Safety protection system for hydraulic equipment production

Abstract

The invention relates to the technical field of industrial production safety, in particular to a hydraulic equipment production safety protection system which comprises a dust monitoring module, an explosion risk prediction module, an environment adjusting module, an execution module and an alarm module. Wherein the dust monitoring module is used for collecting metal dust concentration and component data in an operation area of hydraulic equipment; the explosion risk prediction module is used for predicting the explosion risk of dust and calculating a potential explosion risk level; the environment adjusting module is used for automatically adjusting environment conditions in the production area; the execution module is used for automatically adjusting the operation state of production equipment or starting emergency safety measures; and the alarm module is used for sending real-time alarm information to operators and safety management personnel. According to the invention, through comprehensive measures of real-time monitoring, risk prediction, automatic environment adjustment, instant alarm and the like, the safety and reliability of the industrial production environment are significantly improved, and the occurrence of dust explosion accidents is effectively prevented.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial production safety, and particularly relates to a hydraulic equipment production safety protection system. BACKGROUND

[0002] With the rapid development of industrial production technology, hydraulic equipment plays an increasingly important role in various production and manufacturing fields, especially in metal processing, chemical industry and mining industry, etc. The efficient and stable operation of hydraulic equipment is crucial to ensure production safety and improve production efficiency. However, the production process of these industries is often accompanied by various safety risks, one of which is the metal dust that may cause explosion accidents. Fine metal dust generated in the process of metal processing, etc. If accumulated to a certain concentration in the air and meets the ignition source, an explosion may occur, posing a serious threat to personnel safety and production facilities.

[0003] Although existing safety protection measures can reduce the occurrence of explosion accidents to some extent, these measures often rely on traditional mechanical protection and manual monitoring, which not only has low efficiency, but also is difficult to realize real-time monitoring and early warning of potential explosion risks. In addition, the existing technology cannot accurately control the production environment conditions such as temperature, humidity and ventilation, etc. to actively prevent dust accumulation and explosion risks.

[0004] Therefore, how to effectively monitor and analyze the potential explosion risks in the production environment, and automatically adjust the operating state of the production equipment or take emergency safety measures according to the real-time risk assessment to prevent or reduce the occurrence of explosion accidents, has become a technical problem to be solved. SUMMARY

[0005] Based on the above purpose, the present application provides a hydraulic equipment production safety protection system.

[0006] A hydraulic equipment production safety protection system, comprising a dust monitoring module, an explosion risk prediction module, an environment adjustment module, an execution module and an alarm module; wherein,

[0007] The dust monitoring module is used to collect metal dust concentration and composition data in the operating area of the hydraulic equipment;

[0008] The explosion risk prediction module is used to predict the explosion risk of the dust based on the data collected by the dust monitoring module and in combination with the operating parameters of the hydraulic equipment, and to calculate the potential explosion risk level;

[0009] The environment adjustment module is used to automatically adjust the environmental conditions in the production area according to the risk assessment results of the explosion risk prediction module to reduce the explosion risk;

[0010] Execution module: According to the risk level calculated by the explosion risk prediction module, automatically adjust the running state of production equipment or start emergency safety measures to prevent potential explosion accidents;

[0011] Alarm module: When the risk level calculated by the explosion risk prediction module exceeds the preset threshold, or the execution module takes emergency safety measures, the audible and visual alarm will be activated and real-time alarm messages will be sent to the operating personnel and safety management personnel through the communication network to ensure rapid response.

[0012] Further, the dust monitoring module includes a laser particle size analyzer and an electrochemical composition analyzer; wherein,

[0013] Laser particle size analyzer: used to measure the particle size distribution of metal dust in the air in the hydraulic equipment operating area, the working principle is based on laser scattering technology, which determines the size distribution of dust particles by analyzing the intensity distribution of scattered light, and the laser particle size analyzer can provide accurate data of dust particle size in real time;

[0014] Electrochemical composition analyzer: used to determine the specific chemical composition of metal dust, which analyzes the chemical composition of dust by measuring the change of current generated when metal dust reacts with chemical reagents, and the electrochemical composition analyzer can accurately identify the main metal components in the dust sample, including aluminum and magnesium explosive metal dust, which provides the basis for explosion risk assessment.

[0015] Further, the explosion risk prediction module includes a data integration unit, a machine learning processing unit, and a risk level calculation unit; wherein,

[0016] Data integration unit: used to collect and integrate metal dust concentration and composition data from the dust monitoring module and operating parameters of the hydraulic equipment, including pressure, temperature and working cycle, the data integration unit specifically uses data fusion technology to ensure that the data obtained from different sources is consistent and complete before analysis;

[0017] Machine learning processing unit: receives integrated data from the data integration unit and uses a machine learning algorithm based on support vector machines to analyze the integrated data to predict the explosion risk of metal dust, the machine learning processing unit will learn the relationship between dust concentration, composition and hydraulic equipment operating parameters and explosion risk through training data to establish a prediction model for prediction;

[0018] Risk level calculation unit: calculates the potential explosion risk level according to the prediction results provided by the machine learning processing unit, the risk level calculation unit uses a risk assessment model to map the output of the machine learning processing unit to a predetermined explosion risk level, including low, medium and high levels, to facilitate the subsequent module to take appropriate safety measures.

[0019] Further, the data integration unit specifically includes:

[0020] Data preprocessing subunit: for formatting and cleaning the metal dust concentration and composition data from the dust monitoring module, as well as the operating parameter data of the hydraulic equipment, ensuring that all data are within the same scale and range through standardization processing, using the formula: Data standardization is performed, where X norm is the standardized data, X is the original data, X min and X max are the minimum and maximum values of the original data, respectively;

[0021] Data alignment subunit: for ensuring that the dust monitoring data and the hydraulic equipment operating parameter data are aligned according to the timestamp, so that each piece of dust data matches the corresponding operating parameter data, specifically using time synchronization technology, solving the data missing problem through interpolation or data filling method, ensuring the continuity and integrity of the time series;

[0022] Data merging subunit: for merging the preprocessed and aligned data into a unified data set for the machine learning processing unit, specifically using weighted average method to integrate information from different data sources, using the formula: Data merging is performed, where D combined is the merged data set, D i is the data of the i-th data source, w i is the weight of the i-th data source, and

[0023] Further, the machine learning processing unit specifically includes:

[0024] Feature selection subunit: selects features related to explosion risk prediction from the integrated data set, including the concentration and composition of metal dust, as well as the operating parameters of the hydraulic equipment, feature selection is based on the information gain method to determine the contribution of each feature to the explosion risk prediction ability;

[0025] Model training subunit: uses support vector machine algorithm to train the selected features, support vector machine model finds the optimal separating hyperplane between different classes to maximize the margin between different class data points, the objective function of support vector machine is represented as: where w is the normal vector of the hyperplane, b is the bias term, C is the regularization parameter, and ξ i is the relaxation variable representing the error of the i-th data point, support vector machine trains the model by solving this optimization problem to determine the optimal w and b;

[0026] Prediction and evaluation subunit: use the trained prediction model to predict the explosion risk of new data set, in the prediction process, the risk classification of each data point is based on the function value calculated by the model, the specific formula is: f(x) = w x + b, when the value of f(x) is greater than the preset threshold, it is predicted that the state corresponding to the data point has high explosion risk; otherwise, it is predicted to have low explosion risk.

[0027] Further, the risk level calculation unit specifically comprises:

[0028] Evaluation parameter definition: the risk assessment model used by the risk level calculation unit judges the risk according to the explosion risk probability output by the machine learning processing unit, sets the risk probability as P, and divides the risk level into low, medium and high by setting two threshold values T low and T high

[0029] Set the decision mechanism: when P < T low , the explosion risk level is determined to be low;

[0030] When T low ≤ P < T high , the explosion risk level is determined to be medium;

[0031] When P > T high , the explosion risk level is determined to be high;

[0032] Risk level output: based on the above determination, the risk level calculation unit outputs the specific explosion risk level, which will be directly used to guide the subsequent safety protection measures.

[0033] Further, the environment adjusting module comprises a temperature control unit, a humidity control unit and a ventilation system control unit; wherein,

[0034] Temperature control unit: used to adjust the temperature in the production area according to the risk assessment result, the specific adjustment process includes: if the risk level is low, the current temperature setting is kept unchanged; if the risk level is medium, the temperature is reduced to reduce the possibility of dust explosion; if the risk level is high, the temperature is greatly reduced to below the safety threshold;

[0035] Humidity control unit: used to adjust the humidity in the production area according to the risk assessment result, the specific adjustment process includes: if the risk level is low, the current humidity setting is maintained; if the risk level is medium, the humidity is moderately increased; if the risk level is high, the humidity is greatly increased to a predetermined proportion, and the high humidity environment is used to reduce the flying and accumulation of dust;

[0036] ​Ventilation system control unit: for adjusting the operation of the ventilation system according to the risk assessment results to optimize air flow and reduce potential explosion hazards, the specific adjustment process includes: if the risk level is low, operate according to the normal ventilation program; if the risk level is medium, increase the ventilation intensity and speed up the air circulation to dilute the potential combustible dust; if the risk level is high, maximize the ventilation volume to quickly remove the combustible dust in the air, and at the same time suspend production activities to ensure safety.

[0037] Further, the execution module includes a production speed adjustment unit, a production line state control unit, and an emergency shutdown control unit; wherein,

[0038] Production speed adjustment unit: for automatically adjusting the production line speed according to the risk level, the specific process includes: for low risk level, the production line runs at normal speed to maintain production efficiency; for medium risk level, reduce the production line speed to slow down the dust generation speed and accumulation, thereby reducing the explosion risk; for high risk level, significantly reduce the production speed to the lowest safety level, or suspend the high-risk process according to the situation;

[0039] Production line state control unit: for taking measures when the risk level increases, when a high risk level is detected, it is determined whether the production line needs to be suspended to avoid potential safety risks;

[0040] Emergency shutdown control unit: for executing emergency shutdown in extreme cases, when the risk level suddenly rises to an extremely high level or a direct explosion threat occurs, the emergency shutdown control unit immediately starts the emergency shutdown program to suspend all production activities.

[0041] Further, the production line state control unit further includes a secondary verification mechanism for secondary determination of whether the production line needs to be suspended when a high risk level is detected, the specific steps include:

[0042] Primary risk assessment: when the explosion risk prediction module first reports a high risk level, the production line state control unit will trigger a preliminary safety assessment to collect the current production line operation data, environmental conditions, and recent risk prediction history;

[0043] Secondary verification process: based on the results of the preliminary assessment, the production line state control unit performs secondary verification using the following formula to confirm the risk: R confirm = α · R current + (1-α) · R history , where R confirm represents the confirmed risk level value for the final decision; R history is the average value based on the risk level in the past period of time; α is a weight factor for adjusting the influence weight of the current risk level and the historical risk level, and the value range is between 0 and 1.

[0044] Decision and execution: when R confirm When the preset safety threshold is exceeded, the production line state control unit determines that the production line needs to be suspended to prevent potential explosion accidents, otherwise, if R confirm When the safety threshold is lower, the monitoring continues and the production line continues to operate.

[0045] Further, the alarm module includes an audible and visual alarm unit, a communication network unit and an alarm logic controller; wherein,

[0046] The audible and visual alarm unit is used to activate the audible and visual alarm immediately when the risk level reaches or exceeds the preset high-risk threshold, the audible and visual alarm unit includes a sound generator and a warning light, the sound generator is used to emit continuous or intermittent high-decibel alarm sound, and the warning light is used to emit visual warning signals, including flashing red or yellow light;

[0047] The communication network unit is used to send real-time alarm messages to operators and safety management personnel through the preset communication network when the alarm is triggered, the communication network unit includes a message generator and a message distributor, the message generator is used to generate alarm messages according to the current risk level and related safety data; the message distributor is used to send alarm messages to all registered recipients through email, SMS or dedicated application immediately;

[0048] The alarm logic controller is used to coordinate the work of the audible and visual alarm unit and the communication network unit, to ensure that the alarm is sent quickly and accurately in emergency, the alarm logic controller is used to automatically determine the level and urgency of the alarm according to the data from the explosion risk prediction module and the state of the execution module, and to command the corresponding alarm operation.

[0049] The beneficial effects of the present application are:

[0050] The present application, by integrating the dust monitoring module, the explosion risk prediction module, the environment adjustment module and the execution and alarm module, significantly improves the safety management level of the production environment, first of all, the system can monitor the concentration and composition of metal dust in real time, and identify the dangerous conditions that may form explosive mixtures in time, providing a scientific basis for early warning of explosion risk.

[0051] The present application, by using advanced machine learning algorithms, can accurately predict the risk level of dust explosion, and automatically adjust the production environment conditions or take other safety measures according to the prediction results, such as increasing ventilation, adjusting production speed, or even suspending the production line when necessary, thereby effectively preventing the occurrence of explosion accidents, this proactive prevention strategy greatly improves the timeliness and effectiveness of safety protection compared with traditional passive protection measures.

[0052] The present application can immediately send an alarm to the operator and the safety management personnel through an audible and visual signal and a communication network through the alarm module when the system detects a high-risk state, ensuring rapid response and timely disposal, which not only maximally reduces personnel injury and property loss, but also provides strong technical support for the safety management of enterprises, and significantly improves the safety and reliability of the production process. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present application or prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only a part of the present application, and other drawings can also be obtained by those skilled in the art without creative effort based on these drawings.

[0054] Fig. 1 The present application is a hydraulic equipment production safety protection system schematic diagram;

[0055] Fig. 2 The present application is an explosion risk prediction module schematic diagram. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with specific embodiments.

[0057] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present application should be understood as the usual meaning understood by those skilled in the art to which the present application belongs. The "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0058] As shown in Figs. 1-2 A hydraulic equipment production safety protection system, comprising a dust monitoring module, an explosion risk prediction module, an environment regulation module, an execution module and an alarm module; wherein,

[0059] The dust monitoring module is used to collect metal dust concentration and composition data in the hydraulic equipment operating area;

[0060] The explosion risk prediction module is based on the data collected by the dust monitoring module, and combines the operating parameters (such as pressure, temperature, working cycle) of the hydraulic equipment, uses a machine learning algorithm to predict the explosion risk of the dust, and calculates the potential explosion risk level;

[0061] Environmental conditioning module: automatically adjusts the environmental conditions within the production area based on the risk assessment results from the explosion risk prediction module, such as adjusting the speed and direction of the ventilation system, controlling humidity and temperature, to actively reduce dust concentration and improve environmental conditions to reduce the risk of explosion;

[0062] Execution module: automatically adjusts the operating state of production equipment or initiates emergency safety measures based on the risk level calculated by the explosion risk prediction module, such as increasing ventilation, reducing production speed, or suspending the production line if necessary, to prevent potential explosion accidents;

[0063] Alarm module: when the risk level calculated by the explosion risk prediction module exceeds the preset threshold, or when the execution module takes emergency safety measures, it will activate the sound and light alarm and send real-time alarm messages to operators and safety management personnel through the communication network to ensure rapid response.

[0064] The dust monitoring module includes a laser particle size analyzer and an electrochemical composition analyzer; wherein,

[0065] Laser particle size analyzer: used to measure the particle size distribution of metal dust in the air in the hydraulic equipment operating area, the working principle is based on laser scattering technology, which analyzes the intensity distribution of scattered light to determine the size distribution of dust particles, and this laser particle size analyzer can provide accurate data on dust particle size in real time, thereby evaluating whether the dust may reach the explosive particle size range;

[0066] Electrochemical composition analyzer: used to determine the specific chemical composition of metal dust, which analyzes the chemical composition of dust by measuring the change in current generated when metal dust reacts with chemical reagents, and this electrochemical composition analyzer can accurately identify the main metal components in the dust sample, including aluminum and magnesium explosive metal dust, providing a basis for explosion risk assessment;

[0067] The data output of the above two devices is directly connected to the explosion risk prediction module, ensuring real-time transmission and processing of dust monitoring data, thereby allowing the system to predict and assess potential explosion risks by analyzing the particle size and composition of metal dust.

[0068] The explosion risk prediction module includes a data integration unit, a machine learning processing unit, and a risk level calculation unit; wherein,

[0069] Data integration unit: used to collect and integrate metal dust concentration and composition data from the dust monitoring module and operating parameters of the hydraulic equipment, including pressure, temperature, and working cycle, this data integration unit specifically uses data fusion technology to ensure consistency and completeness of data obtained from different sources before analysis;

[0070] Machine learning processing unit: receives the integrated data from the data integration unit and analyzes the integrated data using a machine learning algorithm of support vector machine to predict the explosion risk of metal dust, the machine learning processing unit will learn the relationship between dust concentration, composition and hydraulic equipment operation parameters and explosion risk through training data to establish a prediction model for prediction;

[0071] Risk level calculation unit: calculates the potential explosion risk level according to the prediction result provided by the machine learning processing unit, the risk level calculation unit uses a risk assessment model to map the output of the machine learning processing unit to a predetermined explosion risk level, including low, medium and high levels, so that the subsequent module can take corresponding safety measures;

[0072] Through the cooperation of the above three units, the explosion risk of dust is accurately predicted and evaluated, by combining dust monitoring data with hydraulic equipment operation parameters and using advanced machine learning algorithms, high-risk states can be effectively identified and corresponding safety measures can be started in time, thereby significantly improving the safety of hydraulic equipment production.

[0073] The data integration unit specifically includes:

[0074] Data preprocessing subunit: used for formatting and cleaning the metal dust concentration and composition data from the dust monitoring module, and the operation parameter data (pressure, temperature and working period) of the hydraulic equipment, through standardization processing to ensure that all data are in the same dimension and range, using the formula: Data standardization is performed, where X norm is the standardized data, X is the original data, X min and X max are the minimum and maximum values of the original data respectively;

[0075] Data alignment subunit: used to ensure that the dust monitoring data and the hydraulic equipment operation parameter data are aligned according to the time stamp, so that each piece of dust data is matched with the corresponding operation parameter data, specifically using time synchronization technology, through interpolation or data filling method to solve the problem of data missing, to ensure the continuity and integrity of time series;

[0076] Data merging subunit: used to merge the preprocessed and aligned data into a unified data set for use by the machine learning processing unit, specifically using weighted average method to integrate information from different data sources, using the formula: Data merging is performed, where D combined is the merged data set, D i is the data of the i-th data source, w i is the weight of the i-th data source, and

[0077] Through the above steps, the data integration unit outputs a fused, consistent, and complete data set that contains not only the key metal dust concentration and composition information but also the operating parameter information of the hydraulic equipment, providing an accurate and comprehensive data basis for the dust explosion risk prediction of the machine learning processing unit.

[0078] The machine learning processing unit specifically includes:

[0079] Feature selection subunit: select features related to explosion risk prediction from the integrated data set, including the concentration, composition of metal dust, and operating parameters of the hydraulic equipment. Feature selection is based on information gain method to determine the contribution of each feature to the explosion risk prediction ability;

[0080] Model training subunit: train the selected features using support vector machine algorithm. The support vector machine model finds the optimal separating hyperplane between different classes to maximize the margin between different class data points. The objective function of support vector machine is represented as: where w is the normal vector of the hyperplane, b is the bias term, C is the regularization parameter, and ξ i is the relaxation variable, representing the error of the i-th data point. Support vector machine trains the model by solving this optimization problem to determine the optimal w and b;

[0081] Prediction and evaluation subunit: use the trained prediction model to predict the explosion risk of new data sets. In the prediction process, the risk classification of each data point is based on the function value calculated by the model, and the specific formula is: f(x) = w·x + b. When the value of f(x) is greater than the preset threshold, it is predicted that the state corresponding to the data point has a high explosion risk; otherwise, it is predicted to have a low explosion risk;

[0082] Through the above steps, the machine learning processing unit not only accurately predicts the explosion risk of metal dust, but also adjusts the model parameters according to the actual situation to adapt to different production environments and conditions. This processing unit provides an effective means to scientifically evaluate and manage the safety risks in the production process of hydraulic equipment.

[0083] The risk level calculation unit specifically includes:

[0084] Evaluation parameter definition: the risk assessment model used by the risk level calculation unit judges the risk according to the explosion risk probability output by the machine learning processing unit, sets the risk probability as P, and sets two thresholds T low and T high to divide the risk level into low, medium, and high;

[0085] Set the decision mechanism: when P < Tlow When T

[0086] When T low < P < T high When P < T

[0087] When P > T high

[0088] Risk level output: Based on the above determination, the risk level calculation unit outputs the specific explosion risk level, which will be directly used to guide subsequent safety protection measures;

[0089] In this way, the risk level calculation unit utilizes the prediction results obtained from the machine learning processing unit, and through a simple and effective threshold comparison method, it intuitively converts the risk probability into a specific risk level, which not only improves the practicality and operability of risk assessment, but also ensures that the prediction results can be effectively used for real-time safety protection decisions.

[0090] The environmental regulation module includes a temperature control unit, a humidity control unit, and a ventilation system control unit; wherein,

[0091] Temperature control unit: used to adjust the temperature in the production area according to the risk assessment results, the specific adjustment process includes: if the risk level is low, keep the current temperature setting unchanged; if the risk level is medium, reduce the temperature to reduce the possibility of dust explosion; if the risk level is high, significantly reduce the temperature below the safety threshold to maximize the reduction of explosion risk;

[0092] Humidity control unit: used to adjust the humidity in the production area according to the risk assessment results, the specific adjustment process includes: if the risk level is low, maintain the current humidity setting; if the risk level is medium, moderately increase the humidity, because appropriate humidity can reduce the flammability of dust; if the risk level is high, significantly increase the humidity to a predetermined proportion, and use a high humidity environment to reduce the flying and accumulation of dust;

[0093] Ventilation system control unit: used to adjust the operation of the ventilation system according to the risk assessment results to optimize air flow and reduce potential explosion hazards, the specific adjustment process includes: if the risk level is low, operate according to the normal ventilation program; if the risk level is medium, increase the ventilation intensity to accelerate air circulation to dilute potential flammable dust; if the risk level is high, maximize the ventilation volume to quickly remove flammable dust in the air, and at the same time suspend production activities to ensure safety;

[0094] ​Through the above measures, the environmental control module can take targeted environmental adjustment measures based on real-time risk assessment results to effectively reduce or avoid the risk of dust explosion. This module ensures the flexibility and responsiveness of the hydraulic equipment production safety protection system and improves the safety of the production environment.

[0095] The execution module includes a production speed adjustment unit, a production line status control unit, and an emergency stop control unit; among which,

[0096] Production speed adjustment unit: used to automatically adjust the production line speed according to the risk level. The specific process includes: for low risk level, the production line runs at normal speed to maintain production efficiency; for medium risk level, the production line speed is reduced to slow down the rate of dust generation and accumulation, thereby reducing the risk of explosion; for high risk level, the production speed is significantly reduced to the minimum safe level, or high-risk processes are suspended as appropriate.

[0097] Production line status control unit: Used to take measures when the risk level increases. When a high risk level is detected, it selects whether the production line needs to be suspended to avoid potential safety risks.

[0098] Emergency Stop Control Unit: Used to execute emergency stop in extreme situations. When the risk level suddenly rises to an extremely high level, or when there is a direct threat of explosion, the emergency stop control unit immediately initiates the emergency stop procedure to suspend all production activities and minimize the risk of personal injury and equipment loss.

[0099] Through the aforementioned safety measures, the execution module takes preventative measures based on real-time risk level assessments, ranging from adjusting production speed to implementing emergency shutdowns when necessary, to ensure the safety of the production environment. The design of this module reflects the system's ability to respond quickly to potential explosion risks and improves the overall safety management level of the hydraulic equipment production process.

[0100] The production line status control unit also includes a secondary verification mechanism, used to make a secondary determination on whether to suspend the production line when a high-risk level is detected. The specific steps include:

[0101] Preliminary Risk Assessment: When the explosion risk prediction module reports a high risk level for the first time, the production line status control unit will trigger a preliminary safety assessment, collecting current production line operating data, environmental conditions, and recent risk prediction history.

[0102] Secondary verification process: Based on the results of the preliminary assessment, the production line status control unit performs secondary verification, using the following formula for risk confirmation: R confirm =α·R current +(1-α)·R history , where R confirm R represents the confirmed risk level value, used for final decision-making;history is the average value of risk level in the past period of time; a is a weight factor, used to adjust the influence weight of current risk level and historical risk level, with a value range of 0 to 1;

[0103] Decision and execution: when R confirm exceeds the preset safety threshold, the production line state control unit determines that the production line needs to be suspended to prevent potential explosion accidents, otherwise, if R confirm is lower than the safety threshold, the monitoring continues and the production line keeps running;

[0104] By introducing this decision mechanism based on secondary verification, the production line state control unit can more accurately assess the severity of the explosion risk and make a decision on whether to suspend the production line. This mechanism increases the reliability of the system, reduces unnecessary production stagnation caused by false positives, and ensures that action can be taken quickly in truly high-risk situations to ensure production safety.

[0105] The alarm module includes an audible and visual alarm unit, a communication network unit, and an alarm logic controller; wherein,

[0106] The audible and visual alarm unit is used to activate the audible and visual alarm immediately when the risk level reaches or exceeds the preset high-risk threshold. The audible and visual alarm unit includes a sound generator and a warning light. The sound generator is used to emit continuous or intermittent high-decibel alarm sounds, and the warning light is used to emit visual warning signals, including flashing red or yellow light.

[0107] The communication network unit is used to send real-time alarm messages to operators and safety management personnel through the preset communication network when the alarm is triggered. The communication network unit includes a message generator and a message distributor. The message generator is used to generate alarm messages based on the current risk level and related safety data. The message distributor is used to send alarm messages to all registered recipients through email, SMS, or a dedicated application in real time.

[0108] The alarm logic controller is used to coordinate the work of the audible and visual alarm unit and the communication network unit, ensuring that the alarm is sent quickly and accurately in emergency situations. The alarm logic controller is used to automatically determine the level and urgency of the alarm based on data from the explosion risk prediction module and the state of the execution module, and to command the corresponding alarm operation.

[0109] Through the above-mentioned component units and mechanisms, the alarm module can quickly activate the audible and visual alarm and send real-time alarm messages through various communication channels when potential safety risks are detected, to ensure that operators and safety management personnel can take necessary measures immediately to minimize or avoid accidents.

[0110] The present application is intended to cover all such alternatives, modifications, and variations as fall within the broad scope of the appended claims. Accordingly, any and all such alternatives, modifications, equivalents, improvements and the like are intended to be encompassed by the present application.

Claims

1. A hydraulic equipment production safety protection system, characterized by, The system includes a dust monitoring module, an explosion risk prediction module, an environmental adjustment module, an execution module, and an alarm module. The dust monitoring module is used to collect metal dust concentration and composition data in the operating area of the hydraulic equipment. The explosion risk prediction module uses machine learning algorithms to predict the explosion risk of dust based on the data collected by the dust monitoring module and the operating parameters of the hydraulic equipment, and calculates the potential explosion risk level. The environmental adjustment module automatically adjusts the environmental conditions in the production area based on the risk assessment results of the explosion risk prediction module to reduce the explosion risk. The execution module automatically adjusts the operating state of the production equipment or initiates emergency safety measures to prevent potential explosion accidents based on the risk level calculated by the explosion risk prediction module. The alarm module will activate the sound and light alarm and send real-time alarm messages to the operators and safety management personnel through the communication network when the risk level calculated by the explosion risk prediction module exceeds the preset threshold or the execution module takes emergency safety measures to ensure rapid response.

2. A hydraulic equipment production safety shield system according to claim 1, characterized in that, The dust monitoring module includes a laser particle size analyzer and an electrochemical composition analyzer. The laser particle size analyzer is used to determine the particle size distribution of metal dust in the air in the operating area of the hydraulic equipment. Its working principle is based on laser scattering technology, which analyzes the intensity distribution of scattered light to determine the size distribution of dust particles. This laser particle size analyzer can provide accurate data on dust particle size in real time. The electrochemical composition analyzer is used to determine the specific chemical composition of metal dust. It analyzes the chemical composition of dust by measuring the current change generated when metal dust reacts with chemical reagents. This electrochemical composition analyzer can accurately identify the main metal components in the dust sample, including aluminum and magnesium, which are explosive metal dust, providing a basis for explosion risk assessment.

3. A hydraulic equipment production safety shield system according to claim 2, characterized in that, The explosion risk prediction module includes a data integration unit, a machine learning processing unit, and a risk level calculation unit. The data integration unit collects and integrates metal dust concentration and composition data from the dust monitoring module and operating parameters of the hydraulic equipment, including pressure, temperature, and working cycle. This data integration unit uses data fusion technology to ensure consistency and integrity of data from different sources before analysis. The machine learning processing unit receives integrated data from the data integration unit and uses a support vector machine machine learning algorithm to analyze the integrated data to predict the explosion risk of metal dust. This machine learning processing unit learns the relationship between dust concentration, composition, and hydraulic equipment operating parameters and explosion risk through training data to establish a prediction model for prediction. The risk level calculation unit calculates the potential explosion risk level based on the prediction results provided by the machine learning processing unit. This risk level calculation unit uses a risk assessment model to map the output of the machine learning processing unit to a predetermined explosion risk level, including low, medium, and high levels, to facilitate the subsequent modules to take appropriate safety measures.

4. A hydraulic equipment production safety shield system according to claim 3, characterized in that, The data integration unit specifically includes: Data preprocessing subunit: for formatting and cleaning the metal dust concentration and composition data from the dust monitoring module, and the operating parameter data of the hydraulic equipment, ensuring that all data are in the same measure and range through standardization processing, using the formula: Data standardization is performed, where X norm is the standardized data, X is the original data, X min and X max are the minimum and maximum values of the original data, respectively; Data alignment subunit: used to ensure that dust monitoring data and hydraulic equipment operating parameter data are aligned according to timestamps, so that each piece of dust data matches the corresponding operating parameter data, specifically using time synchronization technology, solving the problem of data missing through interpolation or data filling method, ensuring the continuity and integrity of the time series; Data merging subunit: used to merge the pre-processed and aligned data sets into a unified data set for the machine learning processing unit, specifically using a weighted average method to integrate information from different data sources, using the formula: Data merging is performed, wherein D combined is the merged data set, D i is the data of the i-th data source, w i is the weight of the i-th data source, and 5. A hydraulic equipment production safety shield system according to claim 4, characterized in that, The machine learning processing unit specifically includes: Feature selection subunit: select features related to explosion risk prediction from the integrated data set, including the concentration and composition of metal dust, and the operating parameters of the hydraulic equipment, and the feature selection is based on the information gain method to determine the contribution of each feature to the explosion risk prediction ability; The model training sub-unit trains the selected features using a support vector machine algorithm, and the support vector machine model maximizes the margin between different classes of data points by finding the optimal separating hyperplane between different classes, and the objective function of the support vector machine is represented as: where w is the normal vector of the hyperplane, b is the bias term, C is the regularization parameter, and ξ i is the slack variable, and e represents the error of the i-th data point, and the support vector machine trains the model by solving this optimization problem to determine the optimal w and b. The prediction and evaluation subunit: uses the trained prediction model to predict the explosion risk of the new data set, and in the prediction process, the risk classification of each data point is based on the function value calculated by the model, and the specific formula is: f(x)=w·x+b, when the value of f(x) is greater than the preset threshold, it is predicted that the state corresponding to the data point has a high explosion risk; otherwise, it is predicted to have a lower explosion risk.

6. A hydraulic equipment production safety shield system according to claim 5, characterized in that, The risk level calculation unit specifically includes: Evaluation parameter definition: the risk assessment model adopted by the risk level calculation unit judges the risk according to the explosion risk probability output by the machine learning processing unit, sets the risk probability as P, and divides the risk level into low, medium and high by setting two thresholds T low and T high . Set decision mechanism: when P < T low then the explosion risk level is determined to be low; When T low ≤ P < T high , then the explosion risk level is determined to be medium; When P > T high then the explosion risk level is determined to be high; Risk level output: based on the above determination, the risk level calculation unit outputs the specific explosion risk level, which will be directly used to guide the subsequent safety protection measures.

7. A hydraulic equipment production safety shield system according to claim 6, characterized in that, The environment adjustment module includes a temperature control unit, a humidity control unit, and a ventilation system control unit; wherein, Temperature control unit: used to adjust the temperature in the production area according to the risk assessment results, the specific adjustment process includes: if the risk level is low, the current temperature setting is kept unchanged; if the risk level is medium, the temperature is lowered to reduce the possibility of dust explosion; if the risk level is high, the temperature is greatly reduced below the safety threshold; Humidity control unit: used to adjust the humidity in the production area according to the risk assessment results, the specific adjustment process includes: if the risk level is low, the current humidity setting is maintained; if the risk level is medium, the humidity is moderately increased; if the risk level is high, the humidity is greatly increased to a predetermined proportion, and the high humidity environment is used to reduce the flying and accumulation of dust; Ventilation system control unit: used to adjust the operation of the ventilation system according to the risk assessment results to optimize air flow and reduce potential explosion hazards, the specific adjustment process includes: if the risk level is low, operate according to the normal ventilation program; if the risk level is medium, increase the ventilation intensity to accelerate air circulation to dilute the potential flammable dust; if the risk level is high, maximize the ventilation volume to quickly remove flammable dust in the air, and at the same time, suspend production activities to ensure safety.

8. A hydraulic equipment production safety shield system according to claim 7, characterized in that, The execution module includes a production speed adjustment unit, a production line state control unit, and an emergency shutdown control unit; wherein, Production speed adjustment unit: used to automatically adjust the production line speed according to the risk level, the specific process includes: for low risk level, the production line runs at normal speed to maintain production efficiency; for medium risk level, reduce the production line speed to slow down the dust generation speed and accumulation, thereby reducing the explosion risk; for high risk level, greatly reduce the production speed to the lowest safety level, or suspend the high-risk process according to the situation; Production line status control unit: for taking measures when risk level increases, when high risk level is detected, choose whether to suspend the production line to avoid potential safety risks; Emergency shutdown control unit: for performing emergency shutdown in extreme cases, when risk level suddenly rises to extremely high, or direct explosion threat occurs, emergency shutdown control unit immediately starts emergency shutdown program to suspend all production activities.

9. A hydraulic equipment production safety shield system according to claim 8, characterized in that, The production line status control unit also includes a secondary verification mechanism for secondary determination whether to suspend the production line when high risk level is detected, the specific steps include: Preliminary risk assessment: when the explosion risk prediction module first reports high risk level, the production line status control unit will trigger preliminary safety assessment, collect current production line operation data, environmental conditions and recent risk prediction history; Secondary verification procedure: Based on the results of the preliminary assessment, the production line status control unit performs secondary verification, using the following formula to confirm the risk: R confirm = a · R current + (1 - a) · R history , where R confirm represents the confirmed risk level value for the final decision; R history is the average value based on the risk level in the past period of time; a is a weight factor for adjusting the influence weight of the current risk level and the historical risk level, with a value range of 0 to 1; Decision and Execution: When R confirm exceeds the preset safety threshold, the production line status control unit determines that the production line needs to be suspended to prevent potential explosion accidents, otherwise, if R confirm is below the safety threshold, the monitoring continues and the production line keeps running.

10. A hydraulic equipment production safety shield system according to claim 9, characterized in that, The alarm module includes an audible and visual alarm unit, a communication network unit and an alarm logic controller; wherein, Audible and visual alarm unit: for activating audible and visual alarm immediately when risk level reaches or exceeds the preset high risk threshold, the audible and visual alarm unit includes a sound generator and a warning light, the sound generator is used to emit continuous or intermittent high decibel alarm sound, the warning light is used to emit visual warning signal, including flashing red or yellow light; Communication network unit: for sending real-time alarm messages to operators and safety management personnel through the preset communication network at the same time of triggering alarm, the communication network unit includes a message generator and a message distributor, the message generator is used to generate alarm messages according to current risk level and related safety data; the message distributor is used to send alarm messages to all registered recipients through email, SMS or special application immediately; Alarm logic controller: for coordinating the work of audible and visual alarm unit and communication network unit, ensuring that the alarm is sent quickly and accurately in emergency, the alarm logic controller is used to automatically determine the level and urgency of the alarm according to the data from the explosion risk prediction module and the state of the execution module, and command the corresponding alarm operation.

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