Dust toxin detection method and system based on safety helmet

Abstract

The invention discloses a dust toxin detection method and system based on a safety helmet, and belongs to the technical field of dust toxin detection. Comprising the following steps: integrating a dust detection sensor into a safety helmet of an operator, and collecting the concentration of an environmental target object in real time; the method comprises the following steps: dividing a working space into working area monomers with equal volumes through three-dimensional modeling, and generating a monomer sample set under a time node by combining the dynamic density (the ratio of the number of personnel to the volume) in each monomer; a three-dimensional melt model is constructed, the melt scale is quantified, the circular domain concentration is calculated, and the dust poison flowing state (diffusion, static or contraction) is represented by comparing the concentration change of adjacent regions; and iteratively evaluating the prediction step length based on the flow state set, and outputting an optimal prediction result. According to the invention, dynamic tracking and accurate prediction of the dust diffusion trend in the three-dimensional space are realized, the monitoring coverage rate and the real-time performance are effectively improved, and intelligent decision support is provided for safety protection of a high-risk operation environment.

Description

Technical Field

[0001] The present invention relates to the technical field of dust and toxicity detection, and in particular to a dust and toxicity detection method and system based on a safety helmet. Background Art

[0002] In industrial production and high-risk working environments, dust and toxic pollution is one of the key factors threatening the health and safety of workers. Real-time and accurate dust and toxic concentration monitoring technology is of great significance for ensuring work safety and optimizing environmental management.

[0003] At present, traditional dust and toxic detection technology mainly relies on fixed sensor networks, which deploy detection equipment at key points in the work area to realize concentration collection and analysis in a static environment; however, this method has significant limitations: first, fixed sensors are difficult to cover dynamic work scenes, especially areas with high personnel mobility, resulting in monitoring blind spots; second, existing technologies lack the ability to dynamically model the dust and toxic diffusion trends in three-dimensional space, and cannot effectively reflect the impact of real-time changing factors such as personnel movement and equipment operation on concentration distribution; in addition, existing systems mostly use data analysis of a single time node, and fail to combine the dynamic changes in personnel density in the work area, resulting in insufficient timeliness and accuracy of the prediction model. In particular, some existing solutions attempt to improve data collection efficiency by dividing the monitoring area and combining it with wireless transmission technology, but the area division method is mostly two-dimensional plane or fixed volume unit, and the synergistic effect of the distribution of workers in three-dimensional space on the diffusion of dust and toxic substances is not considered; although other methods introduce cloud computing for data processing, they do not deeply integrate sensors with equipment worn by personnel (such as safety helmets), resulting in a single data source and weak spatial correlation; in addition, existing prediction models are mostly based on linear regression of historical data, lack of fusion analysis of key parameters such as dynamic density gradient and melt scale quantification, making it difficult to accurately characterize the flow state and diffusion trend of dust and toxic substances. Summary of the invention

[0004] The purpose of the present invention is to provide a dust and toxicity detection method and system based on a safety helmet to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A dust and toxicity detection system based on a safety helmet, the system comprises: a data preparation module, a data preprocessing module, a data fitting module and a data analysis module; The data preparation module collects the target concentration in the environment through a dust and toxicity detection sensor and uploads it to the cloud detection center. The dust and toxicity detection sensor is installed in a safety helmet. The three-dimensional space of the work site is divided into work area monomers to receive the target concentration collected by the dust and toxicity sensor when the operator wears a safety helmet and works in the work area monomer. The data preprocessing module obtains the dynamic density of the operating area monomer by the ratio of the number of operators in the operating area monomer to the volume of the operating area monomer, and analyzes and generates a monomer sample set at a time node based on the dynamic density; The data fitting module is used to construct a three-dimensional melt model, analyze and quantify the melt scale based on the monomer sample set to generate a circular domain concentration; The data analysis module analyzes and characterizes the flow state of dust and toxic substances based on the circular domain concentration, and obtains the predicted step length based on the flow state analysis.

[0006] Further, the data preparation module includes a data acquisition unit and a three-dimensional model unit; The data acquisition unit collects the target concentration in the environment through the dust and toxicity detection sensor installed in the safety helmet, sets a code with unique attributes for each safety helmet, and allocates data storage space to the cloud detection center according to the coding attributes of the safety helmet, and the data storage space is used to store the target concentration; The three-dimensional model unit is used to divide the three-dimensional space of the work site into a number of work area monomers of equal volume. When the workers wear safety helmets and work in the work area monomers, they instruct the dust and toxic sensors on the safety helmets to collect the target object concentration.

[0007] Further, the data preprocessing module includes a dynamic density processing unit and a gradient processing unit; The dynamic density processing unit is used to obtain the number of operators in each operating area monomer, and the ratio of the number of operators in the operating area monomer to the volume of the operating area monomer is the dynamic density of the operating area monomer; The gradient processing unit is used to generate a monomer sample set at different time nodes. The monomer sample set records the dynamic density of monomers in different operating areas at the same time node. The spatial relationship between any two operating area monomers constitutes a gradient vector to generate a gradient set.

[0008] Further, the data fitting module includes a melt fitting unit and a melt concentration processing unit; The melt fitting unit is used to take the dynamic density as the centroid of the monomer sample set and fit the dynamic gradient to obtain the melt scale; The melt concentration processing unit is used to use the melt scale as the radius and the working area monomer as the center of the circle, retrieve the target object concentration uploaded by each dust and toxic detection sensor in the circle when the operator wears a safety helmet and works in the working area monomer, and obtain the average of the target object concentration uploaded by each dust and toxic detection sensor in the circle as the circular domain concentration.

[0009] Further, the data analysis module includes a fluid state marking unit and a dust and toxicity detection prediction unit; The fluid state marking unit quantifies the flow state of the target object when the monomers in different operation areas are taken as the center of the circle based on the circle domain concentration; The dust and toxicity detection prediction unit is used to generate a set of flow states of monomers in different operating areas, and adjust the prediction step length through iterative analysis, evaluate the prediction success probability under the prediction step length, and select and output the prediction step length corresponding to the maximum prediction success probability when the iteration stops.

[0010] The dust and toxicity detection method based on the safety helmet comprises the following steps: Step S1: The concentration of target objects in the environment is collected by a dust and toxicity detection sensor and uploaded to the cloud detection center. The dust and toxicity detection sensor is installed in a safety helmet. The three-dimensional space of the work site is divided into working area monomers to receive the target object concentration collected by the dust and toxicity sensor when the worker wears a safety helmet and works in the working area monomer; Step S2: The dynamic density of the monomer in the operating area is obtained by the ratio of the number of operators in the monomer in the operating area to the volume of the monomer in the operating area. Based on the dynamic density, a monomer sample set at a time node is analyzed and generated; Step S3: construct a three-dimensional melt model, analyze and quantify the melt scale based on the monomer sample set to generate a circular domain concentration; Step S4: Based on the circular concentration, the flow state of dust and toxicity is analyzed and characterized, and based on the flow state, the predicted step length is analyzed.

[0011] Furthermore, the specific implementation process of step S1 includes: The dust and toxicity detection sensor installed in the helmet collects the target concentration in the environment and uploads it to the cloud detection center through a wireless network; a code with unique attributes is set for each helmet, and data storage space is allocated to the cloud detection center according to the coding attributes of the helmet, and the data storage space is used to store the target concentration; A three-dimensional spatial model of the work site is established, and the three-dimensional space of the work site is divided into several work area units of equal volume. When the workers wear safety helmets and work in the work area units, the cloud detection center receives the target object concentration collected by the dust and toxic sensors on the safety helmets in real time.

[0012] Furthermore, the specific implementation process of step S2 includes: The number of operators in each operating area is obtained, and the ratio of the number of operators in the operating area to the volume of the operating area is the dynamic density of the operating area, and the dynamic density is uploaded to the cloud detection center; At the tth time node, a monomer sample set is generated, denoted as ,in, represents the dynamic density of the monomer in the i-th operation area, and I represents the total number of monomers in the operation area; Select Dynamic Density Single sample set The centroid of , and collect all dynamic gradients of the centroid at the tth time node to form a gradient set, ,in, represents the gradient vector between the i-th operating area monomer and the j-th operating area monomer, represents the magnitude of the gradient vector, i.e. the dynamic gradient, and , Represents the dynamic density of the j-th operating area monomer.

[0013] Furthermore, the specific implementation process of step S3 includes: Dynamic density Single sample set When the centroid of the i-th operation area is reached, the dynamic gradient is fitted to calculate the melting scale of the monomer in the i-th operation area. , where It represents the rounding function; Integral Scale As the radius, with the i-th working area monomer as the center, the target concentration uploaded by each dust and toxic detection sensor in the circle when the operator wears a safety helmet and works in the working area monomer is retrieved from the cloud detection center, and the average value of the target concentration uploaded by each dust and toxic detection sensor in the circle is obtained, which is recorded as the circle concentration with the i-th working area monomer as the center .

[0014] Furthermore, the specific implementation process of step S4 includes: Based on the circle concentration, quantify the flow state of the target object when the i-th operating area monomer is the center of the circle ,like , then let , indicating that at the tth time node, the monomer in the i-th operation area has a diffusion trend. , then let , indicating that at the tth time node, the i-th operating area monomer has a stationary behavior. , then let , indicating that at the tth time node, the i-th operating area monomer has a shrinking behavior, In terms of melt size is the radius, and the area of ​​the circle with the i-th operating area monomer as the center, In terms of melt size is the radius, and the area of ​​the circle with the i-th operating area monomer as the center, In terms of melt size is the radius, and the concentration of the circle with the monomer in the i-th operating area as the center; Generate the flow state set of the i-th operating area monomer, denoted as , T represents the current time node, initialization prediction step , evaluate at the prediction step The predicted success probability under , where if , then let ,like , then let ; make Perform iterative calculation of prediction success probability, The iteration stops when , L is the preset threshold, and the prediction step corresponding to the maximum prediction success probability is selected and output when the iteration stops.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the dust and toxicity detection method and system based on a helmet provided by the present invention is used to solve the problems of insufficient dynamic monitoring of three-dimensional space, poor data correlation and low accuracy of prediction models in the prior art. The technical scheme includes: integrating the dust and toxicity detection sensor into the helmet of the operator, collecting the concentration of environmental targets in real time and uploading it to the cloud detection center; dividing the working space into working area monomers of equal volume through three-dimensional modeling, and generating a monomer sample set under the time node by combining the dynamic density (ratio of the number of personnel to volume) in each monomer; constructing a three-dimensional melt model, quantifying the melt scale and calculating the circular domain concentration, and characterizing the dust and toxicity flow state (diffusion, static or contraction) by comparing the concentration changes in adjacent areas; iteratively evaluating the prediction step length based on the flow state set, and outputting the optimal prediction result. The present invention realizes the dynamic tracking and accurate prediction of the dust and toxicity diffusion trend in three-dimensional space through dynamic density gradient analysis, three-dimensional melt modeling and flow state quantification, effectively improving the monitoring coverage and real-time performance, and providing intelligent decision-making support for the safety protection of high-risk working environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0017] Figure 1 It is a schematic diagram of the steps of the dust and toxicity detection method based on a safety helmet of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0019] In the first embodiment: a dust and toxicity detection system based on a safety helmet is provided, the system comprising: a data preparation module, a data preprocessing module, a data fitting module and a data analysis module; The data preparation module collects the target concentration in the environment through a dust and toxicity detection sensor and uploads it to the cloud detection center. The dust and toxicity detection sensor is installed in a safety helmet. The three-dimensional space of the work site is divided into work area monomers to receive the target concentration collected by the dust and toxicity sensor when the operator wears a safety helmet and works in the work area monomer. Wherein, the data preparation module includes a data acquisition unit and a three-dimensional model unit; The data acquisition unit collects the target concentration in the environment through the dust and toxicity detection sensor installed in the safety helmet, sets a code with unique attributes for each safety helmet, and allocates data storage space to the cloud detection center according to the coding attributes of the safety helmet, and the data storage space is used to store the target concentration; The three-dimensional model unit is used to divide the three-dimensional space of the work site into a number of work area monomers of equal volume. When the workers wear safety helmets and work in the work area monomers, they instruct the dust and toxic sensors on the safety helmets to collect the target object concentration.

[0020] The data preprocessing module obtains the dynamic density of the operating area monomer by the ratio of the number of operators in the operating area monomer to the volume of the operating area monomer, and analyzes and generates a monomer sample set at a time node based on the dynamic density; Wherein, the data preprocessing module includes a dynamic density processing unit and a gradient processing unit; The dynamic density processing unit is used to obtain the number of operators in each operating area monomer, and the ratio of the number of operators in the operating area monomer to the volume of the operating area monomer is the dynamic density of the operating area monomer; The gradient processing unit is used to generate a monomer sample set at different time nodes. The monomer sample set records the dynamic density of monomers in different operating areas at the same time node. The spatial relationship between any two operating area monomers constitutes a gradient vector to generate a gradient set.

[0021] The data fitting module is used to construct a three-dimensional melt model, analyze and quantify the melt scale based on the monomer sample set to generate a circular domain concentration; Wherein, the data fitting module includes a melt fitting unit and a melt concentration processing unit; The melt fitting unit is used to take the dynamic density as the centroid of the monomer sample set and fit the dynamic gradient to obtain the melt scale; The melt concentration processing unit is used to use the melt scale as the radius and the working area monomer as the center of the circle, retrieve the target object concentration uploaded by each dust and toxic detection sensor in the circle when the operator wears a safety helmet and works in the working area monomer, and obtain the average of the target object concentration uploaded by each dust and toxic detection sensor in the circle as the circular domain concentration.

[0022] The data analysis module analyzes and characterizes the flow state of dust and toxicity based on the circular domain concentration, and obtains the predicted step length based on the flow state; Wherein, the data analysis module includes a fluid state marking unit and a dust and toxicity detection prediction unit; The fluid state marking unit quantifies the flow state of the target object when the monomers in different operation areas are taken as the center of the circle based on the circle domain concentration; The dust and toxicity detection prediction unit is used to generate a set of flow states of monomers in different operating areas, and adjust the prediction step length through iterative analysis, evaluate the prediction success probability under the prediction step length, and select and output the prediction step length corresponding to the maximum prediction success probability when the iteration stops.

[0023] See also Figure 1 In the second embodiment, a dust and toxicity detection method based on a safety helmet is provided to be applied to the first embodiment, and the method comprises the following steps: Step S1: The concentration of target objects in the environment is collected by a dust and toxicity detection sensor and uploaded to the cloud detection center. The dust and toxicity detection sensor is installed in a safety helmet. The three-dimensional space of the work site is divided into working area monomers to receive the target object concentration collected by the dust and toxicity sensor when the worker wears a safety helmet and works in the working area monomer; Exemplarily, the concentration of target objects in the environment is collected by a dust and toxicity detection sensor installed in a hard hat, and uploaded to a cloud detection center via a wireless network; a code with a unique attribute is set for each hard hat, and data storage space is allocated to the cloud detection center according to the coding attribute of the hard hat, and the data storage space is used to store the concentration of the target object; A three-dimensional spatial model of the work site is established, and the three-dimensional space of the work site is divided into several work area units of equal volume. When the workers wear safety helmets and work in the work area units, the cloud detection center receives the target object concentration collected by the dust and toxic sensors on the safety helmets in real time.

[0024] Step S2: The dynamic density of the monomer in the operating area is obtained by the ratio of the number of operators in the monomer in the operating area to the volume of the monomer in the operating area. Based on the dynamic density, a monomer sample set at a time node is analyzed and generated; Exemplarily, the number of operators in each operating area unit is obtained, and the ratio of the number of operators in the operating area unit to the volume of the operating area unit is the dynamic density of the operating area unit, and the dynamic density is uploaded to the cloud detection center; At the tth time node, a monomer sample set is generated, denoted as ,in, represents the dynamic density of the monomer in the i-th operation area, and I represents the total number of monomers in the operation area; Select Dynamic Density Single sample set The centroid of , and collect all dynamic gradients of the centroid at the tth time node to form a gradient set, ,in, represents the gradient vector between the i-th operating area monomer and the j-th operating area monomer, represents the magnitude of the gradient vector, i.e. the dynamic gradient, and , Represents the dynamic density of the j-th operating area monomer.

[0025] Step S3: construct a three-dimensional melt model, analyze and quantify the melt scale based on the monomer sample set to generate a circular domain concentration; For example, with dynamic density Single sample set When the centroid of the i-th operation area is reached, the dynamic gradient is fitted to calculate the melting scale of the monomer in the i-th operation area. , where It represents the rounding function; Integral Scale As the radius, with the i-th working area monomer as the center, the target concentration uploaded by each dust and toxic detection sensor in the circle when the operator wears a safety helmet and works in the working area monomer is retrieved from the cloud detection center, and the average value of the target concentration uploaded by each dust and toxic detection sensor in the circle is obtained, which is recorded as the circle concentration with the i-th working area monomer as the center .

[0026] Step S4: Analyze and characterize the flow state of dust and poison based on the circular concentration, and obtain the predicted step length based on the flow state; For example, based on the circle concentration, the flow state of the target object is quantified when the i-th operating area monomer is the center of the circle. ,like , then let , indicating that at the tth time node, the monomer in the i-th operation area has a diffusion trend. , then let , indicating that at the tth time node, the i-th operating area monomer has a stationary behavior. , then let , indicating that at the tth time node, the i-th operating area monomer has a shrinking behavior, In terms of melt size is the radius, and the area of ​​the circle with the i-th operating area monomer as the center, In terms of melt size is the radius, and the area of ​​the circle with the i-th operating area monomer as the center, In terms of melt size is the radius, and the concentration of the circle with the monomer in the i-th operating area as the center; Generate the flow state set of the i-th operating area monomer, denoted as , T represents the current time node, initialization prediction step , evaluate at the prediction step The predicted success probability under , where if , then let ,like , then let ; make Perform iterative calculation of prediction success probability, The iteration stops when , L is the preset threshold, and the prediction step length corresponding to the maximum prediction success probability is selected and output when the iteration stops; On the one hand, after selecting the most suitable prediction step length k, if the current time node is T, and a certain operating area monomer has stationary behavior at the current time node, it can be predicted that the operating area monomer after T+k steps will be stationary behavior; On the other hand, if you plan to predict the state behavior of a certain operating area unit at the Hth time node, you can combine the most suitable prediction step k and reverse simulation through the inverse method. The time node of the reverse simulation is (HT) / k.

[0027] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0028] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dust and toxicity detection method based on a safety helmet, characterized in that: The method comprises the following steps: Step S1: The concentration of target objects in the environment is collected by a dust and toxicity detection sensor and uploaded to the cloud detection center. The dust and toxicity detection sensor is installed in a safety helmet. The three-dimensional space of the work site is divided into working area monomers to receive the target object concentration collected by the dust and toxicity sensor when the worker wears a safety helmet and works in the working area monomer; Step S2: The dynamic density of the monomer in the operating area is obtained by the ratio of the number of operators in the monomer in the operating area to the volume of the monomer in the operating area. Based on the dynamic density, a monomer sample set at a time node is analyzed and generated; Step S3: construct a three-dimensional melt model, analyze and quantify the melt scale based on the monomer sample set to generate a circular domain concentration; Step S4: Based on the circular concentration, the flow state of dust and toxicity is analyzed and characterized, and based on the flow state, the predicted step length is analyzed.

2. The dust and toxicity detection method based on a safety helmet according to claim 1, characterized in that: The specific implementation process of step S1 includes: The dust and toxicity detection sensor installed in the helmet collects the target concentration in the environment and uploads it to the cloud detection center through a wireless network; a code with unique attributes is set for each helmet, and data storage space is allocated to the cloud detection center according to the coding attributes of the helmet, and the data storage space is used to store the target concentration; A three-dimensional spatial model of the work site is established, and the three-dimensional space of the work site is divided into several work area units of equal volume. When the workers wear safety helmets and work in the work area units, the cloud detection center receives the target object concentration collected by the dust and toxic sensors on the safety helmets in real time.

3. The dust and toxicity detection method based on a safety helmet according to claim 1, characterized in that: The specific implementation process of step S2 includes: The number of operators in each operating area is obtained, and the ratio of the number of operators in the operating area to the volume of the operating area is the dynamic density of the operating area, and the dynamic density is uploaded to the cloud detection center; At the tth time node, a monomer sample set is generated, denoted as ,in, represents the dynamic density of the monomer in the i-th operation area, and I represents the total number of monomers in the operation area; Select Dynamic Density Single sample set The centroid of , and collect all dynamic gradients of the centroid at the tth time node to form a gradient set, ,in, represents the gradient vector between the i-th operating area monomer and the j-th operating area monomer, represents the magnitude of the gradient vector, i.e. the dynamic gradient, and , Represents the dynamic density of the j-th operating area monomer.

4. The dust and toxicity detection method based on a safety helmet according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Dynamic density Single sample set When the centroid of the i-th operation area is reached, the dynamic gradient is fitted to calculate the melting scale of the monomer in the i-th operation area. , where represents the rounding function; Integral Scale As the radius, with the i-th working area monomer as the center, the target concentration uploaded by each dust and toxic detection sensor in the circle when the operator wears a safety helmet and works in the working area monomer is retrieved from the cloud detection center, and the average value of the target concentration uploaded by each dust and toxic detection sensor in the circle is obtained, which is recorded as the circle concentration with the i-th working area monomer as the center .

5. The dust and toxicity detection method based on a safety helmet according to claim 4, characterized in that: The specific implementation process of step S4 includes: Based on the circle concentration, quantify the flow state of the target object when the i-th operating area monomer is the center of the circle ,like , then let , indicating that at the tth time node, the monomer in the i-th operation area has a diffusion trend. , then let , indicating that at the tth time node, the i-th operating area monomer has a stationary behavior. , then let , indicating that at the tth time node, the i-th operating area monomer has a shrinking behavior, In terms of melt size is the radius, and the area of ​​the circle with the i-th operating area monomer as the center, In terms of melt size is the radius, and the area of ​​the circle with the i-th operating area monomer as the center, In terms of melt size is the radius, and the concentration of the circle with the monomer in the i-th operating area as the center; Generate the flow state set of the i-th operating area monomer, denoted as , T represents the current time node, initialization prediction step , evaluate at the prediction step The predicted success probability under , where if , then let ,like , then let ; make Perform iterative calculation of prediction success probability, The iteration stops when , L is the preset threshold, and the prediction step corresponding to the maximum prediction success probability is selected and output when the iteration stops.

6. A dust and toxicity detection system based on a safety helmet, performing the dust and toxicity detection method according to any one of claims 1 to 5, characterized in that: The system comprises: a data preparation module, a data preprocessing module, a data fitting module and a data analysis module; The data preparation module collects the target concentration in the environment through a dust and toxicity detection sensor and uploads it to the cloud detection center. The dust and toxicity detection sensor is installed in a safety helmet. The three-dimensional space of the work site is divided into work area monomers to receive the target concentration collected by the dust and toxicity sensor when the operator wears a safety helmet and works in the work area monomer. The data preprocessing module obtains the dynamic density of the operating area monomer by the ratio of the number of operators in the operating area monomer to the volume of the operating area monomer, and analyzes and generates a monomer sample set at a time node based on the dynamic density; The data fitting module is used to construct a three-dimensional melt model, analyze and quantify the melt scale based on the monomer sample set to generate a circular domain concentration; The data analysis module analyzes and characterizes the flow state of dust and toxic substances based on the circular domain concentration, and obtains the predicted step length based on the flow state analysis.

7. The dust and toxicity detection system based on a hard hat according to claim 6, characterized in that: The data preparation module includes a data acquisition unit and a three-dimensional model unit; The data acquisition unit collects the target concentration in the environment through the dust and toxicity detection sensor installed in the safety helmet, sets a code with unique attributes for each safety helmet, and allocates data storage space to the cloud detection center according to the coding attributes of the safety helmet, and the data storage space is used to store the target concentration; The three-dimensional model unit is used to divide the three-dimensional space of the work site into a number of work area monomers of equal volume. When the workers wear safety helmets and work in the work area monomers, they instruct the dust and toxic sensors on the safety helmets to collect the target object concentration.

8. The dust and toxicity detection system based on a hard hat according to claim 7, characterized in that: The data preprocessing module includes a dynamic density processing unit and a gradient processing unit; The dynamic density processing unit is used to obtain the number of operators in each operating area monomer, and the ratio of the number of operators in the operating area monomer to the volume of the operating area monomer is the dynamic density of the operating area monomer; The gradient processing unit is used to generate a monomer sample set at different time nodes. The monomer sample set records the dynamic density of monomers in different operating areas at the same time node. The spatial relationship between any two operating area monomers constitutes a gradient vector to generate a gradient set.

9. The dust and toxicity detection system based on a hard hat according to claim 8, characterized in that: The data fitting module includes a melt fitting unit and a melt concentration processing unit; The melt fitting unit is used to take the dynamic density as the centroid of the monomer sample set and fit the dynamic gradient to obtain the melt scale; The melt concentration processing unit is used to use the melt scale as the radius and the working area monomer as the center of the circle, retrieve the target object concentration uploaded by each dust and toxic detection sensor in the circle when the operator wears a safety helmet and works in the working area monomer, and obtain the average of the target object concentration uploaded by each dust and toxic detection sensor in the circle as the circular domain concentration.

10. The dust and toxicity detection system based on a hard hat according to claim 9, characterized in that: The data analysis module includes a fluid state marking unit and a dust and toxicity detection prediction unit; The fluid state marking unit quantifies the flow state of the target object when the monomers in different operation areas are taken as the center of the circle based on the circle domain concentration; The dust and toxicity detection prediction unit is used to generate a set of flow states of monomers in different operating areas, and adjust the prediction step length through iterative analysis, evaluate the prediction success probability under the prediction step length, and select and output the prediction step length corresponding to the maximum prediction success probability when the iteration stops.

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