Dynamic office furniture adjusting method based on environmental factors
Through intelligent sensing networks and augmented reality technology, combined with deep learning algorithms and multi-dimensional data fusion, the office environment is monitored in real time and furniture is automatically adjusted, which solves the problem that traditional furniture cannot adapt to environmental changes and improves user comfort and work efficiency.
Patent Information
- Application Number
- CN202510654513.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-21
- Publication Date
- 2025-08-12
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional office furniture lacks the ability to adapt to environmental changes dynamically and cannot adjust in real time based on factors such as temperature, humidity, light, noise, etc. to provide the best working experience.
Through intelligent sensing networks and augmented reality technology, combined with deep learning algorithms and personalized multi-dimensional data fusion, environmental factors are monitored in real time and adjustment instructions are generated to automatically optimize office furniture configurations, including dynamic adjustment of electric lifting systems and servo motors.
It realizes the real-time adaptation of office furniture to environmental changes, improves user comfort and work efficiency, reduces health risks, and improves productivity.
Smart Images
Figure CN120469260A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of office furniture adjustment, and in particular to a dynamic office furniture adjustment method based on environmental factors. Background Art
[0002] With the continuous progress of society and the development of technology, the office environment has also undergone significant changes. To improve work efficiency and employee health, more and more companies are focusing on intelligent, comfortable, and personalized office environments. However, traditional office furniture designs are often static and lack the ability to respond to environmental changes. This prevents employees from achieving an optimal working experience in varying working conditions. Even when office furniture is adjustable, current adjustment methods mainly include manual, mechanical, and electric adjustment.
[0003] Manual adjustment: This is the most traditional adjustment method, usually operated through physical devices such as knobs, levers or slides. Users can manually adjust the height, angle or position of office furniture. Mechanical adjustment: Mechanical adjustment usually uses principles such as gears, springs or air pressure supports to achieve furniture adjustment. This type of furniture is relatively common, such as pneumatic lift tables, which can adjust the desktop height through operating levers or buttons to meet the needs of different users. Electric adjustment: Electric adjustment is an adjustment method that has developed rapidly in recent years. Users can easily adjust the height, angle, etc. of furniture through buttons, electric switches or remote controls.
[0004] While existing adjustment methods have improved the comfort and flexibility of office furniture to a certain extent, they still have some inherent problems. The core issue is the lack of dynamic adaptability to environmental changes. Existing office furniture adjustment methods generally only perform static adjustments based on user needs, ignoring the various dynamic factors in the office environment, such as temperature, humidity, lighting, and noise, which affect the performance of office furniture. Summary of the Invention
[0005] In response to the shortcomings of the existing technology, the present invention provides a dynamic office furniture adjustment method based on environmental factors. The technical problem to be solved by this invention is: how to combine intelligent sensor networks and personalized multi-dimensional data fusion algorithms to predict and adapt to the office environment and user needs in real time, automatically adjust the office furniture configuration, and improve comfort, health and work efficiency.
[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a dynamic office furniture adjustment method based on environmental factors, comprising:
[0007] S1. Real-time monitoring of environmental factors in the office environment to form environmental factor data, the environmental factors include temperature, humidity, light intensity, noise level, air quality, CO2 concentration, personnel distribution and human posture;
[0008] S2. Transmitting the environmental factor data to the office furniture's central control system via wireless communication. The central control system performs a comprehensive analysis of the environmental factor data based on an individual needs matching algorithm to generate environmental adaptability data required for office furniture adjustment. The environmental adaptability data includes temperature, humidity, and light intensity, as well as personalized adjustment targets.
[0009] S3. Based on intelligent sensor networks and augmented reality technology, the central control system combines edge computing and personalized multi-dimensional data fusion algorithms to introduce an environment-behavior prediction model and a context-adaptive recommendation model to generate adjustment instructions;
[0010] S4. The adjustment command is sent wirelessly to the adjustment control module of the office furniture to drive the adjustment mechanism of the office furniture;
[0011] S5. After the office furniture is adjusted, monitor the adjusted environmental status in real time and evaluate the comfort, health, and work efficiency after the adjustment. If environmental factors change or the user's work needs change, repeat steps S2 to S4 and continue to make dynamic adjustments to maintain the optimal working environment and maximize user comfort and work efficiency.
[0012] Preferably, the environmental factors are collected and processed in real time by sensors, which include temperature and humidity sensors, light sensors, noise sensors, air quality sensors, posture monitoring sensors and position sensors.
[0013] Preferably, the adjustment mechanism includes an electric lifting system, a pneumatic system and a servo motor, which automatically adjusts various parameters of office furniture according to environmental adaptability data to adapt to changes in environmental factors, realize automatic dynamic adjustment, and optimize the user's working posture, comfort and human health.
[0014] Preferably, the environment-behavior prediction model predicts the user's future behavior pattern through a deep learning algorithm and makes adjustments based on environmental changes. The specific mathematical formula is as follows:
[0015]
[0016] in: Behavior Indicates time Given the environmental factors and user behavior data , predicted user behavior, is the environmental data vector, including the measured values of temperature, humidity, and light, is the user's historical behavior data vector, including the user's sitting and standing posture records, and is the weight matrix of the model, which is used to adjust the influence of environmental and behavioral data. b is the bias term, which controls the offset of the prediction results. The Sigmoid activation function ensures that the prediction result is between 0 and 1, indicating the probability of the behavior.
[0017] Preferably, the context-adaptive recommendation model performs context awareness through the following formula:
[0018]
[0019] in: Indicates the recommended adjustment plan based on the current working situation and environmental data is the vector of the current work situation, including office scene type, task, is the environmental factor data vector, According to the situation type The recommended strategy function represents the adjustment plan that should be taken in a given work situation. Environmental factors The adaptability function represents the adjustment plan that should be adopted in a given environment.
[0020] Preferably, the personalized multidimensional data fusion algorithm is optimized by the following formula:
[0021]
[0022] in: The final generated adjustment instructions include various adjustment parameters of office furniture. and are environmental factors and user historical behavior data, Behavior The predicted behavior probability generated by the environment-behavior prediction model, is the adjustment recommendation solution generated by the context-adaptive recommendation model, is a weight parameter used to adjust the impact of environmental factors, user behavior, and contextual recommendations on adjustment instructions.
[0023] Preferably, the wireless method includes Wi-Fi, Bluetooth or Zigbee, which ensures that the adjustment instructions are accurately and stably transmitted to the adjustment control module in a short time.
[0024] Preferably, the comfort assessment tracks the user's sitting posture, standing posture and activity status in real time through posture monitoring sensors, and combines environmental sensor data to assess whether the current adjustment of office furniture meets the user's physiological needs. The health assessment combines ergonomic models and the user's physiological data to assess the health of the current posture. The work efficiency assessment assesses whether the current adjustment helps to improve work efficiency by analyzing the user's input behavior, work progress and the influence of environmental factors.
[0025] The present invention provides a dynamic office furniture adjustment method based on environmental factors. It has the following beneficial effects:
[0026] This environmentally-responsive dynamic office furniture adjustment method, through the integrated application of intelligent sensor networks and augmented reality technology, combines deep learning algorithms with personalized multi-dimensional data fusion to perceive and predict changes in the office environment and user behavior patterns in real time. Using an environment-behavior prediction model and a context-adaptive recommendation model, the system not only responds to current environmental changes but also anticipates user behavior and needs, automatically optimizing office furniture configuration.
[0027] This technology solution's real-time monitoring and adjustment capabilities can effectively improve users' working posture and reduce health issues caused by poor posture or environmental factors. Furthermore, adjusting environmental factors such as temperature, humidity, and noise levels helps create a more comfortable work environment, thereby improving user efficiency and focus. Compared to the static adjustment methods of traditional office furniture, this solution's dynamic adjustment not only provides a comfortable working experience, but also promotes employee health, improving work efficiency, and overall productivity. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 It is a flowchart for realizing the invention;
[0029] Figure 2 It is a schematic diagram of the generation and execution of the regulation instructions for implementing the invention. DETAILED DESCRIPTION
[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0031] Example 1:
[0032] like Figure 1-2As shown, an embodiment of the present invention provides a dynamic office furniture adjustment method based on environmental factors, including: S1. Real-time monitoring of environmental factors in an office environment to form environmental factor data, where the environmental factors include temperature, humidity, light intensity, noise level, air quality, CO2 concentration, personnel distribution, and human posture. The environmental factors are collected and processed in real time by sensors, where the sensors include temperature and humidity sensors, light sensors, noise sensors, air quality sensors, posture monitoring sensors, and position sensors;
[0033] S2. Environmental factor data is transmitted via wireless communication to the office furniture's central control system. The central control system performs a comprehensive analysis based on the environmental factor data and an algorithm matching individual needs to generate environmental adaptability data required for office furniture adjustments. Environmental adaptability data includes temperature, humidity, and light intensity, as well as personalized adjustment targets. These targets are calculated based on the user's work posture and needs. The system provides the current state of the office environment and calculates personalized adjustment targets based on the user's work posture, environmental factors, and health needs. For example, if the system detects that a user has been sitting in a fixed position for an extended period of time and the ambient temperature is high, it generates personalized adjustment targets, adjusting factors such as seat height and back support, as well as the room temperature, to optimize the user's work experience. When the user enters a meeting, the system automatically adjusts the desk and chair layout, lighting intensity, and room temperature to enhance user comfort and work efficiency. Adjustment instructions include adjustment values for various office furniture parameters, such as table height, chair back angle, and cushion firmness, ensuring optimal posture and comfort while working.
[0034] S3. Based on intelligent sensor networks and augmented reality technology, a central control system combines edge computing with personalized multi-dimensional data fusion algorithms, introduces an environment-behavior prediction model and a context-adaptive recommendation model, and then generates adjustment instructions.
[0035] The environment-behavior prediction model uses deep learning algorithms to predict users' future behavior patterns and make adjustments based on environmental changes. The specific mathematical formula is as follows:
[0036]
[0037] in: Behavior Indicates time Given the environmental factors and user behavior data , predicted user behavior (such as long-term sitting, standing, walking, etc.), is the environmental data vector, including the measured values of temperature, humidity, and light, is the user's historical behavior data vector, including the user's sitting and standing posture records, and is the weight matrix of the model, which is used to adjust the influence of environmental and behavioral data. b is the bias term, which controls the offset of the prediction results. The Sigmoid activation function ensures that the prediction result is between 0 and 1, indicating the probability of the behavior.
[0038] The Environment-Behavior Prediction Model integrates user behavior data, environmental change data, and interaction history. Using machine learning and deep learning algorithms, it predicts future user behavior patterns (such as long work periods and short breaks) in real time. Combined with current environmental trends, it automatically calculates the impact of future environmental changes on user comfort and work efficiency. This model can tailor personalized adjustments to different user needs (such as focused work, social discussion, and rest), providing users with the optimal office furniture configuration in advance.
[0039] The context-adaptive recommendation model is context-aware through the following formula:
[0040]
[0041] in: Indicates recommended adjustments (such as desk height, chair back angle, etc.) based on the current working situation and environmental data is the vector of the current work situation, including office scene type (such as personal office, meeting, etc.), task type (such as thinking, discussion, etc.), is the environmental factor data vector (such as temperature, humidity, light, noise, etc.), According to the situation type The recommended strategy function represents the adjustment plan that should be taken in a given work situation. Environmental factors The adaptability function represents the appropriate adjustment plan for a given environment. The context-adaptive recommendation model, based on context-aware technology and intelligent sensing networks, collects real-time office space environmental data (such as temperature, humidity, and noise level) and analyzes current work scenarios (e.g., independent work, team collaboration, and meetings) to intelligently generate optimal office furniture adjustment plans. The model combines environmental conditions, user needs, and task characteristics to recommend appropriate furniture configurations, ensuring optimal comfort and efficiency in different scenarios (e.g., open offices, private offices, and conference rooms).
[0042] Through the environment-behavior prediction model and the situational adaptability recommendation model, the central control system can not only respond to environmental changes in real time, but also predict future environmental changes and adjust office furniture in advance to perfectly adapt it to the user's work needs and environmental characteristics, thereby improving work efficiency and comfort.
[0043] The personalized multidimensional data fusion algorithm is optimized using the following formula:
[0044]
[0045] in: The final generated adjustment instructions include various adjustment parameters of office furniture (such as desktop height, chair back angle, etc.). and are environmental factors and user historical behavior data, Behavior The predicted behavior probability generated by the environment-behavior prediction model, is the adjustment recommendation solution generated by the context-adaptive recommendation model, is a weight parameter used to adjust the impact of environmental factors, user behavior and situational recommendations on adjustment instructions. Through this algorithm, the system can comprehensively consider the real-time environment, user behavior, work situation and historical data to accurately generate adjustment instructions for office furniture, ensuring that each adjustment can be optimized according to user needs and environmental conditions.
[0046] S4. Wirelessly transmit adjustment commands to the office furniture's adjustment control module, which activates the furniture's adjustment mechanism. The adjustment mechanism, which includes an electric lift system, a pneumatic system, and a servo motor, automatically adjusts various parameters of the office furniture based on environmental adaptability data to adapt to changing environmental factors, achieving automated dynamic adjustment and optimizing the user's working posture, comfort, and health. Wireless methods, such as Wi-Fi, Bluetooth, or Zigbee, ensure that adjustment commands are accurately and stably transmitted to the adjustment control module within a short period of time.
[0047] S5. After the office furniture is adjusted, the system monitors the adjusted environmental status in real time and evaluates its comfort, health, and work efficiency. If environmental factors change or the user's work needs change, steps S2 to S4 are repeated, continuously making dynamic adjustments to maintain an optimal work environment and maximize user comfort and work efficiency. The comfort assessment uses posture monitoring sensors to track the user's sitting, standing, and activity status in real time. Combined with environmental sensor data, the system assesses whether the current office furniture adjustments meet the user's physiological needs. For example, the system determines chair comfort based on changes in sitting posture, ensuring that users working for extended periods of time do not experience discomfort due to improper posture. The health assessment combines ergonomic models with the user's physiological data to assess the healthiness of the current posture. For example, if a user maintains a static sitting posture for an extended period, the system will detect this through sensors and issue a reminder, recommending simple exercises or automatically adjusting the desk and chair to prevent muscle strain or spinal problems caused by poor posture. The work efficiency assessment analyzes the user's input behavior, work progress (such as keyboard stroke frequency and mouse operation frequency), and the impact of environmental factors (such as lighting and noise) to assess whether the current adjustments will help improve work efficiency. For example, the system can automatically adjust environmental parameters based on environmental factors (such as light and temperature) to create an optimal working atmosphere to improve user concentration and work output.
[0048] Experimental Examples
[0049] This embodiment uses the following data to generate adjustment instructions based on intelligent sensor networks and augmented reality technology. 1. Implementation of an Environment-Behavior Prediction Model: Objective: Use deep learning algorithms to predict user behavior patterns (such as prolonged sitting, standing, and walking) in real time and dynamically adjust office furniture configuration based on environmental changes.
[0050] Input data:
[0051] Environmental data vector Temperature, humidity, light, , data is collected in real time through environmental sensors. User historical behavior data vector Sitting, standing, , user behavior data comes from posture monitoring sensors, which record the user's working status. Model formula: The environment-behavior prediction model is trained using a deep learning algorithm (such as a multi-layer perceptron (MLP)) and predicts using the following formula:
[0052]
[0053] in: Behavior Indicates that in a given environment data and user historical behavior data Under the circumstance of CNN, the predicted probability of user behavior (such as long-term sitting, standing, etc.) and is the weight matrix in the deep learning model, which is used to adjust the impact of environmental factors and user behavior on the prediction results.
[0054] b is the bias term, which controls the offset of the prediction results.
[0055] The Sigmoid activation function compresses the result between 0 and 1 to represent the predicted probability of the behavior.
[0056] Environmental data (temperature: ,humidity: , light: 300 lux)
[0057] User behavior data (sitting duration: 4 hours, standing duration: 30 minutes)
[0058] Assume that after the deep learning model is trained, the prediction result is:
[0059]
[0060] This indicates that the user has The system can automatically adjust the configuration of office furniture based on the prediction results, such as adjusting the height and angle of the seat, and reminding users to change their posture.
[0061] 2. Implementation of a context-adaptive recommendation model: Goal: Automatically recommend appropriate office furniture configurations (such as desk height, chair back angle, etc.) based on the current work context and environmental data.
[0062] Input data:
[0063] Current work situation vector [Office scenario type, task type], such as whether the user is in individual work, team collaboration, meeting, etc.
[0064] Environmental factor data vector Temperature, humidity, light intensity, .
[0065] Model formula:
[0066] The Situational Adaptive Recommendation Model (SARM) generates recommended adjustment solutions using the following formula:
[0067]
[0068] in: Indicates adjustment solutions (such as desk height and chair back angle) generated based on work scenarios and environmental data.
[0069] Based on the situation type The recommended strategy function represents the adjustment plan that should be taken in a given scenario.
[0070] Based on environmental factors The adaptability function represents the adjustment plan that should be adopted in a specific environment.
[0071] Current work situation (personal work, thinking tasks)
[0072] Environmental data (temperature: ,humidity: , light: 300lux, noise: 40dB)
[0073] Assume that the recommended desk height and chair back angle are calculated by the context-adaptive recommendation model:
[0074] 3. Implementation of personalized multi-dimensional data fusion algorithm:
[0075] Goal: Generate precise adjustment instructions by combining environmental factors, user behavior, work context, and historical data.
[0076] Input data:
[0077] Environmental data , user behavior data , contextual data and predicted user behavior probability Behavior Model formula: Personalized multi-dimensional data fusion algorithm combines environmental, behavioral and situational data to generate the final adjustment instructions :
[0078]
[0079] in:
[0080] The final generated adjustment instructions include parameters such as spring surface height, seat back angle, and seat cushion hardness.
[0081] For environmental factor data, User behavior data.
[0082] Behavior is the predicted behavior probability generated by the environment-behavior prediction model.
[0083] The adjustment recommendation solution generated by the context-adaptive recommendation model.
[0084] is a weight parameter that controls the impact of various factors on the final adjustment instruction.
[0085] Data example:
[0086] Environmental data
[0087] User behavior data Sitting: 4 hours, Standing: 30 minutes
[0088] Predicting behavioral probability Behavior (Probability of the user remaining seated for an extended period of time)
[0089] Recommended adjustment plan Desktop height , chair back angle
[0090] The final adjustment instructions are calculated through a personalized multi-dimensional data fusion algorithm:
[0091]
[0092] Through the aforementioned implementation, the combination of an environment-behavior prediction model and a context-adaptive recommendation model enables the system to automatically adjust office furniture configurations in real time based on user behavior patterns, environmental factors, and work context. A personalized multi-dimensional data fusion algorithm ensures the accuracy and adaptability of adjustment instructions, providing users with an optimal work environment configuration, thereby improving work efficiency and comfort.
[0093] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. A dynamic office furniture adjustment method based on environmental factors, characterized in that: include: S1. Real-time monitoring of environmental factors in the office environment to form environmental factor data, the environmental factors include temperature, humidity, light intensity, noise level, air quality, CO2 concentration, personnel distribution and human posture; S2. Transmitting the environmental factor data to the office furniture's central control system via wireless communication. The central control system performs a comprehensive analysis of the environmental factor data based on an individual needs matching algorithm to generate environmental adaptability data required for office furniture adjustment. The environmental adaptability data includes temperature, humidity, and light intensity, as well as personalized adjustment targets. S3. Based on intelligent sensor networks and augmented reality technology, the central control system combines edge computing and personalized multi-dimensional data fusion algorithms to introduce an environment-behavior prediction model and a context-adaptive recommendation model to generate adjustment instructions; S4. The adjustment command is sent wirelessly to the adjustment control module of the office furniture to drive the adjustment mechanism of the office furniture; S5. After the office furniture is adjusted, monitor the adjusted environmental status in real time and evaluate the comfort, health, and work efficiency after the adjustment. If environmental factors change or the user's work needs change, repeat steps S2 to S4 to continue dynamic adjustments.
2. The method for dynamic office furniture adjustment based on environmental factors according to claim 1, characterized in that: The environmental factors are collected and processed in real time by sensors, which include temperature and humidity sensors, light sensors, noise sensors, air quality sensors, posture monitoring sensors and position sensors.
3. The method for dynamic office furniture adjustment based on environmental factors according to claim 1, characterized in that: The regulating mechanism includes an electric lifting system, a pneumatic system and a servo motor.
4. The method for dynamic office furniture adjustment based on environmental factors according to claim 1, characterized in that: The environment-behavior prediction model uses a deep learning algorithm to predict the user's future behavior patterns and makes adjustments based on environmental changes. The specific mathematical formula is as follows: in: Behavior Indicates time Given the environmental factors and user behavior data , predicted user behavior, is the environmental data vector, including the measured values of temperature, humidity, and light, is the user's historical behavior data vector, including the user's sitting and standing posture records, and is the weight matrix of the model, which is used to adjust the influence of environmental and behavioral data. b is the bias term, which controls the offset of the prediction results. The Sigmoid activation function ensures that the prediction result is between 0 and 1, indicating the probability of the behavior.
5. The method for dynamic office furniture adjustment based on environmental factors according to claim 4, characterized in that: The context-adaptive recommendation model performs context-awareness through the following formula: in: Indicates the recommended adjustment plan based on the current working situation and environmental data is the vector of the current work situation, including office scene type and task type. is the environmental factor data vector, According to the situation type The recommended strategy function represents the adjustment plan that should be taken in a given work situation. Environmental factors The adaptability function represents the adjustment plan that should be adopted in a given environment.
6. The method for dynamic office furniture adjustment based on environmental factors according to claim 5, characterized in that: The personalized multidimensional data fusion algorithm is optimized by the following formula: in: The final generated adjustment instructions include various adjustment parameters of office furniture. and are environmental factors and user historical behavior data, Behavior The predicted behavior probability generated by the environment-behavior prediction model, is the adjustment recommendation solution generated by the context-adaptive recommendation model, is a weight parameter used to adjust the impact of environmental factors, user behavior, and contextual recommendations on adjustment instructions.
7. The method for dynamic office furniture adjustment based on environmental factors according to claim 1, characterized in that: The wireless method includes Wi-Fi, Bluetooth or Zigbee, which ensures that the adjustment instructions are accurately and stably transmitted to the adjustment control module in a short time.
8. The method for dynamic office furniture adjustment based on environmental factors according to claim 1, characterized in that: The comfort assessment uses posture monitoring sensors to track the user's sitting posture, standing posture and activity status in real time, and combines environmental sensor data to assess whether the current adjustment of office furniture meets the user's physiological needs. The health assessment combines ergonomic models and the user's physiological data to assess the health of the current posture. The work efficiency assessment analyzes the user's input behavior, work progress and the impact of environmental factors to assess whether the current adjustment helps improve work efficiency.