Energy optimization management system and management method for hotel guest rooms

Abstract

The invention belongs to the technical field of intelligent control, and discloses an energy optimization management system and method for hotel guest rooms, and the system comprises a central data pool unit which constructs a time sequence database cluster, and carries out the uploading and interaction of data between all units through a data bus; the existence sensing unit is used for integrating multiple sensors by adopting a sensing layer architecture to form a sensing network, capturing existence sensing data and generating a room existence state map based on the existence sensing data; the existence sensing data comprises guest room reservation information data, guest room environment data and guest information data; the partition environment control unit is used for obtaining customer preference data by analyzing a room existence state map; based on the guest preference data and the guest room environment data, selectively activating environment control of different guest room areas by adopting an intelligent optimization algorithm; and the hotel energy utilization rate and customer satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and more specifically, to an energy optimization management system and method for hotel guest rooms. Background Art

[0002] The patent with the publication number CN103136826A discloses an energy and room status management system, which relates to the field of intelligent hotel guest room control. A PC is connected to a network converter, and the network converter is respectively connected to an energy and room status controller and an electric energy meter. The energy and room status controller is respectively connected to the electric energy meter, a water control meter, and an identity recognition power-taking switch. The management system platform is connected to the PC through TCP / IP; this invention patent can improve the collection, storage, and management of energy information; reduce the energy management links and optimize the energy management process; save energy and reduce consumption, and reduce operating costs; facilitate the precise management of guest room resources by hotel guest room staff, and at the same time, the hotel management company can remotely monitor the real-time status of the hotel, promoting the further improvement of management level and the further reduction of operating costs.

[0003] The existing energy optimization management systems and methods for hotel guest rooms mainly have the following problems: The existing intelligent guest room environment control systems often adjust the guest room environment through simple preset scenarios or fixed environmental settings (such as temperature, lighting, etc.), lacking dynamic adjustment based on the individual preferences of guests. Most of them only make simple regional divisions based on the physical location of the guest room, without delving into the details of the furniture layout and activity habits in the guest room. The traditional zoning method is difficult to effectively meet the actual needs of different guests, resulting in insufficient space utilization and comfort. In many existing systems, the environmental transition between regions is relatively abrupt, lacking smooth transition, resulting in an unnatural switch in the environmental perception. For example, from the work area to the rest area, the lighting or temperature may change sharply, causing discomfort. Existing systems often use fixed and standardized buffer widths, without considering the individualized activity data of guests. The existing methods cannot define the buffer zone finely, resulting in low space utilization or inappropriateness; the traditional buffer zone usually adopts a uniform space division method without considering the directionality and frequency of the actual activities of guests.

[0004] In view of this, the present invention proposes an energy optimization management system and method for hotel guest rooms to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: An energy optimization management system for hotel guest rooms, comprising: The central data pool unit constructs a time-series database cluster and uploads and interacts with data between units through a data bus; The presence sensing unit adopts a sensing layer architecture to integrate multiple sensors to form a sensing network, captures presence sensing data, and generates a room presence status map based on the presence sensing data; the presence sensing data includes guest reservation information data, guest room environment data, and guest information data; The partitioned environment control unit analyzes the room presence status map to obtain guest preference data; based on the guest preference data and the guest room environment data, it uses an intelligent optimization algorithm to selectively activate the environmental control of different guest room areas; The gradual adjustment unit designs personalized environmental parameter change curves for different areas based on the guest preference data, obtains the hotel equipment power change curve, and issues a soft transition instruction for scene switching to the equipment collaborative control unit for execution; The equipment collaborative control unit establishes an energy efficiency model library for hotel equipment. According to the hotel equipment power change curve, it adopts an equipment complementary and collaborative mechanism to coordinate the operation of different hotel equipment and obtains equipment operation data; The energy load balancing unit predicts the energy demand index in the future period of time based on the guest preference data, equipment operation data, and personalized environmental parameter change curves; according to the energy demand index, it pre-stores and releases energy at different electricity price periods to balance the energy load.

[0006] Preferably, the method for capturing the presence sensing data includes: Deploy environmental sensors, human activity sensors, and interface devices connected to the hotel management system inside the guest room. Integrate the environmental sensors, human activity sensors, and interface devices into the same sensing network through a standardized interface, and perform periodic data collection to obtain guest room environment data, guest information data, and guest room reservation information data; integrate the guest room environment data, guest information data, and guest room reservation information data to obtain the presence sensing data.

[0007] Preferably, the method for generating the room presence status map includes: Based on the obtained presence sensing data, use the rule engine method to determine the room presence status according to the predefined business rules; the room presence status includes idle, reserved but not used, in use, and abnormal status; perform entity extraction on the room presence status determination result and the presence sensing data as the nodes of the room presence status map, and the logical relationship between entities as the edges of the room presence status map to construct the room presence status map.

[0008] Preferably, the method for obtaining the guest preference data includes: Construct a guest preference prediction model through a spatio-temporal graph neural network. The guest preference prediction model includes an input layer, a spatial feature extraction layer, a temporal feature extraction layer, a spatio-temporal fusion layer, and an output layer. Use the room occupancy status map as the input data for the input layer of the guest preference model, and analyze the spatial dependence relationship in the room occupancy status map through graph convolution in the spatial feature extraction layer. In the spatio-temporal fusion layer, process the guest behavior change pattern in the time dimension through a gated recurrent unit. In the spatio-temporal fusion layer, splice and fuse the obtained spatial dependence relationship and the guest behavior change pattern in the time dimension to obtain guest preference data. The guest preference data includes temperature preference, lighting preference, air quality preference, time pattern preference, area usage habit, device interaction habit, energy usage pattern, scene switching preference, and circadian rhythm preference.

[0009] Preferably, the method for selectively activating the environmental control of different guest room areas includes: The different guest room areas include a rest area, an entertainment area, a work area, and a bathing area. Set each guest room area to contain adjustable environmental parameters to form a -dimensional control vector. With the goal of minimizing energy consumption and maximizing comfort, select the Actor-Critic framework to build a deep neural network, splice and fuse the guest preference data and the guest room environmental data to form a state vector. Use the state vector as the input data of the deep neural network, and gradually extract the deep correlation information in the state vector through the policy network of the fully connected layer. Under the goal of minimizing energy consumption and maximizing comfort, output the global optimal control vector. Upload the global optimal control vector to the central data pool, and send it to the environmental control actuator of the corresponding guest room area through the data bus to adjust the corresponding device status, thereby realizing the selective activation of the environmental control of different guest room areas.

[0010] Preferably, the method for designing the personalized environmental parameter change curve includes: Define the regional boundary and transition zone model, and identify the explicit physical boundary based on the physical structure of the guest room. Naturally form the first layer of boundary between different regions, automatically identify the first layer of boundary by analyzing the CAD drawings and 3D models of the guest room, and use it as the basic framework for regional division. Determine the functional boundary based on the furniture layout. Different furniture combinations indicate different guest room areas. The bed area is defined as the rest area, the table and chair combination is defined as the work area, the sofa and TV area is defined as the entertainment area, and the combination of washbasin, toilet, and shower is defined as the bathing area. By identifying different furniture combinations, define the functional boundaries, which are composed of the union of the edges of the core furniture and the functional activity buffer zones; define the edges of the core furniture and the functional activity buffer zones, construct a regional transition zone model through a distance-weighted interpolation algorithm combined with a cubic spline function, construct a guest preference - region association matrix, analyze the guest activity data and the corresponding environmental requirements, and define the typical scenarios and environmental parameter sets for guests staying in hotel rooms. The typical scenarios include check-in scenarios, sleep preparation scenarios, work scenarios, and leisure scenarios; the parameter sets include temperature, lighting intensity, and humidity and air ventilation frequency. Establish a scenario - parameter multi - dimensional target matrix. The elements in the scenario - parameter multi - dimensional target matrix include typical scenarios, different guest room areas, and types of target environmental parameter sets; define an acceptable range for each target environmental parameter set, adjust the standard target environmental parameter set according to guest preference data, and analyze the adaptability index of guests to changes in guest room environmental data. Establish an environmental parameter change sensitivity model. Through the environmental parameter change sensitivity model, measure the index of the sensitivity of guests' responses to changes in guest room environmental data; calculate the optimal change rate of each parameter in the guest room environmental data. Apply the cubic Bezier curve algorithm to design a smooth transition curve based on the index of the sensitivity of guests' responses to changes in guest room environmental data and the optimal change rate of each parameter in the guest room environmental data, and apply specific physical models to constrain different environmental parameters; establish a correlation matrix between environmental parameters to achieve a parameter linkage mechanism, and coordinate the guest room environmental data through a parameter smooth transition algorithm at the regional junction; and adjust and update the smooth transition curve according to guest preference data to obtain a personalized environmental parameter change curve.

[0011] Preferably, the method for defining the edges of the core furniture and the functional activity buffer zones includes: The method for defining the edges of the core furniture includes defining different furniture combinations corresponding to different guest room areas as the core furniture of that area. For each core furniture, extract its two - dimensional projection contour, construct a polygon boundary representation. For different core furniture, obtain the size and position information of the core furniture through a 3D scanner, calculate the overall contour of the core furniture, and select the corresponding edge definition according to the current state of different furniture combinations. Taking the origin at the lower left corner of the guest room, establish a Cartesian coordinate system, project the core furniture data onto the ground plane. For regular - shaped furniture, directly calculate the four - corner coordinates, which include the lower - left coordinate, upper - left coordinate, upper - right coordinate, and lower - right coordinate, and apply a rotation matrix to process the orientation of the core furniture to obtain its edge; for irregular - shaped furniture, decompose the irregular - shaped furniture into m basic components and merge the boundaries to obtain its edge; for smooth - curved - surface furniture, approximate its edge using parametric curves; the extracted edges are stored in the form of an ordered point set to obtain the edges of the core furniture. The definition method of the functional activity buffer includes that the basic buffer width is determined according to the guest activity data, the basic buffer shape is obtained by non-uniform directional expansion according to the guest activity data, and the basic buffer width is adjusted according to different direction coefficients to obtain the widths of the buffer in different directions; the guest activity data includes the residence time of the guest in different direction areas, the maximum residence time of the guest at all direction angles, the number of times the guest moves along different direction angles, and the total number of times the guest moves at all direction angles; The basic buffer includes a rest buffer, a work buffer, an entertainment buffer, and a bathing buffer; the basic buffer width is obtained by presetting the widths of different buffers.

[0012] Preferably, the method for coordinating the operation of different hotel devices with each other includes: Collect the energy consumption data of various hotel devices under different working conditions, and construct a device energy efficiency mathematical model. The device energy efficiency mathematical model includes a device power-load curve, a start-stop efficiency curve, and a temperature-energy consumption relationship curve; the device power-load curve is obtained by the standard segmented test method; the construction method of the start-stop efficiency curve is obtained based on the industrial energy efficiency test standard; the temperature-energy consumption relationship curve is obtained by the environmental temperature impact test process defined by the international standard test method; Convert the personalized environmental parameter change curve generated by the gradual adjustment unit into a device power change curve, define a device complementary cooperation mechanism, and the complementary cooperation mechanism includes using time sequence complementarity, implementing space complementarity, and performing source-end complementarity; generate a device linkage instruction based on the cooperative optimization algorithm, and issue the device linkage instruction through the energy management intelligent terminal to coordinate the operation of different hotel devices with each other.

[0013] Preferably, the method for balancing the energy load includes: Match and analyze the energy demand index in the predicted future period with the grid electricity price period information. The grid electricity price period is divided into peak period, normal period, and valley period. Identify the overlapping area of the highest demand and the highest electricity price through the time sequence matching algorithm, and balance the energy load for the period corresponding to the overlapping area of the highest demand and the highest electricity price; when it is in the valley period of the electricity price and the highest demand in the future is predicted, activate the energy pre-storage mechanism to store the energy of the hotel guest rooms; preset a grid electricity price threshold, and set an energy release instruction according to the preset grid electricity price threshold during the peak period. When the grid electricity price is greater than or equal to the grid electricity price threshold, trigger the energy release instruction to release the energy of the hotel guest rooms.

[0014] An energy optimization management method for hotel guest rooms includes: S1. Construct a time sequence database cluster, and upload and interact data between each unit through a data bus; S2. Integrate multiple sensors using the perception layer architecture to form a perception network, capture presence perception data, and generate a room presence status map based on the presence perception data; the presence perception data includes guest reservation information data, guest room environment data, and guest information data. S3. Analyze the room presence status map to obtain guest preference data; based on the guest preference data and the guest room environment data, use an intelligent optimization algorithm to selectively activate the environmental control of different guest room areas. S4. Design personalized environmental parameter change curves for different areas based on the guest preference data, obtain the hotel equipment power change curve, and send a soft transition instruction for scene switching to the equipment collaborative control unit for execution. S5. Establish a hotel equipment energy efficiency model library, according to the hotel equipment power change curve, use the equipment complementary and collaborative mechanism to coordinate the operation of different hotel equipment with each other, and obtain the equipment operation data. S6. Predict the energy demand index in the future period of time based on the guest preference data, equipment operation data, and personalized environmental parameter change curve; according to the energy demand index, pre-store and release energy during different electricity price periods to balance the energy load.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention automatically adjusts the environmental control through the guest preference data, can accurately meet the unique needs of each guest, makes the guest room environment more in line with the individual's living habits. As the number of times the user checks in increases, the system will accumulate more detailed preference data, further improving the accuracy of personalized adjustment, continuously optimizing the guest room environment, and providing more accurate services for frequent guests. By automatically identifying the physical boundaries and furniture layouts of the guest rooms, the system can accurately divide different functional areas and adjust the environment according to the needs of each area. By dynamically adjusting the width and shape of the buffer zone, the system can optimize the space layout according to the guest's activity habits and direction needs, avoiding unnecessary space waste; through the refined functional area division, the sense of oppression in the guest room is alleviated. Especially in a smaller guest room space, the discomfort caused by unreasonable space layout can be effectively avoided. Avoid the abrupt feeling during area switching in the existing system. The guest room environment data can smoothly transition between different areas. Through the transition zone model, the conflicts between the environments of different areas are avoided. Guests will not feel uncomfortable due to sudden light changes or drastic temperature fluctuations during activities, enhancing the overall comfort in the guest room. By dynamically adjusting environmental data such as temperature, humidity, and lighting in guest rooms, unnecessary energy waste can be avoided. For example, based on the detection of guests' actual activities, when guests leave the room, the system can automatically adjust the temperature and lighting to save energy; when guests change areas, the system can automatically optimize energy usage to avoid multiple areas being in a high-energy-consuming state simultaneously; for different environmental requirements, the system can optimize energy consumption through intelligent algorithms. For example, the lighting and temperature control in the working area will be automatically adjusted according to the time period of guests' activities, eliminating the need for manual switching, which greatly improves energy efficiency and energy conservation; Guests no longer need to manually adjust various devices to meet their personal needs. The system automatically adjusts the environmental settings in the guest room area according to personal preferences and activity patterns, simplifying the operation for customers and enhancing the convenience of check-in. By anticipating guests' needs in advance and making real-time adjustments, a more considerate service experience can be provided. When guests feel a comfortable and personalized environment, higher customer satisfaction can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic structural diagram of an energy optimization management system for a hotel guest room according to the present invention; Figure 2 It is a schematic flowchart of an energy optimization management method for a hotel guest room according to the present invention; Figure 3 It is a technical roadmap for selectively activating environmental control in different guest room areas provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the 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.

[0018] Embodiment 1 Please refer to Figure 1 and Figure 3 As shown, this Embodiment 1 further illustrates an energy optimization management system for a hotel guest room proposed by the present invention, including: With the intensification of the global energy shortage problem, the hotel industry, as one of the high-energy-consuming industries, faces the challenge of energy conservation and emission reduction. The energy consumption in hotel rooms mainly comes from air conditioners, lighting, hot water supply, and various electrical appliances. The traditional hotel energy management mode often relies on fixed time control or manual adjustment, lacking intelligent and refined management, resulting in energy waste and a decline in the customer experience. Therefore, an energy optimization management system based on intelligent algorithms has become the key research direction, aiming to improve energy use efficiency, reduce operating costs, and provide a more comfortable check-in experience through sensing technology, data analysis, and intelligent control.

[0019] The current hotel room energy management technologies mainly have the following problems: Rigid environmental control mode and lack of personalized adjustment: The air conditioners, lighting, and other equipment in traditional hotel rooms usually rely on fixed time control or manual settings, and cannot be dynamically adjusted according to the specific needs and preferences of guests; the comfort requirements of guests at different times are not considered, resulting in increased energy consumption and affecting the check-in experience.

[0020] Insufficient equipment collaborative control ability: Most of the existing hotel room equipment operates independently, and there is a lack of collaborative adjustment among equipment such as air conditioners, lighting, and curtains, and a systematic energy-saving strategy has not been formed; there is a lack of an energy efficiency optimization model based on the operating status of equipment, resulting in some equipment running for a long time or starting and stopping frequently, affecting energy efficiency and reducing the equipment life.

[0021] Limited energy load management ability: The existing energy management system lacks an accurate energy demand prediction mechanism, resulting in unreasonable energy distribution; it cannot perform energy pre-storage or load balancing adjustment according to electricity price fluctuations, and fails to make full use of low electricity price periods to optimize energy use, increasing the operating costs.

[0022] To effectively solve the above problems, the present invention proposes an energy optimization management system for hotel rooms, including: A central data pool unit, which constructs a time-series database cluster and uploads and exchanges data between units through a data bus; A presence sensing unit, which integrates multiple sensors using a sensing layer architecture to form a sensing network, captures presence sensing data, and generates a room presence status map based on the presence sensing data; the presence sensing data includes guest room reservation information data, guest room environment data, and guest information data; A partitioned environmental control unit, which analyzes the room presence status map to obtain guest preference data; based on the guest preference data and the guest room environment data, it uses an intelligent optimization algorithm to selectively activate the environmental control of different guest room areas; A gradual adjustment unit, which designs personalized environmental parameter change curves for different areas based on the guest preference data, obtains the hotel equipment power change curve, and issues a soft transition instruction for scene switching to the equipment collaborative control unit for execution; The device collaborative control unit establishes an energy efficiency model library for hotel devices. According to the power change curve of hotel devices, it adopts a device complementary and collaborative mechanism to coordinate the operation of different hotel devices with each other and obtains device operation data; The energy load balancing unit predicts the energy demand index in the next period according to the guest preference data, device operation data, and the change curve of personalized environment parameters; according to the energy demand index, it pre-stores and releases energy during different electricity price periods to balance the energy load.

[0023] The methods for capturing presence perception data include: Deploy environmental sensors (such as temperature sensors, humidity sensors, light sensors, CO 2 concentration sensors, noise sensors, etc.), human activity sensors (such as infrared sensors, ultrasonic sensors, motion detectors, etc.), and interface devices docked with the hotel management system in the guest room. Integrate the environmental sensors, human activity sensors, and interface devices into the same perception network through standardized interfaces (such as MQTT, ZigBee, Wi-Fi, or BLE), and perform periodic data collection to obtain guest room environmental data, guest information data, and guest room reservation information data; integrate the guest room environmental data, guest information data, and guest room reservation information data to obtain presence perception data.

[0024] The guest room environmental data includes temperature data, humidity data, light data, CO 2 concentration data, and noise data; the guest information data includes identity information (guest name, identity certificate, membership level, and contact information), occupancy status (occupied, checked out, or vacant), travel trajectory, and historical occupancy data; the guest room reservation information data includes basic reservation information (reservation number, reservation date, check-in time, and check-out time), room type and configuration (room type, bed type, and meals), and special requirements (such as temperature adjustment, lighting mode, air purification requirements, etc.).

[0025] The methods for generating a room presence status map include: Based on the obtained presence perception data, the rule engine method is used to determine the room presence status according to the predetermined business rules; the room presence status includes idle (no current reservation, no access record at the access control, no human activity signal detected), reserved but unused (currently reserved, no access record at the access control, no human activity signal detected), in use (current reservation or no reservation, access record at the access control and no departure, human activity signal detected), and abnormal status (human activity signal detected during non-reservation time period, no one for a long time during the reservation time period); entity extraction is performed on the room presence status determination result and the presence perception data to serve as the nodes of the room presence status graph, and the logical relationships between entities are used as the edges of the room presence status graph to construct the room presence status graph, which is stored and updated through a graph database; the nodes of the room presence status graph include room entities (node attributes include room number, room type, and room capacity), guest room areas (rest area, work area, bathing area, etc.), time points, devices, room presence status, event status (door opening and closing, person detected, room reservation submission, etc.), and guest room environment data (sensor data such as temperature, light, etc.); the logical relationships between entities are, for example: the status of "room-xx" at "time point s1" is "room presence status-idle"; "room-xx" has "event status-door opening"; "room-xx" at "time point s2" has the "device-lamp" in the "guest room area-sleeping area" adjust the "guest room environment parameter-light brightness reduction", etc.

[0026] The methods for obtaining guest preference data include: A guest preference prediction model is constructed through a spatio-temporal graph neural network. The guest preference prediction model includes an input layer, a spatial feature extraction layer, a temporal feature extraction layer, a spatio-temporal fusion layer, and an output layer; the room presence status graph is used as the input data for the input layer of the guest preference model. In the spatial feature extraction layer, the spatial dependence relationships in the room presence status graph are analyzed through graph convolution, that is, the interaction patterns between different guest room areas, sensors, and devices; in the room presence status graph, the interaction patterns between different areas, sensors, and devices mainly refer to their physical connections, device linkage relationships, and associations caused by guest behavior habits. For example: work area - table lamp, interaction pattern: light adjustment (that is, if a guest often uses the table lamp in the work area for supplementary lighting, it can be predicted that this guest likes a brighter office environment and the table lamp brightness will be automatically adjusted in the future).

[0027] In the spatio-temporal fusion layer, the gated recurrent unit is used to process the changing patterns of guest behavior in the time dimension. For example, how guests adjust the device to change the guest room environment data over time. In the spatio-temporal fusion layer, the obtained spatial dependency relationship and the changing patterns of guest behavior in the time dimension are concatenated and fused to obtain guest preference data; Guest preference data includes temperature preference (guest's preference for temperature settings in different guest room areas), lighting preference (guest's preference for brightness, color temperature, and lighting mode), air quality preference (humidity, ventilation frequency, and air purification settings), time mode preference (the changing demand of the guest for the environment at different times), area usage habit (the usage frequency and method of different guest room areas by the guest), device interaction habit (the usage frequency and method of various devices in the room by the guest), energy usage mode (guest's energy-saving or comfort preference), scene switching preference (the acceptance degree of the guest for the adjustment speed of the guest room environment data), and circadian rhythm preference (the guest's work and rest time and the corresponding guest room environment requirements).

[0028] The method for selectively activating the environmental control of different guest room areas includes: Different guest room areas include a rest area (mainly for sleeping and resting), an entertainment area (for leisure, entertainment, and social activities), a work area (mainly for focused activities such as office work and reading), and a bathing area; It is set that each guest room area respectively contains adjustable environmental parameters (such as temperature, illuminance, and air purifier gear), forming a dimensional control vector; With the goal of minimizing energy consumption and maximizing comfort, select the Actor-Critic framework to build a deep neural network, and splice and fuse the guest preference data and the guest room environment data to form a state vector; Use the state vector as the input data of the deep neural network, and gradually extract the deep correlation information in the state vector through the policy network of the fully connected layer. Under the goal of minimizing energy consumption and maximizing comfort, output the global optimal control vector; Calculate the energy consumption of the device under different control vectors through power, for example: the power consumption of air conditioner temperature adjustment, the power demand of lighting brightness control, the energy consumption of air purifier operation, etc., and convert it into a negative reward to ensure that energy consumption is preferentially reduced during the iterative optimization process of the control vector; Calculate the comprehensive comfort score according to the guest room environment data, and use the Gaussian function to measure the deviation between the current guest room environment data and the preset optimal comfort interval, so that the deep neural network can identify the factors that have the greatest impact on comfort, and then optimize the control vector.

[0029] Upload the global optimal control vector to the central data pool, and send it to the environmental control actuator of the corresponding guest room area through the data bus to adjust the corresponding device state, so as to realize the selective activation of the environmental control of different guest room areas.

[0030] For example: Environmental control of the main activity area: Prioritize adjusting the environmental parameters of the area where the guest is currently located to achieve the best comfort; Secondary area maintenance: For areas that the guest has not used but may enter, maintain the equipment in the area in an energy-saving standby state; Inactive area: For areas that the guest has not used for a long time, control the equipment in the area to enter a deep sleep mode.

[0031] The method for designing a personalized environmental parameter change curve includes: Define the area boundary and transition zone model to ensure smooth transition of environmental parameters between areas; Identify the explicit physical boundaries based on the physical structure of the guest room, and the physical structure of the guest room includes clear physical segmentation elements such as walls and partition screens; Naturally form the first layer of boundaries between different areas, automatically identify the first layer of boundaries by analyzing the CAD drawings and 3D models of the guest room, and use it as the basic framework for area division; Determine the functional boundaries based on the furniture layout. Different furniture combinations indicate different guest room areas. The bed area is defined as the rest area, the table and chair combination is defined as the work area, the sofa and TV area is defined as the entertainment area, and the combination of washbasin, toilet and shower is defined as the bathing area; By identifying different furniture combinations, define the functional boundaries, and the functional boundaries are composed of the union of the core furniture edges and the functional activity buffer zones; Define the core furniture edges and the functional activity buffer zones, construct the area transition zone model through the distance-weighted interpolation algorithm combined with the cubic spline function, construct the guest preference - area association matrix, analyze the guest activity data and the corresponding environmental requirements, and define the typical scenarios and environmental parameter sets for the guest staying in the hotel guest room. The typical scenarios include check-in scenario, sleep preparation scenario, work scenario and leisure scenario; The parameter set includes temperature, lighting intensity and humidity air ventilation frequency; For example, check-in scenario: The guest first enters the room; Environmental parameter set: {Welcome temperature, welcome lighting, moderate ventilation}; Sleep preparation scenario: The guest prepares to rest; Environmental parameter set: {Lower temperature, dim the light, increase humidity}; Work scenario: The guest works in the room; Environmental parameter set: {Bright lighting, moderate temperature, frequent air renewal}; Leisure scenario: The guest relaxes and entertains; Environmental parameter set: {Comfortable temperature, soft lighting, moderate humidity}; Establish a scenario - parameter multi-dimensional target matrix. The elements in the scenario - parameter multi-dimensional target matrix include typical scenarios, different guest room areas and target environmental parameter set types; Define the acceptable range for each target environmental parameter set, adjust the standard target environmental parameter set according to the guest preference data, and analyze the adaptability index of the guest to the change of guest room environmental data. The higher the value, the better the guest's adaptability to the change of guest room environmental data, and the lower the value, the more sensitive the guest is to the change of guest room environmental data; Establish an environmental parameter change sensitivity model. Through the environmental parameter change sensitivity model, measure the index of the guest's sensitivity to the change of guest room environmental data ; wherein, represents the frequency of adjusting parameters in the guest room environment data; is the index of the parameter, ; represents the change range of the guest room environment data, which is obtained by weighted averaging the difference between the maximum value and the minimum value of different parameters in the guest room environment data; Calculate the optimal change rate of each parameter in the guest room environment data ; wherein, represents the preset basic change rate, which is the default value set according to the physical characteristics of different environmental parameters (such as the default temperature is 0.5 °C per minute); represents the priority coefficient of the typical scenario, which reflects the importance of the current activity. For high-priority scenarios (such as sleep preparation), the value is greater than 1, and the parameter adjustment is accelerated. For low-priority scenarios, the value is less than 1, and the adjustment speed is slowed down; Apply the cubic Bezier curve algorithm to design a smooth transition curve according to the index of the guest's sensitivity to the change of the guest room environment data and the optimal change rate of each parameter in the guest room environment data. Apply specific physical model constraints for different environmental parameters. For example, the temperature change curve adopts a thermodynamic model, and the light change curve adopts a human eye adaptation model; establish a correlation matrix between environmental parameters to realize the parameter linkage mechanism, and coordinate the guest room environment data through the parameter smooth transition algorithm at the regional junction; and adjust and update the smooth transition curve according to the guest preference data to obtain a personalized environmental parameter change curve.

[0032] According to the personalized environmental parameter change curve and the device power change curve, the system will generate specific control instructions. These instructions not only include the target values of environmental parameters (such as new temperature, light intensity), but also the change rate (such as "raise the temperature from 22 °C to 24 °C within 30 minutes"). At the same time, the instructions will also consider the power limit of the device to ensure that the system stability will not be affected due to excessive device load.

[0033] Characteristics of the soft transition instructions: The instructions must ensure that there will be no excessive power fluctuations during the execution of each device, and also avoid mutual interference between devices. For example, the air conditioner and the lighting system need to work in coordination. While the temperature is gradually rising, the light intensity is also gradually increasing, rather than jumping immediately. These generated soft transition instructions will be transmitted to the device cooperative control unit through communication protocols (such as Wi-Fi, Zigbee, Bluetooth, etc.). The role of the device cooperative control unit is to transmit the instructions to specific devices and ensure that the devices are adjusted according to the predetermined curve.

[0034] The methods for defining the edges of the core furniture and the functional activity buffer zones include: The method for defining the edge of the core furniture includes defining different furniture combinations corresponding to different guest room areas as the core furniture of that area. For each core furniture, extract its two-dimensional projection contour, construct a polygon boundary representation. For different core furniture, obtain the size and position information of the core furniture through a 3D scanner, calculate the overall contour of the core furniture, and select the corresponding edge definition according to the current state of different furniture combinations. Taking the origin at the lower left corner of the guest room, establish a Cartesian coordinate system, project the core furniture data onto the ground plane. For regular-shaped furniture (rectangular beds, square tables, etc.), directly calculate the four-corner coordinates, which include the lower left corner coordinate, the upper left corner coordinate, the upper right corner coordinate, and the lower right corner coordinate, and apply a rotation matrix to process the orientation of the core furniture to obtain its edge. For irregular-shaped furniture, decompose the irregular-shaped furniture (L-shaped sofas, special-shaped tables, etc.) into m basic components and merge the boundaries to obtain its edge. For smooth curved surface furniture (round tables, curved sofas), approximately obtain its edge using parametric curves. The extracted edges are stored in the form of an ordered point set to obtain the edges of the core furniture. The method for defining the functional activity buffer includes that the basic buffer width is determined according to the guest activity data. The shape of the basic buffer is obtained by non-uniform directional expansion according to the guest activity data, and the basic buffer width is adjusted according to different direction coefficients to obtain the widths of the buffer in different directions. For example, for a desk, the buffer in the front side (the direction where the human body is located) is larger (100 - 120 cm), while the buffer in the back side is smaller (20 - 30 cm). The guest activity data includes the residence time of the guest in different direction areas, the maximum residence time of the guest at all direction angles, the number of times the guest moves along different direction angles, and the total number of times the guest moves at all direction angles. The basic buffer includes a rest buffer, a work buffer, an entertainment buffer, and a bathing buffer. The basic buffer width is obtained by presetting the widths of different buffers. For example, the rest buffer: 80 - 100 cm (the space required for getting in and out of bed and making the bed); the work buffer: 70 - 120 cm (the space required for sitting, standing up, and moving); the entertainment buffer: 60 - 80 cm (the space for standing use); the bathing buffer: 50 - 70 cm (the space required for bathing). The width of the buffer in different directions is the basic buffer width multiplied by the direction coefficient; the direction coefficient ; where represents any one of the direction angles in different directions; represents the proportion of the residence time of the guest in any one direction area; represents the weight coefficient for adjusting the movement frequency. According to the expert experience method, ; represents the movement frequency; residence time proportion ; wherein, represents the staying time of the guest at any direction angle; represents the maximum staying time of the guest at all direction angles; represents the maximum staying time; moving frequency ; wherein, represents the number of times the guest moves along any direction angle ; represents the total number of movements of the guest at all direction angles; represents the index of the staying time of the guest at any direction angle.

[0035] The following main problems existing in the prior art are solved: The traditional guest room environment control system regards the entire room as a single area or uses fixed preset areas, and cannot adapt to the real activity patterns of guests; The prior art usually divides the buffer area with a unified standard width, without considering the differences in the actual activity data of guests, resulting in low space utilization efficiency and insufficient comfort; There is a lack of a systematic method to convert guest activity data into environmental control parameters, and the environmental parameters change abruptly at the junction of regions, resulting in an incoherent guest experience; The creativity of this solution is: fusing the definition of the edge of static furniture with dynamic guest behavior data, establishing a non-uniform direction expansion buffer model based on actual usage data, developing a complete mathematical framework to quantitatively convert behavior data into spatial design parameters, and establishing a differential buffer strategy for different functional areas (80 - 100 cm for rest, 70 - 120 cm for work, etc.); It should be noted that the design of the direction coefficient is supported by the following prior art. This formula draws on the accessibility analysis model in space syntax, combines static space characteristics with dynamic pedestrian flow behavior through weight coefficients. The proportion of staying time in the formula is derived from the behavior map technology in architecture, which has been widely used in the design of retail and hotel spaces. The moving frequency parameter is based on the activity frequency model in ergonomics, and similar methods have been used in the design of workspaces and the development of human-computer interaction interfaces; adopting a product-sum structure instead of a simple linear sum is based on the non-linear interaction principle in environmental behavior research proposed by the MIT Media Lab, which can more accurately reflect the characteristics of human spatial behavior. The direction coefficient is essentially an application innovation that combines existing spatial behavior analysis methods with environmental control systems, and its mathematical structure has theoretical support; Beneficial effects compared with the prior art: Through the precise definition of furniture boundaries and the dynamic buffer design based on activity data, the functionality of each area is optimized, avoiding the problem that the space division in the traditional method is too general and cannot meet personalized needs; The buffer is adjusted according to the direction coefficient, which can better adapt to the actual activity needs of guests and avoid space waste. For example, the widths of the buffers on the front and back sides of the desk are no longer uniform, but are adjusted according to the actual usage habits of guests, making the space layout more in line with the actual needs.

[0036] Through the smooth transition of area division and the dynamic buffer design, guests can enjoy a more comfortable and natural living experience. Especially the transition between different activity areas is smoother, and the environmental change will not make guests feel abrupt or uncomfortable.

[0037] The methods for coordinating the operation of different hotel equipment include: Collect the energy consumption data of various hotel equipment under different working conditions, and construct a mathematical model of equipment energy efficiency. The mathematical model of equipment energy efficiency includes equipment power-load curve, start-stop efficiency curve, and temperature-energy consumption relationship curve. The equipment power-load curve is obtained by the standard segmented test method, and data is obtained through equally spaced test points between 0% and 100%. This method has been widely used in the evaluation of HVAC systems. The polynomial fitting model used in the air conditioning system is derived from the energy efficiency evaluation method of ASHRAE Standard 90.1. The cubic law model (the relationship between power and the cube of flow) of fans and pumps is a classic model that has been used in the field of fluid machinery for a long time. These curve fitting techniques have been widely used in building energy management systems.

[0038] The construction method of the start-stop efficiency curve is obtained based on industrial energy efficiency test standards, namely industrial energy efficiency test standards ISO15627 and GB / T21452. The start process model of exponential decay and linear combination is derived from the mature theory in the field of motor control. The energy efficiency penalty calculation method has been promoted by the Building Technology Office of the US Department of Energy and incorporated into multiple commercial building energy efficiency evaluation software. The method for determining the minimum running time is a standard practice in HVAC control systems, mainly used to prevent equipment from being damaged by frequent start and stop.

[0039] The temperature-energy consumption relationship curve is obtained by the environmental temperature impact test process defined by the international standard test method. The international standard test method (AHRI210 / 240), the COP correction model for refrigeration equipment and the efficiency model for heating equipment are widely used in energy efficiency rating systems, such as the energy label in Europe and the Energy Star program in the United States. The query table and interpolation method used in the temperature compensation algorithm are common techniques in industrial control systems and have been implemented in many commercial building automation systems. These models provide a theoretical basis for equipment energy efficiency prediction and are widely used in energy management systems.

[0040] Convert the personalized environmental parameter change curve generated by the gradient adjustment unit into a device power change curve. First, the system receives the multi-dimensional environmental parameter target curve (temperature, humidity, light, etc.) through a parameter parser, applies signal preprocessing technology to eliminate noise and perform legality verification. This process adopts the data processing specifications defined by building automation standard protocols (such as BACnet).

[0041] Secondly, use a physical model conversion engine to convert environmental parameter requirements into load requirements. For temperature parameters, the ASHRAE load calculation method is adopted, considering the characteristics of the building envelope, occupancy load, and equipment load; for humidity, the CIBSE wet load calculation standard is used; for light, the IES lighting calculation method is used; for air quality, the ventilation calculation method based on the European EN13779 standard is used.

[0042] In the third step, apply device characteristic mapping and perform load-power conversion through the pre-established device energy efficiency model library. For the air conditioning system, the part-load performance curve defined by the AHRI standard is used; for pumps and fans, the flow-power relationship of the ISO 5801 standard is used; for the lighting system, the power factor mapping of the IEC62442 standard is used.

[0043] Finally, integrate the multi-device power change curves through a multi-objective optimization algorithm, apply signal processing technologies such as moving average filtering to smooth power fluctuations, and perform power constraint checks to ensure that the total power does not exceed the system capacity. Define a device complementary cooperation mechanism, which includes using temporal complementarity, implementing spatial complementarity, and performing source-end complementarity; using temporal complementarity means avoiding the overlap of device power peaks and achieving peak-shaving operation; implementing spatial complementarity means coordinating devices in adjacent areas to reduce boundary energy losses; performing source-end complementarity means selecting the optimal energy combination from multiple energy combinations; generating device linkage instructions based on the cooperative optimization algorithm and issuing device linkage instructions through the energy management intelligent terminal to coordinate the operation of different hotel devices with each other.

[0044] Methods for balancing energy loads include: Predict the future Match and analyze the energy demand index within a period of time with the grid electricity price period information. The grid electricity price periods are divided into peak periods (14:00 - 17:00, 19:00 - 22:00), normal periods (8:00 - 14:00, 17:00 - 19:00, 22:00 - 23:00), and valley periods (23:00 - 8:00 the next day). Identify the overlapping area of the highest demand and the highest electricity price through the time series matching algorithm, and balance the energy load for the time period corresponding to the overlapping area of the highest demand and the highest electricity price; when it is the valley period of the electricity price and predicting the future highest demand, activate the energy pre-storage mechanism to store the energy of the hotel guest rooms; preset the grid electricity price threshold, and set the energy release instruction according to the preset grid electricity price threshold during the peak period. When the grid electricity price is greater than or equal to the grid electricity price threshold, trigger the energy release instruction to release the energy of the hotel guest rooms.

[0045] The preset grid electricity price threshold is set by the expert experience method. Similarly, set the preset optimal comfort interval, preset basic change rate, preset widths of different buffer zones, and fixed preset areas.

[0046] In this embodiment, by automatically adjusting the environmental control through guest preference data, it can precisely meet the unique needs of each guest, making the guest room environment more in line with the individual's living habits. As the number of times a user checks in increases, the system will accumulate more detailed preference data, further improving the accuracy of personalized adjustment, continuously optimizing the guest room environment, and providing more precise services for frequent guests; By automatically identifying the physical boundaries and furniture layouts of the guest rooms, the system can accurately divide different functional areas and adjust the environment according to the needs of each area. By dynamically adjusting the width and shape of the buffer zone, the system can optimize the space layout according to the guest's activity habits and direction needs, avoiding unnecessary space waste; through refined functional area division, relieve the sense of oppression in the guest room. Especially in a smaller guest room space, it can effectively avoid the discomfort caused by unreasonable space layout; Avoid the abrupt feeling during area switching in the existing system. The guest room environment data can smoothly transition between different areas. Through the transition zone model, avoid the conflict of the environment in different areas. Guests will not feel uncomfortable due to sudden light changes or drastic temperature fluctuations during activities, enhancing the overall comfort in the guest room; By dynamically adjusting environmental data in the guest room such as temperature, humidity, and lighting, unnecessary energy waste can be avoided. For example, based on the detection of the actual activities of the guests, when the guests leave the room, the system can automatically adjust the temperature and lighting to save energy consumption; when the guests change areas, the system can automatically optimize energy use to avoid multiple areas being in a high energy consumption state simultaneously; for different environmental requirements, the system can optimize energy consumption through intelligent algorithms. For example, the lighting and temperature control in the working area will be automatically adjusted according to the time period of the guests' activities, eliminating the need for manual switching, greatly improving energy efficiency and energy conservation; Guests no longer need to manually adjust various devices to meet their personal needs. The system automatically adjusts the environmental settings in the guest room area according to personal preferences and activity patterns, simplifying the operation for customers and enhancing the convenience of check-in. By anticipating the needs of the guests in advance and making real-time adjustments, a more considerate service experience can be provided. When guests feel a comfortable and personalized environment, higher customer satisfaction can be achieved.

[0047] Embodiment 2 Please refer to Figure 2 As shown, for the parts not described in detail in this embodiment, refer to the description content of Embodiment 1. A method for optimizing energy management in a hotel guest room is provided, including: S1. Construct a time series database cluster, and upload and interact data between various units through a data bus; S2. Integrate multiple sensors using a perception layer architecture to form a perception network, capture occupancy perception data, and generate a room occupancy status map based on the occupancy perception data; the occupancy perception data includes guest room reservation information data, guest room environmental data, and guest information data; S3. By analyzing the room occupancy status map, obtain guest preference data; based on the guest preference data and guest room environmental data, use an intelligent optimization algorithm to selectively activate the environmental control of different guest room areas; S4. Design personalized environmental parameter change curves for different areas based on the guest preference data, and obtain the hotel equipment power change curve, and send a soft transition instruction for scene switching to the equipment collaborative control unit for execution; S5. Establish an energy efficiency model library for hotel equipment, according to the hotel equipment power change curve, adopt an equipment complementary and collaborative mechanism to coordinate the operation of different hotel equipment with each other, and obtain equipment operation data; S6. Predict the energy demand index within a future period of time according to the guest preference data, equipment operation data, and personalized environmental parameter change curves; according to the energy demand index, perform energy pre-storage and release during different electricity price periods to balance the energy load.

[0048] Since the electronic device introduced in this embodiment is the electronic device used to implement the energy optimization management system and method for a hotel guest room in the embodiments of the present application, based on the energy optimization management system and method for a hotel guest room introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the energy optimization management system and method for a hotel guest room in the embodiments of the present application, it falls within the protection scope of the present application.

[0049] The above formulas are all calculated by taking the numerical values without dimensions. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0050] The above are only the preferred implementation manners of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary users in the technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. An energy optimization management system for hotel guest rooms, characterized in that: include: The central data pool unit builds a time series database cluster and uploads and interacts data between units through the data bus; The presence sensing unit uses a sensing layer architecture to integrate multiple sensors to form a sensing network, captures presence sensing data, and generates a room presence state map based on the presence sensing data; the presence sensing data includes room reservation information data, room environment data, and guest information data; The zoned environment control unit obtains guest preference data by analyzing the room status map; based on the guest preference data and room environment data, it uses an intelligent optimization algorithm to selectively activate the environment control of different room areas; The gradual adjustment unit designs personalized environmental parameter change curves for different areas based on guest preference data, obtains the power change curve of hotel equipment, and issues scene switching soft transition instructions to the equipment collaborative control unit for execution; Equipment collaborative control unit, establishes a hotel equipment energy efficiency model library, adopts equipment complementary collaborative mechanism according to the power change curve of hotel equipment, coordinates the operation of different hotel equipment, and obtains equipment operation data; Energy load balancing unit, predicting future trends based on guest preference data, equipment operation data, and personalized environmental parameter change curves Energy demand index within a period of time; pre-store and release energy at different electricity price periods according to the energy demand index to balance the energy load.

2. The energy optimization management system for hotel rooms according to claim 1, characterized in that: The method of capturing presence sensing data comprises: Environmental sensors, human activity sensors and interface devices connected to the hotel management system are deployed in the guest rooms. The environmental sensors, human activity sensors and interface devices are integrated into the same perception network through standardized interfaces, and periodic data collection is performed to obtain guest room environmental data, guest information data and room reservation information data; the guest room environmental data, guest information data and room reservation information data are integrated to obtain presence perception data.

3. The energy optimization management system for hotel rooms according to claim 2, characterized in that: The method for generating a room existence state map comprises: Based on the acquired presence perception data, the rule engine method is used to determine the room existence status according to the predetermined business rules; the room existence status includes idle, booked but not used, in use and abnormal state; the room existence status determination results and presence perception data are subjected to entity extraction as nodes of the room existence status graph, and the logical relationships between entities are used as edges of the room existence status graph to construct the room existence status graph.

4. The energy optimization management system for hotel rooms according to claim 3, characterized in that: The method for obtaining the guest preference data includes: A guest preference prediction model is constructed through a spatiotemporal graph neural network. The guest preference prediction model includes an input layer, a spatial feature extraction layer, a temporal feature extraction layer, a spatiotemporal fusion layer, and an output layer. The room existence state map is used as input data for the input layer of the guest preference model. In the spatial feature extraction layer, the spatial dependency relationship in the room existence state map is analyzed through graph convolution. In the spatiotemporal fusion layer, the guest behavior change pattern in the time dimension is processed through the gated recurrent unit. In the spatiotemporal fusion layer, the acquired spatial dependencies and the guest behavior change pattern in the time dimension are spliced ​​and fused to obtain the guest preference data; the guest preference data includes temperature preference, lighting preference, air quality preference, time mode preference, area usage habits, device interaction habits, energy usage patterns, scene switching preferences and day and night regularity preferences.

5. The energy optimization management system for hotel rooms according to claim 4, characterized in that: The method of selectively activating environmental control of different guest room areas includes: Different guest room areas include rest area, entertainment area, work area and bathing area; each guest room area is set to include adjustable environmental parameters, forming dimensional control vector; with the goal of minimizing energy consumption and maximizing comfort, the Actor-Critic framework is selected to build a deep neural network, and the guest preference data and room environment data are spliced ​​and fused to form a state vector; the state vector is used as the input data of the deep neural network, and the deep correlation information in the state vector is gradually extracted through the strategy network of the fully connected layer, and the global optimal control vector is output while meeting the goal of minimizing energy consumption and maximizing comfort; The global optimal control vector is uploaded to the central data pool and sent to the environmental control actuators in the corresponding guest room areas through the data bus to adjust the corresponding equipment status, thereby achieving selective activation of environmental control in different guest room areas.

6. The energy optimization management system for hotel rooms according to claim 5, characterized in that: The method for designing a personalized environmental parameter change curve comprises: Define regional boundaries and transition zone models, identify explicit physical boundaries based on the physical structure of guest rooms; naturally form the first-layer boundaries between different areas, automatically identify the first-layer boundaries by analyzing guest room CAD drawings and 3D models, and use them as the basic framework for regional division; Functional boundaries are determined based on furniture layout. Different furniture combinations correspond to different guest room areas. The bed area is defined as the rest area, the table and chair combination is defined as the work area, the sofa and TV area is defined as the entertainment area, and the washbasin, toilet and shower combination is defined as the bathing area. By identifying different furniture combinations, functional boundaries are defined. Functional boundaries are composed of the union of core furniture edges and functional activity buffers. Core furniture edges and functional activity buffers are defined, and a regional transition zone model is constructed by combining a distance weighted interpolation algorithm with a cubic spline function. A guest preference-region association matrix is ​​constructed, and guest activity data and corresponding environmental requirements are analyzed. Typical scenarios and environmental parameter sets for guests staying in hotel rooms are defined. Typical scenarios include check-in scenarios, sleep preparation scenarios, work scenarios, and leisure scenarios. Parameter sets include temperature, lighting brightness, humidity, and air ventilation frequency. Establish a scenario-parameter multidimensional target matrix, the elements of which include typical scenarios, different guest room areas, and target environment parameter set types; define an acceptable range for each target environment parameter set, adjust the standard target environment parameter set according to guest preference data, and analyze the guest's adaptability to changes in guest room environment data; Establish an environmental parameter change sensitivity model, and use it to measure the guest's sensitivity to changes in guest room environmental data; calculate the optimal change rate of each parameter in the guest room environmental data; The cubic Bezier curve algorithm is used to design a smooth transition curve based on the guest's sensitivity to changes in guest room environmental data and the optimal change rate of each parameter in the guest room environmental data, and specific physical model constraints are applied to different environmental parameters; a correlation matrix between environmental parameters is established to realize a parameter linkage mechanism, and the guest room environmental data is coordinated through a smooth transition algorithm of parameters at the junction of regions; and the smooth transition curve is adjusted and updated through guest preference data to obtain a personalized environmental parameter change curve.

7. The energy optimization management system for hotel rooms according to claim 6, characterized in that: The method for defining the core furniture edge and the functional activity buffer zone comprises: The method for defining the edge of core furniture includes defining different furniture combinations corresponding to different guest room areas as core furniture of the area, extracting the two-dimensional projection contour of each core furniture, constructing a polygonal boundary representation, obtaining the size and position information of the core furniture through a 3D scanner, calculating the overall contour of the core furniture, and selecting the corresponding edge definition according to the current status of different furniture combinations; With the lower left corner of the guest room as the origin, a Cartesian coordinate system is established, and the core furniture data is projected onto the ground plane. For regular-shaped furniture, the four-corner coordinates are directly calculated, including the lower left corner coordinate, the upper left corner coordinate, the upper right corner coordinate, and the lower right corner coordinate. The rotation matrix is ​​used to process the orientation of the core furniture to obtain its edge. For irregular-shaped furniture, the irregular-shaped furniture is decomposed into m basic components, and the boundaries are merged to obtain its edge. For smooth curved furniture, parametric curves are used to approximate its edge. The extracted edges are stored in the form of ordered point sets to obtain the core furniture edge. The method for defining the functional activity buffer includes determining the basic buffer width based on the guest activity data, The basic buffer shape is obtained by non-uniform directional expansion according to the guest activity data, and the basic buffer width is adjusted according to different directional coefficients to obtain the width of the buffer in different directions; the guest activity data includes the guest's stay time in different directional areas, the guest's maximum stay time in all directional angles, the number of times the guest moves along different directional angles, and the total number of times the guest moves in all directional angles; The basic buffer zone includes a rest buffer zone, a work buffer zone, an entertainment buffer zone and a bathing buffer zone; the width of the basic buffer zone is obtained by presetting the widths of different buffer zones.

8. The energy optimization management system for hotel rooms according to claim 7, characterized in that: The method for coordinating the operation of different hotel equipments with each other includes: Collect energy consumption data of various hotel equipment under different working conditions and construct equipment energy efficiency mathematical models, which include equipment power-load curve, start-stop efficiency curve and temperature-energy consumption relationship curve; equipment power-load curve is obtained by standard segmented test method; start-stop efficiency curve construction method is based on industrial energy efficiency test standards; temperature-energy consumption relationship curve is obtained by ambient temperature impact test process defined by international standard test method; The personalized environmental parameter change curve generated by the gradual adjustment unit is converted into the equipment power change curve, and the equipment complementary coordination mechanism is defined. The complementary coordination mechanism includes utilizing timing complementarity, implementing spatial complementarity and executing source-end complementarity. Based on the collaborative optimization algorithm, equipment linkage instructions are generated, and equipment linkage instructions are issued through the energy management intelligent terminal to coordinate the operation of different hotel equipment.

9. The energy optimization management system for hotel rooms according to claim 8, characterized in that: The method for balancing energy loads comprises: Will predict the future The energy demand index within a period of time is matched and analyzed with the grid electricity price period information. The grid electricity price period is divided into peak period, normal period and valley period. The overlapping area of ​​the highest demand and the highest electricity price is identified through the time series matching algorithm, and the energy load is balanced for the period corresponding to the overlapping area of ​​the highest demand and the highest electricity price. During the valley period of electricity price and when the highest demand in the future is predicted, the energy pre-storage mechanism is activated to store energy in the hotel rooms. The grid electricity price threshold is preset, and the energy release instruction is set according to the preset grid electricity price threshold during the peak period. When the grid electricity price is greater than or equal to the grid electricity price threshold, the energy release instruction is triggered to release the energy in the hotel rooms.

10. A method for optimizing the management of energy in hotel rooms, applied to the energy optimization management system for hotel rooms according to any one of claims 1 to 9, characterized in that: include: S1. Build a time series database cluster and upload and interact data between units through the data bus; S2, using the perception layer architecture to integrate multiple sensors to form a perception network, capture presence perception data, and generate a room presence state map based on the presence perception data; the presence perception data includes room reservation information data, room environment data and guest information data; S3. Obtain guest preference data by analyzing the room status map; based on the guest preference data and room environment data, use intelligent optimization algorithms to selectively activate environmental control in different room areas; S4. Design personalized environmental parameter change curves for different areas based on guest preference data, obtain hotel equipment power change curves, and issue scene switching soft transition instructions to the equipment collaborative control unit for execution; S5. Establish a hotel equipment energy efficiency model library, adopt equipment complementary coordination mechanism according to the power change curve of hotel equipment, coordinate the operation of different hotel equipment, and obtain equipment operation data; S6. Predict the future based on guest preference data, equipment operation data and personalized environmental parameter change curves Energy demand index within a period of time; pre-store and release energy at different electricity price periods according to the energy demand index to balance the energy load.

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