A data acquisition and management method and system based on electronic fence

Abstract

This invention discloses a data acquisition and management method and system based on electronic fences, specifically relating to the field of construction safety management. It addresses the issue of real-time monitoring of construction workers by using a GIS system to delineate risk areas and set up electronic fences. The method collects real-time location information of construction workers, extracts individual and collective behavioral characteristics, calculates the differences between individual and collective behavioral patterns, and assesses the consistency and coordination of collective behavior. An interaction event matrix is ​​constructed to analyze the interaction intensity among construction workers, calculates the interaction risk index, and uses graph theory algorithms to identify high-frequency interaction groups and assess the group risk level. The hidden risk index, combined with principal component projection scores and interaction-cooperation heterogeneity indices, enables managers to accurately identify high-risk personnel and develop targeted management and prevention measures based on the electronic fence areas, providing comprehensive data support and decision-making basis for construction site safety management.

Description

Technical Field

[0001] This invention relates to the field of construction safety management, and more specifically, to a data acquisition and management method and system based on electronic fences. Background Technology

[0002] At large construction sites, ensuring the safety of construction workers is always the top priority in management. The construction environment is complex and ever-changing, with various potential risks, such as working at heights, operating heavy machinery, and working in confined spaces. These risks require managers to monitor the location and activities of construction workers in real time to respond and adjust accordingly. Therefore, an increasing number of construction projects are introducing advanced technologies, such as GIS systems and GPS positioning technology, to improve safety management at construction sites through precise location information collection and real-time monitoring. However, simply collecting and monitoring location information is insufficient to comprehensively guarantee safety. How to effectively analyze and utilize this data to further enhance safety management effectiveness has become a pressing issue.

[0003] While GPS positioning and GIS systems are widely used in construction safety management, several shortcomings remain. First, existing systems primarily focus on monitoring individual location information, lacking analysis of collective worker behavior patterns and failing to adequately assess the impact of collective behavior on individual safety. Second, existing risk assessment methods are often simplistic, failing to comprehensively consider the differences and interactive risks between individual and collective behavior patterns, resulting in poor accuracy and comprehensiveness in risk assessment. Furthermore, the lack of effective data fusion technology prevents the full utilization of multi-source data, further limiting the system's safety management capabilities. Therefore, an innovative technology is urgently needed that can dynamically adjust data collection frequency, comprehensively analyze individual and collective behavior patterns, and fuse multi-source data to improve the overall safety management level of construction sites.

[0004] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of existing technologies, embodiments of the present invention provide a data acquisition and management method and system based on electronic fences. This method involves dividing risk areas using a GIS system and setting up electronic fences, integrating GPS modules into the safety equipment of construction workers, configuring and testing data transmission protocols, and activating the data acquisition system to monitor and record the location information and activity trajectories of construction workers in real time. This significantly improves the safety management level of construction sites. By collecting the location information of construction workers, extracting individual and collective behavioral characteristics, calculating the difference between individual and collective behavioral patterns, and combining electronic fence area division and risk area influence factors, a precise assessment of hidden risks of construction workers can be achieved. Introducing a collective coordination index helps assess the normative impact of collective behavior on individual behavior, reflecting the consistency and coordination of collective behavior. By constructing an interaction event matrix, the interaction intensity between construction workers is detected and analyzed, an interaction risk index is calculated, revealing the potential risks brought about by frequent interactions, and graph theory algorithms are used to identify high-frequency interaction groups and assess the overall risk level within the groups. The comprehensive calculation of the hidden risk index, combined with the principal component projection score and the interaction-cooperative heterogeneity index, enables managers to accurately identify high-risk personnel and formulate targeted management and prevention measures based on the division of electronic fence areas. This provides comprehensive data support and decision-making basis for the safety management of construction sites, thereby solving the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] S1 uses a GIS system to set up multiple electronic fence areas with different risk levels and integrates a GPS positioning module into the safety equipment of construction workers to collect and transmit the location information of construction workers in real time.

[0008] S2, collect the location information of construction workers, combine it with the division of electronic fence areas, extract individual and collective behavioral characteristics, calculate the difference and interaction between individual and collective behavioral patterns, obtain the corresponding collective coordination index and interaction risk index, and combine it with the risk area influence factor to calculate the hidden risk index of each construction worker, identify potential high-risk personnel and manage them, including showing the hidden risk level.

[0009] S3 dynamically adjusts the data collection frequency and transmission mode based on the identified level of hidden risk.

[0010] In a preferred embodiment, step S1 includes the following:

[0011] Risk areas are delineated using a GIS system and electronic fences are set up. GPS modules are integrated into the safety equipment of construction workers. Data transmission protocols are configured and tested, and a data acquisition system is started to monitor and record the location information and activity trajectory of construction workers in real time.

[0012] In a preferred embodiment, the process of obtaining the collective synergy index is as follows:

[0013] A1 uses a GPS module to collect real-time location information of construction workers, including timestamps, latitude and longitude, and altitude.

[0014] A2, Individual Feature Extraction: Extracting individual trajectory features, including trajectory length. average speed Number of stops Path change frequency ;

[0015] Collective Feature Extraction: Extracting collective trajectory features, including collective trajectory density. Collective path consistency Collective speed consistency Distribution of group stay points ;

[0016] In this context, a collective is composed of multiple individuals with the same function;

[0017] A3, Individual Behavior Pattern Calculation: Calculate individual behavior patterns based on individual characteristics and define individual behavior vectors. ;

[0018] Collective behavior pattern calculation: Calculate collective behavior patterns based on collective characteristics and define collective behavior vectors. ;

[0019] Calculate the difference between individual behavior patterns and collective behavior patterns The formula is as follows: ;

[0020] A4. Based on the GIS system, define the risk zone impact factors for the electronically fenced areas within the construction site that are marked with different risk levels. ,in, The risk level of the area is indicated by: low risk 1, medium risk 2, and high risk 3.

[0021] A5, combining the degree of difference and regional risk impact factors, calculate the comprehensive risk index for each construction worker. The formula is as follows: ;

[0022] in, The number of areas where construction workers are active; For the first Risk factors affecting each region; For the construction workers at the first Time spent in each area;

[0023] A6, Calculate the collective coordination coefficient The formula reflects the impact of collective behavior on individual risk, as follows: ;

[0024] in, The number of construction workers in the group; For the first Differences in the behavioral patterns of individual construction workers.

[0025] In a preferred embodiment, the process of obtaining the interaction risk index is as follows:

[0026] B1 collects the location information of construction workers, including timestamps, latitude, longitude, and altitude;

[0027] B2, when the distance between two construction workers is less than the distance threshold. And the duration exceeds It is assumed that an interaction event has occurred, and the trajectory data of each pair of construction workers are compared to detect the interaction event;

[0028] B3, Constructing the Interaction Matrix ,in Indicates the first The construction worker and the first The interaction intensity between construction workers is calculated based on the number and duration of interaction events.

[0029] ;

[0030] in, For time steps; For the first Time step The construction worker and the first The distance between construction workers; Duration of the interaction; For indicator functions;

[0031] B4. Calculate the interaction risk for each construction worker with all other construction workers. The interaction risk is calculated using the following formula: ;

[0032] in, For the number of construction workers, The risk function for interaction strength is defined as: ;

[0033] B5. Further analyze the interaction patterns, identify high-risk interaction groups, and use graph theory algorithms to identify high-frequency interaction groups to assess overall risk.

[0034] Building an interaction diagram , where nodes Indicates construction workers, side The edge weights represent the interaction strength, indicating the interaction relationship.

[0035] Community detection algorithms are used to detect interactive groups and calculate the interaction risk index for each group. : ,in, It is the set of interaction edges within the group;

[0036] B6. Calculate the interaction risk index for each construction worker, considering both individual and group interaction risks. : ;

[0037] in, For construction workers The group they belong to The group size.

[0038] In a preferred embodiment, C1, the interaction risk index and collective collaboration index of each construction worker are collected;

[0039] C2, calculate the mean and standard deviation of the interaction risk index and collective coordination index for each construction worker, denoted as follows: , and , ;

[0040] The interaction risk index and collective coordination index of each construction worker are standardized into standard scores;

[0041] C3, Constructing an Interaction-Coordination Matrix Each element of the matrix Elements represent the first The construction worker and the first The relationship between individual construction workers in terms of interactive risks and collective collaboration; ;

[0042] C4. Perform principal component analysis on the interaction-cooperation matrix to extract the principal components and perform dimensionality reduction. Let the eigenvector of the first principal component be... ;

[0043] Calculate the projected score of each construction worker on the first principal component: ;

[0044] in, The total number of construction workers These are the components of the eigenvector corresponding to the first principal component;

[0045] C5, calculate the interaction-cooperative heterogeneity index for each construction worker. This reflects the degree of heterogeneity in their interaction and collaboration, as shown in the following formula: ;

[0046] in, and The average of the standard scores of all construction workers;

[0047] C6 combines the first principal component projection score of each construction worker with the interaction-cooperative heterogeneity index to calculate the hidden risk index. The formula is as follows: .

[0048] In a preferred embodiment, different levels of hidden risk are determined by calculating the hidden risk index of each construction worker and comparing it with pre-set risk threshold one and hidden risk threshold two. The specific process is as follows:

[0049] First, the hidden risk index of each construction worker is obtained based on the comprehensive analysis of the interaction risk index and the collective coordination index;

[0050] The hidden risk index is compared with risk threshold one and hidden risk threshold two;

[0051] If the hidden risk index is lower than the risk threshold one, it means that the construction workers' behavior patterns are highly consistent with the group and they have little interaction with other people, which is a low-risk level.

[0052] If the hidden risk index is between risk threshold one and hidden risk threshold two, it indicates that the construction workers' behavior patterns are somewhat different, or that they have moderate interaction with others, which is considered a medium risk level.

[0053] If the hidden risk index is higher than the hidden risk threshold of two, it is classified as high risk. The construction workers' behavior patterns are significantly different from those of the group, and they frequently interact with others, which is considered a high-risk level.

[0054] In a preferred embodiment, step S3 includes the following:

[0055] For construction workers marked as medium-risk, the upper and lower limits of the data collection frequency are set as follows: and ;

[0056] Based on the behavioral patterns of people at medium-risk levels and the division of electronic fence areas, the data collection frequency is dynamically adjusted, using the following formula: ;

[0057] in, Based on the sampling frequency; The adjusted sampling frequency; To conceal the risk index; and These are the upper and lower limits of the risk threshold;

[0058] Replace the base acquisition frequency with the adjusted acquisition frequency and implement the adjusted acquisition frequency.

[0059] A data acquisition and management system based on electronic fences includes: an area management module, a positioning equipment module, a data acquisition module, a behavior analysis module, a risk assessment module, and a dynamic adjustment module;

[0060] The area management module sets up multiple electronic fence areas with different risk levels through the GIS system, and sends the risk area data to the data acquisition module and the behavior analysis module.

[0061] The positioning equipment module integrates a GPS positioning module into the safety equipment of construction workers, which collects and transmits location information in real time, and transmits the collected location information to the data acquisition module in real time.

[0062] The data acquisition module collects the location information of construction workers, performs preliminary processing and storage, and transmits the processed data to the behavior analysis module;

[0063] The behavior analysis module combines the electronic fence area division to extract individual and collective behavior characteristics, calculate the difference and interaction between individual and collective behavior patterns, and obtain the collective coordination index and interaction risk index accordingly. The collective coordination index and interaction risk index are then sent to the risk assessment module.

[0064] The risk assessment module combines risk area impact factors to calculate the hidden risk index of each construction worker, identify and manage potentially high-risk personnel, including displaying the hidden risk level and sending the assessment results to the dynamic adjustment module;

[0065] The dynamic adjustment module dynamically adjusts the data acquisition frequency and transmission mode based on the identified hidden risk level.

[0066] The technical effects and advantages of the data acquisition and management method and system based on electronic fences of this invention are as follows:

[0067] 1. This invention uses a GIS system to delineate risk areas and set up electronic fences, integrates GPS modules into the safety equipment of construction workers, configures and tests data transmission protocols, and starts a data acquisition system to monitor and record the location information and activity trajectories of construction workers in real time. This can significantly improve the safety management level of construction sites, effectively monitor the activities of construction workers in different risk areas, promptly detect and prevent potential safety hazards, ensure the stability and real-time nature of data transmission, provide accurate and real-time location information support for managers, and improve overall construction efficiency and safety assurance.

[0068] 2. This invention, by collecting location information of construction workers, extracting individual and collective behavioral characteristics, calculating the degree of difference between individual and collective behavioral patterns, and combining this with electronic fence area division and risk area influence factors, enables accurate assessment of hidden risks associated with construction workers. The calculation of the degree of difference between individual and collective behavioral patterns provides a deeper understanding of the extent to which construction workers' behavior deviates from collective standards. The introduction of a collective coordination index helps assess the normative impact of collective behavior on individual behavior, reflecting the consistency and coordination of collective behavior. By constructing an interaction event matrix, detecting and analyzing the interaction intensity among construction workers, and calculating the interaction risk index, the potential risks brought about by frequent interactions can be revealed. Furthermore, graph theory algorithms are used to identify high-frequency interaction groups and assess the overall risk level within these groups. The comprehensive calculation of the hidden risk index, combining principal component projection scores and the interaction-coordination heterogeneity index, allows managers to accurately identify high-risk personnel and formulate targeted management and prevention measures based on the electronic fence area division. This method not only improves the accuracy of risk assessment but also provides comprehensive data support and decision-making basis for safety management at construction sites. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating a data acquisition and management method based on an electronic fence according to the present invention.

[0070] Figure 2 This is a schematic diagram of the structure of a data acquisition and management system based on an electronic fence according to the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0072] Figure 1 This invention provides a data acquisition and management method based on electronic fences, comprising:

[0073] S1 uses a GIS system to set up multiple electronic fence areas with different risk levels and integrates a GPS positioning module into the safety equipment of construction workers to collect and transmit the location information of construction workers in real time.

[0074] S2, collect the location information of construction workers, combine it with the division of electronic fence areas, extract individual and collective behavioral characteristics, calculate the difference and interaction between individual and collective behavioral patterns, obtain the corresponding collective coordination index and interaction risk index, and combine it with the risk area influence factor to calculate the hidden risk index of each construction worker, identify potential high-risk personnel and manage them, including showing the hidden risk level.

[0075] S3 dynamically adjusts the data collection frequency and transmission mode based on the identified level of hidden risk.

[0076] At large construction sites, ensuring the safety of construction workers is the top management priority. To achieve precise personnel location and risk area monitoring, electronic fence technology based on a GIS system has been introduced to the construction site. Through the GIS system, the construction site is divided into multiple zones with different risk levels, such as low-risk, medium-risk, and high-risk zones, each with clearly defined boundaries. To monitor personnel activity in real time, high-precision GPS positioning modules are integrated into the safety equipment of construction workers (such as helmets, name tags, and belts). These modules can collect and transmit the location information of construction workers in real time, including timestamps, latitude, longitude, and altitude, ensuring that managers can immediately grasp the specific location and movement of each construction worker. The application of this technology not only helps improve the level of on-site safety management but also enables rapid location and rescue of trapped personnel in emergencies, greatly enhancing the overall safety assurance capability of the construction site.

[0077] Step S1 includes the following:

[0078] Risk Zone Delineation: Based on the actual conditions of the construction site and the risk assessment report, areas with different risk levels are divided. Using GIS (Geographic Information System) software, different risk levels, such as low-risk, medium-risk, and high-risk areas, are marked and assigned on a map of the construction site. An electronic map with risk levels marked is generated, defining the boundaries and attributes of each area.

[0079] Electronic fence setup: Set up electronic fences in the GIS system to ensure clear boundaries for each risk area. Use the polygon tool in the GIS software to draw each electronic fence, defining its spatial extent and risk level attributes. Complete the risk area map with electronic fences.

[0080] Integrate the GPS module into the safety equipment of construction workers (such as helmets, name tags, and belts) to ensure that the GPS module can be installed stably and will not affect the operation of construction workers.

[0081] Data transmission protocol configuration: Configure the data transmission protocol to ensure that GPS module data can be transmitted stably and timely to the data acquisition system. Based on the technical specifications of the GPS module and receiving system, set the data transmission protocol (such as LoRa, NB-IoT, Wi-Fi, Bluetooth, etc.) to ensure the stability and reliability of data transmission. The configured transmission protocol guarantees smooth data transmission.

[0082] Real-time data transmission test: Test the real-time transmission performance of GPS data to ensure normal system operation. Perform multiple tests in a simulated environment to check the real-time performance, accuracy, and integrity of data transmission, identify and resolve potential problems. A test report confirms that the system can operate stably and meet the real-time data transmission requirements.

[0083] System Launch and Real-time Monitoring: The data acquisition system is launched for real-time monitoring. The system is officially launched to monitor the location information of construction personnel in real time and record their activities in different risk areas. Real-time monitoring data records the location information and activity trajectories of construction personnel.

[0084] This invention uses a GIS system to delineate risk areas and set up electronic fences, integrates GPS modules into the safety equipment of construction workers, configures and tests data transmission protocols, and starts a data acquisition system to monitor and record the location information and activity trajectories of construction workers in real time. This can significantly improve the safety management level of construction sites, effectively monitor the activities of construction workers in different risk areas, promptly detect and prevent potential safety hazards, ensure the stability and real-time nature of data transmission, provide accurate and real-time location information support for managers, and improve overall construction efficiency and safety assurance.

[0085] S2 includes the following:

[0086] On construction sites, safety management is a crucial task, especially in large-scale projects. The behavioral patterns of construction workers and their activities in different areas directly impact the overall safety level. Therefore, calculating the Collective Coordination Index is particularly important, aiming to assess the impact of collective behavior on individual risk. By collecting real-time location information of construction workers, extracting individual and collective behavioral characteristics, and analyzing the differences between individual and collective behavioral patterns, potential safety hazards can be identified more accurately. The Collective Coordination Index comprehensively considers the degree of difference between individual and collective behavioral patterns, spatial behavioral consistency, temporal behavioral consistency, and individual behavioral dispersion. Detailed analysis of this data reflects the consistency and coordination of collective behavior, thus providing managers with a reliable basis for risk assessment and guiding the development of effective safety management strategies.

[0087] The process of obtaining the collective coordination index is as follows:

[0088] A1 uses a GPS module to collect real-time location information of construction workers, including timestamps, latitude, longitude, and altitude.

[0089] Remove noise and outliers, handle missing data, and ensure data accuracy and consistency.

[0090] The cleaned data is converted into a structured data format to facilitate subsequent processing.

[0091] A2, Individual Feature Extraction: Extracting individual trajectory features, including trajectory length. average speed Number of stops Path change frequency .

[0092] Collective Feature Extraction: Extracting collective trajectory features, including collective trajectory density. Collective path consistency Collective speed consistency Distribution of group stay points .

[0093] It should be noted that a collective is composed of multiple individuals with the same function.

[0094] A3, Individual Behavior Pattern Calculation: Calculate individual behavior patterns based on individual characteristics and define individual behavior vectors. ;

[0095] Collective behavior pattern calculation: Calculate collective behavior patterns based on collective characteristics and define collective behavior vectors. ;

[0096] Calculate the difference between individual behavior patterns and collective behavior patterns The formula is as follows: ;

[0097] A4, based on the GIS system, shows the electronic fence areas within the construction site with different risk levels (low risk, medium risk, high risk).

[0098] Define risk area impact factors (in (Indicates the risk level of the area), set as: low risk (1), medium risk (2), high risk (3).

[0099] A5, combining the degree of difference and regional risk impact factors, calculate the comprehensive risk index for each construction worker. The formula is as follows: ;

[0100] in, The number of areas where construction workers are active; For the first Risk factors affecting each region; For the construction workers at the first The time spent in each area.

[0101] A6, Calculate the collective coordination coefficient The formula reflects the impact of collective behavior on individual risk, as follows: ,in, The number of construction workers in the group; For the first Differences in the behavioral patterns of individual construction workers.

[0102] The Collective Coordination Index measures the impact of collective behavior on individual risk at a construction site, reflecting the consistency and coordination of collective behavior. Specifically, the index comprehensively considers the differences between individual and collective behavior patterns, spatial consistency, temporal consistency, and individual behavior dispersion. A higher index indicates greater coordination among collective behavior patterns, smaller differences between individual and collective behavior, more consistent behavior among construction workers within the group, and lower risk. Conversely, a lower index indicates significant differences in behavior patterns among construction workers within the group, poor consistency in collective behavior, and higher dispersion in individual behavior, implying higher potential risk. This index allows for a more accurate assessment of the overall safety situation at the construction site, providing managers with effective risk warnings and management guidance.

[0103] In complex construction sites, interactions between construction workers not only affect work efficiency but also pose a potential threat to overall safety. Calculating an interaction risk index becomes crucial for assessing the impact of these interactions on overall safety risk. By collecting real-time location information of each worker and detecting and analyzing their interaction events, it's possible to understand the frequency and intensity of interactions and how these interactions affect individual and collective safety. The interaction risk index considers the non-linear risk growth of interaction behavior, reflecting the higher probability of accidents and risk propagation that frequent interactions may bring. This index helps identify high-risk interaction groups, assess the overall risk within the group, and thus provide managers with accurate risk warnings, enabling the development of more targeted safety management measures to ensure the safe operation of the construction site.

[0104] The process of obtaining the interaction risk index is as follows:

[0105] B1 collects the location information of construction workers, including timestamps, latitude, longitude, and altitude.

[0106] Remove noise and outliers, handle missing data, and ensure data accuracy and consistency.

[0107] The cleaned data is converted into a structured data format to facilitate subsequent processing.

[0108] B2, when the distance between two construction workers is less than the distance threshold. And the duration exceeds An interaction event is considered to have occurred. The trajectory data of each pair of construction workers is compared to detect the interaction event.

[0109] B3, Constructing the Interaction Matrix ,in Indicates the first The construction worker and the first The interaction intensity between construction workers. The interaction intensity is calculated based on the number of interaction events and their duration.

[0110] ;

[0111] in, For time steps; For the first Time step The construction worker and the first The distance between construction workers; Duration of the interaction; This is an indicator function.

[0112] B4. Calculate the interaction risk for each construction worker with all other construction workers. Interaction risk is calculated using the following formula: ;

[0113] in, For the number of construction workers, The risk function for interaction strength is defined as: ;

[0114] The risk function of interaction intensity takes into account the non-linear growth of interaction intensity, reflecting the higher risk that frequent interactions may bring.

[0115] B5. Further analyze interaction patterns to identify high-risk interaction groups. Use graph theory algorithms (such as community detection algorithms) to identify high-frequency interaction groups and assess overall risk.

[0116] Building an interaction diagram , where nodes Indicates construction workers, side This represents the interaction relationship, with the edge weight representing the interaction strength.

[0117] Community detection algorithms (such as the Louvain algorithm) are used to detect interactive groups and calculate the interaction risk index for each group. : ,in, It is the set of interaction edges within the group.

[0118] B6. Calculate the interaction risk index for each construction worker, considering both individual and group interaction risks. : ;

[0119] in, For construction workers The group they belong to The group size.

[0120] The Interactive Risk Index (IRI) is used to assess the impact of interactions among construction workers on overall safety risk, reflecting the importance of individual-to-group interaction patterns to safety management. The calculation method first collects location information of construction workers, removes noise and outliers, and formats the data for processing. Next, an interaction event is defined as when the distance between two construction workers is less than a certain threshold and the duration exceeds a specified time. These interaction events are detected, and an interaction matrix is ​​constructed, where each element represents the interaction intensity between each construction worker and other construction workers, calculated from the number and duration of the corresponding events. The risk function for interaction intensity adopts a non-linear form, reflecting the higher risk associated with frequent interactions. Subsequently, the interaction risk of each construction worker with all other construction workers is calculated, and high-frequency interaction groups are identified using graph theory algorithms (such as community detection algorithms), constructing an interaction graph and calculating the interaction risk index of each group. Finally, by combining the interaction risks of individuals and groups, the interaction risk index of each construction worker is calculated as the group set to which the construction worker belongs. A higher interaction risk index value indicates a higher frequency of interaction and a greater possibility of risk propagation, while a lower interaction risk index value indicates fewer interactions and lower risk. This index can be used to comprehensively assess the safety status of the construction site and improve management.

[0121] The interaction risk index is used to represent the impact of interactions among construction workers on overall safety risk, reflecting the importance of interaction patterns between individuals and the group for safety management. A larger interaction risk index value indicates frequent and intense interactions between construction workers and other personnel, suggesting higher safety risk, as frequent interactions may lead to a higher probability of accidents and risk propagation; a smaller interaction risk index value indicates less interaction between construction workers and other personnel, generally implying lower safety risk, because individual behavior is more independent and less affected by the behavior of others.

[0122] On construction sites, safety management involves complex behavioral analysis and interaction assessment. To accurately identify potentially high-risk individuals, the complex relationship between individual and collective behavior must be comprehensively considered. The Interaction Risk Index (IRI) aims to assess the impact of interactions among construction workers on overall safety risk, revealing potential accident risks from frequent interactions by analyzing their frequency and intensity. On the other hand, the Collective Coordination Index (CCI) measures the consistency and coordination of construction workers within collective behavioral patterns, reflecting the degree of difference between individual and collective behavioral patterns and their impact on individual risk. Combining these two indicators to calculate the Hidden Risk Index (CRI) not only captures the degree of abnormality in individual behavior but also deeply analyzes the interaction effects between individuals and collective behavior. This comprehensive assessment method can accurately identify high-risk individuals with abnormal behavioral patterns and incoordination with collective behavior. These individuals may increase potential safety hazards due to deviations from collective standards or frequent and complex interactions with others. Calculating the Hidden Risk Index requires combining the projection scores of individual behavior on the principal components with its interaction-coordination heterogeneity index, forming a multi-dimensional, multi-level risk assessment system. The establishment and application of this system not only helps to identify and prevent potential safety hazards in advance, but also provides managers with comprehensive risk assessment data, thereby enabling them to formulate more targeted safety management strategies and ultimately improve the overall safety management level of the construction site.

[0123] C1 collects the interaction risk index and collective collaboration index of each construction worker;

[0124] C2, calculate the mean and standard deviation of the interaction risk index and collective coordination index for each construction worker, denoted as follows: , and , .

[0125] The interactive risk index and collective coordination index of each construction worker were standardized into standard scores (Z-scores).

[0126] Standard scores reflect the degree of an individual's abnormality relative to the group; larger positive values ​​indicate above-mean and larger negative values ​​indicate below-mean.

[0127] C3, Constructing an Interaction-Coordination Matrix Each element of the matrix Elements represent the first The construction worker and the first The relationship between individual construction workers in terms of interactive risks and collective collaboration. ;

[0128] C4. Principal component analysis is performed on the interaction-cooperation matrix to extract the principal components and perform dimensionality reduction. Let the eigenvector of the first principal component be... .

[0129] Calculate the projected score of each construction worker on the first principal component: ;

[0130] in, The total number of construction workers These are the components of the eigenvector corresponding to the first principal component.

[0131] C5, calculate the interaction-cooperative heterogeneity index for each construction worker. This reflects the degree of heterogeneity in their interaction and collaboration, as shown in the following formula: ;

[0132] in, and This represents the average standard score of all construction workers.

[0133] C6. The hidden risk index is calculated by combining the first principal component projection score of each construction worker with the interaction-cooperative heterogeneity index, as shown in the following formula: ;

[0134] The Hidden Risk Index is used to represent and assess the potential safety risks of each construction worker on a construction site, specifically reflecting the implicit impact of the interaction between individual and collective behavioral patterns on overall safety. This index combines each worker's first principal component projection score with their interaction-cooperative heterogeneity index to comprehensively reflect the degree of individual abnormality within the group and the complexity of their interactions with other personnel. A larger Hidden Risk Index value indicates that the worker's behavior differs significantly from collective behavior and that their interactions with other personnel are frequent and complex, suggesting a higher potential safety risk, as such behavior may lead to unforeseen safety hazards and accidents. A smaller Hidden Risk Index value indicates that the worker's behavior is highly consistent with collective behavior and that their interactions with other personnel are less frequent or simpler, generally implying a lower safety risk, as their behavioral patterns are more standardized and less affected by external interference. By assessing the Hidden Risk Index, managers can more accurately identify potentially high-risk personnel and take preventative measures in advance, thereby effectively improving the overall safety level of the construction site.

[0135] By calculating the hidden risk index (CRI) of each construction worker and comparing it with the pre-set risk threshold one and hidden risk threshold two, different hidden risk levels are classified. The specific process is as follows: First, the hidden risk index of each construction worker is obtained based on the comprehensive analysis of the interaction risk index and the collective coordination index.

[0136] The hidden risk index is compared with risk threshold one (e.g., 1.0) and hidden risk threshold two (e.g., 2.5);

[0137] If the hidden risk index is lower than the risk threshold one, it means that the construction workers' behavior patterns are highly consistent with the group and they have little interaction with other people. This is considered a low-risk level. In this case, regular monitoring should be maintained without additional measures, but regular checks should be conducted to ensure that their behavior patterns remain stable.

[0138] If the hidden risk index is between risk threshold one and hidden risk threshold two, it indicates that the construction workers' behavior patterns are somewhat different, or that they have moderate interaction with others, which is a medium-risk level; strengthen patrols and supervision, regularly review their behavior patterns and interactions, pay attention to their activity frequency in different areas, and ensure that their behavior is within a controllable range.

[0139] If the hidden risk index is higher than the hidden risk threshold of two, it is classified as high risk. The construction workers' behavior patterns are significantly different from those of the group, and they frequently interact with others, which is considered a high-risk level. Key monitoring measures are taken, such as restricting their access to high-risk areas, increasing the frequency of real-time monitoring, assigning dedicated personnel to be responsible for safety supervision, providing training and guidance when necessary to improve their behavior patterns, and regularly assessing their risk index to ensure timely adjustment of management strategies.

[0140] Risk reports are generated for each construction worker and corresponding management measures are taken. Low-risk personnel are subject to routine monitoring, medium-risk personnel are subject to enhanced patrols and supervision, and high-risk personnel are subject to key monitoring measures such as restricting access to high-risk areas and increasing the frequency of real-time monitoring. In this way, potential high-risk personnel can be effectively identified and managed to ensure the safe operation of the construction site.

[0141] This invention collects location information of construction workers, extracts individual and collective behavioral characteristics, calculates the degree of difference between individual and collective behavioral patterns, and combines this with electronic fence area division and risk area influence factors to achieve accurate assessment of hidden risks from construction workers. The calculation of the degree of difference between individual and collective behavioral patterns provides a deeper understanding of the extent to which construction workers' behavior deviates from collective standards. The introduction of a collective coordination index helps assess the normative impact of collective behavior on individual behavior, reflecting the consistency and coordination of collective behavior. By constructing an interaction event matrix, detecting and analyzing the interaction intensity among construction workers, and calculating an interaction risk index, the potential risks brought about by frequent interactions can be revealed. Furthermore, graph theory algorithms are used to identify high-frequency interaction groups and assess the overall risk level within these groups. The comprehensive calculation of the hidden risk index, combining principal component projection scores and interaction-coordination heterogeneity indices, allows managers to accurately identify high-risk personnel and formulate targeted management and prevention measures based on the electronic fence area division. This method not only improves the accuracy of risk assessment but also provides comprehensive data support and decision-making basis for safety management at construction sites.

[0142] In complex and dynamic construction sites, timely and accurate monitoring of construction worker behavior is crucial for ensuring safety, especially for workers at medium-risk levels, whose behavioral changes and location shifts can pose potential safety hazards. To achieve this, it is necessary to dynamically adjust data acquisition frequency and transmission modes, employing flexible monitoring strategies to adapt to real-time changes in risk levels. Traditional fixed-frequency data acquisition and a single transmission mode cannot meet this requirement. However, by dynamically adjusting the data acquisition frequency and transmission mode based on a hidden risk index, behavioral changes of medium-risk personnel can be captured more effectively, ensuring timely and accurate data at critical moments. Combining a real-time monitoring system with an automated transmission mode switching mechanism not only improves the efficiency of data acquisition and transmission but also allows for continuous optimization and adjustment of strategies through data feedback mechanisms, making safety monitoring at construction sites more precise and efficient. This innovative approach will provide stronger technical support for construction safety management, ensuring timely preventative measures are taken before potential risks occur.

[0143] Step S3 includes the following:

[0144] For construction workers marked as medium-risk, the upper and lower limits of the data collection frequency are set as follows: and (For example, low frequency is once every 10 minutes, and high frequency is once every 1 minute).

[0145] The data collection frequency is dynamically adjusted based on the behavioral patterns of individuals at medium-risk levels and the division of electronic fence areas. The formula is as follows: ;

[0146] in, Based on the sampling frequency; The adjusted sampling frequency; To conceal the risk index; and These are the upper and lower limits of the risk threshold.

[0147] Replace the base acquisition frequency with the adjusted acquisition frequency, implement the adjusted acquisition frequency, and monitor the data quality and the effectiveness of the acquisition frequency in real time to ensure the accuracy and timeliness of the data.

[0148] Figure 2 The present invention provides a data acquisition and management system based on electronic fences, comprising: an area management module, a positioning equipment module, a data acquisition module, a behavior analysis module, a risk assessment module, and a dynamic adjustment module;

[0149] The area management module sets up multiple electronic fence areas with different risk levels through the GIS system, and sends the risk area data to the data acquisition module and the behavior analysis module.

[0150] The positioning equipment module integrates a GPS positioning module into the safety equipment of construction workers, which collects and transmits location information in real time, and transmits the collected location information to the data acquisition module in real time.

[0151] The data acquisition module collects the location information of construction workers, performs preliminary processing and storage, and transmits the processed data to the behavior analysis module;

[0152] The behavior analysis module combines the electronic fence area division to extract individual and collective behavior characteristics, calculate the difference and interaction between individual and collective behavior patterns, and obtain the collective coordination index and interaction risk index accordingly. The collective coordination index and interaction risk index are then sent to the risk assessment module.

[0153] The risk assessment module combines risk area impact factors to calculate the hidden risk index of each construction worker, identify and manage potentially high-risk personnel, including displaying the hidden risk level and sending the assessment results to the dynamic adjustment module;

[0154] The dynamic adjustment module dynamically adjusts the data acquisition frequency and transmission mode based on the identified hidden risk level.

[0155] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0156] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0157] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely to distinguish one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0158] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data collection and management method based on electronic fences, characterized in that: S1 uses a GIS system to set up multiple electronic fence areas with different risk levels and integrates a GPS positioning module into the safety equipment of construction workers to collect and transmit the location information of construction workers in real time. S2, collect the location information of construction workers, combine it with the division of electronic fence areas, extract individual and collective behavioral characteristics, calculate the difference and interaction between individual and collective behavioral patterns, obtain the corresponding collective coordination index and interaction risk index, and combine it with the risk area influence factor to calculate the hidden risk index of each construction worker, identify potential high-risk personnel and manage them, including showing the hidden risk level. S3 dynamically adjusts the data collection frequency and transmission mode based on the identified level of hidden risk.

2. The data acquisition and management method based on electronic fence according to claim 1, characterized in that: Step S1 includes the following: Risk areas are delineated using a GIS system and electronic fences are set up. GPS modules are integrated into the safety equipment of construction workers. Data transmission protocols are configured and tested, and a data acquisition system is started to monitor and record the location information and activity trajectory of construction workers in real time.

3. The data acquisition and management method based on electronic fence according to claim 2, characterized in that: The process of obtaining the collective coordination index is as follows: A1 uses a GPS module to collect real-time location information of construction workers, including timestamps, latitude and longitude, and altitude. A2, Individual Feature Extraction: Extracting individual trajectory features, including trajectory length. average speed Number of stops Path change frequency ; Collective Feature Extraction: Extracting collective trajectory features, including collective trajectory density. Collective path consistency Collective speed consistency Distribution of group stay points ; In this context, a collective is composed of multiple individuals with the same function; A3, Individual Behavior Pattern Calculation: Calculate individual behavior patterns based on individual characteristics and define individual behavior vectors. ; Collective behavior pattern calculation: Calculate collective behavior patterns based on collective characteristics and define collective behavior vectors. ; Calculate the difference between individual behavior patterns and collective behavior patterns The formula is as follows: ; A4. Based on the GIS system, define the risk zone impact factors for the electronically fenced areas within the construction site that are marked with different risk levels. ,in, The risk level of the area is indicated by: low risk 1, medium risk 2, and high risk 3. A5, combining the degree of difference and regional risk impact factors, calculate the comprehensive risk index for each construction worker. The formula is as follows: ; in, The number of areas where construction workers are active; For the first Risk impact factors for each region; For the construction workers at the first Time spent in each area; A6, Calculate the collective coordination index The formula reflects the impact of collective behavior on individual risk, as follows: ; in, The number of construction workers in the group; For the first Differences in the behavioral patterns of individual construction workers.

4. The data acquisition and management method based on electronic fence according to claim 3, characterized in that: The process of obtaining the interaction risk index is as follows: B1 collects the location information of construction workers, including timestamps, latitude, longitude, and altitude; B2, when the distance between two construction workers is less than the distance threshold. And the duration exceeds It is assumed that an interaction event has occurred, and the trajectory data of each pair of construction workers are compared to detect the interaction event; B3, Constructing the Interaction Matrix ,in Indicates the first The construction worker and the first The interaction intensity between construction workers is calculated based on the number and duration of interaction events. ； in, For time steps; For the first Time step The construction worker and the first The distance between construction workers; Duration of the interaction; For indicator functions; B4. Calculate the interaction risk for each construction worker with all other construction workers. The interaction risk is calculated using the following formula: ; in, For the number of construction workers, The risk function for interaction strength is defined as: ; B5. Further analyze the interaction patterns, identify high-risk interaction groups, and use graph theory algorithms to identify high-frequency interaction groups to assess overall risk. Building an interaction diagram , where nodes Indicates construction workers, side The edge weights represent the interaction strength, indicating the interaction relationship. Community detection algorithms are used to detect interactive groups and calculate the interaction risk index for each group. : ,in, It is the set of interaction edges within the group; B6. Calculate the interaction risk index for each construction worker, considering both individual and group interaction risks. : ; in, For construction workers The group they belong to The group size.

5. The data acquisition and management method based on electronic fence according to claim 4, characterized in that: C1 collects the interaction risk index and collective collaboration index of each construction worker; C2, calculate the mean and standard deviation of the interaction risk index and collective coordination index for each construction worker, denoted as follows: , and , ; The interaction risk index and collective coordination index of each construction worker are standardized into standard scores; C3, Constructing an Interaction-Coordination Matrix Each element of the matrix Elements represent the first The construction worker and the first The relationship between individual construction workers in terms of interactive risks and collective collaboration; ; C4. Perform principal component analysis on the interaction-cooperation matrix to extract the principal components and perform dimensionality reduction. Let the eigenvector of the first principal component be... ; Calculate the projected score of each construction worker on the first principal component: ; in, The total number of construction workers. These are the components of the eigenvector corresponding to the first principal component; C5, calculate the interaction-cooperative heterogeneity index for each construction worker. This reflects the degree of heterogeneity in their interaction and collaboration, as shown in the following formula: ; in, and The average of the standard scores of all construction workers; C6 combines the first principal component projection score of each construction worker with the interaction-cooperative heterogeneity index to calculate the hidden risk index. The formula is as follows: .

6. The data acquisition and management method based on electronic fence according to claim 5, characterized in that: By calculating the hidden risk index of each construction worker and comparing it with pre-set risk threshold one and hidden risk threshold two, different hidden risk levels are classified. The specific process is as follows: First, the hidden risk index of each construction worker is obtained based on the comprehensive analysis of the interaction risk index and the collective coordination index; The hidden risk index is compared with risk threshold one and hidden risk threshold two; If the hidden risk index is lower than the risk threshold one, it means that the construction workers' behavior patterns are highly consistent with the group and they have little interaction with other people, which is a low-risk level. If the hidden risk index is between risk threshold one and hidden risk threshold two, it indicates that the construction workers' behavior patterns are somewhat different, or that they have moderate interaction with others, which is considered a medium risk level. If the hidden risk index is higher than the hidden risk threshold of two, it is classified as high risk. The construction workers' behavior patterns are significantly different from those of the group, and they frequently interact with others, which is considered a high-risk level.

7. The data acquisition and management method based on electronic fence according to claim 6, characterized in that: Step S3 includes the following: For construction workers marked as medium-risk, the upper and lower limits of the data collection frequency are set as follows: and ; Based on the behavioral patterns of people at medium-risk levels and the division of electronic fence areas, the data collection frequency is dynamically adjusted, using the following formula: ; in, Based on the sampling frequency; The adjusted sampling frequency; To conceal the risk index; and These are the upper and lower limits of the risk threshold; Replace the base acquisition frequency with the adjusted acquisition frequency and implement the adjusted acquisition frequency.

8. A data acquisition and management system based on an electronic fence, used to implement the data acquisition and management method based on an electronic fence as described in any one of claims 1-7, characterized in that, include: The module includes a regional management module, a positioning equipment module, a data acquisition module, a behavior analysis module, a risk assessment module, and a dynamic adjustment module. The area management module sets up multiple electronic fence areas with different risk levels through the GIS system, and sends the risk area data to the data acquisition module and the behavior analysis module. The positioning equipment module integrates a GPS positioning module into the safety equipment of construction workers, which collects and transmits location information in real time, and transmits the collected location information to the data acquisition module in real time. The data acquisition module collects the location information of construction workers, performs preliminary processing and storage, and transmits the processed data to the behavior analysis module; The behavior analysis module combines the electronic fence area division to extract individual and collective behavior characteristics, calculate the difference and interaction between individual and collective behavior patterns, and obtain the collective coordination index and interaction risk index accordingly. The collective coordination index and interaction risk index are then sent to the risk assessment module. The risk assessment module combines risk area impact factors to calculate the hidden risk index of each construction worker, identify and manage potentially high-risk personnel, including displaying the hidden risk level and sending the assessment results to the dynamic adjustment module; The dynamic adjustment module dynamically adjusts the data acquisition frequency and transmission mode based on the identified hidden risk level.

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