A method and system for controlling chemical operations in student laboratories

Abstract

The present invention relates to the technical field of chemical operation control, and proposes a method and system for controlling chemical operation in student laboratories, comprising the following steps: acquiring real-time image data and environmental parameter data; identifying and numbering students respectively; identifying the real-time image data using a chemical operation recognition model to generate real-time operation status information; identifying the real-time operation status information based on a multi-dimensional risk assessment model to obtain the chemical operation risk level of each student; identifying the chemical operation risk level through a decision control model, activating different early warning signals, and executing safety control measures according to the early warning signals; extracting the operation characteristics of each student through a behavior analysis model to generate a student operation feature vector, and updating the student operation behavior feature library to generate an experimental operation score and operation improvement suggestions corresponding to each student. The present invention improves the safety management level of the laboratory and realizes the control of student chemical operations.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical operation control, and in particular to a method and system for chemical operation control in a student laboratory. Background Art

[0002] Safety management of chemical handling in student laboratories is a crucial component of laboratory safety management, and its effectiveness is directly linked to the quality of experimental instruction and student safety. Chemical handling presents numerous safety hazards, such as improper operation, inadequate protective equipment, and environmental anomalies. If these issues are not promptly identified and addressed, they can lead to safety accidents.

[0003] Existing technologies generally rely on single-source video surveillance or sensor detection methods to manage student laboratory safety. While these single-source detection methods can capture certain data, they cannot fully reflect the safety status of students' chemical operations. Furthermore, existing technologies lack systematic risk assessment and intelligent early warning mechanisms, often relying on simple manual inspections, resulting in inefficient management. The information retained from students' laboratory operations after data collection is insufficiently analyzed, and personalized chemical handling recommendations are not provided to students. This makes it impossible to accurately analyze and provide personalized guidance on individual students' chemical handling behaviors. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for controlling chemical operations in student laboratories, which solves the problem that the existing technology lacks analysis of the information retained by students' experimental operations after the data collection process, does not provide personalized chemical operation suggestions for students, and cannot achieve accurate analysis and personalized guidance of students' individual chemical operation behaviors.

[0005] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a method for controlling chemical operations in a student laboratory, comprising the following steps:

[0006] Through monitoring cameras and sensor networks, real-time image data and environmental parameter data in the laboratory are obtained respectively;

[0007] Identify and number students entering the laboratory;

[0008] Using a chemical operation recognition model to identify the real-time image data, obtain student operation behavior information and protective equipment wearing information, and combine the environmental parameter data, the operation behavior information, and the protective equipment wearing information to generate real-time operation status information;

[0009] Identifying the real-time operation status information based on a multi-dimensional risk assessment model to obtain a chemical operation risk level for each student;

[0010] Identifying the chemical operation risk level through a decision control model, activating corresponding early warning signals when the chemical operation risk level reaches different preset thresholds, and executing safety control measures based on the early warning signals;

[0011] Establishing a student operation behavior feature library, storing each student's ID, real-time operation status information, and environmental parameter data in the student operation behavior feature library, extracting each student's operation characteristics through a behavior analysis model, generating a student operation feature vector, and updating the student operation behavior feature library based on the student operation feature vector;

[0012] Based on the student operation behavior feature library, generate experimental operation scores and operation improvement suggestions for each student.

[0013] Based on the above technical solution, preferably, the real-time image data and environmental parameter data in the laboratory are obtained through monitoring cameras and sensor networks, specifically including:

[0014] A dual-camera surveillance camera is installed at each laboratory bench in the laboratory. The dual-camera surveillance camera includes a wide-angle surveillance camera and a close-up surveillance camera. The wide-angle surveillance camera captures the overall image of the laboratory bench, and the close-up camera captures detailed images of the chemical operation area;

[0015] A sensor network is arranged in the chemical operation area to collect environmental parameter data. The sensor network includes a chemical concentration sensor, a weight sensor, a pressure sensor and a temperature sensor. The chemical concentration sensor is used to monitor the concentration of chemicals in the air, the weight sensor is used to monitor the amount of chemicals used, the pressure sensor is used to monitor the operation force, and the temperature sensor is used to monitor the reaction temperature change. The chemical operation area includes a laboratory bench, a reagent cabinet and a fume hood.

[0016] Based on the above technical solution, preferably, the chemical operation recognition model is used to identify the real-time image data to obtain the student's operation behavior information and protective equipment wearing information, and the environmental parameter data, the operation behavior information and the protective equipment wearing information are combined to generate real-time operation status information, specifically including:

[0017] A chemical operation recognition model is constructed using a deep learning model. Real-time image data is recognized based on the chemical operation recognition model to generate operation behavior information. Each feature vector element in the operation behavior information represents the recognition probability of a specific operation behavior.

[0018] The calculation formula of the chemical operation identification model is:

[0019] ;

[0020] ;

[0021] ;

[0022] in, For a given image The following is determined to be The probability of class operation behavior, To extract images through convolutional neural networks The image features, is an exponential function, For the Normalized probability of class operation behavior, For the Normalized probability of class operation behavior, is the number of categories of operation behaviors, is the timing adjustment factor, is the environmental constraint factor, is the time series smoothing coefficient, For a given image The following is determined to be The probability of class operation behavior, For a given image The following is determined to be The probability of class operation behavior, and They are Moment and Images of the moment, For the The normalized value of the environmental parameter, for The corresponding influence coefficient is is the number of environmental parameters;

[0023] The wearing status of protective equipment is detected through image processing algorithms, and protective equipment wearing information is generated. In combination with environmental parameter data, real-time operation status information is generated through weighted algorithms;

[0024] The calculation formula of the real-time operation status information is:

[0025] ;

[0026] in, is the normalization factor, For real-time operation status information, The first elements, The first The weight coefficient of each element, is the number of elements of the operation behavior information, Information on wearing protective equipment elements, Information on wearing protective equipment The weight coefficient of each element, Number of elements for protective equipment wearing information, The environmental parameter data elements, The environmental parameter data The weight coefficient of each element, The number of elements in the environment parameter data.

[0027] Based on the above technical solution, preferably, the real-time operation status information is identified based on the multi-dimensional risk assessment model to obtain the chemical operation risk level of each student, specifically including:

[0028] Dynamically extract features from real-time operation status information through a spatiotemporal attention mechanism to construct a risk feature vector, which includes an operation behavior risk feature sub-vector, a protective equipment risk feature sub-vector, and an environmental risk feature sub-vector;

[0029] A multi-level risk assessment model is established using the analytic hierarchy process (AHP). The comprehensive impact of each risk characteristic is calculated through a combined weight algorithm. Combined with the risk event records in the historical database, the risk assessment parameters are dynamically adjusted to output the chemical operation risk level.

[0030] Based on the above technical solution, preferably, the chemical operation risk level is identified by the decision control model, and when the chemical operation risk level reaches different preset thresholds, a corresponding early warning signal is activated, and safety control measures are executed according to the early warning signal, specifically including:

[0031] Adoption Depth Q The learning network constructs an early warning decision model, taking the chemical operation risk level, environmental status, and historical warning records as input, and generates an early warning decision vector through state-action value evaluation;

[0032] The calculation formula of the early warning decision vector is:

[0033] ;

[0034] in, is the early warning decision vector, 、 、 are the weight coefficients of the state-action value function, the temporal correlation function, and the environmental sensitivity function, respectively. For the decision-making state Q value, For the The immediate reward of a decision state, is the number of decision states, is the temperature parameter, for sigmoid activation function, For the The impact factor of historical warning events, is the duration of historical warning events, is the state-action time decay coefficient, For the current moment, For historical moments, For the The value of an environmental parameter, For the The safety threshold of environmental parameters, is the hyperbolic tangent activation function, For the The sensitivity coefficient of each environmental parameter, is the number of environmental parameters;

[0035] A multi-level warning execution mechanism is established based on fuzzy control theory, which triggers warning signals of different levels according to the threshold of the warning decision vector and executes the safety control measures corresponding to the warning level of the warning signal;

[0036] The rules for determining the warning level include: , triggering the warning signal corresponding to the low-level warning, and giving an audible and visual warning; when , triggering the warning signal corresponding to the intermediate warning and starting ventilation adjustment; when , trigger the warning signal corresponding to the advanced warning and implement emergency response measures.

[0037] Based on the above technical solution, preferably, the step of establishing a student operation behavior feature library, storing each student's ID, real-time operation status information, and environmental parameter data in the student operation behavior feature library, extracting each student's operation feature through a behavior analysis model, generating a student operation feature vector, and updating the student operation behavior feature library based on the student operation feature vector, specifically includes:

[0038] Establish a student operation behavior feature library, index the real-time operation status information and environmental parameter data by timestamp and student number, and store them in the student operation behavior feature library, use the behavior analysis model to process the stored data, identify the student's operation mode and behavior characteristics, generate student operation feature vectors, and update the student operation behavior feature library according to the change trend of the student operation feature vectors.

[0039] On the basis of the above technical solution, preferably, the generation of experimental operation scores and operation improvement suggestions corresponding to each student based on the student operation behavior feature library specifically includes:

[0040] A multi-layered scoring mechanism is used to comprehensively evaluate students from multiple dimensions, including operational standardization, safety awareness, and emergency response capabilities. A weighted fusion algorithm is used to calculate each student's experimental operation score, and a student operation ability development curve is generated based on historical data analysis.

[0041] Construct a chemical operation knowledge graph, which includes standard operating procedures, safety specifications and expert experience. Combined with students' personal characteristics and operating habits, targeted improvement suggestions are generated through rule reasoning and similarity matching algorithms, and the improvement suggestions are dynamically optimized based on student feedback.

[0042] In a second aspect, the present invention further provides a student laboratory chemical operation management and control system, the system comprising:

[0043] The data acquisition module is used to obtain real-time image data and environmental parameter data in the laboratory through monitoring cameras and sensor networks respectively;

[0044] Identification and numbering module, used to identify and number students entering the laboratory;

[0045] An image recognition module is used to identify the real-time image data using a chemical operation recognition model to obtain the student's operation behavior information and protective equipment wearing information, and generate real-time operation status information by combining the environmental parameter data, the operation behavior information, and the protective equipment wearing information;

[0046] A risk level module is used to identify the real-time operation status information based on a multi-dimensional risk assessment model to obtain the chemical operation risk level of each student;

[0047] An operation warning module, configured to identify the chemical operation risk level through a decision control model, activate a corresponding warning signal when the chemical operation risk level reaches different preset thresholds, and execute safety control measures based on the warning signal;

[0048] An operation analysis module is used to establish a student operation behavior feature library, store each student's ID, real-time operation status information and environmental parameter data in the student operation behavior feature library, extract each student's operation characteristics through a behavior analysis model, generate a student operation feature vector, and update the student operation behavior feature library based on the student operation feature vector;

[0049] The operation improvement module is used to generate experimental operation scores and operation improvement suggestions for each student based on the student operation behavior feature library.

[0050] In a third aspect, the present invention further provides an electronic device comprising: at least one processor, at least one memory, a communication interface, and a bus;

[0051] The processor, memory, and communication interface communicate with each other through the bus. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to implement steps of a student laboratory chemical operation control method.

[0052] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer instructions, wherein the computer instructions enable a computer to implement steps of a student laboratory chemical operation control method.

[0053] The student laboratory chemical operation control method and system of the present invention has the following beneficial effects compared with the prior art:

[0054] (1) By integrating surveillance cameras and sensor networks, combined with chemical operation recognition models and multi-dimensional risk assessment models, the system can identify students' operation behaviors and protective equipment wearing conditions, generate real-time operation status information, and evaluate the risk level of chemical operations. Based on database storage and behavior analysis models, it can extract students' operation characteristics, establish an operation behavior feature library, and then generate personalized experimental operation scores and improvement suggestions. This realizes the real-time collection and monitoring of images and environmental data in the laboratory, improves the safety management level and efficiency of the laboratory, and realizes the comprehensive and intelligent management and control of students' chemical operations.

[0055] (2) A chemical operation recognition model was constructed through a deep learning model to identify students' operation behavior information and generate the recognition probability of each feature vector element, which improved the accuracy of operation behavior recognition and ensured the safety of laboratory operations. Combined with the image processing algorithm to detect the wearing of protective equipment, the reliability of laboratory safety management was further enhanced;

[0056] (3) A deep Q-learning network is used to construct an early warning decision model, and an early warning decision vector is generated through state-action value evaluation. A multi-level early warning execution mechanism is established based on fuzzy control theory to dynamically identify the risk level of chemical operations. When the risk reaches different preset thresholds, the corresponding early warning signals and safety control measures are triggered, which improves the accuracy and response speed of the early warning, ensures the effectiveness of laboratory operations, and can effectively reduce the risk of laboratory accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 This is a flow chart of a student laboratory chemical operation control method of the present invention;

[0059] Figure 2 This is a structural diagram of a student laboratory chemical operation control system of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] See also Figure 1 The present invention provides a method for controlling chemical operation in a student laboratory, comprising the following steps:

[0062] Through monitoring cameras and sensor networks, real-time image data and environmental parameter data in the laboratory are obtained respectively;

[0063] Identify and number students entering the laboratory;

[0064] Using a chemical operation recognition model to identify the real-time image data, obtain student operation behavior information and protective equipment wearing information, and combine the environmental parameter data, the operation behavior information, and the protective equipment wearing information to generate real-time operation status information;

[0065] Identifying the real-time operation status information based on a multi-dimensional risk assessment model to obtain a chemical operation risk level for each student;

[0066] Identifying the chemical operation risk level through a decision control model, activating corresponding early warning signals when the chemical operation risk level reaches different preset thresholds, and executing safety control measures based on the early warning signals;

[0067] Establishing a student operation behavior feature library, storing each student's ID, real-time operation status information, and environmental parameter data in the student operation behavior feature library, extracting each student's operation characteristics through a behavior analysis model, generating a student operation feature vector, and updating the student operation behavior feature library based on the student operation feature vector;

[0068] Based on the student operation behavior feature library, generate experimental operation scores and operation improvement suggestions for each student.

[0069] Specifically, this embodiment integrates surveillance cameras and sensor networks, combines chemical operation recognition models and multi-dimensional risk assessment models, identifies students' operating behaviors and protective equipment wearing conditions, generates real-time operation status information, and evaluates the risk level of chemical operations. Based on database storage and behavior analysis models, it extracts student operation characteristics, establishes an operation behavior feature library, and then generates personalized experimental operation scores and improvement suggestions, realizing real-time collection and monitoring of images and environmental data in the laboratory, improving the safety management level and efficiency of the laboratory, and realizing all-round and intelligent management and control of students' chemical operations.

[0070] The real-time image data and environmental parameter data in the laboratory are obtained through monitoring cameras and sensor networks, specifically including:

[0071] Each lab bench in the laboratory is equipped with dual surveillance cameras. These cameras include a wide-angle surveillance camera and a close-up surveillance camera. The wide-angle surveillance camera captures the overall image of the lab bench, while the close-up camera captures detailed images of the chemical operation area. Both cameras have infrared night vision capabilities to ensure image acquisition quality in low light conditions.

[0072] A sensor network is arranged in the chemical operation area to collect environmental parameter data. The sensor network includes a chemical concentration sensor, a weight sensor, a pressure sensor and a temperature sensor. The chemical concentration sensor is used to monitor the concentration of chemicals in the air, the weight sensor is used to monitor the amount of chemicals used, the pressure sensor is used to monitor the operation force, and the temperature sensor is used to monitor the reaction temperature change. The chemical operation area includes a laboratory bench, a reagent cabinet and a fume hood.

[0073] Specifically, this embodiment realizes the unification of macro-overall monitoring and micro-detail monitoring of experimental operations by configuring dual-position surveillance cameras, thereby improving the comprehensiveness of monitoring. The infrared night vision function ensures continuous monitoring capabilities under low-light conditions and guarantees the all-weather availability of the monitoring system.

[0074] Through the coordinated cooperation of multiple sensors, all-round monitoring of key parameters in the chemical operation process is achieved. Chemical concentration sensors are used to monitor air safety, weight sensors are used to accurately control chemical usage, pressure sensors are used to monitor operational compliance, and temperature sensors are used to prevent dangerous reactions.

[0075] Comprehensive monitoring of key areas such as laboratory benches, reagent cabinets and fume hoods ensures safe management of the entire chemical use process, forming a complete chemical operation safety monitoring network and avoiding blind spots in monitoring.

[0076] Step S2 specifically includes:

[0077] A facial recognition camera and card swiping device are installed at the laboratory entrance, and a dual authentication mechanism of "facial recognition + student ID card swiping" is adopted to establish a laboratory access personnel database to store student photos, identity information, etc.

[0078] A multi-level coding method of "year + major + class + student number" is used to assign a unique operation serial number to each experimental operation, and the student number is associated with the experimental project number to form a complete experimental record identification.

[0079] Match student identity information with experimental arrangements, establish a correspondence between students, experimental projects, and time periods, and update students' entry and exit records in the laboratory in real time.

[0080] The real-time image data is identified by using a chemical operation recognition model to obtain the student's operation behavior information and protective equipment wearing information, and real-time operation status information is generated by combining the environmental parameter data, the operation behavior information and the protective equipment wearing information. Specifically, the information includes:

[0081] A chemical operation recognition model is constructed using a deep learning model. Real-time image data is recognized based on the chemical operation recognition model to generate operation behavior information. Each feature vector element in the operation behavior information represents the recognition probability of a specific operation behavior.

[0082] The calculation formula of the chemical operation identification model is:

[0083] ;

[0084] ;

[0085] ;

[0086] in, For a given image The following is determined to be The probability of class operation behavior, To extract images through convolutional neural networks The image features, is an exponential function, For the Normalized probability of class operation behavior, For the Normalized probability of class operation behavior, is the number of categories of operation behaviors, is the timing adjustment factor, is the environmental constraint factor, is the time series smoothing coefficient, For a given image The following is determined to be The probability of class operation behavior, For a given image The following is determined to be The probability of class operation behavior, and They are Moment and Images of the moment, For the The normalized value of the environmental parameter, for The corresponding influence coefficient is is the number of environmental parameters;

[0087] The wearing status of protective equipment is detected through image processing algorithms, and protective equipment wearing information is generated. In combination with environmental parameter data, real-time operation status information is generated through weighted algorithms;

[0088] The calculation formula of the real-time operation status information is:

[0089] ;

[0090] in, is the normalization factor, For real-time operation status information, The first elements, The first The weight coefficient of each element, is the number of elements of the operation behavior information, Information on wearing protective equipment elements, Information on wearing protective equipment The weight coefficient of each element, Number of elements for protective equipment wearing information, The environmental parameter data elements, The environmental parameter data The weight coefficient of each element, The number of elements in the environment parameter data.

[0091] In a specific embodiment, the process of constructing the chemical operation identification model is as follows:

[0092] ResNet-50 is used as the basic convolutional neural network architecture, and a labeled chemical operation video dataset is used for model training. A classification system containing 20 types of standard operation behaviors is established. Image features are extracted in real time and the probability of operation behaviors is calculated. A timing adjustment mechanism is introduced to eliminate short-term fluctuations, and constrained optimization is performed in combination with environmental parameters.

[0093] The specific steps for implementing protective equipment testing are as follows:

[0094] Use the YOLO target detection algorithm to identify protective equipment, set a protective equipment detection list (such as goggles, gloves, masks, etc.), detect the wearing position and status of protective equipment in real time, and filter false detection results through confidence thresholds.

[0095] The mechanisms for generating real-time operational status information include:

[0096] Establish a three-layer weighted fusion architecture, design an adaptive weight adjustment algorithm, achieve millisecond-level status information updates, and establish a data caching mechanism to improve processing efficiency.

[0097] Specifically, this embodiment achieves an operational behavior recognition accuracy of over 95%, a protective equipment detection accuracy of over 98%, effectively reduces false alarm and missed alarm rates, and supports stable recognition in complex scenarios; it achieves millisecond-level recognition response speed, smooth video stream processing capabilities, fast status information updates, and low-latency system feedback; it can adapt to different lighting conditions, has strong anti-interference capabilities, stable long-term operating performance, and good environmental adaptability.

[0098] The multi-dimensional risk assessment model is used to identify the real-time operation status information to obtain the chemical operation risk level of each student, specifically including:

[0099] Dynamically extract features from real-time operation status information through a spatiotemporal attention mechanism to construct a risk feature vector. The risk feature vector includes an operation behavior risk feature sub-vector, a protective equipment risk feature sub-vector, and an environmental risk feature sub-vector. The elements of each sub-vector represent the risk contribution of the corresponding dimension.

[0100] A multi-level risk assessment model is established using the analytic hierarchy process. The combined weight algorithm is used to calculate the comprehensive impact of each risk characteristic. Combined with the risk event records in the historical database, the risk assessment parameters are dynamically adjusted to output the chemical operation risk level.

[0101] The calculation formula for the chemical operation risk level is:

[0102] ;

[0103] ;

[0104] in, is the risk level of chemical operation, is the first risk feature vector elements, is the first risk feature vector The weight coefficient of each element, is the risk feature vector, is the maximum value of the risk characteristic vector, is the number of elements in the risk feature vector, 、 、 They are risk level balance factor, time decay balance factor, and environmental dynamic balance factor. For historical moments Risk level records, is the length of historical data, For the current moment, is the time attenuation coefficient, is the environmental dynamic factor, For the The value of an environmental parameter, For the The safety threshold of environmental parameters, For the The influence coefficient of each environmental parameter, is the number of environmental parameters.

[0105] Specifically, this embodiment realizes dynamic feature extraction of operation status information through the spatiotemporal attention mechanism, decomposes risk characteristics into three dimensions: operation behavior, protective equipment, and environment, improves the accuracy of risk assessment, realizes the quantitative representation of risk contribution, and facilitates the accurate measurement of risk level.

[0106] The multi-level risk assessment model provides more comprehensive risk analysis capabilities. The combined weighting algorithm ensures the appropriate weight distribution of various risk factors, and the dynamic adjustment mechanism of historical data improves the accuracy of risk assessment. The introduction of a balancing factor improves the stability of assessment results, while supporting rapid processing of large-scale data and real-time response.

[0107] Adaptive adjustment of risk assessment parameters is achieved through the historical database, the sensitivity of assessment results to environmental changes is ensured through environmental dynamic factors, and the time decay mechanism is used to ensure the reasonable attenuation of the impact of historical data.

[0108] The decision control model identifies the chemical operation risk level, activates a corresponding early warning signal when the chemical operation risk level reaches different preset thresholds, and executes safety control measures according to the early warning signal, specifically including:

[0109] Adoption Depth Q The learning network constructs an early warning decision model, taking the chemical operation risk level, environmental status, and historical warning records as input, and generates an early warning decision vector through state-action value evaluation;

[0110] The calculation formula of the early warning decision vector is:

[0111] ;

[0112] in, is the early warning decision vector, 、 、 are the weight coefficients of the state-action value function, the temporal correlation function, and the environmental sensitivity function, respectively. For the decision-making state Q value, For the The immediate reward of a decision state, is the number of decision states, is the temperature parameter, for sigmoid activation function, For the The impact factor of historical warning events, is the duration of historical warning events, is the state-action time decay coefficient, For the current moment, For historical moments, For the The value of an environmental parameter, For the The safety threshold of environmental parameters, is the hyperbolic tangent activation function, For the The sensitivity coefficient of each environmental parameter, is the number of environmental parameters;

[0113] A multi-level warning execution mechanism is established based on fuzzy control theory, which triggers warning signals of different levels according to the threshold of the warning decision vector and executes the safety control measures corresponding to the warning level of the warning signal;

[0114] The rules for determining the warning level include: , triggering the warning signal corresponding to the low-level warning, and giving an audible and visual warning; when , triggering the warning signal corresponding to the intermediate warning and starting ventilation adjustment; when , trigger the warning signal corresponding to the advanced warning and implement emergency response measures.

[0115] In one embodiment, depth Q The implementation steps of learning network to build early warning decision model include:

[0116] The input layer receives the risk level (a floating point number between 0 and 1), environmental status (sensor data such as temperature and humidity), and historical warning data (warning records of the past 30 days); the hidden layer adopts a three-layer neural network, each layer contains 128 neurons, and the activation function uses ReLU; the output layer generates a warning decision vector with an output dimension of 3, corresponding to low, medium, and high levels of warning.

[0117] The early warning decision vector is implemented through the following steps:

[0118] The state-action value assessment uses the Q-value update formula, with the learning rate set to 0.01 and the discount factor set to 0.9; the time series correlation analysis considers the impact of the past five warning events, and the time decay coefficient is set to 0.8; the environmental sensitivity calculation adjusts the weight according to the rate of change of environmental parameters, and the sensitivity coefficient ranges from 0.1 to 0.5; the weight coefficient is dynamically adjusted according to real-time data, and the initial weight is set to 0.3.

[0119] The multi-level early warning execution mechanism includes:

[0120] For low-level warnings ( ): Trigger the sound and light alarm system, the alarm volume is set to 70 decibels, and a reminder message is sent to the laboratory management system.

[0121] For intermediate warning ( ): Start the ventilation system with a wind speed of 2 meters per second, send a warning message to the laboratory director, and record the warning event in the database for subsequent analysis.

[0122] For advanced warning ( ): Carry out emergency response, including shutting down experimental equipment, evacuating personnel to a safe area within 2 minutes, shutting down related equipment, and ensuring laboratory safety.

[0123] Specifically, this embodiment uses deep Q-learning to achieve intelligent early warning decision-making, with an accuracy rate exceeding 95%. Multi-dimensional input improves the accuracy of early warning judgments and reduces false positives and missed negatives. Millisecond-level early warning decision generation ensures rapid response and rapid early warning signal triggering, with an average response time of less than 1 second. Safety measures are promptly implemented, ensuring laboratory safety.

[0124] The early warning decision-making model can continuously optimize the early warning strategy through learning, adapt to changes in different experimental environments, dynamically adjust decision parameters according to historical early warning records, improve system flexibility, achieve accurate division of early warning levels, avoid excessive intervention and waste of resources, and improve management efficiency.

[0125] The step of establishing a student operation behavior feature library, storing each student's ID, real-time operation status information, and environmental parameter data in the student operation behavior feature library, extracting each student's operation feature through a behavior analysis model, generating a student operation feature vector, and updating the student operation behavior feature library based on the student operation feature vector, specifically includes:

[0126] Establish a student operation behavior feature library, index real-time operation status information and environmental parameter data by timestamp and student number, and store them in the student operation behavior feature library to ensure efficient storage and rapid retrieval of data, support incremental updates of real-time data and batch processing of historical data, use behavior analysis models to process the stored data, identify students' operation patterns and behavior characteristics, generate student operation feature vectors, and update the student operation behavior feature library based on the changing trends of the student operation feature vectors;

[0127] The calculation formula of the student operation characteristic vector is:

[0128] ;

[0129] in, The first elements, The first The weight coefficient of each element, is the operation behavior feature vector The maximum value of is the number of dimensions of the operational behavior characteristics, The first parameter characterizing the environmental elements, The first parameter characterizing the environmental The weight coefficient of each element, is the environmental parameter feature vector The maximum value of is the number of dimensions of the environmental parameter characteristics.

[0130] Specifically, this embodiment realizes the systematic management of student operation data by establishing a student operation behavior feature library, indexes the data by timestamp and student number, ensures efficient storage and rapid retrieval of data, supports incremental updates of real-time data and batch processing of historical data, and improves the flexibility and efficiency of data management.

[0131] Using behavioral analysis models to process stored data can identify students' operational patterns and behavioral characteristics, helping to uncover habitual behaviors and potential problems in experimental operations. The generated student operational feature vectors provide data support for subsequent personalized instruction and improvement suggestions. By analyzing the changing trends of student operational feature vectors and dynamically updating the student operational behavior feature library, this helps to promptly reflect changes in students' learning and operational abilities, providing personalized operational improvement suggestions for each student and improving the relevance of instruction. Through continuous monitoring and analysis of student operational behavior, irregular operations or potential safety hazards can be promptly identified, thereby improving the overall safety management level of the laboratory.

[0132] The experimental operation score and operation improvement suggestions corresponding to each student are generated based on the student operation behavior feature library, specifically including:

[0133] A multi-layered scoring mechanism is used to comprehensively evaluate students from multiple dimensions, including operational standardization, safety awareness, and emergency response capabilities. A weighted fusion algorithm is used to calculate each student's experimental operation score, and a student operation ability development curve is generated based on historical data analysis.

[0134] Construct a chemical operation knowledge graph, which includes standard operating procedures, safety specifications and expert experience. Combined with students' personal characteristics and operating habits, targeted improvement suggestions are generated through rule reasoning and similarity matching algorithms, and the improvement suggestions are dynamically optimized based on student feedback.

[0135] Specifically, this embodiment uses a multi-layer scoring mechanism to comprehensively evaluate students from multiple dimensions such as operational standardization, safety awareness, and emergency response capabilities, accurately reflecting students' comprehensive operational capabilities. A weighted fusion algorithm is used to ensure the reasonable weight distribution of each evaluation dimension, making the scoring results more objective. By constructing a knowledge graph for chemical operations, combined with standard operating procedures, safety regulations, and expert experience, a rich knowledge resource is provided for students. Through rule reasoning and similarity matching algorithms, personalized improvement suggestions are generated based on students' personal characteristics and operating habits, thereby improving the pertinence of teaching. Improvement suggestions are dynamically optimized based on student feedback to ensure the practicality and timeliness of the suggestions, which helps to continuously improve students' operational capabilities and safety awareness.

[0136] See also Figure 2The present invention also provides a student laboratory chemical operation management and control system, the system comprising:

[0137] The data acquisition module is used to obtain real-time image data and environmental parameter data in the laboratory through monitoring cameras and sensor networks respectively;

[0138] Identification and numbering module, used to identify and number students entering the laboratory;

[0139] An image recognition module is used to identify the real-time image data using a chemical operation recognition model to obtain the student's operation behavior information and protective equipment wearing information, and generate real-time operation status information by combining the environmental parameter data, the operation behavior information, and the protective equipment wearing information;

[0140] A risk level module is used to identify the real-time operation status information based on a multi-dimensional risk assessment model to obtain the chemical operation risk level of each student;

[0141] An operation warning module, configured to identify the chemical operation risk level through a decision control model, activate a corresponding warning signal when the chemical operation risk level reaches different preset thresholds, and execute safety control measures based on the warning signal;

[0142] An operation analysis module is used to establish a student operation behavior feature library, store each student's ID, real-time operation status information and environmental parameter data in the student operation behavior feature library, extract each student's operation characteristics through a behavior analysis model, generate a student operation feature vector, and update the student operation behavior feature library based on the student operation feature vector;

[0143] The operation improvement module is used to generate experimental operation scores and operation improvement suggestions for each student based on the student operation behavior feature library.

[0144] Specifically, a student laboratory chemical operation control system of this embodiment realizes comprehensive monitoring and management of student experimental operations by integrating data acquisition, identification number, image recognition, risk level, operation warning, operation analysis and operation improvement modules. The system can identify students' operating behaviors and protective equipment wearing in real time, assess the risk level of chemical operations, and trigger warning signals and safety control measures when the risk reaches a preset threshold. At the same time, by establishing a student operation behavior feature library and generating personalized operation scores and improvement suggestions, the system effectively improves the laboratory safety management level and teaching quality, ensuring the standardization and safety of experimental operations.

[0145] The present invention also discloses an electronic device, comprising: at least one processor, at least one memory communication interface and a bus: wherein the processor, memory and communication interface communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement a distributed photovoltaic power station drone inspection method.

[0146] The present invention also discloses a computer-readable storage medium storing computer instructions that cause the computer to implement all or part of the steps of the distributed photovoltaic power station drone inspection method described in an embodiment of the present invention. The storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling chemical operations in student laboratories, characterized in that: The following steps are involved: Through monitoring cameras and sensor networks, real-time image data and environmental parameter data in the laboratory are obtained respectively; Identify and number students entering the laboratory; Using a chemical operation recognition model to identify the real-time image data, obtain student operation behavior information and protective equipment wearing information, and combine the environmental parameter data, the operation behavior information, and the protective equipment wearing information to generate real-time operation status information; The real-time image data is identified by using a chemical operation recognition model to obtain the student's operation behavior information and protective equipment wearing information, and real-time operation status information is generated by combining the environmental parameter data, the operation behavior information and the protective equipment wearing information. Specifically, the information includes: A chemical operation recognition model is constructed using a deep learning model. Real-time image data is recognized based on the chemical operation recognition model to generate operation behavior information. Each feature vector element in the operation behavior information represents the recognition probability of a specific operation behavior. The calculation formula of the chemical operation identification model is: ； ； ； in, For a given image The following is determined to be The probability of class operation behavior, To extract images through convolutional neural networks The image features, is an exponential function, For the Normalized probability of class operation behavior, For the Normalized probability of class operation behavior, is the number of categories of operation behaviors, is the timing adjustment factor, is the environmental constraint factor, is the time series smoothing coefficient, For a given image The following is determined to be The probability of class operation behavior, For a given image The following is determined to be The probability of class operation behavior, and They are Moment and Images of the moment, For the The normalized value of the environmental parameter, for The corresponding influence coefficient is is the number of environmental parameters; Identifying the real-time operation status information based on a multi-dimensional risk assessment model to obtain a chemical operation risk level for each student; Identifying the chemical operation risk level through a decision control model, activating corresponding early warning signals when the chemical operation risk level reaches different preset thresholds, and executing safety control measures based on the early warning signals; Establishing a student operation behavior feature library, storing each student's ID, real-time operation status information, and environmental parameter data in the student operation behavior feature library, extracting each student's operation characteristics through a behavior analysis model, generating a student operation feature vector, and updating the student operation behavior feature library based on the student operation feature vector; Based on the student operation behavior feature library, generate experimental operation scores and operation improvement suggestions for each student.

2. A student laboratory chemical operation control method according to claim 1, characterized in that: The real-time image data and environmental parameter data in the laboratory are obtained through monitoring cameras and sensor networks, specifically including: A dual-camera surveillance camera is installed at each laboratory bench in the laboratory. The dual-camera surveillance camera includes a wide-angle surveillance camera and a close-up surveillance camera. The wide-angle surveillance camera captures the overall image of the laboratory bench, and the close-up camera captures detailed images of the chemical operation area; A sensor network is arranged in the chemical operation area to collect environmental parameter data. The sensor network includes a chemical concentration sensor, a weight sensor, a pressure sensor and a temperature sensor. The chemical concentration sensor is used to monitor the concentration of chemicals in the air, the weight sensor is used to monitor the amount of chemicals used, the pressure sensor is used to monitor the operation force, and the temperature sensor is used to monitor the reaction temperature change. The chemical operation area includes a laboratory bench, a reagent cabinet and a fume hood.

3. A student laboratory chemical operation control method according to claim 1, characterized in that: The real-time image data is identified by using a chemical operation recognition model to obtain the student's operation behavior information and protective equipment wearing information, and real-time operation status information is generated by combining the environmental parameter data, the operation behavior information and the protective equipment wearing information. Specifically, the information includes: The wearing status of protective equipment is detected through image processing algorithms, and protective equipment wearing information is generated. In combination with environmental parameter data, real-time operation status information is generated through weighted algorithms; The calculation formula of the real-time operation status information is: ； in, is the normalization factor, For real-time operation status information, The first elements, The first The weight coefficient of each element, is the number of elements of the operation behavior information, Information on wearing protective equipment elements, Information on wearing protective equipment The weight coefficient of each element, Number of elements for protective equipment wearing information, The environmental parameter data elements, The environmental parameter data The weight coefficient of each element, The number of elements in the environment parameter data.

4. A student laboratory chemical operation control method according to claim 3, characterized in that: The multi-dimensional risk assessment model is used to identify the real-time operation status information to obtain the chemical operation risk level of each student, specifically including: Dynamically extract features from real-time operation status information through a spatiotemporal attention mechanism to construct a risk feature vector, which includes an operation behavior risk feature sub-vector, a protective equipment risk feature sub-vector, and an environmental risk feature sub-vector; A multi-level risk assessment model is established using the analytic hierarchy process (AHP). The comprehensive impact of each risk characteristic is calculated through a combined weight algorithm. Combined with the risk event records in the historical database, the risk assessment parameters are dynamically adjusted to output the chemical operation risk level.

5. A student laboratory chemical operation control method according to claim 1, characterized in that: The decision control model identifies the chemical operation risk level, activates a corresponding early warning signal when the chemical operation risk level reaches different preset thresholds, and executes safety control measures according to the early warning signal, specifically including: Adoption Depth Q The learning network constructs an early warning decision model, taking the chemical operation risk level, environmental status, and historical warning records as input, and generates an early warning decision vector through state-action value evaluation; The calculation formula of the early warning decision vector is: ； in, is the early warning decision vector, 、 、 are the weight coefficients of the state-action value function, the temporal correlation function, and the environmental sensitivity function, respectively. For the decision-making state Q value, For the The immediate reward of a decision state, is the number of decision states, is the temperature parameter, for sigmoid activation function, For the The impact factor of historical warning events, is the duration of historical warning events, is the state-action time decay coefficient, For the current moment, For historical moments, For the The value of an environmental parameter, For the The safety threshold of environmental parameters, is the hyperbolic tangent activation function, For the The sensitivity coefficient of each environmental parameter, is the number of environmental parameters; A multi-level warning execution mechanism is established based on fuzzy control theory, which triggers warning signals of different levels according to the threshold of the warning decision vector and executes the safety control measures corresponding to the warning level of the warning signal; The rules for determining the warning level include: , triggering the warning signal corresponding to the low-level warning, and giving an audible and visual warning; when , triggering the warning signal corresponding to the intermediate warning and starting ventilation adjustment; when , trigger the warning signal corresponding to the advanced warning and implement emergency response measures.

6. A student laboratory chemical operation control method according to claim 5, characterized in that: The step of establishing a student operation behavior feature library, storing each student's ID, real-time operation status information, and environmental parameter data in the student operation behavior feature library, extracting each student's operation feature through a behavior analysis model, generating a student operation feature vector, and updating the student operation behavior feature library based on the student operation feature vector, specifically includes: Establish a student operation behavior feature library, index the real-time operation status information and environmental parameter data by timestamp and student number, and store them in the student operation behavior feature library, use the behavior analysis model to process the stored data, identify the student's operation mode and behavior characteristics, generate student operation feature vectors, and update the student operation behavior feature library according to the change trend of the student operation feature vectors.

7. A student laboratory chemical operation control method according to claim 1, characterized in that: The above method generates experimental operation scores and operation improvement suggestions for each student based on the student operation behavior feature library, specifically including: A multi-layered scoring mechanism is used to comprehensively evaluate students from multiple dimensions, including operational standardization, safety awareness, and emergency response capabilities. A weighted fusion algorithm is used to calculate each student's experimental operation score, and a student operation ability development curve is generated based on historical data analysis. Construct a chemical operation knowledge graph, which includes standard operating procedures, safety specifications and expert experience. Combined with students' personal characteristics and operating habits, targeted improvement suggestions are generated through rule reasoning and similarity matching algorithms, and the improvement suggestions are dynamically optimized based on student feedback.

8. A student laboratory chemical operation control system, used to implement a student laboratory chemical operation control method according to any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to obtain real-time image data and environmental parameter data in the laboratory through monitoring cameras and sensor networks respectively; Identification and numbering module, used to identify and number students entering the laboratory; An image recognition module is used to identify the real-time image data using a chemical operation recognition model to obtain the student's operation behavior information and protective equipment wearing information, and generate real-time operation status information by combining the environmental parameter data, the operation behavior information, and the protective equipment wearing information; A risk level module is used to identify the real-time operation status information based on a multi-dimensional risk assessment model to obtain the chemical operation risk level of each student; An operation warning module, configured to identify the chemical operation risk level through a decision control model, activate a corresponding warning signal when the chemical operation risk level reaches different preset thresholds, and execute safety control measures based on the warning signal; An operation analysis module is used to establish a student operation behavior feature library, store each student's ID, real-time operation status information and environmental parameter data in the student operation behavior feature library, extract each student's operation characteristics through a behavior analysis model, generate a student operation feature vector, and update the student operation behavior feature library based on the student operation feature vector; The operation improvement module is used to generate experimental operation scores and operation improvement suggestions for each student based on the student operation behavior feature library.

9. An electronic device, characterized in that: include: at least one processor, at least one memory, a communication interface, and a bus; The processor, memory, and communication interface communicate with each other through the bus, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to implement the method according to any one of claims 1 to 7.

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