Intelligent hotel guest room service robot system and application method thereof

Abstract

The invention discloses an intelligent hotel guest room service robot system and an application method thereof, and the method comprises the steps: enabling a robot to actively greet or passively respond to a customer through a voice recognition module, and building the voice interaction with the robot; according to a client instruction, the robot obtains room environment data such as temperature, humidity and illumination by using a multi-mode sensing module, and automatically adjusts a room environment; according to customer demands or historical data, the robot allocates tasks and plans a path through self-adaptive navigation, and personalized services are provided; meanwhile, the robot optimizes the service content through continuous interaction. In a data transmission process, the system ensures that personal data of a client is not leaked or abused through a security and privacy protection module; according to the intelligent hotel guest room service robot system and the application method thereof, the problems in the prior art are solved by adopting a voice interaction technology, multi-mode perception, an environment adaptability algorithm, personalized service and a data security mechanism, and more efficient, more accurate and safer intelligent service is provided.

Description

Technical Field

[0001] The present invention belongs to the field of application of intelligent hotel room service robots, and particularly relates to an intelligent hotel room service robot system and an application method thereof. Background Art

[0002] An intelligent hotel room service robot system usually utilizes robot technology and artificial intelligence (AI) technology, combined with hotel management systems, sensors, speech recognition and other technologies, to design an automated service device. The purpose is to improve the efficiency of hotel room service, enhance the customer experience and save labor costs. This is the core part of the system, usually equipped with a variety of sensors (such as cameras, lidar, ultrasonic sensors, etc.) to achieve autonomous navigation, obstacle avoidance, environment and object recognition; the robot conducts data processing, task instruction distribution and dynamic adjustment with the back-end control center through an integrated operating system; through speech recognition technology, guests can directly interact with the robot to perform operations such as room service requests, information queries, and device switching; according to the needs of the hotel, the system may have customized service functions such as food delivery, cleaning, room maintenance reminders, reception, etc.; the robot can be connected to the hotel's management system through a wireless network, enabling hotel staff to remotely control the robot or view its status through a computer or mobile phone; the robot can automatically deliver guests' requests (such as food and beverages, toiletries, etc.) to the designated room through a preset path, reducing manual intervention; the robot can detect the status of the guest room, such as full garbage, stains, etc., through built-in sensors and automatically start the cleaning function; the robot can greet guests in the hotel lobby and provide information such as room reservations, hotel facilities, and surrounding attractions to enhance the guests' check-in experience; the robot can regularly patrol the corridors or public areas of the hotel to detect potential safety hazards or monitor abnormal situations; combined with the intelligent devices in the hotel (such as air conditioners, lights, curtains, etc.), the robot can help guests adjust the room temperature, lighting, etc. to optimize the check-in environment.

[0003] However, although the intelligent hotel room service robot system brings many advantages to the hotel industry, there are still some defects and challenges in the actual application of existing systems. Existing intelligent robot systems still have certain deficiencies in aspects such as speech recognition accuracy, environmental adaptability, and personalized service. Therefore, an innovative intelligent hotel room service robot system is needed that can overcome the defects of existing systems and improve the overall service quality and customer experience. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent hotel room service robot system and an application method thereof. By adopting speech interaction technology, multi-modal perception, environmental adaptability algorithms, personalized service and data security mechanisms, the problems in the prior art are solved, and more efficient, accurate and secure intelligent services are provided.

[0005] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0006] An intelligent hotel guest room service robot system, comprising:

[0007] A voice interaction module, which is used to adopt voice recognition technology based on deep learning, recognize multiple languages, multiple accents and complex sentences, and perform semantic understanding through natural language processing technology;

[0008] A multi-modal perception module, which is used to integrate multiple sensors and real-time perceive the environmental, obstacle and personnel position information in the guest room through multi-modal perception fusion technology;

[0009] An intelligent environment adjustment module, which is used to combine the temperature, humidity and light environment data in the room, and automatically adjust the room environment by connecting with smart home devices;

[0010] An adaptive navigation and path planning module, which is used to perform dynamic path planning and real-time obstacle avoidance based on reinforcement learning and deep learning algorithms, and the robot makes dynamic path planning and real-time obstacle avoidance according to the guest room layout, personnel flow and real-time changing environmental conditions;

[0011] A personalized service recommendation module, which is used to recommend catering, entertainment and personalized room setting services for guests based on user historical data and preference analysis;

[0012] A data security and privacy protection module, which is used to encrypt and store and transmit all collected user data and adopt international standard privacy protection strategies.

[0013] Another technical problem to be solved by the present invention is to provide an application method for an intelligent hotel guest room service robot, comprising the following steps:

[0014] Step 1: After the customer enters the guest room, the robot actively or passively responds in real time through the voice recognition module to greet the customer and establish a voice interaction connection with the customer, and the customer issues demands to the robot through voice commands;

[0015] Step 2: The robot obtains the environmental information in the guest room through the multi-modal perception module, including temperature, humidity, light and cleaning status, and automatically adjusts the room environment according to the customer's demands;

[0016] Step 3: The robot performs task allocation according to the customer's voice commands or preset demands, and the robot plans the path through the adaptive navigation module;

[0017] Step 4: The robot recommends personalized services to the customer according to the customer's historical data and hobbies, and through continuous interaction, the robot continuously optimizes the service content;

[0018] Step 5: During data transmission and processing, the robot system protects the customer's personal data from being leaked or misused through the data security and privacy protection module.

[0019] Preferably, after the customer enters the guest room, the robot actively or passively responds to the customer's greeting in real time through the speech recognition module and establishes a voice interaction connection with the customer. The method for the customer to send requirements to the robot through voice commands is as follows:

[0020] After the customer enters the guest room, the robot collects the customer's voice signal through the microphone, performs speech recognition, and converts it into text. The setting process is as follows:

[0021] S input =SpeechRecognition(S audio )

[0022] where S audio is the voice input signal of the customer;

[0023] S input is the text input extracted from the voice signal;

[0024] After the robot recognizes the text, it understands the customer's requirements through the natural language processing module, expressed as:

[0025] T intcnt ,P cntitics =NLU(S input )

[0026] where T intcnt is the customer's intention recognized by the robot;

[0027] P cntitics is the extracted entity information;

[0028] According to the customer's voice command, the robot recognizes, judges, and assigns corresponding tasks and calculates the execution, which is:

[0029] T task =TaskAssignment(T intcnt ,P cntitics )

[0030] where T task is the task recognized and assigned by the robot;

[0031] The robot starts to execute the task assigned to it according to the task execution module. In the task of adjusting the temperature, the robot sends a control signal to the air conditioning system:

[0032] C output =DeviceControl(T task ,Pcntitics )

[0033] Among them, C output is the control signal for the smart home device;

[0034] After completing the task, the robot gives feedback through voice or display:

[0035] S fccdback = GenerateSpeechResponse(T task )

[0036] Among them, S fccdback is the voice feedback of the robot.

[0037] Preferably, the method for the robot to obtain the environmental information in the guest room through the multimodal perception module, including temperature, humidity, light, and cleaning status, and automatically adjust the room environment according to the customer's needs is as follows:

[0038] The robot collects different types of environmental data through the multimodal perception module, and sets the collected environmental data as:

[0039] E tcmp = TempSensor(R room )

[0040] E humidity = humiditySensor(R room )

[0041] E light = LightSensor(R room )

[0042] E clcanlincss = CleanlinessSensor(R room )

[0043] Among them, E tcmp is the room temperature information;

[0044] E humidity is the room humidity information;

[0045] E light is the room light intensity information;

[0046] E clcanlincss is the room cleanliness status information;

[0047] The customer puts forward requirements through voice or other input devices, and the robot analyzes the customer's requirements through the natural language processing module and generates operation instructions:

[0048] T intcnt ,Pcntities =NLU(S input )

[0049] where T intcnt is the customer's requirement intention;

[0050] P cntitics is the entity information related to the customer's requirement;

[0051] According to the customer's requirement and the environmental status, the robot decides how to adjust the room environment. The formula for the decision-making process is as follows:

[0052] D action =AdjustEnvironment(T intent , P cntitics , E temp , E humidity , E light , E clcanlincss )

[0053] where D action is the environmental adjustment decision made by the robot;

[0054] According to the environmental adjustment decision, the robot sends instructions through the control system to adjust the equipment and change the room environment. The formula is:

[0055] C output =ControlDevice(D action , P cntitics )

[0056] where C output is the control signal sent by the robot to the room equipment;

[0057] The robot provides real-time feedback according to the actual environmental changes and monitors the adjustment effect according to the feedback. If the adjustment does not reach the expected effect, further adjustment is made. The feedback formula is:

[0058] S fccdback =GenerateFeedback(C output , E tcmp , E humidity , E light , E clcanlincss )

[0059] where S fccdback is the voice or display feedback of the robot to the customer.

[0060] Preferably, the robot performs task allocation according to the customer's voice command or preset requirement. The method for the robot to plan the path through the adaptive navigation module is:

[0061] The robot assigns tasks according to the customer's voice commands or preset requirements. The setting process is as follows:

[0062] T intcnt , P cntitics =NLU(S input )

[0063] Among them, T intcnt is the intention of the customer's demand;

[0064] P cntitics is the relevant entity in the customer's demand;

[0065] Based on the parsed intention and entity information, the robot generates tasks and assigns them to appropriate modules;

[0066] T task =AssignTask(T intcnt , P cntitics )

[0067] The robot senses the environment through sensors and constructs a room map, obtaining obstacle and passage information of the current room. The environmental information includes the current position and target position of the robot;

[0068] E map =SensorMapping(R room )

[0069] P robot =GetPosition(R robot )

[0070] P goal =GetGoalPosition(T task , P cntitics )

[0071] Among them, E map is the room map, including obstacles and passable areas;

[0072] P robot is the current position of the robot;

[0073] P goal is the target position calculated after task assignment;

[0074] The robot uses an adaptive navigation module to plan a path according to the current environment and task requirements. The path planning algorithm is based on the dynamic changes of the robot's current position, target position, and surrounding environment. The path planning uses the A* algorithm, Dijkstra algorithm, or other dynamic programming methods:

[0075] P path =PathPlanning(Emap , P robot , P goal )

[0076] Among them, P path is the optimal path planned from the current position of the robot to the target position;

[0077] During the actual navigation process, when the robot encounters obstacles or environmental changes, resulting in dynamic path adjustment, the navigation module of the robot will monitor the environment in real time and adaptively adjust the path according to the actual situation:

[0078] P adjustcd_path = DynamicPathAdjustment(E map , P path , P robot )

[0079] Among them, P adjustcd_path is the path after dynamic adjustment;

[0080] The robot moves along the planned path and executes tasks. During the execution process, the robot continuously updates the path and adjusts according to the new environmental information:

[0081] C output = NavigateAndExecuteTask(P adjustcd_path , T task )

[0082] Among them, C output is the result of the robot executing the task.

[0083] Preferably, the method for the robot to recommend personalized services to customers based on the customer's historical data and interests and continuously optimize the service content through continuous interaction is:

[0084] The robot collects the customer's historical data and interests. The information is collected through sensors, speech recognition, and user behavior tracking:

[0085] D history = GetHistoricalData(C customer )

[0086] I preforcnccs = GetPreferences(C customer )

[0087] Among them, D history is the customer's historical data, including consumption records and activity participation records; I prefcrcnccs is the customer's interest data;

[0088] Based on the customer's historical data and interests, the robot uses a recommendation algorithm to generate personalized service recommendations by calculating the matching degree between the customer's interests and the service content:

[0089] R rccommcndation = PersonalizedRecommendation(D history , I prcfcrnccs , S scrvicc_catalog )

[0090] where R rccommcndation is a list of personalized service recommendations generated based on the customer's historical data and interests;

[0091] S scrvie_catalog is a list of all available services for recommendation;

[0092] After the robot recommends personalized services to the customer, it further adjusts and optimizes the recommendation strategy through the customer's feedback. The intensity and type of the customer's feedback help the robot optimize the recommended content;

[0093] F fccdback = GetUserFeedback(R rccommcndation , C customer )

[0094]

[0095] where F fccdback is the customer's feedback information;

[0096] FeedbackProcessing is the process of processing the customer's feedback and adjusting the customer's preference data according to the feedback;

[0097] UpdatePreferences is to update the customer's interest data;

[0098] Through continuous interaction, the robot continuously optimizes the service recommendation. After each interaction, the robot adjusts the recommendation model based on the new customer preference data and historical behavior, and continuously optimizes the service recommendation system through dynamic learning based on user behavior:

[0099]

[0100] where, is the updated customer interest data;

[0101] is the new personalized service recommendation generated according to the updated preference data.

[0102] Preferably, in the process of data transmission and processing, the method for the robot system to protect the personal data of customers from being leaked or misused through the data security and privacy protection module is as follows:

[0103] Data encryption uses symmetric encryption and asymmetric encryption algorithms. The formula for the symmetric encryption algorithm is:

[0104] C = E(K, P)

[0105] Where C is the ciphertext after encryption;

[0106] E is the encryption function;

[0107] K is the encryption key;

[0108] P is the original data;

[0109] Asymmetric encryption uses public and private keys to encrypt and decrypt data:

[0110] C = E(K public , P)

[0111] P = D(K privatc , C)

[0112] Where K public is the public key for encrypting data;

[0113] K privatc is the private key for decrypting data;

[0114] Set the original information of the customer as D customer = {ID, Name, Age, Address};

[0115] The result after data anonymization is expressed as:

[0116] D anonymous = {Anonymous_ID, Anonymized_Name.Anonymized_Address}

[0117] Where D customer is the original data of the customer;

[0118] D anonymous is the de-identified data;

[0119] Through access control policies and authentication technologies, ensure that only authorized users can access the data. The basic principle of access control is to control the access permissions of different roles of users to different resources by setting access policies;

[0120] Aacccss(U, R) = Allow if U ∈ R allowed

[0121] Among them, A acccss (U, R) represents the access control decision of user U to resource R;

[0122] U is the user identity;

[0123] R allowed The set of user roles allowed to access;

[0124] To ensure that the data is not tampered with during transmission, a hash function is used to generate the hash value of the data, and verification is performed at the receiving end. Let P be the data to be transmitted and H be the hash value of the data:

[0125] H = Hash(P)

[0126] During data transmission, the receiving party checks the integrity of the data based on the hash value:

[0127] Verify(H rcccived , H cxpccted ) = True if H reccived = H expcctcd

[0128] To prevent the data from being stolen or tampered with during transmission, a secure transmission protocol is adopted. The TLS protocol encrypts the data. The TLS protocol uses public-key and private-key encryption methods to ensure the confidentiality and integrity of data transmission. Let P be the data to be transmitted. After adopting the TLS protocol, the data is encrypted into ciphertext:

[0129] C = TLS_Encrypt(K public , P)

[0130] The receiving party uses the private key for decryption:

[0131] P = TLS_Decrypt(K privatc , C).

[0132] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements an intelligent hotel guest room service robot system and its application method as described in any one of the above.

[0133] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium with a computer program stored thereon. When the program is executed by a processor, it implements an intelligent hotel guest room service robot system and its application method.

[0134] The beneficial effects of the present invention are:

[0135] Through the voice recognition module, the robot can actively conduct voice interactions with customers, enhancing the sense of interaction for customers; the robot can recommend personalized services based on customers' historical data and interests, ensuring that the needs of each customer are precisely met; the robot plans paths through the adaptive navigation module and assigns tasks according to customer needs; the multi-modal perception module can monitor the environment in the guest room in real time and automatically adjust according to different requirements; during the data transmission and processing process, the robot system ensures that customers' personal data will not be leaked or misused through the data security and privacy protection module; through functions such as automated voice interaction, environment adjustment, and task assignment, the robot can greatly improve work efficiency, reduce manual intervention and errors, and lower labor costs; as the robot continuously collects customers' interaction data and optimizes service content based on this data, the system can more precisely meet customers' needs and achieve continuous improvement of services. Brief Description of the Drawings

[0136] Figure 1 It is a schematic flowchart of an intelligent hotel guest room service robot system of the present invention. Detailed Embodiment

[0137] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. The advantages and features of the present invention will be clearer according to the following description and claims.

[0138] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0139] Embodiment

[0140] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0141] An intelligent hotel guest room service robot system includes:

[0142] A voice interaction module, which is used to adopt voice recognition technology based on deep learning, recognize multiple languages, multiple accents, and complex sentences, and perform semantic understanding through natural language processing technology

[0143] A multi-modal perception module, which is used to integrate multiple sensors and real-time sense the environment, obstacles, and personnel position information in the guest room through multi-modal perception fusion technology;

[0144] An intelligent environment adjustment module, which is used to combine the temperature, humidity, and lighting environment data in the room and automatically adjust the room environment by connecting to smart home devices;

[0145] An adaptive navigation and path planning module, which is used to perform dynamic path planning and real-time obstacle avoidance based on reinforcement learning and deep learning algorithms, with the robot making decisions according to the guest room layout, personnel flow, and real-time changing environmental conditions;

[0146] A personalized service recommendation module, which is used to recommend catering, entertainment, and personalized room setting services to guests based on user historical data and preference analysis;

[0147] A data security and privacy protection module, which is used to encrypt the storage and transmission of all collected user data and adopt international standard privacy protection strategies.

[0148] Through modules such as voice interaction, intelligent environment adjustment, and personalized service recommendation, the robot can provide high-quality and convenient services to customers, greatly enhancing the check-in experience; functions such as adaptive navigation and path planning, and multi-modal perception improve the working efficiency and accuracy of the robot, reducing interference and mistakes in manual operations; the robot can replace some manual work, such as voice interaction, environment adjustment, and personalized recommendation, thus saving labor costs and improving work efficiency; the entire system combines multiple intelligent technologies, providing highly automated services, reducing manual intervention, and optimizing the operation process; the data security and privacy protection module ensures the proper protection of customers' personal information and operation data, enhancing the reliability of the system.

[0149] An application method of an intelligent hotel guest room service robot, including the following steps:

[0150] Step 1: After the customer enters the guest room, the robot greets the customer actively or passively in real time through the voice recognition module and establishes a voice interaction connection with the customer. The customer issues demands to the robot through voice commands;

[0151] Step 2: The robot obtains the environmental information in the guest room, including temperature, humidity, lighting, and cleaning status, through the multi-modal perception module and automatically adjusts the room environment according to the customer's needs;

[0152] Step 3: The robot assigns tasks according to the customer's voice commands or preset needs, and the robot plans the path through the adaptive navigation module;

[0153] Step 4: The robot recommends personalized services to the customer based on the customer's historical data, interests, and hobbies, and continuously optimizes the service content through continuous interaction;

[0154] Step 5: During data transmission and processing, the robot system protects the customer's personal data from being leaked or misused through the data security and privacy protection module.

[0155] Through voice interaction, automatic environment adjustment, and personalized service recommendations, the robot provides efficient and considerate services, greatly enhancing the customer's check-in experience; the robot can automatically complete multiple service tasks, reducing labor costs and improving operational efficiency; the system can perceive environmental changes in real time, intelligently plan paths and tasks, adapt to complex and changing hotel environments, and enhance the flexibility and accuracy of services; through data encryption and privacy protection modules, the customer's personal data is effectively protected, enhancing the credibility of the system; through continuous interaction and learning, the robot can continuously optimize services to ensure long-term provision of high-quality personalized experiences.

[0156] After the customer enters the guest room, the robot greets the customer actively or in real-time passively through the voice recognition module and establishes a voice interaction connection with the customer. The method for the customer to issue requirements to the robot through voice commands is as follows:

[0157] After the customer enters the guest room, the robot collects the customer's voice signal through the microphone and performs voice recognition to convert it into text. The setting process is as follows:

[0158] S input = SpeechRecognition(S audio )

[0159] where S audio is the customer's voice input signal;

[0160] S input is the text input extracted from the voice signal;

[0161] After the robot recognizes the text, it understands the customer's needs through the natural language processing module, expressed as:

[0162] T intcnt , P cntitics = NLU(S input )

[0163] where T intcnt is the customer's intention recognized by the robot;

[0164] P cntitics is the extracted entity information;

[0165] According to the customer's voice command, the robot identifies, judges, and assigns corresponding tasks and calculates the execution as:

[0166] T task = TaskAssignment(T intcnt , Pcntitics )

[0167] Among them, T task is the task recognized and assigned by the robot;

[0168] The robot starts to execute the task assigned to it according to the task execution module. In the task of adjusting the temperature, the robot sends a control signal to the air conditioning system:

[0169] C output = DeviceControl(T task , P cntitics )

[0170] Among them, C output is the control signal to the smart home device;

[0171] After completing the task, the robot gives feedback through voice or display:

[0172] S fccdback = GenerateSpeechResponse(T task )

[0173] Among them, S fccdback is the voice feedback of the robot.

[0174] The robot adopts speech recognition and natural language processing technologies, enabling customers to directly interact with the robot through voice without manual operation; providing a convenient and barrier-free interaction experience, enhancing the comfort and convenience of customers when checking in; the robot recognizes customers' needs according to their voice commands and automatically assigns tasks through the intelligent decision-making module; through automatic task allocation and execution, the robot reduces manual intervention and improves service efficiency; the robot not only relies on speech recognition but also combines sensor data (such as temperature, humidity, light, etc.) to adjust the guest room environment in real time; this intelligent control method can automatically adjust the guest room facilities according to real-time environmental changes and customer needs to ensure a comfortable living environment; the robot ensures the security of customers' personal information and usage data through the data encryption and privacy protection module during the whole process; ensuring customers' privacy security, enhancing customers' trust in intelligent services, and avoiding legal and trust risks caused by data leakage; through continuous interaction with customers, the robot can accumulate customers' preferences and historical data and continuously optimize service content; personalized recommendations enhance customers' sense of participation and satisfaction. The robot gradually optimizes the service, improving customer loyalty and the willingness to check in again.

[0175] The method by which the robot obtains the environmental information in the guest room, including temperature, humidity, light, and cleaning status, through the multi-modal perception module and automatically adjusts the room environment according to customer needs is as follows:

[0176] The robot collects different types of environmental data through a multi-modal perception module. The set environmental data is as follows:

[0177] E tcmp = TempSensor(R room )

[0178] E humidity = HumiditySensor(R room )

[0179] E light = LightSensor(R room )

[0180] E clcanlincss = CleanlinessSensor(R room )

[0181] Among them, E tcmp is the room temperature information;

[0182] E humidity is the room humidity information;

[0183] E light is the room light intensity information;

[0184] E clcanlincss is the room cleanliness status information;

[0185] The customer puts forward requirements through voice or other input devices. The robot parses the customer requirements through the natural language processing module and generates operation instructions:

[0186] T intcnt ,P cntitics = NLU(S input )

[0187] Among them, T intcnt is the customer's requirement intention;

[0188] P cntitics is the entity information related to the customer requirements;

[0189] According to the customer requirements and the environmental status, the robot decides how to adjust the room environment. The formula for the decision-making process is as follows:

[0190] D action = AdjustEnvironment(T intcnt ,R cntitics ,E tcmp ,E humidity ,E light ,E clcanlincss )

[0191] Among them, D action is the environmental adjustment decision made by the robot;

[0192] According to the environmental adjustment decision, the robot sends instructions through the control system to adjust the equipment and change the room environment. The formula is:

[0193] C output = ControlDevice(D action , P cntitics )

[0194] Among them, C output is the control signal sent by the robot to the room equipment;

[0195] The robot provides real-time feedback according to the actual environmental changes and monitors the adjustment effect according to the feedback situation. If the adjustment does not reach the expected effect, it will be further adjusted. The feedback formula is:

[0196] S fccdback = GenerateFeedback(C output , E tcmp , E humidity , E light , E clcanlincss )

[0197] Among them, S fccdback is the voice or display feedback of the robot to the customer.

[0198] By integrating multiple sensors such as temperature, humidity, light, and cleaning status, the robot can perceive the guest room environment in real time; the guest room environment can be comprehensively monitored to ensure that customer needs are optimally met; the robot uses a natural language processing module to parse the customer's voice or text instructions, understand the specific needs of the customer, and generate corresponding operation instructions; through natural language interaction, the customer does not need to manually adjust the equipment and can easily adjust the room environment with just voice instructions; the robot determines the direction of adjustment required based on the customer's needs and real-time environmental data and automatically sends control signals to the equipment, such as adjusting the temperature, humidity, or light, etc.; the automated environmental adjustment reduces manual intervention, making the service more efficient and accurate; the robot continuously monitors the adjustment effect to ensure that the environmental change meets the customer's requirements; this real-time feedback mechanism can ensure that the environmental adjustment meets the customer's needs and can improve service satisfaction by continuously optimizing the adjustment process; by continuously accumulating interaction data with the customer, the robot can provide personalized services. For example, the robot can remember the customer's preferences, such as temperature settings, light intensity, etc., and provide more accurate services based on this data; through personalized adjustment, the customer will feel a more considerate service experience, thereby increasing customer satisfaction and loyalty; the robot can optimize the adjustment according to real-time environmental data, such as saving energy by reducing the air conditioner temperature and adjusting the lighting. Through intelligent device control, energy waste is reduced; not only is the resource utilization efficiency improved, but it can also save costs for hotel or property managers, and at the same time provide a more environmentally friendly service for customers.

[0199] Based on the customer's voice instructions or preset requirements, the robot performs task allocation. The method for the robot to plan the path through the adaptive navigation module is as follows:

[0200] The robot performs task allocation according to the customer's voice instructions or preset requirements. The setting process is as follows:

[0201] T intcnt ,P cntitics =NLU(S input )

[0202] Where T intcnt is the intention of the customer's demand;

[0203] P cntitics is the relevant entity in the customer's demand;

[0204] Based on the parsed intention and entity information, the robot generates tasks and assigns them to appropriate modules;

[0205] T task =AssignTask(T intcnt ,P cntitics )

[0206] The robot perceives the environment through sensors and constructs a room map, obtaining information about obstacles and passages in the current room. The environmental information includes the robot's current position and the target position.

[0207] E map = SensorMapping(R room )

[0208] P robot = GetPosition(R robot )

[0209] P goal = GetGoalPosition(T task , P cntitics )

[0210] Among them, E map is the room map, including obstacles and passable areas.

[0211] P robot is the current position of the robot.

[0212] P goal is the target position calculated according to the task assignment.

[0213] The robot uses an adaptive navigation module to plan a path according to the current environment and task requirements. The path planning algorithm is based on the dynamic changes of the robot's current position, target position, and the surrounding environment. The path planning uses the A* algorithm, Dijkstra algorithm, or other dynamic programming methods:

[0214] P path = PathPlanning(E map , P robot , P goal )

[0215] Among them, P path is the optimal path planned, from the robot's current position to the target position.

[0216] During the actual navigation process, when the robot encounters obstacles or environmental changes, resulting in dynamic path adjustment, the robot's navigation module will monitor the environment in real time and adaptively adjust the path according to the actual situation:

[0217] P adjustcd_path = DynamicPathAdjustment(E map , P path , P robot )

[0218] Among them, P adjustcd_path is the path after dynamic adjustment.

[0219] The robot moves along the planned path and executes tasks. During the execution process, the robot continuously updates the path and adjusts according to new environmental information:

[0220] C output = NavigateAndExecuteTask(P adjustcd_path , T task )

[0221] where C output is the result of the robot executing the task.

[0222] Through the natural language processing module, the robot can accurately understand the customer's needs and allocate tasks to different modules; improve service efficiency, automatically process tasks according to the customer's voice or demand instructions, reduce manual intervention, and provide personalized services; the robot uses sensors to perceive the surrounding environment and build a real-time updated room map to ensure sufficient understanding of the dynamic changes in the environment; ensure that the robot can understand the room layout and obstacle positions in real time, thereby reducing errors and obstacle collisions in navigation and providing accurate path planning; the robot calculates the optimal path through path planning algorithms such as the A* algorithm and Dijkstra algorithm to ensure a smooth arrival at the target position from the current position; these algorithms can calculate the shortest path and avoid obstacles, thereby improving navigation efficiency and reducing path travel time; the robot automatically adjusts the path according to the real-time monitored environmental changes during the actual navigation process to ensure the successful completion of tasks; adaptive navigation can handle dynamic environmental changes, such as the sudden appearance of obstacles or changes in passages, and maintain the flexibility and stability of the navigation process; during the execution of tasks, the robot continuously updates the path and adjusts according to new environmental information to ensure the tasks are completed as expected; this real-time adjustment mechanism enables the robot to flexibly respond to environmental changes, ensure timely response to customer needs, and at the same time provide real-time feedback on task completion, increasing user satisfaction; the robot can make intelligent decisions and path planning according to the complexity of tasks and environmental changes to ensure the efficient execution of tasks; through voice commands or preset requirements from customers, the robot automatically executes tasks, greatly improving operation efficiency and continuously optimizing the execution process in a dynamic environment; through voice recognition and natural language processing technologies, the robot can interact with users more intelligently, understand customer needs and execute tasks; improve the convenience and naturalness of interaction, the user experience is more smooth and intelligent, and customers are more satisfied with the service.

[0223] The method by which the robot recommends personalized services to customers based on the customer's historical data, interests and hobbies, and continuously optimizes the service content through continuous interaction is:

[0224] The robot collects the historical data and hobbies of the customers, and the information is collected through sensors, speech recognition, and user behavior tracking:

[0225] D history = GetHistoricalData(C customcr )

[0226] I prcfcrcnccs = GetPreferences(C customcr )

[0227] Among them, D history is the historical data of the customer, including consumption records and activity participation records;

[0228] I prcfrcnccs is the hobby data of the customer;

[0229] Based on the historical data and hobbies of the customer, the robot uses a recommendation algorithm to generate personalized service recommendations by calculating the matching degree between the customer's interests and the service content:

[0230] R rccommcndation = PersonalizedRecommendation(D history , I prcfcrcnces , S scrvice_catalog )

[0231] Among them, R rccommendation is the list of personalized service recommendations generated according to the customer's historical data and hobbies;

[0232] S scrvice_catalog is the list of all available services for recommendation;

[0233] After the robot recommends personalized services to the customer, it further adjusts and optimizes the recommendation strategy through the feedback of the user. The intensity and type of the customer's feedback help the robot optimize the recommended content;

[0234] F fccdback = GetUserFeedback(R rccomncndation , C customcr )

[0235]

[0236] Among them, F fccdback is the feedback information of the customer;

[0237] FeedbackProcessing is the process of processing the customer's feedback, and adjusts the customer's preference data according to the feedback;

[0238] UpdatePreferences is to update the customer's hobby data;

[0239] Through continuous interaction, the robot continuously optimizes service recommendations. After each interaction, the robot adjusts the recommendation model based on the new customer preference data and historical behavior, and continuously optimizes the service recommendation system through dynamic learning based on user behavior:

[0240]

[0241]

[0242] Among them, is the updated customer hobby data;

[0243] is the new personalized service recommendation generated according to the updated preference data.

[0244] Collect the customer's historical data and hobbies through multiple channels, and analyze the data through technologies such as machine learning to form a customer interest profile; be able to understand the customer's needs and preferences more accurately, provide sufficient support for personalized service recommendations, and thus improve customer satisfaction; based on the customer's historical data and hobbies, use recommendation algorithms (such as collaborative filtering, content recommendation, matrix factorization, etc.) to generate personalized service recommendations, enhancing the matching degree between the service and the customer; be able to improve the relevance and adaptability of the service, thereby increasing the customer's acceptance rate and conversion rate; the customer's feedback (such as acceptance, rejection, like, etc.) is used as the basis for dynamic adjustment, and the robot adjusts the customer's preference data through the feedback information to optimize the recommendation strategy; the real-time processing of customer feedback can help the robot quickly adjust the recommended content to make it more in line with the customer's real-time needs, thereby improving the accuracy of the recommendation; through continuous interaction with the customer, the robot can continuously update the customer's hobby data and optimize the recommendation model through behavior learning; this dynamic learning process can improve the adaptability of the system. Over time, the robot can better understand the customer's needs and further improve the quality of personalized services; through personalized service recommendations and continuous optimization, the robot can continuously provide services that meet the customer's needs, increasing the customer's satisfaction and loyalty to the service; improve the customer's satisfaction and loyalty, reduce the customer churn rate, and enhance the customer stickiness of the overall service; by collecting a large amount of customer data and analyzing it, the robot can discover potential laws from the big data and optimize the service recommendations; be able to ensure that the recommendation system is continuously improved and adjusted, accurately grasp the customer's needs, and improve the overall service level.

[0245] In the process of data transmission and processing, the method for the robot system to protect the customer's personal data from being leaked or misused through the data security and privacy protection module is:

[0246] Data encryption uses symmetric encryption and asymmetric encryption algorithms. The formula for the symmetric encryption algorithm is:

[0247] C = E(K, P)

[0248] Where C is the ciphertext after encryption;

[0249] E is the encryption function;

[0250] K is the encryption key;

[0251] P is the original data;

[0252] Asymmetric encryption uses a public key and a private key to encrypt and decrypt data:

[0253] C = E(K public , P)

[0254] P = D(K privatc , C)

[0255] Where K public is the public key, used to encrypt data;

[0256] K privatc is the private key, used to decrypt data;

[0257] Set the original information of the customer as D customcr = {ID, Name, Age, Address};

[0258] The result after data anonymization is expressed as:

[0259] D anonymou s = {Anonymous_ID, Anonymized_Name, Anonymized_Address}

[0260] Where D customcr is the original data of the customer;

[0261] D anonymous is the de-identified data;

[0262] Through access control policies and authentication technologies, ensure that only authorized users can access the data. The basic principle of access control is to control the access rights of different roles of users to different resources by setting access policies;

[0263] A acccss (U, R) = Allow if U ∈ R allowed

[0264] Where A acccss (U, R) represents the access control decision of user U to resource R;

[0265] U is the user identity;

[0266] R allowed The set of user roles allowed to access;

[0267] To ensure that the data is not tampered with during transmission, a hash function is used to generate the hash value of the data, and verification is performed at the receiving end. Let P be the data to be transmitted and H be the hash value of the data:

[0268] H = Hash(P)

[0269] During data transmission, the receiving party checks the integrity of the data based on the hash value:

[0270] Verify(H rcccivcd , H expccted ) = True if H rcccivcd = H expccted

[0271] To prevent the data from being stolen or tampered with during transmission, a secure transmission protocol is used. TLS encrypts the data. The TLS protocol ensures the confidentiality and integrity of data transmission through the use of public and private key encryption. Let P be the data to be transmitted. After using the TLS protocol, the data is encrypted into ciphertext:

[0272] C = TLS_Encrypt(K public , P)

[0273] The receiving party uses the private key for decryption:

[0274] P = TLS_Decrypt(K privatc , C).

[0275] Through symmetric encryption and asymmetric encryption technologies, data remains encrypted during storage and transmission to prevent data theft; effectively protects customers' sensitive information, such as personal identity, financial data, etc., ensuring data confidentiality; the asymmetric encryption method of public key encryption and private key decryption ensures that even if the communication channel is stolen, attackers cannot decrypt the data, and only the legitimate recipient (holding the private key) can decrypt it; guarantees the confidentiality of data during transmission and ensures that only authorized recipients can access the data; through de-identification processing, eliminates the risk of exposure of customer identity information, and even if the data is leaked, the true identity of the customer will not be exposed; enhances customer privacy protection, complies with data privacy regulations (such as GDPR), and reduces the risk of leakage; through access control policies, the system can dynamically manage data access permissions according to factors such as the user's role and permissions, preventing unauthorized access; ensures that only authorized users can access sensitive data, improves the security of the system, and prevents data abuse; uses a hash function to generate a hash value of the data and performs verification at the receiving end to ensure that the data has not been tampered with during transmission; through hash value checking, it can be immediately discovered whether the data has been tampered with, ensuring the integrity of data transmission; encrypts data transmission through the TLS protocol to ensure that the data is not stolen or tampered with during transmission; ensures the confidentiality and integrity of data transmission, avoids man-in-the-middle attacks and data leakage, and enhances data security.

[0276] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements an intelligent hotel guest room service robot system and its application method as described above.

[0277] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by the processor, it implements an intelligent hotel guest room service robot system and its application method as described above.

[0278] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0279] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above.

[0280] The above embodiments of the present invention do not limit the protection scope of the present invention. The implementation manners of the present invention are not limited thereto. All kinds of modifications, substitutions, or changes made to the above structure of the present invention according to the above content of the present invention, in accordance with the common general knowledge and customary means in the art, without departing from the above basic technical idea of the present invention, shall fall within the protection scope of the present invention.

Claims

1. An intelligent hotel room service robot system, characterized in that, It includes: A voice interaction module, which is used to adopt voice recognition technology based on deep learning, recognize multiple languages, multiple accents and complex sentences, and perform semantic understanding through natural language processing technology A multi-modal perception module, which is used to integrate multiple sensors and real-time sense the environmental information, obstacles and personnel position information in the guest room through multi-modal perception fusion technology; An intelligent environment adjustment module, which is used to combine the temperature, humidity and light environment data in the room and automatically adjust the room environment by connecting with smart home devices; An adaptive navigation and path planning module, which is used to perform dynamic path planning and real-time obstacle avoidance based on reinforcement learning and deep learning algorithms, according to the guest room layout, personnel flow and real-time changing environmental conditions; A personalized service recommendation module, which is used to recommend catering, entertainment and personalized room setting services to guests based on user historical data and preference analysis; A data security and privacy protection module, which is used to encrypt and store and transmit all collected user data and adopt international standard privacy protection strategies.

2. An application method for an intelligent hotel room service robot, characterized in that It includes the following steps: Step 1: After the customer enters the guest room, the robot greets the customer actively or in real-time passively through the voice recognition module, establishes a voice interaction connection with the customer, and the customer issues demands to the robot through voice commands; Step 2: The robot obtains the environmental information in the guest room through the multi-modal perception module, including temperature, humidity, light and cleaning status, and automatically adjusts the room environment according to the customer's demands; Step 3: The robot assigns tasks according to the customer's voice commands or preset demands, and the robot plans the path through the adaptive navigation module; Step 4: The robot recommends personalized services to the customer based on the customer's historical data and interests, and continuously optimizes the service content through continuous interaction; Step 5: During the data transmission and processing process, the robot system protects the customer's personal data from being leaked or misused through the data security and privacy protection module.

3. The application method of an intelligent hotel room service robot according to claim 2, characterized in that, The method by which, after the customer enters the guest room, the robot greets the customer actively or in real-time passively through the voice recognition module, establishes a voice interaction connection with the customer, and the customer issues demands to the robot through voice commands is as follows: After the customer enters the guest room, the robot collects the customer's voice signal through the microphone, performs voice recognition and converts it into text. The setting process is as follows: S input = SpeechRecognition(S audio ) Among them, S audio is the voice input signal of the customer; S input It is the text input extracted from the speech signal; After the robot recognizes the text, it understands the customer's demands through the natural language processing module, which is expressed as: T intent , P cutities = NLU(S input ) Among them, T intent is the customer intention recognized by the robot; P cntitics is the extracted entity information; According to the customer's voice commands, the robot recognizes, judges and assigns corresponding tasks, and calculates the execution, which is: T task = TaskAssignment(T intent , P entitics ) Among them, T task is the task recognized and assigned by the robot; The robot starts to execute the tasks assigned to it according to the task execution module. In the task of adjusting the temperature, the robot sends a control signal to the air conditioning system: C output = DeviceControl(T task , P entities ) Among them, C output is the control signal for the smart home device; After completing the task, the robot gives feedback through voice or the display screen: S feedback = GenerateSpeechResponse(T task ) Among them, S feedback is the voice feedback of the robot.

4. A method for applying an intelligent hotel room service robot according to claim 3, characterized in that, The method by which the robot obtains the environmental information in the guest room through the multi-modal perception module, including temperature, humidity, light and cleaning status, and automatically adjusts the room environment according to the customer's demands is as follows: The robot collects different types of environmental data through the multi-modal perception module. The set environmental data for collection is: E temp = TempSensor(R room ) E humidity = HumiditySensor(R room ) E light = LightSensor(R room ) E cleanliness = CleanlinessSensor(R room ) Among them, E temp is the room temperature information; E humidity is the room humidity information; E light is the room light intensity information; E clcanliness is the room cleaning status information; The customer puts forward requirements through voice or other input devices, and the robot analyzes the customer's requirements through the natural language processing module and generates operation instructions: T intent , P cntities = NLU(S input ) Among them, T intcnt is the customer's demand intention; P cntitics Entity information relevant to customer requirements; According to the customer's requirements and environmental status, the robot decides how to adjust the room environment. The formula for the decision-making process is as follows: D action = AdjustEnvironment(T intent , P cntitics , E tcmp , E humidity , E light , E cleanliness ) Among them, D action is the environmental adjustment decision made by the robot; According to the environmental adjustment decision, the robot sends instructions through the control system to adjust the equipment and change the room environment. The formula is: C output = ControlDevice(D action , P cntities ) Among them, C output is the control signal sent by the robot to the room device; The robot provides real-time feedback based on the actual environmental changes and monitors the adjustment effect according to the feedback. If the adjustment does not meet the expected effect, it will be further adjusted. The feedback formula is: S feedback = GenerateFeedback(C output , E temp , E humidity , E light , E cleanliness ) Among them, S feedback The robot's voice or display feedback to the customer.

5. A method for applying an intelligent hotel room service robot according to claim 4, characterized in that, The robot assigns tasks according to the customer's voice instructions or preset requirements. The method for the robot to plan the path through the adaptive navigation module is: The robot assigns tasks according to the customer's voice instructions or preset requirements. The setting process is as follows: T intent , P entities = NLU(S input ) Among them, T intent is the intention of the customer's requirements; P cntities is a relevant entity in the customer requirements; Based on the parsed intent and entity information, the robot generates tasks and assigns them to appropriate modules; T task = AssignTask(T intcnt , P cntitics ) The robot senses the environment through sensors and constructs a room map to obtain obstacle and passage information in the current room. The environmental information includes the robot's current position and target position; E map = SensorMapping(R room ) P robot = GetPosition(R robot ) P goal = GetGoalPosition(T task , P cntities ) Among them, E map is the room map, which includes obstacles and passable areas; P robot is the current position of the robot; P goal is the target position calculated after task assignment; The robot uses the adaptive navigation module to plan the path according to the current environment and task requirements. The path planning algorithm is based on the robot's current position, target position, and dynamic changes in the surrounding environment. The path planning uses the A* algorithm, Dijkstra algorithm, or other dynamic programming methods: P path = PathPlanning(E map , P robot , P goal ) Among them, P path is the optimal path planned from the current position of the robot to the target position; During the actual navigation process, when the robot encounters obstacles or environmental changes, resulting in dynamic path adjustment, the robot's navigation module will monitor the environment in real time and adaptively adjust the path according to the actual situation: P adjusted_path = DynamicPathAdjustment(E map , P path , P robot ) Among them, P adjusted_path is the path dynamically adjusted; The robot moves along the planned path and executes tasks. During the execution process, the robot continuously updates the path and adjusts according to the new environmental information: C output = NavigateAndExecuteTask(P ajusted_path , T task ) Among them, C output is the result of the robot executing the task.

6. A method for applying an intelligent hotel room service robot according to claim 5, characterized in that, The robot recommends personalized services to the customer based on the customer's historical data and interests. The method for the robot to continuously optimize the service content through continuous interaction is: The robot collects the customer's historical data and interests. The information is collected through sensors, speech recognition, and user behavior tracking: D history = GetHistoricalData(C customer ) I preferenccs = GetPreferences(C customer ) Among them, D history is the historical data of the customer, including consumption records and activity participation records; I preferences Customer's hobby data; Based on the customer's historical data and interests, the robot uses a recommendation algorithm to generate personalized service recommendations by calculating the matching degree between the customer's interests and service content: R recommendation = PersonalizedRecommendation(D history , I preferences , S serive_catalog ) Among them, R recommendation is a personalized service recommendation list generated based on the customer's historical data and interests; S service_catalog For the list of all services available for recommendation; After the robot recommends personalized services to the customer, it further adjusts and optimizes the recommendation strategy through the customer's feedback. The intensity and type of the customer's feedback help the robot optimize the recommended content; F feedback = GetUserFeedback(R recommendation , C customer ) Among them, F feedback is the feedback information of the customer; FeedbackProcessing is the process of processing the customer's feedback, and adjusts the customer's preference data according to the feedback; UpdatePreferences is to update the customer's interest data; Through continuous interaction, the robot continuously optimizes the service recommendation. After each interaction, the robot adjusts the recommendation model based on the new customer preference data and historical behavior, and continuously optimizes the service recommendation system through dynamic learning based on user behavior: Among them, is the updated customer hobby data; New personalized service recommendations generated based on updated preference data.

7. The application method of an intelligent hotel room service robot according to claim 6, characterized in that, During the data transmission and processing process, the robot system protects the customer's personal data from being leaked or misused through the data security and privacy protection module. The method is: Data encryption uses symmetric encryption and asymmetric encryption algorithms. The formula for the symmetric encryption algorithm is: C = E(K, P) Among them, C is the encrypted ciphertext; E is the encryption function; K is the encryption key; P is the original data; Asymmetric encryption uses public and private keys to encrypt and decrypt data: C = E(K public , P) P = D(K private , C) Among them, K public is the public key, which is used to encrypt data; K privatc is the private key used to decrypt data; Set the original information of the customer to D customer ={ID, Name, Age, Address}; The result after data anonymization is expressed as: D anonymous = {Anonymous_ID, Anonymized_Name, Anonymized_Address Among them, D customer is the original data of the customer; D anonymous Is the de-identified data; Through access control policies and authentication technologies, it is ensured that only authorized users can access the data. The basic principle of access control is to control the access rights of users with different roles to different resources by setting access policies; A access (U, R) = Allow if U ∈ R allowed Among them, A access (U, R) represents the access control decision of user U to resource R; U is the user identity; R allowed Set of user roles allowed to access; To ensure that the data has not been tampered with during transmission, a hash function is used to generate the hash value of the data, and verification is performed at the receiving end. Let P be the data to be transmitted and H be the hash value of the data: H = Hash(P) During data transmission, the receiving party checks the integrity of the data based on the hash value: Verify(H rcccived , H expcctd ) = True if H rcccived = H expccted The data is not stolen or tampered with during transmission. A secure transmission protocol is adopted, and TLS encrypts the data. The TLS protocol ensures the confidentiality and integrity of data transmission through the encryption method using public and private keys. Let P be the data to be transmitted. After adopting the TLS protocol, the data is encrypted into ciphertext: C = TLS_Encrypt(K public , P) The receiving party uses the private key for decryption: P = TLS_Decrypt(K privatc , C).

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements an intelligent hotel guest room service robot application method as described in any one of claims 2-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements an intelligent hotel guest room service robot application method as described in any one of claims 2-7.

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