Large container crane intelligent early warning system based on personnel behavior analysis

Abstract

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crane operation safety management, and in particular to an intelligent early warning system for large container cranes based on personnel behavior analysis. Background Art

[0002] With the rapid development of the port logistics industry, large container cranes, as core operating equipment, have played an important role in high-intensity operating scenarios. Due to the complexity and dynamism of the crane operating environment, the interaction between operators and equipment is frequent and there are many safety hazards. How to achieve efficient and safe operation monitoring and early warning in a complex and dynamic operating environment is the focus and difficulty of current technical research. The existing crane safety management system mainly focuses on the monitoring of equipment operating status. Sensors are used to collect equipment vibration, speed, displacement and other parameters to determine whether the crane operating status is abnormal. Although these methods provide technical support for equipment safety management, they have significant deficiencies when it comes to personnel behavior analysis and dynamic risk assessment.

[0003] In the existing technology, most safety management systems pay little attention to personnel behavior and only roughly monitor personnel in the working area through area surveillance cameras or simple intrusion detection methods. This method ignores the behavioral characteristics of the operators during operation, such as whether they wear safety equipment correctly and whether there are any violations in entering high-risk areas. In the actual working environment, unsafe behavior of personnel is the main cause of accidents. The existing system lacks in-depth analysis and dynamic evaluation of personnel behavior, which leads to obvious shortcomings in operation safety management.

[0004] The risk assessment methods in the existing technology are relatively simple, usually relying on fixed equipment parameter thresholds or simple logical rules for judgment. This method fails to fully consider the complex correlation between human behavior, equipment operating status and environmental parameters, and cannot dynamically integrate and analyze multi-dimensional data. This type of method lacks the ability to predict risk change trends, resulting in delayed or distorted risk assessment results when the working environment changes rapidly, and thus cannot provide real-time support for early warning and intervention.

[0005] The early warning response mechanism is a key link in the safety management of cranes, but the existing systems usually adopt a single early warning method, such as sound and light alarms or equipment shutdown. Although this method can provide certain risk warnings, it lacks the ability to respond to different risk levels, which can easily cause excessive warnings in low-risk scenarios or insufficient responses in high-risk scenarios. In addition, the existing early warning signal triggering logic is usually fixed and difficult to adjust according to real-time risk changes, resulting in low accuracy and timeliness of early warning responses. Existing systems generally lack intelligent adjustment support for operator behavior. When the early warning signal is triggered, the system usually cannot generate specific optimization suggestions to guide operators to take effective risk avoidance measures. In this case, operators may be at a loss when facing risks, further increasing the possibility of accidents. At the same time, existing technologies usually use fixed rules for system optimization and cannot learn and iterate based on real-time data and historical records, resulting in insufficient system adaptability and difficulty in coping with different operating scenarios and long-term dynamic changes.

[0006] Therefore, how to provide an intelligent early warning system for large container cranes based on personnel behavior analysis is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose an intelligent early warning system for large container cranes based on personnel behavior analysis. The present invention makes full use of multimodal sensor networks, time difference embedding algorithms, Bayesian reasoning models, adaptive multi-objective optimization algorithms and quantum probabilistic behavior prediction algorithms, and describes in detail the complete process from dynamic collection and feature extraction of personnel behavior data to risk assessment, graded early warning and generation of behavior adjustment suggestions. It has the advantages of strong real-time performance, accurate risk assessment, flexible early warning response and high system adaptability.

[0008] The large container crane intelligent early warning system based on personnel behavior analysis according to an embodiment of the present invention comprises the following steps:

[0009] S1. Deploy a multimodal sensor network in the crane operation area, lifting path and surrounding environment. The sensors include a behavior trajectory tracking module, an environmental status monitoring module and an equipment operation status collection module. The sensor network collects behavior data, environmental parameters and equipment status information in real time to build a dynamic data set.

[0010] S2. Based on the dynamic data set, the behavior sequence feature vector is constructed using the time difference embedding algorithm. The relationship between the behavior trajectory and the device status is aggregated through the feature vector, and a multi-level spatiotemporal feature matrix is ​​generated in combination with the dynamic scene influencing factors;

[0011] S3. Using the regional risk dynamic allocation algorithm based on Bayesian reasoning, combined with the spatiotemporal feature matrix and the risk zoning rules of the operating area, the regional risk factor is dynamically calculated. Through the time series inference of continuous data, the potential danger probability of behavior and equipment operation is calculated, and the corresponding regional risk weight distribution is generated;

[0012] S4. Through the adaptive multi-objective risk optimization algorithm, the regional risk factors and the global risk of the operation are integrated to dynamically calculate the comprehensive risk value. The comprehensive risk value makes weighted corrections to high-risk behaviors by optimizing the objective function to generate the risk level of the current operation scenario. The risk level is divided into three categories: low, medium and high.

[0013] S5. Trigger a graded warning response based on the risk level of the comprehensive risk value. The graded warning includes a low-risk sound and light prompt signal, a medium-risk voice reminder signal, and a high-risk operation stop signal. The warning response strategy is dynamically adjusted through a real-time feedback mechanism.

[0014] S6. After the warning signal is triggered, the multi-level spatiotemporal feature matrix and the comprehensive risk value are combined to generate personnel behavior adjustment suggestions through the quantum probabilistic behavior prediction algorithm. The algorithm infers the optimal adjustment path based on the probability distribution of the operation trajectory and transmits it to the operator through a visual interface and voice prompts;

[0015] S7. Based on real-time event records and historical data, the progressive learning algorithm is used to jointly optimize the time difference embedding rules, Bayesian reasoning model and adaptive multi-objective risk optimization algorithm, dynamically adjust the feature extraction rules and risk assessment logic, optimize the adaptability of the system in different operating environments, and form a closed-loop optimization process.

[0016] Optionally, the S2 specifically includes:

[0017] S21, dynamic data set D = {d1, d2, d n} decomposed into behavior trajectory data B t , Equipment status data E t and environmental parameter data P t , the data set is divided according to the time window mechanism T, and each time window contains multimodal data of behavior trajectory, device status and environmental parameters;

[0018] S22. In each time window, the time difference embedding algorithm is used to extract the dynamic characteristics of the behavior trajectory, device status and environmental parameters:

[0019] ΔX k =x t+k X t ,(X∈{B,E,P});

[0020] Where ΔX kRepresents the dynamic change characteristics of the data dimension x within the time window;

[0021] S23, embed the time difference feature into the behavior sequence feature vector, define the feature vector as Fk, feature vector F k Express the dynamic correlation characteristics of behavior trajectory, device status and environmental parameters within the time window;

[0022] S24, combining the influencing factors of the dynamic scene, define the scene weight matrix w = [w b , w e , w p ], weight factor w b , w e , w p Optimize and adjust based on the dynamic characteristics of each data dimension and multi-dimensional scenario influencing factors:

[0023]

[0024] The eigenvector Fk=[ΔB k , ΔE k , ΔP k ] is optimized, and the matrix expression of the weighted eigenvector Vk is:

[0025]

[0026] S25, the weighted feature vector V generated in each time window k Aggregate in time series order to form a time series feature set, which expresses the dynamic change characteristics of behavior trajectories, device states, and environmental parameters in multiple time windows;

[0027] S26. Based on the time series feature set, a multi-level spatiotemporal feature matrix M is constructed. The feature matrix has the time series dimension horizontally and the correlation characteristics of the behavior trajectory, device status and environmental parameters vertically. The feature matrix serves as a multimodal expression of the behavior trajectory, device status and environmental parameters.

[0028] Optionally, the S3 specifically includes:

[0029] S31, divide the operation area into a risk area set R = {r1, r2, r n}, each risk area r i Including behavior trajectory data, equipment status data and environmental parameter data. Combined with the generated spatiotemporal feature matrix M, the corresponding dynamic features in the matrix are mapped to each risk area R i ;

[0030] S32, based on regional feature set R i, using the Bayesian inference model to dynamically calculate the risk factor f for each region i :

[0031]

[0032] Among them, P(H|E) represents the probability of a dangerous event occurring in a region within a specific time window, and dynamic inference is performed by combining the prior probability P(H) and the conditional probability of the real-time feature data E;

[0033] S33. Combine the changing trend of risk factors in the continuous time window and use the time series inference model to predict the potential danger probability of behavior and equipment operation:

[0034] f i (t) = α·f i (t-1)+(1α)·P(H|E t );

[0035] Among them, f i (t) represents the risk factor at the current time t, and α is the historical weight factor;

[0036] S34, combined with regional risk factor f i (t), generate regional risk weight distribution W R ={w1,w2,,w n}, regional risk weight w i Dynamically reflect risk distribution priorities among regions.

[0037] Optionally, the S4 specifically includes:

[0038] S41. Combine the generated regional risk factor fi(t) and regional risk weight distribution w R , extract the multi-dimensional risk factors in the operation scenario and construct a set of comprehensive risk parameters. The comprehensive risk parameters combine the behavior trajectory, equipment status and environmental interference characteristics to provide input for the optimization objective function;

[0039] S42. Define an adaptive multi-objective risk optimization objective function to dynamically integrate regional risk factors and global operational risks:

[0040]

[0041] Among them, R c is the comprehensive risk value, f i and w i are the regional risk factors and weights, δ i is the scene interference factor, λ is the adjustment factor;

[0042] S43. Weighted correction of high-risk behaviors by optimizing the objective function:

[0043]

[0044] Among them, R c ′ is the modified comprehensive risk value, F k Represents the high-risk behavior features in the behavior trajectory feature set, Yk is the weighting factor;

[0045] S44. Based on the revised comprehensive risk value, the operation scenarios are divided into three categories: low risk, medium risk and high risk. The risk level classification rules are dynamically adjusted based on the characteristics of the scenarios.

[0046] S45. Combined with the dynamic changes of real-time operation scenarios, adjust the adjustment factor λ and the high-risk behavior weight γ in the optimization objective function k , so that the comprehensive risk value R c ′Accurately reflect the risk status of the operation scenario.

[0047] Optionally, the S5 specifically includes:

[0048] S51. According to the comprehensive risk value R c ′ and risk level classification rules, set corresponding graded warning trigger signals for the risk levels of the operation scenes, low risk levels trigger sound and light prompt signals, medium risk levels trigger voice reminder signals, prompting operators to adjust operations, high risk levels trigger equipment operation stop signals, and send emergency notifications to the remote management platform for management personnel to intervene;

[0049] S52. Combined with the real-time change trend of the comprehensive risk value, dynamically adjust the trigger conditions and response parameters of the graded warning signals. Low-risk signals are optimized by adjusting the prompt frequency and intensity. Medium-risk signals are optimized by optimizing the content, repetition frequency and volume of voice reminders. High-risk signals adjust the scope and duration of equipment stop according to the urgency of the risk.

[0050] S53, by monitoring the comprehensive risk value R c ′, establish a real-time feedback mechanism, and dynamically optimize the triggering rules, intensity and duration of the early warning signal when the risk value change rate is lower than the preset threshold;

[0051] S54. Coordinate the execution time and trigger logic of signals of different risk levels according to the risk level and the triggering sequence of the warning signal, and prioritize the execution of high-risk signals through priority rules to avoid interference between low-risk signals and high-risk signals;

[0052] S55. Record the trigger type, execution time and response effect of each warning signal, and optimize the trigger rules and response strategies of the warning signal through comprehensive analysis of historical data and real-time feedback.

[0053] Optionally, the S6 specifically includes:

[0054] S61. Based on the generated multi-level spatiotemporal feature matrix and comprehensive risk value, a quantum probabilistic behavior prediction model is established. The model takes behavior trajectory characteristics, device state characteristics and dynamic scene interference factors as inputs, describes the dynamic changes of behavior states through quantum superposition states, and forms a behavior state set.

[0055] S62. Combine the current behavior trajectory and device state, map them to a set of quantum behavior states, and calculate the quantum probability distribution of each behavior state:

[0056]

[0057] Among them, P(ψ i , t) represents the behavior state ψ at time t i The normalized probability of

[0058] S63. Based on the dynamic quantum probability distribution and the comprehensive risk value, the optimal adjustment path is inferred:

[0059]

[0060] Among them, P opt Represents the optimal adjustment path, combining time continuity and behavior state priority w i and the risk adjustment factor λ determine the path optimization results;

[0061] S64. Generate specific behavior adjustment suggestions based on the inferred optimal adjustment path. The suggestions include optimizing operation steps, adjusting behavior trajectories, and changing work positions. The adjustment suggestions are promptly delivered to the operator through a visual interface or voice prompts.

[0062] S65. Record the execution results of behavioral adjustment suggestions and changes in the comprehensive risk value, and optimize the parameter settings of the quantum probability behavior prediction model in combination with real-time feedback data. By adjusting the quantum probability distribution and path generation logic, optimize the model's adaptability to complex dynamic scenarios and the accuracy of behavior prediction.

[0063] Optionally, the S7 specifically includes:

[0064] S71. Based on real-time event records and historical data, extract behavior trajectory characteristics, equipment status data and environmental parameters, combine comprehensive risk values ​​and regional risk factors, and construct a joint optimization data set D opt ;

[0065] S72. Combined with optimized data set D opt , using the progressive learning algorithm to dynamically adjust the size of the feature extraction window and the embedding dimension to form an optimized embedding rule, which isk and the dynamic scene weight factor w t , optimize the embedded time series features:

[0066]

[0067] Among them, T k ′ is the optimized time window, T k is the original time window, w t is the dynamic scene weight factor, ΔR c ′ is the rate of change of the comprehensive risk value, μ is the balance factor;

[0068] S73. Based on the optimized data set, dynamically modify the risk factor distribution in the Bayesian reasoning model, adjust the conditional probability and prior probability of the model to adapt to the changing trends of real-time data and historical data, and adaptively optimize the conditional probability distribution through progressive learning to optimize the assessment accuracy of risk factors;

[0069] S74. Combined with the high-risk behavior characteristics in the real-time event records, the weight factors and adjustment factors in the adaptive multi-objective risk optimization algorithm are adjusted to form a priority processing logic for high-risk behaviors. The optimization model calculates the dynamic risk priority according to the weight distribution:

[0070]

[0071] Among them, P w is the dynamic priority distribution, f i is the risk factor, Δ t is the time interval, α is the time weight adjustment factor;

[0072] S75. Feedback the optimized time difference embedding rules, Bayesian reasoning model and adaptive multi-objective risk optimization algorithm to the data processing module to form a closed-loop optimization process. By continuously collecting real-time and historical data, the optimization logic is updated to optimize the system's long-term adaptability to complex dynamic scenarios.

[0073] Optional modules include:

[0074] Multimodal data acquisition module: Through the sensor network installed in the crane operation area, lifting path and surrounding environment, it collects behavioral data, equipment status and environmental parameters in real time to build a dynamic data set;

[0075] Data processing module: pre-processes and extracts features from multimodal data sets, and constructs a dynamic correlation model of behavior trajectory, device status, and environmental parameters through feature vectors;

[0076] Behavior feature analysis module: uses the time difference embedding algorithm to extract the time series features of behavior trajectory, device status and environmental parameters, and combines the dynamic scene influencing factors to generate a multi-level spatiotemporal feature matrix;

[0077] Dynamic risk assessment module: Combines the spatiotemporal feature matrix and the division rules of the operation area, uses the Bayesian reasoning model to dynamically calculate the regional risk factor, generates a comprehensive risk value through an adaptive multi-objective risk optimization algorithm, and divides the current operation scenario into low risk, medium risk or high risk according to the risk level;

[0078] Graded warning response module: triggers graded warning signals according to the comprehensive risk value. Low risk levels trigger sound and light prompt signals, medium risk levels trigger voice reminder signals, and high risk levels trigger equipment operation stop signals. The warning trigger conditions and response logic are dynamically adjusted through a real-time feedback mechanism.

[0079] Behavior adjustment suggestion module: Combines comprehensive risk value and multi-level spatiotemporal feature matrix, infers the optimal adjustment path through quantum probabilistic behavior prediction algorithm, generates specific behavior adjustment suggestions, and transmits them to operators through visual interface and voice prompts;

[0080] System closed-loop optimization module: Based on real-time event records and historical data, it uses progressive learning algorithms to dynamically optimize time difference embedding rules, Bayesian reasoning models, and adaptive multi-objective risk optimization algorithms, continuously updates feature extraction rules and risk assessment logic, and builds a closed-loop optimization process.

[0081] The beneficial effects of the present invention are:

[0082] The present invention collects operator behavior data, equipment operating status and environmental parameters through a multimodal sensor network, and uses a time difference embedding algorithm and dynamic scene influencing factors to generate a multi-level spatiotemporal feature matrix, thereby realizing dynamic modeling and feature extraction of the operating scene, and significantly improving the system's ability to characterize complex correlations in the operating environment. Combined with the Bayesian reasoning model and the adaptive multi-objective optimization algorithm, it can dynamically calculate regional risk factors and comprehensive risk values, generate real-time updated risk levels, and effectively improve the accuracy and real-time performance of risk assessment.

[0083] The present invention proposes a graded early warning response mechanism based on comprehensive risk values, which provides flexible and diverse early warning response measures through low-risk sound and light prompts, medium-risk voice reminders, and high-risk equipment operation stops, avoiding the problems of excessive warnings or missed reports in traditional single alarm methods. At the same time, the system generates specific behavior adjustment suggestions through quantum probabilistic behavior prediction algorithms to guide operators to optimize operating behaviors in a timely manner, thereby further reducing potential risks. Compared with the prior art, the present invention not only realizes intelligent management of the entire process from risk identification to early warning triggering, but also makes up for the deficiencies in the prior art in adjustment and guidance after responding to risks.

[0084] Through the progressive learning algorithm, the present invention constructs a closed-loop optimization process, which can dynamically optimize feature extraction rules, risk assessment logic and model parameters according to real-time data and historical records, so that the system has the ability of continuous learning and adaptation. This optimization capability ensures the applicability and stability of the system in different operating scenarios and long-term dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] 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:

[0086] Figure 1 This is a flow chart of the intelligent early warning system for large container cranes based on personnel behavior analysis proposed by the present invention;

[0087] Figure 2 A schematic diagram of the behavior adjustment suggestion module of the present invention generating an optimal adjustment path through a quantum probabilistic behavior prediction algorithm;

[0088] Figure 3 The present invention is a flowchart of the progressive learning algorithm in the system closed-loop optimization module for optimizing feature extraction rules and model parameters. DETAILED DESCRIPTION

[0089] 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.

[0090] refer to Figure 1-3 ,The intelligent early warning system for large container cranes based on personnel behavior analysis includes the following steps:

[0091] S1. Deploy a multimodal sensor network in the crane operation area, lifting path and surrounding environment. The sensors include a behavior trajectory tracking module, an environmental status monitoring module and an equipment operation status collection module. The sensor network collects behavior data, environmental parameters and equipment status information in real time to build a dynamic data set.

[0092] S2. Based on the dynamic data set, the behavior sequence feature vector is constructed using the time difference embedding algorithm. The relationship between the behavior trajectory and the device status is aggregated through the feature vector, and a multi-level spatiotemporal feature matrix is ​​generated in combination with the dynamic scene influencing factors;

[0093] S3. Using the regional risk dynamic allocation algorithm based on Bayesian reasoning, combined with the spatiotemporal feature matrix and the risk zoning rules of the operating area, the regional risk factor is dynamically calculated. Through the time series inference of continuous data, the potential danger probability of behavior and equipment operation is calculated, and the corresponding regional risk weight distribution is generated;

[0094] S4. Through the adaptive multi-objective risk optimization algorithm, the regional risk factors and the global risk of the operation are integrated to dynamically calculate the comprehensive risk value. The comprehensive risk value makes weighted corrections to high-risk behaviors by optimizing the objective function to generate the risk level of the current operation scenario. The risk level is divided into three categories: low, medium and high.

[0095] S5. Trigger a graded warning response based on the risk level of the comprehensive risk value. The graded warning includes a low-risk sound and light prompt signal, a medium-risk voice reminder signal, and a high-risk operation stop signal. The warning response strategy is dynamically adjusted through a real-time feedback mechanism.

[0096] S6. After the warning signal is triggered, the multi-level spatiotemporal feature matrix and the comprehensive risk value are combined to generate personnel behavior adjustment suggestions through the quantum probabilistic behavior prediction algorithm. The algorithm infers the optimal adjustment path based on the probability distribution of the operation trajectory and transmits it to the operator through a visual interface and voice prompts;

[0097] S7. Based on real-time event records and historical data, the progressive learning algorithm is used to jointly optimize the time difference embedding rules, Bayesian reasoning model and adaptive multi-objective risk optimization algorithm, dynamically adjust the feature extraction rules and risk assessment logic, optimize the adaptability of the system in different operating environments, and form a closed-loop optimization process.

[0098] In this implementation, S2 specifically includes:

[0099] S21, dynamic data set D = {d1, d2, d n} decomposed into behavior trajectory data B t , Equipment status data E t and environmental parameter data P t , the data set is divided according to the time window mechanism T, and each time window contains multimodal data of behavior trajectory, device status and environmental parameters;

[0100] S22. In each time window, the time difference embedding algorithm is used to extract the dynamic characteristics of the behavior trajectory, device status and environmental parameters:

[0101] ΔX k =x t+k X t , (X∈{B,E,P});

[0102] Where ΔX k Represents the dynamic change characteristics of the data dimension x within the time window;

[0103] S23, embed the time difference feature into the behavior sequence feature vector, define the feature vector as Fk, feature vector F k Express the dynamic correlation characteristics of behavior trajectory, device status and environmental parameters within the time window;

[0104] S24, combining the influencing factors of the dynamic scene, define the scene weight matrix W = [w b , w e , w p ], weight factor w b , w e , w p Optimize and adjust based on the dynamic characteristics of each data dimension and multi-dimensional scenario influencing factors:

[0105]

[0106] The eigenvector Fk=[ΔB k , ΔE k , ΔP k ] is optimized, and the matrix expression of the weighted eigenvector Vk is:

[0107]

[0108] S25, the weighted feature vector V generated in each time window k Aggregate in time series order to form a time series feature set, which expresses the dynamic change characteristics of behavior trajectories, device states, and environmental parameters in multiple time windows;

[0109] S26. Based on the time series feature set, a multi-level spatiotemporal feature matrix M is constructed. The feature matrix has the time series dimension horizontally and the correlation characteristics of the behavior trajectory, device status and environmental parameters vertically. The feature matrix serves as a multimodal expression of the behavior trajectory, device status and environmental parameters.

[0110] In this implementation, S3 specifically includes:

[0111] S31, divide the operation area into a risk area set R = {r1, r2, rn}, each risk area r i Including behavior trajectory data, equipment status data and environmental parameter data. Combined with the generated spatiotemporal feature matrix M, the corresponding dynamic features in the matrix are mapped to each risk area R i ;

[0112] S32, based on regional feature set R i , using the Bayesian inference model to dynamically calculate the risk factor f for each region i :

[0113]

[0114] Among them, P(H|E) represents the probability of a dangerous event occurring in a region within a specific time window, and dynamic inference is performed by combining the prior probability P(H) and the conditional probability of the real-time feature data E;

[0115] S33. Combine the changing trend of risk factors in the continuous time window and use the time series inference model to predict the potential danger probability of behavior and equipment operation:

[0116] f i (t) = α·f i (t-1)+(1α)·P(H|E t );

[0117] Among them, f i (t) represents the risk factor at the current time t, and α is the historical weight factor;

[0118] S34, combined with regional risk factor f i (t), generate regional risk weight distribution W R ={w1,w2,,w n}, regional risk weight w i Dynamically reflect risk distribution priorities among regions.

[0119] In this implementation, S4 specifically includes:

[0120] S41. Combine the generated regional risk factor fi(t) and regional risk weight distribution W R , extract the multi-dimensional risk factors in the operation scenario and construct a set of comprehensive risk parameters. The comprehensive risk parameters combine the behavior trajectory, equipment status and environmental interference characteristics to provide input for the optimization objective function;

[0121] S42. Define an adaptive multi-objective risk optimization objective function to dynamically integrate regional risk factors and global operational risks:

[0122]

[0123] Among them, R c is the comprehensive risk value, f i and w i are the regional risk factors and weights, δ i is the scene interference factor, λ is the adjustment factor;

[0124] S43. Weighted correction of high-risk behaviors by optimizing the objective function:

[0125]

[0126] Among them, R c ′ is the modified comprehensive risk value, F k Represents the high-risk behavior features in the behavior trajectory feature set, Yk is the weighting factor;

[0127] S44. Based on the revised comprehensive risk value, the operation scenarios are divided into three categories: low risk, medium risk and high risk. The risk level classification rules are dynamically adjusted based on the characteristics of the scenarios.

[0128] S45. Combined with the dynamic changes of real-time operation scenarios, adjust the adjustment factor λ and the high-risk behavior weight γ in the optimization objective function k , so that the comprehensive risk value R c ′Accurately reflect the risk status of the operation scenario.

[0129] In this implementation manner, S5 specifically includes:

[0130] S51. According to the comprehensive risk value R c ′ and risk level classification rules, set corresponding graded warning trigger signals for the risk levels of the operation scenes, low risk levels trigger sound and light prompt signals, medium risk levels trigger voice reminder signals, prompting operators to adjust operations, high risk levels trigger equipment operation stop signals, and send emergency notifications to the remote management platform for management personnel to intervene;

[0131] S52. Combined with the real-time change trend of the comprehensive risk value, dynamically adjust the trigger conditions and response parameters of the graded warning signals. Low-risk signals are optimized by adjusting the prompt frequency and intensity. Medium-risk signals are optimized by optimizing the content, repetition frequency and volume of voice reminders. High-risk signals adjust the scope and duration of equipment stop according to the urgency of the risk.

[0132] S53, by monitoring the comprehensive risk value R c ′, establish a real-time feedback mechanism, and dynamically optimize the triggering rules, intensity and duration of the early warning signal when the risk value change rate is lower than the preset threshold;

[0133] S54. Coordinate the execution time and trigger logic of signals of different risk levels according to the risk level and the triggering sequence of the warning signal, and prioritize the execution of high-risk signals through priority rules to avoid interference between low-risk signals and high-risk signals;

[0134] S55. Record the trigger type, execution time and response effect of each warning signal, and optimize the trigger rules and response strategies of the warning signal through comprehensive analysis of historical data and real-time feedback.

[0135] In this implementation manner, S6 specifically includes:

[0136] S61. Based on the generated multi-level spatiotemporal feature matrix and comprehensive risk value, a quantum probabilistic behavior prediction model is established. The model takes behavior trajectory characteristics, device state characteristics and dynamic scene interference factors as inputs, describes the dynamic changes of behavior states through quantum superposition states, and forms a behavior state set.

[0137] S62. Combine the current behavior trajectory and device state, map them to a set of quantum behavior states, and calculate the quantum probability distribution of each behavior state:

[0138]

[0139] Where P(ψi, t) represents the normalized probability of behavior state ψi at time t;

[0140] S63. Based on the dynamic quantum probability distribution and the comprehensive risk value, the optimal adjustment path is inferred:

[0141]

[0142] Among them, P opt Represents the optimal adjustment path, combining time continuity and behavior state priority w i and the risk adjustment factor λ determine the path optimization results;

[0143] S64. Generate specific behavior adjustment suggestions based on the inferred optimal adjustment path. The suggestions include optimizing operation steps, adjusting behavior trajectories, and changing work positions. The adjustment suggestions are promptly delivered to the operator through a visual interface or voice prompts.

[0144] S65. Record the execution results of behavioral adjustment suggestions and changes in the comprehensive risk value, and optimize the parameter settings of the quantum probability behavior prediction model in combination with real-time feedback data. By adjusting the quantum probability distribution and path generation logic, optimize the model's adaptability to complex dynamic scenarios and the accuracy of behavior prediction.

[0145] In this implementation manner, the S7 specifically includes:

[0146] S71. Based on real-time event records and historical data, extract behavior trajectory characteristics, equipment status data and environmental parameters, combine comprehensive risk values ​​and regional risk factors, and construct a joint optimization data set D opt ;

[0147] S72. Combined with optimized data set D opt , using the progressive learning algorithm to dynamically adjust the size of the feature extraction window and the embedding dimension to form an optimized embedding rule, which is k and the dynamic scene weight factor w t , optimize the embedded time series features:

[0148]

[0149] Among them, T k ′ is the optimized time window, T k is the original time window, w t is the dynamic scene weight factor, ΔR c ′ is the rate of change of the comprehensive risk value, μ is the balance factor;

[0150] S73. Based on the optimized data set, dynamically modify the risk factor distribution in the Bayesian reasoning model, adjust the conditional probability and prior probability of the model to adapt to the changing trends of real-time data and historical data, and adaptively optimize the conditional probability distribution through progressive learning to optimize the assessment accuracy of risk factors;

[0151] S74. Combined with the high-risk behavior characteristics in the real-time event records, the weight factors and adjustment factors in the adaptive multi-objective risk optimization algorithm are adjusted to form a priority processing logic for high-risk behaviors. The optimization model calculates the dynamic risk priority according to the weight distribution:

[0152]

[0153] Among them, P w is the dynamic priority distribution, f i is the risk factor, Δ t is the time interval, α is the time weight adjustment factor;

[0154] S75. Feedback the optimized time difference embedding rules, Bayesian reasoning model and adaptive multi-objective risk optimization algorithm to the data processing module to form a closed-loop optimization process. By continuously collecting real-time and historical data, the optimization logic is updated to optimize the system's long-term adaptability to complex dynamic scenarios.

[0155] In this embodiment, the following modules are included:

[0156] Multimodal data acquisition module: Through the sensor network installed in the crane operation area, lifting path and surrounding environment, it collects behavioral data, equipment status and environmental parameters in real time to build a dynamic data set;

[0157] Data processing module: pre-processes and extracts features from multimodal data sets, and constructs a dynamic correlation model of behavior trajectory, device status, and environmental parameters through feature vectors;

[0158] Behavior feature analysis module: uses the time difference embedding algorithm to extract the time series features of behavior trajectory, device status and environmental parameters, and combines the dynamic scene influencing factors to generate a multi-level spatiotemporal feature matrix;

[0159] Dynamic risk assessment module: Combines the spatiotemporal feature matrix and the division rules of the operation area, uses the Bayesian reasoning model to dynamically calculate the regional risk factor, generates a comprehensive risk value through an adaptive multi-objective risk optimization algorithm, and divides the current operation scenario into low risk, medium risk or high risk according to the risk level;

[0160] Graded warning response module: triggers graded warning signals according to the comprehensive risk value. Low risk levels trigger sound and light prompt signals, medium risk levels trigger voice reminder signals, and high risk levels trigger equipment operation stop signals. The warning trigger conditions and response logic are dynamically adjusted through a real-time feedback mechanism.

[0161] Behavior adjustment suggestion module: Combines comprehensive risk value and multi-level spatiotemporal feature matrix, infers the optimal adjustment path through quantum probabilistic behavior prediction algorithm, generates specific behavior adjustment suggestions, and transmits them to operators through visual interface and voice prompts;

[0162] System closed-loop optimization module: Based on real-time event records and historical data, it uses progressive learning algorithms to dynamically optimize time difference embedding rules, Bayesian reasoning models, and adaptive multi-objective risk optimization algorithms, continuously updates feature extraction rules and risk assessment logic, and builds a closed-loop optimization process.

[0163] Embodiment 1:

[0164] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a large container terminal of an international port as a test scenario. The terminal covers an area of ​​about 300,000 square meters and is equipped with 20 large container cranes. The operating environment is complex, the interaction between personnel and equipment is frequent, and there are various safety hazards. The test area mainly covers the container loading and unloading area and the yard area. The operators are responsible for crane operation, equipment inspection and ground guidance. Typical safety issues in the test scenario include personnel entering high-risk areas without wearing safety equipment, abnormal operation of operating equipment, and operational hazards caused by harsh environmental conditions.

[0165] In this scenario, the present invention realizes the real-time collection of operator behavior, equipment status and environmental parameters by deploying a multimodal sensor network. The sensor network covers the crane operating area, lifting path and key components of the equipment. A total of 120 sensor nodes are deployed, including 30 cameras, 20 equipment status monitoring sensors and 70 environmental monitoring sensors. The sensor nodes collect and transmit multimodal data in real time with a time window of 5 seconds to generate a dynamic data set.

[0166] After data collection is completed, the system uses the time difference embedding algorithm to extract dynamic features of the operator's behavior trajectory, and correlates the behavior data with the equipment operating status and environmental parameters to generate a multi-level spatiotemporal feature matrix. This matrix describes in detail the dynamic correlation between personnel behavior, equipment status and environmental changes, and provides important basic data support for the risk assessment module. In one operation scenario, the system identified through the feature matrix that a ground guide did not wear safety equipment and mistakenly entered a high-risk area less than 10 meters away from the crane boom. Combining the feature data and risk zoning rules, the system used the Bayesian reasoning model to dynamically calculate the risk factor of the area as 0.85, and determined the comprehensive risk value to be 0.92 through an adaptive multi-objective optimization algorithm, judging it as a high-risk scenario.

[0167] Based on the comprehensive risk value, the system immediately triggered a high-risk level warning signal. The sound and light alarms in the crane operating room were immediately activated, and voice prompts notified the operating personnel to quickly evacuate the high-risk area. At the same time, the crane suspended the current lifting operation, and the remote management platform received the emergency notification in real time, prompting the management personnel to intervene. As the ground guides successfully evacuated the dangerous area according to the prompted path, the crane operation quickly resumed normal operation after automatically assessing the safety of the environment. The entire warning response process from detection to alarm triggering took only 1.8 seconds, effectively avoiding a possible safety accident.

[0168] In this scenario, the behavior adjustment suggestion module combines the quantum probabilistic behavior prediction algorithm to generate an optimized evacuation path. Based on the current behavior trajectory of the operator and the multi-level spatiotemporal feature matrix, the system calculates the quantum probability distribution of the behavior state, infers the optimal adjustment path and guides the operator to avoid other potential danger areas. The adjustment suggestions are delivered to the operator in the form of voice prompts and visual interfaces, allowing them to quickly leave the high-risk area and return to the safe area. The operator completed the evacuation within 7 seconds, which is 45% shorter than the traditional manual guidance method.

[0169] During the one-month test, the present invention verified multiple typical work scenarios and collected more than 5 million data points, covering 3,500 crane hoisting operations and behavioral data of 1,200 operators. The system detected 225 personnel violations, including 90 cases of not wearing safety equipment, 75 cases of mistakenly entering high-risk areas, and 60 cases of staying too long. The accuracy of risk identification reached 98.7%, which is about 35% higher than the traditional equipment monitoring method. The average early warning response speed was 1.8 seconds, which was 64% shorter than the average response time of the existing system. In addition, the incidence of simulated safety accidents during the test was reduced by 72%, significantly improving operational safety.

[0170] Table 1 System performance table

[0171]

[0172] It can be seen from the above table that the system of the present invention performs well in detecting violations, and is able to detect 225 violations during a one-month test period, including failure to wear safety equipment, mistakenly entering high-risk areas, and staying too long. These behaviors are accurately identified through the system's multimodal sensors and dynamic feature analysis, with an identification accuracy rate of up to 98.7%, which is far superior to the technical capabilities of existing systems that cannot effectively analyze human behavior; in terms of risk identification and response efficiency, the present invention significantly improves the timeliness of risk management. The system's average risk identification time is 1.8 seconds, and the early warning response speed is also 1.8 seconds, which is 64% faster than the 5 seconds of the existing system. Rapid identification and response enable the system to trigger early warning signals and notify operators in a timely manner, effectively avoiding potential safety accidents. Through analysis of simulated scenarios, the present invention reduces the accident rate by 72%, fully demonstrating its practicality and reliability in safety protection. In addition, the system can generate behavioral adjustment suggestions with efficient guidance, such as guiding operators to quickly evacuate high-risk areas, bypass other dangerous areas or return to safe areas. These suggestions are generated through a quantum probabilistic behavior prediction algorithm, with an execution success rate of 96.3% and an average completion time of 7 seconds, which is 45% shorter than traditional manual guidance methods.

[0173] The system of the present invention, through innovative behavior analysis and risk management technology, demonstrated powerful real-time risk identification capabilities, flexible early warning response mechanisms, and effective behavior adjustment suggestions in test scenarios. Test results show that the present invention can solve the problems of lack of attention to personnel behavior, delayed risk assessment, and lack of adjustment suggestions in the prior art, significantly improving the safety and efficiency of crane operations and providing a reliable intelligent safety management solution for port operating environments.

[0174] 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. An intelligent early warning method for large container cranes based on personnel behavior analysis, characterized in that: The steps include: S1. Deploy a multimodal sensor network in the crane operation area, lifting path and surrounding environment. The sensors include a behavior trajectory tracking module, an environmental status monitoring module and an equipment operation status collection module. The sensor network collects behavior data, environmental parameters and equipment status information in real time to build a dynamic data set. S2. Based on the dynamic data set, the behavior sequence feature vector is constructed using the time difference embedding algorithm. The relationship between the behavior trajectory and the device status is aggregated through the feature vector, and a multi-level spatiotemporal feature matrix is ​​generated in combination with the dynamic scene influencing factors; S3. Using the regional risk dynamic allocation algorithm based on Bayesian reasoning, combined with the spatiotemporal feature matrix and the risk zoning rules of the operating area, the regional risk factor is dynamically calculated. Through the time series inference of continuous data, the potential danger probability of behavior and equipment operation is calculated, and the corresponding regional risk weight distribution is generated; S4. Through the adaptive multi-objective risk optimization algorithm, the regional risk factors and the global risk of the operation are integrated to dynamically calculate the comprehensive risk value. The comprehensive risk value makes weighted corrections to high-risk behaviors by optimizing the objective function to generate the risk level of the current operation scenario. The risk level is divided into three categories: low, medium and high. S5. Trigger a graded warning response based on the risk level of the comprehensive risk value. The graded warning includes a low-risk sound and light prompt signal, a medium-risk voice reminder signal, and a high-risk operation stop signal. The warning response strategy is dynamically adjusted through a real-time feedback mechanism. S6. After the warning signal is triggered, the multi-level spatiotemporal feature matrix and the comprehensive risk value are combined to generate personnel behavior adjustment suggestions through the quantum probabilistic behavior prediction algorithm. The algorithm infers the optimal adjustment path based on the probability distribution of the operation trajectory and transmits it to the operator through a visual interface and voice prompts; S7. Based on real-time event records and historical data, the progressive learning algorithm is used to jointly optimize the time difference embedding rules, Bayesian reasoning model and adaptive multi-objective risk optimization algorithm, dynamically adjust the feature extraction rules and risk assessment logic, optimize the adaptability of the system in different operating environments, and form a closed-loop optimization process.

2. The intelligent early warning method for large container cranes based on personnel behavior analysis according to claim 1 is characterized in that: The S2 specifically includes: S21, dynamic data set D = {d1, d2,, d n } decomposed into behavior trajectory data B t , Equipment status data E t and environmental parameter data P t , the data set is divided according to the time window mechanism T, and each time window contains multimodal data of behavior trajectory, device status and environmental parameters; S22. In each time window, the time difference embedding algorithm is used to extract the dynamic characteristics of the behavior trajectory, device status and environmental parameters: ΔX k =X t+k X t ,(X∈{B,E,P}); Where ΔX k Indicates the dynamic change characteristics of data dimension X within the time window; S23, embed the time difference feature into the behavior sequence feature vector, and define the feature vector as F k , the eigenvector F k Express the dynamic correlation characteristics of behavior trajectory, device status and environmental parameters within the time window; S24, combining the influencing factors of the dynamic scene, define the scene weight matrix W = [w b ,w e ,w p ], weight factor w b ,w e ,w p Optimize and adjust based on the dynamic characteristics of each data dimension and multi-dimensional scenario influencing factors: The feature vector F is transformed by the weight matrix W. k =[ΔB k ,ΔE k ,ΔP k ] is optimized, and the weighted feature vector V k The matrix expression is: S25, the weighted feature vector V generated in each time window k Aggregate in time series order to form a time series feature set, which expresses the dynamic change characteristics of behavior trajectories, device states, and environmental parameters in multiple time windows; S26. Based on the time series feature set, a multi-level spatiotemporal feature matrix M is constructed. The feature matrix has the time series dimension horizontally and the correlation characteristics of the behavior trajectory, device status and environmental parameters vertically. The feature matrix serves as a multimodal expression of the behavior trajectory, device status and environmental parameters.

3. The intelligent early warning method for large container cranes based on personnel behavior analysis according to claim 1 is characterized in that: The S3 specifically includes: S31, divide the operation area into risk area set R = {r1, r2,, r n }, each risk area r i Including behavior trajectory data, equipment status data and environmental parameter data. Combined with the generated spatiotemporal feature matrix M, the corresponding dynamic features in the matrix are mapped to each risk area R i ; S32, based on regional feature set R i , using the Bayesian inference model to dynamically calculate the risk factor f for each region i : Among them, P(H|E) represents the probability of a dangerous event occurring in a region within a specific time window, and dynamic inference is performed by combining the prior probability P(H) and the conditional probability of the real-time feature data E; S33. Combine the changing trend of risk factors in the continuous time window and use the time series inference model to predict the potential danger probability of behavior and equipment operation: f i (t)=α·f i (t-1)+(1α)·P(H|E t ); Among them, f i (t) represents the risk factor at the current time t, and α is the historical weight factor; S34, combined with regional risk factor f i (t), generate regional risk weight distribution W R ={w1,w2,,w n }, regional risk weight w i Dynamically reflect risk distribution priorities among regions.

4. The intelligent early warning method for large container cranes based on personnel behavior analysis according to claim 1 is characterized in that: The S4 specifically includes: S41, combined with the generated regional risk factor f i (t) and regional risk weight distribution W R , extract the multi-dimensional risk factors in the operation scenario and construct a set of comprehensive risk parameters. The comprehensive risk parameters combine the behavior trajectory, equipment status and environmental interference characteristics to provide input for the optimization objective function; S42. Define an adaptive multi-objective risk optimization objective function to dynamically integrate regional risk factors and global operational risks: Among them, R c is the comprehensive risk value, f i and w i are the regional risk factors and weights, δ i is the scene interference factor, λ is the adjustment factor; S43. Weighted correction of high-risk behaviors by optimizing the objective function: Among them, R c ′ is the modified comprehensive risk value, F k represents the high-risk behavior features in the behavior trajectory feature set, γ k is the weighting factor; S44. Based on the revised comprehensive risk value, the operation scenarios are divided into three categories: low risk, medium risk and high risk. The risk level classification rules are dynamically adjusted based on the characteristics of the scenarios. S45. Combined with the dynamic changes of real-time operation scenarios, adjust the adjustment factor λ and the high-risk behavior weight γ in the optimization objective function k , so that the comprehensive risk value R c ′ Accurately reflect the risk status of the operation scenario.

5. The intelligent early warning method for large container cranes based on personnel behavior analysis according to claim 1 is characterized in that: The S5 specifically includes: S51. According to the comprehensive risk value R c ′ According to the risk classification rules, the corresponding graded warning trigger signals are set for the risk levels of the operation scenarios. Low risk levels trigger sound and light prompt signals, medium risk levels trigger voice reminder signals, prompting operators to adjust operations, and high risk levels trigger equipment operation stop signals, and emergency notifications are sent to the remote management platform for management personnel to intervene; S52. Combined with the real-time change trend of the comprehensive risk value, dynamically adjust the trigger conditions and response parameters of the graded warning signals. Low-risk signals are optimized by adjusting the prompt frequency and intensity. Medium-risk signals are optimized by optimizing the content, repetition frequency and volume of voice reminders. High-risk signals adjust the scope and duration of equipment stop according to the urgency of the risk. S53, by monitoring the comprehensive risk value R c ′ Establish a real-time feedback mechanism to dynamically optimize the triggering rules, intensity and duration of early warning signals when the risk value change rate is lower than the preset threshold; S54. Coordinate the execution time and trigger logic of signals of different risk levels according to the risk level and the triggering sequence of the warning signal, and prioritize the execution of high-risk signals through priority rules to avoid interference between low-risk signals and high-risk signals; S55. Record the trigger type, execution time and response effect of each warning signal, and optimize the trigger rules and response strategies of the warning signal through comprehensive analysis of historical data and real-time feedback.

6. The large container crane intelligent early warning method based on personnel behavior analysis according to claim 1 is characterized in that: The S6 specifically includes: S61. Based on the generated multi-level spatiotemporal feature matrix and comprehensive risk value, a quantum probabilistic behavior prediction model is established. The model takes behavior trajectory characteristics, device state characteristics and dynamic scene interference factors as inputs, describes the dynamic changes of behavior states through quantum superposition states, and forms a behavior state set. S62. Combine the current behavior trajectory and device state, map them to a set of quantum behavior states, and calculate the quantum probability distribution of each behavior state: Among them, P(ψ i ,t) represents the behavior state ψ at time t i The normalized probability of S63. Based on the dynamic quantum probability distribution and the comprehensive risk value, the optimal adjustment path is inferred: Among them, P opt Represents the optimal adjustment path, combining time continuity and behavior state priority w i and the risk adjustment factor λ determine the path optimization results; S64. Generate specific behavior adjustment suggestions based on the inferred optimal adjustment path. The suggestions include optimizing operation steps, adjusting behavior trajectories, and changing work positions. The adjustment suggestions are promptly delivered to the operator through a visual interface or voice prompts. S65. Record the execution results of behavioral adjustment suggestions and changes in the comprehensive risk value, and optimize the parameter settings of the quantum probability behavior prediction model in combination with real-time feedback data. By adjusting the quantum probability distribution and path generation logic, optimize the model's adaptability to complex dynamic scenarios and the accuracy of behavior prediction.

7. The intelligent early warning method for large container cranes based on personnel behavior analysis according to claim 1 is characterized in that: The S7 specifically includes: S71. Based on real-time event records and historical data, extract behavior trajectory characteristics, equipment status data and environmental parameters, combine comprehensive risk values ​​and regional risk factors, and construct a joint optimization data set D opt ; S72. Combined with optimized data set D opt , using the progressive learning algorithm to dynamically adjust the size of the feature extraction window and the embedding dimension to form an optimized embedding rule, which is k and the dynamic scene weight factor w t , optimize the embedded time series features: Among them, T k ′ is the optimized time window, T k is the original time window, w t is the dynamic scene weight factor, ΔR c ′ is the rate of change of the comprehensive risk value, μ is the balance factor; S73. Based on the optimized data set, dynamically modify the risk factor distribution in the Bayesian reasoning model, adjust the conditional probability and prior probability of the model to adapt to the changing trends of real-time data and historical data, and adaptively optimize the conditional probability distribution through progressive learning to optimize the assessment accuracy of risk factors; S74. Combined with the high-risk behavior characteristics in the real-time event records, the weight factors and adjustment factors in the adaptive multi-objective risk optimization algorithm are adjusted to form a priority processing logic for high-risk behaviors. The optimization model calculates the dynamic risk priority according to the weight distribution: Among them, P w is the dynamic priority distribution, f i is the risk factor, Δ t is the time interval, α is the time weight adjustment factor; S75. Feedback the optimized time difference embedding rules, Bayesian reasoning model and adaptive multi-objective risk optimization algorithm to the data processing module to form a closed-loop optimization process. By continuously collecting real-time and historical data, the optimization logic is updated to optimize the system's long-term adaptability to complex dynamic scenarios.

8. An intelligent early warning system for large container cranes based on personnel behavior analysis is characterized by: Includes the following modules: Multimodal data acquisition module: Through the sensor network installed in the crane operation area, lifting path and surrounding environment, it collects behavioral data, equipment status and environmental parameters in real time to build a dynamic data set; Data processing module: pre-processes and extracts features from multimodal data sets, and constructs a dynamic correlation model of behavior trajectory, device status, and environmental parameters through feature vectors; Behavior feature analysis module: uses the time difference embedding algorithm to extract the time series features of behavior trajectory, device status and environmental parameters, and combines the dynamic scene influencing factors to generate a multi-level spatiotemporal feature matrix; Dynamic risk assessment module: Combines the spatiotemporal feature matrix and the division rules of the operation area, uses the Bayesian reasoning model to dynamically calculate the regional risk factor, generates a comprehensive risk value through an adaptive multi-objective risk optimization algorithm, and divides the current operation scenario into low risk, medium risk or high risk according to the risk level; Graded warning response module: triggers graded warning signals according to the comprehensive risk value. Low risk levels trigger sound and light prompt signals, medium risk levels trigger voice reminder signals, and high risk levels trigger equipment operation stop signals. The warning trigger conditions and response logic are dynamically adjusted through a real-time feedback mechanism. Behavior adjustment suggestion module: Combines comprehensive risk value and multi-level spatiotemporal feature matrix, infers the optimal adjustment path through quantum probabilistic behavior prediction algorithm, generates specific behavior adjustment suggestions, and transmits them to operators through visual interface and voice prompts; System closed-loop optimization module: Based on real-time event records and historical data, it uses progressive learning algorithms to dynamically optimize time difference embedding rules, Bayesian reasoning models, and adaptive multi-objective risk optimization algorithms, continuously updates feature extraction rules and risk assessment logic, and builds a closed-loop optimization process.

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