Offshore worker safety management method and system

Abstract

This application discloses a method and system for safety management of offshore workers. It generates location codes containing positioning information for preset locations in offshore operation scenarios. Offshore workers use mobile terminals to scan these location codes, recording their position changes throughout the entire offshore operation. A cloud management platform receives the scanning records from the mobile terminals and analyzes the workers' dynamic changes in real time to determine their movement trajectory. Simultaneously, the cloud management platform accurately predicts potential hazards during offshore operations based on environmental monitoring data. When abnormalities are detected in the movement trajectory or potential hazards are detected based on environmental monitoring data, the platform immediately generates anomaly and early warning information and notifies relevant personnel and management departments through various means, effectively protecting the safety of offshore workers.

Description

Technical Field

[0001] This application relates to the field of safety management technology, and in particular to a method and system for safety management of offshore workers. Background Technology

[0002] Safety management of personnel working at sea has attracted widespread attention. With the rapid development of marine engineering projects such as offshore wind power, higher requirements have been placed on the safety management of personnel at sea. Traditional safety management methods, such as manual recording and post-event analysis, can no longer meet the real-time and accurate requirements of modern marine engineering for personnel safety monitoring.

[0003] Among the various methods for safety management of personnel working at sea, physical check-in systems, such as magnetic cards or IC cards, are relied upon as identification mechanisms. This method requires personnel to check in at designated locations upon departure and return, recording the departure and return times. However, this approach suffers from poor real-time performance, incomplete data recording, and susceptibility to forgery, failing to accurately reflect the real-time location and dynamic changes of personnel working at sea.

[0004] Other relevant methods for safety management of personnel working at sea employ GPS positioning or other wireless communication technologies to track and monitor their location. However, these technologies have certain limitations in practical applications, such as limited signal coverage, low positioning accuracy, and high equipment costs.

[0005] Furthermore, the relevant technologies rely on manual review and judgment, lack intelligent analysis and early warning functions, making it difficult to detect potential security risks and hazards in a timely manner and to take swift countermeasures. Summary of the Invention

[0006] In view of this, this application provides a method and system for safety management of offshore workers. The cloud management platform receives and analyzes the scanning records of offshore workers sent by mobile terminals. When abnormalities or potential dangers are detected among offshore workers, the platform immediately notifies relevant personnel and management departments through various means, effectively protecting the safety of offshore workers.

[0007] According to one aspect of this application, a method for safety management of offshore workers is provided, applied to a cloud management platform capable of communicating with a mobile terminal. The method includes: receiving encrypted scan records sent by the mobile terminal; decrypting the scan records and determining, based on the decrypted scan records, the movement trajectory of the offshore worker corresponding to user information in the scan records; determining abnormal information of the offshore worker based on the movement trajectory; acquiring environmental monitoring data fed back by the offshore worker, inputting the environmental monitoring data into a risk prediction model to determine early warning information of the environment in which the offshore worker is located, wherein the risk prediction model is trained based on historical environmental monitoring data, and the environmental monitoring data includes at least meteorological data and marine traffic data; displaying the abnormal information or the early warning information, and sending the abnormal information or the early warning information to the terminal associated with the offshore worker corresponding to the abnormal information or the early warning information.

[0008] According to another aspect of this application, a method for safety management of offshore workers is provided, applied to a mobile terminal. The method includes: scanning a location code located at a preset location to identify location information in the location code, and recording the scanning time and user information logged into the mobile terminal at the time of scanning; determining a scanning record based on the scanning time, the location information, and the user information; encrypting the scanning record, and sending the encrypted scanning record to a cloud management platform, so that the cloud management platform can determine abnormal information or warning information of the offshore workers corresponding to the user information in the scanning record based on the scanning record, display the abnormal information or the warning information, and send the abnormal information or the warning information to the terminal associated with the offshore workers corresponding to the abnormal information or the warning information.

[0009] According to another aspect of this application, a safety management system for offshore workers is provided, comprising: a cloud management platform for receiving encrypted scan records sent by the mobile terminal; decrypting the scan records and determining, based on the decrypted scan records, the movement trajectory map of the offshore workers corresponding to user information in the scan records; determining abnormal information of the offshore workers based on the movement trajectory map; acquiring environmental monitoring data fed back by the offshore workers, inputting the environmental monitoring data into a risk prediction model, and determining early warning information of the environment in which the offshore workers are located, wherein the risk prediction model is trained based on historical environmental monitoring data, and the monitoring data includes at least meteorological data and marine traffic data; displaying the abnormal information or the early warning information, and transmitting the abnormal information or the early warning information to the relevant authorities. The warning information is sent to the terminal associated with the offshore worker corresponding to the abnormal information or the warning information; the mobile terminal is used to scan the positioning code located at a preset location to identify the positioning information in the positioning code, and record the scanning time and the user information logged into the mobile terminal at the time of scanning; the scanning record is determined according to the scanning time, the positioning information and the user information; the scanning record is encrypted and sent to the cloud management platform, so that the cloud management platform can determine the abnormal information or warning information of the offshore worker corresponding to the user information in the scanning record, display the abnormal information or the warning information, and send the abnormal information or the warning information to the terminal associated with the offshore worker corresponding to the abnormal information or the warning information.

[0010] By employing the above technical solution, this application provides a method and system for safety management of offshore workers. It generates location codes containing positioning information for preset locations in offshore operation scenarios. Offshore workers use mobile terminals to scan these location codes, recording their position changes throughout the entire offshore operation process. A cloud management platform receives the scanning records from the mobile terminals and analyzes the workers' dynamic changes in real time to determine their movement trajectory. Simultaneously, the cloud management platform accurately predicts potential hazards during offshore operations based on environmental monitoring data. Upon detecting abnormalities in offshore workers through the movement trajectory map or identifying potential hazards based on environmental monitoring data, it immediately generates anomaly and early warning information and notifies relevant personnel and management departments through various means. This helps management personnel make rapid decisions and take corresponding preventative measures, effectively protecting the safety of offshore workers.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0013] Figure 1 A flowchart illustrating the safety management method for offshore workers provided in an embodiment of this application is shown.

[0014] Figure 2 A structural block diagram of the offshore worker safety management system provided in an embodiment of this application is shown. Detailed Implementation

[0015] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0016] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0017] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “attached” to another element, it can be directly connected or attached to the other element, or there may be intermediate elements. Furthermore, “connected” or “attached” as used herein can include wireless connections or wireless interconnections. The term “and / or” as used herein includes all or any unit and all combinations of one or more associated listed items.

[0018] Exemplary embodiments according to this application will now be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in many different forms and should not be construed as being limited to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of this application is thorough and complete, and that the concept of these exemplary embodiments is fully conveyed to those skilled in the art.

[0019] Safety management of personnel working at sea has attracted widespread attention. With the rapid development of marine engineering projects such as offshore wind power, higher requirements have been placed on the safety management of personnel at sea. Traditional safety management methods, such as manual recording and post-event analysis, can no longer meet the real-time and accurate requirements of modern marine engineering for personnel safety monitoring.

[0020] Traditional safety management for personnel at sea relies on physical check-in systems, such as magnetic cards or IC cards, as identification. This method requires personnel to check in at designated locations upon departure and return, recording the departure and return times. However, this method suffers from poor real-time performance, incomplete data recording, and susceptibility to forgery, failing to accurately reflect the real-time location and dynamic changes of personnel at sea. For example, check-in systems typically only record entry and exit times, not detailed information such as location changes, work content, and work environment throughout the entire operation. Some information technology-based personnel safety management methods typically employ RFID, GPS positioning, or other wireless communication technologies to track and monitor personnel locations. However, these technologies have limitations in practical applications, such as limited signal coverage, low positioning accuracy, and high equipment costs. For instance, some systems use RFID (Radio Frequency Identification) technology to track the location of personnel at sea, but these systems typically have the following drawbacks: RFID tags and readers are expensive, and corresponding hardware facilities need to be deployed at each work site. RFID technology is susceptible to interference from seawater, metal, and other environmental factors, affecting signal stability and accuracy. RFID technology may raise privacy concerns among workers, especially without explicit notification and consent. Furthermore, GPS positioning systems and RFID technology require personnel to carry additional equipment, increasing operational burden and potentially affecting the accuracy and completeness of data recordings due to equipment malfunction, loss, or battery depletion. Simultaneously, the lack of effective data sharing and interoperability mechanisms between different systems or platforms leads to severe data silos. This makes it difficult for managers to comprehensively and accurately grasp the overall situation of personnel working at sea, impacting the scientific and effective nature of safety management decisions.

[0021] like Figure 1As shown, this embodiment provides a method for safety management of offshore workers, which can be applied to a cloud management platform and a mobile terminal. The cloud management platform is capable of communicating with the mobile terminal. This embodiment uses the application of this method to a cloud management platform and a mobile terminal as an example for illustration. The method includes:

[0022] Step 101: The mobile terminal scans the location code located at the preset location to identify the location information in the location code and records the scanning time and the user information logged into the mobile terminal at the time of scanning.

[0023] Step 102: The mobile terminal determines the scan record based on the scan time, location information, and user information.

[0024] Step 103: The mobile terminal encrypts the scan record and sends the encrypted scan record to the cloud management platform.

[0025] In this embodiment, a unique QR code containing location information, safety instructions, and other data is generated in advance for key locations in the offshore operation scenario. This QR code serves as the location code for the key location, enabling offshore workers to scan the location code using their mobile terminals when moving to these locations. The scanning record is then sent to the cloud management platform via the mobile terminal, allowing the cloud management platform to monitor personnel activities based on the scanning record and improve the reliability of safety management.

[0026] For example, key locations can be determined based on the characteristics of the actual offshore operation scenario. For instance, location codes can be deployed in prominent locations such as ship entrances and exits, substation entrances, docks, and construction platforms. Furthermore, offshore workers do not need additional equipment; they can use their own mobile terminals with QR code scanning capabilities, such as smartphones, reducing operational burdens and enterprise costs while improving the accessibility and ease of use of safety management. Simultaneously, offshore workers log in with their unique user information on their mobile terminals and scan the location codes at key locations when they move to them, allowing the mobile terminal to recognize the location information within the codes. The mobile terminal records the time of each scan and the user information logged in each time. The location information, scan time, and user information identified in each scan are used as a scan record, and encryption technology is used to ensure the security and uniqueness of the scan record. This encrypted scan record is then sent to a cloud management platform, enabling the platform to monitor offshore workers in real time based on the scan record, ensuring their safety.

[0027] This involves using modern communication technologies (such as 4G / 5G and WiFi) to achieve real-time data transmission between mobile scanning terminals and cloud servers. This ensures that the dynamic location and time information of personnel working at sea can be uploaded to the cloud management platform in real time, facilitating real-time monitoring by management personnel.

[0028] In this embodiment, QR code scanning technology comprehensively records key data such as the identity information of personnel working at sea, their departure and return times, and location changes. This provides rich data support for safety management, enabling managers to gain a more comprehensive understanding of the maritime operations. Furthermore, the location information in the scanned records is carefully encoded and encrypted, ensuring high accuracy and reliability. Compared to other positioning methods, which may be affected by signal interference and error accumulation, leading to inaccurate location information, QR code scan records serve as strong evidence that personnel at sea have reached a specific location, enhancing data credibility.

[0029] It's worth mentioning that the mobile device features offline scanning capabilities, ensuring data recording even in environments without network coverage, and automatically synchronizing to the cloud management platform once the network is restored. Furthermore, the mobile device supports offline data caching and resume interrupted downloads, ensuring data timeliness and reliability.

[0030] Step 104: The cloud management platform receives the encrypted scan record sent by the mobile terminal.

[0031] Step 105: The cloud management platform decrypts the scan records and determines the movement trajectory of the offshore workers corresponding to the user information in the scan records based on the decrypted scan records.

[0032] In this embodiment, the cloud management platform analyzes the movement trajectory of offshore workers in real time based on the received scanning records, accurately analyzes the dynamic changes in the position of offshore workers during the offshore operation, and provides data support for subsequent safety management.

[0033] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, the step of determining the movement trajectory map of the offshore workers corresponding to the user information in the decrypted scan records specifically includes: determining the scan records in the decrypted scan records whose positioning information matches the preset sea departure location as the starting record; determining the user information in the starting record as the target user information, and determining the scan records associated with the target user information as monitoring records; using geographic information system technology, determining the positioning point corresponding to the positioning information in the monitoring records on a preset map; in the preset map, connecting the positioning points according to the scanning time of the positioning information in the monitoring records to determine the movement trajectory map of the target workers, wherein the target workers are the offshore workers corresponding to the target user information; and displaying the movement trajectory map.

[0034] In this embodiment, the cloud management platform receives encrypted scan records from mobile terminals, decrypts the scan records, and then cleans, verifies, and integrates the decrypted scan records to reduce data redundancy and improve data consistency. Furthermore, the cloud management platform associates different scan records of the same person with user information in the preprocessed scan records to create movement trajectory maps of different maritime workers, thereby enabling real-time monitoring of multiple different maritime workers, meeting the diversity of maritime operations, and improving the comprehensiveness of safety management.

[0035] For example, for any scan record associated with user information, the cloud management platform detects whether there is location information that matches the preset departure location. If it does, it means that the person at sea corresponding to the user information has started to go to sea. The scan record with location information matching the preset departure location is then identified as the starting record, and the user information is identified as the target user information. The scan record associated with the target user information is identified as the monitoring record, thereby continuously monitoring the person at sea through the monitoring record.

[0036] Furthermore, using Geographic Information System (GIS) technology, the location information in the monitoring records is mapped onto a preset map to determine the location points corresponding to each location information. Based on the scanning time of the location information in the monitoring records, the location points are connected in chronological order to form a movement trajectory map, which is then visualized.

[0037] It is worth mentioning that the corresponding time information can be marked on or next to the movement trajectory map so that managers can intuitively understand the location of offshore workers at different times.

[0038] In one embodiment, the method for safety management of offshore workers further includes: a cloud management platform determining the departure time of the target worker based on the scan time in the initial record and the current time; if the departure time is longer than a preset time, the cloud management platform determines the timeout information of the target worker based on the departure time that is longer than the preset time; the cloud management platform displays the timeout information and sends the timeout information to the terminal associated with the target worker.

[0039] In this embodiment, the cloud management platform analyzes the scanning records of offshore workers to automatically identify abnormal behaviors, such as failure to return to land within the specified time. The platform then promptly sends the detected timeout information to the terminals associated with the offshore workers through various means (such as SMS, email, and APP push notifications), so that relevant personnel and management departments can take appropriate measures in a timely manner.

[0040] For example, the cloud management platform determines the departure time of the target worker based on the difference between the scan time in the initial record and the current time in the monitoring records. If the departure time exceeds the preset time limit, the cloud management platform immediately generates timeout information, such as calculating the specific duration of the timeout or constructing detailed timeout information based on the duration, such as the number of hours or minutes of the timeout. This timeout information is then generated in an appropriate format (such as text, warning, or notification) and displayed on the cloud management platform. The cloud management platform sends the timeout information to the target worker's terminal through various methods to ensure timely reminders, accurately track and alert workers to timeout situations, and enhance safety management effectiveness.

[0041] If the departure time is less than or equal to the preset time, the cloud management platform checks the monitoring records for location information matching the preset return location to determine if the target worker has returned to land. If the departure time is less than or equal to the preset time, and the monitoring records contain location information matching the preset return location, the cloud management platform identifies the scan record with the matching location information as the end record. Based on the scan times in the end record and the start record, the platform determines the departure time of the target worker and terminates monitoring of that worker.

[0042] Step 106: The cloud management platform determines the abnormal information of the personnel working at sea based on the movement trajectory map.

[0043] Step 107: The cloud management platform displays the abnormal information and sends it to the terminal associated with the offshore worker corresponding to the abnormal information.

[0044] In this embodiment, the cloud management platform reduces the workload of manual monitoring and improves the response speed and accuracy of security management by automating the detection and push of abnormal information.

[0045] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, the step of determining the abnormal information of the offshore workers based on the movement trajectory map specifically includes: determining the movement speed and activity range of the offshore workers based on the movement trajectory map; obtaining the offshore workers' sea departure plan information; if the movement speed or activity range does not conform to the preset conditions in the sea departure plan information, determining the abnormal information based on the movement speed or activity range that does not conform to the sea departure plan information.

[0046] In this embodiment, the cloud management platform analyzes the activity range and movement patterns of offshore workers by drawing movement trajectory maps, thereby assessing whether the offshore workers are following the established work plan and safety regulations. When abnormal behavior is detected, an alarm is immediately triggered to remind managers to pay attention and take appropriate measures.

[0047] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, the step of determining the movement speed of the offshore workers based on the movement trajectory map specifically includes: converting the positioning information corresponding to adjacent points in the movement trajectory map into radians, and determining the radian information of the adjacent points; determining the first radian difference and the second radian difference between adjacent points based on the radian information; determining the actual distance between adjacent points based on the first radian difference, the second radian difference, and a preset Earth radius; determining the time interval between adjacent points based on the scanning time corresponding to the adjacent points; and determining the movement speed of the offshore workers based on the actual distance and the time interval.

[0048] The movement trajectory map includes the location point and the corresponding scan time. The location point is determined based on the location information in the scan record, and the adjacent points are the location points corresponding to adjacent scan times.

[0049] In this embodiment, based on time series analysis and considering the geographical characteristic that the Earth is an irregular sphere or ellipsoid, the Haversine formula is used to more accurately calculate the actual spherical distance between adjacent points in the movement trajectory map, thereby calculating a more accurate movement speed based on the actual distance.

[0050] For example, taking two adjacent positioning points in the scanning time as adjacent points, the latitude and longitude of the two adjacent points can be obtained from the positioning information of the adjacent points in the movement trajectory map, and the latitude and longitude of the two adjacent points can be converted into radians to meet the calculation requirements of trigonometric functions in the geographical distance calculation formula.

[0051] Furthermore, the actual distance d between adjacent points is calculated according to the following formula:

[0052]

[0053] in, These are the latitudes of two adjacent points converted to radians, respectively. Δλ is the difference between the latitudes of two adjacent points converted to radians, Δλ is the difference between the longitudes of two adjacent points converted to radians, and R is the radius of the Earth, which can be taken as an average of approximately 6371 kilometers.

[0054] The movement speed of the personnel working at sea can be calculated based on the actual distance and the scanning time interval between adjacent points.

[0055] It is worth mentioning that statistical analysis can be performed on the movement speed of personnel working at sea, such as calculating average speed, maximum speed, and standard deviation of speed. The calculated movement speed or related quantities are compared with the established permissible speed range, and if they exceed the permissible range, they are marked as abnormal behavior.

[0056] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, the activity range of the offshore workers is determined according to the movement trajectory map, specifically including: obtaining the distance threshold and minimum sample number of the positioning point; determining the neighborhood of the positioning point with the positioning point as the center and the distance threshold as the radius; marking the positioning point as unvisited; determining the positioning points in the neighborhood with a number of positioning points greater than or equal to the minimum sample number as core points; randomly determining a positioning point in the unvisited state as the target point; if the target point is a core point, marking all positioning points in the neighborhood of the target point as visited, and creating a new cluster, adding all positioning points in the neighborhood of the target point to the cluster; if the target point is a core point, and the number of positioning points in the neighborhood of the target point is greater than or equal to the minimum sample number... If there are core points other than the target point, the core points in the neighborhood of the target point are identified as expansion points. The neighborhood of the target point is updated based on the neighborhood of the expansion points, and all localization points in the neighborhood of the expansion points are added to the individual clusters until there are no more expansion points in the neighborhood of the target point. Then, a localization point that is in an unvisited state is randomly identified as the target point. Localization points belonging to multiple clusters are identified as merge points, and the multiple clusters to which the merge point belongs are merged into one cluster. If the target point is not a core point, the target point is marked as visited, and a localization point that is in an unvisited state is randomly identified as the target point again until there are no more localization points in an unvisited state. If there are no more localization points in an unvisited state, the activity range of the offshore workers is determined based on the individual clusters.

[0057] The movement trajectory map includes the location points and the corresponding scanning time. The location points are determined based on the location information in the scanning records.

[0058] In this embodiment, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is used to cluster the various positioning points in the movement trajectory map. The density of the positioning points is used to determine which positioning points belong to the same cluster and to identify noise points. Thus, without relying on a preset number of clusters, positioning points within a certain distance range are grouped into one cluster. The clustering results can be used to obtain activity ranges with irregular shapes and varying densities, which conforms to the actual working scenarios of personnel at sea.

[0059] Specifically, the core idea of ​​this embodiment is "extension," that is, starting from a core point, the range of the cluster is continuously expanded through density reachability. Specifically, an unvisited point p is first selected. If this point is a core point (i.e., it has enough points in its neighborhood), a new cluster C is created, and the points in its neighborhood are added to cluster C. Then, all localized points in cluster C are traversed. For each localized point q in cluster C, if q is also a core point, the points in q's neighborhood are also added to cluster C, and so on, until cluster C stops expanding. Next, the next unvisited point is selected, and the above process is repeated until all points have been visited.

[0060] It should be noted that the neighborhood is determined based on a distance threshold, which defines the range of a location's neighborhood, while the minimum number of samples, MinPts, determines whether a location is a core point. If a location has at least MinPts locations in its neighborhood (including the location itself), then that location is considered a core point.

[0061] For example, the geographical distance calculation formula involved in the above embodiments can be used to calculate the spherical distance between each positioning point in the movement trajectory map, and the existence of each positioning point in the neighborhood of other positioning points can be determined based on the spherical distance between each positioning point.

[0062] Further, three lists can be created first: an unvisited list, a visited list, and a cluster list. Next, the unvisited list is initialized by adding all localities in the movement trajectory map to it, ensuring all localities in the movement trajectory map are in an unvisited state. The visited list and the cluster list are initialized to empty. Then, a locality p is taken from the unvisited list, marked as visited, and added to the visited list. It is checked whether locality p is a core point (i.e., whether it has at least MinPts localities in its neighborhood). If locality p is not a core point, it is marked as visited and added to the visited list, and the process continues by taking the next locality from the unvisited list. If locality p is a core point, a new cluster C is created and added to the cluster list, and all localities q in the neighborhood of locality p are traversed. If a location point q is unvisited, it is marked as visited and added to the visited list and cluster C. If a location point q is visited and belongs to an existing cluster C' (i.e., q is within the neighborhood of C'), cluster C' is merged with cluster C until no more locations can be added. Then, the process continues, taking the next location point from the unvisited list and repeating the above steps until unvisited is empty, meaning all locations have been visited, resulting in multiple clusters. Based on the regions formed by these clusters, the activity ranges of the maritime workers in the movement trajectory map can be determined.

[0063] In one embodiment, the method for safety management of offshore workers further includes: sorting the positioning points in a cluster according to the scanning time to determine the point order; determining the dwell time of the activity range corresponding to the cluster according to the difference in scanning time between the first and last positioning points in the point order; if the dwell time does not conform to the time specified in the sea departure plan information, determining abnormal behavior information based on the dwell time that does not conform to the sea departure plan information; displaying the abnormal behavior information and sending the abnormal behavior information to the terminal associated with the offshore worker corresponding to the abnormal behavior information.

[0064] In this embodiment, for each activity area, the dwell time is determined by statistically analyzing the time span of the location points within that activity area, and the calculated dwell time is compared with a predetermined dwell time limit. If the dwell time exceeds the limit, it is marked as abnormal behavior, and an early warning is issued promptly.

[0065] It is worth mentioning that for each activity area, it can also be determined whether the activity area conforms to the operational area in the sea deployment plan information. If the activity area does not conform to the operational area, the predetermined stay time will be reduced, so that if the offshore workers stay in the non-operational area for too long, abnormal information will be generated in a timely manner, so that relevant personnel can pay attention to any abnormal behavior in a timely manner.

[0066] Furthermore, the analysis results of movement speed, activity range, and dwell time can be integrated to comprehensively determine whether offshore workers have followed the work plan and safety regulations. An assessment report is generated based on the analysis results, including statistics on the movement speed of offshore workers, dwell time, and detailed descriptions of any abnormal behavior. Simultaneously, the assessment results can be fed back to the offshore workers to remind them to comply with the work plan and safety regulations.

[0067] Step 108: The cloud management platform obtains environmental monitoring data fed back by offshore workers, inputs the environmental monitoring data into the risk prediction model, and determines the early warning information of the environment in which the offshore workers are located.

[0068] The risk prediction model is trained based on historical environmental monitoring data, which includes at least meteorological data and marine traffic data.

[0069] Step 109: Display the warning information and send it to the terminal associated with the offshore worker corresponding to the warning information.

[0070] In this embodiment, the cloud management platform accurately predicts and manages the operational risks of personnel working at sea, comprehensively revealing the dynamics and coupling relationships between various complex risk factors, thereby accurately predicting potential hazards. Once a potential hazard is predicted, the cloud management platform will immediately generate an early warning message and notify relevant personnel and management departments through various means, improving the comprehensiveness of safety protection.

[0071] In one embodiment, the method for safety management of offshore workers further includes: preprocessing historical environmental monitoring data and extracting feature vectors from the preprocessed historical environmental monitoring data; determining the initial parameters of a logistic regression model using the maximum likelihood estimation method; training a logistic regression model with initial parameters based on the feature vectors, and updating the parameters in the logistic regression model using the gradient descent method until the loss function of the logistic regression module converges or reaches the maximum number of iterations; and determining a risk prediction model based on the logistic regression model whose loss function has converged or has reached the maximum number of iterations, so that the risk prediction model can output the probability of danger based on the environmental monitoring data.

[0072] In this embodiment, the risk prediction model adopts a logistic regression model. By leveraging the simplicity and intuitiveness of logistic regression, it can meet the high interpretability requirements in offshore operation scenarios and focus on the orderly occurrence of multiple events or the hierarchical superposition of potential factors, revealing the dynamics and coupling relationships between various risk factors in complex offshore operation scenarios.

[0073] For example, historical environmental monitoring data can be preprocessed. For meteorological data such as wind speed and wave height, reasonable ranges can be set based on their physical characteristics to identify outliers. For maritime traffic data such as the number of nearby vessels, significantly unreasonable high or low values ​​can also be identified as outliers and processed. For missing values ​​in meteorological conditions and maritime traffic data, interpolation can be used to fill in the missing values ​​based on the correlation of time series data, or the mean, median, or other values ​​of the specified features can be used.

[0074] Next, feature vectors are extracted from the preprocessed historical environmental monitoring data. For meteorological feature vectors such as wind speed and temperature, Z-score standardization can be used to make their mean 0 and standard deviation 1. For marine traffic feature vectors such as the number of nearby ships, Min-Max normalization can be used to map them to the [0,1] interval.

[0075] Furthermore, the probabilistic model for logistic regression is defined as follows:

[0076]

[0077] Where X = (X1, X2, ..., X...) n ) is the feature vector obtained after the above processing, Y∈{0,1} is the label, β0,β1,...,β n These are the model parameters.

[0078] Next, the model parameters are estimated using the maximum likelihood estimation method. The log loss function is used for logistic regression. The parameters in the logistic regression model are updated using gradient descent until the loss function of the logistic regression module converges or reaches the maximum number of iterations. Training is then complete, and a risk prediction model is obtained, enabling the risk prediction model to output the probability of danger based on environmental monitoring data.

[0079] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, the step of inputting environmental monitoring data into the risk prediction model to determine the early warning information for offshore workers specifically includes: if the probability of danger is greater than a preset threshold, determining the early warning information based on the environmental monitoring data corresponding to the probability of danger greater than the preset threshold.

[0080] In this embodiment, by using the hazard probability output by the risk prediction model and a preset threshold, an effective early warning function is achieved, avoiding over- or under-warning, optimizing the decision-making process, and providing dynamic adaptability. Reasonable threshold settings not only improve the accuracy of early warnings but also ensure that necessary countermeasures are taken promptly when risks occur, protecting the safety of personnel working at sea.

[0081] For example, early warning information typically includes key information such as the type of hazard, the expected time of occurrence, the scope of impact, and recommended countermeasures, which helps managers make quick decisions and take appropriate preventive measures.

[0082] It is worth mentioning that the risk prediction model has a dynamic adjustment function. With the continuous addition of new data and continuous optimization of the model, the risk prediction model can continuously improve its prediction accuracy and adaptability. At the same time, by collecting user feedback on early warning information, the risk prediction model can further optimize the prediction algorithm and early warning strategy, ensuring the continuous improvement and enhancement of the risk prediction model.

[0083] Furthermore, such as Figure 2 As shown, as a specific implementation of the above-mentioned method for managing the safety of offshore workers, this application provides an offshore worker safety management system 200, which includes a cloud management platform 201 and a mobile terminal 202.

[0084] The cloud management platform 201 is used to receive encrypted scan records sent by mobile terminals; decrypt the scan records and determine the movement trajectory of the offshore workers corresponding to the user information in the scan records based on the decrypted scan records; determine abnormal information of the offshore workers based on the movement trajectory; obtain environmental monitoring data fed back by the offshore workers, input the environmental monitoring data into the risk prediction model, determine the early warning information of the environment in which the offshore workers are located, wherein the risk prediction model is trained based on historical environmental monitoring data, and the monitoring data includes at least meteorological data and marine traffic data; display abnormal information or early warning information, and send the abnormal information or early warning information to the terminal associated with the offshore worker corresponding to the abnormal information or early warning information;

[0085] Mobile terminal 202 is used to scan a location code at a preset location to identify the location information in the location code and record the scanning time and the user information logged into the mobile terminal at the time of scanning; determine the scanning record based on the scanning time, location information and user information; encrypt the scanning record and send the encrypted scanning record to the cloud management platform, so that the cloud management platform can determine the abnormal information or warning information of the offshore workers corresponding to the user information in the scanning record, display the abnormal information or warning information, and send the abnormal information or warning information to the terminal associated with the offshore workers corresponding to the abnormal information or warning information.

[0086] Specific limitations regarding the safety management system for offshore workers can be found in the limitations on the methods for managing offshore workers' safety mentioned above, and will not be repeated here. Each module in the aforementioned safety management system for offshore workers can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0087] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0088] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A method for safety management of offshore workers, applied to a cloud management platform, wherein the cloud management platform is capable of communicating with mobile terminals, characterized in that, The method includes: Receive the encrypted scan record sent by the mobile terminal; The scan record is decrypted, and based on the decrypted scan record, the movement trajectory map of the offshore workers corresponding to the user information in the scan record is determined; Based on the movement trajectory map, determine the abnormal information of the offshore workers; The environmental monitoring data fed back by the offshore workers is obtained, and the environmental monitoring data is input into the risk prediction model to determine the early warning information of the environment in which the offshore workers are located. The risk prediction model is trained based on historical environmental monitoring data, and the environmental monitoring data includes at least meteorological data and marine traffic data. Display the abnormal information or the warning information, and send the abnormal information or the warning information to the terminal associated with the offshore worker corresponding to the abnormal information or the warning information; The step of determining the movement trajectory of the offshore workers corresponding to the user information in the decrypted scan records specifically includes: identifying scan records whose location information matches a preset departure location as starting records; identifying the user information in the starting records as target user information, and identifying the scan records associated with the target user information as monitoring records; using geographic information system technology to determine the location points corresponding to the location information in the monitoring records on a preset map; connecting the location points in the preset map according to the scanning time of the location information in the monitoring records to determine the movement trajectory of the target workers, wherein the target workers are the offshore workers corresponding to the target user information; and displaying the movement trajectory. The movement trajectory map includes a location point and the corresponding scan time. The location point is determined based on the location information in the scan record. The method further includes: converting the location information corresponding to adjacent points in the movement trajectory map into radians, determining the radian information of the adjacent points, wherein the adjacent points are the location points corresponding to adjacent scan times; determining a first radian difference and a second radian difference between the adjacent points based on the radian information; determining the actual distance between the adjacent points based on the first radian difference, the second radian difference, and a preset Earth radius; determining the time interval between the adjacent points based on the scan time corresponding to the adjacent points; and determining the movement speed of the offshore workers based on the actual distance and the time interval.

2. The method for safety management of offshore workers according to claim 1, characterized in that, The method further includes: The departure time of the target personnel is determined based on the scan time in the initial record and the current time; If the departure time is longer than the preset time, then the overtime information of the target operator is determined based on the departure time that is longer than the preset time. The timeout information is displayed and sent to the terminal associated with the target operator.

3. The method for safety management of offshore workers according to claim 1, characterized in that, Based on the movement trajectory map, the abnormal information of the offshore workers is determined, specifically including: Based on the movement trajectory map, the activity range of the offshore workers is determined; Obtain the sea departure plan information of the aforementioned offshore workers; If the movement speed or the activity range does not meet the preset conditions in the sea departure plan information, the abnormal information is determined based on the movement speed or the activity range that does not meet the sea departure plan information.

4. The method for safety management of offshore workers according to claim 3, characterized in that, The movement trajectory map includes a location point and the corresponding scan time. The location point is determined based on the location information in the scan record. Determining the activity range of the offshore workers based on the movement trajectory map specifically includes: Obtain the distance threshold and minimum number of samples for the location point; The neighborhood of the location point is determined with the location point as the center and the distance threshold as the radius. Mark the location point as unvisited; The location points in the neighborhood whose number of location points is greater than or equal to the minimum number of samples are identified as core points; Randomly select one of the locations that is not currently visited as the target point; If the target point is the core point, mark all the location points in the neighborhood of the target point as visited, create a new cluster, and add all the location points in the neighborhood of the target point to the cluster. If the target point is the core point, and there are core points other than the target point in the neighborhood of the target point, then the core points other than the target point in the neighborhood of the target point are determined as extension points. The neighborhood of the target point is updated according to the neighborhood of the extension points, and all the location points in the neighborhood of the extension points are added to the cluster until there are no more extension points in the neighborhood of the target point. Then, a location point that is in an unvisited state is randomly determined as the target point. The location points belonging to multiple clusters are determined as merging points, and the multiple clusters to which the merging points belong are merged into one cluster; If the target point is not the core point, the target point is marked as visited, and an unvisited location point is randomly reassigned as the target point until there are no unvisited location points. If no location point is in an unvisited state, the activity range of the offshore workers is determined based on the cluster.

5. The method for safety management of offshore workers according to claim 4, characterized in that, The method further includes: Based on the scan time, the positioning points in each cluster are sorted to determine the point order; The dwell time of the activity range corresponding to each cluster is determined based on the difference in scanning time between the first and last positioning points in the point sequence. If the stay time does not conform to the time specified in the sea departure plan information, abnormal behavior information is determined based on the stay time that does not conform to the sea departure plan information; The abnormal behavior information is displayed and sent to the terminal associated with the offshore worker corresponding to the abnormal behavior information.

6. The method for safety management of offshore workers according to claim 1, characterized in that, The method further includes: The historical environmental monitoring data is preprocessed, and feature vectors are extracted from the preprocessed historical environmental monitoring data. The initial parameters of the logistic regression model are determined using the maximum likelihood estimation method. The logistic regression model with the initial parameters is trained based on the feature vector, and the parameters in the logistic regression model are updated using the gradient descent method until the loss function of the logistic regression module converges or reaches the maximum number of iterations. Based on the logistic regression model whose loss function converges or reaches the maximum number of iterations, the risk prediction model is determined so that the risk prediction model can output the probability of danger based on the environmental monitoring data. The step of inputting the environmental monitoring data into the risk prediction model to determine the early warning information for offshore workers specifically includes: If the probability of danger is greater than a preset threshold, an early warning message is determined based on the environmental monitoring data corresponding to the probability of danger that is greater than the preset threshold.

7. The method for safety management of offshore workers as described in claim 1, applied to a mobile terminal, characterized in that, The method includes: Scan the location code located at the preset location to identify the location information in the location code, and record the scanning time and the user information logged into the mobile terminal at the time of scanning; The scan record is determined based on the scan time, the location information, and the user information; The scan record is encrypted and then sent to the cloud management platform. The cloud management platform determines the abnormal information or warning information of the offshore workers corresponding to the user information in the scan record, displays the abnormal information or warning information, and sends the abnormal information or warning information to the terminal associated with the offshore worker corresponding to the abnormal information or warning information.

8. A safety management system for offshore workers, characterized in that, The system includes: A cloud management platform is used to receive encrypted scan records sent by the mobile terminal; decrypt the scan records and determine the movement trajectory map of the offshore workers corresponding to the user information in the scan records based on the decrypted scan records; determine the abnormal information of the offshore workers based on the movement trajectory map; acquire environmental monitoring data fed back by the offshore workers, input the environmental monitoring data into a risk prediction model, determine the early warning information of the environment in which the offshore workers are located, wherein the risk prediction model is trained based on historical environmental monitoring data, and the monitoring data includes at least meteorological data and marine traffic data; display the abnormal information or the early warning information, and send the abnormal information or the early warning information to the terminal associated with the offshore workers corresponding to the abnormal information or the early warning information; A mobile terminal is used to scan a location code at a preset location to identify the location information in the location code and record the scanning time and the user information logged into the mobile terminal at the time of scanning; a scanning record is determined based on the scanning time, the location information, and the user information; the scanning record is encrypted and sent to a cloud management platform, so that the cloud management platform can determine the abnormal information or warning information of the offshore workers corresponding to the user information in the scanning record, display the abnormal information or warning information, and send the abnormal information or warning information to the terminal associated with the offshore workers corresponding to the abnormal information or warning information. The step of determining the movement trajectory of the offshore workers corresponding to the user information in the decrypted scan records specifically includes: identifying scan records whose location information matches a preset departure location as starting records; identifying the user information in the starting records as target user information, and identifying the scan records associated with the target user information as monitoring records; using geographic information system technology to determine the location points corresponding to the location information in the monitoring records on a preset map; connecting the location points in the preset map according to the scanning time of the location information in the monitoring records to determine the movement trajectory of the target workers, wherein the target workers are the offshore workers corresponding to the target user information; and displaying the movement trajectory. The movement trajectory map includes a location point and the corresponding scan time. The location point is determined based on the location information in the scan record. The method further includes: converting the location information corresponding to adjacent points in the movement trajectory map into radians, determining the radian information of the adjacent points, wherein the adjacent points are the location points corresponding to adjacent scan times; determining a first radian difference and a second radian difference between the adjacent points based on the radian information; determining the actual distance between the adjacent points based on the first radian difference, the second radian difference, and a preset Earth radius; determining the time interval between the adjacent points based on the scan time corresponding to the adjacent points; and determining the movement speed of the offshore workers based on the actual distance and the time interval.

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