Safety production standardization integrated management system and method

Abstract

The invention discloses a safety production standardized comprehensive management system and method, and relates to the technical field of safety production management, the system comprises the following components: a data acquisition module, a data preprocessing module, a model construction and training module, a prediction analysis module and a maintenance management module; according to the method, the time sequence data in the full life cycle of the equipment are continuously collected, the time sequence data comprise key parameters such as operation duration, start-stop times and maintenance records, the improved LSTM neural network is utilized to construct the equipment safety life prediction model, and the model can not only predict the overall life of the equipment, but also can predict the service life of the equipment. The method can accurately predict the residual safe use cycle of the easily-worn part, the accurate prediction capability enables an enterprise to plan a maintenance plan in advance, production interruption and safety accidents caused by sudden equipment faults are avoided, and the accuracy and foresight of equipment safety management are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of safety production management technology, specifically to a comprehensive safety production standardization management system and method. Background Technology

[0002] With the advent of Industry 4.0, intelligent manufacturing and safe production have become key elements for the sustainable development of enterprises. In the industrial production process, equipment, as a core production element, is directly related to production efficiency, product quality, and personnel safety through its safe and stable operation. However, during long-term operation, equipment inevitably experiences wear and tear, aging, and other phenomena, leading to frequent failures and even safety accidents. Therefore, how to conduct full life-cycle safety management of equipment, predict equipment failures in advance, and rationally arrange maintenance plans has become an important issue that enterprises urgently need to address.

[0003] Traditional equipment safety management relies primarily on periodic maintenance and reactive repairs, which have significant limitations. First, periodic maintenance is often based on experience or equipment manufacturer recommendations, failing to accurately reflect the actual operating status and remaining lifespan of the equipment. This can lead to over-maintenance or under-maintenance; over-maintenance increases unnecessary costs, while under-maintenance can cause equipment failures and affect production safety. Second, reactive repairs are performed after equipment failures occur, which not only leads to production interruptions but may also increase repair costs and time due to emergency repairs. Furthermore, traditional methods lack systematic collection and analysis of data throughout the equipment's entire lifecycle, making it difficult to form a comprehensive understanding and accurate prediction of the equipment's safety status, thus limiting the improvement of equipment safety management levels.

[0004] Given the shortcomings of traditional equipment safety management technologies, it is therefore particularly important to develop a standardized and comprehensive management system and methodology for safe production. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a comprehensive management system and method for standardized safety production. This system integrates multiple modules, including data acquisition, preprocessing, model building and training, predictive analysis, and maintenance management, to achieve real-time monitoring and precise management of the entire equipment lifecycle. The system utilizes an improved LSTM neural network to construct an equipment safety life prediction model, which can accurately predict the overall lifespan of the equipment and the remaining safe service life of easily worn components. This provides enterprises with a scientific basis for maintenance decisions. The application of this system significantly improves the accuracy and foresight of equipment safety management, effectively reduces the risk of unplanned downtime and production interruptions, and provides strong protection for the enterprise's safe production and economic benefits.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On the one hand, a comprehensive management system for standardized safety production, which includes the following components: a data acquisition module, a data preprocessing module, a model building and training module, a predictive analysis module, and a maintenance management module;

[0007] The data acquisition module is used to continuously collect time-series data throughout the entire lifecycle of the device. The time-series data includes at least the device's runtime, number of start-ups and shutdowns, maintenance records, fault history, and environmental temperature and humidity changes. The data acquisition module includes a multi-dimensional sensing unit and an adaptive transmission submodule.

[0008] The data preprocessing module is used to clean, denoise, and normalize the collected time-series data, remove abnormal data, and unify the data format; the data preprocessing module adopts a three-level purification mechanism.

[0009] The model building and training module is used to build and train an equipment safety life prediction model based on preprocessed time-series data using an improved LSTM neural network time-series data mining algorithm. The model includes an overall equipment life prediction sub-model and a consumable component life prediction sub-model. The consumable components include at least bearings and seals. In the model building and training module, the memory unit of the improved LSTM neural network adopts a dynamic forgetting mechanism and includes a two-factor attention mechanism sub-module.

[0010] The predictive analysis module is used to call the trained equipment safety life prediction model, input the real-time collected and preprocessed equipment time-series data, and output the overall equipment scrapping time and the remaining safe service life of each easily damaged component; the predictive analysis module adopts a multi-scale fusion prediction strategy.

[0011] The maintenance management module is used to generate component replacement reminders based on the remaining safe service life of consumable components, and to generate procurement suggestions in conjunction with equipment maintenance needs and inventory data; the maintenance management module includes an intelligent decision-making subsystem; the standardization management module is communicatively connected to the data acquisition module, predictive analysis module, and maintenance management module, and is used to integrate equipment operation data, predictive data, and maintenance data to generate a standardized safety production report, realizing standardized management of the entire equipment lifecycle; the standardization management module includes a dynamic risk assessment subsystem.

[0012] Furthermore, the multi-dimensional sensing unit of the data acquisition module includes: a timing sensor for collecting the cumulative running time and single running duration of the equipment; a mechanical motion sensor for collecting the vibration amplitude at the moment of equipment start-up and shutdown through the piezoelectric effect, and calculating the number of start-ups and shutdowns and the start-up and shutdown impact intensity; and an environmental composite sensor for simultaneously collecting temperature, humidity, and dust concentration data. The adaptive transmission submodule adopts a dynamic frame length adjustment mechanism. When the equipment's operating state is stable, the transmission interval is automatically adjusted to 30 seconds / time. When a parameter mutation is detected, it immediately switches to a high-frequency transmission mode of 1 second / time and prioritizes the use of industrial control channels through 5G slicing technology to ensure that the latency of critical data transmission is ≤100ms. The data acquisition module also includes a manual input terminal with electronic signature. Maintenance personnel must pass dual authentication of facial recognition and employee ID before they can input maintenance records. Each record is automatically appended with a timestamp and authentication information to ensure data traceability.

[0013] Furthermore, the three-level purification mechanism of the data preprocessing module includes: the first level is anomaly detection, which uses an improved isolated forest algorithm to cluster the time-series data by constructing 200 isolated trees, calculates the anomaly index AI for each data point, and determines an outlier and removes data points when AI > 0.7. The formula for calculating the anomaly index is: In the formula, the path length is the traversal depth of the data point in the isolated tree, and the feature deviation is the Euclidean distance between the current data and the center of the same cluster. The second level is missing value repair, which uses spatiotemporal fusion interpolation. For short-period missing values, cubic spline interpolation is used. ,in As basis functions, The first stage involves known data points; for long-term missing data, concurrent operating data from similar devices are introduced as auxiliary variables and filled using an improved KNN algorithm; the third stage is feature normalization, employing a segmented standardization strategy, and using Min-Max normalization for monotonically increasing data such as runtime. For data exhibiting temperature and humidity fluctuations, an improved Z-Score standardization was employed. ,in and These are the moving mean and moving standard deviation at time t, respectively, so that the data can be unified in terms of units while preserving the time series trend.

[0014] Furthermore, in the model construction and training module, the memory unit of the improved LSTM neural network adopts a dynamic forgetting mechanism, and its core update formula is: ,in for The output of the forget gate at any moment, It is the sigmoid activation function. The forget gate weight matrix has the dimension equal to the number of hidden layer neurons multiplied by (number of hidden layer neurons + number of input features). for The hidden state at all times for The input feature vector at time t, For the forget gate bias term, This is a dynamic adjustment coefficient, with a value range of 0.1-0.5. This is the equipment health degradation factor.

[0015] Furthermore, the two-factor attention mechanism submodule of the model construction and training module is used to dynamically adjust the weight allocation of input features, and its attention weight calculation formula is as follows: ,in for Time of the first Attention weights for each feature for Time of the first Each input feature for The hidden state at all times , This is the weight matrix. , For bias terms, It represents the Hadamah accumulation. The importance coefficient of the bearing features is pre-calculated using grey relational analysis. Environmental characteristics , For the first The historical contribution of each feature enables the model to adaptively focus on key features: when abnormal bearing vibration is detected, its attention weight is automatically increased; when the ambient temperature and humidity change drastically, the weight of environmental features is dynamically increased. Through this dynamic adjustment, the model reduces the error in predicting the lifespan of wear-prone components by 15-20%, and performs better, especially under complex working conditions.

[0016] Furthermore, the multi-scale fusion prediction strategy of the predictive analysis module includes a short-term fine prediction sub-module and a long-term trend prediction sub-module, and the fusion formula between the two is as follows: ,in For the final predicted remaining lifespan, For short-term forecast results, For long-term prediction results, For dynamic weighting coefficients, when Timing, ;when Timing, ;when Timing, , The correction term is calculated using the following formula: ,in For equipment type coefficients, rotating equipment Static equipment , The characteristic time constant, The moving average of recent prediction errors is used. This fusion strategy solves the problems of insufficient short-term accuracy and long-term trend drift in single-scale prediction. Industrial verification shows that the prediction error for the remaining life of bearings in 30 days is ≤3 days, and the prediction error for the overall life of equipment in 180 days is ≤15 days, which meets the requirements of industrial applications.

[0017] Furthermore, the intelligent decision-making subsystem of the maintenance management module generates the optimal maintenance strategy based on an improved reinforcement learning algorithm, and its reward function is defined as: ,in: for The decision reward value at any given moment. , , These are weighting coefficients, taken as 0.4, 0.4, and 0.2 respectively. To account for the expected costs of traditional preventative maintenance, The expected cost of the strategy recommended by this system. for The probability of failure at time 1 is output by the prediction model. To maintain the standardization and compliance of the solution, a value of 0-1 is used. Based on enterprise safety standard assessments, the intelligent decision-making subsystem continuously interacts with the environment to optimize maintenance time windows and component replacement combinations. When the predicted remaining bearing life is 45 days, the system evaluates the reward values ​​of three options: immediate replacement (high cost, low risk), replacement after 15 days (medium cost, requires monitoring), and replacement after 30 days (low cost, high risk). The largest possible option is used as the recommended strategy. Simultaneously, an economic order quantity correction model is incorporated when generating procurement recommendations. ,in To achieve the optimal purchase quantity, This represents the average daily demand. Cost per order, Unit inventory cost For remaining lifespan, For the total lifespan of the component, For safety, a factor of 1.2 is used to ensure that the procurement plan achieves the optimal balance between economy and safety.

[0018] Furthermore, the risk dynamic assessment subsystem of the standardized management module uses an improved Bowtie analysis method to construct a risk matrix, and its risk value calculation formula is as follows: ,in The probability of failure occurrence is output by the prediction model and takes a value between 0 and 1. The severity of the consequences was determined using Failure Mode and Effects Analysis (FMEA), categorized into 5 levels with values ​​ranging from 1 to 5. This represents the risk exposure value. The effectiveness of existing security measures is determined through an audit score, ranging from 0 to 1. The dynamic risk assessment subsystem updates the risk value every 8 hours and automatically adjusts control measures based on the assessment results. Upon such event, a Level 3 response is immediately triggered, involving system shutdown for inspection and 24-hour monitoring; At that time, a Level II response will be activated, increasing the frequency of inspections to once every 2 hours plus specialized testing; At the same time, the system maintains the normal Level 1 response. Simultaneously, the system automatically generates a standardized report that includes the trend of risk value changes, the implementation status of control measures, and the results of rectification and acceptance. The report format complies with the requirements of GB / T28001 safety production management system and can be directly used for external audits and internal assessments.

[0019] Furthermore, the system also includes a digital twin mapping module. By receiving measured data from the data acquisition module and prediction results from the predictive analysis module in real time, it constructs a high-fidelity digital twin of the equipment. This module dynamically updates the digital twin model in the following way: based on the model state at the previous moment, it calculates the state change based on the physical laws of the equipment, and then uses a Kalman filter algorithm to fuse the deviation between the actual acquired data and the model prediction output to form the model state at the current moment. The digital twin mapping module supports three operating modes: real-time synchronization mode can achieve a 1:1 mapping with the current state of the equipment with a delay of no more than 500ms; predictive simulation mode can simulate the equipment state changes over the next 30 days based on the prediction model output; and hypothetical analysis mode can simulate the evolution of the equipment state under different maintenance strategies. Through this module, managers can intuitively view the internal wear and tear of the equipment and the three-dimensional visualization of bearing wear in a virtual environment, verifying the effectiveness of maintenance plans to reduce actual trial and error costs. At the same time, this module is linked with the standardized management module to automatically generate a maintenance plan visualization report containing digital twin simulation results, improving decision-making efficiency.

[0020] On the other hand, the comprehensive management method for standardized safety production is characterized by the following specific steps:

[0021] S1. Data acquisition step: Continuously collect time-series data throughout the entire life cycle of the equipment. The time-series data includes at least the equipment running time, number of start-stop cycles, maintenance records, fault history, and environmental temperature and humidity change data. The data acquisition step obtains the equipment operating parameters through a multi-dimensional sensing unit and realizes dynamic data transmission through an adaptive transmission submodule.

[0022] S2. Data preprocessing step: The collected time series data is cleaned, denoised, and normalized to remove abnormal data and unify the data format. The data preprocessing step adopts a three-level purification mechanism, which sequentially performs anomaly detection, missing value repair and feature normalization.

[0023] S3. Model Construction and Training Steps: Based on the preprocessed time-series data, an improved LSTM neural network time-series data mining algorithm is used to construct and train an equipment safety life prediction model. The model includes an overall equipment life prediction sub-model and a consumable component life prediction sub-model. The consumable components include at least bearings and sealing rings. In the model construction and training steps, the memory unit of the improved LSTM neural network adopts a dynamic forgetting mechanism, and the weight allocation of the input features is dynamically adjusted through a two-factor attention mechanism sub-module.

[0024] S4. Predictive analysis step: Call the trained equipment safety life prediction model, input the real-time collected and preprocessed equipment time-series data, and output the overall equipment scrapping time and the remaining safe service life of each easily worn component; The predictive analysis step adopts a multi-scale fusion prediction strategy, combining short-term fine prediction and long-term trend prediction results to generate the final prediction.

[0025] S5. Maintenance management steps: Generate component replacement reminders based on the remaining safe service life of consumable components, and generate procurement suggestions in conjunction with equipment maintenance needs and inventory data; the maintenance management steps generate the optimal maintenance strategy through an intelligent decision-making subsystem based on an improved reinforcement learning algorithm;

[0026] S6. Standardized management steps: Integrate equipment operation data, forecast data, and maintenance data to generate a standardized safety production report, achieving standardized management of the entire equipment lifecycle; the standardized management steps use a dynamic risk assessment subsystem to conduct real-time assessment and control of equipment risks.

[0027] Compared with existing technologies, this comprehensive management system and method for standardized safety production has the following beneficial effects:

[0028] I. This system continuously collects time-series data throughout the entire lifecycle of equipment, including key parameters such as runtime, number of start-ups and shutdowns, and maintenance records. It then uses an improved LSTM neural network to build an equipment safe life prediction model. This model can not only predict the overall lifespan of the equipment but also accurately predict the remaining safe service life of easily worn components. This accurate predictive capability enables enterprises to plan maintenance in advance, avoid production interruptions and safety accidents caused by sudden equipment failures, and significantly improve the accuracy and foresight of equipment safety management.

[0029] Second, this system generates component replacement reminders based on the remaining safe service life of easily worn parts through its built-in maintenance management module, and generates procurement suggestions by combining equipment maintenance needs and inventory data. More importantly, this module generates optimal maintenance strategies based on an improved reinforcement learning algorithm through an intelligent decision-making subsystem. This strategy can maximize the economy of maintenance time windows and component replacement combinations while ensuring the safe operation of equipment. This optimization strategy not only reduces maintenance costs, but also reduces production losses caused by unplanned downtime, thereby improving overall production efficiency.

[0030] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0032] Figure 1 A schematic diagram of the architecture of a comprehensive management system for standardized safety production;

[0033] Figure 2 A flowchart illustrating the standardized and comprehensive management methods for safe production. Detailed Implementation

[0034] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0035] Example 1

[0036] In the reactor production workshop of a large chemical enterprise, in order to achieve standardized management of safe production of reactor equipment, data collection work is carried out first. As the core production device, the reactor equipment in this workshop directly affects production safety and efficiency. It is necessary to collect time-series data throughout the entire life cycle of the equipment, including the cumulative running time of the reactor, the number of start-ups and shutdowns in each production process, past maintenance records, historical fault information, and temperature and humidity change data of the surrounding environment of the reactor.

[0037] Data acquisition is accomplished through a multi-dimensional sensing unit. Among them, the timing sensor is installed in the motor control circuit of the reactor to record the cumulative running time of the equipment and the duration of each single operation after each start-up, providing a basis for subsequent assessment of the equipment's fatigue level. The mechanical motion sensor uses a piezoelectric sensor and is installed at the start-stop control valve of the reactor. By sensing the vibration amplitude at the moment the valve starts and stops, it calculates the number of times the equipment starts and stops, and further analyzes the impact intensity of start-stop, avoiding damage to the equipment caused by frequent start-stop or excessive impact. The environmental composite sensor is arranged in key positions around the reactor to simultaneously collect data on temperature, humidity, and dust concentration that may be generated during the production process, preventing harsh environments from affecting equipment operation.

[0038] The adaptive transmission submodule plays a crucial role in data transmission. When the reactor is in a stable operating state with minimal fluctuations in various parameters, the transmission interval is automatically adjusted to a lower frequency to reduce data transmission volume and lower network load. However, when a sudden change in a critical parameter of the reactor is detected, it immediately switches to a high-frequency transmission mode to ensure that abnormal data can be uploaded in a timely manner. At the same time, with the help of 5G slicing technology, the industrial control channel is prioritized to ensure the real-time performance and reliability of data transmission, avoiding data delays caused by network congestion and ensuring timely handling of equipment malfunctions.

[0039] In addition, the data acquisition module is also equipped with a manual input terminal with electronic signature. After maintenance personnel perform maintenance work on the reactor, they must first verify their identity through facial recognition and then enter their personal employee number to complete dual authentication. Only after successful authentication can maintenance records be entered. Each maintenance record will be automatically appended with a corresponding timestamp and authentication information to ensure the authenticity, integrity and traceability of maintenance data and avoid false maintenance records or data tampering.

[0040] After collecting the time-series data from the reactor equipment, the data preprocessing stage is initiated. A three-level purification mechanism is used to process the data to improve its quality and provide a reliable data foundation for subsequent model building and analysis.

[0041] The first level is anomaly detection, employing an improved isolated forest algorithm. This algorithm constructs multiple isolated trees to perform cluster analysis on the collected reactor time-series data, calculates the anomaly index for each data point, and removes data points when the anomaly index exceeds a reasonable threshold by setting a reasonable threshold. The formula for calculating the anomaly index is as follows: In the formula, the path length is the traversal depth of the data point in the isolated tree, and the feature deviation is the Euclidean distance between the current data and the center of the same cluster. For example, if the temperature data of the reactor at a certain moment is much higher than the normal operating range and is determined to be an outlier by the anomaly index calculation, the data will be removed to avoid the outlier data from interfering with the subsequent analysis results.

[0042] The second level is missing value repair, which uses spatiotemporal fusion interpolation. During the operation of the reactor, some data may be missing due to sensor failure, network interruption, etc. For short-period missing values, cubic spline interpolation is used. ,in As basis functions, For known data points, a smooth interpolation curve is constructed based on the known data points before and after the missing data to fill in the missing data. For long-term missing data, it is difficult to accurately fill the missing data by relying solely on its own historical data. In this case, the concurrent operating data of similar reactors is introduced as an auxiliary variable. Through the improved KNN algorithm, the missing values ​​are filled in based on the operating data of similar equipment under similar operating conditions, ensuring the continuity and integrity of the data.

[0043] The third level is feature normalization, which employs a segmented standardization strategy. For different types of time-series data from the reactor, appropriate standardization methods are selected. For data such as runtime, which monotonically increases over time, the Min-Max normalization method is used. This method maps data to a specific range of values, eliminating the influence between data of different magnitudes; for fluctuating data such as temperature, humidity, and vibration amplitude, an improved Z-Score standardization method is used. ,in and Let be the moving mean and moving standard deviation at time t, respectively. By calculating the moving mean and moving standard deviation of the data, the data is transformed into a form that conforms to a standard normal distribution, making data with different fluctuation characteristics comparable and facilitating the subsequent extraction and analysis of data features by the model.

[0044] Based on the preprocessed reactor time series data, we began to build and train a safety life prediction model for the equipment. We adopted an improved LSTM neural network time series data mining algorithm. The model includes a sub-model for predicting the overall life of the reactor and a sub-model for predicting the life of easily worn components.

[0045] In the memory units of the improved LSTM neural network, a dynamic forgetting mechanism is employed, and its core update formula is: ,in for The output of the forget gate at any moment, It is the sigmoid activation function. Here is the forget gate weight matrix. for The hidden state at all times for The input feature vector at time t, For the forget gate bias term, For dynamic adjustment coefficients, As a factor for equipment health decay, the importance of operating data at different times for equipment life prediction varies during the long-term operation of the reactor. The dynamic forgetting mechanism can dynamically adjust the degree of forgetting of historical data based on the timeliness and relevance of the data. For example, the recent operating data of the reactor is more accurate in reflecting the current equipment life status and should be given higher weight, while some irrelevant data from earlier periods can be appropriately forgotten to improve the training efficiency and prediction accuracy of the model.

[0046] The two-factor attention mechanism submodule is used to dynamically adjust the weight allocation of input features. The operating data of the reactor contains multiple features, and different features have different degrees of influence on equipment life prediction. Through the two-factor attention mechanism, different weights can be assigned to different input features based on their importance and historical contribution. The formula for calculating the attention weight is as follows: ,in for Time of the first Attention weights for each feature for Time of the first Each input feature for The hidden state at all times , This is the weight matrix. , For bias terms, It represents the Hadamah accumulation. The feature importance coefficient, For the first The historical contribution of each feature, such as the temperature and pressure data of the reactor, has a significant impact on the overall lifespan of the equipment and the lifespan of easily worn parts. This mechanism can increase the weight of these key features, making the model pay more attention to this important information during training, thereby improving the model's prediction accuracy.

[0047] During model training, the preprocessed reactor time series data is divided into a training set and a validation set. The training set is used to train the model, and by continuously adjusting the model parameters, the model can learn the inherent relationship between reactor operating data and equipment lifespan. The validation set is used to validate the trained model and evaluate its predictive performance. If the model has a large prediction error on the validation set, the model parameters are adjusted or the algorithm is optimized until the model achieves satisfactory prediction accuracy.

[0048] After the model training is completed, the predictive analysis stage begins. The trained reactor equipment safe life prediction model is called, and the real-time collected and preprocessed reactor time series data is input into the model.

[0049] The predictive analysis module employs a multi-scale fusion prediction strategy, which includes a short-term refined prediction sub-module and a long-term trend prediction sub-module. The fusion formula between the two is as follows: ,in For the final predicted remaining lifespan, For short-term forecast results, For long-term prediction results, For dynamic weighting coefficients, when Timing, ;when Timing, ;when Timing, , The correction term is calculated using the following formula: ,in For equipment type coefficient, The characteristic time constant, The short-term fine prediction submodule, which is the moving average of recent prediction errors, mainly predicts the short-term life status of the reactor. It can reflect in detail the impact of short-term operational changes on the remaining life, such as predicting the remaining safe service life of the reactor bearings in the next 15 days, providing a basis for the formulation of short-term maintenance plans. The long-term trend prediction submodule focuses on predicting the long-term life trend of the reactor, analyzing the overall aging trend and life trajectory of the equipment, and providing a reference for the long-term planning of the equipment.

[0050] By using a specific fusion method, short-term detailed forecast results and long-term trend forecast results are combined to generate the final forecast result, which outputs the overall scrap time of the reactor equipment and the remaining safe service life of each consumable component. For example, if the forecast result shows that the overall remaining life of the reactor is 5 years, the remaining safe service life of the bearing is 45 days, and the remaining safe service life of the sealing ring is 90 days, then clear time node information is provided for subsequent maintenance and management.

[0051] Based on the remaining safe service life of each consumable component of the reactor obtained from the predictive analysis, the maintenance management module begins to function. First, it generates corresponding component replacement reminders based on the remaining life of different consumable components. For example, when the predicted remaining safe service life of a bearing is 45 days, the system will send a bearing replacement reminder to the equipment maintenance personnel in advance, reminding them to prepare for replacement; when the remaining safe service life of a sealing ring is less than 30 days, a corresponding replacement warning will also be issued.

[0052] The intelligent decision-making subsystem, based on an improved reinforcement learning algorithm, combines factors such as the reactor's maintenance requirements, historical maintenance costs, failure risks, and inventory data to generate the optimal maintenance strategy. Its reward function is defined as: ,in: for The decision reward value at any given moment. , , These are the weighting coefficients. To account for the expected costs of traditional preventative maintenance, The expected cost of the strategy recommended by this system. for The probability of failure at any given time. To ensure the standardization and compliance of maintenance plans, the system defines a reasonable reward function that comprehensively considers indicators such as reduced maintenance costs, decreased failure probability, and the standardization and compliance of maintenance plans. For example, when the predicted remaining life of the bearing is 45 days, the system evaluates several maintenance options: immediate bearing replacement (relatively high cost, but effectively reduces the risk of reactor shutdown due to bearing failure); bearing replacement after 15 days (medium cost, but requires enhanced monitoring of bearing operation and increased inspection frequency during these 15 days); and bearing replacement after 30 days (low cost, but the probability of bearing failure gradually increases over time, potentially posing a greater risk to the normal operation of the reactor). The intelligent decision-making subsystem calculates the reward value of different options and selects the option with the highest reward value as the recommended maintenance strategy to achieve a balance between maintenance costs and failure risks.

[0053] When generating procurement recommendations, an economic order quantity correction model is introduced. ,in To achieve the optimal purchase quantity, This represents the average daily demand. Cost per order, Unit inventory cost For remaining lifespan, For the total lifespan of the component, To ensure safety, the model comprehensively considers factors such as the average daily demand for consumable parts of the reactor, the cost of a single order, the unit inventory cost, and the remaining and total lifespan of the parts to calculate the optimal purchase quantity. For example, for consumable parts such as bearings, a reasonable purchase quantity is determined based on their remaining and total lifespan, combined with daily consumption. This ensures that there are enough parts in stock when replacement is needed, avoiding equipment downtime due to stockouts, while also preventing over-purchasing that leads to inventory backlog, tying up capital and storage space.

[0054] The standardized management module maintains communication with the data acquisition module, predictive analysis module, and maintenance management module, integrating the operating data, predictive data, and maintenance data of the reactor equipment to generate a comprehensive safety production standardization report.

[0055] The risk dynamic assessment subsystem uses an improved Bowtie analysis method to construct a risk matrix, enabling real-time assessment and control of the risks associated with the reactor equipment. The risk value calculation formula is as follows: ,in The probability of failure occurring. As for the severity of the consequences, This represents the risk exposure value. To assess the effectiveness of existing safety measures, this subsystem measures the degree of risk faced by the equipment by calculating a risk value. The calculation of the risk value takes into account the probability of failure, the severity of the consequences, the risk exposure value, and the effectiveness of existing safety measures.

[0056] The risk dynamic assessment subsystem updates the risk value at fixed time intervals and automatically adjusts control measures based on the assessment results. When the risk value is high, exceeding the set high-risk threshold, a Level 3 response is immediately triggered, and shutdown and inspection measures are taken to conduct a comprehensive inspection and maintenance of the reactor. At the same time, 24-hour monitoring is initiated to closely monitor changes in equipment status until the risk is effectively controlled. When the risk value is at a medium level, within the set medium-risk threshold range, a Level 2 response is initiated, increasing the frequency of inspections, for example, adjusting the inspection frequency from once every 4 hours to once every 2 hours, and conducting special inspections on high-risk links to promptly identify and address potential safety hazards. When the risk value is low, below the medium-risk threshold, a regular Level 1 response is maintained, and the equipment is managed according to the normal inspection plan and maintenance procedures.

[0057] The generated safety production standardization report includes the operating status, forecast results, maintenance status, risk assessment results, and implementation status of control measures of the reactor equipment. It provides the enterprise management with comprehensive and accurate equipment management information, enabling the management to understand the safety status of the equipment in a timely manner, make scientific and reasonable production and management decisions, realize standardized management of the entire life cycle of the reactor equipment, and ensure the safe production of chemical enterprises.

[0058] Example 2

[0059] In the CNC machine tool processing workshop of heavy manufacturing enterprises, CNC machine tools are core production equipment, and their operational stability and safety are crucial to product quality and production progress. Therefore, it is necessary to collect comprehensive data on them. The data collection scope covers the time-series data of the CNC machine tool throughout its entire life cycle, mainly including the cumulative running time of the equipment, the number of start-ups and shutdowns in each processing task, past maintenance records, historical fault information, and environmental temperature and humidity change data in the workshop.

[0060] The multi-dimensional sensing unit is responsible for specific data acquisition. The timing sensor is installed in the spindle drive system of the CNC machine tool to record the cumulative running time of the equipment and the single running duration of each processing task in real time. By analyzing the running time, the fatigue level of each component of the equipment can be determined, providing basic data for subsequent life prediction. The mechanical motion sensor uses a high-precision vibration sensor, which is installed at key moving parts such as the worktable and spindle of the CNC machine tool. By sensing the vibration amplitude at the moment of start and stop of these components, the number of start and stop times of the equipment is calculated, and the start and stop impact intensity is analyzed to prevent component damage or accuracy reduction due to excessive impact during start and stop. The environmental composite sensor is arranged around the CNC machine tool to simultaneously collect data on temperature, humidity and metal dust concentration generated during processing in the workshop. This prevents dust from adhering to the equipment components and affecting its normal operation, and at the same time prevents abnormal temperature and humidity from adversely affecting the electrical system and mechanical accuracy of the equipment.

[0061] The adaptive transmission submodule dynamically adjusts the data transmission strategy based on the operating status of the CNC machine tool. When the CNC machine tool is in a stable machining state and all machining parameters remain stable without significant fluctuations, the data transmission interval is automatically adjusted to a lower frequency to reduce unnecessary data transmission and save network resources. When a sudden change in the key parameters of the CNC machine tool is detected, it immediately switches to a high-frequency transmission mode to ensure that abnormal data can be quickly uploaded to the management system. At the same time, 5G slicing technology is used to prioritize the use of industrial control channels to ensure the real-time performance of data transmission and avoid the inability to detect and handle equipment abnormalities in a timely manner due to data delays, thus preventing the escalation of faults.

[0062] In addition, the data acquisition module is equipped with a manual data entry terminal with electronic signature. After completing the maintenance work on the CNC machine tool, maintenance personnel must first verify their identity through facial recognition and then enter their personal employee number for dual authentication. Only after successful authentication can the maintenance record be entered. Each maintenance record is automatically stamped with timestamps and authentication information to ensure the authenticity and traceability of the maintenance data, preventing falsification or omissions, and providing a reliable basis for subsequent equipment maintenance and analysis.

[0063] After collecting the time-series data of the CNC machine tool, a data preprocessing step is required. A three-level purification mechanism is used to process the data, removing noise, outliers, and missing values, and standardizing the data format, thus laying a good data foundation for subsequent model building and predictive analysis.

[0064] The first stage is anomaly detection, which uses an improved isolated forest algorithm. This algorithm constructs multiple isolated trees to cluster the collected CNC machine tool time-series data, calculates the anomaly index for each data point, and sets a reasonable anomaly index threshold based on the operating characteristics and historical data of the CNC machine tool. When the anomaly index of a data point exceeds the threshold, it is determined to be an outlier and is removed. For example, if the spindle vibration amplitude data of the CNC machine tool at a certain moment is much higher than the normal operating range, and is determined to be an outlier by the anomaly index calculation, then the data is removed to avoid anomaly data interfering with subsequent model training and prediction results.

[0065] The second level is missing value repair, which employs a spatiotemporal fusion interpolation method. During the operation of CNC machine tools, some data may be missing due to sensor failures, network signal interruptions, temporary equipment shutdowns, etc. For short-period missing data, cubic spline interpolation is used to construct a smooth interpolation curve based on known valid data points before and after the missing data, thereby accurately filling in the missing data. For long-period missing data, relying solely on the historical data of the CNC machine tool itself is insufficient for accurate filling. In this case, the concurrent operating data of other CNC machine tools of the same model and under the same working conditions is introduced as an auxiliary variable. Through an improved KNN algorithm, under similar machining tasks and operating environments, the operating data of similar equipment is used to fill in the missing values, ensuring the continuity and integrity of the data and meeting the needs of subsequent analysis.

[0066] The third level is feature normalization, which adopts a segmented standardization strategy. For different types of time-series data from CNC machine tools, appropriate standardization methods are selected. For data such as runtime, which monotonically increases over time, the Min-Max normalization method is used to compress the data into a specific value range, eliminating the impact of differences in data magnitude and making runtime data from different time periods comparable. For fluctuating data such as temperature and humidity, spindle vibration amplitude, and feed rate fluctuations, an improved Z-Score standardization method is used. By calculating the moving mean and moving standard deviation of the data within a certain time window, the data is converted into a form conforming to a standard normal distribution, allowing data with different fluctuation characteristics to be analyzed under the same standard. This facilitates better feature extraction by subsequent models and improves model accuracy.

[0067] Based on the preprocessed CNC machine tool time series data, an improved LSTM neural network time series data mining algorithm is used to construct and train a safe life prediction model for the equipment. This model includes a CNC machine tool overall life prediction sub-model and a consumable component life prediction sub-model, which are used to predict the remaining service life of the overall equipment and the remaining safe service life of each consumable component, respectively.

[0068] The improved LSTM neural network employs a dynamic forgetting mechanism in its memory units. During the long-term operation of CNC machine tools, the value of operational data at different stages for predicting equipment lifespan varies. For example, recent operational data of CNC machine tools better reflects the actual state of the current equipment and has higher reference value for lifespan prediction. However, some early operational data has relatively lower reference value due to factors such as equipment aging and changes in operating conditions. The dynamic forgetting mechanism can dynamically adjust the degree of forgetting of historical data based on the timeliness of the data and its relevance to the current equipment state, retaining valuable data information while forgetting irrelevant or less influential data, thereby improving the training efficiency and prediction accuracy of the model.

[0069] The two-factor attention mechanism submodule is used to dynamically adjust the weight allocation of input features. The operating data of CNC machine tools contains multiple features, such as spindle speed, feed rate, cutting force, running time, vibration amplitude, and ambient temperature and humidity. Different features have different degrees of influence on the lifespan of the equipment and the lifespan of easily worn parts. For example, cutting force and spindle speed directly affect the wear rate of the tool, while vibration amplitude has a significant impact on the lifespan of bearings and guideways. The two-factor attention mechanism can assign corresponding weights to different input features based on their importance and historical contribution, so that the model focuses on features that have a significant impact on lifespan prediction during training, improves the model's ability to capture key features, and thus improves prediction accuracy.

[0070] During model training, the preprocessed CNC machine tool time series data is divided into a training set and a validation set. The training set is used for parameter learning and training of the model. By continuously iterating and optimizing the model parameters, the model can fully learn the inherent correlation between CNC machine tool operating data and equipment lifespan. The validation set is used to evaluate the performance of the trained model. By comparing the model's prediction results with the actual equipment lifespan data, the prediction error is calculated. If the prediction error is large and exceeds the acceptable range, the model's structure, parameters, or optimization algorithm are adjusted, and the model is retrained until the model's prediction performance meets the expected requirements and can accurately predict the overall lifespan of the CNC machine tool and the remaining safe service life of easily worn parts.

[0071] After training the CNC machine tool equipment safety life prediction model, the prediction analysis stage begins. Real-time collected and preprocessed CNC machine tool time series data is input into the model to start the prediction analysis process.

[0072] The predictive analysis module employs a multi-scale fusion prediction strategy, consisting of a short-term fine prediction sub-module and a long-term trend prediction sub-module. The short-term fine prediction sub-module primarily predicts the short-term lifespan of CNC machine tools, accurately reflecting the impact of short-term operational changes on the remaining lifespan. For example, this sub-module can predict the remaining safe service life of cutting tools within the next 10 days, providing an accurate basis for short-term tool replacement plans and preventing excessive tool wear from affecting machining accuracy or causing equipment failure. The long-term trend prediction sub-module focuses on predicting the long-term lifespan trend of CNC machine tools, comprehensively considering factors such as the overall aging degree of the equipment, long-term operating conditions, and maintenance status to analyze the overall lifespan trend of the equipment. This provides a reference for enterprises to formulate long-term equipment upgrade and overhaul plans, helping them to make advance equipment procurement and production arrangements and avoid production delays due to sudden equipment failure.

[0073] By using a specific fusion algorithm, the prediction results of the short-term fine prediction submodule and the long-term trend prediction submodule are combined to generate the final comprehensive prediction result. This result clearly outputs the overall scrap time of the CNC machine tool equipment and the remaining safe service life of each wear-prone component. For example, the prediction result shows that the overall remaining life of the CNC machine tool is 3 years, the remaining safe service life of the bearing is 60 days, and the remaining safe service life of the guide rail is 180 days, providing clear time nodes and objectives for subsequent maintenance and management work.

[0074] Based on the predicted and analyzed remaining safe service life of various consumable parts of the CNC machine tool, the maintenance management module begins to execute corresponding management operations. First, it generates personalized component replacement reminders based on the remaining life of different consumable parts. For example, when the predicted remaining safe service life of a tool is only 5 days, the system will immediately send a tool replacement reminder to the equipment maintenance personnel and operators, reminding them to prepare suitable tools in advance to avoid processing interruptions due to tool failure. When the remaining safe service life of a bearing is less than 30 days, a warning message will also be issued, prompting maintenance personnel to prepare for bearing replacement.

[0075] The intelligent decision-making subsystem, based on an improved reinforcement learning algorithm, combines the maintenance needs of CNC machine tools, historical maintenance cost data, fault risk assessment results, and inventory data to generate the optimal maintenance strategy. This system defines a scientifically sound reward function, comprehensively considering factors such as maintenance cost savings, reduced failure probability, and whether the maintenance plan meets the company's safety production standardization requirements. For example, when the predicted remaining bearing life is 60 days, the system evaluates several possible maintenance plans: Plan 1 is to immediately replace the bearing, which has a relatively high replacement cost but can minimize the risk of CNC machine tool downtime due to bearing failure, ensuring production continuity; Plan 2... Replacing the bearing after 30 days has a moderate replacement cost, but requires enhanced monitoring of the bearing's operating status during these 30 days, increased inspection frequency, and close attention to changes in parameters such as bearing temperature and vibration to promptly identify potential problems. Option three involves replacing the bearing after 50 days, which has the lowest replacement cost. However, the probability of bearing failure gradually increases over time. Once a failure occurs, it may damage the spindle system of the CNC machine tool, leading to higher maintenance costs and longer downtime. The intelligent decision-making subsystem calculates the reward value of different options and selects the option with the highest reward value as the recommended maintenance strategy, thereby optimizing maintenance costs while ensuring safe equipment operation.

[0076] When generating procurement recommendations, an economic order quantity (EOQ) correction model is introduced. This model fully considers key factors such as the average daily demand for consumable parts of CNC machine tools, the cost of a single order, the unit inventory cost, and the remaining and total lifespan of the parts. It calculates the optimal procurement quantity precisely. For example, for consumable parts such as cutting tools that wear out quickly, a reasonable procurement quantity is determined based on their remaining and total lifespan, combined with the daily consumption rate. This ensures that there are enough spare cutting tools in stock when they need to be replaced, avoiding production interruptions due to stockouts, while also avoiding over-purchasing and inventory backlog, thus reducing capital occupation and warehousing costs.

[0077] The standardized management module establishes real-time communication connections with the data acquisition module, predictive analysis module, and maintenance management module, comprehensively integrating the operating data, predictive data, and maintenance data of CNC machine tools, and generating detailed and standardized safety production reports based on this integrated data.

[0078] The risk dynamic assessment subsystem uses an improved Bowtie analysis method to construct a risk matrix, and performs real-time and dynamic assessment and control of the operational risks of CNC machine tools. The subsystem quantifies the degree of risk faced by the equipment by calculating risk values. The calculation of risk values ​​comprehensively considers multiple key factors: failure probability, severity of consequences, risk exposure value, and effectiveness of existing safety measures.

[0079] The risk dynamic assessment subsystem updates and calculates the risk value at fixed time intervals and automatically adjusts the corresponding risk control measures based on the latest risk assessment results. When the calculated risk value is high, exceeding the set high-risk threshold, a Level 3 emergency response is immediately triggered, and the machine is quickly shut down for inspection. Professional technicians conduct a comprehensive and detailed inspection of the CNC machine tool to identify potential safety hazards and fault points. At the same time, a 24-hour uninterrupted monitoring mode is activated, using a real-time monitoring system to closely track changes in various operating parameters of the equipment until the risk is effectively controlled and the equipment returns to a safe operating state. When the risk value is at a medium level, within the set medium-risk threshold range, a Level 2 response is activated, appropriately increasing the frequency of inspections, for example, adjusting the regular inspection from once every 6 hours to once every 2 hours. Special inspections are carried out on high-risk components of the CNC machine tool to promptly identify and address potential problems and prevent further escalation of the risk. When the risk value is low, below the medium-risk threshold, the normal Level 1 response status is maintained, and the CNC machine tool is maintained and managed according to normal equipment management procedures and inspection plans to ensure stable equipment operation.

[0080] The generated safety production standardization report comprehensively covers various aspects, including a summary of the operating status of CNC machine tools, analysis of life prediction results, reports on the implementation of maintenance work, risk assessment results and trends, and the implementation of risk control measures. This report provides the management and equipment management departments of heavy manufacturing enterprises with comprehensive, accurate, and timely equipment management information, helping management to clearly understand the safe operating status and potential risks of CNC machine tools, formulate scientific and reasonable production scheduling and equipment management decisions, achieve standardized and regulated management of the entire life cycle of CNC machine tools, and ensure the safety, stability, and efficiency of the production process in heavy manufacturing enterprises.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A comprehensive management system for standardized safety production, characterized in that: The system comprises the following components: a data acquisition module, a data preprocessing module, a model building and training module, a predictive analysis module, and a maintenance and management module. The data acquisition module is used to continuously collect time-series data throughout the entire lifecycle of the device. The time-series data includes at least the device's runtime, number of start-ups and shutdowns, maintenance records, fault history, and environmental temperature and humidity changes. The data acquisition module includes a multi-dimensional sensing unit and an adaptive transmission submodule. The data preprocessing module is used to clean, denoise, and normalize the collected time-series data, remove abnormal data, and unify the data format. The data preprocessing module employs a three-level purification mechanism; The model building and training module is used to build and train an equipment safety life prediction model based on preprocessed time-series data using an improved LSTM neural network time-series data mining algorithm. The model includes an overall equipment life prediction sub-model and a consumable component life prediction sub-model. The consumable components include at least bearings and seals. In the model building and training module, the memory unit of the improved LSTM neural network adopts a dynamic forgetting mechanism and includes a two-factor attention mechanism sub-module. The predictive analysis module is used to call the trained equipment safety life prediction model, input the real-time collected and preprocessed equipment time-series data, and output the overall equipment scrapping time and the remaining safe service life of each easily damaged component; the predictive analysis module adopts a multi-scale fusion prediction strategy. The maintenance management module is used to generate component replacement reminders based on the remaining safe service life of consumable components, and to generate procurement suggestions in conjunction with equipment maintenance needs and inventory data; the maintenance management module includes an intelligent decision-making subsystem. The standardized management module communicates with the data acquisition module, predictive analysis module, and maintenance management module respectively, and is used to integrate equipment operation data, predictive data, and maintenance data to generate a standardized safety production report; the standardized management module includes a risk dynamic assessment subsystem.

2. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The multi-dimensional sensing unit of the data acquisition module includes: a timing sensor for collecting the cumulative running time and single running duration of the equipment; a mechanical motion sensor for collecting the vibration amplitude at the moment of equipment start-up and shutdown through the piezoelectric effect, and calculating the number of start-ups and shutdowns and the start-up and shutdown impact intensity; and an environmental composite sensor for simultaneously collecting temperature, humidity, and dust concentration data. The adaptive transmission submodule adopts a dynamic frame length adjustment mechanism. When the equipment's operating state is stable, the transmission interval is automatically adjusted to 30 seconds / time. When a parameter change is detected, it immediately switches to a high-frequency transmission mode of 1 second / time and prioritizes the use of industrial control channels through 5G slicing technology. The data acquisition module also includes a manual input terminal with electronic signature. Maintenance personnel must pass dual authentication of facial recognition and employee ID before they can enter maintenance records. Each record is automatically appended with a timestamp and authentication information.

3. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The three-level purification mechanism of the data preprocessing module includes: the first level is anomaly detection, which is based on an improved isolated forest algorithm. It clusters the time-series data by constructing 200 isolated trees, calculates the anomaly index AI for each data point, and removes data points when AI > 0.

7. The formula for calculating the anomaly index is: In the formula, the path length is the traversal depth of the data point in the isolated tree, and the feature deviation is the Euclidean distance between the current data and the center of the same cluster. The second level is missing value repair, which uses spatiotemporal fusion interpolation. For short-period missing values, cubic spline interpolation is used. ,in As basis functions, The first stage involves known data points; for long-term missing data, concurrent operating data from similar devices are introduced as auxiliary variables and filled using an improved KNN algorithm; the third stage is feature normalization, employing a segmented standardization strategy, and using Min-Max normalization for monotonically increasing data such as runtime. For data exhibiting temperature and humidity fluctuations, an improved Z-Score standardization was employed. ,in and Let be the moving mean and moving standard deviation at time t, respectively.

4. The comprehensive management system for standardized safety production according to claim 1, characterized in that, In the model construction and training module, the memory unit of the improved LSTM neural network adopts a dynamic forgetting mechanism, and its core update formula is: ,in for The output of the forget gate at any moment, It is the sigmoid activation function. Here is the forget gate weight matrix. for The hidden state at all times for The input feature vector at time t, For the forget gate bias term, For dynamic adjustment coefficients, This is the equipment health degradation factor.

5. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The two-factor attention mechanism submodule of the model building and training module is used to dynamically adjust the weight allocation of input features, and its attention weight calculation formula is as follows: ,in for Time of the first Attention weights for each feature for Time of the first Each input feature for The hidden state at all times , This is the weight matrix. , For bias terms, It represents the Hadamah accumulation. The feature importance coefficient, For the first The historical contribution of each feature.

6. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The multi-scale fusion prediction strategy of the predictive analysis module includes a short-term fine prediction sub-module and a long-term trend prediction sub-module, and the fusion formula between the two is as follows: ,in For the final predicted remaining lifespan, For short-term forecast results, For long-term prediction results, For dynamic weighting coefficients, when Timing, ;when Timing, ;when Timing, , The correction term is calculated using the following formula: ,in For equipment type coefficient, The characteristic time constant, This is the moving average of recent prediction errors.

7. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The intelligent decision-making subsystem of the maintenance management module generates the optimal maintenance strategy based on an improved reinforcement learning algorithm, and its reward function is defined as: ,in: for The decision reward value at any given moment. , , These are the weighting coefficients. To account for the expected costs of traditional preventative maintenance, The expected cost of the strategy recommended by this system. for The probability of failure at any given time. To maintain the standardization and compliance of the solution, the intelligent decision-making subsystem continuously interacts with the environment to optimize maintenance time windows and component replacement combinations. When the predicted remaining bearing life is 45 days, the system evaluates the reward values ​​of three options: immediate replacement (high cost, low risk), replacement after 15 days (medium cost, requires monitoring), and replacement after 30 days (low cost, high risk), and selects the appropriate option. The largest possible option is used as the recommended strategy. Simultaneously, an economic order quantity correction model is incorporated when generating procurement recommendations. ,in To achieve the optimal purchase quantity, This represents the average daily demand. Cost per order, Unit inventory cost For remaining lifespan, For the total lifespan of the component, This is for the safety factor.

8. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The risk dynamic assessment subsystem of the standardized management module uses an improved Bowtie analysis method to construct a risk matrix, and its risk value calculation formula is as follows: ,in The probability of failure occurring. As for the severity of the consequences, This represents the risk exposure value. The effectiveness of existing security measures is determined through an audit score, ranging from 0 to 1. The dynamic risk assessment subsystem updates the risk value every 8 hours and automatically adjusts control measures based on the assessment results. Upon such event, a Level 3 response is immediately triggered, involving system shutdown for inspection and 24-hour monitoring; At that time, a Level II response will be activated, increasing the frequency of inspections to once every 2 hours plus specialized testing; At the same time, the system maintains the normal Level 1 response and automatically generates a standardized report that includes the trend of risk value changes, the implementation status of control measures, and the results of rectification and acceptance.

9. The comprehensive management system for standardized safety production according to claim 1, characterized in that, The system also includes a digital twin mapping module, which constructs a high-fidelity digital twin of the equipment by receiving measured data from the data acquisition module and prediction results from the predictive analysis module in real time. This module dynamically updates the digital twin model in the following way: based on the model state at the previous moment, it calculates the state change based on the physical laws of the equipment, and then uses a Kalman filter algorithm to fuse the deviation between the actual acquired data and the model prediction output to form the model state at the current moment. The digital twin mapping module supports three operating modes: real-time synchronization mode can achieve a 1:1 mapping with the current state of the equipment with a delay of no more than 500ms; predictive simulation mode can simulate the equipment state changes over the next 30 days based on the prediction model output; and hypothetical analysis mode can simulate the evolution of the equipment state under different maintenance strategies. Through this module, managers can intuitively view the internal wear and tear of the equipment and the three-dimensional visualization of bearing wear in a virtual environment, verifying the effectiveness of maintenance plans to reduce actual trial and error costs. At the same time, this module is linked with the standardized management module and can automatically generate a maintenance plan visualization report containing the digital twin simulation results.

10. A comprehensive management method for standardized safety production, applicable to the comprehensive management system for standardized safety production as described in any one of claims 1-9, characterized in that, The specific steps of this method are as follows: S1. Data acquisition step: Continuously collect time-series data throughout the entire life cycle of the equipment. The time-series data includes at least the equipment running time, number of start-stop cycles, maintenance records, fault history, and environmental temperature and humidity change data. The data acquisition step obtains the equipment operating parameters through a multi-dimensional sensing unit and realizes dynamic data transmission through an adaptive transmission submodule. S2. Data preprocessing step: The collected time series data is cleaned, denoised, and normalized to remove abnormal data and unify the data format. The data preprocessing step adopts a three-level purification mechanism, which sequentially performs anomaly detection, missing value repair and feature normalization. S3. Model Construction and Training Steps: Based on the preprocessed time-series data, an improved LSTM neural network time-series data mining algorithm is used to construct and train an equipment safety life prediction model. The model includes an overall equipment life prediction sub-model and a consumable component life prediction sub-model. The consumable components include at least bearings and sealing rings. In the model construction and training steps, the memory unit of the improved LSTM neural network adopts a dynamic forgetting mechanism, and the weight allocation of the input features is dynamically adjusted through a two-factor attention mechanism sub-module. S4. Predictive analysis step: Call the trained equipment safety life prediction model, input the real-time collected and preprocessed equipment time-series data, and output the overall equipment scrapping time and the remaining safe service life of each easily worn component; The predictive analysis step adopts a multi-scale fusion prediction strategy, combining short-term fine prediction and long-term trend prediction results to generate the final prediction. S5. Maintenance and management steps: Generate component replacement reminders based on the remaining safe service life of consumable components, and generate procurement recommendations based on equipment maintenance needs and inventory data; The maintenance management steps generate the optimal maintenance strategy based on an improved reinforcement learning algorithm through an intelligent decision-making subsystem; S6. Standardized management steps: Integrate equipment operation data, forecast data, and maintenance data to generate a standardized safety production report, achieving standardized management of the entire equipment lifecycle; the standardized management steps use a dynamic risk assessment subsystem to conduct real-time assessment and control of equipment risks.

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