Center type multi-node induction measurement system and method for coping with productive noise

Abstract

The invention discloses a central multi-node induction measurement system and method for coping with productive noise. The method comprises the following steps: measuring noise intensity data in real time through an online noise level meter arranged at a working post position of each near noise source; a worker wears an inductor to record position data and time data of the worker in real time, and individual movement track data is obtained; and the central server obtains the noise intensity data and the individual movement track data in the inductor, carries out processing and analysis based on the noise intensity data and the individual movement track data, and calculates the noise contact level in real time. According to the method, the production efficiency is guaranteed, and excessive contact of noise is avoided; and meanwhile, early warning monitoring can be carried out in advance, so that the safety of noise contact management of a working place is remarkably improved, and the occurrence risk of occupational diseases is reduced.

Description

Technical Field

[0001] The present invention relates to the field of noise measurement, and in particular to a central multi-node induction measurement system and method for dealing with productive noise. Background Art

[0002] In industrial production environments, noise is one of the common occupational hazards. Long-term exposure to high noise environments can damage workers' hearing and physical health. Therefore, monitoring and evaluating workers' noise exposure and providing noise warnings to workers based on noise exposure, thereby reducing workers' noise exposure levels, is crucial to improving occupational safety and health protection.

[0003] The study found that the traditional noise measurement method, for fixed work stations, mainly relies on the measurement of noise level meters with large volume installed on tripods at set positions, or the measurement of noise level meters held by measurement personnel; for mobile work stations, measurement personnel have the workers wear individual noise level meters for measurement.

[0004] However, the above-mentioned traditional method of measuring noise at a fixed position has low work efficiency and is easily limited by manpower and time. It can only reflect the measurement value of one or a period of time, and cannot be associated with the actual contact time of the workers in real time, resulting in large errors and inaccuracies in the later estimation results. However, in the traditional method of measuring noise at mobile posts, although the measured personnel wearing individual noise dosimeters can perform walking measurements in the entire work area, the final measured data only has one result value, which cannot match the contribution value of noise at different work locations and different times during the measurement process in real time. Summary of the invention

[0005] The object of the present invention is to provide a centralized multi-node induction measurement system and method for dealing with productive noise, thereby solving the above-mentioned technical problems pointed out in the prior art.

[0006] The present invention provides a central multi-node induction measurement system for dealing with productive noise, comprising a plurality of online noise level meters, a plurality of sensors and a central server;

[0007] Wherein, the online noise level meter is arranged at each work station near the noise source, and is used to measure the noise intensity data in real time;

[0008] The sensor is worn by each staff member to record the position data and time data of the staff member in real time to obtain individual movement trajectory data;

[0009] The online noise level meter is also used to trigger a linkage mechanism with the sensor to send the noise intensity data to the sensor when the individual movement trajectory data enters the current noise source range;

[0010] The central server is used to obtain the noise intensity data and the individual movement trajectory data on the sensor, and process and analyze the noise intensity data and the individual movement trajectory data to calculate the noise exposure level in real time;

[0011] The central server is further used to perform early warning alarm processing operations on the current staff based on the noise exposure level.

[0012] Preferably, during a specific operation, the sensor is also used to synchronously fuse the position data with the time data to obtain individual movement trajectory data.

[0013] Preferably, the online noise level meter is also used to obtain the coordinates of the noise source during the specific operation process;

[0014] The distance dt between the sensor and the noise source is calculated based on the coordinates of the noise source and the current coordinates of the individual movement trajectory data.

[0015] Preferably, the central server, during the specific operation, is also used to construct a noise spatial distribution model C based on the noise intensity data and the individual movement trajectory data. jk ;

[0016] Based on the noise spatial distribution model C jk Calculate the noise exposure level D ij(t) ;

[0017] Preferably, the noise spatial distribution model C jk Characterizes the noise intensity of the jth noise source at time k;

[0018] Preferably, the noise exposure level D ij(t) The calculation method is:

[0019] D ij(t) =∑C jk ×Δt;

[0020] Where, the noise exposure level D ij(t) Characterizes the noise exposure of the i-th worker at the j-th noise source in the t-th time period; Δt is the time interval;

[0021] Preferably, multiple online noise level meters are configured at different locations to measure the noise intensity data of the environment in real time and send it to the central server in real time;

[0022] The central server is further used to perform real-time data interaction with the plurality of online noise level meters via a communication network, and the central server is used to receive and store noise intensity data measured by the plurality of sound level meters in real time online;

[0023] The central server is further used to generate a spatiotemporal distribution diagram of noise intensity based on the noise intensity data measured by the multiple sound level meters at multiple locations, identify dangerous areas where the noise intensity exceeds a preset threshold, and mark the dangerous areas;

[0024] The central server is also used to notify the corresponding workers through sound, text message or mobile application after obtaining the location information of the dangerous area to achieve real-time warning. That is, based on the location information of the dangerous area, an online reminder is sent to the workers.

[0025] Preferably, the central server also includes an intelligent route planning module, which is used to calculate and output route planning information of the job position selected by the current worker based on the spatiotemporal distribution diagram of the online noise level meter at multiple location points and the job position selected by the current worker; the job position planning route information includes the job position and the corresponding working hours.

[0026] Preferably, the central server further includes a first early warning module:

[0027] The first warning module is used to set the noise action level warning value (i.e. the action level limit standard value) and the maximum working time;

[0028] The first warning module is also used to obtain the current noise exposure level and the working hours of the current noise place in real time, and judge the working hours of the current staff when the current noise exposure level reaches the noise action level warning value; if the current working hours are less than or equal to half of the maximum working hours, the warning mechanism is triggered to send the first warning information to warn the staff. If the current working hours are greater than half of the maximum working hours, continuous monitoring is performed without warning processing.

[0029] Preferably, the central server further includes a second early warning module;

[0030] The second warning module is used to set a maximum threshold for noise exposure level warning;

[0031] The second warning module is further used to determine whether the noise exposure level is greater than or equal to the noise exposure level warning maximum threshold; if so, trigger the warning mechanism and send a second warning message to warn the staff.

[0032] Accordingly, the present invention also proposes a centralized multi-node sensing measurement system and method for dealing with productive noise, comprising the following operating steps:

[0033] Noise intensity data is measured in real time by an online noise level meter installed at each work station near the noise source; the worker wears a sensor to record the worker's position data and time data in real time to obtain individual movement trajectory data;

[0034] When the individual movement trajectory data enters the current noise source range, the online noise level meter and the sensor trigger a linkage mechanism to send the noise intensity data to the sensor;

[0035] The central server obtains the noise intensity data and the individual movement trajectory data from the sensor, and processes and analyzes the noise intensity data and the individual movement trajectory data to calculate the noise exposure level in real time;

[0036] Performing a warning alarm processing operation on the current staff member based on the noise exposure level;

[0037] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0038] From the analysis of the above-mentioned centralized multi-node sensing measurement system and method for dealing with productive noise provided by the present invention, it can be known that in specific applications, first, each noise source is equipped with an online noise level meter to measure and record the noise level data of the surrounding environment in real time, and at the same time, each worker wears a sensor to record its position data and time data in real time, thereby generating individual movement trajectory data; the noise intensity of the workplace is continuously monitored by setting an online noise level meter, and these data provide a basis for subsequent noise exposure assessment and early warning; the sensor worn by the worker can record its position and residence time through a real-time positioning system, generate its dynamic movement trajectory, provide real-time and accurate matching and association of workplace noise data and individual spatiotemporal position data (the so-called spatiotemporal position data is a mapping set of time data and position data), and provide key support for subsequent noise exposure level calculation and early warning mechanism;

[0039] Furthermore, when the individual movement trajectory data of the staff enters the influence range of a certain noise source, the online noise level meter and the sensor are triggered through the linkage mechanism. After the linkage mechanism is activated, the online noise level meter sends the real-time noise data of the current noise source to the worn sensor; it is helpful to evaluate the exposure of the staff in a specific noise environment, and can realize the real-time synchronization of the noise source and the individual position, providing timely and relevant data support for the subsequent noise exposure analysis;

[0040] Furthermore, the central server combines the noise intensity data in the sensor with the individual movement trajectory data, processes and analyzes it through the built-in algorithm, and calculates the noise exposure of the staff in real time; based on the location data of the staff and the received noise intensity data, combined with the noise intensity data, the staff's stay time, location changes and other factors, the noise exposure of the individual in a specific time period is calculated in real time; through the real-time calculation of the noise exposure, it is possible to evaluate whether the staff is in a dangerous noise exposure range in the working environment, provide real-time noise exposure assessment for the individual, and promptly find out whether the staff exceeds the requirements of the occupational exposure limit standard, and then take protective measures;

[0041] At the same time, the central server can also combine the noise spatiotemporal distribution map and the workers' job selection to intelligently calculate and output the optimal job location planning route information, realize job location route optimization and recommendation, which not only ensures production efficiency but also avoids excessive noise exposure; at the same time, it can also conduct early warning monitoring, thereby significantly improving the safety of noise exposure management in the workplace and reducing the risk of occupational diseases;

[0042] Furthermore, based on the noise exposure of the workers, when their noise exposure exceeds a certain preset threshold, the system will activate a warning alarm to remind the workers to pay attention to noise protection;

[0043] Specifically, when the noise exposure of workers reaches a predetermined threshold, a warning alarm is issued in a timely manner to prompt the workers to take actions (such as wearing earplugs or leaving the noise source area), thereby reducing the potential health risks of noise exposure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the overall architecture of a centralized multi-node sensing measurement system for dealing with productive noise;

[0045] Figure 2 A schematic diagram of the overall operation steps of a centralized multi-node sensing measurement method for dealing with productive noise.

[0046] Reference numerals: online noise level meter 10 , sensor 20 , central server 30 . DETAILED DESCRIPTION

[0047] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0049] Embodiment 1

[0050] like Figure 1 As shown, the first embodiment of the present invention provides a central multi-node sensing measurement system for dealing with productive noise, including a plurality of online noise level meters 10, a plurality of sensors 20 and a central server 30;

[0051] The online noise level meter 10 is arranged at each work station near the noise source to measure the noise intensity data in real time;

[0052] The sensor 20 is worn by each staff member to record the position data and time data of the staff member in real time to obtain individual movement trajectory data;

[0053] The online noise level meter 10 is also used to trigger a linkage mechanism (realize signal communication) with the sensor 20 when the individual movement trajectory data enters the current noise source range (or the sensing measurement range of the online noise level meter 10), and send the noise intensity data to the sensor 20;

[0054] The central server 30 is used to obtain the noise intensity data and the individual movement trajectory data on the sensor, and process and analyze the noise intensity data and the individual movement trajectory data to calculate the noise exposure level in real time;

[0055] The central server 30 is further used to perform early warning alarm processing operations on the current staff based on the noise exposure level.

[0056] Preferably, during a specific operation, the sensor 20 is also used to synchronously fuse the position data with the time data to obtain individual movement trajectory data.

[0057] Preferably, the online noise level meter 10 is also used to obtain the coordinates of the noise source during the specific operation process;

[0058] The distance dt between the sensor and the noise source is calculated based on the coordinates of the noise source and the current coordinates of the individual movement trajectory data.

[0059] Preferably, the central server 30, during the specific operation, is also used to construct a noise spatial distribution model C based on the noise intensity data and the individual movement trajectory data. jk ;

[0060] The above noise spatial distribution model C jkCharacterizes the noise intensity of the jth noise source at time k;

[0061] Based on the noise spatial distribution model C jk Calculate the noise exposure level D ij(t) ;

[0062] The noise exposure level D ij(t) The calculation method is:

[0063] D ij(t) =∑C jk ×Δt;

[0064] Where, the noise exposure level D ij(t) Characterizes the noise exposure of the i-th worker at the j-th noise source in the t-th time period; Δt is the time interval;

[0065] Preferably, multiple online noise level meters are configured at different locations to measure the noise intensity data of the environment in real time and send it to the central server in real time;

[0066] The central server is further used to perform real-time data interaction with the plurality of online noise level meters via a communication network, and the central server is used to receive and store noise intensity data measured by the plurality of sound level meters in real time online;

[0067] The central server is further configured to generate a spatiotemporal distribution diagram of noise intensity (i.e., noise intensity data corresponding to a current location and a current time) based on the noise intensity data measured by the multiple sound level meters at multiple locations, identify a dangerous area where the noise intensity exceeds a preset threshold, and mark the dangerous area;

[0068] The central server is also used to notify the corresponding workers through sound, text message or mobile application after obtaining the location information of the dangerous area to achieve real-time warning. That is, based on the location information of the dangerous area, an online reminder is sent to the workers.

[0069] It should be noted that the online noise level meter configured at different locations can not only execute a linkage mechanism with the sensor, but also communicate with the central server in real time, and perform online data interaction and online reminder functions at the same time; when the online noise level meter interacts with the central server, the measurement data of the online noise level meter at multiple locations is obtained through the central server to obtain the time and space distribution, so as to obtain the area with relatively high noise intensity data (i.e., dangerous area), which is marked as a dangerous area; thus, after obtaining the location information of the dangerous area, the central server notifies the corresponding workers through sound, text message or mobile application to realize real-time early warning.

[0070] Preferably, the central server further comprises an intelligent route planning module, which is used to calculate and output route planning information of the job position selected by the current worker based on the spatiotemporal distribution diagram of the online noise level meter at multiple locations and the job position selected by the current worker;

[0071] The work station location planning route information includes the work station location and the corresponding working hours.

[0072] It should be noted that the central server can also see the measurement readings and distribution of online noise level meters at multiple locations in real time, so that each worker can reasonably and independently choose the planned route and work order of the work station based on the measurement readings of the online noise level meters at multiple locations, thereby achieving more reasonable labor organization and avoiding high-intensity noise exposure.

[0073] Of course, the central server can also use the spatiotemporal distribution map of the online noise level meter and receive the current worker's selection of multiple job positions to be selected through intelligent calculation (combining the noise intensity data corresponding to the current position and the current time and the average value of the previous calculation cycle, and estimating the noise intensity data of the current position in the next calculation cycle), and then calculate the preferred job position in the corresponding time period (i.e., the next calculation cycle), so as to obtain the optimal target job position and the corresponding time period, form collective data, and obtain the job position planning route information.

[0074] The intelligent route planning module built into the above-mentioned central server combines the noise spatiotemporal distribution map and the workers' job position selection to intelligently calculate and output the optimal work route and corresponding working time information, helping workers to complete their work tasks efficiently and safely.

[0075] In summary, the embodiment of the present invention performs spatiotemporal analysis on the data of multiple noise level meters through a central server, can accurately identify dangerous areas with high noise intensity, and timely warn relevant personnel through various reminder methods (such as sound, text messages or mobile applications), thereby improving the safety of the workplace.

[0076] The visualization interface on the user terminal device directly displays the noise readings of each monitoring point and its spatial distribution. Workers can use this to rationally plan their work routes and workstation sequences, optimize labor organization, reduce exposure time in high-noise environments, and reduce occupational hazards.

[0077] Preferably, the central server 30 includes a first early warning module (i.e., implementing a first level of initial early warning);

[0078] Among them, the first warning module is used to set the noise action level warning value (i.e. the action level limit standard value) and the maximum working time;

[0079] The first warning module is also used to obtain the current noise exposure level and the working hours of the current noise place in real time, and judge the working hours of the current staff when the current noise exposure level reaches the noise action level warning value; if the current working hours are less than or equal to half of the maximum working hours, the warning mechanism is triggered to send the first warning information to warn the staff. If the current working hours are greater than half of the maximum working hours, continuous monitoring is performed without warning processing.

[0080] The central server 30 also includes a second warning module (i.e., implementing a second level warning);

[0081] The second warning module is used to set a maximum threshold for noise exposure level warning;

[0082] The second warning module is further used to determine whether the noise exposure level is greater than or equal to the noise exposure level warning maximum threshold; if so, trigger the warning mechanism and send a second warning message to warn the staff.

[0083] It should be noted that the first warning message mentioned above is a warning message sent when the detection finds that the noise action level warning value is exceeded and the working time has not reached half of the maximum working time; however, the second warning message mentioned above is a warning message sent when the occupational exposure limit standard is reached.

[0084] In summary, the application proposes a centralized multi-node sensing measurement system for dealing with productive noise. First, each noise source is equipped with an online noise level meter to measure and record the noise level data of the surrounding environment in real time. At the same time, each worker wears a sensor to record their position data and time data in real time, thereby generating individual movement trajectory data. By setting the online noise level meter to the noise intensity of the workplace, these data provide a basis for subsequent noise exposure assessment and early warning. The sensors worn by the workers can record their positions through a real-time positioning system (such as GPS, RFID, Bluetooth, etc.) and generate their dynamic movement trajectories, providing real-time and accurate matching and association of workplace noise data with individual spatiotemporal position data, providing key support for subsequent noise exposure level calculations and early warning mechanisms.

[0085] Furthermore, when the individual movement trajectory data of the staff enters the influence range of a certain noise source, the online noise level meter and the sensor are triggered through the linkage mechanism. After the linkage mechanism is activated, the online noise level meter sends the real-time noise data of the current noise source to the worn sensor; it is helpful to evaluate the exposure of the staff in a specific noise environment, and can realize the real-time synchronization of the noise source and the individual position, providing timely and relevant data support for the subsequent noise exposure analysis;

[0086] Furthermore, the central server combines the noise intensity data in the sensor with the individual movement trajectory data, processes and analyzes it through the built-in algorithm, and calculates the noise exposure of the staff in real time; based on the location data of the staff and the received noise intensity data, combined with the noise intensity data, the staff's stay time, location changes and other factors, the noise exposure of the individual in a specific time period is calculated in real time; through the real-time calculation of the noise exposure, it is possible to evaluate whether the staff is in a dangerous noise exposure range in the working environment, provide real-time noise exposure assessment for the individual, and promptly find out whether the staff exceeds the requirements of the occupational exposure limit standard, and then take protective measures;

[0087] Furthermore, based on the noise exposure level of the staff, when their noise exposure level exceeds a certain preset threshold, the system will activate a warning alarm to remind the staff to pay attention to noise protection;

[0088] Specifically, when the noise exposure level of the staff reaches a predetermined threshold, a warning alarm is issued in time to prompt the staff to take actions (such as wearing earplugs or leaving the noise source area), thereby reducing the potential health risks of noise exposure; in the above-mentioned embodiment of the present application, the real-time warning alarm reduces the risk of excessive exposure of the staff to the noise environment, thereby improving the safety of the workplace and the health protection of the staff;

[0089] The above-mentioned embodiment of the present application measures noise intensity data and personnel location data in real time and triggers a linkage mechanism, so that changes in noise sources can timely affect the behavior and judgment of workers; the dynamic trajectory data and noise intensity data of workers are used to calculate the noise exposure in real time, providing a strong data basis for avoiding occupational hazards;

[0090] The technical solution adopted in the above-mentioned embodiments of the present application combines noise management and individual health protection, realizes personalized real-time monitoring and early warning alarms through technical means, reduces the potential threat of noise to the health of workers, can effectively reduce the impact of noise pollution on workers, and improve the overall safety and health level of the workplace.

[0091] Embodiment 2

[0092] like Figure 2 As shown, the second embodiment of the present invention provides a centralized multi-node sensing measurement method for dealing with productive noise, including the following operation steps:

[0093] Step S10: measuring the noise intensity data in real time by means of an online noise level meter installed at each work station near the noise source; the worker wears a sensor to record the position data and time data of the worker in real time to obtain individual movement trajectory data;

[0094] Step S20: When the individual movement trajectory data enters the current noise source range, the online noise level meter and the sensor trigger a linkage mechanism to send the noise intensity data to the sensor;

[0095] Step S30: the central server obtains the noise intensity data and the individual movement trajectory data in the sensor, and processes and analyzes the noise intensity data and the individual movement trajectory data to calculate the noise exposure level in real time;

[0096] Step S40: performing an early warning alarm processing operation on the current worker based on the noise exposure level; the central server calculates and outputs the planning route information of the work position selected by the current worker based on the spatiotemporal distribution diagram of the online noise level meter at multiple locations and the work position selected by the current worker;

[0097] It should be noted that, in the above-mentioned embodiment of the present application, each noise source is first equipped with an online noise level meter to measure and record the noise level data of the surrounding environment in real time, and each worker wears a sensor to record their position data and time data in real time, thereby generating individual movement trajectory data; the noise intensity of the work station is continuously monitored by setting up an online noise level meter, and these data provide a basis for subsequent noise exposure assessment and early warning; the sensor worn by the worker can record its position through a real-time positioning system (such as GPS, RFID, Bluetooth, etc.), generate its dynamic movement trajectory, provide real-time and accurate noise data and individual position data, and provide key support for subsequent noise exposure calculation and early warning mechanism;

[0098] Furthermore, when the individual movement trajectory data of the staff enters the influence range of a certain noise source, the online noise level meter and the sensor are triggered through the linkage mechanism. After the linkage mechanism is activated, the online noise level meter sends the real-time noise data of the current noise source to the worn sensor; it is helpful to evaluate the exposure of the staff in a specific noise environment, and can realize the real-time synchronization of the noise source and the individual position, providing timely and relevant data support for the subsequent noise exposure analysis;

[0099] Furthermore, the central server combines the noise intensity data from the sensor with the individual movement trajectory data, processes and analyzes them through the built-in algorithm, and calculates the noise exposure of the staff in real time; based on the staff's location data and the received noise intensity data, combined with factors such as the noise source intensity, the staff's stay time, and position changes, the individual's noise exposure in a specific time period is calculated in real time (usually expressed in sound level or noise exposure time); through the real-time calculation of noise exposure, it is possible to evaluate whether the staff is in a dangerous noise exposure range in the working environment, provide individual real-time noise exposure assessment, and promptly detect whether the staff is in an environment with excessive noise, and then take protective measures;

[0100] Furthermore, based on the noise exposure of the workers, when their noise exposure exceeds a certain preset threshold, the system will activate a warning alarm to remind the workers to pay attention to noise protection;

[0101] Specifically, when the noise exposure of the staff reaches a predetermined threshold, a warning alarm is issued in time to prompt the staff to take actions (such as wearing earplugs or leaving the noise source area), thereby reducing the potential health risks of noise exposure; in the above-mentioned embodiment of the present application, the real-time warning alarm reduces the risk of excessive exposure of the staff to the noise environment, thereby improving the safety of the workplace and the health protection of the staff;

[0102] The above-mentioned embodiment of the present application measures noise intensity data and personnel location data in real time and triggers a linkage mechanism, so that changes in noise sources can timely affect the behavior and judgment of workers; the dynamic trajectory data and noise intensity data of workers are used to calculate the noise exposure in real time, providing a strong data basis for avoiding occupational hazards;

[0103] The technical solution adopted in the above-mentioned embodiments of the present application combines noise management and individual health protection, realizes personalized real-time monitoring and early warning alarms through technical means, reduces the potential threat of noise to the health of workers, can effectively reduce the impact of noise pollution on workers, and improve the overall safety and health level of the workplace.

[0104] Specifically, in step S10, the worker wears a sensor to record the worker's position data and time data in real time to obtain individual movement trajectory data, including the following operation steps:

[0105] Step S11: Synchronously fuse the position data and the time data to obtain individual movement trajectory data.

[0106] It should be noted that, in the above-mentioned embodiment of the present application, the sensor worn by the staff collects its position coordinates in real time through the built-in positioning system (such as GPS, Bluetooth, RFID, etc.), and the position data generally includes latitude, longitude, and possible height (such as floor information), etc.; at the same time, the sensor also records a timestamp (usually including date and time) to ensure that the position data can correspond to the time point one by one, which means that each piece of position data will be marked with an exact time, so as to reflect the specific activity period of the staff; further, through the correspondence between the timestamp and the position data, the two can be synchronously fused (that is, to generate spatiotemporal position data, the so-called spatiotemporal position data is a mapping set of timestamps (or time data) and position data), and generate accurate individual movement trajectory data, which contains all the position change information of the staff in a specific time period, forming a complete route map, which can show the whole process of the staff moving from one place to another in the working environment;

[0107] During the specific operation, by combining the timestamp and the location coordinates (i.e., location data), the specific location of the staff at different time points can be accurately recorded. For example, if the staff is near the noise source at a certain timestamp, the noise exposure during that period can be determined.

[0108] The synchronized data will form a continuous route record, which can not only show the location distribution of workers, but also reflect their movement patterns in the work environment. This route can not only be linear (simple movement), but also add more dimensional information according to the complexity of the work environment (such as floor, workstation distribution, etc.);

[0109] After obtaining the individual movement trajectory data, the worker's movement route can be combined with the distribution of noise sources. The noise level meter can record the worker's noise exposure at different locations (i.e., noise intensity data). By analyzing the trajectory data, the individual's total noise exposure (noise exposure level) during working hours can be calculated to further assess health risks.

[0110] The above-mentioned embodiments of the present application utilize the close integration of location data and time so that the activity trajectory of each staff member can be accurately calculated to the specific time and space, so that the calculation of noise exposure not only depends on the location of the staff member, but also takes into account their being within the influence range of the noise source in different time periods. In addition, through time data, the activity trajectory of the staff in different time periods can be accurately distinguished. For example, the staff member may be in a high-noise area during a certain period of the working day and enter a low-noise area during another period. These changes can be accurately captured through time stamps to avoid errors or deviations.

[0111] Specifically, in step S20, when the individual movement trajectory data enters the current noise source range, the online noise level meter and the sensor trigger a linkage mechanism to send the noise intensity data to the sensor, including the following operation steps:

[0112] Step S21: Obtain the coordinates of the noise source;

[0113] Step S22: Calculating the distance dt between the sensor and the noise source based on the coordinates of the noise source and the current coordinates of the individual movement trajectory data.

[0114] It should be noted that the above-mentioned embodiment of the present application first calculates the distance from the sensor to the noise source through the noise source coordinates and the current coordinates, and this distance can be used as a reference data; at the same time, the noise intensity measured by the sensor (what is measured is what is obtained) is also an important reference data, which is transmitted to the central server; when sharing specific data, the real-time data of the noise source (including noise intensity, noise source location, distance and other information) is transmitted to the sensor worn by the individual through wireless communication methods (such as Bluetooth, Wi-Fi, ZigBee, etc.); the noise intensity data is sent to the sensor in a timely manner through the linkage mechanism, providing real-time noise information to help individuals make protection decisions.

[0115] Specifically, in step S30, the noise intensity data and the individual movement trajectory data are processed and analyzed based on the sensor to calculate the noise exposure level in real time, including the following operation steps:

[0116] Step S31: construct a noise spatial distribution model C based on the noise intensity data and the individual movement trajectory data jk ;

[0117] The above noise spatial distribution model C jk Characterizes the noise intensity of the jth noise source at time k;

[0118] Step S32: Based on the noise spatial distribution model C jk Calculate the noise exposure level D ij(t) ;

[0119] The noise exposure level D ij(t) The calculation method is:

[0120] D ij(t) =∑C jk ×Δt;

[0121] Where, the noise exposure level D ij(t) Characterizes the noise exposure of the i-th worker at the j-th noise source in the t-th time period; Δt is the time interval;

[0122] It should be noted that the above-mentioned embodiment of the present application first constructs a noise spatial distribution model based on noise intensity data and individual movement trajectory data, wherein the noise intensity data includes the location information of the noise source and the corresponding noise intensity, such as the distribution and intensity of steady-state noise and non-steady-state noise; the individual movement trajectory data records the activity route and time of an individual (such as a staff member), which can be a time series data describing the location of the staff member at different time points;

[0123] By combining these two types of data, a noise spatial distribution model can be constructed, which shows how the noise intensity changes at different spatial locations and time points. In particular, the focus here is on the noise intensity of the jth noise source at time k, so it is necessary to calculate the noise intensity at a specific location within a certain time period based on the distribution of the noise source and the location information of the staff. The model not only considers the spatial location, but also combines the time dimension, and can update the noise intensity value in real time based on the actual distribution of the noise source and the activity trajectory of the staff. The above noise spatial distribution model provides the necessary basic data for the subsequent noise exposure calculation.

[0124] Furthermore, the individual's noise exposure is calculated through the constructed noise spatial distribution model; by integrating the noise intensity in the noise spatial distribution model, the amount of noise received by the worker in a given time period is obtained, which calculates the individual's total noise exposure to a specific noise source within a certain period of time; by integrating the sum of the noise intensity at all time points, the amount of noise received by each worker in a certain period of time can be evaluated; this helps to evaluate the noise exposure level of different workers.

[0125] Specifically, in step S40, a warning alarm processing operation is performed on the current staff member based on the noise exposure level, including the following operation steps:

[0126] Step S41': setting the maximum threshold of noise exposure level warning (ie, the standard value of occupational exposure limit);

[0127] Step S42': determine whether the noise exposure level is greater than or equal to the noise exposure level warning maximum threshold; if so (if not, continue monitoring), trigger the warning mechanism to warn the staff.

[0128] It should be noted that the warning in the above-mentioned embodiment of the present application includes a visual alarm, which pops up a warning prompt on the console or monitoring system to remind the operator; an audio alarm, which notifies the manager to notice the abnormal noise exposure through an audio alarm; a notification, such as sending a text message or email to notify the relevant staff or management personnel to remind the staff to take appropriate protective measures or adjust the work schedule;

[0129] The above-mentioned system of the embodiment of the present application first sets a "maximum threshold value for warning of noise exposure level", that is, sets an upper limit value for noise reception, which is determined based on factors such as the safety standards, working environment and health risks of the staff, and its purpose is to determine the maximum safe limit of noise exposure that the staff can be exposed to; usually, this threshold is set based on the regulatory standards for occupational health and safety (such as "Occupational Exposure Limits for Hazardous Factors in the Workplace Part 2: Physical Factors" (GBZ2.2) or other local standards); the setting of the above-mentioned maximum threshold value for warning of noise exposure level establishes a "safety limit", that is, when the noise exposure level exceeds this value, an alarm will be triggered, which helps prevent the staff from being exposed to excessive noise at work and avoid health problems such as hearing damage; by reasonably setting the maximum threshold value for warning, the system can accurately determine when intervention measures need to be taken to ensure the health and safety of the staff; this is the basis of the entire warning mechanism and determines the accuracy of the system's response;

[0130] Furthermore, the system will monitor and obtain the noise exposure data of the staff in real time. If the noise exposure level is greater than or equal to the maximum warning threshold set in advance, the warning trigger process will be entered; if the noise exposure is lower than the threshold, the system will continue to monitor and wait for further data updates without triggering the warning; by comparing the noise exposure level with the maximum warning threshold, the system can decide whether to initiate the warning to avoid health risks of workers due to noise exposure;

[0131] Through the technical solution adopted in the above-mentioned embodiment of the present application, by continuously monitoring the noise exposure amount and automatically comparing it with the set threshold, the system can respond quickly when the noise exposure reaches a dangerous level and automatically trigger an early warning, thereby greatly improving the early warning efficiency; and, through the combination of visual alarms, sound alarms and notifications, the solution ensures that no matter where the management personnel are or what they are busy with, they can obtain early warning information in a timely manner. This multi-level early warning method effectively improves the speed of safety information transmission and reduces response delays; through timely early warnings, staff can take measures (such as wearing earplugs, adjusting the working environment, etc.) before the noise exposure is too high, thereby greatly reducing the risk of occupational hearing damage.

[0132] Specifically, in step S40, the early warning alarm processing operation is performed on the current staff member based on the noise exposure level, and the following operation steps are also included:

[0133] Step S41: setting the noise action level warning value (i.e., the action level limit standard value) and the maximum working time;

[0134] Step S42: Real-time acquisition of the current noise exposure level and the working hours of the current noise venue, and when the current noise exposure level reaches the noise action level warning value, the working hours of the current staff are determined; if the current working hours are less than or equal to half of the maximum working hours, the warning mechanism is triggered to send the first warning information to warn the staff. If the current working hours are greater than half of the maximum working hours, continuous monitoring is performed without warning processing.

[0135] The first warning message is sent when the detection finds that the noise action level warning value is exceeded and the working time is less than half of the maximum working time; however, the second warning message mentioned above is sent when the occupational exposure limit standard is reached.

[0136] After step S40, the method further includes executing a prediction process of the duration of reaching the occupational exposure limit standard, specifically including:

[0137] Step S51: Obtain the working time from the initial moment (initial noise exposure moment) to the current moment, and obtain the total noise exposure level up to the current moment; and calculate the average exposure amount per unit time;

[0138] Step S52: Obtain the occupational exposure limit standard, subtract the occupational exposure limit standard from the total noise exposure level to calculate the remaining noise exposure level (or remaining noise exposure amount), and then use the remaining noise exposure level divided by the average exposure amount per unit time to calculate and predict the time when the occupational exposure limit standard will be reached in the future.

[0139] In summary, the centralized multi-node sensing measurement system and method for dealing with productive noise proposed in the example of the present invention continuously monitors the noise intensity data of each workstation and its surroundings by setting a line noise level meter at a workstation near the noise source, so as to obtain the noise intensity data at the specific workstation and its surroundings, and these data provide a basis for subsequent noise exposure assessment and early warning; the sensor worn by the staff can record its position and residence time through the real-time positioning system, generate its dynamic movement trajectory and generate spatiotemporal position data; when the individual movement trajectory data of the staff enters the influence range of a certain noise source, the online noise level meter and the sensor are triggered through the linkage mechanism, and after the linkage mechanism is started, the online noise level meter sends the real-time noise data of the current noise source to the worn sensor and realizes the association and matching with the spatiotemporal position data to form a series of associated data; it is helpful to evaluate the exposure of the staff in a specific noise environment, and can realize the real-time synchronization of the noise source and the individual position, providing timely and relevant data support for the subsequent noise exposure analysis;

[0140] Furthermore, the central server combines the noise intensity data in the sensor with the individual movement trajectory data, processes and analyzes them through the built-in algorithm, and calculates the noise exposure level of the staff in real time; then the evaluation process is performed accordingly. When it is considered that the warning conditions are met, the system will activate the warning alarm to remind the staff to pay attention to noise protection; the above-mentioned technical application improves the intelligent monitoring level of productive noise through real-time data processing of sensors worn by individual terminals and centralized management of the central server; at the same time, early warning monitoring can be carried out in advance, thereby significantly improving the safety of noise exposure management in the workplace and reducing the risk of occupational diseases; therefore, the technical solution adopted in the present invention can effectively monitor and evaluate early warning and manage noise exposure risks, thereby providing strong support for the protection of occupational individual health, and has broad development prospects and application potential in the future.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. A person skilled in the art may modify the technical solutions described in the above embodiments, or replace part or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A centralized multi-node sensing measurement system for dealing with productive noise, characterized in that: It includes multiple online noise level meters, multiple sensors and a central server; Wherein, the online noise level meter is arranged at each work station near the noise source, and is used to measure the noise intensity data in real time; The sensor is worn by each staff member to record the position data and time data of the staff member in real time to obtain individual movement trajectory data; The online noise level meter is also used to trigger a linkage mechanism with the sensor to send the noise intensity data to the sensor when the individual movement trajectory data enters the current noise source range; The central server is used to obtain the noise intensity data and the individual movement trajectory data on the sensor, and process and analyze the noise intensity data and the individual movement trajectory data to calculate the noise exposure level in real time; The central server is further used to perform early warning alarm processing operations on the current staff based on the noise exposure level.

2. A centralized multi-node induction measurement system for dealing with productive noise according to claim 1, characterized in that: During the specific operation, the sensor is also used to synchronously fuse the position data with the time data to obtain individual movement trajectory data.

3. A centralized multi-node induction measurement system for dealing with productive noise according to claim 1, characterized in that: The online noise level meter is also used to obtain the coordinates of the noise source during the specific operation process; The distance dt between the sensor and the noise source is calculated based on the coordinates of the noise source and the current coordinates of the individual movement trajectory data.

4. A centralized multi-node induction measurement system for dealing with productive noise according to claim 1, characterized in that: The central server is also used to construct a noise spatial distribution model C based on the noise intensity data and the individual movement trajectory data during the specific operation. jk ; Based on the noise spatial distribution model C jk Calculate the noise exposure level D ij(t) .

5. A centralized multi-node induction measurement system for dealing with productive noise according to claim 4, characterized in that: The noise spatial distribution model C jk Characterizes the noise intensity of the jth noise source at time k; The noise exposure level D ij(t) The calculation method is: D ij(t) =∑C jk ×Δt; Where, the noise exposure level D ij(t) Characterizes the noise exposure of the i-th worker at the j-th noise source during the t-time period; Δt is the time interval.

6. A centralized multi-node induction measurement system for dealing with productive noise according to claim 5, characterized in that: Multiple online noise level meters are configured at different locations to measure the noise intensity of the environment in real time and send the data to the central server in real time; The central server is further used to perform real-time data interaction with the plurality of online noise level meters via a communication network, and the central server is used to receive and store noise intensity data measured by the plurality of sound level meters in real time online; The central server is further used to generate a spatiotemporal distribution diagram of noise intensity based on the noise intensity data measured by the multiple sound level meters at multiple locations, identify dangerous areas where the noise intensity exceeds a preset threshold, and mark the dangerous areas; The central server is also used to notify corresponding workers through sound, text message or mobile application after obtaining the location information of the dangerous area to achieve real-time warning.

7. A centralized multi-node induction measurement system for dealing with productive noise according to claim 6, characterized in that: The central server further includes an intelligent route planning module, which is used to calculate and output route planning information of the job position selected by the current worker based on the spatiotemporal distribution diagram of the online noise level meter at multiple locations and the job position selected by the current worker; The work station location planning route information includes the work station location and the corresponding working hours.

8. A centralized multi-node induction measurement system for dealing with productive noise according to claim 7, characterized in that: The central server also includes a first early warning module: The first warning module is used to set the noise action level warning value and the maximum working time; The first warning module is also used to obtain the current noise exposure level and the working hours of the current noise place in real time, and judge the working hours of the current staff when the current noise exposure level reaches the noise action level warning value; if the current working hours are less than or equal to half of the maximum working hours, the warning mechanism is triggered to send the first warning information to warn the staff. If the current working hours are greater than half of the maximum working hours, continuous monitoring is performed without warning processing.

9. A centralized multi-node induction measurement system for dealing with productive noise according to claim 6, characterized in that: The central server also includes a second early warning module; The second warning module is used to set a maximum threshold for noise exposure level warning; The second warning module is further used to determine whether the noise exposure level is greater than or equal to the noise exposure level warning maximum threshold; if so, trigger the warning mechanism and send a second warning message to warn the staff.

10. A centralized multi-node sensing measurement method for dealing with productive noise, characterized in that: The steps are as follows: Noise intensity data is measured in real time by an online noise level meter installed at each work station near the noise source; the worker wears a sensor to record the worker's position data and time data in real time to obtain individual movement trajectory data; When the individual movement trajectory data enters the current noise source range, the online noise level meter and the sensor trigger a linkage mechanism to send the noise intensity data to the sensor; The central server obtains the noise intensity data and the individual movement trajectory data from the sensor, and processes and analyzes the noise intensity data and the individual movement trajectory data to calculate the noise exposure level in real time; Based on the noise exposure level, a warning alarm processing operation is performed on the current staff member.

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