Intelligent monitoring and transmission management and control system for occupational health hazard information

Abstract

The invention relates to the technical field of occupational health monitoring, in particular to an intelligent monitoring and transmission management and control system for occupational health hazard information, which comprises a multi-node data acquisition module, a wireless transmission module, a data intelligent processing module, a risk assessment module and a remote regulation and control module, occupational health information is acquired through a multi-node data acquisition module to obtain occupational health hazard initial information; the wireless transmission module receives the initial information, checks module communication indexes and outputs occupational health hazard information; a data intelligent processing module is utilized to obtain a multi-level output result and an information loss condition, and an information closed-loop processing mechanism is established to obtain an occupational hazard information base; the risk assessment module performs hazard trend prediction based on the occupational hazard information base to obtain an occupational health risk assessment result; and the remote regulation and control module remotely controls the occupational health hazard monitoring equipment and the terminal access technology in combination with an occupational health risk assessment result and an occupational hazard information base, thereby providing a basis for occupational health management.

Description

Technical Field

[0001] This invention relates to the field of occupational health monitoring technology, specifically to an intelligent monitoring and transmission control system for occupational health hazard information. Background Technology

[0002] Existing occupational health monitoring and management methods have numerous drawbacks, severely impacting the effective acquisition and control of occupational health hazard information. Traditional monitoring equipment primarily employs a single-point layout, with each monitoring point operating independently, hindering collaborative data collection across multiple nodes. This results in data that only reflects local conditions, leading to a one-sided view and making it difficult to comprehensively and accurately present the overall situation of occupational health hazards. The lack of effective data interaction and sharing mechanisms prevents the effective integration of data collected from different devices, hindering the formation of a comprehensive and systematic global risk assessment and impeding comprehensive judgment and decision-making regarding occupational health hazards. Furthermore, management loopholes easily arise for cross-regional occupational health hazard monitoring equipment, making it impossible to achieve remote and unified management of all devices and failing to meet the needs of modern occupational health monitoring.

[0003] To address the aforementioned issues, a system integrating sensor networks, wireless transmission, intelligent analysis, and remote control functions needs to be designed. This system would effectively solve key problems such as multi-node data fusion, real-time risk assessment, and cross-regional collaborative management, thereby improving the accuracy, timeliness, and management efficiency of occupational health monitoring. Summary of the Invention

[0004] To address the shortcomings of existing methods and the needs of practical applications, in order to achieve the integration of multi-node data, accurate risk assessment, and collaborative management of cross-regional equipment, it is necessary to ensure that different data sources can detect and share information, guaranteeing that the data has high quality and high accuracy, thus providing solid data support for occupational health monitoring from the source. On one hand, this invention provides an intelligent monitoring and transmission control system for occupational health hazard information. The system includes: a multi-node data acquisition module, a wireless transmission module, a data intelligent processing module, a risk assessment module, and a remote control module. The multi-node data acquisition module collects occupational health information in real time to obtain initial occupational health hazard information. The wireless transmission module receives the initial occupational health hazard information and verifies its communication indicators to output occupational health hazard information. The data intelligent processing module obtains multi-level output results and information loss status of the occupational health hazard information. Based on the multi-level output results and information loss status, an information closed-loop processing mechanism is established to obtain an occupational hazard information database. The risk assessment module predicts hazard trends based on the occupational hazard information database to obtain occupational health risk assessment results. The remote control module remotely controls occupational health hazard monitoring equipment and terminal access technology in conjunction with the occupational health risk assessment results and the occupational hazard information database.

[0005] The various modules of this invention work together to achieve comprehensive collection, efficient transmission, intelligent processing, scientific evaluation, and remote control of occupational health hazard information, effectively improving the accuracy, timeliness, and management efficiency of occupational health monitoring, and providing technical support for the health and safety of occupational personnel.

[0006] Optionally, the step of collecting occupational health information in real time through the multi-node data acquisition module and obtaining initial occupational health hazard information includes: setting a distributed data acquisition strategy, a time synchronization mechanism, and a data verification mechanism in the multi-node data acquisition module; the multi-node data acquisition module collecting monitoring information from occupational health hazard monitoring devices based on the distributed data acquisition strategy and the time synchronization mechanism to obtain multi-device monitoring information; and the multi-node data acquisition module performing preliminary verification of the multi-device monitoring information according to the data verification mechanism to obtain initial occupational health hazard information.

[0007] This invention ensures that all data collection nodes collect data under the same time reference, and can integrate and analyze data collected from different nodes in an accurate time sequence, providing reliable time dimension information for accurately analyzing the development trend and pattern of occupational health hazards.

[0008] Optionally, the wireless transmission module receiving the initial occupational health hazard information and verifying the communication indicators of the wireless transmission module to output occupational health hazard information includes: the wireless transmission module receiving the initial occupational health hazard information output by the multi-node data acquisition module in real time; configuring wireless communication technology and equipment and designing data transmission protocols in the wireless transmission module.

[0009] The system of this invention can obtain the latest dynamics of hazardous factors in the occupational environment in the first instance, and can promptly understand the changes at different times, which is conducive to taking rapid countermeasures and ensuring personnel safety and health.

[0010] Optionally, the process of the wireless transmission module receiving the initial occupational health hazard information and verifying its communication indicators to output occupational health hazard information includes: setting transmission communication indicators for the wireless transmission module; verifying the transmission communication indicators in the wireless transmission module to obtain communication indicator verification results; optimizing the wireless transmission module based on the communication indicator verification results to obtain an optimized wireless transmission module; and using the optimized wireless transmission module to wirelessly transmit the initial occupational health hazard information to output occupational health hazard information. This invention verifies the transmission communication indicators, enabling a comprehensive and objective evaluation of the actual transmission performance of the wireless transmission module.

[0011] Optionally, obtaining multi-level output results and information loss status of occupational health hazard information using the data intelligence processing module includes setting up multi-level processing layers in the data intelligence processing module, including a feature extraction layer, a learning layer, and a decision layer. This invention sets up multi-level processing layers, which can not only output the overall hazard assessment results, but also the analysis results of each feature level, as well as the dynamic changes over different time periods, helping to improve the precision of occupational health management.

[0012] Optionally, obtaining the multi-level output results and information loss of occupational health hazard information using the data intelligent processing module includes: the data intelligent processing module receiving occupational health hazard information output by the wireless transmission module in real time; the data intelligent processing module processing the occupational health hazard information using the feature extraction layer to obtain low-dimensional feature information of the occupational health hazard information; the data intelligent processing module processing the low-dimensional feature information through the learning layer to obtain high-dimensional representation information of the occupational health hazard information; the data intelligent processing module processing the high-dimensional representation information according to the decision layer to obtain information prediction results of the occupational health hazard information; and combining the low-dimensional feature information, the high-dimensional representation information, and the information prediction results to obtain the multi-level output results of the occupational health hazard information. The multi-level output results of this invention can meet the diverse needs of different application scenarios and improve the utilization efficiency and value of information.

[0013] Optionally, obtaining the multi-level output results and information loss status of occupational health hazard information using the data intelligent processing module includes: the data intelligent processing module obtaining true information labels based on the occupational health hazard information; and the data intelligent processing module comparing and analyzing the information prediction results in the multi-level output results with the true information labels to obtain the information loss status of the occupational health hazard information. This invention compares and analyzes the information prediction results with the true information labels, enabling a quantitative measurement of the degree of loss of occupational health hazard information during data processing and prediction.

[0014] Optionally, the step of establishing an information closed-loop processing mechanism based on the multi-level output results and the information loss situation, and obtaining the occupational hazard information database through the information closed-loop processing mechanism, includes: introducing a backpropagation algorithm into the data intelligent processing module; and the data intelligent processing module establishing an information closed-loop processing mechanism based on the backpropagation algorithm, the multi-level output results, and the information loss situation.

[0015] The information closed-loop processing mechanism of this invention, combined with the backpropagation algorithm, forms a feedback and optimization loop, which can promptly capture changes and adjust parameters to adapt to new situations, thereby ensuring that the output results can accurately reflect the actual occupational hazard situation.

[0016] Optionally, the step of establishing an information closed-loop processing mechanism based on the multi-level output results and the information loss situation, and obtaining the occupational hazard information database through the information closed-loop processing mechanism, includes: establishing a gradient calculation chain expression in the information closed-loop processing mechanism; jointly optimizing the parameters of the feature extraction layer, learning layer, and decision layer through the gradient calculation chain expression and the backpropagation algorithm to obtain optimized feature extraction layer, optimized learning layer, and optimized decision layer; and the data intelligent processing module using the optimized feature extraction layer, optimized learning layer, and optimized decision layer to perform multi-level processing on the occupational health hazard information to obtain the occupational hazard information database.

[0017] This invention performs joint optimization of the feature extraction layer, learning layer, and decision layer, enabling collaborative work among multiple levels and thereby improving the accuracy of hazard information processing results.

[0018] Optionally, the risk assessment module performs hazard trend prediction based on the occupational hazard information database to obtain occupational health risk assessment results, including: introducing a decision tree prediction model into the risk assessment module; Based on the decision tree prediction model, a risk scoring function is established in the risk assessment module; the risk assessment module predicts the occupational health hazard trend based on the risk scoring function and the occupational hazard information database, and obtains the occupational health risk assessment result.

[0019] The decision tree prediction model of this invention makes the risk assessment results highly interpretable. In the process of occupational health risk assessment, risk prediction can be made based on data in the occupational hazard information database, providing a clear reference for risk management and decision-making. Attached Figure Description

[0020] Figure 1 This is a flowchart of the intelligent monitoring and transmission control system for occupational health hazard information of the present invention; Figure 2 This is a structural diagram of the intelligent monitoring, transmission and control system for occupational health hazard information of the present invention. Detailed Implementation

[0021] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0022] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0023] Please see Figure 1 In practical applications, existing methods cannot effectively achieve data verification and fusion processing, resulting in poor accuracy in risk assessment and a lack of collaborative management mechanisms for cross-regional equipment. To address these issues and provide a data foundation and technical support for occupational health monitoring, this invention provides an intelligent monitoring and transmission management system for occupational health hazard information. The system includes the following steps: An intelligent monitoring and transmission control system for occupational health hazard information includes: a multi-node data acquisition module, a wireless transmission module, a data intelligent processing module, a risk assessment module, and a remote control module.

[0024] S1. Occupational health information is collected in real time through a multi-node data acquisition module to obtain initial information on occupational health hazards. The specific implementation details are as follows: To ensure accurate collection and preliminary processing of occupational health hazard information, the multi-node data acquisition module, based on information networks, multiple types of sensors, and collaborative acquisition technology, comprehensively and in real time acquires information on hazard factors in the occupational environment. Furthermore, the multi-node data acquisition module incorporates distributed data acquisition strategies, time synchronization mechanisms, and data verification mechanisms.

[0025] Distributed strategies and time synchronization mechanisms.

[0026] The multi-node data acquisition module employs a distributed data acquisition strategy, allowing different sensor nodes to independently perform data acquisition, ensuring targeted data collection from each node and improving the efficiency and flexibility of data acquisition. Simultaneously, a time synchronization mechanism is introduced to ensure that data collected by all nodes is on the same time dimension, enabling data collected by different nodes to accurately correspond to the occupational environment status at the same moment, providing a time benchmark for subsequent correlation analysis and trend prediction. The combined effect of these two mechanisms in collecting monitoring information from occupational health hazard monitoring equipment yields multi-device monitoring data.

[0027] The aforementioned distributed data acquisition strategy allows different sensor nodes to independently conduct data acquisition, eliminating reliance on a single central node and preventing delays or interruptions caused by central node failures or excessive load. Furthermore, each node can flexibly adjust its acquisition frequency and method based on its environment and task requirements, improving efficiency and flexibility, and better adapting to complex and ever-changing work environments.

[0028] The time synchronization mechanism ensures that the data collected by all nodes are on the same time dimension, which helps to understand the overall distribution and changing trends of hazard factors in the occupational environment. It provides a reliable time benchmark for subsequent correlation analysis and trend prediction, avoids data analysis errors caused by time differences, and improves the consistency and accuracy of the data.

[0029] Data verification mechanism.

[0030] The multi-node data acquisition module performs preliminary screening and review of the collected monitoring information from multiple devices based on a pre-set data verification mechanism. The data verification mechanism sets reasonable data range thresholds, and each data point is judged according to these thresholds. If any data point meets the following acquisition conditions... ,in This represents the minimum allowable data value set for a specific hazard factor in an occupational health monitoring scenario. This represents the actual data value collected by the node. This represents the maximum allowable value of data for a specific hazard factor in an occupational health monitoring scenario. Based on this, the data point can be valid and retained. If the data point exceeds this range, it is marked as an outlier. For outliers, corresponding processing measures can be taken to recollect the data at that point. Through the above process, the reliability of the collected data can be ensured, providing a high-quality data source for accurate analysis of occupational health hazard information, and ultimately obtaining the initial information on occupational health hazards.

[0031] Through the above data collection and processing, the multi-node data acquisition module can obtain accurate initial information on occupational health hazards. This information provides a foundation for subsequent processing and analysis of occupational health hazard information, and supports the formulation of scientific and reasonable occupational health protection measures and decision-making.

[0032] S2. The wireless transmission module receives the initial occupational health hazard information and verifies the communication indicators of the wireless transmission module to output the occupational health hazard information. The specific implementation details are as follows: The wireless transmission module needs to stably, reliably, and securely transmit the occupational health hazard information acquired by the multi-node data acquisition module to the subsequent modules. To achieve the above goals, the wireless transmission module needs to be rationally designed and planned in terms of communication technology selection, equipment configuration, and data transmission protocol design.

[0033] I. Wireless Communication Technology Selection and Equipment Configuration In this embodiment, factors such as transmission distance, data volume, and real-time requirements of occupational health hazard information are comprehensively considered. The most suitable communication technology for this embodiment can be selected from a variety of wireless communication technologies such as LoRa, NB-IoT, or 5G. LoRa technology can be selected in scenarios such as large factories or mines where the transmission distance is long, the data volume is small, and the real-time requirements are not high. However, for scenarios with large data volumes and high real-time requirements, such as intelligent medical monitoring or high-speed industrial production monitoring, 5G technology needs to be selected to ensure that occupational health hazard data can be transmitted quickly and accurately.

[0034] Next, appropriate wireless transmission equipment should be configured according to wireless communication technology. During equipment selection, the performance stability, reliability, and anti-interference capabilities of the equipment must be fully considered to ensure that it maintains good working condition under complex occupational environment conditions, such as high temperature, high humidity, and strong electromagnetic interference. Simultaneously, the installation location and layout of the equipment should be rationally planned to ensure the stability of the wireless connection channel. These configuration conditions provide hardware guarantees for the transmission of occupational health hazard information. In environments with many metal obstacles, the transmission power and antenna direction of the equipment should be adjusted appropriately to enhance signal penetration.

[0035] II. Data Transmission Protocol Design In data packet format design, it is necessary to rationally divide the data into header, body, and trailer, clearly defining the meaning and function of each part to reduce unnecessary data overhead and improve data transmission efficiency. In one optional embodiment, a data encoding method is used to store and transmit data in binary form, reducing the space occupied by the data.

[0036] In terms of optimizing the transmission control mechanism, the transmission strategy needs to be dynamically adjusted according to the network conditions. Adaptive modulation and coding technology can be used to automatically adjust the modulation method and coding rate according to the channel quality, effectively reducing transmission delay and packet loss rate, and ensuring that occupational health hazard information can reach the centralized processing platform quickly and accurately.

[0037] Simultaneously, a data encryption mechanism is introduced to encrypt occupational health hazard information during transmission. In one optional embodiment, an encryption algorithm is used to encrypt and decrypt the occupational health hazard information, ensuring that even if the information is intercepted during transmission, it cannot be illegally obtained or tampered with, thus effectively protecting the integrity and confidentiality of the occupational health hazard information. Furthermore, a key management system can be established, with encryption keys being changed periodically in one optional embodiment to prevent data security issues caused by key leakage, further providing technical support for the secure and stable transmission of occupational health hazard information.

[0038] In order to ensure the performance and quality of the wireless transmission module, this embodiment sets transmission communication indicators and verifies the above indicators to obtain the communication indicator verification results. In this embodiment, the transmission communication indicators mainly include transmission delay and packet loss rate.

[0039] The above transmission delay is This refers to the time difference between the completion of data collection and the receipt of the data by the centralized processing platform; the aforementioned transmission delay. The following conditions must be met: , in, Indicates system transmission delay. Indicates the data reception time. This indicates the time when data acquisition was completed. By accurately measuring and recording these two time points, the transmission delay can be precisely calculated, providing a reference for evaluating the real-time performance of the wireless transmission module.

[0040] Packet loss rate The packet loss rate refers to the ratio of the number of data packets lost during transmission to the total number of data packets sent. The following relationship must be satisfied: , in, Indicates the system data packet loss rate. Indicates the number of lost data packets. This indicates the total number of data packets sent. Numbering and counting data packets during transmission allows for a more accurate calculation of the packet loss rate, providing a reference indicator for evaluating the reliability of the wireless transmission module.

[0041] The communication performance indicators are tested to obtain the test results.

[0042] The wireless transmission module also includes a transmission delay monitoring system, which can monitor the time interval from data acquisition to reception in real time. By comparing this time interval with a preset transmission delay threshold, it determines whether the transmission delay is within the allowable range. Simultaneously, it records transmission delay data under different time periods and environmental conditions, analyzes the patterns of transmission delay changes and influencing factors, and provides data support for subsequent equipment adjustments and optimizations.

[0043] The wireless transmission module employs a packet retransmission and acknowledgment mechanism to track and count data packets during transmission. After sending a data packet, the sender waits for acknowledgment from the receiver. If no acknowledgment is received within a specified time, the data packet is considered lost and retransmitted. By counting the number of lost data packets and the total number of data packets sent, the packet loss rate is calculated. Simultaneously, the module analyzes the trend of packet loss rate changes and influencing factors, such as network congestion and signal interference, to provide a basis for optimizing the wireless transmission module.

[0044] The wireless transmission module is optimized based on the communication index test results to obtain an optimized wireless transmission module, thereby improving the performance and reliability of the wireless transmission module.

[0045] Further analysis of the transmission delay calculation results reveals that the causes of transmission delay include, but are not limited to, network congestion, insufficient equipment processing capacity, and excessively long signal propagation paths. In an optional embodiment, network monitoring tools can be used to analyze network traffic distribution and determine whether network congestion points exist. By testing the processing speed and response time of the equipment, the equipment's processing capacity can be evaluated to determine whether it meets the requirements. For different causes of transmission delay, corresponding optimization measures need to be taken. If the delay is caused by network congestion, the network topology can be optimized, network bandwidth increased, and load balancing technology adopted. If the delay is caused by insufficient equipment processing capacity, the equipment hardware can be upgraded to improve the equipment's processing speed and performance. If the delay is caused by excessively long signal propagation paths, the installation location of the equipment can be adjusted appropriately, and signal relay equipment can be added.

[0046] The embodiment further analyzes the packet loss rate indicator. In practical applications, the main reasons for the packet loss rate exceeding the preset threshold include signal interference, transmission protocol defects, and equipment failure. In an optional embodiment, the characteristics and distribution of lost data packets can be analyzed to determine whether there is signal interference of a specific frequency; the implementation code of the transmission protocol can be checked to find any logical errors that may lead to packet loss.

[0047] In one alternative embodiment, corresponding optimization measures are taken based on the cause of packet loss. If the packet loss is caused by signal interference, wireless communication technologies with stronger anti-interference capabilities can be used, the transmission frequency and power of the equipment can be adjusted, and signal shielding measures can be added. If the packet loss is caused by defects in the transmission protocol, the transmission protocol can be improved, and data retransmission mechanisms and error correction mechanisms can be added. If the packet loss is caused by equipment failure, the faulty equipment can be replaced in a timely manner, and equipment maintenance and upkeep can be performed.

[0048] The optimized wireless transmission module is used to wirelessly transmit the initial information on occupational health hazards in order to output occupational health hazard information.

[0049] The optimized wireless transmission module transmits initial occupational health hazard information wirelessly, accurately and promptly sending the data to subsequent modules and the data processing platform. Ultimately, it outputs reliable occupational health hazard information, providing information support for occupational health management and decision-making. Simultaneously, continuous monitoring and evaluation of the wireless transmission module's operational status are necessary. Optimization strategies can be adjusted promptly based on actual conditions to ensure the module maintains optimal operating status and provides stable assurance for the transmission of occupational health hazard information.

[0050] S3. Utilize the data intelligent processing module to obtain multi-level output results and information loss status of occupational health hazard information. Based on the multi-level output results and information loss status, establish an information closed-loop processing mechanism. Obtain an occupational hazard information database through this mechanism. The specific implementation details are as follows: First, the data intelligence processing module is used to obtain multi-level output results of occupational health hazard information.

[0051] The data intelligence processing module incorporates multiple processing layers, including a feature extraction layer. Learning layer Decision-making level The three levels mentioned above work together to process the input occupational health hazard information step by step.

[0052] The data intelligent processing module receives occupational health hazard information output by the wireless transmission module in real time, and records the occupational health hazard information as follows: Then, the occupational health hazard information undergoes multi-level processing to obtain multi-level processing results.

[0053] Using feature extraction layers to analyze occupational health hazard information The low-dimensional feature information is obtained through processing, and then passed through the feature extraction layer. Low-dimensional features were then obtained. And satisfy the following relationship: , in, The low-dimensional feature information output by the feature extraction layer. For feature extraction layer, For occupational health hazard information, These are the parameters for the feature extraction layer. The low-dimensional features... Input learning layer Further processing yields a high-dimensional representation of occupational health hazard information. And satisfy the following relationship: , in, The high-dimensional representation information output by the learning layer. For the learning layer, These are the parameters for the learning layer. The above learning layer... It can be a fully connected neural network layer. Simultaneously satisfying... in Representation learning layer The weight matrix, Representation learning layer The bias vector.

[0054] Based on the decision-making level's representation of high-dimensional information The information is processed to obtain the prediction results of occupational health hazard information. High-dimensional representation of information. Input decision layer The final prediction result is obtained: , in, The prediction results output by the decision-making level. For the decision-making level, For decision-making level The parameters. The aforementioned decision-making level. Satisfying the linear model conditions, i.e. ,in For decision-making level The weight matrix, For decision-making level The bias vector.

[0055] By combining low-dimensional feature information, high-dimensional representation information, and information prediction results, multi-level output results of occupational health hazard information are obtained.

[0056] Then, the data intelligence processing module obtains the information loss situation of occupational health hazard information.

[0057] The data intelligent processing module determines the true label of occupational health hazard information based on relevant standards and historical data, and records it as follows: .

[0058] Predicting results from multi-level outputs and information authenticity label Comparative analysis is conducted, and a loss function is used to measure the information loss. The loss function can be expressed as follows: The specific form can be selected according to actual needs, such as the mean squared error loss function.

[0059] Finally, an information closed-loop processing mechanism is established based on the multi-level output results and information loss, and an occupational hazard information database is obtained through the information closed-loop processing mechanism.

[0060] The backpropagation algorithm is introduced into the data intelligence processing module. Therefore, the data intelligence processing module builds an information closed-loop processing mechanism based on the backpropagation algorithm, multi-level output results, and information loss.

[0061] This embodiment introduces a backpropagation algorithm into the data intelligence processing module. Based on the backpropagation algorithm, multi-level output results, and information loss considerations, a closed-loop information processing mechanism is further established. Traditional information processing flows are mostly unidirectional data acquisition, processing, and analysis, resulting in information loss and optimization gaps, making it difficult to achieve global optimization. The occupational health and hygiene monitoring information closed-loop processing mechanism in this embodiment combines a feedback mechanism and a joint optimization strategy, constructing a bidirectional loop of information flow and value flow in occupational health and hygiene monitoring information, enabling iterative improvement of system performance.

[0062] A gradient calculation chain expression was established in the information closed-loop processing mechanism to achieve global parameter optimization of multi-level analysis layers. The gradient calculation chain expression is as follows: , in, Represents the loss function For global parameters gradient, This represents the gradient of the loss with respect to the output of the decision layer. express gradient with respect to its own parameters, This represents the gradient transpose of the local derivatives between the learning layer and the decision layer. express gradient with respect to its own parameters, This represents the gradient transpose of the local derivatives between the extraction layer and the learning layer. express The gradient with respect to its own parameters.

[0063] The feature extraction layer extracts low-dimensional features from the original data, where the parameters... The above parameters control the feature extraction method; the learning layer maps low-dimensional features to a high-dimensional representation space. It can optimize representation learning capabilities; the decision layer generates the final prediction result based on the high-dimensional representation, where parameters... It is mainly used to adjust the decision-making boundaries.

[0064] The local derivatives between the feature extraction layer and the representation learning layer reflect the transformation relationship from features to representations; the local derivatives between the learning layer and the decision layer reflect the transformation relationship from representations to decisions; the gradient of the decision layer loss with respect to the output helps guide the optimization of decision layer parameters.

[0065] Furthermore, it is necessary to provide a gradient hierarchy and explanation for the above gradient calculation chain expression: I. Relevant Gradients at the Decision-Making Level The gradient of the loss with respect to the output of the decision layer is: It is calculated based on the loss function and the output of the decision layer.

[0066] The gradient of the decision-making layer with respect to its own parameters is: Due to the decision-making level Satisfying the linear model conditions ,in For decision-making level The weight matrix, Representation learning layer The output, Indicates the decision-making level The bias vector.

[0067] The gradient transpose of the local derivatives between the learning layer and the decision layer is Due to the decision-making level The input is the learning layer The output results, due to the decision-making level If the linear model conditions are met, then From the perspective of backpropagation, the learning layer To the decision-making level Gradient propagation, decision layer For the learning layer The gradient transpose of the output is .

[0068] II. Learning Layer Related Gradients Gradient of the learning layer with respect to its own parameters Based on the above implementation details, the learning layer... It is a fully connected neural network layer , Representation learning layer Weight matrix, Represents the feature extraction layer The output result, Representation learning layer The bias vector.

[0069] The gradient transpose of the local derivatives between the feature extraction layer and the learning layer is: Because of the learning layer The input is the feature extraction layer. Output Due to the learning layer middle ,but Therefore, from the perspective of backpropagation, the feature extraction layer To the learning layer Gradient propagation, learning layers For feature extraction layer The gradient transpose of the output is .

[0070] III. Relevant Gradients in Feature Extraction Layer gradient of the feature extraction layer with respect to its own parameters Feature extraction layer Feature extraction can be performed using convolutional layers in a convolutional neural network. Let the input be... The convolution kernel is The output is ,but The calculation needs to be performed according to the rules of convolution operations. Generally, it can be calculated by backpropagating the gradient of the cross-correlation operation to obtain a value related to the input. and output Related tensors.

[0071] Using the complete gradient calculation formula described above, the backpropagation algorithm is used to jointly optimize the parameters of the feature extraction layer, representation learning layer, and decision layer in the closed-loop system. During the optimization process, the parameters of each layer are adjusted according to the gradient calculation results, which can achieve iterative improvement of system performance and thus improve the efficiency of data value mining. After parameter optimization, the optimized feature extraction layer, optimized learning layer, and optimized decision layer are obtained.

[0072] The data intelligence processing module utilizes an optimized feature extraction layer, an optimized learning layer, and an optimized decision layer to perform multi-level processing of occupational health hazard information in order to obtain an occupational hazard information database.

[0073] The data intelligence processing module utilizes optimized feature extraction, learning, and decision-making layers to process occupational health hazard information at multiple levels. Through this series of optimized processes, it can more accurately and comprehensively extract and analyze occupational health hazard information. Ultimately, the processed information is integrated to form an occupational hazard information database, providing strong support for occupational health management and decision-making. Simultaneously, the information closed-loop processing mechanism is continuously monitored and evaluated, and optimization strategies are adjusted promptly based on actual conditions to ensure the accuracy and timeliness of the occupational hazard information database.

[0074] S4. The risk assessment module predicts hazard trends based on the occupational hazard information database to obtain occupational health risk assessment results. The specific implementation details are as follows: To ensure that the risk assessment module can effectively predict hazard trends based on the occupational hazard information database and thus obtain accurate occupational health risk assessment results, the risk assessment module in this embodiment includes processing steps such as multi-node data fusion analysis, intelligent risk assessment, hazard trend prediction, and risk assessment report generation.

[0075] Multi-node data fusion analysis Hazard information in the occupational environment is usually collected by multiple monitoring nodes. Since the data collected by a single node has limitations and cannot fully and accurately reflect the overall status of the monitored object, it is necessary to fuse the data from multiple nodes.

[0076] In one optional embodiment, there are a total of Each monitoring node collects data as follows: The merged data It can be calculated using a weighted average or an attention mechanism, and the specific calculation formula is as follows: , in, This indicates the merged data. Indicates the number of data monitoring nodes. This represents the task weight coefficient for different data monitoring nodes. This represents data from different data monitoring nodes.

[0077] The data obtained after fusion processing integrates information from various monitoring nodes, and can more accurately reflect the overall status of the monitored objects; the number of data monitoring nodes clarifies the scale of nodes participating in data fusion.

[0078] The range of values ​​for the task weight coefficients of different data monitoring nodes is as follows: Furthermore, the sum of the task weight coefficients of different data monitoring nodes is 1.

[0079] Multiple coefficient The determination of the monitoring nodes needs to take into account factors such as the importance of the monitoring nodes, data reliability, and acquisition accuracy. In the example, the expert evaluation method can be used to score and evaluate the actual situation of each monitoring node; the entropy weight method can also be used to determine the weight based on the dispersion of the data. The greater the dispersion of the data, the greater the role of the node in the decision-making, and the higher the weight.

[0080] The data, after fusion processing, integrates information from various monitoring nodes, enabling it to more accurately reflect the overall status of the monitored objects and providing a reliable data foundation for subsequent risk assessment.

[0081] Based on the decision tree prediction model, a risk scoring function is established in the risk assessment module. At the same time, the risk assessment module predicts the trend of occupational health hazards based on the risk scoring function and the occupational hazard information database, and obtains the occupational health risk assessment results, providing a scientific basis for formulating occupational disease risk response measures.

[0082] In this embodiment, a decision tree prediction model is introduced into the risk assessment module, and a risk scoring function is established. The decision tree is a machine learning algorithm that makes decisions based on a tree structure. It can continuously divide data features, gradually subdivide the dataset into different subsets, and finally achieve the prediction of the target variable.

[0083] To quantify the magnitude of risk, a risk score R is used to represent the risk assessment result. The risk score is calculated based on the prediction results of multiple decision trees, and the specific formula is as follows: , in, This indicates the result of the risk score calculation. Indicates the number of decision trees. Indicates an indicator function, Indicates the first The predicted results for each tree. This indicates the risk threshold.

[0084] The risk score calculation result usually ranges from 0 to 1, with a higher value indicating a higher risk.

[0085] The more decision trees a model has, the more stable and accurate it usually is. In practical applications, the appropriate number of decision trees can be determined through experiments.

[0086] Different prediction results can be specific risk values ​​or risk categories or risk thresholds, which can be used to determine whether the prediction results have reached the risk level.

[0087] Risk thresholds are used to determine whether the prediction results reach the risk level. The setting of risk thresholds needs to be determined based on the specific risk scenario and business needs, and can be determined through methods such as historical data analysis and expert experience.

[0088] The specific definitions of the above indicator functions are as follows: , That is, when the prediction result of the decision tree Greater than the risk threshold When the predictions are true, the indicator function is 1; otherwise, it is 0. The indicator function is applied to all decision tree predictions, and the average is calculated to obtain the final risk score. .

[0089] In this embodiment, based on occupational health hazard data and real-time data, a machine learning model is used to predict the development trend of occupational health hazard risks, which can provide support for early warning and management decisions regarding occupational fever risks.

[0090] In one optional embodiment, random forest is used for occupational health hazard trend prediction. Random forest improves the accuracy and stability of the model by constructing multiple decision trees and combining the prediction results. It can handle high-dimensional data and has good robustness to noise and outliers. LSTM is used for hazard trend prediction, which can effectively process time series data, capture long-term dependencies in the data, and is suitable for predicting occupational hazard risk data with time characteristics.

[0091] Furthermore, the hazard trend prediction results can be presented in the form of charts such as line graphs, bar charts, or text to intuitively show the changing trends of occupational health hazard risks, so as to formulate reasonable occupational disease prevention measures and response plans, provide scientific and accurate decision-making basis for occupational health risk management, and protect the occupational health and safety of workers.

[0092] S5. The remote control module combines occupational health risk assessment results and an occupational hazard information database to remotely control occupational health hazard monitoring equipment and terminal access technology. The specific implementation details are as follows: The remote control module can remotely control occupational health hazard monitoring equipment. Based on cross-regional remote control technology and multi-terminal access support system, it can acquire, analyze and process occupational health hazard information in real time, thereby responding immediately and effectively preventing and controlling the occurrence and development of occupational health hazard events.

[0093] To meet the operational needs of different users in different scenarios, this embodiment supports remote control of monitoring equipment via PC, mobile devices (such as mobile phones and tablets), and a dedicated monitoring platform. In addition, users can select appropriate terminal devices to operate the monitoring equipment according to actual application needs.

[0094] In an optional embodiment, various parameters of the monitoring device can be adjusted on different terminals. The sensor sampling frequency can be set to high-frequency sampling to obtain more detailed data, or set to low-frequency sampling to save equipment resources and data storage space, depending on the degree of hazard and monitoring requirements of different working environments. The sensor sensitivity can be precisely adjusted according to actual detection needs to ensure accurate detection of different occupational health hazards.

[0095] In one optional embodiment, flexible switching of the device's operating modes is supported. In the normal monitoring mode, the device can effectively collect basic data by performing daily monitoring according to preset parameters and frequencies. When the work environment changes or potential risks arise, the device can be switched to emergency monitoring mode. In emergency monitoring mode, the monitoring frequency of the device can be increased, the timeliness of data transmission can be enhanced, and the occupational health hazards can be grasped more comprehensively and quickly.

[0096] In emergency situations, the equipment's emergency functions can be activated quickly via remote control. When the concentration of occupational health hazards exceeds the safety threshold, the alarm system can be triggered immediately to promptly notify on-site staff and relevant management personnel. At the same time, ventilation equipment, purification equipment, and other protective measures will be automatically activated to further protect the life safety and health of personnel. Through the above response mechanism, various sudden occupational health hazard events can be effectively dealt with to reduce losses and impacts.

[0097] The remote control module in this embodiment also supports access from multiple terminals.

[0098] To facilitate integration and data exchange between this remote control module and third-party platforms, government regulatory platforms, and other systems, this embodiment provides a standardized API interface. Through this interface, occupational health hazard monitoring data can be securely and reliably acquired, enabling data sharing and business collaboration. Simultaneously, it ensures occupational health and safety during the production process, strengthens the supervision of enterprises' occupational health work, and protects workers' rights.

[0099] Through the above implementation plan and measures, the remote control module will be able to effectively combine the results of occupational health risk assessment and the occupational hazard information database to conduct precise and efficient remote control of occupational health hazard monitoring equipment and terminal access technology, providing strong technical support for occupational health management and effectively protecting the occupational health and safety of workers.

[0100] Please see Figure 2 In an optional embodiment, the present invention also provides an intelligent monitoring and transmission control system for occupational health hazard information. This system includes a multi-node data acquisition module, a wireless transmission module, a data intelligent processing module, a risk assessment module, and a remote control module. These modules are interconnected and implement the specific steps of the relevant embodiments of the intelligent monitoring and transmission control system for occupational health hazard information provided by the present invention. The intelligent monitoring and transmission control system for occupational health hazard information of the present invention has a complete structure, is objective and stable, and improves the overall applicability and practical application capability of the present invention.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions 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, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. An intelligent monitoring and transmission control system for occupational health hazard information, characterized in that, The system includes a multi-node data acquisition module, a wireless transmission module, a data intelligent processing module, a risk assessment module, and a remote control module; The multi-node data acquisition module collects occupational health information in real time and obtains initial information on occupational health hazards. The wireless transmission module receives the initial occupational health hazard information and verifies the communication indicators of the wireless transmission module to output occupational health hazard information. The data intelligent processing module is used to obtain multi-level output results and information loss status of occupational health hazard information. Based on the multi-level output results and information loss status, an information closed-loop processing mechanism is built, and an occupational hazard information database is obtained through the information closed-loop processing mechanism. The risk assessment module predicts hazard trends based on the occupational hazard information database to obtain occupational health risk assessment results. The remote control module combines the occupational health risk assessment results and the occupational hazard information database to remotely control the occupational health hazard monitoring equipment and terminal access technology.

2. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The process of collecting occupational health information in real time through the multi-node data acquisition module and obtaining initial information on occupational health hazards includes: A distributed data acquisition strategy, a time synchronization mechanism, and a data verification mechanism are set in the multi-node data acquisition module; The multi-node data acquisition module collects monitoring information from occupational health hazard monitoring equipment based on the distributed data acquisition strategy and time synchronization mechanism to obtain multi-device monitoring information; The multi-node data acquisition module performs preliminary verification of the multi-device monitoring information based on the data verification mechanism to obtain initial information on occupational health hazards.

3. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The wireless transmission module receives the initial occupational health hazard information and verifies the communication indicators of the wireless transmission module to output occupational health hazard information, including: The wireless transmission module receives initial occupational health hazard information output by the multi-node data acquisition module in real time; The wireless transmission module is configured with wireless communication technology and equipment and designed with data transmission protocols.

4. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The wireless transmission module receives the initial occupational health hazard information and verifies the communication indicators of the wireless transmission module to output occupational health hazard information, including: Configure the transmission and communication parameters of the wireless transmission module. The transmission communication indicators in the wireless transmission module are tested to obtain the communication indicator test results. Based on the communication index test results, the wireless transmission module is optimized to obtain the optimized wireless transmission module. The optimized wireless transmission module is used to wirelessly transmit the initial occupational health hazard information in order to output the occupational health hazard information.

5. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The multi-level output results and information loss of occupational health hazard information obtained by the data intelligence processing module include: The data intelligence processing module is configured with multiple processing layers, including a feature extraction layer, a learning layer, and a decision layer.

6. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 5, characterized in that, The multi-level output results and information loss of occupational health hazard information obtained by the data intelligence processing module include: The data intelligent processing module receives occupational health hazard information output by the wireless transmission module in real time; The data intelligent processing module uses the feature extraction layer to process the occupational health hazard information to obtain low-dimensional feature information of the occupational health hazard information; The data intelligent processing module processes the low-dimensional feature information through the learning layer to obtain a high-dimensional representation of occupational health hazard information; The data intelligence processing module processes the high-dimensional representation information based on the decision layer to obtain the information prediction results of occupational health hazard information; By combining the low-dimensional feature information, the high-dimensional representation information, and the information prediction results, a multi-level output result of occupational health hazard information is obtained.

7. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The multi-level output results and information loss of occupational health hazard information obtained by the data intelligence processing module include: The data intelligent processing module obtains the information's true label based on the occupational health hazard information; The data intelligent processing module compares and analyzes the information prediction results in the multi-level output results with the actual information labels to obtain the information loss situation of occupational health hazard information.

8. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The establishment of an information closed-loop processing mechanism based on the multi-level output results and the information loss situation, and the acquisition of an occupational hazard information database through the information closed-loop processing mechanism, includes: A backpropagation algorithm is introduced into the data intelligence processing module. The data intelligent processing module establishes an information closed-loop processing mechanism based on the backpropagation algorithm, the multi-level output results, and the information loss situation.

9. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 8, characterized in that, The establishment of an information closed-loop processing mechanism based on the multi-level output results and the information loss situation, and the acquisition of an occupational hazard information database through the information closed-loop processing mechanism, includes: A gradient calculation chain expression is established in the aforementioned information closed-loop processing mechanism; The parameters of the feature extraction layer, learning layer, and decision layer are jointly optimized using the gradient calculation chain expression and the backpropagation algorithm to obtain the optimized feature extraction layer, optimized learning layer, and optimized decision layer. The data intelligence processing module utilizes the optimized feature extraction layer, the optimized learning layer, and the optimized decision layer to perform multi-level processing on the occupational health hazard information to obtain an occupational hazard information database.

10. The intelligent monitoring and transmission control system for occupational health hazard information according to claim 1, characterized in that, The risk assessment module predicts hazard trends based on the occupational hazard information database to obtain occupational health risk assessment results, including: A decision tree prediction model is introduced into the risk assessment module. A risk scoring function is established in the risk assessment module based on the decision tree prediction model. The risk assessment module predicts occupational health hazard trends based on the risk scoring function and the occupational hazard information database, and obtains occupational health risk assessment results.

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