Sunday), volunteers used their mobile phones to record pedestrian travel behavior in eight designated corners within the study area during two specific time periods: 3:00-4:00 PM and 6:00-7:00 PM each day.

[0192] Because cameras were not permitted at the International Trade Center, this study relied primarily on observation to analyze pedestrian behavior between stores. Therefore, the video resolution required was relatively low, and mobile phone recordings were sufficient. Furthermore, due to limited personnel, the video could not cover the entire study area. If a pedestrian left all video frames, it was assumed they left through the mall entrance. Their behavior prior to that point was also considered valid data.

[0193] In addition to the video recording, volunteers were asked to complete a questionnaire from 4:00 to 6:00 pm every day, which included basic information, preferences for moving between shops (which can be used to determine the complementary weights between shops, ), desire to buy after shopping ( ), the matching degree between shops and pedestrians ( ), the effectiveness of shop guidance measures (the shop guidance coefficient, ) and other major issues. To facilitate data collection, an electronic questionnaire was chosen. Pedestrians could access the survey using a quick response code and complete it at their convenience. In the end, a total of 429 questionnaires were collected.

[0194] Step 2: Parameter calibration

[0195] According to the proposed optimization model, there are four parameters that need to be calibrated, namely, the complementary weights between shops i and n , Purchase desire adjustment parameters , the matching degree between pedestrians and shops and the guidance coefficient of the shop .

[0196] (1) Complementary relationship between different shops

[0197] Based on the analysis of pedestrian behavior within each video, we established a preliminary quantitative framework for the complementary relationships between different shops, which can be divided into the following four types:

[0198] Highly complementary stores: exchange 15% of customers (marked );

[0199] Moderately complementary stores: exchange 10% of their customers (marked );

[0200] Low-complementarity stores: exchange 5% of their customers (marked );

[0201] Non-complementary stores: exchange 0 customers (marked ).

[0202] In the questionnaire survey, the Likert scale was used to quantify the pedestrians’ choices. After entering the surrounding shops (no shopping), the desire to enter the different types of shops is defined as no desire, slight desire, some desire, moderate desire, strong desire and very strong desire, with a score from 0 to 5. After normalization, the complementary relationship between different types of shops is summarized in Table 1, which can be used to calculate the desire of shops in practical applications. and The complementary weight matrix between .

[0203] Table 1 Complementary relationships between different types of shops

[0204]

[0205] (2) Purchase Desire Adjustment Parameters

[0206] The disclosed embodiment investigates pedestrians in a specific type of store. The desire to enter similar stores after shopping is also divided into six levels, from no desire to very strong, with scores ranging from 0 to 5. Based on the normalized questionnaire results, the purchase desire adjustment parameters (1-δn) for different types of stores are obtained as follows:

[0207]

[0208] (3) Matching degree between shops and pedestrians

[0209] Similarly, the present disclosure embodiment also investigates the pedestrians entering the The desires of different types of shops are analyzed and the same six desire levels are defined. However, this embodiment ignores the differences between shops, and the final results correspond only to the shop type. Furthermore, this embodiment believes that gender has a greater impact on matching, so gender is differentiated in the statistical analysis. The matching matrix is shown on the left side of Table 2.

[0210] Table 2 Matching degree and guidance coefficient matrix

[0211]

[0212] Another interesting finding is that men have lower overall desire than women, meaning they are less likely to be attracted. Therefore, attracting female customers would be more effective in increasing the number of customers in your store.

[0213] (4) The guiding role of shops

[0214] In addition, the disclosed embodiments investigated the impact of three types of guidance on pedestrians, using a scale of -2 to 2 points, including very negative, negative, no effect, interested, and very interested. Interesting findings were found, including varying perceptions of different types of guidance, with the majority still finding them positive or ineffective. Pedestrians were more negatively impacted by audio guidance (hawking) than visual guidance (advertising and events), and women were more likely to be attracted to guidance than men.

[0215] Step 3: Algorithm Description

[0216] Generally speaking, the model solution process can be divided into two steps. In the first step, a macro-optimization model is used to search for local optimal layout solutions. Specifically, all shops are randomly placed in different locations, and the total shop appeal of the current layout is calculated. Next, the shop positions are adjusted, and the total appeal of the new and old layouts is compared. The layout with the higher appeal is retained. If the appeal of two layouts is the same, both are retained, and the next round of adjustments is performed. This process is repeated until the layout with the highest appeal is found.

[0217] In the second step, the various layout options obtained in the first step were further verified using a simulation model. Pedestrian behavior was simulated under different layouts, and the pedestrian density in the corridors for each layout was calculated based on the simulation results. Finally, by comparing the corridor densities of the different layouts, the layout with the lowest density was selected as the final layout.

[0218] In the first step, the embodiment of the present disclosure adopts a greedy brute force search algorithm to solve the problem, and in the second step, adopts a social force model based on attractive potential energy to simulate pedestrian density, as shown in Algorithm 1.

[0219] For example, Algorithm 1: Macro to Micro Store Location Optimization Algorithm and Simulation:

[0220] Input: I: Shop; J: Location; Entr, Elev: Entrance, Elevator; Attributes: 、 、 ;

[0221] parameter: Initial attraction ; Total number of pedestrians N.

[0222] Output: Final store location allocation index Total attractiveness of shops and passenger flow density of the channel .

[0223] Step 1: Optimize the store location;

[0224] Step 2: Generate The initial random feasible solution of ;

[0225] Step 3: Randomly initialize all shops ;

[0226] Step 4: If convergence is not achieved, execute Step 5 to Step 8.

[0227] Step 5. Do:

[0228] Step 6. Calculation :

[0229]

[0230] Step 7, end;

[0231] Step 8. Optimization:

[0232]

[0233] according to Dynamic Updates ;

[0234] Step 9, end;

[0235] Step 10, store all the Candidate solutions for the value;

[0236] Step 11, Step 2: Channel density optimization;

[0237] Step 12, for the maximum Candidates for values Perform operations;

[0238] Step 13: Use the social force model based on attractive potential energy to simulate pedestrian behavior and trajectory:

[0239]

[0240]

[0241]

[0242] Count the number of pedestrians in a corridor:

[0243]

[0244] Calculate the average passenger flow density:

[0245]

[0246] Step 14, end;

[0247] Step 15. Select the solution with the minimum average passenger flow density ;

[0248] Step 16, Output: Final 、 and .

[0249] Step 4: Numerical and simulation experiments

[0250] Numerical and simulation experiments are conducted to verify the effectiveness of the proposed optimization model. These experiments are implemented in MATLAB 2016b and AnyLogic Professional 8.8.5 with Java 2.0, respectively, and run on an Intel(R) Core(TM) i5-10210U 1.60GHz PC with 16GB of RAM. The optimization model is tested on data from the B1 floor of the International Trade Center Mall.

[0251] In the numerical experiments, the random adjustment coefficient for store locations was evaluated over multiple iterations, with a standard deviation of 0.05 selected as the random offset. Based on Assumption 1 and the constraints, the layout of 122 stores was optimized without changing the location of public infrastructure. Each iteration was repeated three times, 50,000 times, to observe the algorithm's stability and obtain multiple feasible solutions. To improve computational efficiency, the median values and total store attractiveness in the numerical experiments were rounded to two decimal places.

[0252] In the simulation, pedestrians arriving at the entrance connecting the mall and the subway line were assumed to follow a normal distribution N[20, 1] (p / min), while pedestrians arriving at the elevators were assumed to follow a normal distribution N[15, 1] (p / min). Furthermore, 200 pedestrians were initially set to move within the study area, with a 1:1 ratio of male to female. Based on the results of the numerical experiments, each layout was simulated for 60 minutes and repeated three times to determine the pedestrian density within the corridor. The optimal layout was then determined through comparison.

[0253] (1) Numerical experiments

[0254] The original layout of the study area had a total shop appeal of 60.34. The program was run three times (with two, three, and two layouts each iteration) to obtain seven optimized layouts with total shop appeals of 67.62, 67.54, and 67.71, respectively. The maximum fluctuation in shop appeal was 0.3%, demonstrating the algorithm's stability. Therefore, the two optimized layouts with the highest total shop appeal (Optimization A and Optimization B) were selected for subsequent analysis and simulation experiments. These two optimized layouts follow different design principles.

[0255] Comparing the two layout plans reveals that Optimized Plan A prioritizes complementarity between shops of the same type, while Optimized Plan B prioritizes complementarity between different shop types. For example, both plans relocate low-traffic "family and children's" shops. The former concentrates them in one area, facilitating comparison shopping and increasing the number of in-store shoppers. However, this approach also reduces the likelihood of shoppers patronizing shops in other areas. The latter, on the other hand, distributes these shops across different areas based on their complementarity with dessert and beverage shops and lifestyle stores, providing more potential shoppers for other shop types.

[0256] Furthermore, while some areas in both scenarios housed the same store types, the specific stores assigned to each location varied. Therefore, determining the most suitable layout for the study area through numerical simulation alone was challenging. Therefore, the second step of the algorithm required microscopic pedestrian simulation experiments under different layouts.

[0257] (2) Simulation experiment

[0258] Table 3 sets the simulation parameters of AP-SFM in the embodiment of the present disclosure.

[0259] Table 3 AP-SFM parameter settings

[0260]

[0261] The results of three simulations for each layout scheme allow for a qualitative and quantitative comparison of the optimization effects. On the one hand, a comparison of static local density demonstrates that both optimized layouts not only avoid unreasonable congestion but also reduce pedestrian density within the corridor. In other words, without changing objective conditions such as pedestrian arrival rate, pedestrian heterogeneity, or store types, simply adjusting the mall layout can effectively encourage more pedestrians to enter stores.

[0262] on the other hand, Figure 2 The diagram shows the change of the overall density of the channels under the three layout schemes provided by the embodiment of the present disclosure, as shown in FIG. Figure 2As shown in the original layout, because the complementarity between shops was not effectively considered, pedestrians needed to spend a lot of time walking in the corridor after shopping, looking for new shops they were interested in. Therefore, as more and more pedestrians came to the study area, the overall density of the corridor showed an upward trend.

[0263] In contrast, Optimization A emphasizes the complementarity between similar shops, allowing pedestrians to quickly enter similar shops for comparison shopping. However, once their shopping needs in that area are satisfied, they tend to wander aimlessly in the aisles or seek an exit to leave the mall. This behavior causes a sharp increase in aisle density in Optimization A after the simulation reaches 1500 seconds. Furthermore, Optimization B places greater emphasis on the complementarity between different types of shops, which encourages pedestrians to be attracted to various types of shops and engage in repeated in-store behavior. The dispersed layout of shops of the same type increases the time pedestrians spend in the aisles and delays the time at which pedestrians fully satisfy their shopping needs. Consequently, the gradual increase in aisle density in Optimization B, as simulated, takes longer than in Optimization A, resulting in a later density surge than in Optimization A.

[0264] Both optimization schemes increase the frequency of pedestrians in shops. Figure 3 This is demonstrated visually. Figure 3 A comparison chart of the total in-store traffic of various types of shops under the three layout schemes provided in the embodiment of the present disclosure is shown. Figure 3 As shown in the figure, the total in-store flow of each store in the simulation is calculated and the results are summarized by store type. It is obvious that both optimized layouts effectively increase the total in-store flow of each type of store while keeping the total pedestrian flow unchanged.

[0265] In addition, the embodiment of the present disclosure also calculated the average channel density of the original solution, optimized solution A and optimized solution B, which were 0.161p / m 2 , 0.141p / m 2 and 0.137p / m 2 Therefore, in this case, optimization plan B is more suitable for implementation.

[0266] The above mainly introduces the solutions provided by the embodiments of the present disclosure. It is understandable that in order to implement the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0267] The embodiments of the present disclosure can divide the functional units of the electronic device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical function division. In actual implementation, there may be other division methods.

[0268] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides a layout optimization device for shops in a functional interweaving area of a commercial complex. The layout optimization device for shops in a functional interweaving area of a commercial complex can be a terminal or a chip applied to a terminal. Figure 4 The following is a schematic diagram showing the structure of a layout optimization device for shops in a functional interwoven area of a commercial complex provided by an exemplary embodiment of the present disclosure. Figure 4 As shown, the apparatus 400 includes:

[0269] Acquisition module 401 is used to obtain a pre-built macro-optimization model based on store attributes and a pre-built micro-optimization model based on pedestrian attributes; wherein the macro-optimization model is used to optimize the total attractiveness of stores and the penalty for unbalanced passenger flow in the functional interweaving area of the commercial complex, and the micro-optimization model is used to optimize the passenger flow density of the passages in the functional interweaving area of the commercial complex;

[0270] Processing module 402, for determining the macro optimization goal and constraints of the macro optimization model and the micro optimization goal of the micro optimization model;

[0271] The processing module 402 is further configured to determine a plurality of candidate store layouts using the macro optimization model, the macro optimization goal, and the constraint conditions;

[0272] The processing module 402 is further configured to determine a target shop layout from the plurality of candidate shop layouts using the micro-optimization model and the micro-optimization objective, so as to arrange shops in the functional interweaving area of the commercial complex using the target shop layout.

[0273] In some embodiments, the processing module 402 is further configured to determine the total attractiveness of the shops in the functional interweaving area of the commercial complex based on the shop attributes;

[0274] The acquisition module 401 is further used to obtain the penalty weight of unbalanced customer flow, customer flow ratio limit and initial predicted attractiveness of the store;

[0275] The processing module 402 is further configured to determine an unbalanced passenger flow penalty for the shops in the functional interweaving area of the commercial complex based on the total attractiveness of the shops in the functional interweaving area of the commercial complex, the penalty weight of the unbalanced passenger flow, the passenger flow ratio limit, and the initial predicted attractiveness of the shops;

[0276] The processing module 402 is further configured to construct the macro optimization model based on the total attractiveness of the shops in the functional interweaving area of the commercial complex and the unbalanced passenger flow penalty;

[0277] The macro optimization model is expressed by the following formula:

[0278]

[0279] Among them, I={1,2...,n} represents the set of shops, J={1,2...,m} represents the location set of shops, and x ij ∈{0,1} indicates whether shop i is assigned to location j, represents the attractiveness of store i at location j, represents the penalty weight of unbalanced passenger flow, Indicates passenger flow ratio limit, represents the initial predicted attractiveness of store i at location j;

[0280] The store attributes include the store's size, type, location, brand level, and complementarity; the attractiveness of store i at location j is calculated using the following formula:

[0281]

[0282] in, Indicates the size of shop i, which is equal to the area of location j , Indicates the type of shop i, represents the brand level of shop i, represents the location attractiveness of shop i at location j, Represents a distance Boolean variable, used to characterize the complementary effect between shop i at location j and shop n. If > , =0, represents the distance between the center coordinates of location j and shop n, represents the influence radius of shop i, represents the complementary weight between shop i and shop n, represents the initial predicted attractiveness of store n at location m, , represents the saturation effect function of shop i, represents the number of shops of the same type within the influence radius of shop i at location j, α>0 represents the saturation rate parameter, indicates the purchase desire adjustment parameter, ∈[0,1];

[0283] The location attractiveness of the shop i at location j is expressed by the following formula:

[0284]

[0285] in, The set of entries e representing the functional interweaving area, represents the set of elevators l, ( , ) represents the coordinates of position j, ( , ) represents the coordinates of the entrance e, ( , ) represents the coordinates of elevator l.

[0286] In some embodiments, the macro optimization goal is to maximize the total attractiveness of the shops in the functional interweaving area of the commercial complex;

[0287] The constraint condition is expressed by the following formula:

[0288]

[0289]

[0290]

[0291]

[0292]

[0293] in, represents the area of position j, represents the maximum passenger flow carrying capacity of shop i, Indicates the type of shop i, It represents the total attractiveness of shops in the functional interweaving area.

[0294] In some embodiments, the micro-optimization goal is to minimize the passenger flow density of the passage in the functional interweaving area of the commercial complex;

[0295] The acquisition module 401 is further configured to acquire the range of the passage, the area of the passage, a pedestrian quantity indication function, and a pedestrian position update function in the functional interweaving area of the commercial complex;

[0296] The processing module 402 is further configured to construct the micro-optimization model based on the range of the passage in the functional interweaving area of the commercial complex, the area of the passage, the pedestrian quantity indication function, and the pedestrian position update function;

[0297] The micro-optimization model is expressed by the following formula:

[0298]

[0299]

[0300]

[0301] in, represents the passenger flow density of the channel in the functional interweaving area, C represents the range of the channel in the functional interweaving area, represents the vertex coordinates of the channel, T represents the total time step, P represents the total number of pedestrians in the functional interweaving area, represents the area of the channel, An indicator function representing the number of pedestrians in the channel, represents the position of pedestrian p at time t, represents the position of pedestrian p at time step t+1, represents the pedestrian position update function, represents the actual speed of pedestrian p at time t.

[0302] In some embodiments, the pedestrian position update function is a social force model based on attractive potential energy;

[0303] The acquisition module 401 is further configured to acquire the driving force of pedestrians in the functional interweaving area of the commercial complex, the interaction force between the pedestrians and other pedestrians, the repulsive force between the pedestrians and the walls in the passage, and the attractive potential energy between the shops and the pedestrians;

[0304] The processing module 402 is also used to construct the social force model based on attraction potential energy based on the driving force of pedestrians in the functional intersection area of the commercial complex, the interaction force between the pedestrians and other pedestrians, the repulsive force between the pedestrians and the walls in the channel, and the attraction potential energy between the shops and pedestrians.

[0305] In some embodiments, the social force model based on attractive potential energy is expressed by the following formula:

[0306]

[0307]

[0308]

[0309]

[0310]

[0311]

[0312]

[0313]

[0314] in, represents the mass of pedestrian p, represents the driving force of pedestrian p, represents the interaction force between pedestrian p and other pedestrians q, represents the repulsive force between the pedestrian p and the wall w in the channel, Indicates a shop and pedestrians The attractive potential energy between represents the actual speed of pedestrian p, represents the actual speed of other pedestrian q, represents the expected speed of pedestrian p at time t, represents the unit vector pointing to the desired target at time t, τ represents a certain characteristic time, represents the strength of social interaction, represents the scope of social interaction forces, and are all constants, represents the sum of the radii of pedestrian p and other pedestrians q, represents the radius of pedestrian p, represents the distance between the center of mass of pedestrian p and other pedestrian q, represents the distance between the center of mass of pedestrian p and wall w, represents the normalized vector from other pedestrian q to pedestrian p, represents the normalized vector from the wall w to the pedestrian p, represents the body compressibility coefficient, represents the coefficient of sliding friction, represents the tangent direction of pedestrian p and other pedestrians q, represents the tangent direction of pedestrian p and wall w, Q represents the total number of pedestrians, W represents the total number of walls, Indicates whether pedestrians are not touching each other or touching the wall, represents the position of pedestrian p at time t, represents the position of pedestrian p at the initial time, Indicates the effective force value; Indicates a shop For pedestrians attractiveness; Indicates pedestrians Point to the shop The normalized vector of ; Represents the weight parameter to ensure the consistency of unit magnitude, and its value is , Indicates a shop The scale, Indicates a shop The guidance coefficient, Indicates a shop With pedestrians The distance between Indicates a shop With pedestrians The matching degree between Represents a range constraint, used to determine whether a shop is within the pedestrian's attention range. Indicates a shop and pedestrians The angle between The angle representing the pedestrian's attention range, Indicates the depth of the pedestrian's attention range.

[0315] An embodiment of the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method disclosed in the embodiment of the present disclosure.

[0316] Figure 5 FIG. 1 shows a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present disclosure. Figure 5 As shown, the electronic device 500 includes at least one processor 501 and a memory 502 coupled to the processor 501. The processor 501 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0317] The processor 501 can also be referred to as a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in the embodiments of the present disclosure can be performed by hardware integrated logic circuits in the processor 501 or by software instructions. The processor 501 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present disclosure can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can be located in memory 502, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other storage media commonly used in the art. The processor 501 reads the information in the memory 502 and, in conjunction with its hardware, completes the steps of the method.

[0318] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transferred from a storage medium or a network to a computer system having a dedicated hardware structure, for example, Figure 6 The computer system 600 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 6 A schematic diagram of the structure of a computer system provided by an exemplary embodiment of the present disclosure is shown.

[0319] Computer system 600 is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic equipment can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0320] like Figure 6As shown, computer system 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of computer system 600. Computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0321] Multiple components within computer system 600 are connected to I / O interface 605, including an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. Input unit 606 can be any type of device capable of inputting information into computer system 600. Input unit 606 can receive input numeric or character information and generate key input signals related to user settings and / or function control of an electronic device. Output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 608 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 609 allows computer system 600 to exchange information / data with other devices over a network, such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0322] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device via the ROM 602 and / or the communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the methods disclosed in the embodiments of the present disclosure by any other suitable means (e.g., via firmware).

[0323] An embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0324] The computer-readable storage medium in the embodiments of the present disclosure may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. The computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0325] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0326] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein the computer program implements the above method disclosed in the embodiments of the present disclosure when executed by a processor.

[0327] In embodiments of the present disclosure, computer program code for performing the operations of the present disclosure may be written in one or more programming languages or a combination thereof, including but not limited to object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0328] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0329] The modules, components, or units described in the embodiments of the present disclosure may be implemented in software or hardware. The names of the modules, components, or units do not necessarily limit the modules, components, or units themselves.

[0330] The functions described above herein may be at least partially performed by one or more hardware logic components. For example, and without limitation, exemplary hardware logic components that may be used include: a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), a system on a chip (SOC), a complex programmable logic device (CPLD), and the like.

[0331] The above descriptions are merely some embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned concepts. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0332] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art will appreciate that the above examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for optimizing the layout of shops in the functional interweaving area of a commercial complex, characterized by: include: Obtain a pre-built macro-optimization model based on store attributes and a pre-built micro-optimization model based on pedestrian attributes; wherein the macro-optimization model is used to optimize the total attractiveness of stores and the penalty for unbalanced passenger flow in the functional interweaving area of the commercial complex, and the micro-optimization model is used to optimize the passenger flow density of the passages in the functional interweaving area of the commercial complex; respectively determining the macro optimization objectives and constraints of the macro optimization model and the micro optimization objectives of the micro optimization model; Determining a plurality of candidate store layouts using the macro optimization model, the macro optimization objective, and the constraint conditions; Determining a target shop layout from the plurality of candidate shop layouts using the micro-optimization model and the micro-optimization goal, and performing shop layout in the functional interweaving area of the commercial complex using the target shop layout; The method further comprises: Determining the total attractiveness of the shops in the functional interweaving area of the commercial complex based on the shop attributes; Obtaining a penalty weight for unbalanced passenger flow, a passenger flow ratio limit, and an initial predicted attractiveness of a shop, and determining an unbalanced passenger flow penalty for the shops in the functional interweaving area of the commercial complex based on the total attractiveness of the shops in the functional interweaving area of the commercial complex, the penalty weight for unbalanced passenger flow, the passenger flow ratio limit, and the initial predicted attractiveness of the shops; Constructing the macro optimization model based on the total attractiveness of the shops in the functional interweaving area of the commercial complex and the unbalanced passenger flow penalty; The method further comprises: Obtaining the range of the passage in the functional interweaving area of the commercial complex, the area of the passage, a pedestrian quantity indication function, and a pedestrian position update function; The micro-optimization model is constructed based on the range of the channel in the functional interweaving area of the commercial complex, the area of the channel, the pedestrian quantity indication function and the pedestrian position update function.

2. The method according to claim 1, wherein The macro optimization model is expressed by the following formula: Among them, I={1,2...,n} represents the set of shops, J={1,2...,m} represents the location set of shops, and x ij ∈{0,1} indicates whether shop i is assigned to location j, represents the attractiveness of store i at location j, represents the penalty weight of unbalanced passenger flow, Indicates passenger flow ratio limit, represents the initial predicted attractiveness of store i at location j; The store attributes include the store's size, type, location, brand level, and complementarity; the attractiveness of store i at location j is calculated using the following formula: in, Indicates the size of shop i, which is equal to the area of location j , Indicates the type of shop i, represents the brand level of shop i, represents the location attractiveness of shop i at location j, Represents a distance Boolean variable, used to characterize the complementary effect between shop i at location j and shop n. If > , =0, represents the distance between the center coordinates of location j and shop n, represents the influence radius of shop i, represents the complementary weight between shop i and shop n, represents the initial predicted attractiveness of store n at location m, , represents the saturation effect function of shop i, represents the number of shops of the same type within the influence radius of shop i at location j, α>0 represents the saturation rate parameter, indicates the purchase desire adjustment parameter, ∈[0,1]; The location attractiveness of the shop i at location j is expressed by the following formula: in, The set of entries e representing the functional interweaving area, represents the set of elevators l, ( , ) represents the coordinates of position j, ( , ) represents the coordinates of the entrance e, ( , ) represents the coordinates of elevator l.

3. The method according to claim 2, wherein The macro optimization goal is to maximize the total attractiveness of the shops in the functional interweaving area of the commercial complex; The constraint condition is expressed by the following formula: in, represents the area of position j, represents the maximum passenger flow carrying capacity of shop i, Indicates the type of shop i, It represents the total attractiveness of shops in the functional interweaving area.

4. The method according to claim 1, wherein The micro-optimization goal is to minimize the passenger flow density of the passages in the functional interweaving area of the commercial complex; The micro-optimization model is expressed by the following formula: in, represents the passenger flow density of the channel in the functional interweaving area, C represents the range of the channel in the functional interweaving area, represents the vertex coordinates of the channel, T represents the total time step, P represents the total number of pedestrians in the functional interweaving area, represents the area of the channel, An indicator function representing the number of pedestrians in the channel, represents the position of pedestrian p at time t, represents the position of pedestrian p at time step t+1, represents the pedestrian position update function, represents the actual speed of pedestrian p at time t.

5. The method according to claim 4, wherein The pedestrian position update function is a social force model based on attractive potential energy; the method further includes: Obtaining the driving force of pedestrians in the functional interweaving area of the commercial complex, the interaction force between the pedestrians and other pedestrians, the repulsive force between the pedestrians and the walls in the passage, and the attractive potential energy between the shops and pedestrians; Based on the driving force of pedestrians in the functional intersection area of the commercial complex, the interaction force between the pedestrians and other pedestrians, the repulsive force between the pedestrians and the walls in the passage, and the attractive potential energy between the shops and pedestrians, the social force model based on attractive potential energy is constructed.

6. The method according to claim 5, wherein The social force model based on attractive potential energy is expressed by the following formula: in, represents the mass of pedestrian p, represents the driving force of pedestrian p, represents the interaction force between pedestrian p and other pedestrians q, represents the repulsive force between the pedestrian p and the wall w in the channel, Indicates a shop and pedestrians The attractive potential energy between represents the actual speed of pedestrian p, represents the actual speed of other pedestrian q, represents the expected speed of pedestrian p at time t, represents the unit vector pointing to the desired target at time t, τ represents a certain characteristic time, represents the strength of social interaction, represents the scope of social interaction forces, and are all constants, represents the sum of the radii of pedestrian p and other pedestrians q, represents the radius of pedestrian p, represents the distance between the center of mass of pedestrian p and other pedestrian q, represents the distance between the center of mass of pedestrian p and wall w, represents the normalized vector from other pedestrian q to pedestrian p, represents the normalized vector from the wall w to the pedestrian p, represents the body compressibility coefficient, represents the coefficient of sliding friction, represents the tangent direction of pedestrian p and other pedestrians q, represents the tangent direction of pedestrian p and wall w, Q represents the total number of pedestrians, W represents the total number of walls, Indicates whether pedestrians are not touching each other or touching the wall, represents the position of pedestrian p at time t, represents the position of pedestrian p at the initial time, Indicates the effective force value; Indicates a shop For pedestrians attractiveness; Indicates pedestrians Point to the shop The normalized vector of ; Represents the weight parameter to ensure the consistency of unit magnitude, and its value is , Indicates a shop The scale, Indicates a shop The guidance coefficient, Indicates a shop With pedestrians The distance between Indicates a shop With pedestrians The matching degree between Represents a range constraint, used to determine whether a shop is within the pedestrian's attention range. Indicates a shop and pedestrians The angle between The angle representing the pedestrian's attention range, Indicates the depth of the pedestrian's attention range.

7. A layout optimization device for shops in the functional interweaving area of a commercial complex, characterized by: include: An acquisition module is used to acquire a pre-built macro-optimization model based on store attributes and a pre-built micro-optimization model based on pedestrian attributes; wherein the macro-optimization model is used to optimize the total attractiveness of stores and the penalty for unbalanced passenger flow in the functional interweaving area of the commercial complex, and the micro-optimization model is used to optimize the passenger flow density of the passages in the functional interweaving area of the commercial complex; a processing module, configured to respectively determine the macro optimization objective and constraints of the macro optimization model and the micro optimization objective of the micro optimization model; The processing module is further configured to determine a plurality of candidate store layouts using the macro optimization model, the macro optimization objective, and the constraint conditions; The processing module is further configured to determine a target shop layout from the plurality of candidate shop layouts using the micro-optimization model and the micro-optimization objective, so as to perform shop layout in the functional interweaving area of the commercial complex using the target shop layout; The processing module is further configured to determine the total attractiveness of the shops in the functional interweaving area of the commercial complex based on the shop attributes; The acquisition module is further configured to acquire a penalty weight for unbalanced passenger flow, a passenger flow ratio limit, and an initial predicted attractiveness of a shop; the processing module is further configured to determine an unbalanced passenger flow penalty for shops in the functional interweaving area of the commercial complex based on the total attractiveness of the shops in the functional interweaving area of the commercial complex, the penalty weight for unbalanced passenger flow, the passenger flow ratio limit, and the initial predicted attractiveness of the shops; The processing module is further configured to construct the macro optimization model based on the total attractiveness of the shops in the functional interweaving area of the commercial complex and the unbalanced passenger flow penalty; The acquisition module is further used to obtain the range of the channel in the functional interweaving area of the commercial complex, the area of the channel, the pedestrian quantity indication function and the pedestrian position update function; The processing module is also used to construct the micro-optimization model based on the range of the channel in the functional interweaving area of the commercial complex, the area of the channel, the pedestrian quantity indication function and the pedestrian position update function.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images