Hotel room intelligent equipment control system and method based on Internet of Things

Abstract

The present invention discloses a hotel room intelligent equipment control system and method based on the Internet of Things, which relates to the field of equipment intelligent control technology. The access control authentication mode is alternately switched according to a preset switching rule; the accommodation environment condition data in the accommodation area is collected, and if the accommodation environment condition data is abnormal, the vital sign data of the occupant is collected; the degree of health abnormality of the occupant is judged by the vital sign data, and if the degree of abnormality exceeds expectations, the active control mode of the electrical equipment is switched to the automatic control mode; when it is predicted that the user's unhealthy state will continue, if the collected user behavior data verifies the prediction result, according to the user's current state, after the emergency treatment knowledge map outputs the emergency treatment plan, the user's access control authority and the electrical equipment control authority are transferred. By switching the control authority of the electrical equipment in the accommodation area, the user's safety status is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent equipment control, and in particular to a hotel guest room intelligent equipment control system and method based on the Internet of Things. Background Art

[0002] Intelligent control of hotel equipment is an important trend in the modern hotel industry. Through technologies such as the Internet of Things and artificial intelligence, it realizes the automation and intelligent management of lighting, air conditioning, curtains, televisions, security and other equipment in guest rooms. Guests can remotely control these devices through mobile phone APP or voice commands to enjoy a personalized stay experience, such as adjusting the brightness and color temperature of the lights, setting the appropriate temperature, and timing the opening and closing of curtains. At the same time, the intelligent control system can also intelligently monitor and manage energy consumption to achieve energy conservation and emission reduction. With the continuous advancement of technology, the intelligent control of hotel equipment will develop in a more intelligent and personalized direction, providing guests with a more convenient, comfortable and intelligent accommodation experience.

[0003] In the Chinese invention patent application with publication number CN117970825A, an electronic information control system for hotel guest rooms is disclosed, which belongs to the technical field of hotel electronic information control systems. It includes intelligent door locks, real-time acquisition units and central processing units. Different guest room scenes are set according to the layout and characteristics of the guest rooms. Guests can select different scenes by themselves through the user interface terminal. The system will adjust the equipment and services in the guest rooms accordingly to improve energy efficiency. The purpose of regional linkage control can be achieved by setting regionalization in the guest rooms, realizing intelligent scene control, intelligent sensing control, intelligent check-in and sleep control, as well as intelligent lighting, air conditioning and other control effects, providing a more personalized experience, making the guest room residents feel familiar with home, and assisting the hotel in managing the actual occupancy status of the guest rooms.

[0004] After the user checks into the hotel, in order to ensure the user's safety, the access control rights of the guest room are usually transferred to the user. At the same time, in order to improve the user's comfort, the control rights of various electrical equipment in the guest room will also be transferred to the user, so that the user can actively control each electrical equipment.

[0005] In the existing hotel room smart device control method, in order to save energy and improve the user experience, the existing hotel equipment usually collects real-time environmental condition data in the accommodation area and uses this as feedback to automatically control hotel equipment, such as air conditioners, lamps and humidifiers. However, in fact, different guests have different requirements for the accommodation environment and the safety needs in the accommodation area. In particular, some users with poor physical health conditions and who are still in the rehabilitation stage have higher requirements for accommodation safety. For example, when a user is in the rehabilitation stage, he cannot accept too much disturbance. In the existing hotel room smart device control method, it is difficult to consider the safety needs and accommodation needs of this type of rehabilitation users in multiple dimensions.

[0006] To this end, the present invention provides a hotel room intelligent equipment control system and method based on the Internet of Things. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] In view of the deficiencies of the prior art, the present invention provides a hotel room intelligent device control system and method based on the Internet of Things, which alternately switches the access control authentication mode according to the pre-set switching rules; collects the occupancy environment condition data in the occupancy area, and if there is an abnormality in the occupancy environment condition data, collects the vital sign data of the occupant; determines the abnormal degree of the occupant's health by the vital sign data, and if the abnormal degree exceeds expectations, switches the active control mode of the electrical equipment to the automatic control mode; when it is predicted that the user's unhealthy state will continue, if the collected user behavior data verifies the prediction result, according to the user's current state, after the emergency treatment knowledge map outputs the emergency treatment plan, the user's access control authority and the electrical equipment control authority are transferred. By switching the control authority of the electrical equipment in the occupancy area, the user's safety status is guaranteed, thereby solving the technical problems recorded in the background technology.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] The method for controlling hotel guest room intelligent equipment based on the Internet of Things includes: when the occupancy status data in the hotel triggers the access control switching mechanism, the access control authentication mode is alternately switched according to the preset switching rules;

[0012] Collect the occupancy environment condition data in the occupancy area. If the occupancy environment condition data is abnormal, collect the vital sign data of the occupant; judge the health abnormality degree of the occupant based on the vital sign data. If the abnormality degree exceeds the expectation, switch the active control mode of the electrical equipment to the automatic control mode; wherein, the method of generating the health abnormality value Hut for judging the health abnormality degree of the occupant is as follows:

[0013]

[0014] Where: F1 and F2 are the weight coefficients of abnormal nodes and abnormal degree, respectively, and their values ​​fall within [0,1]; N(t) is the number of abnormal nodes at time t, D(t) is the abnormal degree of the vital sign data at time t, and T is the distribution interval of the time nodes;

[0015] When it is predicted that the user's unhealthy state will continue, if the collected user behavior data verifies the prediction result, the emergency handling knowledge graph outputs an emergency handling plan based on the user's current state, and then transfers the user's access control authority and electrical equipment control authority.

[0016] Furthermore, the user is guided to enter identity information and access control information, and at least the flow data, unlocking time, and number of people unlocking each time in each check-in area or public area of ​​the guest room within the observation period in the hotel are recorded, and the data are summarized to generate an access control security information set; the check-in value Sa is generated from the access control security information set within the observation period. When the check-in value Sa is greater than the pre-set security threshold, the access control switching trigger condition is met.

[0017] Furthermore, under dimensionless conditions, the way to generate the occupancy value Sa from the access control security information set is as follows:

[0018]

[0019] Where n is the number of time nodes, Kp is the length of the observation period; V(i,j) is the difference in the flow of people from time node i to time node j in the public area, Xr is the total number of people entering the corresponding guest room from time node i to time node j, and R is the maximum diameter of the guest room distribution area.

[0020] Furthermore, a sensor network in the occupancy area is activated to monitor the noise level, air quality level, air humidity data and light intensity data in the room, and the data is aggregated to generate a set of environmental condition data in the occupancy area;

[0021] Within the preset environmental analysis period T, the abnormality of the environmental conditions is calculated and the corresponding environmental abnormality value Qut is obtained as follows:

[0022]

[0023] Where: p is the number of environmental condition parameters, w i is the weight coefficient of parameter i, and its value falls within [0,1]. i (t) is the actual measured value of parameter i at time t, x i,r is the middle value of the qualified interval of parameter i, x i,m is the width of the qualified interval of parameter i; erf(x) is the error function, and Φ(x) is the cumulative distribution function.

[0024] Furthermore, if the environmental abnormal value exceeds the abnormal threshold, the user's vital sign data is collected by the monitoring network arranged in the occupancy area and used as feedback data, which is aggregated to generate a user vital sign data set;

[0025] After aligning the user's vital sign data, the vital sign data in the user's vital sign data set is used as input, and the trained abnormal data recognition model is used to perform anomaly recognition. If abnormal data exists, the abnormal node where the vital sign data produces the abnormality is output, and the abnormality degree of the corresponding abnormal data is obtained and used as the abnormality degree.

[0026] Furthermore, health abnormality values ​​are generated based on the distribution state and abnormality degree of abnormal nodes in the user's vital sign data;

[0027] If there are multiple health abnormal values ​​that exceed the abnormal threshold continuously, the user's active control authority over indoor electrical equipment will be revoked; using the user's real-time vital sign data as input, the trained equipment automatic control model is used to automatically control the electrical equipment in the occupancy area, thereby adjusting the environmental conditions in the occupancy area.

[0028] Furthermore, after automatically controlling the environmental condition parameters in the occupancy area, the user's vital sign data is continuously collected; the user's vital sign data is predicted using the trained vital sign data prediction model, and the corresponding health abnormality value is obtained. If the health abnormality value still exceeds the abnormal threshold, the image acquisition module in the occupancy area is started; the image acquisition module collects the user's posture, movement and expression data from multiple angles, and generates a user behavior data set after aggregation.

[0029] Furthermore, the user's current status data is used as input, and the user's health status is verified using the trained user status evaluation model. The user is guided to issue early warning instructions through voice commands. If the user continues to fail to respond to the guidance, an early warning instruction is automatically issued to the outside.

[0030] Furthermore, when issuing an early warning instruction to the outside, feature extraction is performed on the vital sign data and shape data to obtain the corresponding emergency features; user first aid emergency treatment is used as the target word, and an emergency treatment knowledge graph is pre-constructed; based on the correspondence between the emergency features and the emergency treatment plan, the emergency treatment knowledge graph provides the corresponding emergency treatment plan for the users in the occupancy area;

[0031] Send emergency response plans, user vital signs data, and behavior data to the cloud and support team.

[0032] Furthermore, after receiving the warning command, the cameras installed in the public areas and corridors collect image data in real time;

[0033] The collected image data is detected and identified. If the security team is identified, the access control authority of the occupancy area and the control authority of electrical equipment will be transferred to the external security team, and the access control switching mechanism will be cancelled.

[0034] The hotel room intelligent device control system based on the Internet of Things includes an access control management unit, which switches the access control authentication mode alternately according to the preset switching rules when the occupancy status data in the hotel triggers the access control switching mechanism;

[0035] The environmental analysis unit collects the data of the living environment conditions in the living area. If there is any abnormality in the living environment condition data, the vital sign data of the living user is collected;

[0036] The mode switching unit determines the abnormality of the health of the occupant based on the vital sign data. If the abnormality exceeds the expected level, the active control mode of the electrical equipment is switched to the automatic control mode.

[0037] The security warning unit, when predicting that the user's unhealthy state will continue, will issue a warning instruction to the outside if the collected user behavior data verifies the prediction result;

[0038] The authority control unit transfers the user's access control authority and electrical equipment control authority based on the user's current status and after the emergency handling plan is output from the emergency handling knowledge graph.

[0039] (III) Beneficial effects

[0040] The present invention provides a hotel room intelligent equipment control system and method based on the Internet of Things, which has the following beneficial effects:

[0041] 1. Make the access control authentication in the selection and combination state, build the access control switching mechanism according to the check-in status and visitor distribution status in the hotel, and ensure the safety of users' check-in by switching the access control authentication mode.

[0042] 2. Based on the environmental abnormality value Qut, the abnormality and comfort level in the occupancy area can be evaluated, and the environmental conditions in the occupancy area can be adjusted to ensure that the user's occupancy area can maintain a non-abnormal state for a long time and avoid the electrical equipment in the occupancy area from entering a high-frequency adjustment state.

[0043] 3. Use the collected vital sign data as feedback to provide feedback on the accommodation conditions in the accommodation area. When the user is in poor health, adjust the environmental conditions in the accommodation area as a starting point to ensure the user's safety.

[0044] 4. Describe and evaluate the user's feedback after check-in based on the health abnormality Hut, confirm the current comfort level of the room, and switch the control authority of the electrical equipment in the check-in area to ensure the user's health status and assist the user in recovery.

[0045] 5. By switching the control authority of the electrical equipment in the occupancy area, the environmental conditions in the occupancy area are automatically adjusted, and different control models are actively switched to avoid the environmental conditions interfering with the user's rehabilitation progress and living status after the user enters the rehabilitation stage. In addition, the environmental conditions in the occupancy area are adaptively controlled, which can not only reduce the frequency of manual control, but also prevent the high-frequency switching of the working status of electrical equipment.

[0046] 6. Predict the user's health status and determine whether the user's health status will improve. If it does not improve, use this as an image acquisition and health status verification node to verify the user's health status. When the rehabilitation user enters an unhealthy state, intervene in the current abnormal state through early warning.

[0047] 7. Based on the user's physical performance data as feedback, an emergency response plan is given in advance as a reference to achieve rapid emergency response to the user's abnormal state. By transferring control authority directly to the outside, the user's safety is guaranteed while ensuring timely treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a block diagram of the hotel room intelligent device control method of the present invention;

[0049] Figure 2 This is a flow chart of the hotel room intelligent device control method of the present invention;

[0050] Figure 3 This is a schematic diagram of the structure of the hotel room intelligent equipment control system of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0052] See also Figure 1-2 The present invention provides a hotel room intelligent device control method based on the Internet of Things, comprising:

[0053] Step 1: When the occupancy status data in the hotel triggers the access control switching mechanism, the access control authentication mode is switched alternately according to the pre-set switching rules;

[0054] The step 1 includes the following contents:

[0055] Step 101: After the user enters the check-in state, guide the user to enter identity information and access control information, and complete information entry of multiple access control authentication methods such as RFID card, face recognition, fingerprint recognition and password input;

[0056] Record the traffic data, unlocking time, number of people unlocked each time, etc. in each check-in area or public area of ​​the guest room within the observation period of the hotel, and generate a collection of access control security information after aggregation;

[0057] Step 102: Under dimensionless conditions, an occupancy value Sa is generated from the access control security information set within the observation period. When the occupancy value Sa is greater than a preset security threshold, the access control switching trigger condition is met, in the following manner:

[0058]

[0059] Where n is the number of time nodes, Kp is the length of the observation period; V(i,j) is the difference in the flow of people from time node i to time node j in the public area, Xr is the total number of people entering the corresponding guest room from time node i to time node j, and R is the maximum diameter of the guest room distribution area;

[0060] After the access control switching mechanism is triggered, the access control mode is switched alternately according to the preset switching rules, for example, one or more combinations are randomly selected from several preset access control authentication modes, or the authentication order is adjusted and replaced;

[0061] When using, combine the contents in steps 101 and 102:

[0062] If the user may stay permanently, considering the safety of the user's stay, collecting a variety of different access control authentication information, so that the access control authentication is in a selection and combination state, can increase the safety of the user's stay. At the same time, building an access control switching mechanism based on the occupancy status and visitor distribution status in the hotel, using the changes in the hotel's occupancy environment as feedback, by switching the access control authentication method, it can ensure the safety of the user's stay from the perspective of preventing unauthorized personnel from entering the occupancy area.

[0063] When using, combine the contents in steps 101 and 102:

[0064] In the existing hotel room smart device control method, in order to save energy and improve the user experience, the existing hotel equipment usually collects real-time environmental condition data in the accommodation area and uses this as feedback to automatically control hotel equipment, such as air conditioners, lamps and humidifiers. However, in fact, different guests have different requirements for the accommodation environment and the safety needs in the accommodation area. In particular, some users with poor physical health conditions and who are still in the rehabilitation stage have higher requirements for accommodation safety. For example, when a user is in the rehabilitation stage, he cannot accept too much disturbance. In the existing hotel room smart device control method, it is difficult to consider the safety needs and accommodation needs of this type of rehabilitation users in multiple dimensions.

[0065] Step 2: Collect the occupancy environment condition data in the occupancy area. If the occupancy environment condition data is abnormal, collect the vital sign data of the occupant;

[0066] The step 2 includes the following contents:

[0067] Step 201: After the user enters the check-in area, a sensor network in the check-in area is started, wherein the sensor network includes a noise sensor, a humidity sensor, an air quality sensor, etc. The sensor network is used to monitor the noise level, air quality level, air humidity data, light intensity data and other check-in environment condition data in the room, and the environmental condition data set in the check-in area is generated after being aggregated;

[0068] Step 202: within the preset environment analysis period T, calculate the abnormality of the environment conditions and obtain the corresponding environment abnormality value Qut, in the following manner:

[0069]

[0070] Where: p is the number of environmental condition parameters, w i is the weight coefficient of parameter i, and its value falls within [0,1]. i (t) is the actual measured value of parameter i at time t, x i,r is the middle value of the qualified interval of parameter i, xi,m is the width of the qualified interval of parameter i; erf(x) is the error function, Φ(x) is the cumulative distribution function;

[0071] Pre-set abnormal thresholds based on historical data and management expectations of environmental conditions in the occupied area;

[0072] If the environmental abnormal value obtained during the current environmental analysis cycle exceeds the abnormal threshold, it means that the environmental conditions in the occupancy area are abnormal, which may affect the user's recovery status. At this time, an abnormal reminder is issued to the outside;

[0073] During use, after the user verifies the access control and checks in, the environmental condition data in the occupancy area is collected and monitored, and then the environmental abnormality value Qut is constructed. Based on the environmental abnormality value Qut, the abnormality degree and comfort level in the occupancy area can be evaluated. This is used as feedback to adjust the environmental conditions in the occupancy area to ensure that the user's occupancy area can remain in a non-abnormal state for a long time, and to avoid the electrical equipment in the occupancy area from entering a high-frequency adjustment state.

[0074] Step 203: After receiving the abnormal reminder, the monitoring network arranged in the occupancy area, such as the heart rate, breathing and other vital sign monitoring equipment embedded in the bedside or mattress, collects the user's vital sign data and uses it as feedback data, including heart rate, breathing and body temperature data, etc., and generates a user vital sign data set after aggregation;

[0075] When using, combine the contents in steps 201 to 203:

[0076] By collecting the user's vital sign data, when the user is in a recovery state, the collected vital sign data can be used as feedback to provide feedback on the accommodation conditions in the accommodation area. When the user's physical health is not good, the environmental conditions in the accommodation area can be adjusted as a starting point to ensure the user's safety.

[0077] Step 3: Determine the degree of health abnormality of the occupant based on the physical sign data. If the degree of abnormality exceeds expectations, switch the active control mode of the electrical equipment to the automatic control mode;

[0078] The step three includes the following contents:

[0079] Step 301: training a neural convolutional network with the labeled sample data to obtain a trained abnormal data recognition model;

[0080] After aligning the user's vital sign data using the timestamp, the vital sign data in the user's vital sign data set is used as input, and the trained abnormal data recognition model is used to perform abnormal recognition. If abnormal data exists, the time node at which the vital sign data is abnormal is output and used as the abnormal node. The abnormal degree of the corresponding abnormal data is obtained and used as the abnormal degree;

[0081] Step 302: Generate a health abnormality value Hut based on the distribution state and abnormality degree of abnormal nodes of the user's vital sign data in the following manner:

[0082]

[0083] Where: F1 and F2 are the weight coefficients of abnormal nodes and abnormal degree, respectively, and their values ​​fall within [0,1]; N(t) is the number of abnormal nodes at time t, D(t) is the abnormal degree of the vital sign data at time t, and T is the distribution interval of the time nodes;

[0084] Pre-set physical sign thresholds based on historical data and management expectations for user physical sign data;

[0085] If there are multiple health abnormal values ​​Hut that exceed the abnormal threshold continuously, it means that the user's vital sign data is gradually getting worse. At this time, if the user does not actively adjust the environmental conditions in the accommodation area, it means that the user is not sensitive enough to his or her own health status. In order to avoid further deterioration of the vital sign data, the user's active control authority over indoor electrical equipment is cancelled, and automatic control instructions are issued to the outside;

[0086] During use, after the user checks in and enters the observation stage, the user's vital sign data is continuously collected, and a health abnormality Hut is constructed based on the continuously acquired vital sign data. The user's feedback after check-in is described and evaluated based on the health abnormality Hut to further confirm the current comfort level of the stay, and by switching the control authority of electrical equipment in the occupancy area, the user's health status is protected and the user's recovery is assisted.

[0087] Step 303: After receiving the automatic control instruction, the machine learning algorithm is trained with the labeled sample data to obtain a trained automatic control model of the equipment in the occupancy area;

[0088] Using the user's real-time vital sign data as input, the trained device automatic control model is used to automatically control the electrical equipment in the occupancy area, such as air purifiers, humidifiers, smart lights, smart curtains, air conditioners and audio equipment, to adjust the environmental conditions in the occupancy area, such as temperature, ventilation and humidity;

[0089] When using, combine the contents in steps 301 to 303:

[0090] When it is determined that the user may intentionally be in uncomfortable environmental conditions, the environmental conditions in the occupancy area are automatically adjusted by switching the control authority of the electrical equipment in the occupancy area, so that different control models are actively switched to avoid the user's rehabilitation progress and living status being interfered with by the uncomfortable environmental conditions after entering the rehabilitation stage. In addition, the environmental conditions in the occupancy area are adaptively controlled, which can not only reduce the frequency of manual control, but also prevent the high-frequency switching of the working status of the electrical equipment.

[0091] Step 4: When it is predicted that the user's unhealthy state will continue, if the collected user behavior data verifies the prediction result, an early warning instruction is issued to the outside;

[0092] The step 4 includes the following contents:

[0093] Step 401: After automatically controlling the environmental condition parameters in the occupancy area, the user's vital sign data is continuously collected; the labeled sample data is used to train a neural convolution network to obtain a trained vital sign data prediction model;

[0094] Use the trained vital sign data prediction model to predict the user's vital sign data and obtain the corresponding health abnormality value. If the health abnormality value still exceeds the abnormal threshold, start the image acquisition module in the occupancy area; the image acquisition module collects the user's posture, movement, expression and other behavior data from multiple angles, and aggregates them to generate a user behavior data set;

[0095] Step 402: After training the neural network using the labeled sample data, a trained user status evaluation model is obtained;

[0096] The user's current status data is used as input, and the trained user status evaluation model is used to verify the user's health status, to determine whether the user's current status is abnormal. When the user is unwell, the user is guided to issue a warning command through voice commands. If the user continues to fail to respond to the guidance, a warning command is automatically issued to the outside.

[0097] When using, combine the contents in steps 401 to 402:

[0098] After entering the observation stage, the user's health status is predicted based on the collected vital sign data and corresponding historical data to determine whether the user's health status will improve. If there is no improvement, this is used as an image acquisition and health status verification node to verify the user's health status. This way, when the rehabilitation user enters an unhealthy state, the current abnormal state can be intervened through early warning.

[0099] Step 5: Based on the user's current status, after the emergency handling knowledge graph outputs the emergency handling plan, the user's access control authority and power equipment control authority are transferred;

[0100] The step five includes the following contents:

[0101] Step 501: When issuing an early warning instruction to the outside, extract features from the vital sign data and shape data to obtain corresponding emergency features;

[0102] Taking user first aid emergency treatment as the target word, after deep retrieval and entity relationship construction, the emergency treatment knowledge graph is pre-built; based on the correspondence between emergency characteristics and emergency treatment plans, the emergency treatment knowledge graph provides corresponding emergency treatment plans for users in the area;

[0103] Send emergency response plans, user vital signs data, and behavior data to the cloud and support team;

[0104] When in use, taking into account the user's current poor health condition, the user's physical performance data is used as feedback. When the user makes an emergency response, an emergency response plan is given in advance as a reference, so as to achieve rapid emergency response to the user's abnormal state and avoid further continuation of the current abnormal state.

[0105] Step 502: After receiving the warning instruction, the preset emergency management cycle is entered, and the cameras installed in the public areas and corridors collect image data in real time;

[0106] The collected image data is detected and identified. If the security team is identified, the access control authority of the occupancy area and the control authority of electrical equipment are transferred to the external security team, and the access control switching mechanism is cancelled;

[0107] When in use, the contents of steps 501 and 502 are:

[0108] After introducing an external security team for users, considering that users may be unable to actively open the door or transfer access control rights, for example, when they are physically disabled or have difficulty in real-time operation, control rights will be transferred directly to the outside, or with the user's permission, the rights will be transferred with the user's default permission, ensuring the user's safety while ensuring timely treatment.

[0109] See also Figure 3 The present invention provides a hotel room intelligent equipment control system based on the Internet of Things, including:

[0110] The access control management unit switches the access control authentication mode alternately according to the preset switching rules when the occupancy status data in the hotel triggers the access control switching mechanism;

[0111] The environmental analysis unit collects the data of the living environment conditions in the living area. If there is any abnormality in the living environment condition data, the vital sign data of the living user is collected;

[0112] The mode switching unit determines the abnormality of the health of the occupant based on the vital sign data. If the abnormality exceeds the expected level, the active control mode of the electrical equipment is switched to the automatic control mode.

[0113] The security warning unit, when predicting that the user's unhealthy state will continue, will issue a warning instruction to the outside if the collected user behavior data verifies the prediction result;

[0114] The authority control unit transfers the user's access control authority and electrical equipment control authority based on the user's current status and after the emergency handling plan is output from the emergency handling knowledge graph.

[0115] The construction method of the knowledge graph of emergency treatment for rehabilitation patients is as follows:

[0116] It is necessary to clarify the construction goals of the knowledge graph of emergency treatment for rehabilitation patients. This usually includes improving the efficiency, accuracy and personalization of emergency treatment for rehabilitation patients, and providing timely and effective emergency treatment guidance for rehabilitation patients.

[0117] Data source determination: Determine the data source based on the emergency treatment needs of rehabilitation patients. These data sources may include case records, emergency guidelines, expert experience, etc. from rehabilitation hospitals, rehabilitation centers, and emergency centers.

[0118] Data preprocessing: Preprocess the collected raw data, including data cleaning, format conversion, deduplication, etc., to ensure the accuracy and consistency of the data.

[0119] Entity recognition: Natural language processing technology is used to identify entities related to emergency treatment of rehabilitation patients from pre-processed data, such as disease names, symptoms, first aid measures, etc.

[0120] Relationship extraction: Further analyze the relationship between entities, such as the relationship between disease and symptoms, the relationship between symptoms and first aid measures, etc. These relationships can be realized through relationship extraction technology in natural language processing.

[0121] Attribute extraction: In addition to entities and relationships, it is also necessary to extract attribute information of entities, such as the cause of the disease, course of disease, preventive measures, etc. This attribute information can provide more comprehensive emergency treatment guidance for rehabilitation patients.

[0122] Data integration: Integrate knowledge obtained from different data sources to form a unified knowledge base. This includes the integration of entities, relationships, and attributes.

[0123] Entity disambiguation: Disambiguate ambiguous entities to ensure the uniqueness of each entity in the knowledge graph.

[0124] Co-reference resolution: Processing different expressions of the same entity that may exist in different data sources to ensure that they are correctly identified and associated in the knowledge graph.

[0125] Ontology construction: According to the knowledge characteristics of emergency treatment of rehabilitation patients, the ontology library is constructed. The ontology library is the core part of the knowledge graph, which defines the concepts of entities, relationships and attributes in the knowledge graph and the relationships between them.

[0126] Knowledge representation: Using ontology representation, entities, relationships, and attributes in the knowledge graph are represented in the form of triples (entity 1, relationship, entity 2). This helps to achieve effective organization and storage of knowledge.

[0127] Knowledge storage: Choose appropriate storage technology to store knowledge graphs. Common storage technologies include graph databases, relational databases, etc. According to the scale and complexity of the knowledge graph, choose appropriate storage technology to ensure the efficiency of query and update.

[0128] Quality assessment: Perform quality assessment on the knowledge in the knowledge graph, including the accuracy of entities, the correctness of relationships, the completeness of attributes, etc. This can be achieved through manual review, expert evaluation, etc.

[0129] Knowledge update: With the continuous development of medical knowledge and technology, the knowledge of emergency treatment of rehabilitation patients also needs to be constantly updated. Therefore, it is necessary to establish an effective knowledge update mechanism to ensure the timeliness and accuracy of the knowledge graph.

[0130] Application development: Based on the constructed knowledge graph of emergency treatment for rehabilitation patients, various applications can be developed, such as intelligent question-answering systems, auxiliary diagnosis systems, etc. These applications can provide personalized emergency treatment guidance for rehabilitation patients.

[0131] User feedback: Collect user feedback on the application and optimize and improve the knowledge graph based on the feedback.

[0132] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A hotel room intelligent device control method based on the Internet of Things, characterized in that: include, When the occupancy status data in the hotel triggers the access control switching mechanism, the access control authentication mode is switched alternately according to the pre-set switching rules; Collect the occupancy environment condition data in the occupancy area. If the occupancy environment condition data is abnormal, collect the vital sign data of the occupant; judge the degree of health abnormality of the occupant based on the vital sign data. If the degree of abnormality exceeds expectations, switch the active control mode of the electrical equipment to the automatic control mode; generate a health abnormality value to judge the degree of health abnormality of the occupant The way is as follows: ； Where: and are the weight coefficients of abnormal nodes and abnormal degree, and their values ​​fall into within; For time The number of abnormal nodes, For time The degree of abnormality of the physical sign data, is the distribution interval of the time node; When it is predicted that the user's unhealthy state will continue, if the collected user behavior data verifies the prediction result, the emergency handling knowledge graph outputs an emergency handling plan based on the user's current state, and then transfers the user's access control authority and electrical equipment control authority.

2. The hotel room intelligent device control method according to claim 1, characterized in that: Guide users to enter their identity information and access control information, and at least record the flow of people in each check-in area or guest room's public area during the observation period in the hotel, unlocking time, and the number of people unlocked each time, and then generate an access control security information set after summarizing; the occupancy value is generated from the access control security information set during the observation period , when the check-in value When it is greater than the preset security threshold, the access control switching trigger condition is met.

3. The hotel room intelligent device control method according to claim 2, characterized in that: Under dimensionless conditions, the occupancy value is generated from the access control security information set The way is as follows: ； in, is the number of time nodes, is the length of the observation period; It is a time point in the public area To the time node The difference in traffic volume, For time node To the time node The total number of people entering the corresponding guest room during the day. It is the maximum diameter of the guest room distribution area.

4. The hotel room intelligent device control method according to claim 3, characterized in that: Start the sensor network in the occupied area to monitor the noise level, air quality level, air humidity data and light intensity data in the room, and generate a data set of environmental conditions in the occupied area after aggregation; In the pre-set environment analysis cycle Within, calculate the abnormality of environmental conditions and obtain the corresponding environmental abnormality value.

5. The hotel room intelligent device control method according to claim 4, characterized in that: If the environmental abnormal value exceeds the abnormal threshold, the monitoring network deployed in the occupancy area collects the user's vital sign data and uses it as feedback data, which is aggregated to generate a user vital sign data set; After aligning the user's vital sign data, the vital sign data in the user's vital sign data set is used as input, and the trained abnormal data recognition model is used to perform anomaly recognition. If abnormal data exists, the abnormal node where the vital sign data produces the abnormality is output, and the abnormality degree of the corresponding abnormal data is obtained and used as the abnormality degree.

6. The hotel room intelligent device control method according to claim 5, characterized in that: Generate health abnormality values ​​based on the distribution status and abnormality degree of abnormal nodes in user's vital sign data; If there are multiple health abnormal values ​​that exceed the abnormal threshold continuously, the user's active control authority over indoor electrical equipment will be revoked; using the user's real-time vital sign data as input, the trained equipment automatic control model is used to automatically control the electrical equipment in the occupancy area, thereby adjusting the environmental conditions in the occupancy area.

7. The hotel room intelligent device control method according to claim 6, characterized in that: After automatically controlling the environmental condition parameters in the occupancy area, the user's vital sign data is continuously collected; the user's vital sign data is predicted using the trained vital sign data prediction model, and the corresponding health abnormality value is obtained. If the health abnormality value still exceeds the abnormal threshold, the image acquisition module in the occupancy area is started; The image acquisition module collects the user's posture, movement and expression data from multiple angles, and aggregates them to generate a user behavior data set.

8. The hotel room intelligent device control method according to claim 7, characterized in that: The user's current status data is used as input, and the trained user status evaluation model is used to verify the user's health status. The user is guided to issue early warning instructions through voice commands. If the user continues to fail to respond to the guidance, an early warning instruction is automatically issued to the outside.

9. The hotel room intelligent device control method according to claim 8, characterized in that: When issuing an early warning command to the outside, feature extraction is performed on the vital sign data and shape data to obtain the corresponding emergency features; the user's first aid emergency treatment is used as the target word, and the emergency treatment knowledge graph is pre-constructed; based on the correspondence between the emergency features and the emergency treatment plan, the emergency treatment knowledge graph provides the corresponding emergency treatment plan for the users in the area; Send emergency response plans, user vital signs data, and behavior data to the cloud and support team.

10. The hotel room intelligent device control method according to claim 9, characterized in that: After receiving the warning command, the cameras installed in the public areas and corridors collect image data in real time; The collected image data is detected and identified. If the security team is identified, the access control authority of the occupancy area and the control authority of electrical equipment will be transferred to the external security team, and the access control switching mechanism will be cancelled.

11. Hotel room intelligent equipment control system based on the Internet of Things, characterized by: include, The access control management unit switches the access control authentication mode alternately according to the preset switching rules when the occupancy status data in the hotel triggers the access control switching mechanism; The environmental analysis unit collects the data of the living environment conditions in the living area. If there is any abnormality in the living environment condition data, the vital sign data of the living user is collected; The mode switching unit determines the abnormal health degree of the occupant based on the vital sign data. If the abnormal degree exceeds the expected level, the active control mode of the electrical equipment is switched to the automatic control mode; wherein a health abnormality value is generated to determine the abnormal health degree of the occupant The way is as follows: ； Where: and are the weight coefficients of abnormal nodes and abnormal degree, and their values ​​fall into within; For time The number of abnormal nodes, For time The degree of abnormality of the physical sign data, is the distribution interval of the time node; The security warning unit, when predicting that the user's unhealthy state will continue, will issue a warning instruction to the outside if the collected user behavior data verifies the prediction result; The authority control unit transfers the user's access control authority and electrical equipment control authority based on the user's current status and after the emergency handling plan is output from the emergency handling knowledge graph.

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