Intelligent early warning system for large container crane based on personnel behavior analysis

Abstract

The application discloses a large container crane intelligent early warning system based on personnel behavior analysis, comprising the following steps: collecting the behavior of operating personnel, the running state of equipment and environmental parameters in real time through a multi-modal sensor network, constructing a dynamic data set, extracting behavior characteristics using a time difference embedding algorithm, generating a multi-level space-time feature matrix in combination with dynamic scene influence factors, dynamically calculating regional risk factors using a Bayesian inference model, and generating a comprehensive risk value through an adaptive multi-objective optimization algorithm, dividing the risk level into three categories of low, medium and high, triggering a graded early warning response according to the risk level, and generating optimized behavior adjustment suggestions in combination with a quantum probability behavior prediction algorithm, dynamically optimizing feature extraction rules and risk assessment logic through a progressive learning algorithm, and forming a closed-loop optimization process. The application realizes intelligent analysis and real-time early warning of personnel behavior, and improves the safety and adaptability in the crane operating environment.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of the port logistics industry, large container cranes, as core operation equipment, play an important role in high-intensity operation scenarios. Due to the complexity and dynamics of the crane operation environment, the interaction between the operating personnel and the 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 operation environment is the focus and difficulty of current technical research. Existing crane safety management systems mainly focus on monitoring the operating state of the equipment. Through sensors, parameters such as equipment vibration, speed, and displacement are collected to determine whether the crane operating state is abnormal. These methods provide technical support for equipment safety management, but there are significant shortcomings when it comes to personnel behavior analysis and dynamic risk assessment.

[0003] In the prior art, most safety management systems pay less attention to personnel behavior. They only use regional monitoring cameras or simple intrusion detection methods to roughly monitor personnel in the operating area. This approach ignores the behavior characteristics of operating personnel during operation, such as whether they are wearing safety equipment correctly or whether they have violated the rules by entering high-risk areas. In actual operating environments, unsafe behavior by personnel is the main cause of accidents. The lack of in-depth analysis and dynamic assessment of personnel behavior in existing systems makes the operation safety management have obvious shortcomings.

[0004] The risk assessment methods in the prior art are relatively simple, usually relying on fixed equipment parameter thresholds or simple logical rules for judgment. This approach fails to fully consider the complex correlation between personnel behavior, equipment operating state, and environmental parameters, and cannot dynamically integrate and analyze multidimensional data. This type of method lacks the ability to predict risk trends, resulting in lagging or distorted risk assessment results in rapidly changing operating environments, which cannot provide real-time support for early warning and intervention.

[0005] The early warning response mechanism is a key link of the crane safety management, but the existing system usually adopts a single early warning mode, such as sound and light alarm or device shutdown. Although this mode can provide certain risk prompt, due to the lack of hierarchical response ability for different risk levels, it is easy to cause excessive early warning in a low-risk scene or insufficient response in a high-risk scene. 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 precision and timeliness of the early warning response. The existing system generally lacks intelligent adjustment support for the behavior of the operating personnel. When the early warning signal is triggered, the system usually cannot generate specific optimization suggestions to guide the operating personnel to take effective risk avoidance measures. In this case, the operating personnel may be at a loss when facing risks, further increasing the possibility of accidents. At the same time, the existing technology usually adopts fixed rules for system optimization and cannot learn and iterate according to real-time data and historical records, resulting in insufficient system adaptability and difficulty in coping with different operation scenarios and long-term dynamic changes.

[0006] Therefore, how to provide a large container crane intelligent early warning system based on personnel behavior analysis is a problem that those skilled in the art need to solve. SUMMARY

[0007] One object of the present application is to provide a large container crane intelligent early warning system based on personnel behavior analysis. The present application makes full use of a multi-modal sensor network, a time difference embedding algorithm, a Bayesian inference model, an adaptive multi-objective optimization algorithm and a quantum probability behavior prediction algorithm. The complete process from dynamic collection and feature extraction of personnel behavior data to risk assessment, hierarchical early warning and behavior adjustment suggestion generation is described in detail. The present application 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 the embodiment of the present application comprises the following steps:

[0009] S1, a multi-modal sensor network is arranged in the crane operation area, hoisting path and surrounding environment. The sensors include a behavior trajectory tracking module, an environment state monitoring module and a device running state acquisition module. Behavior data, environment parameters and device state information are collected in real time through the sensor network to construct a dynamic data set;

[0010] S2, based on the dynamic data set, a behavior sequence feature vector is constructed using a time difference embedding algorithm. The association between the behavior trajectory and the device state is aggregated through the feature vector, and a multi-level spatio-temporal feature matrix is generated in combination with dynamic scene influence factors;

[0011] S3, using a regional risk dynamic allocation algorithm based on Bayesian inference, combining the spatio-temporal feature matrix and the risk zoning rules of the operation region, dynamically calculating the regional risk factor, calculating the potential risk probability of behavior and device operation through the time series inference of continuous data, and generating the corresponding regional risk weight distribution;

[0012] S4, through an adaptive multi-objective risk optimization algorithm, integrating the regional risk factor and the global risk of operation, dynamically calculating the comprehensive risk value, and the comprehensive risk value being weighted and corrected to high-risk behavior through an optimization objective function, generating the risk level of the current operation scene, the risk level being divided into three categories of low, medium and high;

[0013] S5, triggering a hierarchical early warning response according to the risk level of the comprehensive risk value, the hierarchical early warning including an audible and visual prompt signal for low risk, a voice reminder signal for medium risk, and an operation stop signal for high risk, and dynamically adjusting the early warning response strategy through a real-time feedback mechanism;

[0014] S6, after the early warning signal is triggered, combining the multi-level spatio-temporal feature matrix and the comprehensive risk value, generating a personnel behavior adjustment suggestion through a quantum probability behavior prediction algorithm, the algorithm inferring an optimal adjustment path according to the probability distribution of the operation trajectory, and delivering it to the operation personnel through a visual interface and a voice prompt mode;

[0015] S7, based on real-time event records and historical data, using a progressive learning algorithm to jointly optimize the time difference embedding rule, the Bayesian inference model and the adaptive multi-objective risk optimization algorithm, dynamically adjusting the feature extraction rule and the risk evaluation logic, optimizing the adaptability of the system in different operation environments, and forming a closed-loop optimization process.

[0016] Optionally, the S2 specifically includes:

[0017] S21, decomposing a dynamic data set D={d1, d2, …, d n} into behavior trajectory data B t , device state data E t and environmental parameter data P t , dividing the data set according to a time window mechanism T, each time window containing multi-modal data of behavior trajectory, device state and environmental parameters;

[0018] S22, in each time window, using a time difference embedding algorithm to extract the dynamic features of behavior trajectory, device state and environmental parameters:

[0019] ΔX k =x t+k X t ,(X∈{B,E,P});

[0020] wherein, ΔX krepresenting the dynamic change characteristics of the data dimension x within the time window;

[0021] S23, embedding the time difference characteristics into the behavior sequence feature vector, defining the feature vector as Fk, and the feature vector F k representing the dynamic correlation characteristics of the behavior trajectory, device state and environmental parameters within the time window;

[0022] S24, combining the influence factors of the dynamic scene to define the scene weight matrix w = [w b , w e , w p ], the weight factor w b , w e , w p Optimizing adjustment according to the dynamic characteristics of each data dimension and the multi-dimensional scene influence factor:

[0023]

[0024] Optimizing the feature vector Fk = [ΔB k , ΔE k , ΔP k ] through the weight matrix w, and the matrix expression of the weighted feature vector Vk is:

[0025]

[0026] S25, aggregating the weighted feature vectors V k generated within each time window in time sequence order to form a time series feature set, and the time series feature set represents the dynamic change characteristics of the behavior trajectory, device state and environmental parameters within multiple time windows;

[0027] S26, based on the time series feature set, constructing a multi-level space-time feature matrix M, the feature matrix is transverse to the time series dimension and longitudinal to the correlation characteristics of the behavior trajectory, device state and environmental parameters, and the feature matrix is a multi-modal expression of the behavior trajectory, device state and environmental parameters.

[0028] Optionally, the S3 specifically comprises:

[0029] S31, dividing the work area into a risk area set R = {r1, r2, …, r n}, each risk area r i includes behavior trajectory data, device state data and environmental parameter data. In combination with the generated space-time feature matrix M, the corresponding dynamic characteristics in the matrix are mapped to each risk area R i ;

[0030] S32, based on the area feature set R i, and the risk factor f of each region is dynamically calculated by using a Bayesian inference model i :

[0031]

[0032] wherein P(H|E) represents the probability of a dangerous event occurring in the region within a specific time window, and is dynamically inferred in combination with the prior probability P(H) and the conditional probability of real-time feature data E;

[0033] S33, in combination with the change trend of the risk factor within the continuous time window, a time series inference model is used to predict the potential dangerous probability of the behavior and device operation:

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

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

[0036] S34, in combination with the region risk factor f i (t), a region risk weight distribution W R = {w1, w2,, w n} is generated, and the region risk weight w i dynamically reflects the risk distribution priority among regions.

[0037] Optionally, the S4 specifically comprises:

[0038] S41, in combination with the generated region risk factor fi(t) and the region risk weight distribution w R , a multi-dimensional risk factor under the operation scenario is extracted, a comprehensive risk parameter set is constructed, and the comprehensive risk parameter combines the behavior trajectory, the device state and the environmental interference characteristics to provide input for the optimization objective function;

[0039] S42, an adaptive multi-objective risk optimization objective function is defined, which is used to dynamically integrate the region risk factor and the global risk of the operation:

[0040]

[0041] wherein R c is a comprehensive risk value, f i and w i are respectively a region risk factor and a weight, δ i is a scene interference factor, and λ is a regulation factor;

[0042] S43, a high-risk behavior is weighted and corrected by the optimization objective function:

[0043]

[0044] wherein R c is the modified comprehensive risk value, F k represents a high-risk behavior feature in the behavior trajectory feature set, Yk is a weighting factor;

[0045] S44, according to the modified comprehensive risk value, the operation scene is divided into low risk, medium risk and high risk three categories, the risk level division rule combines the scene characteristics dynamic adjustment;

[0046] S45, combined with the dynamic change of real-time operation scene, adjust the adjustment factor λ and high-risk behavior weight γ in the optimization objective function k , make the comprehensive risk value R c ' accurately reflect the risk state of the operation scene.

[0047] Optionally, the S5 specifically includes:

[0048] S51, according to the comprehensive risk value R c ' and the risk level division rule, the risk level of the operation scene is set with corresponding hierarchical early warning trigger signal, low risk level triggers sound and light prompt signal, medium risk level triggers voice prompt signal, prompts the operator to adjust the operation, high risk level triggers device operation stop signal, and sends emergency notification to remote management platform for management personnel intervention;

[0049] S52, combined with the real-time change trend of the comprehensive risk value, dynamically adjust the trigger condition and response parameter of the hierarchical early warning signal, the low risk signal is adjusted by adjusting the prompt frequency and intensity, the medium risk signal is optimized by optimizing the content, repetition frequency and volume of voice prompt to optimize the warning effect, and the high risk signal is adjusted according to the risk emergency degree to adjust the range and duration of device stop;

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

[0051] S54, according to the risk level and the early warning signal trigger sequence, coordinate the execution time and trigger logic of different risk level signals, through priority rule, make the high risk signal execute first, avoid the interference between low risk signal and high risk signal;

[0052] S55, record the trigger type, execution time and response effect of each early warning signal, through the comprehensive analysis of historical data and real-time feedback, optimize the trigger rule and response strategy of early warning signal.

[0053] Optionally, the S6 specifically includes:

[0054] S61, based on the generated multi-level spatio-temporal feature matrix and the comprehensive risk value, a quantum probability behavior prediction model is established, which takes behavior trajectory features, device state features 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, in combination with the current behavior trajectory and the device state, it is mapped to the quantum behavior state set, and the quantum probability distribution of each behavior state is calculated:

[0056]

[0057] Wherein, P(ψ i , t) represents the normalized probability of behavior state ψ i at time t;

[0058] S63, according to the dynamic quantum probability distribution, in combination with the comprehensive risk value, the optimal adjustment path is predicted:

[0059]

[0060] Wherein, P opt represents the optimal adjustment path, in combination with the time continuity, the behavior state priority w i and the risk adjustment factor λ to determine the path optimization result;

[0061] S64, in combination with the predicted optimal adjustment path, specific behavior adjustment suggestions are generated, including optimization operation steps, adjustment behavior trajectory and change of working position, through visual interface or voice prompt mode, the adjustment suggestions are timely delivered to the job personnel;

[0062] S65, the execution result of the behavior adjustment suggestion and the change of the comprehensive risk value are recorded, the parameter setting of the quantum probability behavior prediction model is optimized in combination with the real-time feedback data, through adjusting the quantum probability distribution and the path generation logic, the adaptability of the model to complex dynamic scene and the accuracy of behavior prediction are optimized.

[0063] Optionally, the S7 specifically includes:

[0064] S71, based on real-time event records and historical data, behavior trajectory features, device state data and environmental parameters are extracted, in combination with the comprehensive risk value and the regional risk factor, a joint optimization data set D opt is constructed;

[0065] S72, in combination with the optimization data set D opt , the size of the feature extraction window and the embedding dimension are dynamically adjusted by using the progressive learning algorithm, and the optimized embedding rule is formed, which adjusts the window Tk and dynamic scene weight factor w t , optimize the embedded time series features:

[0066]

[0067] wherein, 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 change rate of the comprehensive risk value, and μ is the balance factor;

[0068] S73, based on the optimized data set, dynamically correct the risk factor distribution in the Bayesian inference model, adjust the conditional probability and prior probability of the model, and adapt to the change trend of real-time data and historical data, and adaptively optimize the conditional probability distribution through progressive learning, and optimize the evaluation accuracy of the risk factor;

[0069] S74, combined with the high-risk behavior characteristics in the real-time event record, adjust the weight factor and the adjustment factor in the adaptive multi-target risk optimization algorithm, form the priority processing logic of the high-risk behavior, and the optimization model calculates the dynamic risk priority according to the weight distribution:

[0070]

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

[0072] S75, the optimized time difference embedding rule, Bayesian inference model and adaptive multi-target risk optimization algorithm are fed back to the data processing module to form a closed loop optimization process, and through continuous acquisition of real-time data and historical data, the optimization logic is updated, and the long-term adaptability of the system to complex dynamic scenes is optimized.

[0073] Optionally, the following modules are included:

[0074] Multi-modal data acquisition module: through the sensor network installed in the crane operation area, hoisting path and surrounding environment, real-time acquisition of behavior data, equipment state and environmental parameters, construction of dynamic data set;

[0075] Data processing module: pre-process and feature extract the multi-modal data set, and construct the dynamic correlation model of behavior trajectory, equipment state and environmental parameters through the feature vector;

[0076] Behavior feature analysis module: use time difference embedding algorithm to extract time sequence features of behavior trajectory, device state and environmental parameters, and generate multi-level spatio-temporal feature matrix combined with dynamic scene influence factors;

[0077] Dynamic risk assessment module: combined with the spatio-temporal feature matrix and the division rule of the work area, the regional risk factor is dynamically calculated by using the Bayesian inference model, the comprehensive risk value is generated by using the adaptive multi-objective risk optimization algorithm, and the current work scene is divided into low risk, medium risk or high risk according to the risk level;

[0078] Hierarchical early warning response module: according to the comprehensive risk value, trigger the hierarchical early warning signal, trigger the sound and light prompt signal for low risk level, trigger the voice prompt signal for medium risk level, trigger the device operation stop signal for high risk level, and dynamically adjust the early warning trigger condition and response logic through real-time feedback mechanism;

[0079] Behavior adjustment suggestion module: combined with the comprehensive risk value and the multi-level spatio-temporal feature matrix, the optimal adjustment path is predicted by using quantum probability behavior prediction algorithm, specific behavior adjustment suggestions are generated, and the suggestions are delivered to the workers through visual interface and voice prompt;

[0080] System closed loop optimization module: based on real-time event records and historical data, the time difference embedding rule, Bayesian inference model and adaptive multi-objective risk optimization algorithm are dynamically optimized by using progressive learning algorithm, the feature extraction rule and risk assessment logic are continuously updated, and the closed loop optimization process is constructed.

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

[0082] The present application collects the behavior data of workers, the running state of equipment and environmental parameters through a multi-modal sensor network, and generates a multi-level spatio-temporal feature matrix by using time difference embedding algorithm and dynamic scene influence factors, realizes dynamic modeling and feature extraction of work scene, significantly improves the description ability of the system to complex correlation in work environment, combined with Bayesian inference model and adaptive multi-objective optimization algorithm, can dynamically calculate regional risk factor and comprehensive risk value, generate real-time updated risk level, effectively improve the accuracy and real-time of risk assessment.

[0083] The application proposes a hierarchical early warning response mechanism based on comprehensive risk value, provides flexible and diverse early warning response measures through low-risk sound and light prompts, medium-risk voice reminders and high-risk device operation stop, avoids the problem of excessive early warning or missed reports in the traditional single alarm mode, and simultaneously, the system generates specific behavior adjustment suggestions through a quantum probability behavior prediction algorithm to guide the operating personnel to timely optimize the operation behavior, thereby further reducing the potential risk, compared with the prior art, the application not only realizes intelligent management of the whole process from risk identification to early warning triggering, but also makes up for the deficiency of the prior art in adjustment and guidance after responding to the risk.

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

[0085] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, together with the embodiments of the application, for explaining the application, and do not constitute a limitation on the application. In the drawings:

[0086] Fig. 1 The flowchart of the large container crane intelligent early warning system based on personnel behavior analysis proposed by the application;

[0087] Fig. 2 The schematic diagram of the behavior adjustment suggestion module of the application generating the optimal adjustment path through the quantum probability behavior prediction algorithm;

[0088] Fig. 3 The flowchart of the progressive learning algorithm in the system closed-loop optimization module of the application optimizing the feature extraction rule and the model parameter. DETAILED DESCRIPTION

[0089] The application will now be further described in detail in conjunction with the drawings. These drawings are all simplified schematic diagrams, and only illustrate the basic structure of the application in a schematic manner, and therefore only show the components related to the application.

[0090] REFERENCE Figs. 1-3 The large container crane intelligent early warning system based on personnel behavior analysis includes the following steps:

[0091] S1, a multi-modal sensor network is arranged in the crane operation area, the lifting path and the surrounding environment, the sensors include a behavior trajectory tracking module, an environment state monitoring module and a device running state acquisition module, behavior data, environment parameters and device state information are collected in real time through the sensor network to construct a dynamic data set;

[0092] S2, constructing a behavior sequence feature vector using a time difference embedding algorithm based on a dynamic data set, aggregating the association between the behavior trajectory and the device state through the feature vector, and generating a multi-level spatio-temporal feature matrix in combination with a dynamic scene influence factor;

[0093] S3, using a regional risk dynamic allocation algorithm based on Bayesian inference, combining the spatio-temporal feature matrix and the risk zoning rules of the work area, dynamically calculating the regional risk factor, calculating the potential risk probability of behavior and device operation through time series inference of continuous data, and generating the corresponding regional risk weight distribution;

[0094] S4, integrating the regional risk factor and the global risk of the work through an adaptive multi-objective risk optimization algorithm, dynamically calculating the comprehensive risk value, and the comprehensive risk value is weighted and corrected for high-risk behavior through an optimization objective function, generating the risk level of the current work scene, and the risk level is divided into low, medium and high;

[0095] S5, triggering a hierarchical early warning response according to the risk level of the comprehensive risk value, the hierarchical early warning including an audible and visual prompt signal for low risk, a voice reminder signal for medium risk, and an operation stop signal for high risk, and dynamically adjusting the early warning response strategy through a real-time feedback mechanism;

[0096] S6, after the early warning signal is triggered, combining the multi-level spatio-temporal feature matrix and the comprehensive risk value, generating a personnel behavior adjustment suggestion through a quantum probability behavior prediction algorithm, the algorithm inferring the optimal adjustment path according to the probability distribution of the operation trajectory, and delivering it to the workers through a visual interface and a voice prompt method;

[0097] S7, based on real-time event records and historical data, using a progressive learning algorithm to jointly optimize the time difference embedding rules, the Bayesian inference model and the adaptive multi-objective risk optimization algorithm, dynamically adjusting the feature extraction rules and the risk evaluation logic, optimizing the adaptability of the system in different work environments, and forming a closed-loop optimization process.

[0098] In the embodiment, the S2 specifically includes:

[0099] S21, decomposing a dynamic data set D = {d1, d2, …, d n} into behavior trajectory data B t , device state data E t and environmental parameter data P t , and dividing the data set according to a time window mechanism T, each time window containing multi-modal data of behavior trajectory, device state and environmental parameters;

[0100] S22, within each time window, the dynamic characteristics of the behavior trajectory, device state and environmental parameters are extracted using a time difference embedding algorithm:

[0101] ΔX k = x t+k X t , (X ∈ {B, E, P});

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

[0103] S23, embedding the time difference characteristics into the behavior sequence feature vector, defining the feature vector as Fk, and the feature vector F k expresses the dynamic correlation characteristics of the behavior trajectory, device state and environmental parameters within the time window;

[0104] S24, combining the influence factors of the dynamic scene, defining the scene weight matrix W = [w b , w e , w p ], and the weight factor w b , w e , w p is optimized and adjusted according to the dynamic characteristics of each data dimension and the multi-dimensional scene influence factor:

[0105]

[0106] The feature vector Fk = [ΔB k , ΔE k , ΔP k ] is optimized by the weight matrix w, and the matrix expression of the weighted feature vector Vk is:

[0107]

[0108] S25, the weighted feature vector V k generated within each time window is aggregated in time sequence order to form a time sequence feature set, and the time sequence feature set expresses the dynamic change characteristics of the behavior trajectory, device state and environmental parameters within multiple time windows;

[0109] S26, based on the time sequence feature set, a multi-level space-time feature matrix M is constructed, the feature matrix is transverse to the time sequence dimension, and longitudinal to the correlation characteristics of the behavior trajectory, device state and environmental parameters, and the feature matrix is a multi-modal expression of the behavior trajectory, device state and environmental parameters.

[0110] In this embodiment, the S3 specifically includes:

[0111] S31, dividing the work area into a risk area set R = {r1, r2, …, rn}, each risk area r i including behavior trajectory data, equipment state data and environmental parameter data. In combination with the generated spatio-temporal feature matrix M, the corresponding dynamic features in the matrix are mapped to each risk area R i ;

[0112] S32, based on the area feature set R i , the risk factor f of each area is dynamically calculated using the Bayesian inference model i :

[0113]

[0114] Where P(H|E) represents the probability of the region occurring a dangerous event within a specific time window, which is dynamically inferred in combination with the prior probability P(H) and the conditional probability of real-time feature data E;

[0115] S33, in combination with the risk factor change trend within the continuous time window, the potential dangerous probability of behavior and equipment operation is predicted using the time series inference model:

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

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

[0118] S34, in combination with the area risk factor f i (t), the area risk weight distribution W R = {w1, w2,, w n} is generated, and the area risk weight w i dynamically reflects the risk distribution priority among areas.

[0119] In the embodiment, the S4 specifically includes:

[0120] S41, in combination with the generated area risk factor fi(t) and the area risk weight distribution W R , the multi-dimensional risk factor under the operation scene is extracted, the comprehensive risk parameter set is constructed, and the comprehensive risk parameter combines the behavior trajectory, the equipment state and the environmental interference characteristics, to provide input for the optimization objective function;

[0121] S42, define an adaptive multi-objective risk optimization objective function, which is used to dynamically integrate the area risk factor and the global risk of operation:

[0122]

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

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

[0125]

[0126] Among them, R c F' is the corrected overall risk value. k This represents high-risk behavioral characteristics within the set of behavioral trajectory features. Yk As a weighting factor;

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

[0128] S45. Based on the dynamic changes in the real-time operation scenario, adjust and optimize the adjustment factor λ and the high-risk behavior weight γ in the objective function. k This makes the overall risk value R c It accurately reflects the risk status of the work scenario.

[0129] In this embodiment, S5 specifically includes:

[0130] S51, Based on the comprehensive risk value R c The risk level classification rules set corresponding graded early warning trigger signals for the risk level of the work scenario. Low risk level triggers sound and light prompts, medium risk level triggers voice reminders to prompt operators to adjust their operations, and high risk level triggers equipment operation stop signals and sends emergency notifications to the remote management platform for management personnel to intervene.

[0131] S52. Based on the real-time trend of the comprehensive risk value, dynamically adjust the triggering conditions and response parameters of the graded early warning signals. For low-risk signals, adjust the prompting frequency and intensity; for medium-risk signals, optimize the warning effect by optimizing the content, repetition frequency and volume of the voice reminder; for high-risk signals, adjust the range and duration of equipment shutdown according to the urgency of the risk.

[0132] S53, By monitoring the comprehensive risk value R c The dynamic changes of risk value are investigated, and a real-time feedback mechanism is established. When the rate of change of risk value is lower than the preset threshold, the triggering rules, intensity and duration of the early warning signal are dynamically optimized.

[0133] S54, according to the risk level and the early warning signal trigger sequence, coordinating the execution time and trigger logic of different risk level signals, through priority rules, making high-risk signals execute preferentially, avoiding interference between low-risk signals and high-risk signals;

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

[0135] In this embodiment, S6 specifically includes:

[0136] S61, based on the generated multi-level space-time feature matrix and comprehensive risk value, a quantum probability behavior prediction model is established, which takes behavior trajectory features, device state features and dynamic scene interference factors as input, describes the dynamic change of behavior state through quantum superposition state, and forms a behavior state set;

[0137] S62, combine the current behavior trajectory and device state, and map it to the quantum behavior state set, and calculate the quantum probability distribution of each behavior state:

[0138]

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

[0140] S63, according to the dynamic quantum probability distribution, combined with the comprehensive risk value, the optimal adjustment path is predicted:

[0141]

[0142] Wherein, P opt represents the optimal adjustment path, combined with time continuity, behavior state priority w i and risk adjustment factor λ to determine the path optimization result;

[0143] S64, combined with the predicted optimal adjustment path, generate specific behavior adjustment suggestions, including optimization operation steps, adjustment behavior trajectory and change of working position, through visual interface or voice prompt mode, the adjustment suggestion is timely delivered to the job personnel;

[0144] S65, record the execution result of behavior adjustment suggestion and the change of comprehensive risk value, combine real-time feedback data to optimize the parameter setting of quantum probability behavior prediction model, through adjusting quantum probability distribution and path generation logic, optimize the adaptability of the model to complex dynamic scene and the accuracy of behavior prediction.

[0145] In this embodiment, S7 specifically includes:

[0146] S71, based on real-time event records and historical data, extract behavior trajectory features, device state data and environmental parameters, combine comprehensive risk values and regional risk factors to construct a joint optimization dataset D opt ;

[0147] S72, combined with the optimization dataset D opt , the size of the feature extraction window and the embedding dimension are dynamically adjusted using the progressive learning algorithm to form the optimized embedding rule. The rule adjusts the window T k and the dynamic scene weight factor w t , optimizes the time series features of the embedding:

[0148]

[0149] Where 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 change rate of the comprehensive risk value, and μ is the balance factor.

[0150] S73, based on the optimization dataset, dynamically correct the risk factor distribution in the Bayesian inference model, adjust the conditional probability and prior probability of the model to adapt to the change trend of real-time data and historical data, and adaptively optimize the conditional probability distribution through progressive learning to optimize the evaluation accuracy of the risk factor.

[0151] S74, combined with the high-risk behavior features in the real-time event records, adjust the weight factor and the adjustment factor in the adaptive multi-objective risk optimization algorithm to form the priority processing logic of the high-risk behavior, and the optimization model calculates the dynamic risk priority according to the weight distribution:

[0152]

[0153] Where P w is the dynamic priority distribution, f i is the risk factor, Δ t is the time interval, and α is the time weight adjustment factor.

[0154] S75, the optimized time difference embedding rule, Bayesian inference model and adaptive multi-objective risk optimization algorithm are fed back to the data processing module to form a closed-loop optimization process. Through continuous collection of real-time data and historical data, the optimization logic is updated, and the long-term adaptability of the system to complex dynamic scenes is optimized.

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

[0156] Multi-modal data acquisition module: Real-time collection of behavior data, equipment status and environmental parameters through a sensor network installed in the crane operation area, lifting path and surrounding environment, and construction of a dynamic data set;

[0157] Data processing module: Preprocessing and feature extraction of the multi-modal data set, and construction of a dynamic correlation model of behavior trajectory, equipment status and environmental parameters through a feature vector;

[0158] Behavior feature analysis module: Extraction of time series features of behavior trajectory, equipment status and environmental parameters using a time difference embedding algorithm, and generation of a multi-level spatio-temporal feature matrix combined with dynamic scene influence factors;

[0159] Dynamic risk assessment module: Dynamic calculation of regional risk factors using a Bayesian inference model combined with the spatio-temporal feature matrix and the division rules of the operation area, generation of a comprehensive risk value through an adaptive multi-objective risk optimization algorithm, and classification of the current operation scene as low risk, medium risk or high risk according to the risk level;

[0160] Hierarchical early warning response module: Triggering of a hierarchical early warning signal according to the comprehensive risk value, triggering of an audible and visual prompt signal for low risk level, triggering of a voice reminder signal for medium risk level, and triggering of a device operation stop signal for high risk level, and dynamic adjustment of the early warning trigger conditions and response logic through a real-time feedback mechanism;

[0161] Behavior adjustment suggestion module: Combination of the comprehensive risk value and the multi-level spatio-temporal feature matrix, speculation of the optimal adjustment path through a quantum probability behavior prediction algorithm, generation of specific behavior adjustment suggestions, and delivery of the suggestions to the operation personnel through a visual interface and voice prompts;

[0162] System closed-loop optimization module: Dynamic optimization of the time difference embedding rules, Bayesian inference model and adaptive multi-objective risk optimization algorithm using a progressive learning algorithm based on real-time event records and historical data, continuous updating of the feature extraction rules and risk assessment logic, and construction of a closed-loop optimization process.

[0163] Example 1:

[0164] In order to verify the feasibility of the application in implementation, the application is applied to a large container terminal of an international port as a test scene, the terminal covers an area of about 300,000 square meters, is equipped with 20 large container cranes, has a complex operation environment, and 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 operation personnel are responsible for crane operation, equipment inspection and ground guiding work, and the typical safety problems in the test scene include personnel entering high-risk areas without wearing safety equipment, abnormal operation of equipment and operation hazards caused by adverse environmental conditions.

[0165] In this scenario, the application realizes the real-time collection of the behavior of the operating personnel, the state of the equipment, and the environmental parameters by deploying a multi-modal sensor network. The sensor network covers the crane operating area, the lifting path, and the key components of the equipment, with a total of 120 sensor nodes, including 30 cameras, 20 equipment state monitoring sensors, and 70 environmental monitoring sensors. The sensor nodes collect and transmit multi-modal data in real time with a time window of 5 seconds, generating a dynamic data set.

[0166] After data collection is complete, the system uses a time difference embedding algorithm to extract dynamic features of the behavior trajectory of the operating personnel, correlates the behavior data with the equipment operating state and environmental parameters, and generates a multi-level spatio-temporal feature matrix. This matrix details the dynamic correlation of personnel behavior, equipment state, and environmental changes, providing important basic data support for the risk assessment module. In a one-time operating scenario, the system identifies through the feature matrix that a ground guide personnel is not wearing safety equipment and has entered a high-risk area less than 10 meters from the crane boom. Combined with feature data and risk zoning rules, the system uses a Bayesian inference model to dynamically calculate the risk factor of this area as 0.85, and determines the comprehensive risk value as 0.92 through an adaptive multi-objective optimization algorithm, determining it as a high-risk scenario.

[0167] Based on the comprehensive risk value, the system immediately triggers a high-risk level warning signal, and the audible and visual alarm in the crane operating room is started, with a voice prompt notifying the operating personnel to quickly evacuate the high-risk area. At the same time, the crane suspends the current lifting operation, and the remote management platform receives the emergency notification in real time, prompting the management personnel to intervene. As the ground guide personnel successfully evacuates the dangerous area according to the prompt path, the crane operation quickly resumes normal operation after automatically assessing the environmental safety. The entire warning response process takes only 1.8 seconds from detection to alarm triggering, effectively avoiding a potential safety accident.

[0168] In this scenario, the behavior adjustment suggestion module generates an optimized evacuation path based on the quantum probability behavior prediction algorithm. The system calculates the quantum probability distribution of the behavior state based on the current behavior trajectory of the operating personnel and the multi-level spatio-temporal feature matrix, infers the optimal adjustment path, and guides the operating personnel to bypass other potential dangerous areas. The adjustment suggestion is delivered to the operating personnel in the form of a voice prompt and a visual interface, allowing them to quickly leave the high-risk area and return to a safe zone. The operating personnel complete the evacuation within 7 seconds, which is 45% shorter than the traditional manual guidance method.

[0169] In a month-long test, the application verified multiple typical work scenarios, collected over 5 million data points, covered 3,500 crane lifting operations and 1,200 worker behavior data, the system detected 225 personnel violations, including 90 times of not wearing safety equipment, 75 times of entering high-risk areas, and 60 times of staying too long, the risk identification accuracy rate reached 98.7%, which was about 35% higher than the traditional equipment monitoring method, the average warning response speed was 1.8 seconds, which was 64% shorter than the average response time of existing systems, in addition, the simulated safety accident rate during the test period was reduced by 72%, significantly improving the safety of work.

[0170] Table 1 System performance table

[0171]

[0172] From the above table, it can be seen that the system of the application performs excellently in violation detection, which can detect 225 violations in a month-long test, including not wearing safety equipment, entering high-risk areas, and staying too long, etc. These behaviors are accurately identified through the system's multi-modal sensors and dynamic feature analysis, with an identification accuracy rate of 98.7%, which is much higher than the technical ability of existing systems that cannot effectively analyze personnel behavior. In terms of risk identification and response efficiency, the application significantly improves the timeliness of risk management, with an average risk identification time of 1.8 seconds and a warning response speed of 1.8 seconds, which is 64% faster than the 5 seconds of existing systems. Fast identification and response enable the system to timely trigger warning signals and notify workers, effectively preventing potential safety accidents; through the analysis of simulated scenarios, the application reduces the accident rate by 72%, fully demonstrating its practicality and reliability in safety protection. In addition, the system can generate behavior adjustment suggestions with high guidance effect, such as guiding workers to quickly evacuate high-risk areas, bypass other dangerous areas, or return to safe areas. These suggestions are generated through quantum probability behavior prediction algorithms, with a successful execution rate of 96.3% and an average completion time of 7 seconds, which is 45% shorter than the traditional manual guidance method.

[0173] The system of the application exhibits strong real-time risk identification capability, flexible warning response mechanism, and effective behavior adjustment suggestions through innovative behavior analysis and risk management technology in the test scenario. The test results prove that the application can solve the problems of lack of attention to personnel behavior, lagging risk assessment, and lack of adjustment suggestions in existing technology, significantly improving the safety and efficiency of crane operation, and providing a reliable intelligent safety management solution for port operation environment.

[0174] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A large container crane intelligent early warning method based on personnel behavior analysis, characterized in that, Comprise the following steps: S1, in the crane operation area, hoisting path and surrounding environment layout multi-modal sensor network, the sensor includes behavior trajectory tracking module, environment state monitoring module and equipment running state acquisition module, through the sensor network real-time collection behavior data, environmental parameters and equipment state information, construct dynamic data set; S2, based on the dynamic data set, the time difference embedding algorithm is used to construct the behavior sequence feature vector, the association relationship between the behavior trajectory and the equipment state is aggregated through the feature vector, and a multi-level space-time feature matrix is generated combined with the dynamic scene influence factor; Specifically comprising: S21, Dynamic Data Set Decomposed into behavioral trajectory data Equipment status data and environmental parameter data Data sets are processed using a time window mechanism. The data is segmented, with each time window containing multimodal data including behavioral trajectories, device status, and environmental parameters; S22, in each time window, the dynamic characteristics of the behavior trajectory, the equipment state and the environmental parameters are extracted by using the time difference embedding algorithm: ； wherein, represents the dynamic change characteristics of the data dimension within the time window ; S23, embedding the time difference feature into the behavior sequence feature vector, defining the feature vector as , the feature vector expresses the dynamic correlation characteristics of the behavior trajectory, device state and environmental parameters in the time window; S24, combine the influence factors of dynamic scenes, define the scene weight matrix , weight factor Optimize and adjust according to the dynamic characteristics of each data dimension and the multi-dimensional scene influence factor: ； By the weight matrix On the eigenvector Optimization, weighted eigenvector The matrix expression is: ； S25, generating a weighted feature vector in each time window aggregating in time sequence order to form a time sequence feature set, the time sequence feature set expressing dynamic change characteristics of the behavior trajectory, the device state and the environmental parameter in the plurality of time windows; S26, based on the time series feature set, constructing a multi-level space-time feature matrix The feature matrix has a time series dimension in the horizontal direction and a correlation characteristic of the behavior trajectory, the device state and the environmental parameter in the vertical direction, and the feature matrix is a multi-modal expression of the behavior trajectory, the device state and the environmental parameter. S3, using the regional risk dynamic allocation algorithm based on Bayesian inference, combining the space-time feature matrix and the risk zoning rules of the operation area, dynamically calculating the regional risk factor, through the time series inference of continuous data, calculating the potential danger probability of behavior and equipment operation, and generating the corresponding regional risk weight distribution; S4, through the adaptive multi-objective risk optimization algorithm, integrating the regional risk factor and the global risk of operation, dynamically calculating the comprehensive risk value, the comprehensive risk value is weighted and corrected to high-risk behavior through the optimization objective function, generating the risk level of the current operation scene, the risk level is divided into low, medium and high three categories; S5, according to the risk level of the comprehensive risk value, trigger the graded warning response, the graded warning includes sound and light prompt signal of low risk, voice prompt signal of medium risk and operation stop signal of high risk, through the real-time feedback mechanism, dynamically adjust the warning response strategy; S6, after the warning signal is triggered, combining the multi-level space-time feature matrix and the comprehensive risk value, generating personnel behavior adjustment suggestion through quantum probability behavior prediction algorithm, the algorithm according to the probability distribution of operation trajectory infers the optimal adjustment path, and transmits to the operation personnel through visual interface and voice prompt mode; S7, based on real-time event record and historical data, using progressive learning algorithm to jointly optimize the time difference embedding rule, Bayesian inference model and adaptive multi-objective risk optimization algorithm, dynamically adjusting the feature extraction rule and risk evaluation logic, optimizing the adaptability of the system in different operation environments, forming a closed-loop optimization process.

2. The method of claim 1, wherein the method is based on a personnel behavior analysis of the large container crane. The S3 specifically comprises: S31, dividing the work area into a risk region set Each risk region includes behavior trajectory data, equipment state data and environmental parameter data; in combination with the generated space-time feature matrix Map the corresponding dynamic features in the matrix to each risk region ; S32、based on the regional feature set , dynamically calculate the risk factor of each region by using the Bayesian inference model : ； wherein, represents the probability of a dangerous event occurring in the region within a specific time window, combined with the prior probability and the conditional probability of real-time feature data are dynamically inferred; S33, combining the risk factor change trend in the continuous time window, using the time series inference model to predict the potential danger probability of behavior and equipment operation: ； wherein, represents the current time a risk factor, is a historical weight factor; S34, combine regional risk factors generate regional risk weight distribution regional risk weight dynamically reflect risk distribution priorities between regions.

3. The method of claim 1, wherein the method is based on a personnel behavior analysis of the large container crane. The S4 specifically comprises: S41, combine the generated regional risk factors and regional risk weight distribution , extract multi-dimensional risk factors in the operation scene, construct a comprehensive risk parameter set, and provide input for the optimization objective function by combining behavior trajectory, equipment state and environmental disturbance characteristics; S42, define the adaptive multi-objective risk optimization objective function, used to dynamically integrate the regional risk factor and the global risk of operation: ； wherein, is a comprehensive risk value, and are a regional risk factor and a weight, respectively, is a scenario disturbance factor, is a regulation factor; S43, through the optimization objective function, the high-risk behavior is weighted and corrected: ； wherein, is the modified overall risk value, represents a high-risk behavior feature in the behavior trajectory feature set, is a weighting factor; S44, according to the corrected comprehensive risk value, the operation scene is divided into low risk, medium risk and high risk three categories, the risk level division rule is dynamically adjusted combined with the scene characteristics; S45, adjust the adjustment factor in the optimization objective function combined with the dynamic change of real-time operation scene and high-risk behavior weight , make the comprehensive risk value accurately reflect the risk state of the operation scene.

4. The method of claim 1, wherein the method is based on a personnel behavior analysis of the large container crane. The S5 specifically comprises: S51、 according to the comprehensive risk value and risk level classification rules, the risk level of the operation scene is set with corresponding classified warning trigger signals, the low risk level triggers an audible and visual prompt signal, the medium risk level triggers a voice prompt signal, prompting the operator to adjust the operation, the high risk level triggers a device operation stop signal, and an emergency notification is sent to the remote management platform for management personnel intervention; S52, combined with the real-time change trend of the comprehensive risk value, dynamically adjust the triggering conditions and response parameters of the hierarchical early warning signal, adjust the prompt frequency and intensity of the low-risk signal, optimize the content, repetition frequency and volume of the voice reminder of the medium-risk signal to optimize the warning effect, and adjust the range and duration of the device stop according to the risk emergency level of the high-risk signal; S53, by monitoring the dynamic change of the comprehensive risk value establish a real-time feedback mechanism, when the risk value change rate is lower than the preset threshold, dynamically optimize the triggering rule, intensity and duration of the early warning signal; S54, according to the risk level and the triggering sequence of the early warning signal, coordinate the execution time and triggering logic of different risk level signals, and make the high-risk signal execute preferentially through priority rules to avoid interference between low-risk signals and high-risk signals; S55, record the triggering type, execution time and response effect of each early warning signal, and optimize the triggering rules and response strategies of the early warning signal through comprehensive analysis of historical data and real-time feedback.

5. The method of claim 1, wherein the method is based on a personnel behavior analysis of the large container crane. S6 specifically includes: S61, based on the generated multi-level spatio-temporal feature matrix and the comprehensive risk value, a quantum probability behavior prediction model is established, which takes behavior trajectory features, device state features and dynamic scene interference factors as inputs, describes the dynamic change of behavior state through quantum superposition state, and forms a behavior state set; S62, combine the current behavior trajectory and device state, and map it to the quantum behavior state set to calculate the quantum probability distribution of each behavior state: ； wherein, represents the normalized probability of being in a state at time t. S63, according to the dynamic quantum probability distribution, combined with the comprehensive risk value, the optimal adjustment path is predicted: ； wherein, represents the optimal adjustment path, incorporating time continuity, behavior state priority and risk adjustment factor determines the path optimization result; S64, combined with the predicted optimal adjustment path, specific behavior adjustment suggestions are generated, including optimization operation steps, adjustment behavior trajectory and change of working position, and the adjustment suggestions are timely delivered to the workers through visual interface or voice prompt mode; S65, record the execution result of the behavior adjustment suggestion and the change of the comprehensive risk value, optimize the parameter setting of the quantum probability behavior prediction model combined with the real-time feedback data, and optimize the adaptability of the model to complex dynamic scenes and the accuracy of behavior prediction through adjusting the quantum probability distribution and path generation logic.

6. The method of claim 1, wherein the method is based on a personnel behavior analysis of a large container crane. S7 specifically includes: S71, based on real-time event records and historical data, extract behavior trajectory features, device state data and environmental parameters, combine comprehensive risk values and regional risk factors to build a joint optimization dataset ; S72, combine the optimization dataset , dynamically adjust the size of the feature extraction window and the embedding dimension using the progressive learning algorithm, form the optimized embedding rule, which adjusts the window and dynamic scene weight factor , optimize the time series features of embedding: ； wherein, is the optimized time window, is the original time window, is the dynamic scenario weight factor, is the rate of change of the integrated risk value, is the balancing factor; S73, based on the optimized data set, dynamically correct the risk factor distribution in the Bayesian inference model, adjust the conditional probability and prior probability of the model, and make it adapt to the change trend of real-time data and historical data, and adaptively optimize the conditional probability distribution through progressive learning to optimize the evaluation accuracy of the risk factor; S74, combined with the high-risk behavior characteristics in the real-time event record, adjust the weight factor and adjustment factor in the adaptive multi-objective risk optimization algorithm to form the priority processing logic of high-risk behaviors, and the model calculates the dynamic risk priority according to the weight distribution: ； wherein, is a dynamic priority distribution, is a risk factor, is a time interval, is a time weight adjustment factor; S75, feedback the optimized time difference embedding rule, Bayesian inference model and adaptive multi-objective risk optimization algorithm to the data processing module to form a closed-loop optimization process, continuously collect real-time data and historical data to update the optimization logic, and optimize the long-term adaptability of the system to complex dynamic scenes.

7. An intelligent warning system for large container cranes based on personnel behavior analysis, characterized in that, The modules include: Multi-modal data acquisition module: through the sensor network installed in the crane operation area, lifting path and surrounding environment, real-time acquisition of behavior data, device state and environmental parameters, and construction of dynamic data set; Data processing module: preprocess and feature extraction on multi-modal data set, construct dynamic correlation model of behavior trajectory, device state and environmental parameters through feature vector; specifically including: dynamic data sets Decomposed into behavioral trajectory data Equipment status data and environmental parameter data Data sets are processed using a time window mechanism. The data is segmented, with each time window containing multimodal data including behavioral trajectories, device status, and environmental parameters; Within each time window, use time difference embedding algorithm to extract dynamic features of behavior trajectory, device state and environmental parameters: ； wherein, represents the dynamic change characteristics of the data dimension within the time window ; The time difference feature is embedded into the behavior sequence feature vector, and a feature vector is defined as , a feature vector expresses the dynamic correlation characteristics of the behavior trajectory, device state and environmental parameters in the time window; Incorporating dynamic scene impact factors, define scene weight matrix , weight factors Optimally adjusted according to dynamic characteristics of each data dimension and multi-dimensional scene impact factors: ； By the weight matrix To the eigenvector Optimization, weighted eigenvector The matrix expression is: ； generate a weighted feature vector within each time window aggregating in time sequence order to form a time sequence feature set, the time sequence feature set expressing dynamic change characteristics of the behavior track, the device state and the environmental parameter within the multiple time windows; Based on the time series feature set, a multi-level space-time feature matrix is constructed The feature matrix has time series dimension in the horizontal direction and the associated characteristics of the behavior trajectory, the device state and the environmental parameters in the vertical direction, and the feature matrix is a multi-modal expression of the behavior trajectory, the device state and the environmental parameters; Behavior feature analysis module: use time difference embedding algorithm to extract time sequence features of behavior trajectory, device state and environmental parameters, generate multi-level spatio-temporal feature matrix combined with dynamic scene influence factors; Dynamic risk assessment module: combine spatio-temporal feature matrix and division rules of work area, use Bayesian inference model to dynamically calculate regional risk factors, generate comprehensive risk value through adaptive multi-objective risk optimization algorithm, and divide current work scene into low risk, medium risk or high risk according to risk level; Hierarchical early warning response module: trigger hierarchical early warning signal according to comprehensive risk value, trigger sound and light prompt signal for low risk level, trigger voice reminder signal for medium risk level, trigger device operation stop signal for high risk level, and dynamically adjust early warning trigger condition and response logic through real-time feedback mechanism; Behavior adjustment suggestion module: combine comprehensive risk value and multi-level spatio-temporal feature matrix, predict optimal adjustment path through quantum probability behavior prediction algorithm, generate specific behavior adjustment suggestion, and deliver it to workers through visual interface and voice prompt; System closed-loop optimization module: based on real-time event record and historical data, use progressive learning algorithm to dynamically optimize time difference embedding rules, Bayesian inference model and adaptive multi-objective risk optimization algorithm, continuously update feature extraction rules and risk assessment logic, and build closed-loop optimization process.

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