Process evaluation method based on AI event time sequence duration analysis

Abstract

The invention provides a process evaluation method based on AI event time sequence duration analysis, which comprises the following steps: S1, data collection: directly acquiring or extracting a video from a data source by adopting a data collection device; s2, video analysis: analyzing and detecting the video, splitting the video according to events and generating detection data; s3, time sequence analysis: comparing the time sequence of each event with a system specified time sequence, and judging whether the steps are correct or not; s4, time length analysis: comparing the time length of each event with the time length specified by the system, and judging whether the duration time is in a reasonable range or not; s5, precondition analysis: comparing conditions before and after each event with system specified conditions, and judging whether the conditions are met or not; and S6, result storage and feedback: storing an analysis result and feeding back the result to related personnel. According to the invention, the accuracy of experiment operation of students can be automatically analyzed, and the effect and efficiency of experiment teaching are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of process automation evaluation, and in particular to a process evaluation method based on AI event timing and duration analysis. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, its application in the field of education has gradually received attention. In the teaching of physics, chemistry, and biology experiments, the traditional experimental process often requires students to strictly follow the operating steps. If steps are missed or timing errors occur, the experimental results may be inaccurate or even fail. Most of the current technical implementations can only analyze and judge the frame data obtained in real time during the experiment. The advantage of this technology is that it can quickly identify whether the student's operation meets the experimental requirements, feedback errors in real time, and help students correct the operation in time. However, this real-time judgment method also has obvious limitations.

[0003] First, real-time frame judgment can only analyze the current operation, and cannot combine the overall timing of the experiment with historical operation data for comprehensive evaluation. This means that the system cannot identify certain chain reactions or potential problems caused by errors in previous steps, leading to deviations in experimental results. This limitation makes the system incapable of handling complex experimental processes.

[0004] Secondly, due to the lack of historical data support, the system cannot conduct in-depth analysis and summary of students' operating habits, error patterns, etc., and thus cannot provide targeted personalized suggestions and long-term improvement strategies. In this way, although it can help students correct their mistakes in a short period of time, it cannot play a long-term role in promoting the overall improvement of students' experimental ability. Summary of the invention

[0005] In order to make up for the deficiencies in the prior art, the present invention provides a process evaluation method based on AI event timing and duration analysis, which can automatically analyze the accuracy of students' experimental operations and greatly improve the effect and efficiency of experimental teaching.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The process evaluation method based on AI event timing analysis includes the following steps:

[0008] S1. Data collection, using a data collection device to directly acquire or extract videos from data sources;

[0009] S2. Video analysis: analyzing and detecting the video, splitting the video into events and generating detection data;

[0010] S3. Timing analysis: compare the timing of each event with the timing specified by the system to determine whether its steps are correct;

[0011] S4. Duration analysis: compare the duration of each event with the duration specified by the system to determine whether its duration is within a reasonable range;

[0012] S5. Precondition analysis: compare the conditions before and after each event with the system-specified conditions to determine whether they meet the conditions;

[0013] S6. Result storage and feedback: store the analysis results and feed back the results to relevant personnel.

[0014] Furthermore, the method further comprises the following steps:

[0015] S7. System optimization: training historical data through machine learning algorithms to optimize the accuracy of event analysis and conditional judgment.

[0016] Furthermore, in step S2, the detection data includes the video source address, event type, location, and confidence level.

[0017] Furthermore, in step S4, the system specifies that the duration is determined based on historical data or a statistical model.

[0018] Furthermore, in step S5, the system-specified conditions include environmental status, availability of system resources, and dependencies between events.

[0019] Furthermore, in step S5, the precondition analysis uses a rule engine or a logic judgment module, and the rules of the rule engine are pre-set or dynamically generated through a machine learning model.

[0020] Compared with the prior art, the present invention has the following beneficial technical effects:

[0021] The process evaluation method based on AI event timing and duration analysis of the present invention can effectively analyze and judge the timing, duration and related conditions of events in multiple application scenarios, so as to make accurate decisions; it can reduce errors caused by human factors and improve the reliability and safety of the overall operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0023] Many specific details are described in the following description to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from the description, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Example 1

[0025] like Figure 1 As shown, the process evaluation method based on AI event timing duration analysis includes the following steps:

[0026] S1. Data collection: directly acquiring or extracting video from a data source using a data collection device; the data collection device may be a camera or a sensor.

[0027] S2. Video analysis: Analyze and detect the video, split the video by event and generate detection data; the detection data includes the video source address, event type, location, and confidence. The confidence can be user-defined to solve the problem of inaccurate detection under different environmental conditions.

[0028] S3. Timing analysis: compare the timing of each event with the system-specified timing to determine whether the steps are correct; the system-specified timing can be freely adjusted by the user, or steps can be added or deleted.

[0029] S4. Duration analysis: compare the duration of each event with the duration specified by the system to determine whether its duration is within a reasonable range; the system-specified duration is determined based on historical data or statistical models, and can automatically adjust the duration judgment criteria according to different scenarios.

[0030] S5. Precondition analysis: compare the conditions before and after each event with the system-specified conditions to determine whether they meet the conditions; the system-specified conditions include environmental status, availability of system resources, and dependencies between events. Precondition analysis uses a rule engine or logic judgment module, and the rules of the rule engine are pre-set or dynamically generated through a machine learning model.

[0031] S6. Result storage and feedback: Store the analysis results for subsequent analysis and optimization, and feedback the results to relevant personnel. The feedback mechanism may include alarm notifications, status updates, user prompts, etc. to ensure the accuracy and timeliness of information transmission.

[0032] S7. System optimization: Use machine learning algorithms to train historical data and optimize the accuracy of event analysis and condition judgment. The system also needs to regularly update the model and rule base to ensure that it can cope with new scenarios and changes in conditions.

[0033] The process evaluation method based on AI event timing duration analysis of the present invention can be applied to the following fields:

[0034] 1. Industrial Automation

[0035] Production process optimization: AI can determine whether the equipment on the production line is operating normally based on the sequence and duration of events, and optimize the production process in real time. If a piece of equipment fails to complete an operation within the scheduled time, the system can automatically adjust the production line to avoid capacity loss or product quality issues.

[0036] Fault prediction and maintenance: AI systems can monitor the operating hours and conditions of equipment, predict possible failures based on historical data, and schedule preventive maintenance, thereby reducing downtime and maintenance costs.

[0037] 2. Smart Manufacturing

[0038] Process monitoring and adjustment: During the manufacturing process, AI can determine whether process parameters need to be adjusted by analyzing the timing and conditions of events to ensure the stability of product quality. For example, in semiconductor manufacturing, AI can monitor the timing of each step to ensure that the manufacturing process is carried out strictly in accordance with the design specifications.

[0039] Quality control: AI can identify potential quality issues by judging the timing and conditions of the production process and intervene before the problem spreads.

[0040] 3. Intelligent transportation

[0041] Traffic signal optimization: By analyzing the timing and duration of traffic events, AI can dynamically adjust the duration of traffic lights to alleviate traffic congestion and optimize urban traffic flow.

[0042] Accident prevention and management: AI can monitor the behavior of vehicles on the road, predict possible traffic accidents by analyzing their timing and conditions, and take preventive measures or notify relevant departments in advance.

[0043] 4. Smart Home

[0044] Home security monitoring: The AI ​​system can analyze the timing and conditions of events in the home environment (such as door and window openings, temperature changes, etc.) to determine whether any abnormal situation has occurred and issue an alarm in a timely manner.

[0045] Energy consumption management: By monitoring the usage time and conditions of home appliances, AI can determine whether there is unnecessary energy consumption and automatically adjust the operating status of the equipment to save energy.

[0046] 5. Healthcare

[0047] Patient monitoring: AI can determine whether there are health risks based on the timing and condition changes of the patient's physiological signals (such as heart rate, blood pressure, etc.) and notify medical staff in a timely manner.

[0048] Operating room management: During surgery, AI can determine the progress of the operation based on the timing and conditions of events, and provide real-time suggestions to ensure the smooth progress of the operation.

[0049] 6. Financial Transactions

[0050] Abnormal transaction detection: AI can help prevent financial fraud and money laundering by analyzing the timing and conditions of transactions and identifying abnormal transaction behaviors.

[0051] Algorithmic trading optimization: In high-frequency trading, AI can optimize trading strategies and improve returns by judging the timing and conditions of market events.

[0052] 7. Smart Education

[0053] Learning process analysis: AI can judge students’ learning status based on the timing and conditions of their operations during the learning process, identify weak links in learning, and provide personalized learning suggestions.

[0054] 8. Supply Chain Management

[0055] Inventory management: By analyzing the timing and conditions of inventory replenishment and shipment, AI can optimize inventory management, reduce inventory costs and improve supply chain efficiency.

[0056] Transport Scheduling: AI can optimize transport routes and scheduling strategies and reduce logistics costs by analyzing the timing and conditions of the transport process.

[0057] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to replace some of the technical features therein with equivalents. However, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A process evaluation method based on AI event timing analysis, characterized in that: The following steps are involved: S1. Data collection, using a data collection device to directly acquire or extract videos from data sources; S2. Video analysis: analyzing and detecting the video, splitting the video into events and generating detection data; S3. Timing analysis: compare the timing of each event with the timing specified by the system to determine whether its steps are correct; S4. Duration analysis: compare the duration of each event with the duration specified by the system to determine whether its duration is within a reasonable range; S5. Precondition analysis: compare the conditions before and after each event with the system-specified conditions to determine whether they meet the conditions; S6. Result storage and feedback: store the analysis results and feed back the results to relevant personnel.

2. The process evaluation method based on AI event timing duration analysis according to claim 1 is characterized in that: The following steps are also included: S7. System optimization: training historical data through machine learning algorithms to optimize the accuracy of event analysis and conditional judgment.

3. The process evaluation method based on AI event timing duration analysis according to claim 2 is characterized in that: In step S2, the detection data includes the video source address, event type, location, and confidence level.

4. The process evaluation method based on AI event timing analysis according to claim 3 is characterized in that: In step S4, the system specifies that the duration is determined based on historical data or a statistical model.

5. The process evaluation method based on AI event timing duration analysis according to claim 4 is characterized in that: In step S5, the system-specified conditions include environmental status, availability of system resources, and dependencies between events.

6. The process evaluation method based on AI event timing duration analysis according to claim 5 is characterized in that: In step S5, the precondition analysis uses a rule engine or a logic judgment module, and the rules of the rule engine are pre-set or dynamically generated through a machine learning model.

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