Safety production closed-loop management system and method based on AI multi-dimensional early warning

Abstract

The invention discloses a safety production closed-loop management system and method based on AI multi-dimensional early warning, and aims to solve the problems of data acquisition and analysis splitting, insufficient early warning precision, management closed-loop deficiency, insufficient model iteration capability and the like in traditional safety production management. The system comprises a data acquisition module, an AI multi-dimensional analysis module, an early warning module and a closed-loop management module, multi-source data of equipment, environment, personnel and materials are acquired and preprocessed and then input into a risk assessment model to calculate a comprehensive risk index, and graded early warning is realized in combination with anomaly recognition. And a work order is generated based on the closed-loop management module, the rectification and verification effect is tracked, and the weight of the model is trained and optimized through historical data. According to the invention, multi-dimensional accurate early warning and full-process closed-loop management are realized, the intelligent and refined level of safety production management is improved, and the system is suitable for multiple fields of chemical engineering, buildings, mechanical manufacturing and the like.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of safety production management, and particularly relates to a safety production closed-loop management system and method based on AI multidimensional early warning. BACKGROUND

[0002] In the fields of industrial production, chemical manufacturing and construction, safety production is a core link for guaranteeing personnel life safety and avoiding property loss. With the expansion of production scale, the improvement of process complexity and the increase of equipment automation degree, the traditional safety production management mode gradually exposes limitations in many aspects:

[0003] Firstly, data collection and analysis are fragmented. Existing management systems mostly rely on single type sensors or manual records, and it is difficult to integrate multi-dimensional information such as equipment operation parameters (such as vibration, temperature), environmental data (such as humidity, harmful gas concentration), personnel operation behavior, etc. The data is scattered and stored in different platforms, and there is a lack of unified analysis framework, which leads to one-sidedness in safety risk identification - for example, only the state of the equipment is concerned and the superimposed influence of personnel violation operation is ignored, or only the environmental indicators are monitored and the equipment failure warning is not associated, which is difficult to form a comprehensive risk assessment.

[0004] Secondly, the risk early warning is lagging and the precision is insufficient. The traditional early warning method is mostly based on fixed threshold value judgment, without considering the correlation and dynamic change law between parameters. For example, when the temperature of a certain equipment exceeds the standard, if only the single point threshold value is used to trigger the alarm, the coordinated risk of the vibration value of the surrounding equipment and the environmental humidity may be ignored. At the same time, the fixed threshold value cannot adapt to the differences in working conditions in different production stages (such as starting and full load running), which is easy to cause false alarm or missed alarm, and reduces the actual guiding value of the early warning.

[0005] Thirdly, the closed loop of management process is missing. Most systems lack a standardized disposal tracking mechanism after early warning, and there is a phenomenon of “early warning ending”. For example, after the safety alarm is triggered, the dispatch of disposal work order, the confirmation of responsible person, the implementation of rectification measures and the verification of effect depend on manual coordination, the information transmission is lagging and the responsibility tracing is difficult, which leads to the long-term suspension of some hidden dangers, forming a vicious cycle of “early warning - shelving - early warning again”.

[0006] Fourthly, the intelligent iteration capability is insufficient. The risk assessment model of the existing system is mostly statically set, without dynamic optimization combined with historical accident data, rectification effect and other information. For example, once the risk weight coefficient is determined, it will remain unchanged for a long time, which cannot reflect the changes in risk characteristics caused by equipment aging, process upgrading or change in personnel operation habits, leading to the gradual disconnection between the assessment model and the actual production scene.

[0007] Against this backdrop, integrating artificial intelligence technology to achieve collaborative analysis of multi-dimensional data, dynamic early warning, and closed-loop management of the entire process has become a development trend in the field of work safety. By constructing an AI-driven risk assessment model and integrating multi-source data such as equipment, environment, and personnel, it is possible to achieve accurate quantification and graded early warning of risks. Furthermore, relying on a closed-loop management mechanism to ensure full-chain tracking of hazard handling can effectively compensate for the shortcomings of traditional management models and improve the intelligence and refinement of work safety management. Summary of the Invention

[0008] The technical problems to be solved by this invention are the following in traditional safety production management: the separation of data collection and analysis leading to one-sided risk assessment; the insufficient accuracy and lag of early warning methods based on fixed thresholds; the lack of a closed-loop management process after early warning, resulting in ineffective follow-up on hazard handling; and the lack of intelligent iteration capability of risk assessment models, which cannot adapt to changes in production scenarios. The invention aims to achieve multi-dimensional and accurate early warning and full-process closed-loop management of safety production.

[0009] The technical solution adopted in this invention is: a closed-loop management system for safe production based on AI multi-dimensional early warning, comprising:

[0010] The data acquisition module is used to collect equipment operating parameters, environmental monitoring data, personnel operation information, and material status data at the production site.

[0011] The AI ​​multi-dimensional analysis module receives data output from the data acquisition module and calculates a comprehensive risk index using a constructed risk assessment model. The formula for calculating the comprehensive risk index is as follows:

[0012] Where m is the number of dimensions for device class parameters; p i x represents the risk weight of the i-th equipment parameter, ranging from 0 to 1, reflecting the degree of impact of this parameter on equipment safety; i The standardized value of the i-th equipment parameter is 0-10 after normalization; n is the number of environmental parameter dimensions; q j y represents the risk weight of the j-th environmental parameter, with a value ranging from 0 to 1; j Let be the standardized value of the j-th environmental parameter, ranging from 0 to 10; α and β are the combined weights of equipment-related and environmental-related parameters, respectively, with α + β = 1, used to balance the proportion of the two types of parameters in the risk assessment;

[0013] The early warning module compares the comprehensive risk index S with a preset safety threshold. When S>T, a graded early warning is triggered, where T is the preset threshold set according to industry safety standards.

[0014] The closed-loop management module generates a handling work order after receiving an early warning signal, tracks the handling process, and records the handling results, forming a closed-loop management link from early warning to resolution.

[0015] As a further aspect of the present invention: the AI ​​multi-dimensional analysis module further includes an anomaly identification unit, which calculates the parameter anomaly degree using the following formula:

[0016] Where, d k For the k-th item, real-time data is collected; μ k This is the historical average of the parameter, reflecting the average level of the parameter under normal operating conditions; σ k This represents the historical standard deviation of the parameter, reflecting the range of parameter fluctuation under normal operating conditions; when E k A value greater than 3 is considered a significant anomaly and is used to quickly identify parameters that deviate from the normal range.

[0017] As a further aspect of the present invention: the closed-loop management module includes a rectification verification unit, which evaluates the effectiveness of the rectification by calculating the rectification completion rate.

[0018] Where l represents the number of rectification and verification items; s t S represents the actual score for the t-th verification term, ranging from 0 to 10. max The full score for a single verification item is 10 points; when C≥0.8, the rectification is deemed qualified, and it is used to quantitatively evaluate the completion quality of the rectification work.

[0019] A closed-loop management method for safe production based on AI multi-dimensional early warning includes the following steps:

[0020] S1. Multi-source data acquisition: Collect equipment vibration values, temperature, humidity, personnel positioning information, and material concentration data during the production process through sensors and IoT devices;

[0021] S2. Data Preprocessing: Normalize the collected data using the following formula:

[0022] Where v is the original data value, v min v max These are the historical minimum and maximum values ​​of the parameter, respectively. After processing, the value of z ranges from 0 to 10, which is used to eliminate the dimensional differences between different parameters.

[0023] S3, AI Risk Calculation: Input the pre-processed data into the risk assessment model to calculate multi-dimensional risk values;

[0024] S4. Tiered Early Warning Trigger: Issues an early warning signal based on the risk level.

[0025] S5. Closed-loop handling tracking: Generate handling plans and verify handling results to form a management closed loop.

[0026] As a further aspect of the present invention: the multi-dimensional risk value calculation in step S3 includes a sub-item of personnel behavior risk, and the calculation formula is as follows:

[0027]

[0028] Where f is the actual number of violations, F is the allowable violation threshold, t is the duration of personnel absence from their posts, T is the maximum allowable absence time, and γ is the weight of the behavior factor, ranging from 0.6 to 0.8, used to comprehensively assess personnel operational risks.

[0029] As a further aspect of the present invention: the dynamic adjustment formula for the threshold of the graded early warning in step S4 is:

[0030] Among them, T k Let T0 be the warning threshold for the k-th quarter; T0 be the initial threshold; θ be the adjustment coefficient, ranging from 0.05 to 0.1; N k N represents the actual number of security incidents that occurred in the previous quarter. total This represents the total number of warnings in the previous quarter, used to dynamically optimize warning sensitivity based on actual safety conditions.

[0031] As a further aspect of the present invention, it also includes a historical data training module, which optimizes the risk weights using the following formula:

[0032] Among them, w i+1 The updated weight values; w i λ represents the current weight value; λ is the learning rate, ranging from 0.01 to 0.05; L is the loss function, reflecting the deviation between predicted and actual risk, used to iteratively optimize weight parameters through historical data to improve the accuracy of risk assessment.

[0033] As a further aspect of the present invention: the disposal scheme generation in step S5 includes resource scheduling priority calculation:

[0034] Where P is the scheduling priority, 0-1; ω1 and ω2 are weight coefficients, ω1+ω2=1; S is the current risk value; S max d is the maximum risk threshold; d is the distance between the accident source and the resource point; D is the maximum response distance, used to determine the allocation order of emergency resources.

[0035] As a further aspect of the present invention: the early warning module supports multi-channel collaborative early warning, and the formula for calculating the early warning information dissemination efficiency is:

[0036] Where h is the number of terminals receiving the warning; r ut represents the acknowledgment status of the u-th terminal, where 1 indicates receipt and 0 indicates non-receipt; t represents the time from message transmission to full acknowledgment, used to evaluate the effectiveness of the warning message transmission.

[0037] As a further aspect of the present invention, it also includes a management effectiveness evaluation step, the calculation formula of which is:

[0038] Where g is the total number of historical warning events, T m t is the standard processing time for event m. m The actual processing time is used to quantitatively assess the improvement in the efficiency of overall safety production management.

[0039] The beneficial effects of this invention are:

[0040] 1. Solve the problem of fragmented data collection and analysis to achieve comprehensive risk assessment. By integrating multi-source data such as equipment operating parameters, environmental monitoring data, and personnel operation information, a unified AI analysis framework is built, breaking down data silos. This is achieved using a comprehensive risk index calculation formula. It enables collaborative assessment of factors such as equipment, environment, and personnel, avoiding the one-sidedness of single-dimensional analysis and making risk identification more comprehensive and more in line with actual production scenarios.

[0041] 2. Overcome the bottlenecks of delayed and insufficient risk warnings, and improve the timeliness and accuracy of warnings. Employ dynamic anomaly identification. and threshold adaptive adjustment mechanism It replaces traditional fixed threshold judgments. By analyzing the correlation between parameters and changes in operating conditions, it can quickly identify significant anomalies and dynamically optimize warning thresholds based on historical safety events, reducing false alarms and missed alarms, making warnings more accurate and responses more timely.

[0042] 3. Fill the gaps in the closed-loop management process and ensure full-chain controllability in hazard handling. A complete chain of "early warning - work order - rectification - verification" is constructed through a closed-loop management module, combined with rectification completion calculations. and resource scheduling priority assessment Achieve standardized tracking and quantitative verification of the handling process. Avoid information delays caused by manual coordination, clarify the accountability mechanism, and eliminate the vicious cycle of unresolved hidden dangers.

[0043] 4. Overcome the shortcomings of insufficient intelligent iteration capabilities and achieve continuous model optimization. Utilize historical data training modules. By iteratively optimizing risk weights through a loss function, the model can dynamically adapt to scenarios such as equipment aging, process upgrades, and changes in personnel operating habits. This is combined with management effectiveness assessment. Continuously improve the efficiency of risk assessment and handling, ensure that the system keeps pace with actual production needs in the long term, and avoid the disconnect between models and scenarios. Attached Figure Description

[0044] Figure 1 The flowchart shows the safety production closed-loop management system and method based on AI multi-dimensional early warning, which is the subject of this invention. Detailed Implementation

[0045] The present invention will be further described below.

[0046] Example 1: Safety Production Management in Chemical Production Workshops

[0047] Scenario characteristics: It involves flammable and explosive chemicals. The core risk points are the stability of equipment operation (such as reactor pressure and temperature) and the concentration of toxic gases in the environment. Personnel violations (such as failure to wear protective equipment according to procedures) can easily lead to a chain of accidents.

[0048] 1. Multi-source data acquisition (corresponding to method step S1)

[0049] The following data is collected through sensors and IoT devices:

[0050] Equipment parameters: reactor temperature (v1 = 180℃), reactor pressure (v2 = 2.5MPa), pump vibration value (v3 = 0.8mm / s);

[0051] Environmental parameters: Toxic gas concentration in the workshop (v4 = 30 mg / m³) 3 ), humidity (v5 = 65%);

[0052] Personnel information: Number of violations (f = 2 times), duration of absence from post (t = 15 minutes);

[0053] Material state: Raw material concentration (v6 = 85%).

[0054] 2. Data preprocessing (corresponding to step S2, formula:) )

[0055] Taking the reactor temperature (v1 = 180℃) as an example, its historical minimum value is ℃v min =100℃, maximum value ℃ v max =200℃, normalized:

[0056] (Range 0-10).

[0057] Similarly, other parameters were processed, and the results are as follows:

[0058] Parameter Original value v min ]]> v max ]]> Normalized value z Reaction kettle temperature 180℃ 100℃ 200℃ 8 Reaction kettle pressure 2.5 MPa 0.5 MPa 3 MPa 8 Pump body vibration value 0.8 mm / s 0.2 mm / s 1 mm / s 7.5 Toxic gas concentration 30 mg / m 3 ]] 10 mg / m 3 ]] 50 mg / m 3 ]] 5 Humidity 65％ 30％ 80％ 7 Raw material concentration 85％ 50％ 100％ 7

[0059] 3. AI Risk Calculation (corresponding to method step S3)

[0060] Comprehensive Risk Index S (Formula: ):

[0061] Equipment parameters (m=3): reactor temperature (p1=0.4, x1=8), reactor pressure (p2=0.3, x2=8), pump vibration (p3=0.3, x3=7.5);

[0062] Environmental parameters (n=2): concentration of toxic gases (q1=0.6, y1=5), humidity (q2=0.4, y2=7);

[0063] Overall weighting: Equipment risk accounts for a higher proportion in chemical engineering scenarios, so we take α = 0.6 and β = 0.4 (α + β = 1).

[0064] Calculate: S = 0.6 × (0.4 × 8 + 0.3 × 8 + 0.3 × 7.5) + 0.4 × (0.6 × 5 + 0.4 × 7) = 0.6 × (3.2 + 2.4 + 2.25) + 0.4 × (3 + 2.8) = 0.6 × 7.85 + 0.4 × 5.8 = 4.71 + 2.32 = 7.03.

[0065] Personnel Behavior Risk B (Formula: ):

[0066] The permissible violation threshold F = 3 times, the maximum permissible off-duty time T = 20 minutes, and the behavioral factor weight γ = 0.7 (high operational risk for personnel in chemical industry scenarios);

[0067] calculate:

[0068] 4. Anomaly Detection (Formula: )

[0069] Taking the pump body vibration value (real-time data dk = 0.8 mm / s) as an example, its historical mean μk = 0.5 mm / s and standard deviation σk = 0.1 mm / s;

[0070] calculate: If the value is less than 3, it is considered a non-significant abnormality.

[0071] 5. Tiered early warning trigger (corresponding to method step S4, threshold adjustment formula: )

[0072] Initial threshold T0 = 6 (chemical industry standard), number of safety incidents in the previous quarter Nk = 5, total number of warnings Ntotal = 50, adjustment coefficient θ = 0.05;

[0073] This quarter's threshold:

[0074] The overall risk index S = 7.03 > Tk = 6.03, triggering a level-two warning.

[0075] 6. Closed-loop processing tracking (corresponding to method step S5)

[0076] Resource scheduling priority P (formula: ):

[0077] The current risk value S = 7.03, the maximum risk threshold Smax = 10; the distance between the accident source and the nearest resource point d = 50m, the maximum response distance D = 200m; the weights ω1 = 0.7 (risk priority), ω2 = 0.3;

[0078] calculate: (Medium priority)

[0079] Early warning propagation efficiency E (formula: ):

[0080] Number of receiving terminals h = 10 (workshop director, safety officer, etc.), all terminals confirm reception (∑r u =10), confirmation time t = 2 minutes;

[0081] calculate: (Moderate dissemination efficiency)

[0082] Rectification and verification (formula:) ):

[0083] The rectification verification items l=3 (reactor cooling, gas concentration detection, personnel training), with actual scores s1=9 points, s2=8 points, s3=10 points, and a single item full score Smax=10;

[0084] calculate: The rectification was deemed satisfactory.

[0085] 7. Weight Optimization (Formula: )

[0086] Initially, the equipment pressure weight w_i = 0.3, the learning rate λ = 0.02, and the partial derivative of the loss function... (If the predicted risk is lower than the actual risk, the weighting needs to be increased);

[0087] Updated weight: w i+1 =0.3 + 0.02 × 0.5 = 0.31.

[0088] 8. Management effectiveness evaluation (formula: )

[0089] The total number of historical early warning events g = 10, the average standard handling time T_m = 60 minutes, and the average actual handling time t_m = 45 minutes;

[0090] calculate: (Efficiency increased by 25%).

[0091] Example 2: Safety Management at Construction Sites

[0092] Scenario characteristics: High-altitude operations are common (tower cranes, scaffolding), and environmental factors (pocket humidity, wind speed) and personnel violations (not wearing safety belts, improper operation of machinery) are the main risks. Equipment vibration (tower cranes) and personnel positioning are the key monitoring points.

[0093] 1. Multi-source data acquisition

[0094] Equipment parameters: tower crane vibration value (v1 = 1.2 mm / s), scaffolding load-bearing capacity (v2 = 800 kg);

[0095] Environmental parameters: pit humidity (v3 = 90%), wind speed (v4 = 12 m / s);

[0096] Personnel information: Number of violations (f = 1 time), duration of absence from post (t = 30 minutes).

[0097] 2. Data preprocessing (formula: )

[0098] Parameter Original value v min ]]> v max ]]> Normalized value z Tower crane vibration value 1.2 mm / s 0.5 mm / s 2 mm / s 5.25 Scaffold load 800 kg 500 kg 1000 kg 6 Foundation pit humidity 90％ 60％ 100％ 7.5 Wind speed 12 m / s 0 m / s 20 m / s 6

[0099] 3. AI Risk Calculation

[0100] Overall Risk Index S:

[0101] Equipment (m=2): Tower crane vibration (p1=0.5, x1=5.25), scaffolding load-bearing (p2=0.5, x2=6);

[0102] Environmental factors (n=2): Pit humidity (q1=0.3, y1=7.5), wind speed (q2=0.7, y2=6);

[0103] Overall weighting: The building environment has higher risks, so we take α = 0.4 and β = 0.6;

[0104] Calculate: S = 0.4 × (0.5 × 5.25 + 0.5 × 6) + 0.6 × (0.3 × 7.5 + 0.7 × 6) = 0.4 × (2.625 + 3) + 0.6 × (2.25 + 4.2) = 0.4 × 5.625 + 0.6 × 6.45 = 2.25 + 3.87 = 6.12.

[0105] Personnel Behavior Risk B:

[0106] The permissible violation threshold is F = 2 times, the maximum absence time is T = 40 minutes, and γ = 0.8 (the operational risk for construction workers is extremely high).

[0107] calculate:

[0108] 4. Anomaly Detection

[0109] Taking wind speed (dk = 12 m / s) as an example, the historical mean μk = 6 m / s and the standard deviation σk = 2 m / s;

[0110] calculate: Not exceeding 3, not significantly abnormal.

[0111] 5. Triggering of tiered early warnings

[0112] Initial threshold T0 = 5 (construction industry standard), number of safety incidents in the previous quarter Nk = 8, total number of warnings Ntotal = 40.

[0113] θ = 0.1;

[0114] This quarter's threshold:

[0115] S = 6.12 > 5.1, triggering a Level 1 warning.

[0116] 6. Closed-loop handling and tracking

[0117] Resource scheduling priority P:

[0118] S=6.12, Smax=10; d=30m, D=100m; ω1=0.6, ω2=0.4;

[0119] calculate:

[0120] Early warning propagation efficiency E:

[0121] h = 8 (project manager, safety officer, etc.), ∑r u =8, t = 1 minute;

[0122] calculate: (Extremely high transmission efficiency).

[0123] Rectification and verification:

[0124] l = 2 (tower crane reinforcement, personnel safety training), s1 = 10 points, s2 = 9 points;

[0125] calculate: The rectification was satisfactory.

[0126] 7. Management effectiveness evaluation

[0127] g = 8, T_m = 40 minutes, t_m = 25 minutes;

[0128] calculate: (Efficiency improved by 37.5%).

[0129] Example 3: Safety Production Management in a Machinery Manufacturing Workshop

[0130] Scenario characteristics: The equipment is running at high speed (machine tools, stamping machines). Equipment failure (such as abnormal speed or excessive temperature) and dust concentration in the workshop are the main risks. Personnel violations (such as failure to stop and maintain the machine according to the procedure) have a relatively small impact.

[0131] Core calculation results (application of key formulas)

[0132] Comprehensive risk index S: α = 0.8 (high equipment weight), β = 0.2; S = 5.8 (equipment parameters dominate).

[0133] Anomaly identification: Machine tool speed dk = 3200 r / min, μk = 2500 r / min, σk = 200 r / min. The condition was determined to be significantly abnormal.

[0134] Warning threshold: Tk = 4.5, S = 5.8 > 4.5, triggering a level 3 warning.

[0135] Rectification verification: C = 0.85 (qualified), management efficiency M = 0.2 (improved by 20%).

[0136] Comparative Analysis of Examples

[0137]

[0138] Summarize

[0139] All three embodiments fully utilize all mathematical formulas in the system and method, verifying the universality of the invention: through multi-dimensional data integration (equipment, environment, personnel), dynamic risk calculation (S, B), anomaly identification (E), adaptive early warning (Tk), closed-loop handling (C, P), and model iteration (wi+1), precise management of safe production in different scenarios is achieved. Scenario differences are adapted through weight adjustment (α, β, γ, ω), demonstrating the system's flexibility and practicality.

[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A closed-loop safety production management system based on AI multi-dimensional early warning, characterized in that, include: The data acquisition module is used to collect equipment operating parameters, environmental monitoring data, personnel operation information, and material status data at the production site. The AI ​​multi-dimensional analysis module receives data output from the data acquisition module and calculates a comprehensive risk index using a constructed risk assessment model. The formula for calculating the comprehensive risk index is as follows: Where m is the number of dimensions for device class parameters; p i x represents the risk weight of the i-th equipment parameter, ranging from 0 to 1, reflecting the degree of impact of this parameter on equipment safety; i The standardized value of the i-th equipment parameter is 0-10 after normalization; n is the number of environmental parameter dimensions; q j y represents the risk weight of the j-th environmental parameter, with a value ranging from 0 to 1; j Let be the standardized value of the j-th environmental parameter, ranging from 0 to 10; α and β are the combined weights of equipment-related and environmental-related parameters, respectively, with α + β = 1, used to balance the proportion of the two types of parameters in the risk assessment; The early warning module compares the comprehensive risk index S with a preset safety threshold. When S>T, a graded early warning is triggered, where T is the preset threshold set according to industry safety standards. The closed-loop management module generates a handling work order after receiving an early warning signal, tracks the handling process, and records the handling results, forming a closed-loop management link from early warning to resolution.

2. The safety production closed-loop management system based on AI multi-dimensional early warning as described in claim 1, characterized in that, The AI ​​multi-dimensional analysis module also includes an anomaly detection unit, which calculates the anomaly degree of parameters using the following formula: Where, d k For the k-th item, real-time data is collected; μ k This is the historical average of the parameter, reflecting the average level of the parameter under normal operating conditions; σ k This represents the historical standard deviation of the parameter, reflecting the range of parameter fluctuation under normal operating conditions; when E k A value greater than 3 is considered a significant anomaly and is used to quickly identify parameters that deviate from the normal range.

3. The safety production closed-loop management system based on AI multi-dimensional early warning as described in claim 1, characterized in that, The closed-loop management module includes a rectification verification unit, which evaluates the effectiveness of the measures by calculating the rectification completion rate. Where l represents the number of rectification and verification items; s t S represents the actual score for the t-th verification term, ranging from 0 to 10. max The full score for a single verification item is 10 points; when C≥0.8, the rectification is deemed qualified, and it is used to quantitatively evaluate the completion quality of the rectification work.

4. A closed-loop management method for safe production based on AI multi-dimensional early warning, characterized in that, Includes the following steps: S1. Multi-source data acquisition: Collect equipment vibration values, temperature, humidity, personnel positioning information, and material concentration data during the production process through sensors and IoT devices; S2. Data Preprocessing: Normalize the collected data using the following formula: Where v is the original data value, v min v max These are the historical minimum and maximum values ​​of the parameter, respectively. After processing, the value of z ranges from 0 to 10, which is used to eliminate the dimensional differences between different parameters. S3, AI Risk Calculation: Input the pre-processed data into the risk assessment model to calculate multi-dimensional risk values; S4. Tiered Early Warning Trigger: Issues an early warning signal based on the risk level. S5. Closed-loop handling tracking: Generate handling plans and verify handling results to form a management closed loop.

5. A closed-loop management method for safe production based on AI multi-dimensional early warning, as described in claim 4, is characterized in that... The multi-dimensional risk value calculation in step S3 includes a sub-item of personnel behavior risk, and the calculation formula is as follows: Where f is the actual number of violations, F is the allowable violation threshold, t is the duration of personnel absence from their posts, T is the maximum allowable absence time, and γ is the weight of the behavior factor, ranging from 0.6 to 0.8, used to comprehensively assess personnel operational risks.

6. A closed-loop management method for safe production based on AI multi-dimensional early warning, as described in claim 4, is characterized in that... The dynamic adjustment formula for the threshold of the graded early warning mentioned in step S4 is as follows: Among them, T k Let T0 be the warning threshold for the k-th quarter; T0 be the initial threshold; θ be the adjustment coefficient, ranging from 0.05 to 0.1; N k N represents the actual number of security incidents that occurred in the previous quarter. total This represents the total number of warnings in the previous quarter, used to dynamically optimize warning sensitivity based on actual safety conditions.

7. A closed-loop safety production management system based on AI multi-dimensional early warning as described in claim 1, characterized in that, It also includes a historical data training module, which optimizes risk weights using the following formula: Among them, w i+1 The updated weight values; w i λ represents the current weight value; λ is the learning rate, ranging from 0.01 to 0.05; L is the loss function, reflecting the deviation between predicted and actual risk, used to iteratively optimize weight parameters through historical data to improve the accuracy of risk assessment.

8. A closed-loop management method for safe production based on AI multi-dimensional early warning, as described in claim 4, is characterized in that... The processing plan generation in step S5 includes resource scheduling priority calculation: Where P is the scheduling priority, 0-1; ω1 and ω2 are weight coefficients, ω1+ω2=1; S is the current risk value; S max d is the maximum risk threshold; d is the distance between the accident source and the resource point; D is the maximum response distance, used to determine the allocation order of emergency resources.

9. A closed-loop safety production management system based on AI multi-dimensional early warning as described in claim 1, characterized in that, The early warning module supports multi-channel collaborative early warning, and the formula for calculating the early warning information dissemination efficiency is as follows: Where h is the number of terminals receiving the warning; r u t represents the acknowledgment status of the u-th terminal, where 1 indicates receipt and 0 indicates non-receipt; t represents the time from message transmission to full acknowledgment, used to evaluate the effectiveness of the warning message transmission.

10. A closed-loop management method for safe production based on AI multi-dimensional early warning, as described in claim 4, is characterized in that... It also includes a management effectiveness evaluation step, calculated using the following formula: Where g is the total number of historical warning events, T m t is the standard processing time for event m. m The actual processing time is used to quantitatively assess the improvement in the efficiency of overall safety production management.

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