Enterprise safety production joint control information management system

Abstract

The invention discloses an enterprise safety production joint control information management system, which relates to the technical field of production joint control information management and comprises a collection module, a data processing module, a safety management module and a response module. And selectable. According to the invention, dynamic assessment of the safety risk is realized, intelligent early warning is carried out according to the change trend and severity of the risk, the accuracy and timeliness of early warning are improved, quantitative analysis of the degree of dispersion of the safety risk is realized, a scientific basis is provided for emergency response, rapid response and intelligent scheduling are realized, the accident processing efficiency is improved, and the safety risk is protected. According to the method and the system, the real-time monitoring of the wind speed and the flow velocity data is realized, the quantitative evaluation of the emergency response capability is realized, accurate guidance is provided for the formulation and implementation of emergency measures, the accuracy and effectiveness of emergency processing are improved, dynamic early warning and accurate positioning of a risk source can be realized by using the real-time monitored wind speed and flow velocity data, and the accuracy and timeliness of early warning are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production joint control information management, and in particular to an enterprise safety production joint control information management system. Background Art

[0002] In modern enterprise operations, safe production is a key factor in ensuring the stable development of the enterprise and the safety of life and property of employees. With the rapid development of information technology, the enterprise safe production joint control information management method has gradually become an important means to improve the level of safety management. This method integrates information technology, the Internet of Things, big data analysis and other advanced technologies to achieve safety monitoring, risk assessment and emergency response of the entire production process of the enterprise.

[0003] However, the existing enterprise safety production joint control information management system may rely on fixed safety thresholds for risk warning, lacking the ability of dynamic adjustment and intelligent analysis, which may result in low warning accuracy and easy omissions or false alarms. The existing system may rely on manual judgment and decision-making, lacking rapid response and intelligent scheduling mechanisms, resulting in inefficient accident handling and increased losses. In addition, existing methods may rely on qualitative analysis in the process of safety assessment and lack quantitative evaluation indicators, resulting in objective and inaccurate evaluation results, making it difficult to provide strong support for enterprise decision-making. In addition, the existing safety production management system may only focus on the monitoring of single parameters, such as temperature, pressure, etc., but ignores the monitoring of key parameters such as wind speed and flow rate, resulting in an incomplete safety risk assessment. Summary of the invention

[0004] The purpose of the present invention is to provide an enterprise safety production joint control information management system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solution: an enterprise safety production joint control information management system, comprising:

[0006] Collection module: used to collect temperature data and flow rate data at the enterprise production site in real time, and transmit the temperature data and flow rate data to the data center for storage in real time;

[0007] Data processing module: used for cleaning and denoising temperature data and flow rate data, and outputting current temperature value and current flow rate value;

[0008] Safety management module; used to input the current temperature value and the current flow rate value, output the temperature anomaly detection index, the flow rate anomaly detection index and the comprehensive anomaly detection index, and determine the alarm response level based on the temperature anomaly detection index, the flow rate anomaly detection index and the comprehensive anomaly detection index;

[0009] Response module: determines and executes corresponding measures and emergency plans based on the alarm response level, then notifies relevant departments and personnel, and continuously monitors the safety status of the production site during the execution of alarm measures.

[0010] Optionally, the security management module includes: a multiple detection sub-module, a comprehensive index sub-module, a regulation sub-module and a response sub-module.

[0011] Optionally, the calculation formula of the multiple detection submodules is as follows:

[0012] When T>TA;

[0013]

[0014] When T≤TA;

[0015]

[0016] When V>VA;

[0017]

[0018] When V≤VA;

[0019]

[0020] in:

[0021] SSAQ T Indicates temperature anomaly detection index, SSAQ V represents the flow rate anomaly detection index;

[0022] T represents the current temperature value, TA represents the historical average temperature, TU represents the historical standard deviation of the temperature, WWG T Indicates the weight coefficient of temperature, TMA indicates the maximum temperature value, and TMI indicates the minimum temperature value;

[0023] V represents the current flow rate value, VA represents the historical average flow rate, VU represents the historical standard deviation of the flow rate, WWG v Indicates the weight coefficient of flow rate, VMA indicates the maximum flow rate value, and VMI indicates the minimum flow rate value;

[0024] The processing process of the multiple detection submodule is as follows: the current temperature value T and the current flow rate value V are input into the multiple detection submodule, and based on the historical average temperature value TA, the temperature weight coefficient WWG T , the historical average value of flow velocity VA and the weight coefficient of flow velocity WWG v , output temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V .

[0025] Optionally, the calculation formula of the comprehensive index submodule is as follows:

[0026] CDSA=(SSAQ T 2 +SSAQ V 2 +2×CDSAQ×SSAQ T ×SSAQ V ) 0.5 ;

[0027] in:

[0028] CDSA represents the comprehensive anomaly detection index, SSAQ T Represents the temperature anomaly detection index, SSAQ in the dual-item detection submodule V represents the flow rate anomaly detection index in the dual-item detection submodule, CDSAQ represents the correlation coefficient between temperature and flow rate;

[0029] The processing process of the comprehensive index submodule is as follows: the temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V It is input to the comprehensive index submodule and outputs the comprehensive anomaly detection index CDSA based on the correlation coefficient CDSAQ between temperature and flow rate.

[0030] Optionally, the processing process of the adjustment submodule is as follows:

[0031] First up: WWG T，new =WWG T +α×(CDSA new -CDSA old );

[0032] Second: Set the iteration termination condition:

[0033] Termination condition 1: The number of iterations i is 98;

[0034] Termination condition 2: |CDSA k -CDSA k-1 |<0.0257;

[0035] in:

[0036] WWG T，new Represents the weight coefficient of temperature after iteration;

[0037] α represents the adjustment coefficient, which is used to control the rate of change of the weight coefficient, CDSA new Represents the comprehensive anomaly detection index after iteration, CDSA oldrepresents the comprehensive anomaly detection index before iteration;

[0038] Comprehensive anomaly detection index CDSA based on iteration new Compared with the comprehensive anomaly detection index CDSA before iteration old The temperature weight coefficient WWG is adjusted by adjusting the coefficient α T The change range of the temperature after the iteration is output. T，new , the weight coefficient of the temperature after iteration WWG T，new Replace the temperature weight coefficient WWG in the multiple detection submodules T , and finally the loop is terminated by termination conditions one and two.

[0039] Optionally, the processing process of the response submodule is as follows:

[0040] Level 1;

[0041] S1: SSAQ T ﹤T1,X1﹤CDSA﹤X2;

[0042] S2: SSAQ V ﹤T1,X1﹤CDSA﹤X2;

[0043] When any of S1 and S2 is met, a warning alarm is issued;

[0044] Level 2;

[0045] S1: SSAQ T ﹥T1, CDSA﹥X1;

[0046] S2: SSAQ V ﹥T1, CDSA﹥X1;

[0047] When any of S1 and S2 is met, a primary alarm is issued;

[0048] Level 3:

[0049] S1: SSAQ T ﹥T2, CDSA﹥X2;

[0050] S2: SSAQ V ﹥T2, CDSA﹥X2;

[0051] S3: SSAQ T ﹥T3, SSAQ V ﹥T3;

[0052] When any of S1, S2 and S3 is met, a high-level alarm is issued;

[0053] Wherein: threshold T1 = 0.3, threshold T2 = 0.65, threshold T3 = 0.8, threshold X1 = 0.4, threshold X2 = 0.7;

[0054] The temperature anomaly detection index SSAQ T , flow rate anomaly detection index SSAQ V The comprehensive anomaly detection index CDSA is input into the response submodule, and based on the calculation of threshold T1, threshold T2, threshold T3, threshold X1 and threshold X2, three abnormal response levels of Level 1, Level 2 and Level 3 are output.

[0055] Optionally, the collection module uses high-precision temperature sensors and flow rate sensors to collect temperature data and flow rate data at the enterprise's production site, and configures a data collector and a data acquisition system to collect data output by the sensors in real time.

[0056] Optionally, the response submodule further processes:

[0057] Level 1: Remind the operator and promptly remind the operator of the current abnormal situation through system prompts and warning sounds;

[0058] Level 2: Operators are required to immediately check relevant equipment and process parameters and take necessary countermeasures;

[0059] Level 3: Activate the emergency plan and immediately activate the emergency plan formulated by the enterprise, notify relevant departments and personnel to arrive quickly and prepare to take emergency measures.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. The present invention outputs temperature anomaly detection index and flow rate anomaly detection index through multiple detection submodules. The temperature anomaly detection index and flow rate anomaly detection index can accurately reflect the real-time abnormal state of temperature and flow rate. By calculating the difference between the current value and the historical average value, the anomaly detection is more accurate. The temperature anomaly detection index and flow rate anomaly detection index take into account the difference between the current value and the historical average value, and also introduce multiple factors such as historical standard deviation, weight coefficient, maximum value and minimum value, so that the anomaly detection is more comprehensive and accurate. By dynamically adjusting the weight coefficient and sensitivity parameters, the temperature anomaly detection index and flow rate anomaly detection index can adapt to different production environments and process requirements, and improve the adaptability and flexibility of anomaly detection. The temperature anomaly detection index and flow rate anomaly detection index can reflect the abnormal state of temperature and flow rate in real time, which is helpful for enterprises to discover potential safety hazards in time. Through accurate anomaly detection and timely early warning, enterprises can take measures to eliminate safety hazards and reduce safety risks as soon as possible. According to the monitoring results of the temperature anomaly detection index and the flow rate anomaly detection index, enterprises can optimize and adjust the production process to improve production efficiency and product quality.

[0062] 2. The present invention outputs a comprehensive anomaly detection index through a comprehensive index submodule. The comprehensive anomaly detection index can comprehensively evaluate the abnormal state of temperature and flow rate. By calculating the square of the single anomaly detection index and the mutual influence between them, a comprehensive anomaly detection index can be obtained. The comprehensive anomaly detection index takes into account the mutual influence between temperature and flow rate, making anomaly detection more comprehensive and accurate. The calculation result of the comprehensive anomaly detection index can be directly used to determine the alarm response level, providing a basis for risk warning for enterprises. The comprehensive anomaly detection index not only takes into account the size of the single anomaly detection index, but also takes into account the mutual influence between them, making anomaly detection more comprehensive and accurate. The calculation method of the comprehensive anomaly detection index can be adjusted and optimized according to actual conditions to adapt to different production environments and process requirements. The comprehensive anomaly detection index can comprehensively evaluate the abnormal state of temperature and flow rate to improve the accuracy of warning. According to the calculation result of the comprehensive anomaly detection index, enterprises can formulate more reasonable alarm strategies to avoid false alarms and missed alarms. Through timely warnings and alarms, enterprises can quickly initiate emergency plans and thus improve emergency response capabilities.

[0063] 3. The present invention iterates the weight coefficient of the temperature by adjusting the sub-module, and iterates the weight coefficient of the temperature by the comprehensive anomaly detection index, so that the adjustment of the weight coefficient can be more comprehensive and accurate. The iterative process of the temperature weight coefficient is guided by the comprehensive anomaly detection index, which can make the adjustment of the weight coefficient more scientific and based on evidence. The enterprise safety production joint control information management method can achieve more intelligent management through iterative calculation. The system can automatically adjust the weight coefficient and abnormal response strategy according to real-time data and historical data, thereby improving management efficiency and accuracy. The iterative method enables the system or equipment to respond more quickly when facing abnormal situations, which helps the enterprise to take timely measures to eliminate safety hazards and prevent the situation from further deteriorating. Iterative calculation is a continuous optimization process. With the increase of iteration times and the accumulation of data, the enterprise safety production joint control information management method can be continuously improved and improved, which is convenient for enterprises to continuously improve their safety management level and reduce the risks and losses of safety accidents.

[0064] 4. The present invention can clarify the response level through the response submodule. The response submodule determines the alarm response level according to the comprehensive anomaly detection index and the single temperature anomaly detection index and flow rate anomaly detection index, so that the enterprise can clearly know the current safety risk level, and this submodule provides corresponding alarm measures for different alarm response levels, so that the enterprise can take targeted measures to eliminate safety hazards. Through clear response levels and corresponding measures, the enterprise can quickly launch emergency plans to improve emergency efficiency. This submodule enables the enterprise to take different response measures according to different safety risk levels through a graded response method to improve the pertinence and efficiency of emergency response. The alarm response level and corresponding measures of this submodule can be adjusted and optimized according to actual conditions to adapt to different production environments and process requirements. Through the implementation of this submodule, the enterprise can conduct safety management more scientifically and improve the level of safety management. Through timely early warning and alarm and targeted response measures, the enterprise can reduce the accident rate and ensure production safety. The implementation of this submodule can also enhance employees' safety awareness and increase employees' attention to production safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is the method flow chart of the enterprise's safety production joint control information management system;

[0066] Figure 2 This is the overall structural diagram of the enterprise's safety production joint control information management system;

[0067] Figure 3 It is a structural diagram of the security management module. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] The enterprise's production safety joint control information management system is different from the existing enterprise production safety joint control information management system. The existing production joint control information management system often relies on fixed safety thresholds for risk warnings, lacks the ability of dynamic adjustment and intelligent analysis, resulting in low accuracy of warnings, easy to miss or misreport. After an accident occurs, the existing methods often rely on manual judgment and decision-making, lack of rapid response and intelligent scheduling mechanisms, resulting in low efficiency in accident handling and increased losses;

[0070] The algorithm unit of the company's joint safety production control information management system can realize dynamic assessment of safety risks, provide intelligent early warning according to the changing trends and severity of risks, improve the accuracy and timeliness of early warnings, and realize quantitative analysis of the degree of dispersion of safety risks, providing a scientific basis for emergency response, achieving rapid response and intelligent scheduling, and improving accident handling efficiency.

[0071] Example 1: Please refer to Figures 1 to 3 This implementation provides an enterprise safety production joint control information management system, including:

[0072] Collection module: used to collect temperature data and flow rate data at the enterprise production site in real time, and transmit the temperature data and flow rate data to the data center for storage in real time;

[0073] Data processing module: used for cleaning and denoising temperature data and flow rate data, and outputting current temperature value and current flow rate value;

[0074] Safety management module; used to input the current temperature value and the current flow rate value, output the temperature anomaly detection index, the flow rate anomaly detection index and the comprehensive anomaly detection index, and determine the alarm response level based on the temperature anomaly detection index, the flow rate anomaly detection index and the comprehensive anomaly detection index;

[0075] Response module: Determine and execute corresponding measures and emergency plans based on the alarm response level, then notify relevant departments and personnel, and continuously monitor the safety status of the production site during the execution of alarm measures;

[0076] The safety management module includes: multiple detection sub-modules, comprehensive index sub-modules, adjustment sub-modules and response sub-modules.

[0077] In this embodiment, the system not only provides information on whether the threshold is exceeded, but also provides quantitative indicators of the degree of abnormality, which helps enterprises better understand the safety status of the production process and take corresponding measures. The system can handle multiple monitoring parameters such as temperature and flow rate, and consider their mutual influence, which helps to more comprehensively evaluate the safety risks in the production process. Compared with simple threshold comparison, the calculation of these sub-modules in this system takes into account the historical distribution of monitoring values, through the average value, standard deviation, weight coefficient, possible maximum and minimum values, and the mutual influence between different monitoring parameters, which makes anomaly detection more accurate and comprehensive. The system can handle abnormal situations of multiple monitoring parameters such as temperature and flow rate, and evaluate the overall degree of abnormality through a comprehensive anomaly detection index, and determine the alarm response level based on the maximum value of the comprehensive anomaly detection index and the single anomaly detection index, providing a more flexible alarm mechanism.

[0078] See also Figures 1 to 3 ,The processing process of multiple detection submodules is as follows:

[0079] When T>TA;

[0080]

[0081] When T≤TA;

[0082]

[0083] When V>VA;

[0084]

[0085] When V≤VA;

[0086]

[0087] in:

[0088] SSAQ T Indicates temperature anomaly detection index, SSAQ V represents the flow rate anomaly detection index;

[0089] T represents the current temperature value, TA represents the historical average temperature, TU represents the historical standard deviation of the temperature, WWG T Indicates the weight coefficient of temperature, TMA indicates the maximum temperature value, and TMI indicates the minimum temperature value;

[0090] V represents the current flow rate value, VA represents the historical average flow rate, VU represents the historical standard deviation of the flow rate, WWG v Indicates the weight coefficient of flow rate, VMA indicates the maximum flow rate value, and VMI indicates the minimum flow rate value;

[0091] The processing process of the multiple detection submodule is as follows: the current temperature value T and the current flow rate value V are input into the multiple detection submodule, and based on the historical average temperature value TA and the temperature weight coefficient WWG T , the historical average value of flow velocity VA and the weight coefficient of flow velocity WWG v , output temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V .

[0092] In this embodiment, when T is higher than the historical average, we may want the anomaly detection index to be more sensitive to high temperature anomalies. Therefore, the weight of high temperature anomalies is increased by introducing a term related to the temperature range TMA-TMI and the difference T-TMA between the current temperature and the maximum value in the formula; conversely, when T is lower than or equal to the historical average, we may want the anomaly detection index to be more sensitive to low temperature anomalies, so the weight of low temperature anomalies is increased by introducing a term related to the temperature range and the difference T-TA between the current temperature and the average value to ensure that anomalies can be accurately captured within the low temperature range, and the same is true for the current flow rate value V;

[0093] In actual application, the mutuality between the current flow rate value V and the current temperature value T is reflected in the chemical reaction process. Some chemical reactions need to be carried out under specific temperature and flow rate conditions. In the fluid transportation process, changes in temperature and flow rate may affect the properties and transportation efficiency of the fluid. Therefore, these parameters need to be monitored to ensure the stability and safety of the system. When storing flammable, explosive or toxic substances, temperature and flow rate monitoring is crucial to prevent accidents such as leakage, fire and explosion. In environments where precise control of temperature and flow rate is required, such as laboratories, data centers, etc., dual monitoring helps to ensure the stability and reliability of the system. Temperature and flow rate are interrelated in many industrial processes. For example, in a chemical reaction process, changes in temperature may affect the reaction rate, and then affect the flow rate of the material. Similarly, changes in flow rate may also affect the temperature. Therefore, monitoring temperature and flow rate can provide important information about the process status. In enterprise safety production, when changes in temperature and flow rate may have a significant impact on the production process or equipment safety, double monitoring is required. For example, in chemical production, certain chemical reactions need to be carried out under specific temperature and flow rate conditions to ensure the safety and efficiency of the reaction. If the temperature and flow rate deviate from the set range, it may cause the reaction to be out of control, equipment damage or safety accidents. If the sensor is used directly for numerical monitoring, due to various factors, such as sensor accuracy, environmental interference, data transmission errors, etc., there may be a certain risk of false alarms or missed alarms. Therefore, this submodule considers the historical distribution of monitoring values ​​such as mean and standard deviation, weight coefficient, and possible maximum and minimum values. The formula can more accurately identify abnormal situations. This helps to reduce false alarms and missed alarms;

[0094] This SSAQ T and SSAQ V It can accurately reflect the real-time abnormal status of temperature and flow rate. By calculating the difference between the current value and the historical average value and considering the historical standard deviation and weight coefficient, it makes the abnormal detection more accurate. By introducing the maximum and minimum values ​​to adjust the sensitivity of abnormal detection, especially when approaching the limit value, it can issue early warnings to avoid potential safety risks. The introduction of weight coefficients WT and WV allows enterprises to adjust the importance of temperature and flow rate in abnormal detection according to actual conditions, and respond to different production environments more flexibly. SSAQ T and SSAQ V Not only the difference between the current value and the historical average is considered, but also multiple factors such as historical standard deviation, weight coefficient, maximum value and minimum value are introduced to make anomaly detection more comprehensive and accurate. By dynamically adjusting the weight coefficient and sensitivity parameters, SSAQ T and SSAQ VIt can adapt to different production environments and process requirements, improving the adaptability and flexibility of abnormality detection. T and SSAQ V It can reflect the abnormal status of temperature and flow rate in real time, help enterprises to timely discover potential safety hazards and improve monitoring efficiency. Through accurate abnormal detection and timely warning, enterprises can take measures to eliminate safety hazards and reduce safety risks as soon as possible. According to SSAQ T and SSAQ V Based on the monitoring results, enterprises can optimize and adjust the production process to improve production efficiency and product quality.

[0095] See also Figures 1 to 3 , the comprehensive index processing process is as follows:

[0096] CDSA=(SSAQ T 2 +SSAQ V 2 +2×CDSAQ×SSAQ T ×SSAQ V ) 0.5 ;

[0097] in:

[0098] CDSA represents the comprehensive anomaly detection index, SSAQ T Represents the temperature anomaly detection index, SSAQ in the dual-item detection submodule V represents the flow rate anomaly detection index in the dual-item detection submodule, CDSAQ represents the correlation coefficient between temperature and flow rate;

[0099] The processing process of the comprehensive index submodule is as follows: The temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V It is input to the comprehensive index submodule and outputs the comprehensive anomaly detection index CDSA based on the correlation coefficient CDSAQ between temperature and flow rate.

[0100] In this embodiment, the comprehensive anomaly detection index CDSA comprehensively considers the abnormal conditions of temperature and flow rate and their mutual influence, and can more comprehensively evaluate the safety risks in the production process. In some cases, a single anomaly detection index may not accurately reflect the overall safety situation. For example, when both the temperature and flow rate are close to their respective thresholds, the single anomaly detection index may not be high but the overall safety risk may be high. At this time, by comparing the CDSA threshold, potential safety risks can be more accurately identified;

[0101] CDSA can comprehensively evaluate the abnormal status of temperature and flow rate. By calculating the sum of squares of single abnormal detection indexes and their mutual influence, a comprehensive abnormal detection index is obtained. By introducing the correlation coefficient CDSAQ, CDSA can consider the mutual influence between temperature and flow rate, making abnormal detection more comprehensive and accurate. The calculation results of CDSA can be directly used to determine the alarm response level, providing a basis for risk warning for enterprises. CDSA not only considers the size of single abnormal detection index, but also considers the mutual influence between them, making abnormal detection more comprehensive and accurate. The calculation method of CDSA can be adjusted and optimized according to actual conditions to adapt to different production environments and process requirements. CDSA can comprehensively evaluate the abnormal status of temperature and flow rate and improve the accuracy of early warning. According to the calculation results of CDSA, enterprises can formulate more reasonable alarm strategies to avoid false alarms and missed alarms. Through timely early warnings and alarms, enterprises can quickly launch emergency plans and improve emergency response capabilities.

[0102] See also Figures 1 to 3 , the processing of the adjustment submodule is as follows:

[0103] First up: WWG T，new =WWG T +α×(CDSA new -CDSA old );

[0104] Second: Set the iteration termination condition:

[0105] Termination condition 1: The number of iterations i is 98;

[0106] Termination condition 2: |CDSA k -CDSA k-1 |<0.0257;

[0107] in:

[0108] WWG T，new Represents the weight coefficient of temperature after iteration;

[0109] α represents the adjustment coefficient, which is used to control the rate of change of the weight coefficient, CDSA new Represents the comprehensive anomaly detection index after iteration, CDSA old represents the comprehensive anomaly detection index before iteration;

[0110] Comprehensive anomaly detection index CDSA based on iteration new Compared with the comprehensive anomaly detection index CDSA before iteration old The temperature weight coefficient WWG is adjusted by adjusting the coefficient α T The change range of the temperature after the iteration is output.T，new , the weight coefficient of the temperature after iteration WWG T，new Replace the temperature weight coefficient WWG in the multiple detection submodules T , and finally the loop is terminated by termination conditions one and two.

[0111] In this embodiment, the temperature weight coefficient WWG is calculated by iteration. T It can be dynamically adjusted according to the feedback of the comprehensive anomaly detection index CDSA, making the calculation of the single anomaly detection index SDI more accurate. This dynamic adjustment can reflect the real-time status of the system or equipment, thereby improving the sensitivity and accuracy of anomaly detection. T Iterative optimization, the enterprise safety production joint control information management method can take corresponding response strategies according to different abnormality levels, which helps enterprises to timely discover and deal with potential safety hazards and reduce the probability of safety accidents. Iterative calculation can make the state of the system or equipment more stable, reduce false alarms and missed alarms caused by inaccurate abnormality detection, and help improve the stability and reliability of the entire enterprise safety production joint control information management system. CDSA is an indicator that comprehensively reflects the overall abnormality level of the system or equipment. It contains information on multiple single abnormality detection indexes. Through CDSA, WWG is iterated T , can make the adjustment of weight coefficient more comprehensive and accurate, because CDSA can reflect the overall status of the system or device. If WWG is modified directly T There may be a lack of sufficient evidence and feedback mechanisms, resulting in inaccurate or unreasonable adjustments, but CDSA can be used to guide WWG T The iterative process can make the adjustment of the weight coefficient more scientific and based on evidence, and the real-time and dynamic nature of CDSA can also make the adjustment of the weight coefficient more timely and effective. Through iterative calculation, the enterprise safety production joint control information management method can achieve more intelligent management. The system can automatically adjust the weight coefficient and abnormal response strategy according to real-time data and historical data, thereby improving management efficiency and accuracy. The iterative method can make the system or equipment respond more quickly when facing abnormal situations, which helps enterprises to take timely measures to eliminate safety hazards and prevent the situation from further deteriorating. Iterative calculation is a continuous optimization process. With the increase in the number of iterations and the accumulation of data, the enterprise safety production joint control information management method can be continuously improved and improved, which helps enterprises to continuously improve their safety management level and reduce the risks and losses of safety accidents.

[0112] See also Figures 1 to 3 , the response submodule processing process is as follows:

[0113] Level 1;

[0114] S1: SSAQT ﹤T1,X1﹤CDSA﹤X2;

[0115] S2: SSAQ V ﹤T1,X1﹤CDSA﹤X2;

[0116] When any of S1 and S2 is met, a warning alarm is issued;

[0117] Level 2;

[0118] S1: SSAQ T ﹥T1, CDSA﹥X1;

[0119] S2: SSAQ V ﹥T1, CDSA﹥X1;

[0120] When any of S1 and S2 is met, a primary alarm is issued;

[0121] Level 3:

[0122] S1: SSAQ T ﹥T2, CDSA﹥X2;

[0123] S2: SSAQ V ﹥T2, CDSA﹥X2;

[0124] S3: SSAQ T ﹥T3, SSAQ V ﹥T3;

[0125] When any of S1, S2 and S3 is met, a high-level alarm is issued;

[0126] Wherein: threshold T1 = 0.3, threshold T2 = 0.65, threshold T3 = 0.8, threshold X1 = 0.4, threshold X2 = 0.7;

[0127] The response submodule is further:

[0128] Level 1: Remind the operator and promptly remind the operator of the current abnormal situation through system prompts and warning sounds;

[0129] Among them: the operator needs to make a preliminary assessment of the alarm information, understand the specific situation and possible causes of the abnormality, and prepare corresponding preliminary response measures based on the preliminary assessment results, such as adjusting process parameters, strengthening monitoring, etc., and then record the abnormal situation, preliminary assessment results and preliminary response measures, and report to the superior;

[0130] Level 2: Operators are required to immediately check relevant equipment and process parameters and take necessary countermeasures;

[0131] Among them: the operator shall immediately check the equipment and process parameters involved in the alarm, confirm the specific location and cause of the abnormal situation, and take necessary countermeasures according to the inspection results, such as shutting down for maintenance, adjusting the process flow, etc., to prevent the abnormal situation from further deteriorating. While taking countermeasures, the operator shall strengthen the monitoring of relevant equipment and process parameters to ensure that the abnormal situation is effectively controlled, and then notify the relevant departments and personnel of the abnormal situation, inspection results and countermeasures so that the relevant personnel can understand the situation in time and provide support. Finally, the abnormal situation, inspection results, countermeasures and notifications shall be recorded and reported to the superior;

[0132] Level 3: Activate the emergency plan and immediately activate the emergency plan formulated by the enterprise, notify relevant departments and personnel to arrive quickly and prepare to take emergency measures;

[0133] Among them: According to the emergency plan, take emergency measures to prevent the situation from escalating, such as shutting down production, evacuating personnel, etc., and under the premise of ensuring safety, cut off the dangerous sources that may cause the accident to further expand as soon as possible, and contact external rescue in time. If the situation is serious, contact external rescue forces in time, such as fire brigades and hospitals, etc. Finally, record the abnormal situation, the activation of the emergency plan, the adoption of emergency measures, the cutting off of dangerous sources and the contact with external rescue, and report to the superior.

[0134] The temperature anomaly detection index SSAQ T , flow rate anomaly detection index SSAQ V The comprehensive anomaly detection index CDSA is input into the response submodule, and based on the calculation of threshold T1, threshold T2, threshold T3, threshold X1 and threshold X2, three abnormal response levels of Level 1, Level 2 and Level 3 are output.

[0135] In this embodiment, when multiple anomalies occur simultaneously but none of them reaches the threshold, multiple single anomaly detection indexes may be abnormal at the same time, but the degree of these anomalies individually does not reach the trigger threshold of Level 2 or Level 3. At this time, although each single anomaly does not seem serious, their cumulative effect may still have a significant impact on the overall performance of the system or device. The comprehensive anomaly detection index is an indicator that comprehensively reflects the overall degree of anomalies of the system or device. In some cases, even if the single anomaly detection index does not trigger an alarm, the comprehensive anomaly detection index continues to be high, which may mean that there are some potential problems or risks in the system or device that are not easily captured by single detection; and

[0136] This system can clearly define the response level. The response submodule is based on the comprehensive anomaly detection index and the single SSAQ. T and SSAQ VTo determine the alarm response level, so that the enterprise can clearly know the current safety risk level, and for different alarm response levels, this sub-module provides corresponding alarm measures, so that the enterprise can take targeted measures to eliminate safety hazards, and through clear response levels and corresponding measures, the enterprise can quickly launch emergency plans and improve emergency efficiency. This sub-module uses a graded response method to enable the enterprise to take different response measures according to different safety risk levels, thereby improving the pertinence and efficiency of emergency response. The alarm response level and corresponding measures of this sub-module can be adjusted and optimized according to actual conditions to adapt to different production environments and process requirements. Through the implementation of this sub-module, enterprises can conduct safety management more scientifically and improve the level of safety management. Through timely early warnings and alarms and targeted response measures, enterprises can reduce the accident rate and ensure production safety. The implementation of this sub-module can also enhance employees' safety awareness and increase their attention to safe production.

[0137] In the specific implementation process, multiple sub-modules in this system are used to form an enterprise safety production joint control information management system. By inputting the current temperature value T and the current flow rate value V into multiple detection sub-modules, the temperature anomaly detection index SSAQ is output. T and flow rate anomaly detection index SSAQ V , this SSAQ T and SSAQ V It can accurately reflect the real-time abnormal status of temperature and flow rate. By calculating the difference between the current value and the historical average value, it makes the abnormal detection more accurate. By introducing the maximum and minimum values ​​to adjust the sensitivity of abnormal detection, it can issue early warnings to avoid potential safety risks. The introduction of weight coefficients WT and WV allows enterprises to adjust the importance of temperature and flow rate in abnormal detection according to actual conditions, and respond to different production environments more flexibly. SSAQ T and SSAQ V The difference between the current value and the historical average is taken into account, and multiple factors such as historical standard deviation, weight coefficient, maximum value and minimum value are introduced to make anomaly detection more comprehensive and accurate. By dynamically adjusting the weight coefficient and sensitivity parameters, SSAQ T and SSAQ V It can adapt to different production environments and process requirements, improving the adaptability and flexibility of abnormality detection. T and SSAQ V It can reflect the abnormal status of temperature and flow rate in real time, helping enterprises to discover potential safety hazards in time. Through accurate abnormal detection and timely warning, enterprises can take measures to eliminate safety hazards and reduce safety risks as soon as possible. According to SSAQ T and SSAQ VBased on the monitoring results, enterprises can optimize and adjust the production process to improve production efficiency and product quality.

[0138] The temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V Input to the comprehensive index submodule, and output the comprehensive anomaly detection index CDSA. CDSA can comprehensively evaluate the abnormal status of temperature and flow rate. By calculating the sum of squares of single anomaly detection indexes and their mutual influence, a comprehensive anomaly detection index can be obtained. By introducing the correlation coefficient CDSAQ, CDSA can consider the mutual influence between temperature and flow rate, making anomaly detection more comprehensive and accurate. The CDSA calculation results can be directly used to determine the alarm response level, providing a basis for risk warning for enterprises. CDSA not only considers the size of single anomaly detection index, but also considers the mutual influence between them, making anomaly detection more comprehensive and accurate. The calculation method of CDSA can be adjusted and optimized according to actual conditions to adapt to different production environments and process requirements. CDSA can comprehensively evaluate the abnormal status of temperature and flow rate to improve the accuracy of warning. According to the calculation results of CDSA, enterprises can formulate more reasonable alarm strategies to avoid false alarms and missed alarms. Through timely warnings and alarms, enterprises can quickly launch emergency plans and improve emergency response capabilities.

[0139] Iterate the temperature weight coefficient WWG based on the comprehensive anomaly detection index CDSA T , iterate WWG through CDSA T , which can make the adjustment of weight coefficient more comprehensive and accurate, and guide WWG through CDSA T The iterative process can make the adjustment of the weight coefficient more scientific and based on evidence, and the real-time and dynamic nature of CDSA can also make the adjustment of the weight coefficient more timely and effective. Through iterative calculation, the enterprise safety production joint control information management method can achieve more intelligent management. The system can automatically adjust the weight coefficient and abnormal response strategy according to real-time data and historical data, thereby improving management efficiency and accuracy. The iterative method can enable the system or equipment to respond more quickly when facing abnormal situations, which helps the enterprise to take timely measures to eliminate safety hazards and prevent the situation from further deteriorating. Iterative calculation is a continuous optimization process. With the increase in the number of iterations and the accumulation of data, the enterprise safety production joint control information management method can be continuously improved and improved, which will help the enterprise to continuously improve the level of safety management and reduce the risks and losses of safety accidents.

[0140] The temperature anomaly detection index SSAQ T , flow rate anomaly detection index SSAQ VThe comprehensive anomaly detection index CDSA is input into the response submodule, and three abnormal response levels of Level 1, Level 2 and Level 3 are output. The system can clearly define the response level. The response submodule responds according to the comprehensive anomaly detection index and the single SSAQ T and SSAQ V To determine the alarm response level, so that the enterprise can clearly know the current safety risk level, and for different alarm response levels, this sub-module provides corresponding alarm measures, so that the enterprise can take targeted measures to eliminate safety hazards, and through clear response levels and corresponding measures, the enterprise can quickly launch emergency plans and improve emergency efficiency. This sub-module uses a graded response method to enable the enterprise to take different response measures according to different safety risk levels, thereby improving the pertinence and efficiency of emergency response. The alarm response level and corresponding measures of this sub-module can be adjusted and optimized according to actual conditions to adapt to different production environments and process requirements. Through the implementation of this sub-module, enterprises can conduct safety management more scientifically and improve the level of safety management. Through timely early warnings and alarms and targeted response measures, enterprises can reduce the accident rate and ensure production safety. The implementation of this sub-module can also enhance employees' safety awareness and increase their attention to safe production.

[0141] This allows multiple sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0142] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 ,The collection module uses high-precision temperature sensors and flow velocity sensors to collect ,temperature data and flow velocity data at the enterprise production ,site, and is configured with data collector and data ,acquisition system to collect the data output by the sensors in real time.

[0143] In this embodiment, high-precision temperature sensors and flow rate sensors are used to ensure the accuracy of data collection, and the time interval, frequency and other details of the collection are determined to ensure the continuity and integrity of the data. Data collection processes and specifications are formulated to ensure the safety and reliability of the collection process.

[0144] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise safety production joint control information management system, characterized by: include: Collection module; It is used to collect temperature data and flow rate data at the enterprise production site in real time, and transmit the temperature data and flow rate data to the data center for storage in real time; Data processing module; Used to clean and denoise the temperature data and flow rate data, and output the current temperature value and current flow rate value; Security management module; Used to input the current temperature value and the current flow rate value, output the temperature anomaly detection index, the flow rate anomaly detection index and the comprehensive anomaly detection index, and determine the alarm response level based on the temperature anomaly detection index, the flow rate anomaly detection index and the comprehensive anomaly detection index; Response module; Determine and implement corresponding measures and emergency plans based on the alarm response level, then notify relevant departments and personnel, and continuously monitor the safety status of the production site during the execution of alarm measures.

2. The enterprise safety production joint control information management system according to claim 1 is characterized by: The safety management module includes: a multi-detection submodule, a comprehensive index submodule, a regulation submodule and a response submodule.

3. The enterprise safety production joint control information management system according to claim 2 is characterized by: The calculation formula of the multiple detection submodules is as follows: When T>TA; When T≤TA; When V>VA; When V≤VA; in: SSAQ T Indicates temperature anomaly detection index, SSAQ V represents the flow rate anomaly detection index; T represents the current temperature value, TA represents the historical average temperature, TU represents the historical standard deviation of the temperature, WWG T Indicates the weight coefficient of temperature, TMA indicates the maximum temperature value, and TMI indicates the minimum temperature value; V represents the current flow rate value, VA represents the historical average flow rate, VU represents the historical standard deviation of the flow rate, WWG v Indicates the weight coefficient of flow rate, VMA indicates the maximum flow rate value, and VMI indicates the minimum flow rate value; The processing process of the multiple detection submodule is as follows: the current temperature value T and the current flow rate value V are input into the multiple detection submodule, and based on the historical average temperature value TA, the temperature weight coefficient WWG T , the historical average value of flow velocity VA and the weight coefficient of flow velocity WWG v , output temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V .

4. The enterprise safety production joint control information management system according to claim 3 is characterized by: The calculation formula of the comprehensive index submodule is as follows: CDSA=(SSAQ T 2 +SSAQ V 2 +2×CDSAQ×SSAQ T ×SSAQ V ) 0.5 ; in: CDSA represents the comprehensive anomaly detection index, SSAQ T Represents the temperature anomaly detection index, SSAQ in the dual-item detection submodule V represents the flow rate anomaly detection index in the dual-item detection submodule, CDSAQ represents the correlation coefficient between temperature and flow rate; The processing process of the comprehensive index submodule is as follows: the temperature anomaly detection index SSAQ T and flow rate anomaly detection index SSAQ V It is input to the comprehensive index submodule and outputs the comprehensive anomaly detection index CDSA based on the correlation coefficient CDSAQ between temperature and flow rate.

5. The enterprise safety production joint control information management system according to claim 4 is characterized by: The processing process of the adjustment submodule is as follows: First up: WWG T，new =WWG T +α×(CDSA new -CDSA old ); Second: Set the iteration termination condition: Termination condition 1: The number of iterations i is 98; Termination condition 2: |CDSA k -CDSA k-1 |<0.0257; in: WWG T，new Represents the weight coefficient of temperature after iteration; α represents the adjustment coefficient, which is used to control the rate of change of the weight coefficient, CDSA new Represents the comprehensive anomaly detection index after iteration, CDSA old represents the comprehensive anomaly detection index before iteration; Comprehensive anomaly detection index CDSA based on iteration new Compared with the comprehensive anomaly detection index CDSA before iteration old The temperature weight coefficient WWG is adjusted by adjusting the coefficient α T The change range of the temperature after the iteration is output. T，new , the weight coefficient of the temperature after iteration WWG T，new Replace the temperature weight coefficient WWG in the multiple detection submodules T , and finally the loop is terminated by termination conditions one and two.

6. The enterprise safety production joint control information management system according to claim 5 is characterized by: The processing process of the response submodule is as follows: Level 1; S1:SSAQ T ﹤T1,X1﹤CDSA﹤X2; <h2 style=";text-align:left;direction:ltr">S2: SSAQ<h2 style=";text-align:left;direction:ltr"> V <h2 style=";text-align:left;direction:ltr"> T1, X1, CDSA, X2 When any of S1 and S2 is met, a warning alarm is issued; Level 2; S1:SSAQ T ﹥T1,CDSA﹥X1; <h2 style=";text-align:left;direction:ltr">S2: SSAQ<h2 style=";text-align:left;direction:ltr"> V <h2 style=";text-align:left;direction:ltr"> T1,CDSA﹥X1; When any of S1 and S2 is met, a primary alarm is issued; Level 3: S1:SSAQ T ﹥T2,CDSA﹥X2; <h2 style=";text-align:left;direction:ltr">S2: SSAQ<h2 style=";text-align:left;direction:ltr"> V <h2 style=";text-align:left;direction:ltr"> T2,CDSA﹥X2; S3: SSAQ T ﹥T3, SSAQ V ﹥T3; When any of S1, S2 and S3 is met, a high-level alarm is issued; Wherein: threshold T1 = 0.3, threshold T2 = 0.65, threshold T3 = 0.8, threshold X1 = 0.4, threshold X2 = 0.7; The temperature anomaly detection index SSAQ T , flow rate anomaly detection index SSAQ V The comprehensive anomaly detection index CDSA is input into the response submodule, and based on the calculation of threshold T1, threshold T2, threshold T3, threshold X1 and threshold X2, three abnormal response levels of Level 1, Level 2 and Level 3 are output.

7. The enterprise safety production joint control information management system according to claim 1 is characterized by: The collection module uses high-precision temperature sensors and flow rate sensors to collect temperature data and flow rate data at the enterprise's production site, and is configured with a data collector and a data acquisition system to collect data output by the sensors in real time.

8. The enterprise safety production joint control information management system according to claim 6 is characterized by: The further processing of the response submodule is as follows: Level 1: Remind the operator and promptly remind the operator of the current abnormal situation through system prompts and warning sounds; Level 2: Operators are required to immediately check relevant equipment and process parameters and take necessary countermeasures; Level 3: Activate the emergency plan and immediately activate the emergency plan formulated by the enterprise, notify relevant departments and personnel to arrive quickly and prepare to take emergency measures.

Images