Road construction equipment safety monitoring system based on Internet of Things

Abstract

The invention discloses a highway construction equipment safety monitoring system based on the Internet of Things, and the system comprises an equipment state data collection module which is used for obtaining equipment position data, equipment operation state data and equipment interaction data in real time through a distributed Internet of Things sensor network; the central data processing unit is used for receiving and storing the road construction equipment operation state data set in real time; the ant colony heuristic path search module is used for generating a global risk assessment path of the abnormal operation state of the equipment; the genetic taboo search module is used for dynamically generating a local risk optimization result; the risk assessment information fusion module is used for fusing the global risk assessment path and the local risk optimization result; and the safety early warning module is used for generating an equipment abnormity early warning signal in real time according to the comprehensive risk assessment information. According to the invention, the safety accident risk caused by the fact that the equipment abnormity is not found in time is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to a highway construction equipment safety monitoring system based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, various industries have gradually introduced intelligent monitoring systems to improve equipment operation safety and management efficiency. In the field of highway construction, a large number of large-scale mechanical equipment are involved in the construction process. The operating status of these equipment directly affects the project progress and construction safety. However, due to the complex construction site environment and the wide variety of equipment, the existing safety monitoring methods are difficult to meet the needs of modern highway construction. The existing monitoring methods mainly rely on manual inspections and traditional sensor monitoring, which have certain technical defects and limitations.

[0003] At present, the safety management of highway construction equipment mainly relies on the following methods: on the one hand, construction personnel regularly inspect the equipment and judge the operating status of the equipment through manual observation, recording and experience. However, this method relies on subjective judgment of personnel, is inefficient, and has problems of omissions and delays; on the other hand, some construction sites have installed monitoring systems based on single sensors, such as GPS positioning devices, vibration sensors or temperature sensors to obtain some operating status data of the equipment. However, a single monitoring method is difficult to achieve all-round and real-time monitoring of the equipment, especially in the case of collaborative operation of multiple devices, drastic changes in equipment status or sudden abnormal events. The reliability and accuracy of the existing monitoring system are low.

[0004] In recent years, with the improvement of data processing capabilities, some highway construction companies have begun to introduce remote monitoring systems based on wireless communications, uploading equipment operation data to the management platform, and combining simple data analysis algorithms to make abnormal judgments. However, such systems still have the following defects in actual applications: First, the data collection method is relatively simple and it is difficult to fully reflect the real-time status of the equipment, resulting in a lag in risk warning; second, the risk assessment mechanism of the existing system is relatively rough, usually based on threshold judgment, and triggers an alarm when the equipment parameters exceed the preset range, but fails to fully consider the mutual influence between equipment and complex abnormal patterns, resulting in a high false alarm rate. In addition, the existing monitoring system mainly uses fixed rules for anomaly identification, lacks adaptive optimization capabilities, and makes it difficult to dynamically adjust the monitoring strategy, resulting in difficulty in effective response when the construction environment changes.

[0005] In summary, the existing highway construction equipment safety monitoring technology has obvious deficiencies in the comprehensiveness of data collection, the accuracy of risk assessment and the real-time nature of abnormal warning, and cannot meet the needs of modern highway construction for efficient and intelligent safety monitoring. Therefore, there is an urgent need for an intelligent monitoring method based on the Internet of Things to improve the real-time perception capability of the equipment's operating status, and to improve construction safety and management efficiency by optimizing risk assessment and early warning strategies. Summary of the invention

[0006] One purpose of the present invention is to propose a highway construction equipment safety monitoring system based on the Internet of Things. The present invention greatly reduces the risk of safety accidents caused by failure to timely discover equipment abnormalities.

[0007] A highway construction equipment safety monitoring system based on the Internet of Things according to an embodiment of the present invention includes the following modules: The equipment status data acquisition module is used to obtain equipment location data, equipment operation status data and equipment interaction data in real time through a distributed IoT sensor network, build a highway construction equipment operation status data set, and transmit it to the central data processing unit; A central data processing unit for receiving and storing data sets of the operation status of highway construction equipment in real time; The ant colony heuristic path search module performs ant colony heuristic path search based on the dynamic node abnormal risk level function, dynamic path cost function and path heuristic function, and dynamically constructs the global risk assessment path of abnormal equipment operation status; Genetic taboo search module, which is used to further conduct local refined search of preliminary potential abnormal equipment areas based on the global risk assessment path, and dynamically generate local risk optimization results; The risk assessment information fusion module is used to fuse the global risk assessment path with the local risk optimization results and form comprehensive risk assessment information including risk level, risk distribution range and abnormal status confirmation information; The safety warning module is used to generate equipment abnormality warning signals in real time based on comprehensive risk assessment information, and transmit the equipment abnormality warning signals to the highway construction site safety management terminal to guide on-site personnel to respond and handle abnormal conditions in a timely manner.

[0008] A highway construction equipment safety monitoring method based on the Internet of Things is applied to a highway construction equipment safety monitoring system based on the Internet of Things, comprising: S1. Deploy a distributed IoT sensor network at the highway construction site to collect real-time data sets on the operation status of highway construction equipment; S2. Transmitting the highway construction equipment operation status data set to the central data processing unit in real time via the wireless communication network, and establishing a real-time data monitoring database in the central data processing unit; S3. Preliminary processing of the highway construction equipment operation status data set to form an initial state set of highway construction equipment operation data; S4. Use the ant colony heuristic path search algorithm to perform a global search on the initial state set of highway construction equipment operation data, predict the risk area of ​​highway construction equipment, and generate a global risk assessment path; S5. Using the global risk assessment path as an input parameter, applying the genetic taboo search algorithm to perform a local detailed search and analysis on the potential abnormal area determined in step S4 to generate a local risk optimization result; S6. The global risk assessment path is integrated with the local risk optimization results to form comprehensive risk assessment information, which clarifies the risk level, risk distribution range and abnormal status confirmation information of the operation status of highway construction equipment; S7. Based on the comprehensive risk assessment information, the preset safety warning mechanism generates and releases equipment abnormality warning signals in real time, and transmits the equipment abnormality warning signals to the highway construction site safety management terminal, thereby realizing safety monitoring between highway construction equipment.

[0009] Optionally, the S1 includes the following steps: S11. Divide the monitoring area at the highway construction site into monitoring areas, defined as the monitoring area set , each monitoring area in the monitoring area set Corresponding to the on-site construction scope of the i-th highway construction; S12. In each monitoring area According to the type of highway construction equipment, construction characteristics and safety monitoring requirements, multiple IoT sensor nodes are deployed to form an IoT sensor network collection. : ; in, Indicates In the monitoring area sensor nodes, n represents the total number of monitoring areas divided at the highway construction site, Indicates The number of sensor nodes in the monitoring area; S13. Through sensor nodes Real-time collection of equipment location data, equipment operation status data, and equipment interaction data to form a highway construction equipment operation status data set : ; in, Indicates at time , No. In the monitoring area The highway construction equipment operation status data collected by sensor nodes, Time series of real-time data collection for monitoring systems; S14. Collect the real-time highway construction equipment operation status data set Transmitted to the central data processing unit via wireless communication network.

[0010] Optionally, S3 includes the following steps: S31. Real-time collection of highway construction equipment operation status data set Perform data cleaning to remove abnormal data and invalid data in the data set due to transmission failure, sensor abnormality or environmental interference, and form a cleaned highway construction equipment operation status data set; S32. Perform data normalization on the cleaned highway construction equipment operation status dataset to eliminate the analysis error caused by the dimension difference between the data collected by different sensor nodes, and generate a normalized highway construction equipment operation status dataset ; S33. Standardized highway construction equipment operation status dataset Perform feature extraction and select state feature vectors based on the type of highway construction equipment and construction safety requirements , build the initial state set of highway construction equipment operation data .

[0011] Optionally, S4 includes the following steps: S41. Initial state collection of highway construction equipment operation data Each initial state eigenvector in Defined as the path search node in the ant colony heuristic path search algorithm, forming a search node set : ; in, Indicates In the monitoring area The path search node corresponding to each sensor node is Indicates time No. In the monitoring area The sensor nodes correspond to the initial state feature vectors in the initial state set of highway construction equipment operation data; S42. Search nodes for each path , combined with the degree of difference in equipment operating status parameters, equipment status change trend and equipment abnormality history, define the node abnormality risk level function: ; in, Represents a path search node The risk level of the global search phase, , and is the weight factor, Represents the initial state eigenvector relative to the reference state eigenvector The degree of difference, represents the changing trend of the initial state eigenvector over time, It is a function to accumulate the abnormal history of the path search node; S43. Risk level based on global search stage Construct a dynamic adaptive path cost function between path search nodes to measure the cost between any two path search nodes. and The cost of risk transfer between: ; in, Searching for a node for a path Searching for nodes with paths The risk transfer cost between them is used to describe the degree of transmission of abnormal device status risk between any two path search nodes. and Represents the path search node Searching for nodes with paths The risk level in the global search phase at time t is, , They represent adjacent path search nodes, is the Euclidean distance of feature differences between path search nodes, is the sensitivity adjustment index. The dynamic adaptive path cost function reflects the transmission probability of abnormal equipment operation risk between different equipment nodes. The larger the sum of risk levels, the higher the possibility of abnormal propagation. S44. Constructing a path search heuristic function based on node abnormality risk level Dynamically predict the risk area, and the path search heuristic function is used to guide the path search: ; in, is the sensitivity index of feature differences, which is used to dynamically adjust the impact of feature differences between nodes. When the node abnormality risk level is higher than the preset value and the feature difference is higher than the preset value, the value of the path search heuristic function becomes larger, guiding the ant colony heuristic path search algorithm to prioritize the exploration of risk areas of potential abnormal equipment operation status; S45. Comprehensive pheromone concentration and path search heuristic functions , using the improved path selection probability formula to dynamically determine the path selection probability in the global path search , used to preliminarily locate the risk areas of potential abnormal equipment operation status: ; in, and is the time-varying weight factor that changes with the dynamic data of the highway construction site, is a dynamic adaptive factor based on the recent fluctuations in highway construction equipment operation data. Represents a path search node The set of adjacent nodes, r represents all possible path targets; S46. Dynamically adapt to changes in the construction environment based on path selection probability, optimize in real time and form a global risk assessment path .

[0012] Optionally, S5 includes the following steps: S51. Global risk assessment path The identified potential abnormal regions are used as the initial input of the genetic taboo search algorithm to form a local search population set. , each individual in the population The corresponding device node set to be optimized in the potential abnormal area: ; in, Represents the local search population set Middle Individuals, represents the size of the local search population; S52. For local search population set Individuals in , define the local anomaly risk fitness function: ; in, , and is the weight coefficient of the fitness function, Represents a population of individuals The cumulative value of historical risk assessment, is the local abnormal risk fitness level; S53. According to the local abnormal risk fitness level For the current local search population set Perform genetic taboo search algorithm operations to generate the next generation of population individuals ; S54. According to the local abnormal risk fitness level Constructing a dynamic tabu table for genetic tabu search algorithm , the dynamic taboo table is used for taboo constraints in high fitness areas: ; in, is the dynamic risk threshold: ; in, is the baseline risk factor, is the risk amplification factor, is the preset risk reference value; S55. After the preset termination condition is met, the individual with the highest local abnormal risk fitness is selected from the local search population set outside the dynamic taboo table as the local risk optimization result according to the following formula: : ; in, Represents the local search population set Medium The diversity adjustment factor is used to evaluate the distribution difference of the device node state feature vector within an individual: ; in, Represents an individual The number of device nodes contained in For individuals The center vector of all path search nodes within, Represents a single device operation status node within a local search population individual, To select the diversity adjustment coefficient, it is used to balance the trade-off between individual risk and internal node diversity. Represents an exponential function.

[0013] Optionally, the S6 includes the following steps: S61. Global risk assessment pathway and local risk optimization results Perform feature fusion to construct comprehensive risk assessment information and define the fusion function as: ; in, , and is the fusion weight coefficient, Represents a path search node The overlapping confidence of nodes identified as high risk in both global and local searches, For comprehensive risk assessment information; S62. Based on comprehensive risk assessment information Define comprehensive risk level evaluation indicators , the device nodes are divided into low risk, medium risk and high risk levels according to the preset risk threshold: ; in, , They are the lower threshold and high-risk critical threshold used to distinguish the abnormal risk level of device nodes; S63. Evaluation indicators based on comprehensive risk level Sort the risk levels of device nodes and determine the high-risk node set : ; in, It is a dynamic threshold parameter of the comprehensive risk level, which is determined by the statistical distribution characteristics of the abnormal risk level of historical equipment at the highway construction site and the real-time risk level fluctuation; S63. Based on high risk node set Constructing a comprehensive risk area set for highway construction equipment , locate and dynamically track abnormal equipment operation status areas within the construction site: ; in, represents the set of comprehensive risk areas for highway construction equipment determined by comprehensive risk assessment, Represents the ith monitoring area, i.e., the ith specific construction area divided within the highway construction site. The monitoring area corresponds to the abnormal status node of the highway construction equipment The actual geographical monitoring area is used to identify the specific spatial location of abnormal equipment when an abnormality occurs in the equipment, so as to locate and dynamically track the operating status of the abnormal equipment.

[0014] The beneficial effects of the present invention are: The present invention adopts an ant colony heuristic path search algorithm. In the operation status monitoring of highway construction equipment, a global risk assessment path is established by constructing a node abnormality risk level function, a dynamic path cost function and a path heuristic function. The path search direction can be dynamically adjusted according to the changes in the equipment operation status to make the identification of abnormal equipment more accurate. In addition, the path search process combines historical abnormality records and state change trend multi-dimensional factors, avoiding the false alarm and missed alarm problems caused by fixed rule judgments in traditional methods, and greatly reducing the risk of safety accidents caused by failure to timely discover equipment abnormalities.

[0015] The present invention introduces a genetic taboo search algorithm, further performs local optimization search on high-risk areas based on the global risk assessment path, adopts a local abnormal risk fitness function to conduct in-depth analysis of equipment in potential abnormal areas, and combines the dynamic taboo table to optimize the search process to avoid local optimal traps, thereby improving the ability to accurately locate abnormal areas, reducing redundant alarms, and improving the accuracy of construction site safety management.

[0016] The present invention adopts a multi-level information fusion mechanism to integrate the global risk assessment path with the local risk optimization results, construct comprehensive risk assessment information, and dynamically generate safety warning signals based on risk level, risk distribution range and abnormal status confirmation information. By introducing an adaptive risk weight adjustment mechanism, it ensures that the early warning system can dynamically adjust the risk assessment standards according to changes in real-time data, reduce the occurrence of false alarms and missed alarms, and effectively enhance the ability to prevent safety incidents at construction sites in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a highway construction equipment safety monitoring system based on the Internet of Things proposed by the present invention. DETAILED DESCRIPTION

[0018] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0019] refer to Figure 1 , a highway construction equipment safety monitoring system based on the Internet of Things, including the following modules: The equipment status data acquisition module is used to obtain equipment location data, equipment operation status data and equipment interaction data in real time through a distributed IoT sensor network, build a highway construction equipment operation status data set, and transmit it to the central data processing unit; A central data processing unit for receiving and storing data sets of the operation status of highway construction equipment in real time; The ant colony heuristic path search module performs ant colony heuristic path search based on the dynamic node abnormal risk level function, dynamic path cost function and path heuristic function, and dynamically constructs the global risk assessment path of abnormal equipment operation status; Genetic taboo search module, which is used to further conduct local refined search of preliminary potential abnormal equipment areas based on the global risk assessment path, and dynamically generate local risk optimization results; The risk assessment information fusion module is used to fuse the global risk assessment path with the local risk optimization results and form comprehensive risk assessment information including risk level, risk distribution range and abnormal status confirmation information; The safety warning module is used to generate equipment abnormality warning signals in real time based on comprehensive risk assessment information, and transmit the equipment abnormality warning signals to the highway construction site safety management terminal to guide on-site personnel to respond and handle abnormal conditions in a timely manner.

[0020] A highway construction equipment safety monitoring method based on the Internet of Things is applied to a highway construction equipment safety monitoring system based on the Internet of Things, comprising: S1. Deploy a distributed IoT sensor network at the highway construction site to collect real-time data sets on the operation status of highway construction equipment; S2. Transmit the highway construction equipment operation status data set to the central data processing unit in real time through the wireless communication network, and establish a real-time data monitoring database in the central data processing unit. The data storage structure includes timestamp, equipment identification, equipment status parameters and historical abnormal records. The received data is checked for data integrity and deduplication to serve as the data basis for global risk assessment and local optimization analysis; S3. Preliminary processing of the highway construction equipment operation status data set to form an initial state set of highway construction equipment operation data; S4. Use the ant colony heuristic path search algorithm to perform a global search on the initial state set of highway construction equipment operation data, predict the risk area of ​​highway construction equipment, and generate a global risk assessment path; S5. Using the global risk assessment path as an input parameter, applying the genetic taboo search algorithm to perform a local detailed search and analysis on the potential abnormal area determined in step S4 to generate a local risk optimization result; S6. The global risk assessment path is integrated with the local risk optimization results to form comprehensive risk assessment information, which clarifies the risk level, risk distribution range and abnormal status confirmation information of the operation status of highway construction equipment; S7. Based on the comprehensive risk assessment information, construct an equipment abnormality warning signal. When the comprehensive risk level exceeds the set threshold, the equipment abnormality warning mechanism is triggered, and the equipment abnormality warning signal is transmitted to the highway construction site safety management terminal. The safety management terminal provides the management personnel with equipment operation abnormality reports and response suggestions based on the equipment abnormality warning signal and the equipment operation status at the construction site, thereby realizing dynamic safety monitoring of highway construction equipment. At the same time, the warning feedback information is stored in the risk assessment database for subsequent optimization of equipment monitoring strategies, thereby realizing safety monitoring between highway construction equipment.

[0021] In this implementation, S1 includes the following steps: S11. Divide the monitoring area at the highway construction site into monitoring areas, defined as the monitoring area set , each monitoring area in the monitoring area set Corresponding to the on-site construction scope of the i-th highway construction; S12. In each monitoring area According to the type of highway construction equipment, construction characteristics and safety monitoring requirements, multiple IoT sensor nodes are deployed to form an IoT sensor network collection. : ; in, Indicates In the monitoring area sensor nodes, n represents the total number of monitoring areas divided at the highway construction site, Indicates The number of sensor nodes in the monitoring area; S13. Through sensor nodes Real-time collection of equipment location data, equipment operation status data, and equipment interaction data to form a highway construction equipment operation status data set : ; in, Indicates at time , No. In the monitoring area The highway construction equipment operation status data collected by sensor nodes, Time series of real-time data collection for monitoring systems; S14. Collect the real-time highway construction equipment operation status data set Transmitted to the central data processing unit via wireless communication network.

[0022] This implementation achieves accurate collection and real-time transmission of equipment status data by deploying a distributed IoT sensor network at the highway construction site. By rationally deploying monitoring areas and sensor networks, it ensures all-round monitoring of the operating status of all key equipment at the construction site. Wireless communication technology is used to transmit equipment operating status data to the central data processing unit with low latency, providing real-time support for subsequent analysis. The reliability of data analysis is improved by removing invalid and abnormal data and performing standardization and normalization processing.

[0023] In this implementation, S3 includes the following steps: S31. Real-time collection of highway construction equipment operation status data set Perform data cleaning to remove abnormal data and invalid data in the data set due to transmission failure, sensor abnormality or environmental interference, and form a cleaned highway construction equipment operation status data set; S32. Perform data normalization on the cleaned highway construction equipment operation status dataset to eliminate the analysis error caused by the dimension difference between the data collected by different sensor nodes, and generate a normalized highway construction equipment operation status dataset ; S33. Standardized highway construction equipment operation status dataset Perform feature extraction and select state feature vectors based on the type of highway construction equipment and construction safety requirements , build the initial state set of highway construction equipment operation data .

[0024] This implementation method improves the accuracy of data analysis by performing preliminary processing on the equipment operation status data set to form an initial state set of highway construction equipment operation data. It improves the availability of equipment data through data cleaning, standardization, and feature extraction, avoids noise interference affecting the analysis results, constructs feature vectors so that equipment status information can be stored and processed in a structured manner, performs dimensionality reduction on the data, reduces redundant information, and ensures the real-time response capability of the equipment monitoring system.

[0025] In this implementation, S4 includes the following steps: S41. Initial state collection of highway construction equipment operation data Each initial state eigenvector in Defined as the path search node in the ant colony heuristic path search algorithm, forming a search node set : ; in, Indicates In the monitoring area The path search node corresponding to each sensor node is Indicates time No. In the monitoring area The sensor nodes correspond to the initial state feature vectors in the initial state set of highway construction equipment operation data; S42. Search nodes for each path , combined with the degree of difference in equipment operating status parameters, equipment status change trend and equipment abnormality history, define the node abnormality risk level function: ; in, Represents a path search node The risk level of the global search phase, , and is the weight factor, Represents the initial state eigenvector relative to the reference state eigenvector The degree of difference, represents the changing trend of the initial state eigenvector over time, It is a function to accumulate the abnormal history of the path search node; S43. Risk level based on global search stage Construct a dynamic adaptive path cost function between path search nodes to measure the cost between any two path search nodes. and The cost of risk transfer between: ; in, Searching for a node for a path Searching for nodes with paths The risk transfer cost between them is used to describe the degree of transmission of abnormal device status risk between any two path search nodes. and Represents the path search node Searching for nodes with paths The risk level in the global search phase at time t is, , They represent adjacent path search nodes, is the Euclidean distance of feature differences between path search nodes, is the sensitivity adjustment index. The dynamic adaptive path cost function reflects the transmission probability of abnormal equipment operation risk between different equipment nodes. The larger the sum of risk levels, the higher the possibility of abnormal propagation. S44. Constructing a path search heuristic function based on node abnormality risk level Dynamically predict the risk area, and the path search heuristic function is used to guide the path search: ; in, is the sensitivity index of feature differences, which is used to dynamically adjust the impact of feature differences between nodes. When the node abnormality risk level is higher than the preset value and the feature difference is higher than the preset value, the value of the path search heuristic function becomes larger, guiding the ant colony heuristic path search algorithm to prioritize the exploration of risk areas of potential abnormal equipment operation status; S45. Comprehensive pheromone concentration and path search heuristic functions , using the improved path selection probability formula to dynamically determine the path selection probability in the global path search , used to preliminarily locate the risk areas of potential abnormal equipment operation status: ; in, and is the time-varying weight factor that changes with the dynamic data of the highway construction site, is a dynamic adaptive factor based on the recent fluctuations in highway construction equipment operation data. Represents a path search node The set of adjacent nodes, r represents all possible path targets; S46. Dynamically adapt to changes in the construction environment based on path selection probability, optimize in real time and form a global risk assessment path .

[0026] This implementation method uses an improved ant colony heuristic path search algorithm to perform a global search on the equipment operation status data to achieve accurate prediction of potential abnormal risk areas. By constructing a dynamic path cost and pheromone update mechanism, the global search capability of the ant colony search algorithm in complex environments is improved to avoid falling into local optimality. Combined with the node abnormal risk level and the dynamic path selection probability, the algorithm can adaptively adjust to the real-time changes in the equipment status, improve the accuracy of anomaly detection, and use pheromone propagation and reinforcement learning mechanisms to dynamically optimize the equipment operation status and improve the search efficiency of the global risk assessment path.

[0027] In this implementation, S5 includes the following steps: S51. Global risk assessment path The identified potential abnormal regions are used as the initial input of the genetic taboo search algorithm to form a local search population set. , each individual in the population The corresponding device node set to be optimized in the potential abnormal area: ; in, Represents the local search population set Middle Individuals, represents the size of the local search population; S52. For local search population set Individuals in , define the local anomaly risk fitness function: ; in, , and is the weight coefficient of the fitness function, Represents a population of individuals The cumulative value of historical risk assessment, is the local abnormal risk fitness level; S53. According to the local abnormal risk fitness level For the current local search population set Perform genetic taboo search algorithm operations to generate the next generation of population individuals ; S54. According to the local abnormal risk fitness level Constructing a dynamic tabu table for genetic tabu search algorithm , the dynamic taboo table is used for taboo constraints in high fitness areas: ; in, is the dynamic risk threshold: ; in, is the baseline risk factor, is the risk amplification factor, is the preset risk reference value; S55. After the preset termination condition is met, the individual with the highest local abnormal risk fitness is selected from the local search population set outside the dynamic taboo table as the local risk optimization result according to the following formula: : ; in, Represents the local search population set Medium The diversity adjustment factor is used to evaluate the distribution difference of the device node state feature vector within an individual: ; in, Represents an individual The number of device nodes contained in For individuals The center vector of all path search nodes within, Represents a single device operation status node within a local search population individual, To select the diversity adjustment coefficient, it is used to balance the trade-off between individual risk and internal node diversity. Represents an exponential function.

[0028] This implementation adopts an improved genetic taboo search algorithm to perform a local and detailed search on the abnormal area determined by the global risk assessment path, so that the determination of the abnormal state is more accurate. The fitness function is used to perform local risk assessment on the basis of the global risk assessment, so that the algorithm can more accurately determine the scope and degree of the abnormal state of the equipment. By introducing a dynamic taboo table, the search process can effectively avoid the trap of the historical optimal solution and improve the diversity of local search. The crossover and mutation operations are used to enable the local search population to converge to the optimal abnormal area in continuous optimization, thereby improving the accuracy and stability of fault detection.

[0029] In this implementation, S6 includes the following steps: S61. Global risk assessment pathway and local risk optimization results Perform feature fusion to construct comprehensive risk assessment information and define the fusion function as: ; in, , and is the fusion weight coefficient, Represents a path search node The overlapping confidence of nodes identified as high risk in both global and local searches, For comprehensive risk assessment information; S62. Based on comprehensive risk assessment information Define comprehensive risk level evaluation indicators , the device nodes are divided into low risk, medium risk and high risk levels according to the preset risk threshold: ; in, , They are the lower threshold and high-risk critical threshold used to distinguish the abnormal risk level of device nodes; S63. Evaluation indicators based on comprehensive risk level Sort the risk levels of device nodes and determine the high-risk node set : ; in, It is a dynamic threshold parameter of the comprehensive risk level, which is determined by the statistical distribution characteristics of the abnormal risk level of historical equipment at the highway construction site and the real-time risk level fluctuation; S63. Based on high risk node set Constructing a comprehensive risk area set for highway construction equipment , locate and dynamically track abnormal equipment operation status areas within the construction site: ; in, represents the set of comprehensive risk areas for highway construction equipment determined by comprehensive risk assessment, Represents the ith monitoring area, i.e., the ith specific construction area divided within the highway construction site. The monitoring area corresponds to the abnormal status node of the highway construction equipment The actual geographical monitoring area is used to identify the specific spatial location of abnormal equipment when an abnormality occurs in the equipment, so as to locate and dynamically track the operating status of the abnormal equipment.

[0030] This implementation method constructs comprehensive risk assessment information by integrating the global risk assessment path and the local risk optimization results, making equipment status monitoring more accurate and systematic. Through multi-dimensional information fusion, the results of global search and local search are comprehensively analyzed to improve the overall accuracy of risk assessment. The equipment risk status is divided into low risk, medium risk and high risk levels, making safety management more refined and facilitating the formulation of targeted safety measures. By continuously tracking changes in equipment status, the comprehensive risk assessment information can be updated at any time to ensure that the risk assessment results always meet the actual conditions of the current construction environment. Example

[0031] At 9:30 am on May 15, 2024, at the construction site of a provincial highway expansion project, the system monitored an excavator numbered PL-EXC2023, and its operating status data showed abnormal fluctuations. Sensor data showed that the engine temperature of the equipment remained at 98°C for 15 consecutive minutes, exceeding the safety threshold of 85°C~95°C for normal operating temperature. At the same time, the pressure fluctuations of the hydraulic system were abnormal, and the pressure value suddenly dropped from 320bar to 210bar, and then quickly rebounded to 340bar in a short time, forming an unstable pressure pulse curve. Based on the ant colony heuristic path search algorithm, the system marked the equipment as high-risk equipment and generated an early warning report at 9:32 to notify the construction site management personnel.

[0032] At 9:35, the construction management center's security monitoring platform received an abnormal warning signal from the equipment. The system automatically generated an equipment status risk assessment report, recording the start time of the abnormality, the duration of the abnormality, the abnormal characteristic curve, and the historical equipment operation status data comparison results. The analysis report showed that the hydraulic system of the equipment had briefly experienced similar abnormal fluctuations in the past three days, but did not reach the level that triggered the alarm at the time. Combined with historical data analysis, the system determined that the hydraulic system of the equipment was at risk of wear or leakage. If not handled in time, the equipment's hydraulic pump would fail, causing the equipment to suddenly stop working, and even affecting the collaborative operation of surrounding construction equipment.

[0033] At 9:40, the construction management personnel assigned maintenance personnel to go to the site to inspect the equipment based on the system warning instructions.

[0034] At 9:45, after the maintenance personnel arrived at the equipment location, they checked the abnormal report generated by the system through a handheld terminal and found a slight leakage at a joint of the hydraulic pipeline. The temperature of the hydraulic oil was higher than normal and it was initially determined to be a slight leakage caused by aging of the seal.

[0035] At 9:50, maintenance personnel used special testing instruments to conduct a pressure test on the hydraulic system and found that the hydraulic system pressure of the equipment remained unstable and was accompanied by slight bubbles, which further verified the leakage problem.

[0036] At 10:10, maintenance personnel replaced the damaged seal ring and refilled the hydraulic oil. They then conducted a system pressure stability test and confirmed that the pressure had returned to the normal range of 320 bar to 330 bar and the hydraulic temperature had dropped to 92°C.

[0037] The system performed an automatic re-inspection at 10:20, and the equipment returned to normal operation. The risk level was reduced from high risk to normal, and construction continued.

[0038] Second case: Detection of abnormal data interaction between devices At 14:15 on the afternoon of June 2, 2024, in the construction area of ​​a highway bridge section, the system detected that a roller PL-COM2025 had an abnormal data interaction with another paver PL-PAV3008 during operation. The system monitoring data showed: The position coordinates of the PL-COM2025 device drifted abnormally three times within 5 minutes, with a position deviation of more than 1.2 meters, far exceeding the allowable deviation within the normal construction range of the equipment (±0.3 meters).

[0039] The operating rate data reported by PL-PAV3008 suddenly dropped by 35%, but no mechanical failure was detected in the equipment itself.

[0040] At 14:18, the genetic taboo search algorithm intervened in the analysis. After combining the global risk assessment path calculation, the system determined that there was a risk of device signal interference or sensor failure, and then triggered the device interaction abnormality alarm. The system generated an abnormality analysis report: Abnormal start time: 2024 June 2, 14:15; Affected equipment: PL-COM2025 (road roller), PL-PAV3008 (paving machine); Abnormal data: Abnormal position coordinates: deviation 1.2 meters; equipment data interaction failure rate: 45%; paver operation rate decrease: 35%; Possible causes: GPS module data is lost; wireless communication between devices is disturbed; sensor hardware failure; At 14:20, after receiving the alarm, the construction safety manager assigned technicians to go to the site with portable debugging equipment to troubleshoot the problem.

[0041] At 14:35, technicians found that the construction site was close to high-voltage transmission lines, causing electromagnetic interference to the equipment's wireless communications and affecting the stability of the GPS signal.

[0042] At 14:45, the technicians adjusted the equipment's communication frequency band and recalibrated the GPS receiver. Testing found that the signal had returned to normal, the equipment data interaction had returned to normal levels, the system alarm was automatically lifted, and construction continued.

[0043] In order to further verify the advantages of the method of the present invention, we compared and analyzed the application data of this system with the traditional method. The specific data are as follows: index Traditional method (manual inspection) The method of the present invention (intelligent monitoring + dynamic optimization) Improvement Equipment abnormality discovery time Average 5 hours Average 20 minutes -92% False Positive Rate 18.2% 4.7% -13.5% Equipment failure recovery time Average 6 hours Average 2 hours -66.7% Device communication abnormality recovery time About 2 hours 30 minutes -75% Construction progress delay time 64 hours (90 days) 12 hours (90 days) -52 hours Equipment downtime (within 90 days) 17 times 4 times -76.5% This embodiment verifies its effectiveness in the safety monitoring of highway construction equipment through actual construction cases. It collects data based on IoT sensors and combines ant colony heuristic path search for global risk assessment. It uses genetic taboo search for local optimization to more accurately identify equipment anomalies and provide timely warnings. By comparing data from real cases, it can be seen that the equipment anomaly detection time, false alarm rate, and fault recovery time are superior to existing methods, which effectively improves the safety management level of the construction site, reduces equipment downtime, and improves construction efficiency.

[0044] The present invention adopts an ant colony heuristic path search algorithm. In the operation status monitoring of highway construction equipment, a global risk assessment path is established by constructing a node abnormality risk level function, a dynamic path cost function and a path heuristic function. The path search direction can be dynamically adjusted according to the changes in the equipment operation status to make the identification of abnormal equipment more accurate. In addition, the path search process combines historical abnormality records and state change trend multi-dimensional factors, avoiding the false alarm and missed alarm problems caused by fixed rule judgments in traditional methods, and greatly reducing the risk of safety accidents caused by failure to timely discover equipment abnormalities.

[0045] The present invention introduces a genetic taboo search algorithm, further performs local optimization search on high-risk areas based on the global risk assessment path, adopts a local abnormal risk fitness function to conduct in-depth analysis of equipment in potential abnormal areas, and combines the dynamic taboo table to optimize the search process to avoid local optimal traps, thereby improving the ability to accurately locate abnormal areas, reducing redundant alarms, and improving the accuracy of construction site safety management.

[0046] The present invention adopts a multi-level information fusion mechanism to integrate the global risk assessment path with the local risk optimization results, construct comprehensive risk assessment information, and dynamically generate safety warning signals based on risk level, risk distribution range and abnormal status confirmation information. By introducing an adaptive risk weight adjustment mechanism, it ensures that the early warning system can dynamically adjust the risk assessment standards according to changes in real-time data, reduce the occurrence of false alarms and missed alarms, and effectively enhance the ability to prevent safety incidents at construction sites in advance.

[0047] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A highway construction equipment safety monitoring system based on the Internet of Things, characterized in that: Includes the following modules: The equipment status data acquisition module is used to obtain equipment location data, equipment operation status data and equipment interaction data in real time through a distributed IoT sensor network, build a highway construction equipment operation status data set, and transmit it to the central data processing unit; A central data processing unit for receiving and storing data sets of the operation status of highway construction equipment in real time; The ant colony heuristic path search module performs ant colony heuristic path search based on the dynamic node abnormal risk level function, dynamic path cost function and path heuristic function, and dynamically constructs the global risk assessment path of abnormal equipment operation status; Genetic taboo search module, which is used to further conduct local refined search of preliminary potential abnormal equipment areas based on the global risk assessment path, and dynamically generate local risk optimization results; The risk assessment information fusion module is used to fuse the global risk assessment path with the local risk optimization results and form comprehensive risk assessment information including risk level, risk distribution range and abnormal status confirmation information; The safety warning module is used to generate equipment abnormality warning signals in real time based on comprehensive risk assessment information, and transmit the equipment abnormality warning signals to the highway construction site safety management terminal to guide on-site personnel to respond and handle abnormal conditions in a timely manner.

2. A method for safety monitoring of highway construction equipment based on the Internet of Things, applied to a safety monitoring system for highway construction equipment based on the Internet of Things as claimed in claim 1, characterized in that: include: S1. Deploy a distributed IoT sensor network at the highway construction site to collect real-time data sets on the operation status of highway construction equipment; S2. Transmitting the highway construction equipment operation status data set to the central data processing unit in real time via the wireless communication network, and establishing a real-time data monitoring database in the central data processing unit; S3. Preliminary processing of the highway construction equipment operation status data set to form an initial state set of highway construction equipment operation data; S4. Use the ant colony heuristic path search algorithm to perform a global search on the initial state set of highway construction equipment operation data, predict the risk area of ​​highway construction equipment, and generate a global risk assessment path; S5. Using the global risk assessment path as an input parameter, applying the genetic taboo search algorithm to perform a local detailed search and analysis on the potential abnormal area determined in step S4 to generate a local risk optimization result; S6. Integrate the global risk assessment path with the local risk optimization results to form comprehensive risk assessment information, which clarifies the risk level, risk distribution range and abnormal status confirmation information of the operation status of highway construction equipment; S7. Based on the comprehensive risk assessment information, the preset safety warning mechanism generates and releases equipment abnormality warning signals in real time, and transmits the equipment abnormality warning signals to the highway construction site safety management terminal, thereby realizing safety monitoring between highway construction equipment.

3. A highway construction equipment safety monitoring method based on the Internet of Things according to claim 2, characterized in that: The S1 comprises the following steps: S11. Divide the monitoring area at the highway construction site into monitoring areas, defined as the monitoring area set , each monitoring area in the monitoring area set Corresponding to the on-site construction scope of the i-th highway construction; S12. In each monitoring area According to the type of highway construction equipment, construction characteristics and safety monitoring requirements, multiple IoT sensor nodes are deployed to form an IoT sensor network collection. : ； in, Indicates In the monitoring area sensor nodes, n represents the total number of monitoring areas divided at the highway construction site, Indicates The number of sensor nodes in the monitoring area; S13. Through sensor nodes Real-time collection of equipment location data, equipment operation status data, and equipment interaction data to form a highway construction equipment operation status data set : ； in, Indicates at time , No. In the monitoring area The highway construction equipment operation status data collected by sensor nodes, Time series of real-time data collection for monitoring systems; S14. Collect the real-time highway construction equipment operation status data set Transmitted to the central data processing unit via wireless communication network.

4. A highway construction equipment safety monitoring method based on the Internet of Things according to claim 3, characterized in that: The S3 comprises the following steps: S31. Real-time collection of highway construction equipment operation status data set Perform data cleaning to remove abnormal data and invalid data in the data set due to transmission failure, sensor abnormality or environmental interference, and form a cleaned highway construction equipment operation status data set; S32. Perform data normalization on the cleaned highway construction equipment operation status dataset to eliminate the analysis error caused by the dimension difference between the data collected by different sensor nodes and generate a normalized highway construction equipment operation status dataset ; S33. Standardized highway construction equipment operation status dataset Perform feature extraction and select state feature vectors based on the type of highway construction equipment and construction safety requirements , build the initial state set of highway construction equipment operation data .

5. A highway construction equipment safety monitoring method based on the Internet of Things according to claim 4, characterized in that: The S4 comprises the following steps: S41. Initial state collection of highway construction equipment operation data Each initial state eigenvector in Defined as the path search node in the ant colony heuristic path search algorithm, forming a search node set : ； in, Indicates In the monitoring area The path search node corresponding to each sensor node is Indicates time No. In the monitoring area The sensor nodes correspond to the initial state feature vectors in the initial state set of highway construction equipment operation data; S42. Search nodes for each path , combined with the degree of difference in equipment operating status parameters, equipment status change trend and equipment abnormality history, define the node abnormality risk level function: ； in, Represents a path search node The risk level of the global search phase, , and is the weight factor, Represents the initial state eigenvector relative to the reference state eigenvector The degree of difference, represents the changing trend of the initial state eigenvector over time, It is a function to accumulate the abnormal history of the path search node; S43. Risk level based on global search stage Construct a dynamic adaptive path cost function between path search nodes to measure the cost between any two path search nodes. and The cost of risk transfer between: ； in, Searching for a node for a path Searching for nodes with paths The risk transfer cost between them is used to describe the degree of transmission of abnormal device status risk between any two path search nodes. and Represents the path search node Searching for nodes with paths The risk level in the global search phase at time t is, , They represent adjacent path search nodes, is the Euclidean distance of feature differences between path search nodes, is the sensitivity adjustment index. The dynamic adaptive path cost function reflects the transmission probability of abnormal equipment operation risk between different equipment nodes. The larger the sum of risk levels, the higher the possibility of abnormal propagation. S44. Constructing a path search heuristic function based on node abnormality risk level Dynamically predict the risk area, and the path search heuristic function is used to guide the path search: ； in, is the sensitivity index of feature differences, which is used to dynamically adjust the impact of feature differences between nodes. When the node abnormality risk level is higher than the preset value and the feature difference is higher than the preset value, the value of the path search heuristic function becomes larger, guiding the ant colony heuristic path search algorithm to prioritize the exploration of risk areas of potential abnormal equipment operation status; S45. Comprehensive pheromone concentration and path search heuristic functions , using the improved path selection probability formula to dynamically determine the path selection probability in the global path search , used to preliminarily locate the risk areas of potential abnormal equipment operation status: ； in, and is the time-varying weight factor that changes with the dynamic data of the highway construction site, is a dynamic adaptive factor based on the recent fluctuations in highway construction equipment operation data. Represents a path search node The set of adjacent nodes, r represents all possible path targets; S46. Dynamically adapt to changes in the construction environment based on path selection probability, optimize in real time and form a global risk assessment path .

6. A highway construction equipment safety monitoring method based on the Internet of Things according to claim 5, characterized in that: The S5 comprises the following steps: S51. Global risk assessment path The identified potential abnormal regions are used as the initial input of the genetic taboo search algorithm to form a local search population set. , each individual in the population The corresponding device node set to be optimized in the potential abnormal area: ； in, Represents the local search population set Middle Individuals, represents the size of the local search population; S52. For local search population set Individuals in , define the local anomaly risk fitness function: ； in, , and is the weight coefficient of the fitness function, Represents a population of individuals The cumulative value of historical risk assessment, is the local abnormal risk fitness level; S53. According to the local abnormal risk fitness level For the current local search population set Perform genetic taboo search algorithm operations to generate the next generation of population individuals ; S54. According to the local abnormal risk fitness level Constructing a dynamic tabu table for genetic tabu search algorithm , the dynamic taboo table is used for taboo constraints in high fitness areas: ； in, is the dynamic risk threshold: ;in, is the baseline risk factor, is the risk amplification factor, is the preset risk reference value; S55. After the preset termination condition is met, the individual with the highest local abnormal risk fitness is selected from the local search population set outside the dynamic taboo table as the local risk optimization result according to the following formula: : ； in, Represents the local search population set Medium The diversity adjustment factor is used to evaluate the distribution difference of the device node state feature vector within an individual: ； in, Represents an individual The number of device nodes contained in For individuals The center vector of all path search nodes within, Represents a single device operation status node within a local search population individual, To select the diversity adjustment coefficient, it is used to balance the trade-off between individual risk and internal node diversity. Represents an exponential function.

7. A highway construction equipment safety monitoring method based on the Internet of Things according to claim 6, characterized in that: The S6 comprises the following steps: S61. Global risk assessment pathway and local risk optimization results Perform feature fusion to construct comprehensive risk assessment information and define the fusion function as: ； in, , and is the fusion weight coefficient, Represents a path search node The overlapping confidence of nodes identified as high risk in both global and local searches, For comprehensive risk assessment information; S62. Based on comprehensive risk assessment information Define comprehensive risk level evaluation indicators , the device nodes are divided into low risk, medium risk and high risk levels according to the preset risk threshold: ； in, , They are the lower threshold and high-risk critical threshold used to distinguish the abnormal risk level of device nodes; S63. Evaluation indicators based on comprehensive risk level Sort the risk levels of device nodes and determine the high-risk node set : ； in, It is a dynamic threshold parameter of the comprehensive risk level, which is determined by the statistical distribution characteristics of the abnormal risk level of historical equipment at the highway construction site and the real-time risk level fluctuation; S63. Based on high risk node set Constructing a comprehensive risk area set for highway construction equipment , locate and dynamically track abnormal equipment operation status areas within the construction site: ； in, represents the set of comprehensive risk areas for highway construction equipment determined by comprehensive risk assessment, Represents the ith monitoring area, i.e., the ith specific construction area divided within the highway construction site. The monitoring area corresponds to the abnormal status node of the highway construction equipment The actual geographical monitoring area is used to identify the specific spatial location of abnormal equipment when an abnormality occurs in the equipment, so as to locate and dynamically track the operating status of the abnormal equipment.

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