A smart exhibition intelligent management method and system based on artificial intelligence

Abstract

The present invention relates to the technical field of exhibition intelligent management, and discloses an intelligent exhibition intelligent management method and system based on artificial intelligence, including: obtaining the exhibition information of exhibitors; making an initial arrangement of the exhibition layout according to the exhibition information; making assumptions about the exhibition path using an improved ant colony algorithm, and evaluating the adaptability of the initial arrangement; screening the initial arrangement according to the adaptability to obtain the final arrangement; recording the check-in flow of each exhibitor in each cycle, and managing and adjusting the exhibition according to the flow conditions. By combining artificial intelligence and optimization algorithms, the intelligent management of exhibitions is realized, which can not only optimize the layout of booths and improve space utilization, but also adjust the exhibition operation according to real-time traffic data, avoid congestion, and enhance the experience of exhibitors and visitors.

Description

Technical Field

[0001] The present invention relates to the technical field of exhibition intelligent management, and specifically to an intelligent exhibition intelligent management method and system based on artificial intelligence. Background Art

[0002] With the development of globalization and digitalization, exhibitions, as an important platform for enterprises to display products and acquire customers, are facing increasingly complex management challenges. Traditional exhibition management methods usually rely on manual layout and simple traffic monitoring, which is difficult to effectively deal with the uncertainty and complexity of exhibitor and visitor traffic. Especially in large-scale exhibitions, how to optimize booth layout, improve exhibition experience, avoid congestion and guide visitor flow has become the core issue of exhibition management.

[0003] At the same time, the rapid development of technologies such as artificial intelligence, big data analysis and optimization algorithms has provided new solutions for exhibition management. Based on these technologies, exhibition management can achieve a more intelligent, dynamic and personalized operation model, which not only improves the experience of exhibitors and visitors, but also improves the efficiency and accuracy of exhibition operations. However, how to combine these advanced technologies to achieve accurate path planning, real-time traffic management and dynamic layout adjustment is still a key issue in the current technological development. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing intelligent management method for exhibitions has problems such as unreasonable layout and exhibitors cannot fully display their products.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent management method for smart exhibitions based on artificial intelligence, comprising:

[0007] Obtain exhibitors' exhibition information;

[0008] Make initial arrangements for the exhibition layout based on the exhibitor information;

[0009] Using the improved ant colony algorithm to make assumptions about the exhibition path and evaluate the adaptability of the initial arrangement;

[0010] According to the adaptability, the initial arrangement is screened to obtain a final arrangement;

[0011] The check-in flow of each exhibitor is recorded in each cycle, and the management of the exhibition is adjusted according to the flow situation.

[0012] As a preferred solution of the intelligent management method of smart exhibition based on artificial intelligence described in the present invention, wherein: the exhibition information includes the floor area of ​​the exhibitor, the number of booths of the exhibitor at the exhibition, the historical flow of people and the real-time flow of people of the exhibitor;

[0013] When people pass through the access point, they punch in to record the traffic data.

[0014] As a preferred solution of the intelligent management method of a smart exhibition based on artificial intelligence described in the present invention, wherein: the initial arrangement includes obtaining the arrangable positions in the exhibition to form an exhibition map, in which the total number of booths and the area of ​​each booth are used as arrangement elements to randomly arrange the booths;

[0015] When arranging, constraints include: inherent constraints and conditional constraints;

[0016] The inherent constraints include that no two booths can have overlapping parts;

[0017] The conditional constraints include shape constraints on each booth, distance constraints between booths, and other constraints that can be set;

[0018] The shape constraint of each booth includes adaptively changing the booth shape and angle according to the requirements of the booth arrangement when the booth is arranged; during the booth shape change process, the average horizontal and vertical values ​​of the booth size are both greater than the variable threshold L;

[0019] The distance constraint between the booths includes a preset minimum distance between the booths;

[0020] ;

[0021] in, Indicates the area of ​​the booth. represents the reduction factor, .

[0022] As a preferred solution of the intelligent exhibition intelligent management method based on artificial intelligence described in the present invention, wherein: the exhibition path includes, in each of the initial arrangements, obtaining a traversable area; assuming that each exhibitor moves in one direction in the path;

[0023] The improved ant colony algorithm is used to make assumptions about the exhibition path.

[0024] As a preferred solution of the intelligent management method of smart exhibition based on artificial intelligence described in the present invention, wherein: the improved ant colony algorithm includes taking the exhibitors as individual ants in the ant colony, simulating pheromones through each booth, and making assumptions about the exhibition path;

[0025] Assume that, in the map, the length of the paths connected around booth i is recorded as , and mark The starting and ending points of the path; the probability of individuals checking in on the left and right sides is , ; The probability that the individual's target appears in the i-th booth is ,After finding the target in the booth, the individual leaves the exhibition and ends the path;

[0026] Generate pheromone concentration increment and pheromone increment according to the exhibitor's historical traffic situation;

[0027] The concentration increment of the pheromone is expressed as:

[0028] ;

[0029] in, represents the increment of the pheromone concentration in the path around the i-th booth; j represents the index of the historical record; J represents the number of historical records; represents the increment factor of pheromone concentration; Indicates the check-in traffic of the exhibitor corresponding to the i-th booth visited in the j-th record; represents the total check-in flow in the jth record;

[0030] The pheromone increment includes, with respect to booth i, The paths corresponding to the starting and ending points are assigned pheromone increments;

[0031]

[0032] The initial pheromone in the path is 0. For each booth i, In the corresponding path section, pheromone increments are assigned; the pheromone increments of different booths are superimposed on the path to form pheromones in the path, and the starting and ending points of the pheromone increment of each booth and the booth corresponding to the pheromone increment are marked;

[0033] Homogenize the starting and ending points in the path and calculate them as nodes;

[0034] The ants move in a fixed direction. When ant y is between adjacent nodes a and b, it obtains the booth mark corresponding to the pheromone between a and b. Suppose the booths on the left and right sides of the path are f and g respectively. According to the concentration increment of the pheromone in booths f and g, and , calculate the probability of an individual checking in on the left and right sides in the path respectively and ;

[0035] ;

[0036] ;

[0037] Among them, both f and g can be empty. When f is empty, ; When g is empty, ; represents the adjustment factor;

[0038] If an exhibitor has multiple booths or there are multiple nodes in the paths around the booths, after checking in at a booth, the pheromones emitted by all the exhibitors' booths will be cleared;

[0039] In any booth i, the ants have The probability of finding the target at booth i;

[0040] ;

[0041] in, Indicated in , the number of times the last clock-in occurred at booth i before the exhibitor left;

[0042] When ant y checks in at each booth, the total probability of finding the target is evaluated as:

[0043] ;

[0044] Where K represents the current number of clock-in times; k represents the serial number index of the clock-in location; represents the probability of finding the target at the check-in position k; Indicates the total probability of finding the target when checking in currently.

[0045] As a preferred solution of the intelligent exhibition intelligent management method based on artificial intelligence described in the present invention, wherein: the adaptability includes, assuming that the maximum number of individuals in the ant colony is Y, and looping the exhibition path hypothesis process of the ant individuals Y times;

[0046] Get the number of times each exhibitor is clocked in during Y cycles;

[0047] ;

[0048] in, Indicates the number of times exhibitor m was clocked in. represents the sum of the lengths of the paths around all booths of exhibitor m, represents the adaptability parameter of exhibitor m;

[0049] ;

[0050] in, express The maximum value of express The minimum value of express Very bad, , M represents the number of exhibitors;

[0051] Screening the initial arrangement includes: As the adaptability of each of the initial arrangements, the arrangement with the smallest adaptability is selected as the final arrangement result.

[0052] As a preferred solution of the intelligent management method of smart exhibition based on artificial intelligence described in the present invention, the management adjustment includes obtaining the actual punch-in flow of each exhibitor in the records of each cycle and visualizing the flow;

[0053] By dragging booths, the exhibitors' booths can be adjusted at the management level and the booths can be adjusted before the next cycle starts;

[0054] At the same time, guidance instructions were issued to exhibition staff to disperse the flow of people.

[0055] An artificial intelligence-based smart exhibition intelligent management system using any method described in the present invention, characterized in that:

[0056] Collection module, to obtain exhibitors’ exhibition information;

[0057] Arrangement module, which makes initial arrangement of exhibition layout according to the exhibitor information;

[0058] An analysis module, using an improved ant colony algorithm to make assumptions about the exhibition path and evaluate the adaptability of the initial arrangement;

[0059] A selection module is used to screen the initial arrangement according to the adaptability to obtain a final arrangement;

[0060] The adjustment module records the clock-in flow of each exhibitor in each cycle and makes management adjustments to the exhibition based on the flow conditions.

[0061] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0062] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0063] Beneficial effects of the invention: The intelligent management method for smart exhibitions based on artificial intelligence provided by the invention realizes the intelligent management of exhibitions by combining artificial intelligence and optimization algorithms. It can not only optimize the layout of booths and improve space utilization, but also adjust exhibition operations according to real-time traffic data to avoid congestion and enhance the experience of exhibitors and visitors. Through data-driven management decisions, exhibition organizers can accurately predict and guide the flow of people, optimize the overall smoothness of the exhibition and the exhibition effect, thereby improving the operational efficiency of the exhibition and the satisfaction of exhibitors, and providing innovative intelligent management solutions for the exhibition industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0065] Figure 1 An overall flow chart of an intelligent management method for a smart exhibition based on artificial intelligence provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides an intelligent management method for a smart exhibition based on artificial intelligence, comprising:

[0068] S1: Obtain the exhibitor's exhibition information.

[0069] Furthermore, the exhibition information includes the floor space occupied by the exhibitor, the number of booths of the exhibitor at the exhibition, the historical flow of people and the real-time flow of people of the exhibitor; wherein the flow of people is recorded by punching in when visiting.

[0070] By obtaining the exhibitors’ floor space, number of booths, historical visitor flow, and real-time visitor flow, we can fully understand the scale and influence of each exhibitor, as well as the traffic conditions of the booths. This information provides a decision-making basis for the intelligent exhibition management system, allowing the system to take into account the actual needs of each booth and the historical performance of exhibitors during the exhibition layout and path optimization process, so as to more accurately adjust the layout and traffic guidance strategy, and improve the overall operation efficiency of the exhibition and the visitor experience.

[0071] S2: Initially arrange the exhibition layout according to the exhibition information.

[0072] Furthermore, the arrangable positions in the exhibition are obtained to form an exhibition map. In the exhibition map, the total number of booths and the area of ​​each booth are used as arrangement elements, and the booths are randomly arranged to obtain all arrangement results.

[0073] When arranging, constraints include: inherent constraints and conditional constraints.

[0074] The inherent constraint is that no two booths can have overlapping parts.

[0075] The conditional constraints include shape constraints on each booth, distance constraints between booths, and other configurable constraints.

[0076] By obtaining the map of the exhibition and the floor space of each booth, combined with the needs of exhibitors and traffic conditions, the system can preliminarily allocate booths to the exhibition space. In this process, it is necessary to ensure that certain constraints are followed to avoid problems such as booth overlap, shape mismatch, and insufficient booth spacing, so as to achieve a basically compliant and conflict-free exhibition layout plan.

[0077] Through preliminary arrangement, the system can provide a starting point (entrance) and a traversable path for subsequent optimization algorithms (such as ant colony algorithm), ensure that the basis of the arrangement meets the actual operation requirements, and lay the foundation for subsequent dynamic adjustments.

[0078] The shape constraint for each booth includes adaptively changing the booth shape and angle according to the requirements of the booth arrangement when the booth is arranged; during the booth shape change process, the average horizontal and vertical values ​​of the booth size are both greater than the variable threshold L.

[0079] The distance constraint between the booths includes a preset minimum distance between the booths.

[0080] ;

[0081] in, Indicates the area of ​​the booth. represents the reduction factor, .

[0082] Among them, other constraints that can be set, for example, functional division constraints: allocate corresponding areas according to different booth types (such as business exhibitions, technology exhibitions, lecture areas, rest areas, etc.) to ensure that the functional areas between various booths are reasonably divided. For example, the rest area and the commercial display area should be kept at a certain distance to avoid interference. Safety constraints: ensure that there are enough passages around each booth to meet emergency evacuation requirements. The width of the safety passage for each booth can be preset according to the area of ​​the booth and the number of exhibitors.

[0083] S3: Using the improved ant colony algorithm to make assumptions about the exhibition path and evaluate the adaptability of the initial arrangement.

[0084] In each of the initial arrangements, a traversable area is obtained, and each exhibitor is assumed to move in one direction in the path. The improved ant colony algorithm is used to make assumptions about the exhibitor path.

[0085] It should be noted that the improved ant colony algorithm includes treating exhibitors as individual ants in an ant colony, simulating pheromones through each booth, and making assumptions about the exhibition path.

[0086] Assume that, in the map, the length of the paths connected around booth i is recorded as , and mark The starting and ending points of the path; the probability of individuals checking in on the left and right sides is , (The path appears at most on the booths on the left and right sides, and only the probability of checking in on both sides is considered); the probability of an individual's target appearing in the i-th booth is ,After the individual found the target in the booth, they left the exhibition and ended the path.

[0087] Based on the exhibitor's historical traffic conditions, the pheromone concentration increment and pheromone increment are generated.

[0088] The concentration increment of the pheromone is expressed as:

[0089] ;

[0090] in, represents the increment of the pheromone concentration in the path around the i-th booth; j represents the index of the historical record; J represents the number of historical records; represents the increment factor of pheromone concentration; Indicates the check-in traffic of the exhibitor corresponding to the i-th booth visited in the j-th record; Represents the total check-in flow in the jth record.

[0091] The pheromone increment includes, with respect to booth i, The paths corresponding to the starting and ending points are assigned pheromone increments.

[0092]

[0093] The initial pheromone in the path is 0. For each booth i, In the corresponding path interval, pheromone increments are assigned; the pheromone increments of different booths are superimposed on the path to form pheromones in the path, and the starting and ending points of the pheromone increment of each booth and the booth corresponding to the pheromone increment are marked; the starting and ending points in the path are homogenized and calculated as nodes.

[0094] It should be noted that the step of using pheromone increments to accumulate pheromones also deviates from the original ant colony algorithm. Instead, it homogenizes the nodes through the pheromones of the entire path.

[0095] The ants move in a fixed direction. When ant y is between adjacent nodes a and b, it obtains the booth mark corresponding to the pheromone between a and b (it can be 2, because there are at most 2 booths on both sides of the path). Suppose the booths on the left and right sides of the path are f and g respectively. According to the concentration increment of the pheromone in booths f and g, and , calculate the probability of an individual checking in on the left and right sides in the path respectively and .

[0096] In the improvement of this method, pheromones do not play the role of path guidance, but through the concept of pheromones, the selection of possible target locations is simulated.

[0097] ;

[0098] ;

[0099] Among them, both f and g can be empty. When f is empty, ; When g is empty, ; Represents the adjustment coefficient. If an exhibitor has multiple booths or there are multiple nodes in the paths around the booths, after checking in at a booth, the pheromones emitted by all booths of the exhibitor will be cleared; in fact, this part of the constraint is equivalent to a constraint condition (each exhibitor can only be visited once).

[0100] In any booth i, the ants have The probability of finding the target at booth i.

[0101] ;

[0102] in, Indicated in The number of times the last time an exhibitor clocked in before leaving was at booth i.

[0103] When ant y checks in at each booth, the total probability of finding the target is evaluated as:

[0104] ;

[0105] Where K represents the current number of clock-in times; k represents the serial number index of the clock-in location; represents the probability of finding the target at the check-in position k; Indicates the total probability of finding the target when clocking in. Because when attending an exhibition, you will compare multiple booths, and it is possible that you will find that the content of a booth you clocked in meets your target needs during the comparison process. If you clock in again, you will end the path, go directly to the booth you like, and leave the venue. At this time, it is expressed as "finding the target when clocking in".

[0106] S4: Screening the initial arrangement according to the adaptability to obtain a final arrangement.

[0107] Furthermore, let the maximum number of individuals in the ant colony be Y, and repeat the exhibition path assumption process of the ant individuals Y times. Obtain the number of times each exhibitor is clocked in during the Y cycles.

[0108] ;

[0109] in, Indicates the number of times exhibitor m was clocked in. represents the sum of the lengths of the paths around all booths of exhibitor m, represents the fitness parameter of exhibitor m.

[0110] ;

[0111] in, express The maximum value of express The minimum value of express Very bad, , M represents the number of exhibitors.

[0112] Will As the adaptability of each of the initial arrangements, the arrangement with the smallest adaptability is selected as the final arrangement result.

[0113] By simulating the ant colony algorithm, based on the adaptability evaluation of the initial layout, the exhibition layout is optimized and the optimal booth layout is selected. The initial layout may have a certain amount of optimization space, so through multiple cycles of iteration, the adaptability of different layouts is evaluated, and finally the most suitable layout result is selected. Adaptability is used as an evaluation criterion here, mainly measuring the number of exhibitors' check-ins and the rationality of the path, that is, the exhibitors' exposure and path smoothness.

[0114] Specifically, the adaptability parameter measures the display effect and traffic distribution of each exhibitor by calculating the number of times each exhibitor is clocked in in each cycle, and the path length reflects the distance traveled by exhibitors and visitors, which in turn affects the efficiency and smoothness of the exhibition. After multiple rounds of iterations, an optimally adaptable layout can be selected to maximize the display effect of exhibitors, make the exhibition traffic distribution more balanced, and avoid overly crowded or deserted areas.

[0115] S5: Record the check-in flow of each exhibitor in each cycle, and make management adjustments to the exhibition based on the flow situation.

[0116] In the records of each cycle, the actual check-in traffic of each exhibitor is obtained and the traffic is visualized; by dragging the booths, the exhibitors' booths are adjusted at the management level, and the booths are adjusted before the start of the next cycle; at the same time, guidance instructions are issued to the exhibition staff to disperse the traffic.

[0117] By real-time monitoring and recording the clock-in flow of each exhibitor, the exhibition layout and guidance strategy can be dynamically adjusted to optimize the exhibition site visit experience, reduce congestion, and improve the distribution balance of traffic. The clock-in flow data at the exhibition provides exhibition managers with actual traffic information on each booth, helping to identify booths with high or low traffic and make corresponding adjustments.

[0118] According to the punch-in traffic, if the flow of visitors to certain booths is too large, it may cause congestion in the area. Managers can optimize by dragging booths or adjusting the distance between booths to reduce congested areas and improve the overall exhibition experience. Based on traffic monitoring, the exhibition management system can issue guidance instructions, adjust the exhibition route or arrange staff to conduct real-time traffic diversion. For example, by adjusting the opening and guidance of entrances, exits, and channels, the flow of visitors can be dispersed to avoid excessive congestion in a hot spot area.

[0119] On the other hand, this embodiment also provides an intelligent exhibition management system based on artificial intelligence, which includes:

[0120] Collection module, obtains exhibitors' exhibition information.

[0121] The layout module makes an initial layout of the exhibition according to the exhibition information.

[0122] The analysis module uses the improved ant colony algorithm to make assumptions about the exhibition path and evaluate the adaptability of the initial arrangement.

[0123] A selection module is provided to screen the initial arrangement according to the adaptability to obtain a final arrangement.

[0124] The adjustment module records the clock-in flow of each exhibitor in each cycle and makes management adjustments to the exhibition based on the flow conditions.

[0125] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0126] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0127] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0128] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0129] Example 2 is an embodiment of the present invention, which provides an intelligent management method for smart exhibitions based on artificial intelligence. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0130] This experiment is based on a large international exhibition with 100 exhibitors and a total exhibition area of ​​5,000 square meters. Each exhibitor has booths of different sizes, ranging from 50 square meters to 200 square meters. The exhibition is expected to attract more than 3,000 visitors. During the experiment, an intelligent management method for smart exhibitions based on artificial intelligence was used and compared with traditional exhibition layout and traffic monitoring methods.

[0131] First, the detailed exhibition information of 100 exhibitors was obtained, including the exhibitors' floor space, number of booths, historical traffic data and real-time traffic. Then, based on this information, the traditional method was used to make a preliminary layout of the exhibition. The traditional method only randomly arranges the booths according to the area of ​​the booths and the layout requirements of the exhibition, and does not consider the optimization of the exhibition traffic. The comparison group uses the artificial intelligence management method in the present invention to optimize the exhibition path through the improved ant colony algorithm, and adjusts the booth layout and management based on traffic data and exhibitor intention data.

[0132] During the experimental implementation phase, the flow data of each exhibitor's check-in flow was recorded during the exhibition to monitor the flow changes of the exhibition path in real time. The method of the present invention dynamically adjusts the layout of the exhibition based on real-time data, especially to guide and disperse the flow of people in areas with dense flow of people. The experimental data collection period is a complete exhibition period (three days in total).

[0133] After the experiment, the exhibition effects of the two methods were compared, including the following key indicators:

[0134] Traffic flow: Compare the traffic flow of each booth in the exhibition generated by the traditional method and the method of the present invention.

[0135] Exhibitor and Visitor Intention Achievement Rate: Measures the achievement of business cooperation intentions between exhibitors and visitors.

[0136] Negative review rate: The negative review rate is calculated by analyzing the feedback data from exhibitors and visitors collected after the exhibition.

[0137] The experimental data are shown in Table 1.

[0138] Table 1 Comparison of experimental results

[0139]

[0140] According to the experimental results, Data Table 1 clearly shows the significant differences between the traditional method and the method of the present invention. There is a clear gap between the traditional method and the method of the present invention in key parameters such as the number of exhibitors, the total flow of people at the exhibition, and the flow of people at the booths. The method of the present invention can effectively increase the flow of people at each booth by using an artificial intelligence algorithm to optimize the path and arranging it according to the historical flow data of exhibitors. Specifically, the average flow of people at each booth under the traditional method is 15 people, while the average flow of people at each booth under the method of the present invention is increased to 22 people, an increase of about 47%.

[0141] In the comparison of exhibitors' intention achievement rate and visitors' intention achievement rate, the method of the present invention is 15% and 15% higher than the traditional method respectively. This difference reflects that unreasonable arrangement will lead to poor interaction between exhibitors and visitors.

[0142] The significant decrease in the negative review rate (from 12% to 4%) is another highlight of the method of the present invention. Since the present invention can adjust the booth layout in real time during the exhibition, it avoids the congestion and dissatisfaction of exhibitors and visitors caused by over-centralization or unreasonable booth configuration in traditional methods. It effectively reduces negative feedback during the exhibition and improves the overall satisfaction of exhibitors and visitors.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent management method for smart exhibitions based on artificial intelligence, characterized in that: include: Obtain exhibitors' exhibition information; Make initial arrangements for the exhibition layout based on the exhibitor information; Using the improved ant colony algorithm to make assumptions about the exhibition path and evaluate the adaptability of the initial arrangement; According to the adaptability, the initial arrangement is screened to obtain a final arrangement; Record the clock-in flow of each exhibitor in each cycle, and make management adjustments to the exhibition based on the flow; The exhibition path includes, in each of the initial arrangements, obtaining a traversable area; Assume that each participant moves in one direction along the path; Using the improved ant colony algorithm to make assumptions about the exhibition path; The improved ant colony algorithm includes treating the exhibitors as individual ants in the ant colony, simulating pheromones through each booth, and making assumptions about the exhibition path; Assume that, in the map, the length of the paths connected around booth i is recorded as , and mark The starting and ending points; The probabilities of individuals checking in on the left and right sides of the path are , ; The probability that the individual's target appears in the i-th booth is ,After finding the target in the booth, the individual leaves the exhibition and ends the path; Generate pheromone concentration increment and pheromone increment according to the exhibitor's historical traffic situation; The concentration increment of the pheromone is expressed as: ； in, represents the increment of the pheromone concentration in the path around the i-th booth; j represents the index of the historical record; J represents the number of historical records; represents the increment factor of pheromone concentration; Indicates the check-in traffic of the exhibitor corresponding to the i-th booth visited in the j-th record; represents the total check-in flow in the jth record; The pheromone increment includes, with respect to booth i, The paths corresponding to the starting and ending points are assigned pheromone increments; ； The initial pheromone in the path is 0. For each booth i, In the corresponding path section, pheromone increments are assigned; the pheromone increments of different booths are superimposed on the path to form pheromones in the path, and the starting and ending points of the pheromone increment of each booth and the booth corresponding to the pheromone increment are marked; Homogenize the starting and ending points in the path and calculate them as nodes; The ants move in a fixed direction. When ant y is between adjacent nodes a and b, it obtains the booth mark corresponding to the pheromone between a and b. Suppose the booths on the left and right sides of the path are f and g respectively. According to the concentration increment of the pheromone in booths f and g, and , calculate the probability of an individual checking in on the left and right sides in the path respectively and ; ； ； Among them, both f and g can be empty. When f is empty, ; When g is empty, ; represents the adjustment factor; If an exhibitor has multiple booths or there are multiple nodes in the paths around the booths, after checking in at a booth, the pheromones emitted by all the exhibitors' booths will be cleared; In any booth i, the ants have The probability of finding the target at booth i; ； in, Indicated in , the number of times the last clock-in occurred at booth i before the exhibitor left; When ant y checks in at each booth, the total probability of finding the target is evaluated as: ； Where K represents the current number of clock-in times; k represents the serial number index of the clock-in location; represents the probability of finding the target at the check-in position k; Indicates the total probability of finding the target when checking in currently.

2. The method for intelligent management of smart exhibitions based on artificial intelligence according to claim 1, characterized in that: The exhibition information includes the exhibitor's floor space, the number of booths of the exhibitor at the exhibition, the exhibitor's historical flow of visitors and real-time flow of visitors; When people pass through the access point, they punch in to record the traffic data.

3. The method for intelligent management of smart exhibitions based on artificial intelligence as claimed in claim 2, characterized in that: The initial arrangement includes obtaining the arrangable positions in the exhibition to form an exhibition map, in which the total number of booths and the area of ​​each booth are used as arrangement elements to randomly arrange the booths; When arranging, constraints include: inherent constraints and conditional constraints; The inherent constraints include that no two booths can have overlapping parts; The conditional constraints include shape constraints on each booth, distance constraints between booths, and other constraints that can be set; The shape constraint of each booth includes adaptively changing the booth shape and angle according to the requirements of the booth arrangement when the booth is arranged; during the booth shape change process, the average horizontal and vertical values ​​of the booth size are both greater than the variable threshold L; The distance constraint between the booths includes a preset minimum distance between the booths; ； Among them, S represents the area of ​​the booth, α represents the reduction coefficient, .

4. The method for intelligent management of smart exhibitions based on artificial intelligence as claimed in claim 3, characterized in that: The adaptability includes, assuming that the maximum number of individuals in the ant colony is Y, and looping the exhibition path hypothesis process of the ant individuals Y times; Get the number of times each exhibitor is clocked in during Y cycles; ； in, Indicates the number of times exhibitor m was clocked in. represents the sum of the lengths of the paths around all booths of exhibitor m, represents the adaptability parameter of exhibitor m; ； in, express The maximum value of express The minimum value of express Very bad, , M represents the number of exhibitors; Screening the initial arrangement includes: As the adaptability of each of the initial arrangements, the arrangement with the smallest adaptability is selected as the final arrangement result.

5. The method for intelligent management of smart exhibitions based on artificial intelligence as claimed in claim 4, characterized in that: The management adjustment includes obtaining the actual check-in flow of each exhibitor in the records of each cycle and visualizing the flow; By dragging booths, the exhibitors' booths can be adjusted at the management level and the booths can be adjusted before the next cycle starts; At the same time, guidance instructions were issued to exhibition staff to disperse the flow of people.

6. An artificial intelligence-based smart exhibition intelligent management system using the method as described in any one of claims 1 to 5, characterized in that: Collection module, to obtain exhibitors’ exhibition information; Arrangement module, which makes initial arrangement of exhibition layout according to the exhibitor information; An analysis module, using an improved ant colony algorithm to make assumptions about the exhibition path and evaluate the adaptability of the initial arrangement; A selection module is used to screen the initial arrangement according to the adaptability to obtain a final arrangement; The adjustment module records the clock-in flow of each exhibitor in each cycle and makes management adjustments to the exhibition based on the flow conditions.

7. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods of claims 1-5 when executing the computer program.

8. 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 5 are implemented.

Images