Environmental health monitoring method and system based on office furniture noise detection

Abstract

The invention relates to the technical field of office environment monitoring, in particular to an environment health monitoring method and system based on office furniture noise detection, which can provide an accurate and detailed noise monitoring result, optimize an office environment and guarantee the physical and psychological health of employees. The method is applied to noise environment monitoring of an office area, office furniture in the office area is provided with noise sensors, and the method comprises the following steps: collecting real-time noise monitoring data collected by each noise sensor in real time; for each piece of real-time noise monitoring data, key features in the real-time noise monitoring data are identified and extracted, and office furniture position information, office furniture types and real-time noise decibel values corresponding to the noise sensors are obtained; carrying out numerical conversion on the office furniture position information and the office furniture type, and carrying out data combination to obtain an office furniture space vector; the office furniture space vector comprises an office furniture position identification parameter and an office furniture type identification parameter.

Description

Technical Field

[0001] The present invention relates to the technical field of office environment monitoring, and in particular to an environmental health monitoring method and system based on office furniture noise detection. Background Art

[0002] With the continuous development of modern office environment, the noise pollution problem in office area has received increasing attention. Noise not only affects the work efficiency and mental health of employees, but may also have a long-term negative impact on their physical health. Therefore, it is particularly important to effectively monitor and manage noise in the office environment.

[0003] Existing methods for monitoring office environment noise mainly rely on fixed noise monitoring equipment, which are usually installed in specific locations and cannot fully cover the entire office area. Since the noise levels generated by different locations, different types of office furniture and activities vary greatly, relying solely on measurement data from a single or a few fixed points cannot accurately assess the noise conditions of the overall office environment and its impact on employees. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an environmental health monitoring method and system based on office furniture noise detection, which can provide accurate and detailed noise monitoring results, optimize the office environment, and protect the physical and mental health of employees.

[0005] In a first aspect, the present invention provides an environmental health monitoring method based on office furniture noise detection, the method is applied to noise environment monitoring in an office area, where office furniture in the office area is equipped with noise sensors, the method comprising: Collect real-time noise monitoring data collected by various noise sensors in real time; For each piece of real-time noise monitoring data, identify and extract key features in the real-time noise monitoring data, and obtain office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; The office furniture location information and the office furniture type are converted into numerical values, and the data are combined to obtain an office furniture space vector; the office furniture space vector includes an office furniture location identification parameter and an office furniture type identification parameter; Using the office furniture space vector as an index address, locating it in a preset office environment noise reference matrix, and extracting the reference noise decibel value corresponding to the office furniture space vector; Calculate the noise difference between the real-time noise decibel value and the reference noise decibel value corresponding to each piece of office furniture, and based on the preset spatial distribution rules, perform data array on multiple noise differences to obtain a spatial matrix of office environment noise differences; Inputting the office environment noise difference space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score; The office environment noise health score is compared with the preset office environment noise health threshold. If the office environment noise health score is higher than the preset office environment noise health threshold, the noise level of the office environment at this time is determined to be qualified; if the office environment noise health score is not higher than the preset office environment noise health threshold, the noise level of the office environment at this time is determined to be unqualified.

[0006] Furthermore, the method for identifying and extracting key features in the real-time noise monitoring data includes: Receive real-time data from various noise sensors; Clean the received data to remove outliers, duplicate values, and invalid data; Identify and extract key features from each piece of real-time data; Using the metadata attached to the sensor, determine the specific location of the noise sensor; Compare this location information with the layout of the office area to determine the location of the office furniture corresponding to the sensor; Using the metadata attached to the sensor, determine the type of office furniture where the noise sensor is located; Extract noise decibel values ​​directly from real-time data.

[0007] Furthermore, the method for obtaining the office furniture space vector includes: In an office environment, a coordinate system is established to describe the location of each piece of office furniture; For each sensor with a known position, a position identification parameter is assigned according to its coordinate value relative to the reference point; Each type of office furniture is assigned a type identification parameter; The location identification parameters and the office furniture type identification parameters are combined to obtain a multi-dimensional vector that comprehensively describes the specific office furniture, namely, the office furniture space vector.

[0008] Furthermore, the method for constructing the office environment noise reference matrix includes: Collect historical noise data of the office environment; clean the collected data to remove invalid, abnormal and duplicate data; Categorize the cleaned data by location and type of office furniture; For each category, the average value of its historical noise data is calculated as the benchmark value; According to the collected data, the number of rows and columns of the matrix is ​​set; the number of rows corresponds to different office furniture position identification parameters, and the number of columns corresponds to different office furniture type identification parameters; Fill the benchmark value of each classification into the corresponding row and column intersections of the matrix to obtain the office environment noise benchmark matrix; each intersection represents the benchmark noise decibel value under the corresponding office furniture location identification parameters and office furniture type identification parameters.

[0009] Furthermore, the method for obtaining the office environment noise difference space matrix includes: For each office furniture location and type, subtract the baseline noise decibel value from the real-time noise decibel value to obtain the noise difference value; According to the actual layout of the office area, the entire space is divided into multiple small cells; each cell corresponds to a specific office furniture position; Create a matrix whose number of rows and columns corresponds to the partitioning of space in different dimensions; Fill each noise difference into the corresponding row and column intersection to obtain the office environment noise difference space matrix.

[0010] Furthermore, the method for constructing the office environment noise evaluation model includes: Collect historical office environment noise monitoring data, including noise data at different times, locations, and types of office furniture; Preprocess the collected data, including data cleaning, denoising and standardization; Extract the features related to noise evaluation from the preprocessed data and select the key features that have the greatest impact on noise evaluation; Choose deep learning algorithms as the basis of the model, including support vector machines, neural networks, and random forests; Divide the extracted key features into a training data set and a validation data set; Use the training data set to train the model and optimize the model parameters through cross-validation and grid search methods; Use the validation dataset to validate the trained model; Adjust and optimize the model based on the validation results; The office environment noise evaluation model is deployed in practical applications to obtain the office environment noise health score.

[0011] Furthermore, factors affecting the setting of the preset office environment noise health threshold include hearing protection, mental health, space layout, personnel density, employee feedback, sensor accuracy and data processing capabilities.

[0012] On the other hand, the present application also provides an environmental health monitoring system based on office furniture noise detection, the system comprising: Real-time data acquisition module, which collects real-time noise monitoring data collected by various noise sensors in real time; A feature extraction module, for each piece of real-time noise monitoring data, identifies and extracts key features in the real-time noise monitoring data, and obtains office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; A space vector construction module converts the office furniture position information and the office furniture type into numerical values, and combines the data to obtain an office furniture space vector; the office furniture space vector includes an office furniture position identification parameter and an office furniture type identification parameter; A reference noise positioning module, which uses the office furniture space vector as an index address, locates the office furniture in a preset office environment noise reference matrix, and extracts a reference noise decibel value corresponding to the office furniture space vector; The matrix construction module calculates the noise difference between the real-time noise decibel value and the reference noise decibel value corresponding to each piece of office furniture, and based on the preset spatial distribution rules, performs data array on multiple noise differences to obtain the office environment noise difference spatial matrix; An office environment noise evaluation module, which inputs the office environment noise difference space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score; The determination module compares the office environment noise health score with a preset office environment noise health threshold. If the office environment noise health score is higher than the preset office environment noise health threshold, it is determined that the noise level of the office environment at this time is qualified; if the office environment noise health score is not higher than the preset office environment noise health threshold, it is determined that the noise level of the office environment at this time is unqualified.

[0013] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: by integrating sensors into office furniture, it achieves extensive coverage of different locations within the office area; it can capture more comprehensive and detailed noise data, including those corners and areas that are difficult to reach with traditional fixed-point monitoring equipment; The feature extraction step enables the system to identify and record the key attributes of each noise event, thereby improving the accuracy of noise source location and noise level assessment; using the preset office environment noise benchmark matrix as a reference, it can effectively compare the difference between real-time noise and expected standards, help identify abnormal noise conditions, and provide a basis for noise control strategies; With the help of the office environment noise difference space matrix and the preset evaluation model, the noise health score of the office environment can be automatically generated, which simplifies the noise management process and provides objective data support; the judgment module can quickly determine whether the current noise condition meets the health standard, and issue warnings or suggest actions in time, which helps to quickly respond to noise exceeding the standard and protect employee health; The detailed noise data analysis provided by the system can help managers gain a deeper understanding of the noise sources and their impacts, and then formulate effective improvement measures to continuously improve the quality of the office environment; Effectively managing and reducing noise pollution helps create a quieter, more comfortable working environment, which not only directly improves employee productivity, but also enhances their job satisfaction and loyalty; In summary, this method can not only provide more accurate and detailed noise monitoring results, but also effectively assist enterprises and organizations to optimize the office environment and protect the physical and mental health of employees. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the present invention; Figure 2 It is a flowchart of the method for constructing the office environment noise benchmark matrix; Figure 3 It is the structural diagram of the environmental health monitoring system based on office furniture noise detection. DETAILED DESCRIPTION

[0017] In the description of this application, those skilled in the art should know that this application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, this application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0018] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0019] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0020] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0021] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0022] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0023] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0024] The present application is described below in conjunction with the drawings in the present application.

[0025] Embodiment 1: Figure 1 to Figure 2 As shown, the environmental health monitoring method based on office furniture noise detection of the present invention specifically includes the following steps: S1, real-time collection of noise monitoring data collected by each noise sensor; Choose noise sensors with high sensitivity and wide frequency response range to ensure that they can accurately capture noise signals of different frequencies; arrange sensors according to the layout of the office area and the expected location of noise sources; Use wireless communication technologies such as low-power wide area network, Wi-Fi, ZigBee, Bluetooth, etc. to connect sensors to the central processing system to ensure real-time and stable data transmission; ensure that the wireless network covers the entire office area to avoid signal blind spots that affect the integrity of data collection; Set the collection frequency to reflect the noise changes without generating too much redundant data. For office environments, collecting data every few seconds can meet the needs. Use local or cloud databases to store the collected data to ensure data security and accessibility. Before sending the data to the central system, preliminary data filtering and compression are performed at the sensor end to remove outliers and reduce the transmission burden; each set of collected data is accurately time-stamped so that it can be correctly sorted and associated in subsequent analysis; Make sure the chosen solution has good compatibility, can easily integrate with the existing IT infrastructure, and support the addition of new devices or the update of existing devices in the future.

[0026] In this step, the selection of noise sensors with high sensitivity and wide frequency response range ensures that noise signals of different frequencies in the office environment can be captured, thus improving the accuracy and reliability of noise monitoring. The use of wireless communication technologies such as low-power wide area networks, Wi-Fi, ZigBee, and Bluetooth not only achieves a stable connection between the sensor and the central processing system, but also ensures the real-time data transmission, while reducing energy consumption and extending the working life of the sensor. The sensors are arranged reasonably and the wireless network is ensured to cover the entire office area, avoiding signal blind spots, making noise monitoring more comprehensive and providing a complete data basis for subsequent analysis. Reduce data redundancy and improve response speed: set a reasonable sampling rate to reduce unnecessary The amount of data saved not only saves storage space but also speeds up data processing; local or cloud database storage is used, combined with encryption technology and privacy protection measures to ensure data security; at the same time, it is convenient to call and analyze historical data at any time to support decision-making; preliminary data filtering and compression are performed on the sensor side to remove outliers, improve data quality, and thus improve the credibility of subsequent analysis results; adding time tags to each set of data helps track noise change trends, facilitates long-term series analysis, and further understands the laws of noise generation and its impact on employees; the selected solution is easy to integrate with existing IT infrastructure, supports the addition or update of future equipment, and ensures that the system can continue to evolve with the development of office environment and technology.

[0027] S2. For each piece of real-time noise monitoring data, identify and extract key features in the real-time noise monitoring data, and obtain office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; The method for identifying and extracting key features in the real-time noise monitoring data comprises: Receive real-time data from each noise sensor, including the original noise measurement value and time stamp information; Clean the received data to remove outliers, duplicate values ​​and invalid data to ensure the accuracy and reliability of the data; Identify and extract key features from each piece of real-time noise monitoring data, including the location information and type of office furniture; Use metadata or location identification technology attached to the sensor to determine the specific location of the noise sensor; Compare this location information with the layout of the office area to determine the location of the office furniture corresponding to the sensor; Using the metadata attached to the sensor, determine the type of office furniture where the noise sensor is located; Extract noise decibel values ​​directly from real-time noise monitoring data.

[0028] In this step, the received data is cleaned to remove outliers, duplicate values ​​and invalid data, thereby ensuring the accuracy and reliability of the data relied on for subsequent analysis; not only the credibility of the final result is improved, but also the misjudgment caused by erroneous data is reduced; the metadata attached to the sensor or advanced location recognition technology can be used to accurately locate the specific location of each noise sensor, and then the noise source can be more accurately associated with the specific office furniture; the location information of the sensor is compared with the layout map of the office area to determine the location of the office furniture corresponding to the sensor, and the type of office furniture can be further confirmed in combination with the metadata attached to the sensor; the source of noise is made clearer and provides a basis for targeted noise reduction measures; the noise decibel value is directly extracted from the real-time noise monitoring data, which simplifies the data processing process and ensures the immediacy and accuracy of the noise intensity measurement; the effective implementation of step S2 provides technical support for creating a quieter and healthier office environment; it helps identify and solve noise problems, thereby improving employee work efficiency and mental health.

[0029] S3, converting the office furniture location information and the office furniture type into numerical values, and combining the data to obtain an office furniture space vector; the office furniture space vector includes an office furniture location identification parameter and an office furniture type identification parameter; The method for obtaining the office furniture space vector comprises: In an office environment, establish a coordinate system to describe the location of each piece of office furniture; For each sensor with a known position, a position identification parameter is assigned according to its coordinate value relative to the reference point; this is done by directly reading the sensor's own positioning information, or manually inputting it through a pre-drawn office area layout map; Each type of office furniture is assigned a type identification parameter; if there are multiple different models of the same type of office furniture, the code can be further subdivided to distinguish the differences between these specific models; The location identification parameters and the office furniture type identification parameters are combined to obtain a multi-dimensional vector that comprehensively describes the specific office furniture, namely, the office furniture space vector.

[0030] In this step, by introducing a coordinate system to accurately describe the location of each piece of office furniture and assigning a unique location identification parameter, the noise source can be located more accurately. This helps identify which specific locations or types of office furniture are the main noise contributors, thereby providing a basis for taking targeted measures. The introduction of office furniture type identification parameters enables different types of office furniture to be treated differently. This not only increases the richness of the data, but also supports more complex data analysis. The location identification parameters and office furniture type identification parameters are combined into a multidimensional vector that comprehensively describes specific office furniture, simplifying the subsequent data processing process. As an index address, it can be directly used to quickly access the preset office environment noise benchmark matrix, improving the speed and efficiency of data query. The numerical office furniture space vector provides a solid foundation for realizing automated and intelligent noise management and optimization. This method is easy to adapt to office environments of different sizes and a variety of office furniture types. Whether it is a small office or a large enterprise park, it can be easily expanded by simply adjusting the coordinate system and type coding rules according to actual conditions. Accurate space vectors help managers better understand the noise distribution in the office environment, so as to make more scientific and reasonable decisions. The unified numerical standard ensures that all office furniture is described under the same framework, enhancing the consistency and comparability between data.

[0031] S4, using the office furniture space vector as an index address, locating it in a preset office environment noise reference matrix, and extracting the reference noise decibel value corresponding to the office furniture space vector; in the office environment noise reference matrix, each row represents a different office furniture position identification parameter, each column represents a different office furniture type identification parameter, and the intersection of the row and the column represents the reference noise decibel value under the corresponding office furniture position identification parameter and office furniture type identification parameter; The method for constructing the office environment noise reference matrix comprises: Collect historical noise data of the office environment; historical noise data comes from fixed noise monitoring equipment or other temporary noise monitoring activities; clean the collected data to remove invalid, abnormal and duplicate data; Categorize the cleaned data by location and type of office furniture; For each category, calculate the average of its historical noise data as a baseline value; the average represents the noise level of office furniture under ideal or standard conditions in that location and type; According to the collected data, the number of rows and columns of the matrix is ​​set; the number of rows corresponds to different office furniture position identification parameters, and the number of columns corresponds to different office furniture type identification parameters; Fill the benchmark value of each classification into the corresponding row and column intersections of the matrix to obtain the office environment noise benchmark matrix; each intersection represents the benchmark noise decibel value under the corresponding office furniture location identification parameters and office furniture type identification parameters.

[0032] In this step, by collecting historical noise data and classifying them according to the location and type of office furniture, comprehensive coverage of the noise levels of office furniture in different locations and types in the entire office environment is ensured; the method of cleaning the data and calculating the average value as the benchmark value improves the accuracy of the benchmark noise decibel value and reflects the noise level under ideal or standard conditions; the constructed benchmark matrix acts as a fast lookup table, allowing the system to quickly locate the benchmark noise decibel value corresponding to any given office furniture space vector; the response speed and efficiency of the real-time noise monitoring system are greatly improved, and the calculation complexity is reduced; the office environment noise benchmark matrix provides managers with an intuitive and quantitative tool to evaluate the noise conditions of the office environment; by comparing the actual measured values ​​with the benchmark values, it is easier to identify abnormal noise sources, so as to take targeted measures to improve the working environment; building an exclusive benchmark matrix through historical data of specific places can better meet the needs of various office scenarios; accurate noise monitoring and effective management can help reduce unnecessary noise interference, thereby improving employees' work efficiency and mental health, helping to protect employees' physical health and reduce the risk of diseases caused by noise.

[0033] S5, calculating the noise difference between the real-time noise decibel value and the reference noise decibel value corresponding to each piece of office furniture, and based on a preset spatial distribution rule, performing data array on multiple noise differences to obtain a spatial matrix of office environment noise differences; The method for obtaining the office environment noise difference space matrix comprises: For each office furniture position and type, the noise difference value is obtained by subtracting the baseline noise decibel value from the real-time noise decibel value. The noise difference value represents the change in the noise level of a specific office furniture in the current environment relative to the ideal or standard state. According to the actual layout of the office area, the entire space is divided into multiple small cells; each cell corresponds to a specific office furniture position; Using the office furniture space vector as an index, ensure that each noise difference can be correctly mapped to the corresponding spatial position; Create a matrix whose number of rows and columns correspond to the spatial divisions in different dimensions; then, fill each noise difference into the corresponding row and column intersection to obtain the office environment noise difference spatial matrix; The preset spatial distribution rules include: Spatial continuity: The spatial distribution of noise differences should have a certain degree of continuity, that is, the noise differences between adjacent office furniture should not have too large jumps; this helps identify hot spots and potential propagation paths of noise pollution; Correlation between furniture type and location: Consider the impact of office furniture type and location on noise distribution. Different types of office furniture have different noise absorption, reflection and transmission characteristics due to their different structures and uses. Therefore, when calculating the noise difference, the correlation between furniture type and location needs to be considered. Relationship between human activity density and noise level: Consider the impact of human activity density on noise level; in areas with dense human activity, the noise level is usually higher; while in areas with less human activity, the noise level is relatively lower; therefore, when calculating the noise difference, it is necessary to refer to the distribution of human activity density; Consideration of environmental background noise: Consider the impact of environmental background noise on the measurement results; environmental background noise includes noise from external sources such as traffic, weather conditions, and construction equipment; when calculating the noise difference, the impact of background noise should be eliminated or corrected from the real-time noise monitoring data to ensure the accuracy of the results; Normalization of data arrays: Specify the format and standard of the noise difference data array; the data array should be arranged and organized in a unified format for subsequent analysis and processing; at the same time, the data in the array needs to be normalized to eliminate differences between different sensors or measurement conditions.

[0034] In this step, by comparing the real-time noise level of each office furniture position with the preset baseline value, it is possible to accurately identify which areas or types of office furniture generate abnormal noise; the preset spatial distribution rules ensure the continuity of noise differences in space, which helps to identify hot spots of noise pollution and potential noise propagation paths; understanding how different types of office furniture affect noise distribution can help optimize the design and layout of the office; choosing more effective sound-absorbing materials or rearranging the location of high-noise equipment to create a quieter and more comfortable working environment; the office environment noise difference spatial matrix provides intuitive data support for management to make wise decisions about noise management and control; at the same time, it also provides a basis for long-term planning and improvement, which helps to continuously improve the quality of the office environment; accurate noise monitoring and management can reduce unnecessary noise interference, improve the psychological state of employees, and thus improve work efficiency and satisfaction; it can also prevent physical health problems caused by long-term exposure to adverse noise environments; the noise difference data array is standardized to ensure the consistency and comparability of the data, which is conducive to subsequent analysis and processing.

[0035] S6. Inputting the office environment noise difference space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score; The method for constructing the office environment noise evaluation model comprises: Collect a large amount of office environment noise monitoring data, including noise data at different times, locations, and types of office furniture; Pre-process the collected data, including data cleaning, denoising and standardization, to ensure data quality and consistency; Extract features related to noise evaluation from the preprocessed data, such as noise difference, furniture type, location information, human activity density, etc. Through feature selection methods, the key features that have the greatest impact on noise evaluation are screened out to improve the accuracy and efficiency of the model; Choose deep learning algorithms as the basis of the model, including support vector machines, neural networks, and random forests; Divide the extracted key features into a training data set and a validation data set; Use the training data set to train the model and optimize the model parameters through cross-validation, grid search and other methods to improve the generalization ability of the model; Use the validation data set to validate the trained model and evaluate its accuracy, stability and other indicators; Adjust and optimize the model based on the verification results to ensure its reliability in practical applications; The office environment noise evaluation model is deployed in practical applications to obtain the office environment noise health score.

[0036] In this step, by collecting a large amount of noise monitoring data at different times and locations and preprocessing to ensure data quality and consistency, the model can more accurately reflect the noise conditions in the actual office environment; the application of feature extraction and selection methods further improves the accuracy of the model; the model is trained based on historical data, which can provide a scientific basis to judge the effectiveness of existing noise control measures and guide future work environment optimization strategies; managers can quickly identify problem areas based on health scores and take corresponding improvement measures; the use of deep learning algorithms and optimization of model parameters through methods such as cross-validation and grid search ensures that the model not only has a good fit for the training data, but also can effectively respond to new data that has never been seen, thereby enhancing its generalization ability; accurate noise monitoring and management can reduce unnecessary noise interference, improve the psychological state of employees, and thus improve work efficiency and satisfaction; in the long run, it can also prevent long-term exposure to adverse The model allows flexible adjustment of parameter settings according to the characteristics of the specific office environment, ensuring that the solution is suitable for offices of all sizes and types; it helps to achieve more personalized noise management and optimization; after the model is deployed in actual applications, it can realize automated processing processes, simplifying system integration and maintenance; at the same time, as new data continues to flow in, the model can be dynamically updated to maintain the timeliness and accuracy of the results, and support real-time monitoring and feedback mechanisms; the construction and application of the model provides a basis for continuous improvement for management; by regularly re-evaluating the noise conditions of the entire office environment, noise management and control strategies can be continuously optimized based on the latest data and analysis results to ensure that the office environment is always in the best condition; ultimately, the application of this step is crucial to creating a healthier and more comfortable working environment, helping to improve the overall well-being of employees and reduce the long-term negative impact of noise pollution on human health.

[0037] S7, comparing the office environment noise health score with a preset office environment noise health threshold, if the office environment noise health score is higher than the preset office environment noise health threshold, then determining that the noise level of the office environment at this time is qualified; if the office environment noise health score is not higher than the preset office environment noise health threshold, then determining that the noise level of the office environment at this time is unqualified; The factors affecting the setting of the preset office environment noise health threshold include: Hearing protection: Long-term exposure to high noise environments may cause hearing damage, so you need to ensure that the noise level does not exceed the hearing protection standard; Mental health: Continuous noise disturbance may cause psychological problems such as stress and anxiety, and the impact of these factors on employees' emotions needs to be considered; Space layout: Different types of office spaces have different noise tolerances; for example, open office areas generally allow for higher background noise, while work areas that require concentration require lower noise levels. Density of people: In a high-density office environment, the frequency of communication between people increases, which may generate more noise; on the contrary, a low-density environment is relatively quiet; Employee feedback: Through questionnaires or direct communication, employees’ feelings and suggestions on the current noise situation can be understood, which can serve as an important basis for setting thresholds; Sensor accuracy: The technical performance of the existing noise sensor determines the measurement accuracy and stability that can be achieved; if the sensor itself has large errors, a more relaxed threshold needs to be set to accommodate this uncertainty; Data processing capabilities: The overall performance of the system, including model computing power and real-time response speed, will also affect the choice of thresholds; too strict thresholds may lead to frequent alarms, while too loose thresholds may miss real problems.

[0038] In this step, by setting a reasonable noise threshold, we can effectively prevent hearing damage caused by long-term exposure to high noise environments and protect the physical health of employees; considering the negative impact of continuous noise on mental health can help create a more comfortable working environment with less psychological burden; setting different noise tolerances according to different types of office spaces can better meet various work needs and improve overall office efficiency; considering the impact of office environments of different densities on noise levels, the evaluation is more in line with the actual situation and promotes the reasonable planning of office areas; incorporating employees' actual feelings and suggestions makes the threshold setting closer to user needs and enhances employee participation and satisfaction; setting thresholds based on historical data and technical performance ensures the objectivity and reliability of the evaluation results, provides management with a scientific basis to judge the effectiveness of existing noise control measures, and guides future improvement strategies; fully considers The measurement error under the existing technical conditions is set with an appropriate threshold range to ensure the stability and accuracy of the system; the threshold is adjusted according to the computing power and real-time response speed of the system to avoid the problem of frequent alarms caused by too strict thresholds or problems missed due to too loose thresholds, thereby improving the practicality of the system and user experience; it allows flexible adjustment of parameter settings according to the characteristics of the specific office environment to ensure that the solution can be applied to offices of various sizes and types, supporting long-term continuous improvement; the model deployed in actual applications can realize automated processing procedures, simplify system integration and maintenance work, and support real-time monitoring and feedback mechanisms to detect problems in a timely manner and take corresponding measures; accurate noise monitoring and management can reduce unnecessary noise interference, improve the psychological state of employees, and thus improve work efficiency and satisfaction; in the long run, it can also prevent physical health problems caused by long-term exposure to adverse noise environments.

[0039] Embodiment 2: Figure 3 As shown, the environmental health monitoring system based on office furniture noise detection of the present invention specifically includes the following modules: Real-time data acquisition module, which collects real-time noise monitoring data collected by various noise sensors in real time; A feature extraction module, for each piece of real-time noise monitoring data, identifies and extracts key features in the real-time noise monitoring data, and obtains office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; A space vector construction module converts the office furniture position information and the office furniture type into numerical values, and combines the data to obtain an office furniture space vector; the office furniture space vector includes an office furniture position identification parameter and an office furniture type identification parameter; A reference noise positioning module, which uses the office furniture space vector as an index address, locates the office furniture in a preset office environment noise reference matrix, and extracts a reference noise decibel value corresponding to the office furniture space vector; The matrix construction module calculates the noise difference between the real-time noise decibel value and the reference noise decibel value corresponding to each piece of office furniture, and based on the preset spatial distribution rules, performs data array on multiple noise differences to obtain the office environment noise difference spatial matrix; An office environment noise evaluation module, which inputs the office environment noise difference space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score; The determination module compares the office environment noise health score with a preset office environment noise health threshold. If the office environment noise health score is higher than the preset office environment noise health threshold, it is determined that the noise level of the office environment at this time is qualified; if the office environment noise health score is not higher than the preset office environment noise health threshold, it is determined that the noise level of the office environment at this time is unqualified.

[0040] By installing noise sensors on different types of office furniture, the system can cover the entire office area more widely, capture noise data generated by more locations and activities, and provide a more comprehensive assessment of noise conditions; The feature extraction module can identify and extract key features, allowing the system to more accurately reflect the noise level at a specific location and time point, providing a scientific basis for noise management; The spatial vector building module and the baseline noise positioning module allow the system to adjust its evaluation criteria according to different office furniture locations and types, thus better adapting to changes in various office environments; The matrix construction module forms a spatial matrix of office environment noise differences by performing data array on multiple noise differences, which helps to quickly and intuitively understand the noise distribution of the entire office area; The office environment noise evaluation module analyzes the noise data in combination with the preset model to generate a health score, and compares it with the preset threshold through the judgment module to promptly give a qualified judgment to help managers make decisions quickly; The ultimate goal is to improve the quality of the office environment, ensure that employees work at a healthy noise level, improve work efficiency and mental health, and reduce the health risks caused by long-term noise exposure; the detailed noise data analysis provided by the system can help companies identify the source of noise and take targeted measures to reduce noise; In summary, this system can not only provide more accurate and detailed noise monitoring results, but also effectively assist enterprises and organizations to optimize the office environment and protect the physical and mental health of employees.

[0041] The various variations and specific embodiments of the environmental health monitoring method based on office furniture noise detection in the aforementioned embodiment 1 are also applicable to the environmental health monitoring system based on office furniture noise detection in this embodiment. Through the aforementioned detailed description of the environmental health monitoring method based on office furniture noise detection, those skilled in the art can clearly understand the implementation method of the environmental health monitoring system based on office furniture noise detection in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0042] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0043] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An environmental health monitoring method based on office furniture noise detection, characterized in that: The method is applied to noise environment monitoring in an office area, where office furniture in the office area is equipped with noise sensors, and the method comprises: Collect real-time noise monitoring data collected by various noise sensors in real time; For each piece of real-time noise monitoring data, identify and extract key features in the real-time noise monitoring data, and obtain office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; The office furniture location information and the office furniture type are converted into numerical values, and the data are combined to obtain an office furniture space vector; the office furniture space vector includes an office furniture location identification parameter and an office furniture type identification parameter; Using the office furniture space vector as an index address, locating it in a preset office environment noise reference matrix, and extracting the reference noise decibel value corresponding to the office furniture space vector; Calculate the noise difference between the real-time noise decibel value and the reference noise decibel value corresponding to each piece of office furniture, and based on the preset spatial distribution rules, perform data array on multiple noise differences to obtain a spatial matrix of office environment noise differences; Inputting the office environment noise difference space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score; The office environment noise health score is compared with the preset office environment noise health threshold. If the office environment noise health score is higher than the preset office environment noise health threshold, the noise level of the office environment at this time is determined to be qualified; if the office environment noise health score is not higher than the preset office environment noise health threshold, the noise level of the office environment at this time is determined to be unqualified.

2. The environmental health monitoring method based on office furniture noise detection according to claim 1, characterized in that: The method for identifying and extracting key features in the real-time noise monitoring data comprises: Receive real-time data from various noise sensors; Clean the received data to remove outliers, duplicate values, and invalid data; Identify and extract key features from each piece of real-time data; Using the metadata attached to the sensor, determine the specific location of the noise sensor; Compare this location information with the layout of the office area to determine the location of the office furniture corresponding to the sensor; Using the metadata attached to the sensor, determine the type of office furniture where the noise sensor is located; Extract noise decibel values ​​directly from real-time data.

3. The environmental health monitoring method based on office furniture noise detection according to claim 1, characterized in that: The method for obtaining the office furniture space vector comprises: In an office environment, a coordinate system is established to describe the location of each piece of office furniture; For each sensor with a known position, a position identification parameter is assigned according to its coordinate value relative to the reference point; Each type of office furniture is assigned a type identification parameter; The location identification parameters and the office furniture type identification parameters are combined to obtain a multi-dimensional vector that comprehensively describes the specific office furniture, namely, the office furniture space vector.

4. The environmental health monitoring method based on office furniture noise detection according to claim 1, characterized in that: The method for constructing the office environment noise reference matrix comprises: Collect historical noise data of the office environment; clean the collected data to remove invalid, abnormal and duplicate data; Categorize the cleaned data by location and type of office furniture; For each category, the average value of its historical noise data is calculated as the benchmark value; According to the collected data, the number of rows and columns of the matrix is ​​set; the number of rows corresponds to different office furniture position identification parameters, and the number of columns corresponds to different office furniture type identification parameters; Fill the benchmark value of each category into the corresponding row and column intersections of the matrix to obtain the office environment noise benchmark matrix; each intersection represents the benchmark noise decibel value under the corresponding office furniture location identification parameters and office furniture type identification parameters.

5. The environmental health monitoring method based on office furniture noise detection according to claim 1, characterized in that: The method for obtaining the office environment noise difference space matrix comprises: For each office furniture location and type, subtract the baseline noise decibel value from the real-time noise decibel value to obtain the noise difference value; According to the actual layout of the office area, the entire space is divided into multiple small cells; each cell corresponds to a specific office furniture position; Create a matrix whose number of rows and columns corresponds to the partitioning of space in different dimensions; Fill each noise difference into the corresponding row and column intersection to obtain the office environment noise difference space matrix.

6. The environmental health monitoring method based on office furniture noise detection according to claim 1, characterized in that: The method for constructing the office environment noise evaluation model comprises: Collect historical office environment noise monitoring data, including noise data at different times, locations, and types of office furniture; Preprocess the collected data, including data cleaning, denoising and standardization; Extract the features related to noise evaluation from the preprocessed data and select the key features that have the greatest impact on noise evaluation; Choose deep learning algorithms as the basis of the model, including support vector machines, neural networks, and random forests; Divide the extracted key features into a training data set and a validation data set; Use the training data set to train the model and optimize the model parameters through cross-validation and grid search methods; Use the validation dataset to validate the trained model; Adjust and optimize the model based on the validation results; The office environment noise evaluation model is deployed in practical applications to obtain the office environment noise health score.

7. The environmental health monitoring method based on office furniture noise detection according to claim 1, characterized in that: The factors affecting the setting of the preset office environment noise health threshold include hearing protection, mental health, space layout, personnel density, employee feedback, sensor accuracy and data processing capabilities.

8. An environmental health monitoring system based on office furniture noise detection, characterized in that: The system comprises: Real-time data acquisition module, which collects real-time noise monitoring data collected by various noise sensors in real time; A feature extraction module, for each piece of real-time noise monitoring data, identifies and extracts key features in the real-time noise monitoring data, and obtains office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; A space vector construction module converts the office furniture location information and the office furniture type into numerical values, and combines the data to obtain an office furniture space vector; the office furniture space vector includes an office furniture location identification parameter and an office furniture type identification parameter; A reference noise positioning module, which uses the office furniture space vector as an index address, locates the office furniture in a preset office environment noise reference matrix, and extracts a reference noise decibel value corresponding to the office furniture space vector; The matrix construction module calculates the noise difference between the real-time noise decibel value and the reference noise decibel value corresponding to each piece of office furniture, and based on the preset spatial distribution rules, performs data array on multiple noise differences to obtain the office environment noise difference spatial matrix; An office environment noise evaluation module, which inputs the office environment noise difference space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score; The determination module compares the office environment noise health score with a preset office environment noise health threshold. If the office environment noise health score is higher than the preset office environment noise health threshold, it is determined that the noise level of the office environment at this time is qualified; if the office environment noise health score is not higher than the preset office environment noise health threshold, it is determined that the noise level of the office environment at this time is unqualified.

9. An environmental health monitoring electronic device based on office furniture noise detection, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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