Intelligent guest room energy-saving control system based on CAN-BUS

Abstract

The invention discloses a guest room intelligent energy-saving control system based on CAN-BUS, and relates to the technical field of equipment intelligent control, and the guest room intelligent energy-saving control system comprises an intelligent regulation and control module which is used for controlling various electrical equipment in a guest room; the intelligent regulation and control module comprises an environment sensing unit, an auxiliary decision making unit and an environment regulation and control unit. By designing the intelligent regulation and control module, balance between energy saving and comfort is achieved, the problem of energy waste of a traditional guest room is solved, the energy-saving rigid requirement and the comfortable and flexible experience are balanced, and the customer experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment intelligent control, in particular to a CAN-BUS-based guest room intelligent energy-saving control system. Background Art

[0002] As a key component of hotel intelligence, guest room control systems can improve hotel operational efficiency and service quality while reducing operating costs. Through guest room control systems, hotels can achieve precise temperature and lighting control in guest rooms, providing consumers with comfortable and personalized services.

[0003] Traditional guest room control systems support complex multi-device collaboration strategies, which increases energy waste and restricts the optimization of energy saving and comfort. For example, during daytime sunlight, they only rely on air conditioning for cooling, without linking curtains to reduce energy consumption.

[0004] Patent CN119225199B discloses a hotel room smart device control system and method based on the Internet of Things. The above implementation ensures the health status of users, reduces the frequency of manual control, and prevents high-frequency switching of the working status of electrical equipment.

[0005] The above patent solves the problem that it is difficult to consider the safety needs and accommodation needs of rehabilitation users in multiple dimensions, which leads to serious energy waste in guest rooms and insufficient comfort for customers. However, there is still room for improvement in the balance between energy saving and comfort in guest rooms. This application combines customer behavior, environment, energy consumption and other data to achieve a balance between the rigid demand for energy saving in guest rooms and the flexible experience of comfort, solving the problem of energy waste in traditional guest rooms.

[0006] To this end, the present application proposes a CAN-BUS-based intelligent energy-saving control system for guest rooms that achieves a balance between energy saving and comfort in guest rooms. Summary of the Invention

[0007] The purpose of the present invention is to provide a CAN-BUS-based intelligent energy-saving control system for guest rooms to solve the technical problem of energy waste in traditional guest rooms raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: a CAN-BUS-based guest room intelligent energy-saving control system, comprising an intelligent control module, the intelligent control module being used to control various electrical appliances in the guest room; The intelligent control module includes: an environment perception unit, an auxiliary decision unit and an environment control unit, wherein the environment perception unit is connected to the auxiliary decision unit via a CAN bus, and the auxiliary decision unit is connected to the environment control unit via a CAN bus; The environmental perception unit uses sensors to monitor the environmental parameters in the guest room in real time, perceive the guest's behavior, identify the guest's behavior pattern, and fuse the environmental parameters with the behavior pattern to generate guest room status knowledge; The auxiliary decision-making unit uses Raspberry Pi as an edge node to obtain the current working status and energy consumption data of the guest room equipment, uses cloud big data to analyze energy consumption patterns and guest room status knowledge, and generates auxiliary strategies based on environmental comfort requirements; The environment control unit controls the electrical equipment in the guest room according to the auxiliary strategy. When the customer actively adjusts the equipment, it responds first and overrides the auxiliary strategy, and records the customer's historical operations.

[0009] Preferably, the environment control unit is connected to an adaptive control module via a CAN bus, and the adaptive control module matches the scene mode according to the customer's preferences; The adaptive control module includes: a scene mode unit, a preference learning unit and a mode switching unit. The preference learning unit is connected to the scene mode unit and the environment control unit via a CAN bus. The preference learning unit is connected to the mode switching unit via a CAN bus. The scenario mode unit uses Raspberry Pi to perform cluster analysis on historical customer operation data to create basic scenario modes. Based on the average value of the historical customer operation data, it sets the initial parameter values ​​of the basic scenario modes. The preference learning unit uses machine learning algorithms to learn the current customer's historical operation data, identify the current customer's personalized preferences, fine-tune the parameters of the basic scene mode, and generate a personalized scene mode that meets the current customer's preferences; The mode switching unit performs trend analysis on the current customer's historical operation data, predicts the customer's future behavior pattern, automatically switches the scene mode, and provides an interactive interface for customers to actively select and customize the scene mode.

[0010] Preferably, the environment control unit is connected to an emotion recognition module via a CAN bus, and the emotion recognition module analyzes the guest's emotions and adjusts the guest room environment; The emotion recognition module includes: a language interaction unit, an emotion analysis unit and an auxiliary regulation unit. The language interaction unit is connected to the emotion analysis unit via a CAN bus, and the emotion analysis unit is connected to the auxiliary regulation unit via a CAN bus. The language interaction unit integrates a microphone and an intelligent sound system, and is equipped with voice commands for switching room scene modes. It supports customers to control room equipment and switch scene modes by voice, and provides voice query information services. The emotion analysis unit uses speech recognition and sentiment analysis algorithms to analyze the intonation, speaking speed, and volume characteristics of the customer's voice, identify the customer's emotional state, and generate an emotion analysis report; Based on the emotion analysis report, the auxiliary adjustment unit matches the music and lighting atmosphere that suits the current mood, recommends it to the customer through voice broadcast and interactive interface, links with the air quality monitoring system in the guest room, predicts the trend of air quality changes, and adjusts the working status of the air purification equipment.

[0011] Preferably, the environment perception unit is connected to an auxiliary service module via a CAN bus, and the auxiliary service module is used to provide room service for customers; The auxiliary service module includes a demand analysis unit, a task integration unit, and a service planning unit. The demand analysis unit is connected to the task integration unit and the environment perception unit via a CAN bus. The task integration unit is connected to the service planning unit via a CAN bus. The demand analysis unit predicts customer demand based on room status knowledge, identifies service types based on actual customer needs, analyzes service priorities, and generates service task orders; The task integration unit conducts correlation analysis on the service task books of different guest rooms, merges similar service task books, and sorts the merged service task books based on task type, service priority, and room location factors; The service planning unit assigns tasks to the service robot based on the location, working status, and task progress of the service robot, combined with the merged room service task book, and adjusts the path of the service robot in real time based on the real-time traffic information of the service robot.

[0012] Preferably, the auxiliary decision unit is connected to an intelligent security module via a CAN bus, and the intelligent security module is used to warn of equipment failures in the guest room; The intelligent security module includes: equipment warning unit, emergency response unit and intelligent evacuation unit. The equipment warning unit is connected to the emergency response unit and auxiliary decision unit through the CAN bus, and is connected to the intelligent evacuation unit through the CAN bus. The equipment early warning unit uses deep learning algorithms to analyze the energy consumption data and environmental parameters of guest room equipment, predict future energy consumption and environmental trends, identify energy consumption anomalies and environmental parameter violations, and determine potential safety risks and early warning levels; The event response unit triggers a response mechanism based on the warning information, pushes warning information to customers and room management personnel, and automatically unlocks the door, turns on the room lights, and automatically calls the preset emergency contact number in an emergency; When a customer leaves a guest room for evacuation, the intelligent evacuation unit dynamically displays the evacuation route and direction through indicator lights outside the guest room.

[0013] Preferably, the adaptive control module is connected to a customer service module via a network signal, and the customer service module provides guests with diversified services; The customer service module includes: a personalized adjustment unit, a health monitoring unit, and a privacy protection unit. The personalized adjustment unit is connected to the privacy protection unit via a network signal, and the health monitoring unit is connected to the privacy protection unit via a network signal. The personalized adjustment unit controls the motor via the CAN bus to adjust the angles of the head and foot of the bed according to customer needs, and uses the pneumatic system to adjust the firmness of the mattress. The health monitoring unit uses a sensor network to detect the user's movements during sleep, body temperature changes, and breathing conditions. It analyzes the user's sleep depth and quality, identifies abnormal body temperature and breathing patterns, and generates a health report. The privacy protection unit generates a communication key between the sensor network and the network, stores the communication key in a secure storage area of ​​the sensor network, and uses an encryption algorithm to encrypt the customer privacy data monitored by the sensor network.

[0014] Preferably, the behavior pattern includes: the time when the customer enters and leaves the room, the customer's adjustment behavior of the air conditioner, the customer's triggering frequency and triggering time of the lighting, and the customer's adjustment frequency and triggering time of the curtains.

[0015] Preferably, the auxiliary strategies include: a control strategy for dynamically adjusting guest room equipment according to environmental comfort, and an energy consumption optimization strategy based on time-of-use electricity price response.

[0016] Preferably, the basic scene modes include: a sleep mode with no activity for a long time after lights out, an office mode with desk lamp on, a meeting mode with multiple people entering the guest room, and an energy-saving mode when no one is in the guest room.

[0017] Preferably, the service types include: room cleaning service, item delivery service, and room dining service.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves a balance between energy saving and comfort by designing an intelligent control module, solving the problem of energy waste in traditional guest rooms, balancing the rigid demand for energy saving with the flexible experience of comfort, and improving the customer experience; 2. The present invention is designed with an adaptive control module to achieve the setting of personalized scene modes, solving the problem of traditional guest rooms requiring manual adjustment of multiple electrical devices, improving customer satisfaction and repeat stay rates, and meeting guests' personalized needs for scene modes; 3. The present invention is designed with an emotion recognition module to realize the perception and auxiliary regulation of customer emotions, thereby improving the level of refined hotel management, solving the problem of single experience in traditional room service, and improving comfort; 4. The present invention realizes intelligent management and optimization of room service by designing an auxiliary service module, improves the efficiency of room service, solves the problem of lagging traditional room service, and improves customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the intelligent control module of the present invention; Figure 2 Schematic diagram of the adaptive control module of the present invention; Figure 3 This is a schematic diagram of the emotion recognition module of the present invention; Figure 4 This is a schematic diagram of the auxiliary service module of the present invention; Figure 5 This is a schematic diagram of the intelligent security module of the present invention; Figure 6 A schematic diagram of the customer service module of the present invention; Figure 7 This is a schematic diagram of the service types of the present invention; Figure 8 Schematic diagram of the system workflow of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] Example 1, please refer to Figure 1 and Figure 8 , a CAN-BUS-based guest room intelligent energy-saving control system, including an intelligent control module, which is used to control various electrical appliances in the guest room; the intelligent control module includes: an environmental perception unit, an auxiliary decision unit and an environmental control unit, the environmental perception unit is connected to the auxiliary decision unit via a CAN bus, and the auxiliary decision unit is connected to the environmental control unit via a CAN bus; the environmental perception unit uses sensors to monitor the environmental parameters in the guest room in real time, perceive the customer's behavior, identify the customer's behavior pattern, and fuse the environmental parameters with the behavior pattern to generate guest room status knowledge; the auxiliary decision unit uses Raspberry Pi as an edge node to obtain the current working status and energy consumption data of the guest room equipment, uses cloud big data to analyze the energy consumption pattern and guest room status knowledge, and generates auxiliary strategies in combination with environmental comfort requirements; the environmental control unit controls the electrical equipment in the guest room according to the auxiliary strategy, and when the customer actively adjusts the equipment, it responds first and overrides the auxiliary strategy, and records the customer's historical operations.

[0022] Furthermore, the environmental perception unit uses sensors such as temperature and humidity sensors, light sensors, and CO2 concentration sensors to monitor the environmental parameters in the guest room in real time. It uses smart switches and door magnetic sensors to capture the customer's behavioral data such as operating the guest room's electrical equipment and entering and exiting the room. In this way, the customer's behavioral patterns such as the time of entry and exit of the guest room, the customer's adjustment range and trigger time of the air conditioner, the customer's trigger frequency and trigger time of the light, and the customer's adjustment frequency and trigger time of the curtain are obtained. The environmental parameters are aligned with the behavioral pattern data timestamps to establish an event timeline. The environmental parameters are associated with the behavioral patterns, such as the customer's entry and exit and the light switch status, the curtain opening and closing degree and the light intensity, and the door switch status and the air conditioning cooling, thereby obtaining the guest room status knowledge. The environmental perception unit transmits the guest room status knowledge data to the Raspberry Pi in real time through the CAN bus. At the same time, the auxiliary decision unit obtains the current working status and energy consumption data of the guest room equipment in real time through the Raspberry Pi. The cloud big data analysis platform receives the data from the Raspberry Pi. The cloud uses machine learning algorithms to identify energy consumption patterns and guest room status knowledge, and generates energy consumption curves for future time periods, thereby predicting future energy consumption results. The auxiliary decision unit combines environmental comfort requirements and energy consumption prediction results to generate energy-saving auxiliary strategies, including: control strategies that dynamically adjust guest room equipment according to environmental comfort, and energy consumption optimization strategies that respond to time-of-use electricity prices. Some functions include pre-cooling or pre-heating guest rooms during periods of low electricity prices, automatically turning off non-essential equipment such as lights when the room is unoccupied, and automatically closing curtains when the air conditioner is turned on, fully considering the customer's comfort needs and ensuring both energy saving and comfort. The multi-node parallel communication characteristics of the CAN bus ensure efficient and reliable data transmission of each unit. The Raspberry Pi is connected to the guest room equipment through the CAN bus, which supports real-time data transmission, thus meeting the guest room equipment's demand for immediate response. The Raspberry Pi communicates with the cloud and can pre-process data, such as filtering redundant information, thereby reducing the cloud load and improving system response speed. The environmental control unit receives the auxiliary strategy from the auxiliary decision-making unit, and determines the electrical equipment that needs to be regulated and its target state based on the auxiliary strategy. The environmental control unit accurately controls the air conditioner, lights, curtains and other equipment in the guest room through electrical signals, thereby achieving a balance between energy saving and comfort. For example, when the air conditioner is turned on, the curtains are automatically closed to block direct sunlight, reduce indoor heat accumulation caused by solar radiation, and thus reduce the air conditioning cooling load, thereby reducing energy consumption; when the customer is sleeping, the air conditioner silent mode is started and the curtains are closed to reduce the impact of the external environment on the customer's sleep, thereby improving the customer's comfort. When the customer actively adjusts the status of the electrical equipment, the environmental control unit overwrites the original auxiliary strategy according to the customer's new instructions. At the same time, the environmental control unit records the customer's historical operations so that the system can subsequently learn and understand the customer's behavior patterns.

[0023] Example 2, please refer to Figure 2 and Figure 8 , a CAN-BUS-based guest room intelligent energy-saving control system, the environment control unit is connected to the adaptive control module via the CAN bus, and the adaptive control module matches the scene mode according to the customer's preference; the adaptive control module includes: a scene mode unit, a preference learning unit and a mode switching unit, the preference learning unit is connected to the scene mode unit and the environment control unit via the CAN bus, and the preference learning unit is connected to the mode switching unit via the CAN bus; the scene mode unit uses Raspberry Pi to perform cluster analysis on past customer historical operation data to create a basic scene mode, and sets the initial parameter value of the basic scene mode based on the average value of the customer's historical operation data; the preference learning unit uses a machine learning algorithm to learn the current customer's historical operation data, identify the current customer's personalized preferences, fine-tune the parameters of the basic scene mode, and generate a personalized scene mode that meets the current customer's preferences; the mode switching unit performs trend analysis on the current customer's historical operation data, predicts the customer's future behavior pattern, automatically switches the scene mode, and provides an interactive interface for customers to actively select and customize scene modes.

[0024] Furthermore, the scene mode unit receives the past customer operation data transmitted by the environment control unit, including air conditioning temperature settings, light brightness settings, curtain opening and closing angles, etc., records the triggering time and operation frequency of past customer behaviors, and uses Raspberry Pi to perform cluster analysis on these data, and divides the operation parameter data into different clusters. Each cluster represents a basic scene mode. According to the clustering results, different basic scene modes are identified and named, such as a sleep mode with no activity for a long time after turning off the lights, an office mode with the desk lamp on, a meeting mode with multiple people entering the guest room, an energy-saving mode with no one in the guest room, etc. The average value of the operation parameters in each mode is taken and used as the initial parameter value of the basic scene mode, such as the brightness of the desk lamp when the customer is working, the working status of the air conditioner, etc., and the triggering conditions of different scene modes are set, such as triggering the meeting mode when multiple people enter the guest room, triggering the energy-saving mode when no one in the guest room, triggering the office mode when the desk lamp is on, etc. The preference learning unit receives the current customer's real-time operation data transmitted by the environmental control unit and identifies characteristic data that can reflect the customer's preferences, such as the customer's commonly used lighting brightness, air conditioning temperature setting range, and the closure of curtains when the customer sleeps. It uses machine learning algorithms to learn the mapping relationship between customer preferences and operation data, thereby identifying the current customer's personalized preferences, such as the customer's high-frequency lighting use, air conditioning temperature setting and other data. It then adjusts the parameters of the basic scene mode based on the current customer's personalized preferences and generates a personalized scene mode that meets the current customer's preferences, thereby ensuring that the customer always has the best user experience; The mode switching unit receives the current customer's historical operation data transmitted by the environmental control unit, aligns the timestamp of the customer's current operation data with the timestamp of the customer's historical operation data, identifies similar operation behaviors in the same time period, the same operation of the same device and other similar behaviors, thereby predicting the customer's future behavior patterns. When the mode switching unit determines that the customer is about to trigger a certain behavior pattern based on the customer's operation data, the mode switching unit will automatically adjust the status of the smart devices in the guest room. For example, when the mode switching unit predicts that the customer is about to fall asleep, the mode switching unit automatically switches to sleep mode. The mode switching unit dims the lights and adjusts the air-conditioning temperature through the environmental control unit. At the same time, in order to ensure that the switched mode meets customer expectations, by introducing an interactive interface, customers can actively select or customize scene modes according to their own wishes. The mode switching unit adjusts multiple electrical appliances in the guest room at the same time through the environmental control unit based on customer feedback.

[0025] Example 3, please refer to Figure 1 、 Figure 3 and Figure 8 , a CAN-BUS-based guest room intelligent energy-saving control system, the environment control unit is connected to the emotion recognition module via the CAN bus, the emotion recognition module analyzes the customer's emotions and adjusts the guest room environment; the emotion recognition module includes: a language interaction unit, an emotion analysis unit and an auxiliary adjustment unit, the language interaction unit is connected to the emotion analysis unit via the CAN bus, and the emotion analysis unit is connected to the auxiliary adjustment unit via the CAN bus; the language interaction unit integrates a microphone and an intelligent audio system, sets voice commands for switching guest room scene modes, supports customers to control guest room equipment and switch scene modes by voice, and provides voice query information services; the emotion analysis unit adopts speech recognition and emotion analysis algorithms to analyze the tone, speaking speed, and volume characteristics in the customer's voice, identify the customer's emotional state, and generate an emotion analysis report; the auxiliary adjustment unit matches the music and lighting atmosphere suitable for the current mood according to the emotion analysis report, recommends it to the customer through voice broadcast and interactive interface, links the air quality monitoring system in the guest room, predicts the trend of air quality changes, and adjusts the working status of the air purification equipment.

[0026] Furthermore, the language interaction unit is preset with voice commands for switching room scene modes and controlling room electrical equipment, such as turning on lights, switching to sleep mode, and reporting the weather. At the same time, it supports customers to actively enter voice commands through the user interaction interface. When the customer issues a voice command through the microphone, the language interaction unit uses language recognition technology to convert the voice signal into text commands, extract the key commands in the text commands, and match the key commands with the commands preset in the language interaction unit or set by the customer. Based on the matching results, the language interaction unit generates corresponding light switch signals, air conditioning temperature adjustment signals, or curtain motor control signals, etc. After receiving the signals through the CAN-BUS interface, the environment control unit performs the corresponding operations. In addition, the language interaction unit provides operation feedback to the customer through the intelligent audio system, explaining to the customer that the operation has been performed or responding to the customer's query content; The emotion analysis unit uses speech recognition and sentiment analysis algorithms on the language signals collected by the language interaction unit. It uses Mel-frequency cepstral coefficients and fundamental frequency to analyze the acoustic characteristic parameters of the customer's voice, such as intonation, speaking rate, and volume. It then uses a pre-trained deep learning model to classify the customer's acoustic characteristic parameters and map them to preset emotion labels, such as joy, anger, and calmness. It then generates an emotion analysis report containing the customer's emotion labels. The emotion analysis results are then revised based on the customer's historical operation records transmitted by the environment control unit, such as the customer's frequent adjustment of the air conditioning temperature. The auxiliary adjustment unit associates the emotional data transmitted by the emotion analysis unit with the environmental parameters transmitted by the environmental perception unit, and predicts the future air quality through the air quality monitoring system. When the air quality in the guest room is about to deteriorate and the customer is in a negative emotion such as anxiety, the auxiliary adjustment unit generates an instruction to adjust the air purification equipment in advance, and then adjusts the working state of the air purification equipment through the environmental control unit, such as switching to silent mode, improving ventilation efficiency, etc., to ensure that the air quality of the guest room will not affect the customer's mood and avoid the customer's perception of the environment due to emotions. Through advance adjustment, in addition, the auxiliary adjustment unit recommends music or lighting atmosphere suitable for the current mood, such as promoting light music or warm lights when happy, etc., and recommends them to customers through voice broadcast and interactive interface, thereby assisting customers in regulating their emotions.

[0027] Example 4, please refer to Figure 4 、 Figure 7 and Figure 8, a CAN-BUS-based guest room intelligent energy-saving control system, the environment perception unit is connected to the auxiliary service module via the CAN bus, the auxiliary service module is used to provide guest room services for customers; the auxiliary service module includes a demand analysis unit, a task integration unit, and a service planning unit, the demand analysis unit is connected to the task integration unit and the environment perception unit via the CAN bus, and the task integration unit is connected to the service planning unit via the CAN bus; the demand analysis unit predicts customer demand based on guest room status knowledge, identifies service type based on customer actual demand, analyzes service priority, and generates a service task book; the task integration unit performs correlation analysis on service task books of different guest rooms, merges similar service task books, and sorts the merged service task books according to factors such as task type, service priority, and guest room location; the service planning unit assigns service robot tasks based on the service robot's location, working status, and task progress, combined with the merged guest room service task book, and adjusts the service robot's path in real time based on the service robot's real-time traffic information.

[0028] Furthermore, the demand analysis unit predicts customer needs based on the room status knowledge transmitted by the environmental perception unit, such as predicting the need for room cleaning during the customer's absence period, and determines the customer's actual needs based on the services input or selected by the customer through the user interaction interface. Based on the customer's actual needs and predicted needs, the demand analysis unit classifies the customer's needs and identifies the type of service required, such as room cleaning service, item delivery service, room service, etc. The service is sorted according to the urgency of the service, the importance of the customer, and the service priority rules set by the hotel, and a service task book is generated. The service task book includes the service type, room number, required items, execution time, etc. The task integration unit receives the service task orders for different guest rooms generated by the demand analysis unit and identifies similar tasks that can be merged. For example, if multiple guest rooms require cleaning services and are located in close proximity and have similar priorities, the task integration unit will merge these tasks into one task to improve service efficiency. The system will sort the merged and unmerged service task orders based on factors such as task type, service priority, and room location. The service planning unit receives the service task book delivered by the task integration unit. The service planning unit obtains the service robot's location, working status, task progress and other data in real time. The service planning unit assigns the room service task book to different service robots in turn based on the task allocation logic such as the nearest principle and task priority, such as assigning tasks to the service robot closest to the target guest room, giving priority to tasks with higher priorities, giving priority to tasks on the same floor, etc. The service planning unit combines the hotel map with real-time traffic information to adjust the service robot's path in real time to improve service efficiency.

[0029] Example 5, please refer to Figure 5and Figure 8 , a CAN-BUS-based guest room intelligent energy-saving control system, the auxiliary decision-making unit is connected to the intelligent security module via the CAN bus, and the intelligent security module is used to warn of equipment failures in the guest room; the intelligent security module includes: an equipment warning unit, an emergency response unit and an intelligent evacuation unit, the equipment warning unit is connected to the emergency response unit and the auxiliary decision-making unit via the CAN bus, and is connected to the intelligent evacuation unit via the CAN bus; the equipment warning unit uses a deep learning algorithm to analyze the energy consumption data and environmental parameters of the guest room equipment, predict future energy consumption and environmental trends, identify energy consumption anomalies and environmental parameter violations, and determine potential safety risks and warning levels; the event response unit triggers a response mechanism based on the warning information, pushes warning information to customers and room management personnel, and automatically unlocks the door, turns on the room lights, and automatically dials the preset emergency contact number in an emergency; when the customer leaves the room for evacuation, the intelligent evacuation unit dynamically displays the evacuation path and evacuation direction through the indicator light outside the room.

[0030] Furthermore, the equipment early warning unit receives the energy consumption data and environmental parameters of the guest room equipment transmitted by the auxiliary decision unit, and uses a pre-trained prediction model to predict future energy consumption and environmental trends. The prediction model uses the historical energy consumption data and environmental parameters of the guest room and combines it with a deep learning algorithm for training. The prediction model performs online predictions on the energy consumption data and environmental parameter data transmitted in real time by the auxiliary decision unit, and uses an anomaly detection algorithm to identify energy consumption anomalies and environmental parameter violations. Based on the anomaly detection results of the equipment early warning unit, potential safety risks such as equipment failure, power waste, or environmental discomfort are identified. In addition, the equipment early warning unit classifies warnings into different levels according to the severity and urgency of the risk, so that the subsequent system can take corresponding measures. After the event response unit receives the warning information transmitted by the equipment warning unit, the event response unit first identifies the type of warning, the degree of urgency, the specific room number or location information, etc. According to the type of warning information, the event response unit automatically selects the corresponding response mechanism. For example, when the electrical equipment is abnormal, it automatically cuts off the power and pushes the equipment abnormality information to the customer and the room manager; in an emergency, such as when the smoke alarm detects a fire, the event response unit triggers the emergency response mechanism, automatically unlocks the door, turns on the room lights, and automatically dials the preset emergency contact number, so that the customer can quickly know and leave the room, while ensuring that rescue personnel can quickly enter the room; when the intelligent evacuation unit receives the emergency response signal transmitted by the event response unit, the intelligent evacuation unit calculates the optimal evacuation path according to the hotel layout, and activates the indicator lights along the way through the CAN bus, thereby dynamically displaying the evacuation path and evacuation direction.

[0031] Example 6, please refer to Figure 6 and Figure 8, a CAN-BUS-based guest room intelligent energy-saving control system, the adaptive control module is connected to the customer service module through network signals, and the customer service module provides diversified services for guests; the customer service module includes: a personalized adjustment unit, a health monitoring unit, and a privacy protection unit, the personalized adjustment unit is connected to the privacy protection unit through network signals, and the health monitoring unit is connected to the privacy protection unit through network signals; the personalized adjustment unit controls the motor through the CAN bus according to customer needs, adjusts the angles of the head and foot of the bed, and uses the pneumatic system to adjust the hardness of the mattress; the health monitoring unit obtains the customer's turning movements, body temperature changes, and breathing conditions during sleep through the sensor network, analyzes the customer's sleep depth and quality, identifies abnormal body temperature conditions and abnormal breathing patterns, and generates a health report; the privacy protection unit generates a communication key between the sensor network and the network, stores the communication key in a secure storage area of ​​the sensor network, and uses an encryption algorithm to encrypt the customer's privacy data monitored by the sensor network.

[0032] Furthermore, customers can input personalized needs through the user interface, such as adjusting the headboard angle and mattress firmness, or directly control the system through voice commands. The personalized adjustment unit sends commands to the bed motor controller via the CAN bus, thereby adjusting the angles of the headboard and footboard. It also adjusts the air pressure in the mattress airbag by controlling the air pump and solenoid valve in the pneumatic system to adjust the mattress firmness. The health monitoring unit uses distributed pressure sensors embedded in the mattress to obtain the frequency of the customer's tossing and turning movements during sleep, detects changes in the customer's body temperature through non-contact sensors, and detects the frequency of chest and abdominal rise and fall through micro-motion sensors or millimeter-wave radar. Based on parameters such as tossing and turning frequency and respiratory rate, it analyzes the customer's sleep depth and quality, and identifies abnormal body temperature and breathing patterns, such as abnormal fever or hypothermia, to generate a health report including sleep duration and abnormal event records. The privacy protection unit is responsible for key management, including key generation, storage, distribution, update, and destruction. The privacy protection unit stores the key in a secure storage area of ​​the sensor network. When the health monitoring unit monitors customer data through the sensor network, the privacy protection unit encrypts the customer's private data using an encryption algorithm, thereby ensuring the security of the customer's private data.

[0033] Working Principle: The intelligent control module obtains room status knowledge based on the room's environmental parameters and customer behavior data. It uses the Raspberry Pi to obtain room status knowledge and energy consumption data, and uses the cloud to generate auxiliary strategies to adjust the status of electrical equipment. The adaptive control module identifies the customer's personalized preferences based on the current real-time customer operation data and generates personalized scene modes. When switching scene modes, the intelligent control module further adjusts the state of electrical equipment according to the parameters of the scene mode; The emotion recognition module identifies the customer's emotional state based on the customer's verbal instructions and recommends music or lighting atmosphere that suits the current mood. The auxiliary service module assigns service robots to perform service tasks based on customer needs.

[0034] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A CAN-BUS-based guest room intelligent energy-saving control system, characterized by: It includes an intelligent control module, which is used to control various electrical appliances in the guest room; The intelligent control module includes: an environment perception unit, an auxiliary decision unit and an environment control unit, wherein the environment perception unit is connected to the auxiliary decision unit via a CAN bus, and the auxiliary decision unit is connected to the environment control unit via a CAN bus; The environmental perception unit uses sensors to monitor the environmental parameters in the guest room in real time, perceive the guest's behavior, identify the guest's behavior pattern, and fuse the environmental parameters with the behavior pattern to generate guest room status knowledge; The auxiliary decision-making unit uses Raspberry Pi as an edge node to obtain the current working status and energy consumption data of the guest room equipment, uses cloud big data to analyze energy consumption patterns and guest room status knowledge, and generates auxiliary strategies based on environmental comfort requirements; The environment control unit controls the electrical equipment in the guest room according to the auxiliary strategy. When the customer actively adjusts the equipment, it responds first and overrides the auxiliary strategy, and records the customer's historical operations.

2. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 1, characterized in that: The environmental control unit is connected to an adaptive control module via a CAN bus, and the adaptive control module matches the scene mode according to the customer's preferences; The adaptive control module includes: a scene mode unit, a preference learning unit and a mode switching unit. The preference learning unit is connected to the scene mode unit and the environment control unit via a CAN bus. The preference learning unit is connected to the mode switching unit via a CAN bus. The scenario mode unit uses Raspberry Pi to perform cluster analysis on historical customer operation data to create basic scenario modes. Based on the average value of the historical customer operation data, it sets the initial parameter values ​​of the basic scenario modes. The preference learning unit uses machine learning algorithms to learn the current customer's historical operation data, identify the current customer's personalized preferences, fine-tune the parameters of the basic scene mode, and generate a personalized scene mode that meets the current customer's preferences; The mode switching unit performs trend analysis on the current customer's historical operation data, predicts the customer's future behavior pattern, automatically switches the scene mode, and provides an interactive interface for customers to actively select and customize the scene mode.

3. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 1, characterized in that: The environment control unit is connected to an emotion recognition module via a CAN bus, and the emotion recognition module analyzes the guest's emotions and adjusts the guest room environment; The emotion recognition module includes: a language interaction unit, an emotion analysis unit and an auxiliary regulation unit. The language interaction unit is connected to the emotion analysis unit via a CAN bus, and the emotion analysis unit is connected to the auxiliary regulation unit via a CAN bus. The language interaction unit integrates a microphone and an intelligent sound system, and is equipped with voice commands for switching room scene modes. It supports customers to control room equipment and switch scene modes by voice, and provides voice query information services. The emotion analysis unit uses speech recognition and sentiment analysis algorithms to analyze the intonation, speaking speed, and volume characteristics of the customer's voice, identify the customer's emotional state, and generate an emotion analysis report; Based on the emotion analysis report, the auxiliary adjustment unit matches the music and lighting atmosphere that suits the current mood, recommends it to the customer through voice broadcast and interactive interface, links with the air quality monitoring system in the guest room, predicts the trend of air quality changes, and adjusts the working status of the air purification equipment.

4. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 1, characterized in that: The environment sensing unit is connected to an auxiliary service module via a CAN bus, and the auxiliary service module is used to provide room services to customers; The auxiliary service module includes a demand analysis unit, a task integration unit, and a service planning unit. The demand analysis unit is connected to the task integration unit and the environment perception unit via a CAN bus. The task integration unit is connected to the service planning unit via a CAN bus. The demand analysis unit predicts customer demand based on room status knowledge, identifies service types based on actual customer needs, analyzes service priorities, and generates service task orders; The task integration unit conducts correlation analysis on the service task books of different guest rooms, merges similar service task books, and sorts the merged service task books based on task type, service priority, and room location factors; The service planning unit assigns tasks to the service robot based on the location, working status, and task progress of the service robot, combined with the merged room service task book, and adjusts the path of the service robot in real time based on the real-time traffic information of the service robot.

5. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 1, characterized in that: The auxiliary decision-making unit is connected to an intelligent security module via a CAN bus, and the intelligent security module is used to warn of equipment failures in the guest room; The intelligent security module includes: equipment warning unit, emergency response unit and intelligent evacuation unit. The equipment warning unit is connected to the emergency response unit and auxiliary decision unit through the CAN bus, and is connected to the intelligent evacuation unit through the CAN bus. The equipment early warning unit uses deep learning algorithms to analyze the energy consumption data and environmental parameters of guest room equipment, predict future energy consumption and environmental trends, identify energy consumption anomalies and environmental parameter violations, and determine potential safety risks and early warning levels; The event response unit triggers a response mechanism based on the warning information, pushes warning information to customers and room management personnel, and automatically unlocks the door, turns on the room lights, and automatically calls the preset emergency contact number in an emergency; When a customer leaves a guest room for evacuation, the intelligent evacuation unit dynamically displays the evacuation route and direction through indicator lights outside the guest room.

6. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 2, characterized in that: The adaptive control module is connected to the customer service module via a network signal, and the customer service module provides guests with diversified services; The customer service module includes: a personalized adjustment unit, a health monitoring unit, and a privacy protection unit. The personalized adjustment unit is connected to the privacy protection unit via a network signal, and the health monitoring unit is connected to the privacy protection unit via a network signal. The personalized adjustment unit controls the motor via the CAN bus to adjust the angles of the head and foot of the bed according to customer needs, and uses the pneumatic system to adjust the firmness of the mattress. The health monitoring unit uses a sensor network to detect the user's movements during sleep, body temperature changes, and breathing conditions. It analyzes the user's sleep depth and quality, identifies abnormal body temperature and breathing patterns, and generates a health report. The privacy protection unit generates a communication key between the sensor network and the network, stores the communication key in a secure storage area of ​​the sensor network, and uses an encryption algorithm to encrypt the customer privacy data monitored by the sensor network.

7. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 1, characterized in that: The behavior pattern includes: the time when the customer enters and leaves the room, the customer's adjustment behavior of the air conditioner, the customer's triggering frequency and triggering time of the lighting, and the customer's adjustment frequency and triggering time of the curtains.

8. The CAN-BUS-based intelligent energy-saving control system for guest rooms according to claim 1, characterized in that: The auxiliary strategies include: a control strategy for dynamically adjusting guest room equipment according to environmental comfort, and an energy consumption optimization strategy based on time-of-use electricity price response.

9. The CAN-BUS-based guest room intelligent energy-saving control system according to claim 2, characterized in that: The basic scene modes include: a sleep mode with no activity for a long time after turning off the lights, an office mode with the desk lamp on, a meeting mode with multiple people entering the guest room, and an energy-saving mode when no one is in the guest room.

10. The CAN-BUS-based intelligent energy-saving control system for guest rooms according to claim 4, characterized in that: The service types include: room cleaning service, item delivery service, and room dining service.

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