Service recommendation system and method for public area

Abstract

The invention discloses a service recommendation system and method for a public area, relates to the technical field of public area service recommendation, and aims to optimize distribution and environment adjustment of public resources in a shared office space, construct a user behavior influence matrix through multi-modal data fusion and quantify the interaction strength between users and the public resources. And dynamically detecting the conflict risk of the public resources, generating a dynamically optimized resource allocation scheme on this basis to reduce resource conflicts, and preferentially adjusting the environmental factors of the resource dense area by dividing the resource dense area and combining the environmental adjustment efficiency function to improve the user comfort, thereby not only improving the utilization efficiency of the public resources, but also improving the user experience. In addition, user demand changes can be responded in real time, personalized service recommendation is provided, and therefore resource management and user experience of the shared office space are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of public area service recommendation, and in particular to a public area service recommendation system and method. Background Art

[0002] With the popularization of the shared office model, the efficient use of public resources in office areas has become increasingly important. How to optimize resource allocation and improve user experience through intelligent technology has become an urgent problem to be solved.

[0003] Prior art, such as the invention patent application with publication number CN112395493A, discloses a method, server, and device for intelligent recommendation of shared office, the method comprising: receiving a request for booking a shared workstation sent by a user terminal; predicting the probability of a shared workstation being selected by the user, including obtaining user records of all workstations that have been selected, calculating the similarity between all shared workstations based on the user records, forming a workstation similarity matrix, predicting the probability of a shared workstation being selected by the user based on the calculated workstation similarity matrix and combining the user's historical selection records; sending the shared workstation with a high probability of being selected to the user terminal so that the user terminal displays the recommended workstation for the user to select. The present invention solves the problem of the lack of improving user experience when booking a shared workstation in the prior art, and provides a method, server, device, and system for intelligent recommendation of shared office that can enable users to find and book suitable shared workstations faster, reduce user time costs, and improve user experience.

[0004] There are at least the following technical problems with the above scheme: 1. The above scheme mainly revolves around the intelligent recommendation method, server and equipment for shared office, and lacks the acquisition and analysis of multimodal data of the office area, which will lead to the inability to deeply understand the comprehensive behavior of users in the office environment. In addition, the above scheme only focuses on the workstation selection records, ignoring important factors such as environmental data, making it difficult to fully grasp user needs and the actual conditions of the office environment, which limits the accuracy of the recommendation and makes it impossible to provide users with recommendations that are more in line with their actual needs based on richer information.

[0005] 2. The above scheme lacks a dynamic detection mechanism for the risk of public resource conflicts, which will lead to the inability to predict conflicts in resource use in advance, and lacks the ability to detect conflict risks by analyzing the behavior impact matrix, combining resource usage records and timestamps, and setting a peak demand assessment time period. When the peak period of shared workstation demand arrives, a large number of users may compete for certain workstations at the same time, but they cannot take countermeasures in advance, which reduces the user experience and may even cause problems with unreasonable resource allocation.

[0006] 3. The above solution lacks a link for generating a dynamically optimized resource allocation plan, which will lead to inflexible and unreasonable resource allocation. In addition, simply recommending workstations based on the similarity matrix of workstations and the user's historical selection records without considering the actual demand and carrying capacity of resources at different times and in different scenarios will result in over-recommendation of some workstations and idleness of some workstations, unable to achieve efficient utilization of resources. Summary of the Invention

[0007] The object of the present invention is to provide a service recommendation system and method for a public area, which solves the problems existing in the background technology.

[0008] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a service recommendation system for a public area, including: a data fusion-driven behavior analysis module, configured to obtain corresponding multi-modal data within a specified office area, and construct a behavior influence matrix through the spatio-temporal distribution of user activities to quantify the interaction intensity between users and public resources within the specified office area.

[0009] A conflict risk assessment module, configured to dynamically detect the conflict risk of public resources within the specified office area according to the behavior influence matrix.

[0010] A resource allocation conflict optimization module, configured to generate a corresponding dynamically optimized resource allocation plan for the specified office area according to the conflict risk of public resources within the specified office area.

[0011] An environment dynamic adjustment module, configured to divide the resource-intensive areas within the specified office area, and combine with the resource allocation plan to preferentially adjust the environmental factors of the resource-intensive areas through an environmental adjustment efficiency function.

[0012] The present invention in a second aspect provides a service recommendation method for a public area, including: Step 1, data fusion-driven behavior analysis: Obtain corresponding multi-modal data within the specified office area, and construct a behavior influence matrix through the spatio-temporal distribution of user activities to quantify the interaction intensity between users and public resources within the specified office area.

[0013] Step 2, conflict risk assessment: Dynamically detect the conflict risk of public resources within the specified office area according to the behavior influence matrix.

[0014] Step 3, resource allocation conflict optimization: Generate a corresponding dynamically optimized resource allocation plan for the specified office area according to the conflict risk of public resources within the specified office area.

[0015] Step 4, environment dynamic adjustment: Divide the resource-intensive areas within the specified office area, and combine with the resource allocation plan to preferentially adjust the environmental factors of the resource-intensive areas through an environmental adjustment efficiency function.

[0016] The beneficial effects of the present invention are as follows: 1. A service recommendation system and method for public areas provided by the present invention, in the process of data fusion-driven behavior analysis, obtains corresponding multi-modal data within a specified office area, including user behavior data, environmental data, and interaction data, and constructs a behavior impact matrix through the spatio-temporal distribution of user activities, which is beneficial to accurately quantify the interaction intensity between users and public resources, thereby providing more comprehensive and accurate data support for subsequent resource allocation and adjustment. Based on the multi-modal data fusion method, it can effectively capture the actual needs of users in different environments, avoid the limitations of a single data source, and improve the flexibility and accuracy of the recommendation system.

[0017] 2. In the process of conflict risk assessment of the embodiments of the present invention, by constructing a behavior impact matrix to dynamically detect the conflict risk of public resources within a specified office area, it is beneficial for the system to evaluate the usage intensity and potential conflicts of resources in real time according to the actual user behavior and demand changes, thereby effectively avoiding the problems of resource shortage or over-allocation, and ensuring the rationality and fairness of resource allocation.

[0018] 3. In the process of the resource allocation conflict optimization module of the embodiments of the present invention, by generating a dynamically optimized resource allocation plan according to the conflict risk of public resources, it is beneficial to optimize the usage efficiency of public resources, reduce conflicts, and maximize the utilization of space resources. By calculating the resource allocation optimization index, it helps the system to comprehensively optimize according to factors such as resource demand intensity, conflict risk, and user interaction overlap degree, so as to achieve more accurate and personalized resource recommendation, avoid overcrowding and idleness of resources, and improve the work efficiency of users and the space utilization rate.

[0019] 4. In the process of the environmental dynamic adjustment module of the embodiments of the present invention, by dividing resource-intensive areas and combining with the resource allocation plan, and preferentially adjusting the environmental factors of resource-intensive areas through the environmental adjustment efficiency function, it is beneficial to adjust according to the resource distribution and the actual needs of users. The resource-intensive areas are obtained through the analysis and quantification of the usage density of public resources within a specified office area, representing areas where public resource demands are relatively concentrated and conflicts are likely to occur. The environmental dynamic adjustment module takes resource-intensive areas as the core object, and through the resource allocation plan and the environmental adjustment efficiency function, preferentially optimizes the environmental factors of resource-intensive areas, reduces resource conflicts, and improves the overall usage efficiency and user satisfaction. The two complement each other and jointly improve the management level and service quality of the office area.

[0020] 5. In the process of quantifying the interaction intensity between users and public resources in the embodiments of the present invention, by comprehensively considering factors such as the interaction frequency, spatial distance, and resource availability of users, the quantification value of the interaction intensity between users and resources is calculated, which is beneficial to accurately describe the interaction between users and public resources in different scenarios and effectively evaluate the usage requirements of resources and the real needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0023] Figure 2 It is a schematic diagram of the implementation step flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0025] Please refer to Figure 1 As shown, the present invention provides a service recommendation system for a public area, and the system includes: a data fusion-driven behavior analysis module, a conflict risk assessment module, a resource allocation conflict optimization module, and an environment dynamic adjustment module.

[0026] The data fusion-driven behavior analysis module is connected to the conflict risk assessment module, the conflict risk assessment module is connected to the resource allocation conflict optimization module, and the resource allocation conflict optimization module is connected to the environment dynamic adjustment module.

[0027] The data fusion-driven behavior analysis module is used to obtain corresponding multi-modal data in a specified office area, and construct a behavior influence matrix through the spatio-temporal distribution of user activities to quantify the interaction intensity between users and public resources in the specified office area.

[0028] In a specific embodiment, the process of obtaining the corresponding multi-modal data in the specified office area is as follows: The multi-modal data includes user behavior data, environmental data, and interaction data. By means of the cameras installed in the specified office area, the Bluetooth beacons built into public mobile devices, and the usage records of public non-mobile resources, the interaction frequency between users and public resources in the specified office area is obtained. The public resources include public mobile resources and public non-mobile resources. And the signal strength of the user device is obtained through the Wi-Fi access point, and then the corresponding position coordinates (x 1 , y 1 ) of the user in the specified office area are obtained. And according to the position coordinates (x 2 , y 2 ) of a certain public resource in the specified office area, through the position calculation formula: the spatial distance d between the user and the public resource is obtained, and in this way, the interaction frequency and spatial distance between each user and each public resource in the specified office area are obtained.

[0029] It should be noted that the user behavior data includes but is not limited to the movement trajectory, stay time, and meeting participation of the user in the office area. The environmental data includes but is not limited to temperature and humidity data, light intensity, and noise level. The interaction data includes but is not limited to device usage records and spatial interaction records. The process of obtaining the interaction frequency between the user and the public resource is as follows: For example, when a user enters the area where the shared meeting room is located, the system obtains the interaction frequency between the user and the public resource through the following steps: The system determines the position coordinates of the user through the Wi-Fi access point or Bluetooth beacon. If the user is in the meeting room, the system obtains the signal strength of the user device to determine the position. When the user opens the meeting room door and enters, the device records the entry and exit actions of the user. The system identifies this behavior through the sensor or camera and regards this behavior as an interaction. The system records the action of the user entering the meeting room as an "interaction", and compares it with the interactions of other users to count the "interaction frequency" of this meeting room.

[0030] In a specific embodiment, the process of quantifying the interaction intensity between users and public resources in the specified office area is as follows: Through the calculation formula: the quantization value α ij of the interaction intensity between user i and public resource j is obtained, where ζ ij , d ij respectively represent the interaction frequency and spatial distance between user i and public resource j, and State j represents the availability evaluation value corresponding to public resource j. State j takes the value of 1 or 0. When public resource j is in an available state, State j takes the value of 1, otherwise it is 0. Then the behavior influence matrix R = [αij] is constructed.

[0031] It should be noted that the rows in the behavior impact matrix represent each user, the columns represent each public resource, and the matrix elements represent the quantified values of the interaction intensity between each user and each public resource.

[0032] The conflict risk assessment module is used to dynamically detect the conflict risk of public resources in a specified office area based on the behavior impact matrix.

[0033] In a specific embodiment, the process of dynamically detecting the conflict risk of public resources in the specified office area is as follows: According to the behavior impact matrix R = [αij], the quantified values of the interaction intensity between each user and each public resource are obtained. Through the usage record of public resource j, the interaction timestamps between each user and public resource j are obtained. Set the demand peak assessment time period, divide the demand peak assessment time period into each demand time period, and accumulate the quantified values of the interaction intensity between each user and public resource j within each demand time period. The result obtained is the quantified value of the demand intensity of public resource j within each demand time period. Then, the demand time period corresponding to the quantified value of the demand intensity ranked first within each demand time period is the demand peak of public resource j, and the quantified value of the demand intensity ranked first is denoted as Q max 。

[0035] Obtain the maximum carrying capacity C of public resource j from the management manual of the specified office area. If Q max > C, it indicates that there is a conflict risk between public resource j and users. Otherwise, it indicates that there is no conflict risk between public resource j and users.

[0036] Through the calculation formula: Obtain the quantified value OL of the interaction overlap degree between user i1 and user i2 for public resource j within the demand time period t, where respectively represent the quantified values of the interaction intensity between user i1 and user i2 for public resource j within the time period t.

[0037] It should be noted that the process of setting the demand peak assessment time period is mainly based on the analysis of historical data and real-time monitoring data of the use of public resources in the office area. First, collect the usage records of public resources in different time periods, including interaction frequency and duration, etc., identify the time range with intensive resource use, and then combine the actual operation situation of the office area, such as office hours, rest hours, and special event arrangements, to further accurately determine the time period when resource conflicts occur, ensure that the assessment time period covers the interval with large fluctuations in resource demand. Finally, the identified high-demand time period is used as the demand peak assessment time period.

[0038] It should also be noted that The meaning is as follows: For example, for existing users p1, p2, and p3, the quantization values of their interaction intensities with the public resource j within the time period t are 5, 3, and 2 respectively. The minimum value of the interaction intensities corresponding to p1 and p2 is 3, the minimum value of the interaction intensities corresponding to p2 and p3 is 2, and the minimum value of the interaction intensities corresponding to p1 and p3 is 2. Then the calculation process is: OL = ∑(min(5, 3, 2)) = 3 + 2 + 2 = 7.

[0039] In the process of conflict risk assessment in the embodiments of the present invention, by constructing a behavior impact matrix to dynamically detect the conflict risk of public resources in a specified office area, it is beneficial for the system to evaluate the usage intensity and potential conflicts of resources in real time according to the actual user behavior and demand changes, thereby effectively avoiding the problems of resource shortage or over-allocation, and ensuring the rationality and fairness of resource allocation.

[0040] The resource allocation conflict optimization module is used to generate a corresponding dynamically optimized resource allocation plan for the specified office area according to the conflict risk of the public resources in the specified office area.

[0041] In a specific embodiment, the specific process of generating the corresponding dynamically optimized resource allocation plan for the specified office area is as follows: According to the quantization value of the interaction intensity between the users and the public resources in the specified office area and the conflict risk of the public resources, calculate the resource allocation optimization index corresponding to each public resource in the specified office area. If the resource allocation optimization index of the public resource j is greater than the resource allocation optimization index of the public resource j1, it indicates that the resource conflict corresponding to the public resource j is less than the resource conflict of the public resource j1. Then when the user applies for the public resource j1, recommend the public resource j for the user to use.

[0042] It should be noted that resource conflicts include service convenience, conflict risk, and user overlap degree.

[0043] In a specific embodiment, the specific calculation process of calculating the resource allocation optimization index corresponding to each public resource in the specified office area is as follows: The resource allocation optimization index S corresponding to the public resource j j The corresponding calculation formula is: where d x,j represents the spatial distance between the xth user and the public resource j, x is the number corresponding to each user, x = 1, 2,..., n, n is the number of users, and n is a positive integer, Q j represents the quantization value of the demand intensity between the user and the public resource j during the demand peak assessment time period, λj is the set correction factor corresponding to the public resource j, and OLj represents the quantization value of the interaction overlap degree between the user and the public resource j during the demand peak assessment time period.

[0044] It should be noted that, assuming there are three types of public resources, namely meeting rooms, printers, and workstations, in a shared office area, the role of the correction factor is to balance the importance and particularity of the three public resources when calculating the optimization index. For example, for a meeting room, its usage is affected by the reservation duration and actual utilization rate. The use of the printer is related to the conflict of frequent operations of users, and the workstation is restricted by the space layout and workstation availability. By considering the conflict sensitivity of different resources obtained from historical data analysis, such as historical data showing that the reservation conflict rate of the meeting room during peak hours is as high as 80%, while the usage conflict of the printer is concentrated when the print queue length exceeds 5, and the peak hours only account for 50% of the total usage, indicating that the meeting room has a higher conflict sensitivity. Therefore, a larger correction factor, such as 1.5, can be set for it, while the printer has a low sensitivity and is set to 1.2. In addition, since the demand for workstations is highly correlated with the actual residence time and regional distribution of users, historical data shows that the utilization rate during peak hours is close to the average availability rate, indicating relatively little conflict, and the correction factor is set to 1.0.

[0045] In the process of the resource allocation conflict optimization module of the embodiment of the present invention, a dynamically optimized resource allocation scheme is generated according to the conflict risk of public resources, which is beneficial to optimizing the utilization efficiency of public resources, reducing conflicts, and enhancing the maximized utilization of space resources. By calculating the resource allocation optimization index, it helps the system to conduct comprehensive optimization according to factors such as the demand intensity of resources, conflict risk, and degree of user interaction overlap, so as to achieve more accurate and personalized resource recommendations, avoid overcrowding and idleness of resources, and improve the work efficiency of users and space utilization rate.

[0046] In the process of the environmental dynamic adjustment module of the embodiment of the present invention, by dividing the resource-intensive areas and combining with the resource allocation scheme, the environmental factors of the resource-intensive areas are preferentially adjusted through the environmental adjustment efficiency function, which is beneficial to adjusting according to the resource distribution and the actual needs of users. The resource-intensive areas are obtained through the analysis and quantification of the usage density of public resources in the designated office area, representing the areas where the demand for public resources is relatively concentrated and conflicts are likely to occur. The environmental dynamic adjustment module takes the resource-intensive areas as the core objects, and through the resource allocation scheme and the environmental adjustment efficiency function, preferentially optimizes the environmental factors of the resource-intensive areas, reduces resource conflicts, improves the overall utilization efficiency and user satisfaction, and the two complement each other to jointly improve the management level and service quality of the office area.

[0047] The environmental dynamic adjustment module is used to divide the resource-intensive areas in the designated office area, combine with the resource allocation scheme, and then preferentially adjust the environmental factors of the resource-intensive areas through the environmental adjustment efficiency function.

[0048] In a specific embodiment, the process of obtaining the resource-intensive areas within the specified office area is as follows: The specified office area is divided into each office sub-area, and each public resource included in each office sub-area is obtained. According to the behavior influence matrix within the specified office area, the resource allocation optimization index corresponding to each public resource within the specified office area, and according to the set demand peak evaluation time period, the resource intensity quantization value corresponding to each office sub-area within the demand peak evaluation time period is statistically obtained. If the resource intensity quantization value of a certain office sub-area within the demand peak evaluation time period is greater than the set resource intensity threshold, then this office sub-area is recorded as a resource-intensive area, and thus each resource-intensive area is obtained.

[0049] It should be noted that the division of the specified office area is based on the distribution, usage situation, and intensity of user activities within each office sub-area. For example, in a shared office space, the manager divides the space into each office sub-area according to the public resource configuration within the area and the usage frequency of each resource. For example, in a certain area, there are multiple shared workstations, printers, and meeting rooms. If the usage frequency of the public resources is relatively high and the activities of users in this area are concentrated, the system will divide this area into an office sub-area.

[0050] It should also be noted that the resource intensity quantization value is a quantization index that measures the degree of concentration of resource usage within each office sub-area by combining the behavior influence matrix, the resource allocation optimization index, and the demand peak evaluation time period. Within the set demand peak evaluation time period, first, the quantization value of the interaction intensity between each user and each public resource in the behavior influence matrix is obtained, and based on these values, the resource usage situation within each office sub-area is statistically obtained. Combining the resource allocation optimization index of each public resource, the usage intensity of each public resource within each office sub-area during the demand peak period is evaluated. The resource allocation optimization index reflects the conflict risk and usage demand of the resource. The higher the index, the stronger the demand for this resource. Through weighted summation, the resource intensity quantization value corresponding to each office sub-area within the demand peak evaluation time period is obtained. The process of weighted summation is a prior art and will not be elaborated here too much.

[0051] In a specific embodiment, the process of preferentially adjusting the environmental factors of the resource-intensive areas through the environmental adjustment efficiency function is as follows: The environmental adjustment efficiency of each environmental factor corresponding to each resource-intensive area is calculated through the environmental adjustment efficiency function. Combining the resource allocation scheme that is dynamically optimized and corresponding within the specified office area, if the environmental adjustment efficiency of a certain resource-intensive area corresponding to a certain environmental factor is greater than the set environmental adjustment efficiency threshold of a certain environmental factor, then the environmental factor within this resource-intensive area is preferentially adjusted.

[0052] It should be noted that in a shared office area, if a sub-office area is divided into a resource-intensive area, and the temperature and light in this sub-office area are the main environmental factors affecting user comfort, through the environmental adjustment efficiency function, the system first calculates the adjustment efficiency of the temperature and light in this resource-intensive area. If the calculated temperature adjustment efficiency is 0.8 and the light adjustment efficiency is 0.6, the set temperature environment adjustment efficiency threshold is 0.7, and the light environment adjustment efficiency threshold is 0.5, then the temperature adjustment efficiency exceeds the threshold, while the light adjustment efficiency does not exceed. Therefore, the system will preferentially adjust the temperature environmental factor, and the adjustment methods include adjusting the temperature setting of the air conditioner, increasing or decreasing the ventilation volume, etc. At the same time, the light factor will not be adjusted immediately unless it exceeds the set threshold after the environmental adjustment efficiency is recalculated.

[0053] In a specific embodiment, the environmental adjustment efficiency of each resource-intensive area corresponding to each environmental factor is calculated through the environmental adjustment efficiency function, and the specific process is as follows: Through the calculation formula: The environmental adjustment efficiency βqy of the qth resource-intensive area corresponding to the yth environmental factor is obtained, where G′qy represents the actual value of the yth environmental factor corresponding to the current qth resource-intensive area, G′q′y represents the target value of the yth environmental factor corresponding to the qth resource-intensive area, and G′q″y represents the adjustment cost of the yth environmental factor in the qth resource-intensive area from the actual value to the target value.

[0054] In the process of quantifying the interaction intensity between users and public resources in the embodiments of the present invention, by comprehensively considering factors such as the interaction frequency, spatial distance, and resource availability of users, the quantification value of the interaction intensity between users and resources is calculated, which is beneficial to accurately describe the interaction between users and public resources in different scenarios and effectively evaluate the usage requirements of resources and the true needs of users.

[0055] Please refer to Figure 2 As shown in the figure, a service recommendation method for a public area includes the following steps: Step 1, data fusion-driven behavior analysis: Obtain the corresponding multi-modal data in the specified office area, and construct a behavior influence matrix through the spatio-temporal distribution of user activities to quantify the interaction intensity between users and public resources in the specified office area.

[0056] Step 2, conflict risk assessment: According to the behavior influence matrix, dynamically detect the conflict risk of public resources in the specified office area.

[0057] Step 3, resource allocation conflict optimization: According to the conflict risk of public resources in the specified office area, generate a dynamically optimized resource allocation plan corresponding to the specified office area.

[0058] Step 4. Environmental dynamic adjustment: Divide the resource-intensive areas within the specified office area, and in combination with the resource allocation plan, and then preferentially adjust the environmental factors of the resource-intensive areas through the environmental adjustment efficiency function.

[0059] A service recommendation system and method for a public area provided by the present invention, in the process of data fusion-driven behavior analysis, obtains corresponding multi-modal data within the specified office area, including user behavior data, environmental data, and interaction data, and constructs a behavior influence matrix through the spatio-temporal distribution of user activities, which is beneficial to accurately quantify the interaction intensity between users and public resources, thereby providing more comprehensive and accurate data support for subsequent resource allocation and adjustment. Based on the multi-modal data fusion method, it can effectively capture the actual needs of users in different environments, avoid the limitations of a single data source, and improve the flexibility and accuracy of the recommendation system.

[0060] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A service recommendation system for a public area, characterized in that: include: The data fusion-driven behavior analysis module is used to obtain the corresponding multimodal data in the specified office area, and construct a behavior influence matrix through the spatiotemporal distribution of user activities to quantify the interaction intensity between users and public resources in the specified office area; The conflict risk assessment module is used to dynamically detect the conflict risk of public resources in a specified office area based on the behavior impact matrix; A resource allocation conflict optimization module is used to generate a corresponding dynamically optimized resource allocation plan within a specified office area based on the conflict risk of public resources within the specified office area; The dynamic environment adjustment module is used to divide the resource-intensive areas within the designated office area, and in combination with the resource allocation plan, the environmental factors of the resource-intensive areas are adjusted preferentially through the environmental adjustment efficiency function.

2. A service recommendation system for public areas according to claim 1, characterized in that: The specific process of obtaining the corresponding multimodal data in the designated office area is as follows: Multimodal data includes user behavior data, environmental data, and interaction data. Through the cameras set up in the designated office area, the Bluetooth beacons built into public mobile devices, and the usage records of public non-mobile resources, the interaction frequency between users and public resources in the designated office area is obtained. Public resources include public mobile resources and public non-mobile resources. The signal strength of the user's device is obtained through the Wi-Fi access point, and the corresponding position coordinates (x1, y1) of the user in the designated office area are obtained. According to the position coordinates (x2, y2) corresponding to a certain public resource in the designated office area, the position calculation formula is used: The spatial distance d between the user and the public resource is obtained, thereby obtaining the interaction frequency and spatial distance between each user and each public resource in the specified office area.

3. A service recommendation system for public areas according to claim 2, characterized in that: The specific process of quantifying the interaction intensity between users and public resources in a specified office area is as follows: By calculation formula: Get the quantified value α of the interaction intensity between user i and public resource j ij , where ζ ij ,d ij They are respectively represented as the interaction frequency and spatial distance between user i and public resource j, State j State represents the availability evaluation value corresponding to public resource j. j The value is 1 or 0. When public resource j is available, State j The value is 1, otherwise it is 0, and then the behavior influence matrix R = [αij] is constructed.

4. A service recommendation system for public areas according to claim 3, characterized in that: The specific process of dynamically detecting the conflict risk of public resources in a designated office area is as follows: According to the behavior influence matrix R = [αij], the interaction intensity quantification value of each user and each public resource is obtained. Through the usage record of public resource j, the interaction timestamp of each user and public resource j is obtained. The demand peak evaluation time period is set, and the demand peak evaluation time period is divided into each demand time period. The interaction intensity quantification value of each user and public resource j in each demand time period is accumulated respectively. The result is the demand intensity quantification value of public resource j in each demand time period. The demand time period corresponding to the demand intensity quantification value ranked first in each demand time period is the demand peak of public resource j. The demand intensity quantification value ranked first is recorded as Q max ; Get the maximum carrying capacity C of public resource j from the management manual of the designated office area, if Q max >C, it indicates that there is a conflict risk between public resource j and the user, otherwise, it indicates that there is no conflict risk between public resource j and the user; By calculation formula: The quantitative value OL of the degree of interaction overlap between user i1 and user i2 on public resource j in the demand time period t is obtained, where They are respectively represented as the quantitative values ​​of the interaction intensity of user i1 and user i2 on public resource j in time period t.

5. A service recommendation system for public areas according to claim 4, characterized in that: The specific process of generating a dynamically optimized resource allocation plan corresponding to a specified office area is as follows: According to the quantified value of the interaction intensity between users and public resources in the designated office area and the conflict risk of public resources, the resource allocation optimization index corresponding to each public resource in the designated office area is calculated. If the resource allocation optimization index of public resource j is greater than the resource allocation optimization index of public resource j1, it indicates that the resource conflict corresponding to public resource j is less than the resource conflict of public resource j1. When the user applies for public resource j1, public resource j will be recommended to the user for use.

6. A service recommendation system for public areas according to claim 5, characterized in that: The resource allocation optimization index corresponding to each public resource in the specified office area is calculated, and the specific calculation process is as follows: Resource allocation optimization index S corresponding to public resource j j The corresponding calculation formula is: where d x,j It is expressed as the spatial distance between the xth user and the public resource j, where x is the number corresponding to each user, x = 1, 2, ..., n, n is the number of users, and n is a positive integer, Q j It is expressed as the quantitative value of the demand intensity between the user and the public resource j during the peak demand assessment period, λj is the correction factor corresponding to the set public resource j, and OLj is expressed as the quantitative value of the degree of interaction overlap between the user and the public resource j during the peak demand assessment period.

7. A service recommendation system for public areas according to claim 6, characterized in that: The specific process of obtaining the resource-intensive area within the specified office area is as follows: The designated office area is divided into various office sub-areas, and the public resources contained in each office sub-area are obtained. According to the behavior impact matrix in the designated office area and the resource allocation optimization index corresponding to each public resource in the designated office area, as well as the set demand peak assessment time period, the resource intensity quantified value corresponding to each office sub-area in the demand peak assessment time period is statistically obtained. If the resource intensity quantified value of an office sub-area in the demand peak assessment time period is greater than the set resource intensity threshold, the office sub-area is recorded as a resource-intensive area, so as to obtain various resource-intensive areas.

8. A service recommendation system for public areas according to claim 7, characterized in that: The environmental factors of resource-intensive areas are preferentially regulated by the environmental regulation efficiency function, and the specific process is as follows: The environmental regulation efficiency function is used to calculate the environmental regulation efficiency of each environmental factor in each resource-intensive area. Combined with the dynamically optimized resource allocation plan in the designated office area, if the environmental regulation efficiency of a certain environmental factor in a resource-intensive area is greater than the set environmental regulation efficiency threshold of a certain environmental factor, the environmental factor in the resource-intensive area will be adjusted first.

9. A service recommendation system for public areas according to claim 8, characterized in that: The environmental regulation efficiency function is used to calculate the environmental regulation efficiency of each resource-intensive area corresponding to each environmental factor. The specific process is as follows: By calculation formula: The environmental regulation efficiency βqy of the qth resource-intensive area corresponding to the yth environmental factor is obtained, where G′qy represents the actual value of the current qth resource-intensive area corresponding to the yth environmental factor, G′q′y represents the target value of the qth resource-intensive area corresponding to the yth environmental factor, and G′q″y represents the regulation cost of the yth environmental factor in the qth resource-intensive area from the actual value to the target value.

10. A method for recommending services in a public area by executing a service recommendation system for a public area according to any one of claims 1 to 9, characterized in that: The steps include: Step 1: Data fusion-driven behavior analysis: Obtain the corresponding multimodal data in the specified office area, and construct a behavior influence matrix through the spatiotemporal distribution of user activities to quantify the interaction intensity between users and public resources in the specified office area; Step 2: Conflict risk assessment: Based on the behavior impact matrix, dynamically detect the conflict risk of public resources in the designated office area; Step 3: Optimize resource allocation conflicts: Generate a dynamically optimized resource allocation plan for the designated office area based on the conflict risk of public resources in the designated office area; Step 4: Dynamically adjust the environment: Divide the resource-intensive areas within the designated office area, and combine the resource allocation plan to prioritize the environmental factors of the resource-intensive areas through the environmental adjustment efficiency function.

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