Dust concentration online monitoring method and system based on individual movement trajectory tracking

Abstract

The invention discloses a dust concentration online monitoring method and system based on individual movement trajectory tracking. The method comprises the following steps: collecting movement track data and environmental dust concentration data of an individual through a wearable device; moving track data and environment dust concentration real-time monitoring data are transmitted to a central processing system; analyzing and processing the moving track data and the environmental dust concentration data by using a central processing system, calculating the dust receiving amount of the individual in real time, predicting the dust receiving amount of the current ith individual at the nth space position corresponding to the nth target time node in the future, and outputting the dust receiving amount as a calculation result; a calculation result is transmitted to an online monitoring platform, and the individual dust receiving amount is displayed in real time; and when the individual dust receiving amount exceeds a preset threshold value, an early warning alarm processing operation is triggered. According to the monitoring method, the accuracy of occupational health protection and the foreseeability of exposure risk assessment early warning are improved, dual monitoring management measures of independent monitoring of workers and centralized and efficient management of employers are realized, and the intelligent level of dust hazard protection is improved.

Description

Technical Field

[0001] The present invention relates to dust concentration monitoring to which an individual is exposed, and in particular to an online dust concentration monitoring method and system based on individual movement trajectory tracking. Background Art

[0002] Dust, as an important occupational hazard, not only affects the quality of the working environment, but also directly threatens the health of workers. Therefore, it is necessary to improve the real-time and accurate monitoring of the dust concentration to which workers are exposed (or the individual dust exposure), which can provide a scientific basis for effectively preventing and controlling dust concentration and evaluating the effect of intervention.

[0003] At present, dust monitoring in industrial environments mainly relies on traditional monitoring equipment such as fixed samplers and portable dust monitors. Although these devices can provide dust concentration data at specific locations, they have obvious limitations.

[0004] Researchers found that traditional dust monitoring methods have the following main problems:

[0005] 1. Limitations of monitoring methods: Traditional fixed-point monitoring methods can only obtain dust concentration data at specific sampling points, and cannot reflect the dust distribution status of the entire working environment. Since dust concentration in industrial environments has obvious spatiotemporal distribution characteristics, it is difficult to fully assess the dust exposure risk in the workplace relying solely on data from fixed sampling points. At the same time, the number of fixed monitoring equipment is limited by factors such as cost and maintenance, making it difficult to achieve comprehensive coverage of a large range of working areas. At the same time, the fixed-point monitoring method is also unable to achieve real-time dust monitoring and synchronous coupling of data;

[0006] 2. Insufficient individual exposure assessment: Existing dust monitoring methods cannot accurately reflect the actual dust exposure of workers during movement. Workers often need to move between different areas in their daily work, and the dust concentration they are exposed to will change significantly with their location. It is difficult to accurately assess an individual's actual dust exposure by relying solely on fixed-point monitoring data, which can easily lead to deviations in occupational health risk assessments. At the same time, relying solely on fixed-point monitoring can only achieve one data result for one device, which is difficult to reflect the changes in dust concentration over time and spatial distribution during the monitoring process;

[0007] In order to better protect the occupational health of workers, it is urgent to develop a monitoring method that can organically combine individual movement trajectories with environmental dust concentrations to achieve accurate assessment and prediction of individual dust intake. This will not only help improve the pertinence and effectiveness of occupational health protection, but also provide important technical support for the continuous improvement of the working environment. Summary of the invention

[0008] The present invention provides a method for monitoring dust intake based on individual movement trajectory, which has important practical significance and application value. This method needs to be able to track individual movement trajectory in real time, and at the same time, combine environmental dust concentration data, and use intelligent algorithms to accurately calculate and predict individual dust intake, thereby providing more scientific and effective technical support for occupational health protection.

[0009] In order to achieve the above technical purpose, the present invention adopts the following technical solution:

[0010] The present invention provides an online dust concentration monitoring method based on individual movement trajectory tracking, comprising the following steps:

[0011] S1. Collect individual movement trajectory data and environmental dust concentration data through wearable devices;

[0012] S2, transmitting the movement trajectory data and the ambient dust concentration data to a central processing system;

[0013] S3, using the central processing system to analyze and process the movement trajectory data and the ambient dust concentration data, calculate the individual dust reception in real time and predict the dust reception of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future and output it as the calculation result;

[0014] S4, transmitting the calculation results to the online monitoring platform to display the individual dust reception amount in real time;

[0015] S5. When the individual dust reception exceeds the preset threshold, the early warning alarm processing operation is triggered.

[0016] Preferably, as an implementable embodiment, the step S1 of collecting individual movement trajectory data includes:

[0017] S11, using a GPS module to obtain the geographic location coordinates of the individual;

[0018] S12, performing time synchronization fusion on the data of the acquired geographic location coordinates to form complete movement trajectory data.

[0019] Preferably, as an implementable embodiment, the step S2 of transmitting the movement trajectory data and the ambient dust concentration data to the central processing system includes:

[0020] S21, using a low-power Bluetooth communication module to transmit movement trajectory data and environmental dust concentration data from the wearable device to the mobile terminal;

[0021] S22, transmitting the movement trajectory data and the ambient dust concentration data from the mobile terminal to the central processing system via the mobile communication network;

[0022] S23. The central processing system receives the movement trajectory data and the environmental dust concentration data.

[0023] Preferably, as an implementable embodiment, the use of a central processing system to analyze and process the movement trajectory data and the ambient dust concentration data, and to calculate the individual dust reception amount in real time includes:

[0024] S31, obtaining the ambient dust concentration data in the current space, and then obtaining the spatial distribution characteristics of the ambient dust concentration data, combining the movement trajectory data and the spatial distribution characteristics of the ambient dust concentration data, and calculating the dust reception amount of the individual at the current position in the space corresponding to different time periods;

[0025] S32. Analyze the historical data of the spatial distribution characteristics of the ambient dust concentration data using a machine learning algorithm to predict the dust receiving amount of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future;

[0026] S33. When it is predicted that the dust reception amount corresponding to the jth spatial position at the nth target time node in the future of the ith individual exceeds the standard content threshold, a regional position warning information for the jth spatial position is sent to avoid the alarm information of the current individual going to the jth spatial position.

[0027] Preferably, as an implementable embodiment, the method of using the central processing system to analyze and process the movement trajectory data and the environmental dust concentration data to calculate the individual dust reception amount in real time includes:

[0028] S34, discretizing the movement trajectory data into a corresponding sequence table of time points and position points corresponding to the time points;

[0029] S35. For each time point, calculate the dust concentration at the spatial position;

[0030] S36, if the duration of the current time point exceeds the standard threshold, it is confirmed as a valid time point, and the dust receiving amount at each valid time point is calculated;

[0031] S37, accumulating the dust reception amount of all valid time points of the current individual to obtain the total dust reception amount.

[0032] Preferably, as an implementable embodiment, the real-time display of individual dust reception amount in step S4 includes:

[0033] S41, transmitting the calculated dust receiving amount data to the online monitoring platform;

[0034] S42. Use data visualization technology to display the trend of individual dust intake in the form of charts;

[0035] S43. Use different colors to mark different dust receiving levels;

[0036] S44. Provide multi-dimensional data display, including time dimension, space dimension and cumulative reception dimension.

[0037] Preferably, as an implementable solution, the operation of triggering the early warning alarm processing in step S5 includes:

[0038] S51, setting multiple levels of dust receiving thresholds corresponding to different alarm levels;

[0039] S52, when the dust receiving amount exceeds the preset current level threshold, sending an alarm message of the corresponding level to the individual;

[0040] S53: Record the time and location of alarm triggering and store them.

[0041] Preferably, as an implementable method, the environmental dust concentration data in the current space is obtained, and then the spatial distribution characteristics of the environmental dust concentration data are obtained, and the dust receiving amount of the individual at the current position in the space corresponding to different time periods is calculated by combining the movement trajectory data and the spatial distribution characteristics of the environmental dust concentration data, which specifically includes:

[0042] Obtain the ambient dust concentration data in the current space and use the sensor network to monitor the dust concentration in different areas in real time;

[0043] The Kriging interpolation method is used to perform spatial interpolation on the dust concentration data to construct a spatial distribution model of ambient dust concentration.

[0044] Combined with the individual's movement trajectory data, analyze its spatial position in different time periods, and determine the individual's activity range in the current space by setting a time window;

[0045] The formula for calculating the dust intake of an individual in a specific period of time is:

[0046]

[0047] Among them, R ij represents the dust intake of the i-th individual at the j-th spatial position in the time period [t1, t2], C jk (xy) is the dust concentration at the jth spatial position at time k, and Δt is the time interval.

[0048] Preferably, as an implementable embodiment; further comprising the following steps:

[0049] Through historical data analysis, the spatiotemporal characteristics of environmental dust concentration are extracted, and the feature matrix F and label matrix L are constructed, where F includes dust concentration, meteorological conditions and time information;

[0050] The LSTM algorithm is used to train the feature matrix to predict the dust reception of the i-th individual at the j-th spatial position at the n-th time node in the future. The prediction model is:

[0051] P ij =g(F ij ， θ);

[0052] Among them, P ij is the dust intake of the i-th individual at the future time node, g is the deep learning model function, and θ is the model parameter.

[0053] During the prediction process, the danger threshold T is set and the prediction results are monitored in real time;

[0054] When the predicted value P ij When the danger threshold T is exceeded, a warning message is generated, which includes the degree of dust concentration exceeding the standard at the jth spatial position; and the warning message is sent to the device of the i-th individual through a smartphone application or other instant messaging tools to ensure that the individual receives the warning information in a timely manner.

[0055] The embodiment of the present invention provides an online dust concentration monitoring system based on individual movement trajectory tracking, including a wearable device, a central processing system, an online monitoring platform, and a wireless communication module;

[0056] The wearable device is used to collect individual movement trajectory data and environmental dust concentration data; and transmit the movement trajectory data and environmental dust concentration data to a central processing system;

[0057] The central processing system is used to analyze and process the movement trajectory data and the ambient dust concentration data, calculate the individual dust reception in real time, and predict the dust reception of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future and output it as the calculation result; transmit the calculation result to the online monitoring platform to display the individual dust reception in real time;

[0058] The online monitoring platform is used to trigger a warning alarm processing operation when the individual dust reception exceeds a preset threshold;

[0059] The central processing system establishes communication connections with the wearable device and the online monitoring platform respectively through the wireless communication module.

[0060] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0061] From the analysis of the above-mentioned online monitoring method of dust concentration based on individual mobile trajectory tracking provided by the present invention, it can be known that in specific application, first, the individual mobile trajectory data and environmental dust concentration data are collected through wearable devices; the mobile trajectory data and environmental dust concentration data are transmitted to the central processing system; the mobile trajectory data and environmental dust concentration data are analyzed and processed by the central processing system, and the individual dust reception amount is calculated in real time and the dust reception amount of the jth spatial position corresponding to the nth target time node in the future of the current i-th individual is predicted and output as the calculation result; the calculation result is transmitted to the online monitoring platform, and the individual dust reception amount is displayed in real time; when the individual dust reception amount exceeds the preset threshold, the early warning alarm processing operation is triggered.

[0062] It can be seen from the analysis of the above technical scheme that in the process of executing the above steps, the real-time monitoring of individual dust exposure is realized through wearable devices. At the same time, the wearable device design requires the ability to dynamically track the relationship between the individual's movement trajectory and the dust concentration in the environment, so as to realize individualized accurate monitoring and avoid the limitations of traditional fixed-point monitoring (of course, the wearable device can also synchronously sense and obtain dust online monitoring equipment to obtain environmental dust concentration data online); then, correlation data analysis is performed. This correlation analysis of spatial position and dust concentration realizes more accurate prediction and early warning, which is actually equivalent to providing the possibility of active protection rather than passive response;

[0063] Ultimately, a comprehensive analysis of mobile trajectory data and environmental data is achieved (the dust reception amount at the jth spatial position corresponding to the nth target time node in the future of the current ith individual is predicted and output as the calculation result), and an individual exposure file is established based on the calculation results, so that long-term health management can be achieved by the online monitoring center. This method improves the accuracy of occupational health protection and the predictability of exposure risk assessment and early warning, realizes the implementation of active prevention and intervention measures, and improves the level of intelligence of dust hazard protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the main process of an online dust concentration monitoring method based on individual movement trajectory tracking;

[0065] Figure 2 A schematic diagram of a process for collecting individual movement trajectory data in an online dust concentration monitoring method based on individual movement trajectory tracking;

[0066] Figure 3 A schematic diagram of the main process of transmitting the movement trajectory data and the ambient dust concentration data to a central processing system in an online dust concentration monitoring method based on individual movement trajectory tracking;

[0067] Figure 4A schematic diagram of the main process of analyzing and processing the movement trajectory data and the environmental dust concentration data by a central processing system in an online dust concentration monitoring method based on individual movement trajectory tracking;

[0068] Figure 5 A schematic diagram of the main process of analyzing and processing the movement trajectory data and the environmental dust concentration data by using a central processing system in another embodiment of an online dust concentration monitoring method based on individual movement trajectory tracking;

[0069] Figure 6 A schematic diagram of the main process of real-time display of individual dust receiving amount in an online dust concentration monitoring method based on individual movement trajectory tracking;

[0070] Figure 7 A schematic diagram of the main process of triggering early warning alarm processing operations in an online dust concentration monitoring method based on individual movement trajectory tracking;

[0071] Figure 8 This is a schematic diagram of the architecture of an online dust concentration monitoring system based on individual mobile trajectory tracking.

[0072] Reference numerals: dust concentration online monitoring system based on individual movement trajectory tracking 100; wearable device 10; central processing system 20; online monitoring platform 30; wireless communication module 40. DETAILED DESCRIPTION

[0073] 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.

[0074] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0075] Embodiment 1

[0076] like Figure 1 As shown, the first embodiment of the present invention provides an online dust concentration monitoring method based on individual movement trajectory tracking, including the following operation steps:

[0077] S1. Collect individual movement trajectory data and environmental dust concentration data through wearable devices;

[0078] In an embodiment of the present invention, in addition to the wearable device being able to collect movement trajectory data and environmental dust concentration data along with the movement of the operator, an online dust monitoring device can also be installed in the above-mentioned specific workplace, and the online dust monitoring device can collect environmental dust concentration data in real time (that is, the online dust monitoring device also has the function of sampling environmental dust concentration data); in this way, the individual wearable device is used to collect individual movement trajectories on the one hand, and on the other hand, the online dust monitoring device can be used to obtain environmental dust concentration data online through synchronous sensing.

[0079] S2, transmitting the movement trajectory data and the ambient dust concentration data to a central processing system;

[0080] S3, using the central processing system to analyze and process the movement trajectory data and the ambient dust concentration data, calculate the individual dust reception in real time and predict the dust reception of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future and output it as the calculation result;

[0081] S4, transmitting the calculation results to the online monitoring platform to display the individual dust reception amount in real time;

[0082] S5. When the individual dust reception exceeds the preset threshold, the early warning alarm processing operation is triggered.

[0083] Research has found that existing technical solutions lack an effective method to correlate and analyze individual movement trajectories with environmental dust concentration data. Existing monitoring systems usually separate location information and dust concentration data, and are unable to establish a dynamic correlation between the two, which makes it difficult to accurately evaluate individual dust reception under different time and space conditions. Existing dust monitoring methods make it difficult to provide personalized protection recommendations based on the specific circumstances of different workers. Due to the lack of real-time tracking of individual movement trajectories, it is impossible to formulate targeted protection measures based on the actual exposure of the workers. However, an analysis of the above technical solution of the embodiments of the present invention shows that it has obviously overcome the above technical defects;

[0084] From the analysis of the above-mentioned online monitoring method of dust concentration based on individual mobile trajectory tracking provided by the present invention, it can be known that in specific application, first, the individual mobile trajectory data and environmental dust concentration data are collected through wearable devices; the mobile trajectory data and environmental dust concentration data are transmitted to the central processing system; the mobile trajectory data and environmental dust concentration data are analyzed and processed by the central processing system, and the individual dust reception amount is calculated in real time and the dust reception amount of the jth spatial position corresponding to the nth target time node in the future of the current i-th individual is predicted and output as the calculation result; the calculation result is transmitted to the online monitoring platform, and the individual dust reception amount is displayed in real time; when the individual dust reception amount exceeds the preset threshold, the early warning alarm processing operation is triggered.

[0085] During the execution of the above steps, real-time monitoring of individual dust exposure is achieved through wearable devices. At the same time, the wearable device design requires the ability to dynamically track the relationship between the individual's movement trajectory and the dust concentration in the environment, to achieve individualized and accurate monitoring, and avoid the limitations of traditional fixed-point monitoring (of course, wearable devices can also synchronously sense and obtain dust online monitoring equipment to obtain environmental dust concentration data online); then perform correlation data analysis. This correlation analysis of spatial position and dust concentration achieves more accurate prediction and warning, which is actually equivalent to providing the possibility of active protection rather than passive response;

[0086] Ultimately, a comprehensive analysis of mobile trajectory data and environmental data is achieved (the dust reception amount at the jth spatial position corresponding to the nth target time node in the future of the current ith individual is predicted and output as the calculation result), and an individual exposure file is established based on the calculation results, so that long-term health management can be achieved by the online monitoring center. This method improves the accuracy of occupational health protection and the predictability of exposure risk assessment and early warning, realizes the implementation of active prevention and intervention measures, and improves the level of intelligence of dust hazard protection.

[0087] like Figure 2 As shown, the step S1 of collecting individual movement trajectory data includes:

[0088] S11, using a GPS module to obtain the geographic location coordinates of the individual;

[0089] S12, performing time synchronization fusion on the data of the acquired geographic location coordinates to form complete movement trajectory data.

[0090] like Figure 3 As shown, the step S2 of transmitting the movement trajectory data and the ambient dust concentration data to the central processing system includes:

[0091] S21, using a low-power Bluetooth communication module to transmit movement trajectory data and environmental dust concentration data from the wearable device to the mobile terminal;

[0092] S22, transmitting the movement trajectory data and the ambient dust concentration data from the mobile terminal to the central processing system via the mobile communication network;

[0093] S23. The central processing system receives the movement trajectory data and the environmental dust concentration data.

[0094] like Figure 4 As shown, the use of the central processing system to analyze and process the movement trajectory data and the ambient dust concentration data, and to calculate the individual dust reception amount in real time includes:

[0095] S31, obtaining the ambient dust concentration data in the current space, and then obtaining the spatial distribution characteristics of the ambient dust concentration data, combining the movement trajectory data and the spatial distribution characteristics of the ambient dust concentration data, and calculating the dust reception amount of the individual at the current position in the space corresponding to different time periods;

[0096] S32. Analyze the historical data of the spatial distribution characteristics of the ambient dust concentration data using a machine learning algorithm to predict the dust receiving amount of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future;

[0097] S33. When it is predicted that the dust reception amount corresponding to the jth spatial position at the nth target time node in the future of the ith individual exceeds the standard content threshold, a regional position warning information for the jth spatial position is sent to avoid the alarm information of the current individual going to the jth spatial position.

[0098] The following is a specific implementation plan for the next parallel:

[0099] like Figure 5 As shown, the method of using the central processing system to analyze and process the movement trajectory data and the environmental dust concentration data to calculate the individual dust reception amount in real time includes:

[0100] S34, discretizing the movement trajectory data into a corresponding sequence table of time points and position points corresponding to the time points;

[0101] S35. For each time point, calculate the dust concentration at the spatial position;

[0102] S36, if the duration of the current time point exceeds the standard threshold, it is confirmed as a valid time point, and the dust receiving amount at each valid time point is calculated;

[0103] S37, accumulating the dust reception amount of all valid time points of the current individual to obtain the total dust reception amount.

[0104] like Figure 6 As shown, the real-time display of individual dust reception amount in step S4 includes:

[0105] S41, transmitting the calculated dust receiving amount data to the online monitoring platform;

[0106] S42. Use data visualization technology to display the trend of individual dust intake in the form of charts;

[0107] S43. Use different colors to mark different dust receiving levels;

[0108] S44. Provide multi-dimensional data display, including time dimension, space dimension and cumulative reception dimension.

[0109] like Figure 7 As shown, the triggering of the early warning alarm processing operation in step S5 includes:

[0110] S51, setting multiple levels of dust receiving thresholds corresponding to different alarm levels;

[0111] S52, when the dust receiving amount exceeds the preset current level threshold, sending an alarm message of the corresponding level to the individual;

[0112] S53: Record the time and location of alarm triggering and store them.

[0113] Preferably, as an implementable method, the environmental dust concentration data in the current space is obtained, and then the spatial distribution characteristics of the environmental dust concentration data are obtained, and the dust receiving amount of the individual at the current position in the space corresponding to different time periods is calculated by combining the movement trajectory data and the spatial distribution characteristics of the environmental dust concentration data, which specifically includes:

[0114] Obtain the ambient dust concentration data in the current space and use the sensor network to monitor the dust concentration in different areas in real time;

[0115] The Kriging interpolation method is used to perform spatial interpolation on the dust concentration data to construct a spatial distribution model of ambient dust concentration.

[0116] Combined with the individual's movement trajectory data, analyze its spatial position in different time periods, and determine the individual's activity range in the current space by setting a time window;

[0117] The formula for calculating the dust intake of an individual in a specific period of time is:

[0118]

[0119] Among them, R ij represents the dust intake of the i-th individual at the j-th spatial position in the time period [t1, t2], C jk (xy) is the dust concentration at the jth spatial position at time k, and Δt is the time interval.

[0120] Embodiment 2 of the present invention provides an online dust concentration monitoring method based on individual mobile trajectory tracking. On the one hand, it realizes the accurate calculation of dust reception at a time point, and the effective time point judgment improves the data credibility. At the same time, it also designs the cumulative reception statistics to facilitate the subsequent long-term exposure assessment.

[0121] Preferably, as an implementable embodiment; further comprising the following steps:

[0122] Through historical data analysis, the spatiotemporal characteristics of environmental dust concentration are extracted, and the feature matrix F and label matrix L are constructed, where F includes dust concentration, meteorological conditions and time information;

[0123] The LSTM algorithm is used to train the feature matrix to predict the dust reception of the i-th individual at the j-th spatial position at the n-th time node in the future. The prediction model is:

[0124] P ij =g(F ij 'θ);

[0125] Among them, P ij is the dust intake of the i-th individual at the future time node, g is the deep learning model function, and θ is the model parameter.

[0126] During the prediction process, the danger threshold T is set and the prediction results are monitored in real time;

[0127] When the predicted value P ij When the danger threshold T is exceeded, a warning message is generated, which includes the degree of dust concentration exceeding the standard at the jth spatial position; and the warning message is sent to the device of the i-th individual through a smartphone application or other instant messaging tools to ensure that the individual receives the warning information in a timely manner.

[0128] Embodiment 2 of the present invention provides an online dust concentration monitoring method based on individual mobile trajectory tracking, which adopts spatial interpolation technology to achieve accurate modeling of dust concentration distribution. At the same time, the LSTM deep learning algorithm provides accurate prediction capabilities to realize the subsequent multi-level warning threshold setting to achieve graded warning and regional location warning to avoid exposure to high-risk areas.

[0129] Embodiment 2

[0130] like Figure 8 As shown, the second embodiment of the present invention provides a dust concentration online monitoring system 100 based on individual movement trajectory tracking, including a wearable device 10, a central processing system 20, an online monitoring platform 30, and a wireless communication module 40;

[0131] The wearable device 10 is used to collect individual movement trajectory data and environmental dust concentration data; and transmit the movement trajectory data and environmental dust concentration data to a central processing system;

[0132] The central processing system 20 is used to analyze and process the movement trajectory data and the ambient dust concentration data, calculate the individual dust reception in real time and predict the dust reception of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future and output it as the calculation result; transmit the calculation result to the online monitoring platform to display the individual dust reception in real time;

[0133] The online monitoring platform 30 is used to trigger a warning alarm processing operation when the individual dust reception exceeds a preset threshold;

[0134] The central processing system 20 establishes communication connections with the wearable device and the online monitoring platform respectively through the wireless communication module.

[0135] Embodiment 2 of the present invention provides an online dust concentration monitoring system based on individual mobile trajectory tracking, which realizes the accurate quantification of individual dust exposure, establishes an active early warning and protection mechanism, thereby improving the intelligent level of dust hazard prevention and control; the online dust concentration monitoring system based on individual mobile trajectory tracking can effectively solve the problems existing in the prior art, significantly improve the ability to prevent and control dust hazards in the occupational environment, and provide reliable technical guarantee for protecting the occupational health of workers.

[0136] In summary, the second embodiment of the present invention provides an online dust concentration monitoring system based on individual movement trajectory tracking, which realizes real-time dust reception monitoring based on individual movement trajectory, and improves the accuracy and timeliness of monitoring. At the same time, through the combination of wearable devices and online monitoring platforms, real-time display and early warning of dust reception are realized, which effectively improves the timeliness of individual protection.

[0137] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0138] 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 method for online monitoring of dust concentration based on individual movement trajectory tracking, characterized in that: The following steps are involved: S1. Collect individual movement trajectory data and environmental dust concentration data through wearable devices; S2, transmitting the movement trajectory data and the ambient dust concentration data to a central processing system; S3, using the central processing system to analyze and process the movement trajectory data and the ambient dust concentration data, calculate the individual dust reception in real time and predict the dust reception of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future and output it as the calculation result; S4, transmitting the calculation results to the online monitoring platform to display the individual dust reception amount in real time; S5. When the individual dust reception exceeds the preset threshold, the early warning alarm processing operation is triggered.

2. The method according to claim 1, characterized in that The step S1 of collecting individual movement trajectory data includes: S11, using a GPS module to obtain the geographic location coordinates of the individual; S12, performing time synchronization fusion on the data of the acquired geographic location coordinates to form complete movement trajectory data.

3. The method according to claim 1, characterized in that The step S2 of transmitting the movement trajectory data and the ambient dust concentration data to the central processing system includes: S21, using a low-power Bluetooth communication module to transmit movement trajectory data and environmental dust concentration data from the wearable device to the mobile terminal; S22, transmitting the movement trajectory data and the ambient dust concentration data from the mobile terminal to the central processing system via the mobile communication network; S23. The central processing system receives the movement trajectory data and the environmental dust concentration data.

4. The method according to claim 3, characterized in that The use of the central processing system to analyze and process the movement trajectory data and the ambient dust concentration data to calculate the individual dust receiving amount in real time includes: S31, obtaining the ambient dust concentration data in the current space, and then obtaining the spatial distribution characteristics of the ambient dust concentration data, combining the movement trajectory data and the spatial distribution characteristics of the ambient dust concentration data, and calculating the dust reception amount of the individual at the current position in the space corresponding to different time periods; S32. Analyze the historical data of the spatial distribution characteristics of the ambient dust concentration data using a machine learning algorithm to predict the dust receiving amount of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future; S33. When the dust receiving amount corresponding to the jth spatial position at the nth target time node in the future of the i-th individual is predicted to exceed the standard content threshold, a regional position warning information for the j-th spatial position is sent to avoid the alarm information of the current individual going to the j-th spatial position.

5. The method according to claim 3, characterized in that: The method of using the central processing system to analyze and process the movement trajectory data and the environmental dust concentration data to calculate the individual dust reception amount in real time includes: S34, discretizing the movement trajectory data into a corresponding sequence table of time points and position points corresponding to the time points; S35. For each time point, calculate the dust concentration at the spatial position; S36, if the duration of the current time point exceeds the standard threshold, it is confirmed as a valid time point, and the dust receiving amount at each valid time point is calculated; S37, accumulating the dust reception amount of all valid time points of the current individual to obtain the total dust reception amount.

6. The method according to claim 1, characterized in that The real-time display of individual dust receiving amount in step S4 includes: S41, transmitting the calculated dust receiving amount data to the online monitoring platform; S42. Use data visualization technology to display the trend of individual dust intake in the form of charts; S43. Different colors are used for identification according to different dust receiving levels; S44. Provide multi-dimensional data display, including time dimension, space dimension and cumulative reception dimension.

7. The method according to claim 1, characterized in that The triggering of the early warning alarm processing operation in step S5 includes: S51, setting multiple levels of dust receiving thresholds corresponding to different alarm levels; S52, when the dust receiving amount exceeds the preset current level threshold, sending an alarm message of the corresponding level to the individual; S53: Record the time and location of alarm triggering and store them.

8. The method according to claim 4, characterized in that The ambient dust concentration data in the current space is obtained, and then the spatial distribution characteristics of the ambient dust concentration data are obtained, and the spatial distribution characteristics of the ambient dust concentration data are combined with the movement trajectory data. Calculate the dust intake of the individual at the current position in the space corresponding to different time periods, including: Obtain the ambient dust concentration data in the current space and use the sensor network to monitor the dust concentration in different areas in real time; The Kriging interpolation method is used to perform spatial interpolation on the dust concentration data to construct a spatial distribution model of ambient dust concentration. Combined with the individual's movement trajectory data, analyze its spatial position in different time periods, and determine the individual's activity range in the current space by setting a time window; The formula for calculating the dust intake of an individual in a specific period of time is: Among them, R ij Indicates that the i-th individual is in the time period [ t1 ， t2 ] The dust receiving amount at the jth spatial position, C jk ( xy ) is the dust concentration at the jth spatial position at time k, and Δt is the time interval.

9. The method according to claim 4, characterized in that Further comprising the steps of: Through historical data analysis, the spatiotemporal characteristics of environmental dust concentration are extracted, and the feature matrix F and label matrix L are constructed, where F includes dust concentration, meteorological conditions and time information; The LSTM algorithm is used to train the feature matrix to predict the dust reception of the i-th individual at the j-th spatial position at the n-th time node in the future. The prediction model is: P.S ij g ( F ij ， θ ) 100. Among them, P ij is the dust intake of the i-th individual at the future time node, g is the deep learning model function, and θ is the model parameter. During the prediction process, the danger threshold T is set and the prediction results are monitored in real time; When the predicted value P ij When the danger threshold T is exceeded, a warning message is generated, which includes the degree of dust concentration exceeding the standard at the jth spatial position; and the warning message is sent to the device of the i-th individual through a smartphone application or other instant messaging tools to ensure that the individual receives the warning information in a timely manner.

10. An online dust concentration monitoring system based on individual movement trajectory tracking, characterized in that: Including wearable devices, central processing system and online monitoring platform, wireless communication module; The wearable device is used to collect individual movement trajectory data and environmental dust concentration data; and transmit the movement trajectory data and environmental dust concentration data to a central processing system; The central processing system is used to analyze and process the movement trajectory data and the environmental dust concentration data, calculate the individual dust reception in real time, and predict the dust reception of the current i-th individual at the j-th spatial position corresponding to the n-th target time node in the future and output it as the calculation result; The calculation results are transmitted to the online monitoring platform to display the individual dust reception in real time; The online monitoring platform is used to trigger a warning alarm processing operation when the individual dust reception exceeds a preset threshold; The central processing system establishes communication connections with the wearable device and the online monitoring platform respectively through the wireless communication module.

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