An Internet of Things-based intelligent conference room management system and method

Abstract

The present invention discloses an intelligent conference room management system and method based on the Internet of Things, which relates to the technical field of the Internet of Things and includes an intelligent scheduling module, a parameter dynamic update module, and a device collaboration module. The intelligent scheduling module of the system according to the present invention can highly coordinate device states by establishing a scheduling model between devices; the parameter dynamic update module dynamically adjusts device operation parameters according to real-time collected environmental data to optimize resource allocation; while the device collaboration module provides remote management and monitoring through a cloud platform to promote data sharing and cross-device collaboration between devices. These three modules complement each other, enabling conference room devices to automatically optimize operations in a dynamically changing environment, improving energy efficiency, reducing human management costs, and providing a more intelligent and comfortable environmental experience for conference room users.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to an intelligent conference room management system and method based on the Internet of Things. Background Art

[0002] With the rapid development of the Internet of Things technology, more and more devices are interconnected through wireless communication technology, forming an intelligent and networked system. The Internet of Things technology is widely applied in multiple fields such as home automation, industrial control, and smart cities. Especially in the field of intelligent conference room management, the application of the Internet of Things has brought great convenience. Modern conference rooms usually include multiple devices such as air conditioners, lights, audio systems, projectors, and video conferencing devices. These devices are connected through sensors and the Internet, and can real-time obtain environmental data and automatically adjust according to requirements. With the development of cloud computing technology, remote management, data sharing, and collaboration of devices have also become key ways to achieve intelligent management. Based on the combination of these technologies, the intelligent conference room management system realizes real-time monitoring of environmental data and intelligent scheduling of device states.

[0003] However, there are still many problems in the existing intelligent conference room management systems, especially in the coordination and scheduling between devices and environmental adaptive adjustment. Most of the existing technologies rely on simple preset rules or locally optimized scheduling methods, and it is difficult to achieve efficient and accurate management in a complex multi-device and multi-scenario environment. In addition, most systems lack the design of intelligent scheduling models and cannot dynamically adjust the operating parameters of devices based on the real-time state of devices and environmental changes, resulting in energy waste and unstable device states. Specifically, the following problems generally exist in the current technology: First, it is impossible to comprehensively and dynamically obtain the state data of various devices and establish the dependency relationship between devices based on this; second, most of the existing scheduling models are limited to a single device or a single scenario, lacking intelligent collaboration in a multi-device and multi-scenario environment; third, most systems lack effective resource optimization algorithms and it is difficult to achieve automatic adjustment of device states and optimal allocation of resources. Summary of the Invention

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

[0005] Therefore, the technical problems solved by the present invention are: the existing intelligent conference room management methods have problems such as inability to comprehensively and real-time collect device state data, lack of effective modeling of the dependency relationship between devices, imperfect resource optimization algorithms, and how to achieve cross-device and cross-region data sharing and collaboration through a cloud platform.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent conference room management system based on the Internet of Things, including an intelligent scheduling module, a parameter dynamic update module, and a device collaboration module; the intelligent scheduling module is used to obtain the working state data of various devices, analyze the dependency relationships between devices, and establish an intelligent scheduling model between devices; the parameter dynamic update module is used to dynamically update the operating parameters of devices based on the real-time collected conference room environment data, automatically adjust the working state of devices, optimize resource allocation and the conference room environment; the device collaboration module is used to remotely manage and monitor devices through a cloud platform, and perform cross-region and cross-device data sharing and collaboration.

[0007] As a preferred solution of the intelligent conference room management system based on the Internet of Things according to the present invention, wherein: the establishment of the intelligent scheduling model between devices includes obtaining the real-time working state data of lights, air conditioners, and video conferencing devices in the conference room, and by coordinating the working state of devices, using a Markov model to model the dependency relationships between devices, and by modeling the state transitions of different devices, forming a multi-dimensional scheduling model. The establishment of the intelligent scheduling model between devices is expressed as:

[0008]

[0009] Wherein, Q ij (t state ) represents the similarity measure between device i and device j during the state transition process, S i (t state ) represents the state of device i at time step t state , t state is the time of device state change, S j (t state ) represents the state of device j at time step t state , S k (t state ) represents the state of device k at time step t sate , σ ij represents the standard deviation of the state difference between device i and device j, n represents the total number of devices, k represents the index of the kth device in the system, used to traverse all devices, σ ik represents the standard deviation of the state difference between device i and device k.

[0010] As a preferred solution of the intelligent conference room management system based on the Internet of Things according to the present invention, wherein: the establishment of the intelligent scheduling model between devices further includes analyzing the device state differences based on the intelligent scheduling model between devices. When the device state difference is equal to 0, it means that the devices are completely coordinated. When the device state difference is equal to 1, it means that the device states are completely uncoordinated; based on the similarity measure threshold θ Q (tstate ) Determine the coordination between devices. When Q ij (t state ) ≥ θ Q (t state ) is true, the coordination between devices is high and the devices do not need to be adjusted. When Q ij (t state ) < θ Q (t state ) is true, the coordination between devices is low and the devices need to be adjusted.

[0011] As a preferred solution of the intelligent conference room management system based on the Internet of Things according to the present invention, wherein: the operation parameters of the dynamic update device include dynamically adjusting the operation state of the device after establishing an intelligent scheduling model between devices, dynamically updating the operation parameters of each device based on the real-time collected conference room environment data, associating the current state of the device with the environmental variables, and calculating the power change of the device according to the sensitivity of different devices to environmental changes, and constructing a dynamic power model, expressed as:

[0012]

[0013] Wherein, P i (t power ) represents the dynamic power of device i at time step t power , t power is the time for device power adjustment, P i0 is the initial power of the device, S j (t power ) represents the state of device j at time step t power , β ij (t power ) represents the dependence coefficient between device i and device j, X k (t power ) represents the value of environmental factor m at time step t power , γ im (t power ) represents the sensitivity coefficient of device i to environmental factor m, δ i (t power ) represents the adaptive adjustment factor of device i, φ ij represents the influence factor of the state difference between device u and device j on power adjustment, n represents the total number of devices, and M represents the total number of environmental factors; based on the mutual influence between devices, the distance between devices, the device type and the interaction, construct the dependence coefficient between device i and device j, expressed as:

[0014]

[0015] Wherein, α ijis the induction coefficient between device i and device j, D i represents the spatial position coordinates of device i, D j represents the spatial position coordinates of device j, σ ij is the standard deviation of the state difference between devices; the sensitivity coefficient of device i to environmental factor m is expressed as:

[0016]

[0017] where λ im is the induction constant of device i to environmental factor m, T m is the current value of environmental factor m, T env is the expected value of the environment, τ im is the standard deviation of the sensitivity of the device to environmental changes; the adaptive adjustment factor of device i is obtained through training with the historical performance data of the device and is expressed as:

[0018]

[0019] where ∈ is the adjustment flexibility constant, P i0 is the initial power of the device, P max is the maximum power of the device, μ i is the adaptive adjustment exponent of the device.

[0020] As a preferred solution of the Internet of Things-based intelligent conference room management system described in the present invention, wherein: the optimization of resource allocation and conference room environment includes that when the temperature exceeds the upper limit of T env , the power demand of the air conditioning equipment increases to lower the room temperature, and when the temperature is lower than the lower limit of T env , the power demand of the air conditioner decreases or even shuts down; when the illumination intensity in the conference room exceeds the set value, the intelligent lighting equipment automatically reduces the brightness to reduce power consumption; the equipment is dynamically adjusted based on the change of the number of people in the conference room. When the number of people in the conference room decreases, the device status enters the standby or low-power mode. Through the state difference and environmental factors in the dynamic power model, the system dynamically adjusts the device power; when the number of people in the conference room increases, as the environmental data changes with the increase of people, the power demands of the air conditioner and lighting equipment will increase accordingly, and the adaptive factor in the dynamic power model enables the device to respond and automatically increase the power.

[0021] As a preferred solution of the Internet of Things-based intelligent conference room management system described in the present invention, wherein: the remote management and monitoring of devices through the cloud platform includes using the cloud platform to provide remote monitoring functions. The conference room administrator monitors the status of the devices at a remote location, views the operation status of the devices in real time, and the administrator remotely controls the devices in the conference room.

[0022] As a preferred solution of the Internet of Things-based intelligent conference room management system described in the present invention, wherein: the data sharing and collaboration include data sharing between different devices based on a cloud platform, collaborative operations between devices, and automatic allocation of device resources for each participant; the different devices include control devices, video conferencing devices, and air conditioners in the conference room.

[0023] Another object of the present invention is to provide an Internet of Things-based intelligent conference room management method, which can dynamically update the operating parameters of devices, automatically adjust the working state of devices, optimize resource allocation and the conference room environment based on the real-time collected conference room environment data, and solve the problem of imperfect resource optimization algorithms in current intelligent conference room management technologies.

[0024] As a preferred solution of the Internet of Things-based intelligent conference room management method described in the present invention, wherein: it includes obtaining the working state data of various devices, analyzing the dependency relationships between devices, and establishing an intelligent scheduling model between devices; dynamically updating the operating parameters of devices, automatically adjusting the working state of devices, optimizing resource allocation and the conference room environment based on the real-time collected conference room environment data; realizing remote management and monitoring of devices through a cloud platform, and performing data sharing and collaboration across regions and devices.

[0025] A computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the Internet of Things-based intelligent conference room management method.

[0026] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of the Internet of Things-based intelligent conference room management method.

[0027] Advantages of the present invention: The intelligent scheduling module of the Internet of Things-based intelligent conference room management system provided by the present invention can make the device states highly coordinated by establishing a scheduling model between devices; the parameter dynamic update module dynamically adjusts the device operating parameters according to the real-time collected environmental data to optimize resource allocation; while the device collaboration module provides remote management and monitoring through a cloud platform to promote data sharing and cross-device collaboration between devices. These three modules complement each other, enabling the conference room devices to automatically optimize their operations in a dynamically changing environment, improving energy efficiency, reducing human management costs, and providing a more intelligent and comfortable environmental experience for conference room users. The present invention achieves better results in terms of intelligence, efficiency, and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. 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.

[0029] Figure 1 It is the overall module diagram of an intelligent conference room management system based on the Internet of Things provided for the first embodiment of the present invention.

[0030] Figure 2 It is the overall flowchart of an intelligent conference room management method based on the Internet of Things provided for the third embodiment of the present invention. Detailed implementation manners

[0031] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the 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 shall fall within the protection scope of the present invention.

[0032] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides an intelligent conference room management system based on the Internet of Things, including:

[0033] An intelligent scheduling module 100, a parameter dynamic update module 200, and a device cooperation module 300.

[0034] Furthermore, the intelligent scheduling module 100 is used to obtain the working state data of various devices, analyze the dependency relationships between devices, and establish an intelligent scheduling model between devices; the parameter dynamic update module 200 is used to dynamically update the operating parameters of devices based on the real-time collected conference room environment data, automatically adjust the working state of devices, optimize resource allocation and the conference room environment; the device cooperation module 300 is used to realize remote management and monitoring of devices through the cloud platform, and perform cross-regional and cross-device data sharing and cooperation.

[0035] Furthermore, establishing an intelligent scheduling model between devices includes obtaining the real-time working state data of lights, air conditioners, and video conferencing devices in the conference room, modeling the dependency relationships between devices by coordinating the working states of devices, and forming a multi-dimensional scheduling model by modeling the state transitions of different devices. The establishment of the intelligent scheduling model between devices is expressed as:

[0036]

[0037] Among them, Q ij (t state ) represents the similarity measure during the state transition between device i and device j, S i (t state ) represents the state of device i at time step t state moment, t state is the time of device state change, S j (t state ) represents the state of device j at time step t state moment, S k (t state ) represents the state of device k at time step t state moment, σ ij represents the standard deviation of the state difference between device i and device j, reflecting the sensitivity of the state transition between devices, n represents the total number of devices, k represents the index of the kth device in the system, used to traverse all devices, σ ik represents the standard deviation of the state difference between device i and device k.

[0038] It should be noted that establishing an intelligent scheduling model between devices also includes analyzing the state differences between devices based on the intelligent scheduling model between devices. The device state difference |S i (t state ) - S j (t state )| reflects the deviation of the device state. When the device state difference is equal to 0, it means the devices are completely coordinated. When the device state difference is equal to 1, it means the device states are completely uncoordinated; based on the similarity measure threshold θ Q (t state ) to judge the coordination between devices. A preferred solution includes setting the similarity measure threshold θ Q (t state ) to 0.8. When Q ij (t) state ) ≥ θ Q (t state )), the coordination between devices is high and the devices do not need to be adjusted; when Q ij (t state ) < θ Q (t state ), the coordination between devices is low and the devices need to be adjusted.

[0039] It should also be noted that by establishing an intelligent scheduling model between devices, the automatic coordination of device states is achieved, eliminating the possible inconsistencies and conflicts between devices. This dynamic coordination significantly improves the overall efficiency of the system. In an environment where multiple devices operate in parallel, it can adjust the working state in real time according to the dependency relationship between devices, avoiding the inefficient operation of devices when interfering with each other and reducing energy consumption. By adopting the Markov model, the process of device state change is quantified and modeled, making the scheduling process highly flexible and accurate, and capable of better adapting to the requirements of different scenarios.

[0040] Furthermore, the dynamic update of device operation parameters includes, after establishing the intelligent scheduling model between devices, dynamically adjusting the operation state of devices, dynamically updating the operation parameters of each device based on the real-time collected conference room environment data, associating the current state of the device with environmental variables, and calculating the power change of the device according to the sensitivity of different devices to environmental changes, and constructing a dynamic power model, expressed as:

[0041]

[0042] Among them, P i (t power ) represents the dynamic power of device i at time step t power , t power is the time for device power adjustment, P i0 is the initial power of the device, S j (t power ) represents the state of device j at time step t power , β ij (t power ) represents the dependency coefficient between device i and device j, X k (t power ) represents the value of environmental factor m at time step t power , including temperature, humidity, and light intensity, γ im (t power ) represents the sensitivity coefficient of device i to environmental factor m, δ i (t power ) represents the adaptive adjustment factor of device i, φ ij represents the influence factor of the state difference between device i and device j on power adjustment, n represents the total number of devices, M represents the total number of environmental factors, which are used to describe the data of the environment in the conference room; based on the mutual influence between devices, the distance between devices, the device type and the interaction, the dependency coefficient between device i and device j is constructed, expressed as:

[0043]

[0044] Among them, α ij is the induction coefficient between device i and device j, D i represents the spatial position coordinates of device i, D j represents the spatial position coordinates of device j, σ ij is the standard deviation of the state difference between devices; the environmental factor directly affects the device power adjustment, and each device has different responses to different environmental factors. The sensitivity coefficient of device i to environmental factor m is expressed as:

[0045]

[0046] Among them, λ in is the induction constant of device i to environmental factor m, T m is the current value of environmental factor m, T env is the expected value of the environment, τ im is the standard deviation of the device's sensitivity to environmental changes; the adaptive adjustment factor of device i represents the flexibility of device power adjustment, and it is adaptively adjusted according to the changes in the environment and device status. The adaptive adjustment factor of device i is obtained through training with the historical performance data of the device and is expressed as:

[0047]

[0048] Among them, ∈ is the adjustment flexibility constant, P i0 is the initial power of the device, P max is the maximum power of the device, μ i is the adaptive adjustment exponent of the device, reflecting the flexibility of the device's response to adjustment requirements.

[0049] It should be noted that optimizing resource allocation and the meeting room environment includes that when the temperature exceeds the upper limit of T env , the power demand of the air conditioning equipment increases to lower the room temperature. When the temperature is lower than the lower limit of T env , the air conditioning power demand decreases or even shuts down; when the illumination intensity in the meeting room exceeds the set value, such as 800 lux, the intelligent lighting equipment automatically reduces the brightness to reduce power consumption; the equipment is dynamically adjusted based on the changes in the number of people in the meeting room. When the number of people in the meeting room decreases, the device status enters the standby or low-power mode. Through the state difference and environmental factors in the dynamic power model, the system dynamically adjusts the device power; when the number of people in the meeting room increases, as the number of people increases, the environmental data changes, and the power demands of the air conditioning and lighting equipment will increase accordingly. The adaptive factor in the dynamic power model enables the device to respond and automatically increase the power.

[0050] It should also be noted that through dynamic adjustment based on environmental data, the device can automatically respond to environmental changes without manual intervention, improving the automation and intelligence levels of device operation. The dynamic update of the device status ensures that the conference room environment always remains in the optimal state, avoiding both energy waste and ensuring environmental comfort, achieving fine regulation of device power. Especially when devices work in cooperation, through precise adjustment of the status of each device, redundant operation of the devices can be effectively reduced and energy consumption can be lowered. This dynamic adjustment mechanism based on the environment can also support the flexible change of devices in different working scenarios, improving the adaptability of the system and the reasonable allocation of resources.

[0051] Furthermore, remote management and monitoring of devices are achieved through the cloud platform, including using the cloud platform to provide remote monitoring functions. The conference room administrator can monitor the status of devices at a remote location and view the running conditions of the devices in real time. The administrator can remotely control the devices in the conference room, including adjusting the air conditioning temperature, light brightness, and device switches.

[0052] It should be noted that data sharing and collaboration include data sharing between different devices based on the cloud platform, performing collaborative operations between devices, and automatically allocating device resources for each participant; different devices include the control devices, video conferencing devices, and air conditioners in the conference room.

[0053] It should also be noted that through the remote management function of the cloud platform, the administrator can adjust the conference room devices anytime and anywhere, greatly improving the convenience and flexibility of the system. The collaboration function of the cloud platform enables data sharing between different devices, realizing efficient collaborative operations between devices, ensuring that device resources can be flexibly allocated according to needs, and avoiding resource waste. The remote monitoring and adjustment functions not only improve the management efficiency of the conference room but also can respond to emergencies in a timely manner, such as device failures or drastic environmental changes, ensuring the stability and comfort of the conference room environment.

[0054] Embodiment 2 is an embodiment of the present invention, which provides an intelligent conference room management system based on the Internet of Things. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0055] First, build a conference room equipment management system in a simulation environment, covering common conference room equipment such as air conditioners, intelligent lighting, and video conferencing equipment. Select three types of equipment: air conditioners, intelligent lighting, and video conferencing equipment. Set the power requirement of the air conditioner to 300W, the maximum power to 800W, and the initial power to 500W. Set the power requirement of the intelligent lighting to 100W, the maximum power to 250W, and the initial power to 150W. Set the power requirement of the video conferencing equipment to 150W, the maximum power to 400W, and the initial power to 200W. The real-time working status of the equipment is collected through sensors, and data such as the current power, temperature, humidity, and light intensity of the equipment are obtained. Use the Markov model to analyze the dependency relationship between the equipment, and establish a scheduling model between the equipment through the state transition matrix. For example, the power adjustment of the air conditioner is closely related to the temperature change, while the power requirement of the intelligent lighting is affected by the light intensity. The dependency relationship between the equipment is realized by calculating the equipment state difference. When the equipment state difference is small, the system considers that the equipment works well in cooperation. Dynamically adjust the working status of the equipment by collecting the environmental data (such as temperature, humidity, and light intensity) of the conference room in real time. Assume that the temperature in the conference room is 25°C and the light intensity is 850 lux. The system automatically adjusts the power of the air conditioner and lighting equipment. By analyzing the relationship between the equipment and the environmental factors, dynamically update the power requirements of the air conditioner, lighting, and video conferencing equipment. With the support of the conference room equipment collaboration module, the cloud platform provides remote management functions for the equipment. The administrator can monitor and adjust the equipment status in real time. The administrator remotely adjusts functions such as the air conditioner temperature, light brightness, and equipment switch. All equipment shares data through the cloud platform to ensure the coordinated operation of the equipment. For example, when the number of people in the conference room increases, the system automatically increases the power of the air conditioner and lighting to maintain a comfortable environment. Refer to Table 1. During the experiment, the data is recorded in real time, including the operating power of the equipment, environmental data, and changes in the equipment status, etc., for subsequent analysis. The main data of the experiment includes equipment state difference, power requirement, equipment response time, etc.

[0056] Table 1 Experimental Data Record Sheet

[0057]

[0058]

[0059] Through the analysis of the equipment state difference, it can be seen that the state differences of the air conditioner, lighting, and video conferencing equipment remain within a small range (0.1 - 0.3) at each time step, indicating that these equipment work well in cooperation under the scheduling model. According to the intelligent scheduling model, the similarity measure between the equipment is always higher than 0.8, indicating a high degree of coordination between the equipment.

[0060] The table shows that as the time step increases, the air conditioner power demand is gradually adjusted to match the change in the conference room ambient temperature. When the indoor temperature changes slightly, the air conditioner power is slightly adjusted and always maintained within a suitable power range. The change in the air conditioner power within 6 time steps does not exceed 30W, demonstrating the flexibility and energy-saving ability of the device.

[0061] When the illumination intensity of the lighting equipment exceeds the set value, the power is reduced in a timely manner. For example, at time step 2, the lighting power is reduced from 150W to 120W, indicating that the system automatically adjusts the equipment to reduce energy consumption.

[0062] The power demand change of the equipment is closely related to environmental factors. Especially when the temperature and illumination intensity change, the equipment can quickly respond and adjust the power to optimize the comfort of the conference room. For example, when the number of people in the conference room increases, the air conditioner and lighting power will increase accordingly to ensure that the indoor temperature and brightness are maintained within the set range.

[0063] In the prior art, the equipment usually cannot adaptively adjust the power according to real-time environmental data, while the dynamic adjustment mechanism of the present invention enables the equipment to respond to environmental changes in real time, achieving the goals of optimizing energy consumption and improving the comfort of the conference room.

[0064] Example 3, referring to Figure 2 , is an embodiment of the present invention, which provides an Internet of Things-based intelligent conference room management method, including S1: obtaining the working state data of various devices, analyzing the dependency relationship between devices, and establishing an intelligent scheduling model between devices; S2: dynamically updating the operating parameters of the devices based on the real-time collected conference room environmental data, automatically adjusting the working state of the devices, and optimizing resource allocation and the conference room environment; S3: realizing remote management and monitoring of the devices through the cloud platform, and performing data sharing and collaboration across regions and devices.

[0065] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered a definitional sequence of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.

[0067] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0068] It should be understood that the various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gates for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An intelligent conference room management system based on the Internet of Things, characterized in that: include: Intelligent scheduling module (100), parameter dynamic update module (200), device collaboration module (300); The intelligent scheduling module (100) is used to obtain the working status data of various types of equipment, analyze the dependency relationship between the equipment, and establish an intelligent scheduling model between the equipment; The parameter dynamic updating module (200) is used to dynamically update the operating parameters of the equipment based on the real-time collected conference room environment data, automatically adjust the working state of the equipment, and optimize resource allocation and conference room environment; The device collaboration module (300) is used to realize remote management and monitoring of devices through a cloud platform, and to carry out cross-region and cross-device data sharing and collaboration; Establishing an intelligent scheduling model between devices includes obtaining real-time working status data of lighting, air conditioning, and video conferencing equipment in the conference room, coordinating the working status of the equipment, using the Markov model to model the dependencies between the equipment, and modeling the state transition of different equipment to form a multi-dimensional scheduling model. Establishing an intelligent scheduling model between devices is expressed as: Among them, Q ij (t state ) represents the similarity measure between device i and device j during the state transfer process, S i (t state ) represents the time step t of device i state The state at time t state is the time for the device status to change, S j (t state ) represents the time step t of device j state The state at the moment, S k (t state ) indicates that device k is at time step t state The state at the moment, σ ij represents the standard deviation of the state difference between device i and device j, n represents the total number of devices, k represents the index of the kth device in the system, which is used to traverse all devices, σ ik Represents the standard deviation of the state difference between device i and device k; Dynamically updating the operating parameters of the equipment includes establishing an intelligent scheduling model between devices, dynamically adjusting the operating status of the equipment, dynamically updating the operating parameters of each device based on the real-time collected conference room environment data, associating the current status of the device with the environmental variables, and calculating the power change of the device according to the sensitivity of different devices to environmental changes. The dynamic power model is constructed, which is expressed as: Among them, P i (t power ) represents the time step t of device i power Dynamic power at time t power is the time for equipment power adjustment, P i0 is the initial power of the device, S j (t power ) represents the time step t of device j power The state at the moment, β ij (t power ) represents the dependency coefficient between device i and device j, X m (t power ) represents the environmental factor m at time step t power The value of γ im (t power ) represents the sensitivity coefficient of device i to environmental factor m, δ i (t power ) represents the adaptive adjustment factor of device i, φ ij represents the influence factor of the status difference between device i and device j on power adjustment, n represents the total number of devices, and M represents the total number of environmental factors; Based on the mutual influence between devices, the distance between devices, device types and interactions, the dependency coefficient between device i and device j is constructed and expressed as: Among them, α ij is the inductance between device i and device j, D i Indicates the spatial position coordinates of device i, D j represents the spatial position coordinates of device j, σ ij is the standard deviation of the state differences between devices; The sensitivity coefficient of device i to environmental factor m is expressed as: Among them, λ im is the induction constant of device i to environmental factor m, T m is the current value of the environmental factor m, T env is the expected value of the environment, τ im is the standard deviation of the device’s sensitivity to environmental changes; The adaptive adjustment factor of device i is obtained by training the historical performance data of the device and is expressed as: Where, ∈ is the adjustment flexibility constant, P i0 is the initial power of the device, P max is the maximum power of the device, μ i is the adaptive adjustment index of the device.

2. The intelligent conference room management system based on the Internet of Things as claimed in claim 1, characterized in that: The establishing of the intelligent scheduling model between devices also includes analyzing the device status difference based on the intelligent scheduling model between devices, when the device status difference is equal to 0, it means that the devices are completely coordinated, and when the device status difference is equal to 1, it means that the device status is completely uncoordinated; Based on the similarity measurement threshold θ Q (t state ) to judge the coordination between devices. ij (t state )≥θ Q (t state ) When the equipment is in close coordination, no equipment adjustment is required; When Q ij (t state )<θ Q (t state ), the coordination between devices is low and adjustments need to be made between devices.

3. The intelligent conference room management system based on the Internet of Things as claimed in claim 1, characterized in that: The optimization of resource allocation and conference room environment includes when the temperature exceeds T env When the temperature is lower than T, the power demand of the air conditioning equipment increases, lowering the room temperature. env When the lower limit is reached, the air conditioning power demand is reduced or even shut down; When the light intensity in the conference room exceeds the set value, the intelligent lighting equipment automatically lowers the brightness to reduce power consumption; Dynamically adjust the equipment based on the change of people in the conference room. When the number of people in the conference room decreases, the equipment state enters standby or low power consumption mode. The system dynamically adjusts the equipment power based on the state difference and environmental factors in the dynamic power model. When more people enter the conference room, the environmental data will change with the increase in the number of people, and the power demand of air conditioning and lighting equipment will increase accordingly. The adaptive factors in the dynamic power model enable the equipment to respond and automatically increase power.

4. The intelligent conference room management system based on the Internet of Things as claimed in claim 3, characterized in that: The remote management and monitoring of equipment through the cloud platform includes using the cloud platform to provide a remote monitoring function, where the conference room administrator monitors the status of the equipment at a remote location, views the operation of the equipment in real time, and the administrator remotely controls the equipment in the conference room.

5. The intelligent conference room management system based on the Internet of Things as claimed in claim 4, characterized in that: The data sharing and collaboration include sharing data between different devices based on the cloud platform, performing collaborative operations between devices, and automatically allocating device resources to each participant; The different devices include control equipment, video conferencing equipment and air conditioning in the conference room.

6. A method for using the smart conference room management system based on the Internet of Things as claimed in any one of claims 1 to 5, characterized in that: This includes obtaining the working status data of various types of equipment, analyzing the dependencies between equipment, and establishing an intelligent scheduling model between equipment; Based on the real-time collected conference room environment data, the equipment operating parameters are dynamically updated, the equipment working status is automatically adjusted, and resource allocation and conference room environment are optimized; Remote management and monitoring of equipment can be achieved through the cloud platform, and data sharing and collaboration across regions and devices can be carried out.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method of the smart conference room management system based on the Internet of Things described in claim 6 are implemented.

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 of the smart conference room management system based on the Internet of Things described in claim 6 are implemented.

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