Office furniture noise detection-based environmental health monitoring method and system

Abstract

The application 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 accurate and detailed noise monitoring results, optimize the office environment, and guarantee the physical and mental health of employees; the method is applied to noise environment monitoring of an office area, noise sensors are arranged on office furniture in the office area, 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, identifying and extracting key features in the real-time noise monitoring data, obtaining office furniture position information corresponding to the noise sensor, an office furniture type and a real-time noise decibel value; performing numerical conversion on the office furniture position information and the office furniture type, combining data, and obtaining 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 application relates to the technical field of office environment monitoring, and in particular to an environment health monitoring method and system based on office furniture noise detection. BACKGROUND

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

[0003] The existing office environment noise monitoring method mainly relies on fixed noise monitoring equipment, which is usually installed in a specific location and cannot comprehensively cover the entire office area. Due to the large difference in noise level generated by different positions, different types of office furniture and activities, relying on the measurement data of a single or a few fixed points cannot accurately assess the overall noise condition of the office environment and its impact on employees. SUMMARY

[0004] To solve the above technical problems, the present application provides an environment 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 application provides an environment health monitoring method based on office furniture noise detection, which is applied to noise environment monitoring in an office area. The office furniture in the office area is equipped with a noise sensor. The method comprises:

[0006] Real-time noise monitoring data collected by each noise sensor is collected in real time;

[0007] For each real-time noise monitoring data, key features in the real-time noise monitoring data are identified and extracted to obtain office furniture position information, office furniture type and real-time noise decibel value corresponding to the noise sensor;

[0008] The office furniture position information and the office furniture type are converted into numerical values and combined to obtain an office furniture space vector. The office furniture space vector includes office furniture position identification parameters and office furniture type identification parameters;

[0009] The office furniture space vector is used as an index address to locate a pre-set office environment noise benchmark matrix and extract a benchmark noise decibel value corresponding to the office furniture space vector;

[0010] Calculate the noise difference value between the real-time noise decibel value and the reference noise decibel value corresponding to each office furniture, and based on the preset space distribution rule, array the plurality of noise difference values to obtain an office environment noise difference value space matrix;

[0011] Input the office environment noise difference value space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score;

[0012] Compare the office environment noise health score with a preset office environment noise health threshold value. If the office environment noise health score is higher than the preset office environment noise health threshold value, 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 value, it is determined that the noise level of the office environment at this time is unqualified.

[0013] Further, the method for identifying and extracting key features in the real-time noise monitoring data comprises:

[0014] Receiving real-time data from each noise sensor;

[0015] Cleaning the received data to remove outliers, repeated values and invalid data;

[0016] Identify and extract key features from each real-time data;

[0017] Using the metadata attached to the sensor to determine the specific location of the noise sensor;

[0018] Compare this position information with the layout map of the office area to determine the office furniture position corresponding to the sensor;

[0019] Using the metadata attached to the sensor to determine the type of office furniture at the location of the noise sensor;

[0020] Directly extract the noise decibel value from the real-time data.

[0021] Further, the method for obtaining the office furniture space vector comprises:

[0022] In the office environment, a coordinate system is established to describe the position of each office furniture;

[0023] For each sensor with a known position, a position identification parameter is assigned according to its coordinate value relative to the reference point;

[0024] Each type of office furniture is assigned a type identification parameter;

[0025] Combine the position identification parameter and the office furniture type identification parameter to obtain a multi-dimensional vector that comprehensively describes a specific office furniture, i.e. an office furniture space vector.

[0026] Further, the method for constructing the office environment noise benchmark matrix comprises:

[0027] Collect historical noise data of the office environment; clean the collected data to remove invalid, abnormal and repeated data;

[0028] Classify the cleaned data according to the positions and types of office furniture;

[0029] For each classification, calculate the average value of its historical noise data as a benchmark value;

[0030] According to the collected data, set the number of rows and columns of the matrix; 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;

[0031] Fill the benchmark value of each classification into the corresponding row-column intersection point of the matrix to obtain the office environment noise benchmark matrix; each intersection point represents the benchmark noise decibel value under the corresponding office furniture position identification parameter and office furniture type identification parameter.

[0032] Further, the method for obtaining the office environment noise difference value space matrix comprises:

[0033] For each office furniture position and type, subtract the benchmark noise decibel value from the real-time noise decibel value to obtain the noise difference value;

[0034] According to the actual layout of the office area, divide the whole space into a plurality of small unit cells; each unit cell corresponds to a specific office furniture position;

[0035] Create a matrix whose number of rows and columns correspond to the space division in different dimensions;

[0036] Fill each noise difference value into the corresponding row-column intersection point to obtain the office environment noise difference value space matrix.

[0037] Further, the method for constructing the office environment noise evaluation model comprises:

[0038] Collect historical office environment noise monitoring data, including noise data of different times, different positions and different types of office furniture;

[0039] Preprocess the collected data, including data cleaning, denoising and standardization;

[0040] Extract features related to noise evaluation from the preprocessed data, and select the key features that have the greatest impact on noise evaluation;

[0041] A deep learning algorithm is selected as the basis of the model, including support vector machines, neural networks, and random forests;

[0042] The extracted key features are divided into a training data set and a validation data set;

[0043] The model is trained using the training data set, and the model parameters are optimized through cross-validation and grid search methods;

[0044] The trained model is validated using the validation data set;

[0045] The model is adjusted and optimized based on the validation results;

[0046] The office environment noise evaluation model is deployed in practical applications to obtain office environment noise health scores.

[0047] Further, 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 capacity.

[0048] On the other hand, the present application also provides an environmental health monitoring system based on office furniture noise detection, which comprises:

[0049] A real-time data acquisition module that collects real-time noise monitoring data collected by each noise sensor in real time;

[0050] A feature extraction module that identifies and extracts key features from each real-time noise monitoring data, obtains office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor;

[0051] A space vector construction module that 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 office furniture location identification parameters and office furniture type identification parameters;

[0052] A reference noise positioning module that uses the office furniture space vector as an index address to locate a pre-set office environment noise reference matrix and extract the reference noise decibel value corresponding to the office furniture space vector;

[0053] A matrix construction module that calculates the noise difference value between the real-time noise decibel value and the reference noise decibel value corresponding to each office furniture, and arranges multiple noise difference values in a data array based on a pre-set space distribution rule to obtain an office environment noise difference value space matrix;

[0054] The office environment noise assessment module inputs the office environment noise difference space matrix into a preset office environment noise assessment model to obtain an office environment noise health score.

[0055] The judgment 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, the noise level of the office environment is determined to be acceptable. 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 is determined to be unacceptable.

[0056] Thirdly, this application 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 connected via the bus, and the computer program, when executed by the processor, implements the steps of any of the methods described above.

[0057] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows: by integrating sensors into office furniture, extensive coverage of different locations in the office area is achieved; more comprehensive and detailed noise data can be captured, including corners and areas that are difficult to reach by traditional fixed-point monitoring equipment;

[0059] 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 localization and noise level assessment. Using a preset office environment noise benchmark matrix as a reference, the system can effectively compare the difference between real-time noise and expected standards, help identify abnormal noise situations, and provide a basis for noise control strategies.

[0060] By leveraging the office environment noise difference space matrix and a preset evaluation model, the noise health score of the office environment can be automatically generated, simplifying the noise management process and providing objective data support. The judgment module can quickly determine whether the current noise level meets health standards and issue timely warnings or suggestions for action, which helps to respond quickly to situations where noise exceeds the standard and protect employee health.

[0061] The detailed noise data analysis provided by the system can help managers gain a deeper understanding of the sources of noise and their impact, and then formulate effective improvement measures to continuously improve the quality of the office environment;

[0062] Through effective management and noise pollution reduction, it helps to create a more quiet and comfortable working environment, which not only can directly improve the work efficiency of employees, but also can enhance their job satisfaction and loyalty;

[0063] In summary, this method can not only provide more accurate and detailed noise monitoring results, but also effectively assist enterprises and organizations in optimizing office environment and protecting the physical and mental health of employees. BRIEF DESCRIPTION OF DRAWINGS

[0064] Fig. 1 is a flowchart of the present application;

[0065] Fig. 2 is a flowchart of the construction method of the office environment noise benchmark matrix;

[0066] Fig. 3 is a structural diagram of the environmental health monitoring system based on office furniture noise detection. DETAILED DESCRIPTION

[0067] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, device, electronic device and computer readable storage medium. Therefore, the present application can be specifically implemented as the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), hardware and software combined form. In addition, in some embodiments, the present application can also be implemented as a computer program product in one or more computer readable storage media, which contains computer program code.

[0068] The above computer readable storage medium can adopt any combination of one or more computer readable storage media. The computer readable storage medium includes: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination thereof. More specific examples of computer readable storage medium include: portable computer disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, compact disk read-only memory, optical storage device, magnetic storage device or any combination thereof. In this application, the computer readable storage medium can be any tangible medium containing or storing programs, which can be used or combined with instruction execution system, device or device.

[0069] The acquisition, storage, use, processing and other data in the technical solution of the present application comply with the relevant provisions of national laws.

[0070] The present application provides a method, device and electronic equipment described by flowchart and / or block diagram.

[0071] It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks 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, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0072] These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0073] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0074] The application will be described in relation to the drawings outlined below.

[0075] Embodiment one: as shown in the figure, the environmental health monitoring method based on office furniture noise detection of the application specifically includes the following steps: Figs. 1-2

[0076] S1, real-time collection of real-time noise monitoring data collected by each noise sensor;

[0077] Select a noise sensor with high sensitivity and a wide frequency response range to ensure that different frequency noise signals can be accurately captured; arrange the sensor according to the layout of the office area and the expected noise source position;

[0078] Use low-power wide-area networks, Wi-Fi, ZigBee, Bluetooth, and other wireless communication technologies to connect the sensor to the central processing system, ensure the real-time and stability of data transmission; ensure that the wireless network covers the entire office area to avoid signal blind spots affecting the integrity of data collection;

[0079] Set the collection frequency to reflect the changes in noise without generating too much redundant data; for an office environment, collecting data every few seconds can meet the demand; use local or cloud database to store the collected data to ensure data security and accessibility;

[0080] ​Preliminary data filtering and compression are performed at the sensor end before sending data to the central system to remove outliers and reduce transmission burden. Each set of collected data is labeled with an accurate time stamp to enable correct sorting and correlation during subsequent analysis.

[0081] The selected solution ensures good compatibility, easy integration with existing IT infrastructure, and support for future addition of new devices or updating of existing devices.

[0082] In this step, a high-sensitivity, wide-frequency-range noise sensor is selected to ensure that different frequency noise signals in the office environment can be captured, improving the accuracy and reliability of noise monitoring. Low-power wide-area networks, Wi-Fi, ZigBee, Bluetooth, and other wireless communication technologies are used to not only achieve stable connection between the sensor and the central processing system but also ensure real-time data transmission while reducing energy consumption and prolonging the service life of the sensor. The sensors are arranged reasonably and the wireless network covers the entire office area to avoid signal blind spots, making noise monitoring more comprehensive and providing a complete data foundation for subsequent analysis. Reducing data redundancy improves response speed: setting a reasonable sampling rate reduces unnecessary data volume without affecting data analysis, saving storage space and speeding up data processing. Local or cloud database storage methods are used in combination with encryption technology and privacy protection measures to ensure data security. At the same time, historical data can be called and analyzed at any time to support decision-making. Preliminary data filtering and compression are performed at the sensor end to remove outliers and improve data quality, thereby improving the credibility of subsequent analysis results. Each set of data is labeled with a time stamp to help track noise trends and facilitate long-term sequence analysis, further understanding the rules of noise generation and its impact on employees. The selected solution is easy to integrate with existing IT infrastructure and supports the addition or updating of future devices, ensuring that the system can evolve with the development of the office environment and technology.

[0083] S2, for each real-time noise monitoring data, identifying and extracting key features in the real-time noise monitoring data, obtaining office furniture location information, office furniture type, and real-time noise decibel value corresponding to the noise sensor;

[0084] The method of identifying and extracting key features in the real-time noise monitoring data includes:

[0085] Receive real-time data from each noise sensor, including the original measurement value of noise and timestamp information;

[0086] Clean the received data to remove outliers, duplicates, and invalid data to ensure data accuracy and reliability;

[0087] From each real-time noise monitoring data, identify and extract key features; including office furniture location information and office furniture type;

[0088] Using sensor-attached metadata or location recognition technology, determine the specific location of the noise sensor;

[0089] Compare this location information with the layout of the office area to determine the office furniture position corresponding to the sensor;

[0090] Using sensor-attached metadata, determine the office furniture type at the location of the noise sensor;

[0091] Directly extract the noise decibel value from real-time noise monitoring data.

[0092] In this step, by cleaning the received data, removing outliers, repeated values and invalid data, the accuracy and reliability of the data relied on subsequent analysis are ensured; not only improve the credibility of the final result, but also reduce the misjudgment caused by false data; Using sensor-attached metadata or advanced location recognition technology, the specific location of each noise sensor can be accurately located, and then the noise source and the specific office furniture can be more accurately associated; Compare the location information of the sensor with the layout of the office area to determine the office furniture position corresponding to the sensor, and further confirm the office furniture type combined with the sensor-attached metadata; Make the noise source more clear and obvious, provide the basis for targeted noise reduction measures; Directly extract the noise decibel value from real-time noise monitoring data, simplify the data processing process, and at the same time ensure the immediacy and accuracy of noise intensity measurement; The effective implementation of S2 step provides technical support for creating a quieter and healthier office environment; Help identify and solve noise problems, thereby improving employee work efficiency and mental health.

[0093] S3, the office furniture position information and the office furniture type are converted into numerical values, and the data is combined to obtain an office furniture space vector; The office furniture space vector includes office furniture position identification parameters and office furniture type identification parameters;

[0094] The method for obtaining the office furniture space vector includes:

[0095] In the office environment, a coordinate system is established to describe the position of each office furniture;

[0096] For each sensor with known position, a position identification parameter is given according to its coordinate value relative to the reference point; By directly reading the positioning information of the sensor itself, or by manually inputting through the pre-prepared office area layout map;

[0097] Each type of office furniture is assigned a type identification parameter; if there are multiple different models of the same type of office furniture, they can be further subdivided into codes to distinguish the differences between these specific models.

[0098] By combining the location identifier parameter and the office furniture type identifier parameter, a multi-dimensional vector that comprehensively describes a specific office furniture is obtained, namely the office furniture spatial vector.

[0099] In this step, by introducing a coordinate system to accurately describe the location of each piece of office furniture and assigning it a unique location identifier parameter, noise sources can be located more accurately. This helps identify which specific locations or types of office furniture are the main noise contributors, providing a basis for targeted measures. The introduction of office furniture type identifier parameters allows 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. Combining the location identifier parameter and the office furniture type identifier parameter into a comprehensive multi-dimensional vector describing a specific piece of office furniture simplifies subsequent data processing. As an index address, it can be directly used to quickly access the preset office environment noise baseline matrix, improving the speed and efficiency of data queries. The numerical office furniture spatial vector provides a solid foundation for achieving automated and intelligent noise management and optimization. This method is easily adaptable to office environments of different sizes and diverse types of office furniture. Whether it is a small office or a large corporate park, it can be easily expanded by simply adjusting the coordinate system and type coding rules according to the actual situation. Accurate spatial vectors help managers better understand the noise distribution in the office environment, thereby making more scientific and reasonable decisions. A unified numerical standard ensures that all office furniture is described within the same framework, enhancing the consistency and comparability between data.

[0100] S4. Using the office furniture space vector as an index address, locate it in the preset office environment noise reference matrix, and extract the reference noise decibel value corresponding to the office furniture space vector; in the office environment noise reference matrix, each row represents different office furniture location identification parameters, each column represents different office furniture type identification parameters, and the intersection of the row and column represents the reference noise decibel value under the corresponding office furniture location identification parameters and office furniture type identification parameters;

[0101] The method for constructing the office environment noise baseline matrix includes:

[0102] Collect historical noise data for 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;

[0103] The cleaned data was categorized according to the location and type of office furniture;

[0104] For each category, calculate the average value of its historical noise data as a reference value; the average value represents the noise level of office furniture in an ideal or standard state under this location and type;

[0105] According to the collected data, set the number of rows and columns of the matrix; the number of rows corresponds to different office furniture location identification parameters, and the number of columns corresponds to different office furniture type identification parameters;

[0106] Fill in the reference value of each category into the corresponding row-column intersection point of the matrix to obtain the office environment noise reference matrix; each intersection point represents the reference noise decibel value under the corresponding office furniture location identification parameter and office furniture type identification parameter.

[0107] In this step, by collecting historical noise data and classifying according to the location and type of office furniture, the noise level of office furniture in different positions and types in the entire office environment is comprehensively covered; the method of cleaning data and calculating the average value as the reference value improves the accuracy of the reference noise decibel value, reflecting the noise level in an ideal or standard state; the constructed reference matrix as a quick lookup table enables the system to quickly locate the reference noise decibel value corresponding to any given office furniture space vector; greatly improves the response speed and efficiency of the real-time noise monitoring system, reduces the computational complexity; the office environment noise reference matrix provides a intuitive and quantitative tool for managers to evaluate the noise condition of the office environment; by comparing the actual measurement value with the reference value, it is easier to identify abnormal noise sources, so as to take targeted measures to improve the working environment; by constructing a special reference matrix through the historical data of a specific place, it can better adapt to the needs of various office scenes; accurate noise monitoring and effective management can help reduce unnecessary noise interference, thereby improving the work efficiency and mental health of employees, helping to protect the physical health of employees and reduce the risk of diseases caused by noise.

[0108] S5, calculate the noise difference value between the real-time noise decibel value and the reference noise decibel value corresponding to each office furniture, and based on a preset spatial distribution rule, array a plurality of noise difference values to obtain an office environment noise difference value spatial matrix;

[0109] The method for obtaining the office environment noise difference value spatial matrix comprises:

[0110] For each office furniture location and type, subtract the reference noise decibel value from the real-time noise decibel value to obtain the noise difference value; the noise difference value represents the change of the noise level of the specific office furniture under the current environment relative to the ideal or standard state;

[0111] According to the actual layout of the office area, the entire space is divided into multiple small unit cells; each unit cell corresponds to a specific office furniture position;

[0112] Using the office furniture space vector as an index, it is ensured that each noise difference value can be correctly mapped to the corresponding space position;

[0113] Create a matrix whose row and column numbers correspond to the space division in different dimensions; then, fill each noise difference value into the corresponding row-column intersection point to obtain the office environment noise difference space matrix;

[0114] The preset space distribution rule includes:

[0115] Spatial continuity: requires the distribution of noise difference values in space to have a certain continuity, that is, the noise difference values of adjacent office furniture should not have a large jump; it helps to identify hot spots of noise pollution and potential transmission paths;

[0116] Furniture type and position association: consider the influence of office furniture type and position on noise distribution; different types of office furniture have different structures and purposes, and their absorption, reflection and propagation characteristics of noise are also different; therefore, when calculating the noise difference value, the association between furniture type and position needs to be considered;

[0117] Personnel activity density and noise level relationship: consider the influence of personnel activity density on noise level; in areas with high personnel activity density, noise level is usually high; while in areas with less personnel activity, noise level is relatively low; therefore, when calculating the noise difference value, the distribution of personnel activity density needs to be referred to;

[0118] Consideration of environmental background noise: consider the influence of environmental background noise on measurement results; environmental background noise includes noise from external traffic, weather conditions, building equipment and other sources; when calculating the noise difference value, the influence of background noise should be removed or corrected from the real-time noise monitoring data to ensure the accuracy of the results;

[0119] Normalization of data array: specify the format and standard of noise difference value data array; the data array should be arranged and organized according to 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.

[0120] In this step, by comparing the real-time noise level of each office furniture position with the preset reference value, it can accurately identify which areas or types of office furniture produce abnormal noise; the preset spatial distribution rule ensures the continuity of noise difference in space, which helps to identify the hot spot area of noise pollution and the potential noise propagation path; understanding how different types of office furniture affect noise distribution can help optimize the design and arrangement of the office; selecting more effective sound-absorbing materials or rearranging the location of high-noise equipment to create a more quiet and comfortable working environment; the office environment noise difference spatial matrix provides intuitive data support for management, facilitating wise decisions on 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 poor noise environment; standardized processing of noise difference data array ensures data consistency and comparability, which is beneficial for subsequent analysis and processing.

[0121] S6, inputting the office environment noise difference spatial matrix into a preset office environment noise evaluation model to obtain an office environment noise health score;

[0122] The method for constructing the office environment noise evaluation model comprises:

[0123] A large amount of office environment noise monitoring data is collected, including noise data of different times, different positions, and different types of office furniture;

[0124] The collected data is preprocessed, including data cleaning, denoising, and standardization, to ensure data quality and consistency;

[0125] Features related to noise evaluation are extracted from the preprocessed data, such as noise difference, furniture type, position information, and personnel activity density;

[0126] Through feature selection methods, the key features that have the greatest impact on noise evaluation are selected to improve the accuracy and efficiency of the model;

[0127] Deep learning algorithms are selected as the basis of the model, including support vector machines, neural networks, and random forests;

[0128] The extracted key features are divided into training data sets and validation data sets;

[0129] The training data sets are used to train the model, and model parameters are optimized through cross-validation and grid search methods to improve the generalization ability of the model;

[0130] Use a validation dataset to validate the trained model and evaluate its accuracy, stability, and other metrics.

[0131] The model is adjusted and optimized based on the verification results to ensure its reliability in practical applications.

[0132] The office environment noise assessment model is deployed in practical applications to obtain an office environment noise health score.

[0133] In this step, a large amount of noise monitoring data from different times and locations is collected and preprocessed to ensure data quality and consistency, enabling the model to more accurately reflect the noise situation in the actual office environment. The application of feature extraction and selection methods further improves the model's accuracy. The model is trained on historical data, providing a scientific basis for judging the effectiveness of existing noise control measures and guiding future work environment optimization strategies. Managers can quickly identify problem areas and take corresponding improvement measures based on health scores. Deep learning algorithms are used, and model parameters are optimized through methods such as cross-validation and grid search, ensuring that the model not only has a good fit to the training data but also effectively handles new, unseen data, thereby enhancing its generalization ability. Accurate noise monitoring and management can reduce unnecessary noise interference, improve employees' mental state, and thus increase work efficiency and satisfaction. In the long term, it can also prevent the spread of noise due to prolonged exposure to adverse conditions. The model addresses health problems caused by noise pollution; it allows for flexible parameter adjustments based on the specific characteristics of the office environment, ensuring the solution is applicable to office spaces of various sizes and types; it facilitates more personalized noise management and optimization; once deployed in practical applications, the model enables automated processing, simplifying system integration and maintenance; simultaneously, with the continuous influx of new data, the model can be dynamically updated, maintaining the timeliness and accuracy of results, and supporting real-time monitoring and feedback mechanisms; the model's construction and application provide management with a foundation for continuous improvement; by regularly reassessing the noise situation of the entire office environment, noise management and control strategies can be continuously optimized based on the latest data and analysis results, ensuring the office environment is always in optimal condition; ultimately, the application of this step is crucial for creating healthier and more comfortable working conditions, helping to improve the overall well-being of employees and reduce the long-term negative impacts of noise pollution on human health.

[0134] S7. Compare the office environment noise health score 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 is deemed to be acceptable. 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 is deemed to be unacceptable.

[0135] The factors affecting the setting of the preset office environment noise health threshold include:

[0136] Hearing protection: Long-term exposure to high noise environments can cause hearing damage, so it is necessary to ensure that the noise level does not exceed the standard of hearing protection;

[0137] Mental health: Continuous noise interference can cause psychological problems such as stress and anxiety, and these factors need to be considered in terms of their impact on employee emotions;

[0138] Spatial layout: Different types of office space have different noise tolerance; for example, open office areas usually allow higher background noise, while work areas that require concentration require lower noise levels;

[0139] Personnel density: In high-density office environments, the frequency of communication between people increases, which may generate more noise; conversely, low-density environments are relatively quiet;

[0140] Employee feedback: Through questionnaires or direct communication, understand the feelings and suggestions of employees about the current noise situation, which can be an important basis for setting thresholds;

[0141] Sensor accuracy: The technical performance of existing noise sensors determines the measurement accuracy and stability that can be achieved; if the sensor itself has a large error, a more lenient threshold needs to be set to accommodate this uncertainty;

[0142] Data processing capability: The overall performance of the system, including model calculation capability and real-time response speed, also affects the choice of threshold; too strict a threshold may cause frequent alarms, while too lenient a threshold may miss real problems.

[0143] In this step, by setting a reasonable noise threshold, it can effectively prevent hearing damage caused by long-term exposure to high noise environment, protect the health of employees; considering the negative impact of continuous noise on mental health, it helps to create a more comfortable and less psychologically burdened working environment; setting different noise tolerance according to different types of office space can better meet various work demands and improve overall office efficiency; considering the influence of different density of office environment on noise level, the evaluation is more in line with the actual situation, and the reasonable planning of office area is promoted; the actual feelings and suggestions of employees are taken into account, so that the threshold setting is closer to user needs, and the employee participation and satisfaction are enhanced; based on historical data and technical performance, the threshold is set to ensure the objectivity and reliability of the evaluation results, providing a scientific basis for management to judge the effectiveness of existing noise control measures and guide future improvement strategies; fully considering the measurement error under the existing technical conditions, setting appropriate threshold range ensures the stability and accuracy of the system; adjusting the threshold according to the calculation capacity and real-time response speed of the system avoids the problem of frequent alarms caused by too strict threshold or the problem of missing issues caused by too loose threshold, improving the practicability and user experience of the system; allowing flexible adjustment of parameter settings according to the characteristics of specific office environment ensures that the scheme can be applied to various sizes and types of office spaces, supporting long-term continuous improvement; the model deployed to practical application can realize automatic processing flow, simplify system integration and maintenance work, while supporting real-time monitoring and feedback mechanism to discover problems in time 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 environment.

[0144] Embodiment two: as shown in the figure, the environmental health monitoring system based on office furniture noise detection of the application specifically includes the following modules; Fig. 3

[0145] Real-time data acquisition module, which collects real-time noise monitoring data collected by each noise sensor in real time;

[0146] Feature extraction module, which identifies and extracts key features in each real-time noise monitoring data, obtains office furniture position information, office furniture type and real-time noise decibel value corresponding to the noise sensor;

[0147] Space vector construction module, which converts the office furniture position information and the office furniture type into numerical values and combines the data to obtain the office furniture space vector; the office furniture space vector includes office furniture position identification parameter and office furniture type identification parameter;

[0148] ​The reference noise positioning module locates the office furniture space vector as an index address in a preset office environment noise reference matrix to extract a reference noise decibel value corresponding to the office furniture space vector.

[0149] The matrix construction module calculates noise difference values between real-time noise decibel values and reference noise decibel values of each office furniture, and arranges a plurality of noise difference values into a data array based on a preset space distribution rule to obtain an office environment noise difference value space matrix.

[0150] The office environment noise evaluation module inputs the office environment noise difference value space matrix into a preset office environment noise evaluation model to obtain an office environment noise health score.

[0151] The determination module compares the office environment noise health score with a preset office environment noise health threshold value. If the office environment noise health score is higher than the preset office environment noise health threshold value, 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 value, it is determined that the noise level of the office environment at this time is unqualified.

[0152] The system can more widely cover the entire office area by installing noise sensors on different types of office furniture, capture more noise data generated by positions and activities, and provide more comprehensive noise condition evaluation;

[0153] The feature extraction module can identify and extract key features, so that the system can more accurately reflect the noise level at a specific position and time point, and provide a scientific basis for noise management;

[0154] The space vector construction module and the reference noise positioning module allow the system to adjust its evaluation criteria according to different office furniture positions and types, so as to better adapt to changes in various office environments;

[0155] The matrix construction module forms an office environment noise difference value space matrix by arranging a plurality of noise difference values into a data array, which helps to quickly and intuitively understand the noise distribution of the entire office area;

[0156] The office environment noise evaluation module analyzes noise data in combination with a preset model to generate a health score, and compares the health score with a preset threshold value through the determination module to timely give a pass or fail judgment, helping managers make decisions quickly;

[0157] The ultimate goal is to improve the quality of the office environment, ensure that employees work in a healthy noise level, improve work efficiency and mental health, and reduce the health risks brought by long-term noise exposure; the detailed noise data analysis provided by the system can help enterprises identify noise sources and take targeted measures to reduce noise;

[0158] In summary, the system can not only provide more accurate and detailed noise monitoring results, but also effectively assist enterprises and organizations in optimizing the office environment and protecting the physical and mental health of employees.

[0159] The various variations and specific embodiments of the environment health monitoring method based on office furniture noise detection in the foregoing embodiment one are also applicable to the environment health monitoring system based on office furniture noise detection in the present embodiment. Through the foregoing detailed description of the environment health monitoring method based on office furniture noise detection, those skilled in the art can clearly understand the implementation method of the environment health monitoring system based on office furniture noise detection in the present embodiment. Therefore, in the interest of brevity, the implementation method of the environment health monitoring system based on office furniture noise detection in the present embodiment will not be described in detail herein.

[0160] In addition, the present application also 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, the transceiver, the memory and the processor are connected through the bus respectively, the computer program is executed by the processor to realize each process of the method for controlling output data, and the same technical effect can be achieved, to avoid repetition, which will not be described here.

[0161] The above only describes the preferred embodiments of the present application. It should be noted that those skilled in the art can make several improvements and modifications without departing from the technical principles of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. An environmental health monitoring method based on office furniture noise detection, characterized by, The method is applied to noise environment monitoring of an office area, office furniture in the office area is provided with a noise sensor, and the method comprises the following steps: Real-time noise monitoring data collected by each noise sensor is collected 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 sensor are obtained; The office furniture position information and the office furniture types are converted into numerical values, and data is combined 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; The office furniture space vector is taken as an index address to position in a preset office environment noise benchmark matrix, and a benchmark noise decibel value corresponding to the office furniture space vector is extracted; Noise difference values between real-time noise decibel values and benchmark noise decibel values corresponding to each office furniture are calculated, and an office environment noise difference value space matrix is obtained based on a preset space distribution rule; The office environment noise difference value space matrix is input 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 a preset office environment noise health threshold value, if the office environment noise health score is higher than the preset office environment noise health threshold value, it is determined that the noise degree of the office environment at this time is qualified, and if the office environment noise health score is not higher than the preset office environment noise health threshold value, it is determined that the noise degree of the office environment at this time is unqualified.

2. The method for monitoring environmental health based on noise detection of office furniture according to claim 1, wherein, The method for identifying and extracting key features in the real-time noise monitoring data comprises the following steps: Real-time data from each noise sensor is received; The received data is cleaned to remove abnormal values, repeated values and invalid data; From each piece of real-time data, key features are identified and extracted; The specific position of the noise sensor is determined by using the metadata attached to the sensor; The position information is compared with a layout map of the office area to determine the office furniture position corresponding to the sensor; The type of office furniture at the position of the noise sensor is determined by using the metadata attached to the sensor; The noise decibel value is directly extracted from the real-time data.

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

4. The method for monitoring environmental health based on noise detection of office furniture according to claim 1, wherein, The method for constructing the office environment noise benchmark matrix comprises the following steps: Historical noise data of an office environment is collected; the collected data is cleaned to remove invalid, abnormal and repeated data; The cleaned data is classified according to the position and type of office furniture; For each classification, the average value of the historical noise data thereof is calculated as a 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 in the corresponding row and column intersection point of the matrix with the reference value of each category to obtain an office environment noise reference matrix; each intersection point represents the reference noise decibel value under the corresponding office furniture position identification parameter and office furniture type identification parameter.

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

6. The method for monitoring environmental health based on office furniture noise detection according to claim 1, wherein, The method for constructing the office environment noise evaluation model comprises: Collect historical office environment noise monitoring data, including noise data of different times, different positions, and different types of office furniture; Preprocess the collected data, including data cleaning, denoising, and standardization; Extract features related to noise evaluation from the preprocessed data and select the key features that have the greatest impact on noise evaluation; Select a deep learning algorithm 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; Train the model using the training data set and optimize the model parameters through cross-validation and grid search methods; Verify the trained model using the validation data set; Adjust and optimize the model according to the verification results; Deploy the office environment noise evaluation model to practical applications to obtain the office environment noise health score.

7. The method for monitoring environmental health based on office furniture noise detection according to claim 1, wherein, 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 capacity.

8. An environmental health monitoring system based on office furniture noise detection, characterized by, The system comprises: A real-time data acquisition module that collects real-time noise monitoring data collected by each noise sensor in real time; A feature extraction module that, for each real-time noise monitoring data, identifies and extracts key features from the real-time noise monitoring data to obtain office furniture position information, office furniture type, and real-time noise decibel value corresponding to the noise sensor; A space vector construction module that 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 office furniture position identification parameters and office furniture type identification parameters; A reference noise positioning module that uses the office furniture space vector as an index address to locate the preset office environment noise reference matrix and extract the reference noise decibel value corresponding to the office furniture space vector. The matrix construction module calculates a noise difference value between a real-time noise decibel value corresponding to each office furniture and a reference noise decibel value, and obtains an office environment noise difference value space matrix based on a preset space distribution rule; The office environment noise evaluation module inputs the office environment noise difference value 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 value, and determines that the noise level of the office environment is qualified if the office environment noise health score is higher than the preset office environment noise health threshold value; and determines that the noise level of the office environment is unqualified if the office environment noise health score is not higher than the preset office environment noise health threshold value.

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 on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that, The computer program is executed by the processor to implement the steps in the method of any one of claims 1-7.

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

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