A method for identifying and assessing storage safety risks of explosive and hazardous materials

Abstract

The invention discloses a method for identifying and assessing safety risks in the storage of explosive and dangerous goods, and relates to the technical field of safety risk identification. The method uses a data mining algorithm to perform abnormal detection and analysis on comprehensive storage data and harmful gas data of a gunpowder storage environment, identifies potential safety risks by monitoring the change trend and pattern of the data, and issues early warnings to help managers take timely measures to prevent accidents. At the same time, based on a data mining algorithm model, more comprehensive and accurate decision support and optimization suggestions are provided to managers. By analyzing historical data and trends, potential problems in the gunpowder storage process are identified, and corresponding improvement suggestions are put forward. In addition, intelligent safety management can realize intelligent monitoring and management of the gunpowder storage environment through an automated early warning mechanism and emergency response. Once an abnormal situation is found, an early warning can be issued immediately, and corresponding control measures can be automatically taken.

Description

Technical Field

[0001] The present invention relates to the technical field of safety risk identification, and in particular to a method for identifying and assessing safety risks in the storage of explosive and dangerous goods. Background Art

[0002] Explosive dangerous goods refer to substances that can produce explosive reactions and cause serious harm under certain conditions. Any substance with dangerous properties such as explosion, flammability, toxicity, corrosion and radioactivity can cause combustion and explosion under certain conditions during transportation, loading and unloading, production, use, storage and custody. Gunpowder is a typical explosive dangerous good. Since the ingredients of gunpowder include flammable substances such as nitrates, charcoal and sulfur, as well as explosive chemical reactions, it has the risk of explosion under certain conditions. If the gunpowder is affected by external factors such as severe impact, friction, static discharge or high temperature, it may cause unpredictable explosion.

[0003] At present, traditional gunpowder storage safety analysis and identification often rely on manual experience and periodic detection methods. This method has problems such as untimely monitoring and inaccurate data analysis. Especially in the assessment and identification of harmful gases that appear during the storage of gunpowder, manual methods are often not easy to quickly and accurately identify and assess the concentration and risk level of harmful gases, which can easily cause safety hazards. In addition, the assessment and identification of the storage environment also faces certain challenges, including static electricity problems and improper control of environmental parameters. These problems may increase safety risks in the gunpowder storage process. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for identifying and assessing storage safety risks of explosive and dangerous goods, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for identifying and assessing storage safety risks of explosive and dangerous goods, comprising the following steps:

[0006] S1. First, several integrated detection points are arranged inside the gunpowder storage workshop and the gunpowder storage tank, and detection equipment is installed to detect the comprehensive storage data and harmful gas data of the gunpowder storage, and the collected comprehensive storage data and harmful gas data are classified and summarized to generate a first data set and a second data set. At the same time, a data mining algorithm model is constructed based on the collected data sets, i represents the starting position of the summation, and i=1 represents the summation starting from (Dcxs*a1);

[0007] S2, after dimensionless processing, the electromagnetic data set, environmental data set and gunpowder storage data set of the collected first data set are used to calculate and obtain the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs respectively, and summarize them to generate a comprehensive storage coefficient Cfxs;

[0008] The comprehensive storage coefficient Cfxs is obtained by the following formula:

[0009] ;

[0010] Wherein, a1, a2 and a3 represent preset proportional coefficients of electromagnetic coefficient Dcxs, environmental coefficient Hjxs and gunpowder storage coefficient Ycxs respectively, and a1+a2+a3≠1, 0<a1<0.47, 0<a2<0.57, 0<a3<0.77, and the specific values ​​are adjusted and set by the user, and A is the first correction constant;

[0011] S3, performing dimensionless processing on the harmful gas data set of the collected second data set to calculate and obtain the harmful gas coefficient Kqxs;

[0012] The harmful gas coefficient Kqxs is obtained by the following formula:

[0013] ;

[0014] In the formula, CO2 represents the concentration of carbon dioxide gas, NOx represents the concentration of nitrogen oxide gas, H2S represents the concentration of hydrogen sulfide gas, NH3 represents the concentration of ammonia gas, C8H 10 Indicates xylene gas concentration;

[0015] S4, analyzing and calculating the comprehensive storage coefficient Cfxs and the harmful gas coefficient Kqxs obtained by the first data set and the second data set, and summarizing them to generate a comprehensive safety factor Aqxs;

[0016] S5. Finally, a preliminary comparative evaluation is performed by setting the first evaluation threshold Q and the harmful gas coefficient Kqxs to generate a corresponding evaluation result. At the same time, a second comparative evaluation is performed by setting the second evaluation threshold E and the obtained comprehensive safety factor Aqxs to generate a relevant safety evaluation result.

[0017] Preferably, by arranging a number of integrated detection points in the gunpowder storage workshop and the gunpowder storage tank, and installing detection equipment, the detection equipment includes a first integrated sensor group, a second integrated sensor group, a third integrated sensor group and a fourth integrated sensor group, for real-time detection of electromagnetic data, environmental data and gunpowder storage data of gunpowder storage;

[0018] The first integrated sensor group, the second integrated sensor group and the third integrated sensor group are used for real-time detection of electromagnetic, environmental and powder tank internal data in the integrated storage data;

[0019] The first integrated sensor group includes a charge sensor, a current sensor, an electric field sensor, an electromagnetic radiation sensor and a resistance sensor, and is used to detect electromagnetic data in the gunpowder storage workshop in real time;

[0020] The second integrated sensor group includes a temperature sensitive resistor, a humidity sensor, an ultraviolet sensor, an atmospheric pressure sensor and an air flow sensor, and is used to detect the environmental data of the gunpowder storage in real time;

[0021] The third integrated sensor group includes an oxygen sensor, a temperature sensor, an electronic humidity sensor and an infrared sensor. These sensors are installed inside the gunpowder storage tank and sealed synchronously with the gunpowder, and a data transmission interface is provided. After the gunpowder is sealed, the data detected by the sensors are transmitted in real time from the outside of the gunpowder storage tank;

[0022] The fourth integrated sensor group is used to detect harmful gases generated by gunpowder during storage in real time;

[0023] The fourth integrated sensor group includes a carbon dioxide sensor, a xylene sensor, a nitrogen oxide sensor, a hydrogen sulfide sensor and an ammonia sensor, which are used for real-time detection of harmful gases generated during the storage of gunpowder.

[0024] Preferably, the comprehensive storage data and harmful gas data detected by the detection equipment are dimensionlessly processed to remove outliers, denoise and normalize, and the comprehensive storage data and harmful gas data are classified and summarized to generate a first data set and a second data set and collect them;

[0025] The first data set includes an electromagnetic data set, an environmental data set, and a gunpowder storage data set;

[0026] The electromagnetic data set includes electrostatic charge Ddh, electrostatic current Ddl, electric field strength Dqd, electromagnetic radiation Dfs and resistance Ddz;

[0027] The environmental data set includes temperature Hwd, humidity Hsd, ultraviolet intensity Hzw, atmospheric pressure Hdq and air flow rate Hkq;

[0028] The gunpowder storage data set includes stored oxygen concentration Yyq, gunpowder humidity Ysd, gunpowder water content Yhs, gunpowder temperature Ywd and gunpowder temperature change rate Ybh;

[0029] The second data set includes a harmful gas data set; the harmful gas data set includes carbon dioxide gas concentration CO2, nitrogen oxide gas concentration NOx, hydrogen sulfide gas concentration H2S, ammonia gas concentration NH3 and xylene gas concentration C8H 10 .

[0030] Preferably, feature extraction and feature selection are performed on the collected first data set and second data set, wherein the feature extraction is performed by converting the original data into features used by the data mining algorithm model, and the feature selection is used to select features related to gunpowder storage safety according to domain knowledge and experience, including the gunpowder storage environment and the gunpowder storage environment inside the storage tank, perform feature scaling and dimensionality reduction preprocessing steps, optimize the performance and efficiency of the data mining algorithm model, set the classification algorithm as the basic framework of the model, and perform integrated construction of the data mining algorithm model;

[0031] Then, through the data mining algorithm model, a large amount of historical safety data on gunpowder storage is mined on the Internet, and the data mining algorithm model is trained in combination with the comprehensive storage data and harmful gas data collected at the actual storage site. The model is evaluated using cross-validation technology to ensure the generalization ability of the data mining algorithm model on unseen data. The model is tuned according to the evaluation results, including adjusting hyperparameters and feature selection, and the trained data mining algorithm model is deployed in practical applications for the prediction and monitoring of gunpowder storage safety risks. The performance of the model is monitored regularly, and the data mining algorithm model is updated in a timely manner to cope with changes in data distribution or new safety risks. The data mining algorithm model and data collection methods are continuously optimized, and the accuracy and practicality of the model are adjusted.

[0032] Preferably, the electromagnetic data set is extracted based on the collected first data set, dimensionless processing is performed, and then the electromagnetic coefficient Dcxs is obtained by summarizing, analyzing and calculating;

[0033] The electromagnetic coefficient Dcxs is obtained by the following formula:

[0034] ;

[0035] In the formula, i represents the starting position of the product, and i=1 means that the multiplication starts from the electrostatic charge Ddh.

[0036] Preferably, the environmental data set is extracted based on the collected first data set, dimensionless processed, and then summarized, analyzed and calculated to obtain the environmental coefficient Hjxs;

[0037] The environmental coefficient Hjxs is obtained by the following formula:

[0038] .

[0039] Preferably, the gunpowder storage data set is extracted based on the collected first data set, dimensionless processing is performed, and then a summary analysis and calculation is performed to obtain the gunpowder storage coefficient Ycxs;

[0040] The gunpowder storage coefficient Ycxs is obtained by the following formula:

[0041] .

[0042] Preferably, the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs obtained from the first data set are summarized and calculated to obtain a comprehensive storage coefficient Cfxs, and then the comprehensive storage coefficient Cfxs is correlated with the obtained harmful gas coefficient Kqxs to obtain a comprehensive safety factor Aqxs;

[0043] The comprehensive safety factor Aqxs is obtained by the following formula:

[0044] ;

[0045] In the formula, b1 and b2 represent the preset proportional coefficients of the comprehensive storage coefficient Cfxs and the harmful gas coefficient Kqxs respectively, and b1≠b2, 0<b1<0.37, 0<b2<0.63, the specific value is adjusted and set by the user, and B is the second correction constant.

[0046] Preferably, a preliminary comparative evaluation is performed by presetting the first evaluation threshold Q and the harmful gas coefficient Kqxs obtained by analyzing and calculating the second data set, and a risk assessment result of harmful gases inside the gunpowder storage workshop is obtained. The specific risk assessment scheme is as follows;

[0047] When the harmful gas coefficient Kqxs> the first evaluation threshold Q, it means that the concentration of harmful gases in the current gunpowder storage workshop is in an abnormal state. At this time, it is necessary to increase the power of the ventilation fan in the storage workshop by 50%, and generate an early warning message to intelligently remind nearby staff not to approach, and use appropriate absorbents and chemicals to absorb the leaked substances;

[0048] When the harmful gas coefficient Kqxs ≤ the first evaluation threshold Q, it means that the harmful gas inside the current gunpowder storage workshop is in a normal state. At this time, there is no need to generate early warning information, and the second evaluation mechanism is started at the same time.

[0049] Preferably, the second evaluation mechanism is triggered by the first evaluation mechanism. The second evaluation mechanism is to perform a secondary comparative evaluation by presetting the second evaluation threshold E and the obtained comprehensive safety factor Aqxs, and is used to analyze the storage environment of the gunpowder during storage and the environment in the gunpowder storage tank for real-time evaluation. The specific evaluation scheme is as follows;

[0050] When the comprehensive safety factor Aqxs> the second evaluation threshold E, it means that the current gunpowder storage environment is abnormal. At this time, an early warning message is generated to notify the staff to improve the environment inside the gunpowder storage workshop. First, use a humidifier to control the humidity. In the dry season, the power of the humidifier is automatically increased by 50%. Conductive carpets are laid in key areas to help release static electricity accumulation. Measures are taken to prevent static electricity accumulation for staff entering the storage workshop. At the same time, the windows of the gunpowder storage workshop are shielded to optimize the light intensity in the storage workshop.

[0051] When the comprehensive safety factor Aqxs ≤ the second evaluation threshold E, it means that the current gunpowder storage environment is in a normal state. At this time, there is no need to generate early warning information and continue to perform detection.

[0052] The present invention provides a method for identifying and assessing the storage safety risks of explosive and dangerous goods. It has the following beneficial effects:

[0053] (1) This method can detect and predict the anomaly of the comprehensive storage data and harmful gas data of the gunpowder storage environment by using data mining algorithms. By monitoring the changing trends and patterns of the data, potential safety risks and abnormal situations can be identified, and early warnings can be issued to help managers take timely measures to prevent accidents. At the same time, based on the data mining algorithm model, more comprehensive and accurate decision support and optimization suggestions can be provided to managers. By analyzing historical data and trends, potential problems in the gunpowder storage process can be identified, and corresponding improvement suggestions can be put forward to help managers formulate more effective safety management strategies and measures. In addition, intelligent safety management can realize intelligent monitoring and management of the gunpowder storage environment through automated early warning mechanisms and emergency responses. Once an abnormal situation is found, an early warning can be issued immediately, and corresponding control measures can be automatically taken to ensure the safety of the gunpowder storage process.

[0054] (2) In addition to real-time monitoring and early warning during the storage of gunpowder, this method can also perform preventive maintenance and optimization. By continuously collecting and analyzing a large amount of historical safety data, it can identify potential problems and hidden dangers in the storage of gunpowder and put forward corresponding improvement suggestions. This preventive maintenance and optimization measure can effectively reduce the frequency of safety accidents and improve the efficiency and reliability of safety management.

[0055] (3) This method not only has the function of real-time monitoring, but also can realize remote management and monitoring. Through the Internet and other communication technologies, relevant personnel can remotely monitor and manage the gunpowder storage environment anytime and anywhere. Once an abnormal situation is found, corresponding measures can be taken immediately without on-site operation, which greatly improves the convenience and flexibility of safety management. At the same time, it can also record and store a large amount of monitoring data and safety events, providing a basis for post-analysis and accountability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The present invention is a schematic diagram of the steps of a method for identifying and assessing storage safety risks of explosive and dangerous goods. DETAILED DESCRIPTION

[0057] 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.

[0058] Example 1: The present invention provides a method for identifying and assessing the storage safety risks of explosive and dangerous goods. Figure 1 , including the following steps:

[0059] S1. First, several integrated detection points are arranged inside the gunpowder storage workshop and the gunpowder storage tank, and detection equipment is installed to detect the comprehensive storage data and harmful gas data of the gunpowder storage, and the collected comprehensive storage data and harmful gas data are classified and summarized to generate a first data set and a second data set, and a data mining algorithm model is constructed based on the collected data sets;

[0060] S2, after dimensionless processing, the electromagnetic data set, environmental data set and gunpowder storage data set of the collected first data set are used to calculate and obtain the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs respectively, and summarize them to generate a comprehensive storage coefficient Cfxs;

[0061] The comprehensive storage coefficient Cfxs is obtained by the following formula:

[0062] ;

[0063] Wherein, a1, a2 and a3 represent the preset proportional coefficients of the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs respectively, and a1+a2+a3≠1, 0<a1<0.47, 0<a2<0.57, 0<a3<0.77, and the specific values ​​are adjusted and set by the user;

[0064] S3, performing dimensionless processing on the harmful gas data set of the collected second data set to calculate and obtain the harmful gas coefficient Kqxs;

[0065] The harmful gas coefficient Kqxs is obtained by the following formula:

[0066] ;

[0067] In the formula, CO2 represents the concentration of carbon dioxide gas, NOx represents the concentration of nitrogen oxide gas, H2S represents the concentration of hydrogen sulfide gas, NH3 represents the concentration of ammonia gas, C8H 10 Indicates xylene gas concentration;

[0068] S4, analyzing and calculating the comprehensive storage coefficient Cfxs and the harmful gas coefficient Kqxs obtained by the first data set and the second data set, and summarizing them to generate a comprehensive safety factor Aqxs;

[0069] S5. Finally, a preliminary comparative evaluation is performed by setting the first evaluation threshold Q and the harmful gas coefficient Kqxs to generate a corresponding evaluation result. At the same time, a second comparative evaluation is performed by setting the second evaluation threshold E and the obtained comprehensive safety factor Aqxs to generate a relevant safety evaluation result.

[0070] In this embodiment, multiple integrated detection points are arranged in the gunpowder storage workshop and inside the gunpowder storage tank, and corresponding detection equipment is installed. These devices include sensors and monitoring instruments for detecting comprehensive storage data and harmful gas data of gunpowder storage. The data collected by these detection equipment are classified and summarized to generate a first data set and a second data set. The first data set includes an electromagnetic data set, an environmental data set and a gunpowder storage data set, while the second data set includes a harmful gas data set. The electromagnetic data set, the environmental data set and the gunpowder storage data set are extracted from the first data set, and corresponding coefficients are calculated and obtained respectively, including the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs. These coefficients reflect the characteristics and states of different aspects of the gunpowder storage environment. The harmful gas data set is extracted from the second data set, and after dimensionless processing, the harmful gas coefficient Kqxs is calculated and obtained. These coefficients reflect the concentration of harmful gases in the gunpowder storage environment. The obtained comprehensive storage coefficient and harmful gas coefficient are summarized and calculated to generate a comprehensive safety coefficient Aqxs. This comprehensive safety coefficient reflects the overall safety status of the gunpowder storage environment and is an important indicator for evaluating the safety of gunpowder storage. According to the preset first evaluation threshold Q and second evaluation threshold E, the harmful gas coefficient and the comprehensive safety coefficient are preliminarily and twice compared and evaluated. According to the evaluation results, the system can generate corresponding safety evaluation results, trigger the early warning mechanism, and take corresponding measures in time to ensure the safety of the gunpowder storage process;

[0071] For example, assuming that the electromagnetic data of the gunpowder storage workshop shows that the static electricity is high, the environmental data shows that the temperature and humidity are appropriate, the gunpowder storage data shows that the gunpowder temperature and oxygen concentration are normal, and at the same time, the harmful gas data shows that the carbon dioxide concentration is high, based on these data, the system can calculate the corresponding coefficients and comprehensively analyze to obtain the comprehensive safety factor Aqxs. If the comprehensive safety factor Aqxs is lower than the preset second evaluation threshold E, the system will trigger the early warning mechanism.

[0072] Example 2: This example is an explanation of Example 1. Figure 1 Specifically: by arranging a number of integrated detection points in the gunpowder storage workshop and the gunpowder storage tank, and installing detection equipment, the detection equipment includes a first integrated sensor group, a second integrated sensor group, a third integrated sensor group and a fourth integrated sensor group, the comprehensive storage data and harmful gas data in the storage process of the gunpowder are detected in real time;

[0073] The first integrated sensor group, the second integrated sensor group and the third integrated sensor group are used to perform real-time detection of electromagnetic data, environmental data and gunpowder storage data of gunpowder storage;

[0074] The first integrated sensor group includes a charge sensor, a current sensor, an electric field sensor, an electromagnetic radiation sensor and a resistance sensor, and is used to detect electromagnetic data in the gunpowder storage workshop in real time;

[0075] The second integrated sensor group includes a temperature sensitive resistor, a humidity sensor, an ultraviolet sensor, an atmospheric pressure sensor and an air flow sensor, and is used to detect the environmental data of the gunpowder storage in real time;

[0076] The third integrated sensor group includes an oxygen sensor, a temperature sensor, an electronic humidity sensor and an infrared sensor. These sensors are installed inside the gunpowder storage tank and sealed synchronously with the gunpowder, and a data transmission interface is provided. After the gunpowder is sealed, the data detected by the sensors are transmitted in real time from the outside of the gunpowder storage tank;

[0077] The fourth integrated sensor group is used to detect harmful gases generated by gunpowder during storage in real time;

[0078] The fourth integrated sensor group includes a carbon dioxide sensor, a xylene sensor, a nitrogen oxide sensor, a hydrogen sulfide sensor and an ammonia sensor, which are used for real-time detection of harmful gases generated during the storage of gunpowder.

[0079] In this embodiment, by deploying multiple integrated detection points and various sensor groups, various data inside the gunpowder storage workshop and storage tank can be monitored in real time, including comprehensive storage data and harmful gas data, thereby realizing comprehensive monitoring of the gunpowder storage process. Different sensor groups are responsible for collecting data of different dimensions, including electromagnetic, environmental, internal conditions and harmful gas data, thereby realizing the acquisition of multi-faceted information on the gunpowder storage environment, which is conducive to a comprehensive assessment of safety risks. The third integrated sensor group is installed inside the gunpowder storage tank and is sealed synchronously with the gunpowder. By setting a data transmission interface, real-time monitoring and transmission of data inside the tank are realized. Such a design can obtain the situation inside the gunpowder storage tank in a timely manner, which is helpful to discover potential safety hazards. The fourth integrated sensor group is responsible for real-time detection of harmful gases generated during the gunpowder storage process, and can quickly identify the concentration of harmful gases, so as to take corresponding measures in time to ensure the safety of the staff. Through the real-time monitoring system, abnormal conditions can be discovered in time during the gunpowder storage process, and measures can be taken to deal with them, thereby improving the safety and work efficiency of gunpowder storage and reducing potential safety risks.

[0080] Example 3: This example is an explanation of Example 2. Figure 1 Specifically: after dimensionless processing, the comprehensive storage data and harmful gas data detected by the detection equipment are subjected to outlier removal, denoising and normalization processing, the comprehensive storage data and harmful gas data are classified and summarized, and a first data set and a second data set are generated and collected;

[0081] The first data set includes an electromagnetic data set, an environmental data set, and a gunpowder storage data set;

[0082] The electromagnetic data set includes electrostatic charge Ddh, electrostatic current Ddl, electric field strength Dqd, electromagnetic radiation Dfs and resistance Ddz;

[0083] The environmental data set includes temperature Hwd, humidity Hsd, ultraviolet intensity Hzw, atmospheric pressure Hdq and air flow rate Hkq;

[0084] The gunpowder storage data set includes stored oxygen concentration Yyq, gunpowder humidity Ysd, gunpowder water content Yhs, gunpowder temperature Ywd and gunpowder temperature change rate Ybh;

[0085] The second data set includes a harmful gas data set; the harmful gas data set includes carbon dioxide gas concentration CO2, nitrogen oxide gas concentration NOx, hydrogen sulfide gas concentration H2S, ammonia gas concentration NH3 and xylene gas concentration C8H 10 .

[0086] In this embodiment, the quality and accuracy of the data are effectively improved through dimensionless processing, outlier removal, denoising and normalization processing, which helps to eliminate noise and outliers in the data, making subsequent analysis more reliable and accurate. The comprehensive storage data and harmful gas data are classified into the first data set and the second data set respectively, and summarized according to the dimensions of electromagnetic, environment and gunpowder storage, so that the data structure is clearer and easier to understand. This classification and summary is helpful for the subsequent analysis and processing of different types of data. The electromagnetic data set, environmental data set and gunpowder storage data set in the first data set cover multiple aspects of the gunpowder storage environment, which helps to fully understand the situation of the gunpowder storage environment and provide more dimensional data support for safety assessment. The second data set includes a harmful gas data set, which is crucial for evaluating the safety of the gunpowder storage environment and can provide an important basis for subsequent safety assessment. Through the processing and classification and summary of the data, a rich data source is provided for the subsequent data mining algorithm model, which helps the system to realize intelligent safety management. Through the analysis and pattern recognition of the data, potential safety hazards and abnormal situations can be discovered in time, thereby providing scientific decision support for managers.

[0087] Example 4: This example is an explanation of Example 3. Figure 1 Specifically: feature extraction and feature selection are performed on the first data set and the second data set collected, wherein the feature extraction converts the original data into features used by the data mining algorithm model, and the feature selection is used to select features related to gunpowder storage safety according to domain knowledge and experience, including the gunpowder storage environment and the gunpowder storage environment inside the storage tank, perform feature scaling and dimensionality reduction preprocessing steps, optimize the performance and efficiency of the data mining algorithm model, set the classification algorithm as the basic framework of the model, and integrate and construct the data mining algorithm model;

[0088] Then, through the data mining algorithm model, a large amount of historical safety data on gunpowder storage is mined on the Internet, and the data mining algorithm model is trained in combination with the comprehensive storage data and harmful gas data collected at the actual storage site. The model is evaluated using cross-validation technology to ensure the generalization ability of the data mining algorithm model on unseen data. The model is tuned according to the evaluation results, including adjusting hyperparameters and feature selection, and the trained data mining algorithm model is deployed in practical applications for the prediction and monitoring of gunpowder storage safety risks. The performance of the model is monitored regularly, and the data mining algorithm model is updated in a timely manner to cope with changes in data distribution or new safety risks. The data mining algorithm model and data collection methods are continuously optimized, and the accuracy and practicality of the model are adjusted.

[0089] In this embodiment, by extracting and selecting features from the first data set and the second data set, the most representative and distinguishing features can be screened out from massive data, providing important feature variables for subsequent model construction, which helps to improve the accuracy and predictive ability of the model, while reducing the data dimension and simplifying the complexity of the model. Classic data mining algorithms such as decision trees, random forests and support vector machines are selected as the basic framework, which can effectively cope with classification and regression tasks in the field of gunpowder storage safety. By cross-validating and tuning the model, the generalization ability and performance of the model are further improved, ensuring the reliability and effectiveness of the model in practical applications. The data mining algorithm model is trained by using a large amount of historical safety data on gunpowder storage on the Internet, combined with the comprehensive storage data and harmful gas data actually collected. This helps the model learn rules and patterns from historical data, improve the ability to identify and predict gunpowder storage safety risks, and deploy the trained data mining algorithm model into practical applications for real-time monitoring and prediction of gunpowder storage safety risks. By regularly monitoring the performance of the model and updating the model in time to respond to changes in data distribution or new safety risks, the safety of the gunpowder storage process can be guaranteed. By continuously optimizing the data mining algorithm model and data collection methods, and constantly adjusting the accuracy and practicality of the model, it can adapt to changes in the gunpowder storage environment and evolving safety needs, providing continuous support and guarantee for safety management.

[0090] Example 5: This example is an explanation of Example 3. Figure 1 Specifically: extracting the electromagnetic data set according to the first data set collected, dimensionless processing, and performing summary analysis and calculation to obtain the electromagnetic coefficient Dcxs;

[0091] The electromagnetic coefficient Dcxs is obtained by the following formula:

[0092] ;

[0093] In the formula, i represents the starting position of the product, and i=1 means that the multiplication starts from the electrostatic charge Ddh.

[0094] By extracting the environmental data set based on the first data set collected, dimensionless processing is performed, and then the environmental coefficient Hjxs is obtained by summarizing, analyzing and calculating;

[0095] The environmental coefficient Hjxs is obtained by the following formula:

[0096] .

[0097] By extracting the gunpowder storage data set based on the collected first data set, after dimensionless processing, a summary analysis and calculation is performed to obtain the gunpowder storage coefficient Ycxs;

[0098] The gunpowder storage coefficient Ycxs is obtained by the following formula:

[0099] .

[0100] In this embodiment, first, the electromagnetic, environmental and gunpowder storage data sets are extracted from the first data set, and dimensionless processing is performed on them so that they have a unified dimension and range. Then, a summary analysis is performed for each data set, and a comprehensive characteristic value is calculated, which represents the electromagnetic environment, environmental conditions and gunpowder storage conditions in the gunpowder storage workshop respectively. According to these characteristic values, the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs are calculated using the corresponding calculation formula. These coefficients reflect the comprehensive characteristics of various aspects of the gunpowder storage process, and provide useful information for subsequent safety management and risk prediction.

[0101] Example 6: This example is an explanation of Example 5. Please refer to Figure 1 Specifically: the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs obtained from the first data set are summarized and calculated to obtain the comprehensive storage coefficient Cfxs, and then the comprehensive storage coefficient Cfxs is correlated with the obtained harmful gas coefficient Kqxs to obtain the comprehensive safety factor Aqxs;

[0102] The comprehensive safety factor Aqxs is obtained by the following formula:

[0103] ;

[0104] In the formula, b1 and b2 represent the preset proportional coefficients of the comprehensive storage coefficient Cfxs and the harmful gas coefficient Kqxs respectively, and b1≠b2, 0<b1<0.37, 0<b2<0.63, and the specific values ​​are adjusted and set by the user.

[0105] In this embodiment, the electromagnetic coefficient Dcxs, environmental coefficient Hjxs and gunpowder storage coefficient Ycxs obtained from the first data set are summarized and calculated to obtain a comprehensive storage coefficient Cfxs. Then, the comprehensive storage coefficient Cfxs is correlated with the obtained harmful gas coefficient Kqxs to obtain a comprehensive safety factor Aqxs. This comprehensive safety factor reflects the comprehensive safety situation of the gunpowder storage environment. It comprehensively considers various aspects of the gunpowder storage environment and the situation of harmful gases. This correlated calculation can more comprehensively evaluate the safety of the gunpowder storage environment and provide a more reliable basis for safety management.

[0106] Example 7: This example is an explanation of Example 1. Figure 1Specifically: a preliminary comparative evaluation is performed by presetting the first evaluation threshold Q and the harmful gas coefficient Kqxs obtained by analyzing and calculating the second data set, and a risk assessment result of harmful gases inside the gunpowder storage workshop is obtained. The specific risk assessment scheme is as follows;

[0107] When the harmful gas coefficient Kqxs> the first evaluation threshold Q, it means that the concentration of harmful gases in the current gunpowder storage workshop is in an abnormal state. At this time, it is necessary to increase the power of the ventilation fan in the storage workshop by 50%, and generate an early warning message to intelligently remind nearby staff not to approach, and use appropriate absorbents and chemicals to absorb the leaked substances;

[0108] When the harmful gas coefficient Kqxs ≤ the first evaluation threshold Q, it means that the harmful gas inside the current gunpowder storage workshop is in a normal state. At this time, there is no need to generate early warning information, and the second evaluation mechanism is started at the same time.

[0109] The second evaluation mechanism is triggered by the first evaluation mechanism. The second evaluation mechanism is to conduct a secondary comparative evaluation by presetting the second evaluation threshold E and the obtained comprehensive safety factor Aqxs, which is used to analyze the storage environment of the gunpowder during storage and the environment inside the gunpowder storage tank for real-time evaluation. The specific evaluation scheme is as follows;

[0110] When the comprehensive safety factor Aqxs> the second evaluation threshold E, it means that the current gunpowder storage environment is abnormal. At this time, an early warning message is generated to notify the staff to improve the environment inside the gunpowder storage workshop. First, use a humidifier to control the humidity. In the dry season, the power of the humidifier is automatically increased by 50%. Conductive carpets are laid in key areas to help release static electricity accumulation. Measures are taken to prevent static electricity accumulation for staff entering the storage workshop. At the same time, the windows of the gunpowder storage workshop are shielded to optimize the light intensity in the storage workshop.

[0111] When the comprehensive safety factor Aqxs ≤ the second evaluation threshold E, it means that the current gunpowder storage environment is in a normal state. At this time, there is no need to generate early warning information and continue to perform detection.

[0112] In this embodiment, the obtained harmful gas coefficient Kqxs and the comprehensive safety factor Aqxs are evaluated and compared according to the preset first evaluation threshold Q and the second evaluation threshold E, which can effectively monitor the safety of the gunpowder storage environment, and take corresponding countermeasures when abnormal situations occur to ensure the safety of the gunpowder storage process. Through these evaluations and countermeasures, the safety hazards in the gunpowder storage environment can be discovered and resolved in time to ensure the safety of the gunpowder storage process.

[0113] 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 alterations may be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identifying and assessing storage safety risks of explosive and dangerous goods, characterized by: The following steps are involved: S1. First, several integrated detection points are arranged inside the gunpowder storage workshop and the gunpowder storage tank, and detection equipment is installed to detect the comprehensive storage data and harmful gas data of the gunpowder storage, and the collected comprehensive storage data and harmful gas data are classified and summarized to generate a first data set and a second data set, and a data mining algorithm model is constructed based on the collected data sets; S2, after dimensionless processing, the electromagnetic data set, environmental data set and gunpowder storage data set of the collected first data set are used to calculate and obtain the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs respectively, and summarize them to generate a comprehensive storage coefficient Cfxs; The comprehensive storage coefficient Cfxs is obtained by the following formula: ； Wherein, a1, a2 and a3 represent the preset proportional coefficients of the electromagnetic coefficient Dcxs, the environmental coefficient Hjxs and the gunpowder storage coefficient Ycxs respectively, and a1+a2+a3≠1, 0<a1<0.47, 0<a2<0.57, 0<a3<0.77, and the specific values ​​are adjusted and set by the user, i represents the starting position of summation, and i=1 represents the summation starting from (Dcxs*a1); S3, performing dimensionless processing on the harmful gas data set of the collected second data set to calculate and obtain the harmful gas coefficient Kqxs; The harmful gas coefficient Kqxs is obtained by the following formula: ； In the formula, CO2 represents the concentration of carbon dioxide gas, NOx represents the concentration of nitrogen oxide gas, H2S represents the concentration of hydrogen sulfide gas, NH3 represents the concentration of ammonia gas, C8H 10 Indicates xylene gas concentration; S4, analyzing and calculating the comprehensive storage coefficient Cfxs and the harmful gas coefficient Kqxs obtained by the first data set and the second data set, and summarizing them to generate a comprehensive safety factor Aqxs; S5. Finally, a preliminary comparative evaluation is performed by setting the first evaluation threshold Q and the harmful gas coefficient Kqxs to generate a corresponding evaluation result. At the same time, a second evaluation threshold E is set to perform a secondary comparative evaluation with the obtained comprehensive safety factor Aqxs to generate a relevant safety evaluation result.

2. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 1, characterized in that: By arranging a number of integrated detection points in the gunpowder storage workshop and the gunpowder storage tank, and installing detection equipment, the detection equipment includes a first integrated sensor group, a second integrated sensor group, a third integrated sensor group and a fourth integrated sensor group, the comprehensive storage data and harmful gas data in the storage process of the gunpowder are detected in real time; The first integrated sensor group, the second integrated sensor group and the third integrated sensor group are used to perform real-time detection of electromagnetic data, environmental data and gunpowder storage data of gunpowder storage; The first integrated sensor group includes a charge sensor, a current sensor, an electric field sensor, an electromagnetic radiation sensor and a resistance sensor; The second integrated sensor group includes a temperature sensitive resistor, a humidity sensor, an ultraviolet sensor, an atmospheric pressure sensor, and an air flow sensor; The third integrated sensor group includes an oxygen sensor, a temperature sensor, an electronic humidity sensor and an infrared sensor; The fourth integrated sensor group is used for real-time detection of harmful gases; The fourth integrated sensor group includes a carbon dioxide sensor, a xylene sensor, a nitrogen oxide sensor, a hydrogen sulfide sensor and an ammonia sensor.

3. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 2, characterized in that: After dimensionless processing of the comprehensive storage data and harmful gas data detected by the detection equipment, outliers are removed, noise is removed, and normalization is performed, and the comprehensive storage data and harmful gas data are classified and summarized to generate a first data set and a second data set and collect them; The first data set includes an electromagnetic data set, an environmental data set, and a gunpowder storage data set; The electromagnetic data set includes electrostatic charge Ddh, electrostatic current Ddl, electric field strength Dqd, electromagnetic radiation Dfs and resistance Ddz; The environmental data set includes temperature Hwd, humidity Hsd, ultraviolet intensity Hzw, atmospheric pressure Hdq and air flow rate Hkq; The gunpowder storage data set includes stored oxygen concentration Yyq, gunpowder humidity Ysd, gunpowder water content Yhs, gunpowder temperature Ywd and gunpowder temperature change rate Ybh; The second data set includes a harmful gas data set; The harmful gas data set includes carbon dioxide gas concentration CO2, nitrogen oxide gas concentration NOx, hydrogen sulfide gas concentration H2S, ammonia gas concentration NH3 and xylene gas concentration C8H 10 .

4. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 3, characterized in that: The first data set and the second data set collected are subjected to feature extraction and feature selection, wherein the feature extraction is performed by converting the original data into features used by the data mining algorithm model, and the feature selection is used to select features related to the safety of gunpowder storage according to domain knowledge and experience, including the environment in which the gunpowder is stored and the gunpowder storage environment inside the storage tank, perform feature scaling and dimension reduction preprocessing steps, optimize the performance and efficiency of the data mining algorithm model, set the classification algorithm as the basic framework of the model, and perform integrated construction of the data mining algorithm model; Then, through the data mining algorithm model, a large amount of historical safety data on gunpowder storage is mined on the Internet, and the data mining algorithm model is trained in combination with the comprehensive storage data and harmful gas data collected at the actual storage site. The model is evaluated using cross-validation technology to ensure the generalization ability of the data mining algorithm model on unseen data. The model is tuned according to the evaluation results, including adjusting hyperparameters and feature selection, and the trained data mining algorithm model is deployed in practical applications for the prediction and monitoring of gunpowder storage safety risks. The performance of the model is monitored regularly, and the data mining algorithm model is updated in a timely manner to cope with changes in data distribution or new safety risks. The data mining algorithm model and data collection methods are continuously optimized, and the accuracy and practicality of the model are adjusted.

5. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 3, characterized in that: By extracting the electromagnetic data set based on the first data set collected, dimensionless processing is performed, and then the electromagnetic coefficient Dcxs is obtained by summarizing, analyzing and calculating; The electromagnetic coefficient Dcxs is obtained by the following formula: ； In the formula, i represents the starting position of the product, and i=1 means that the multiplication starts from the electrostatic charge Ddh.

6. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 5, characterized in that: By extracting the environmental data set based on the first data set collected, dimensionless processing is performed, and then the environmental coefficient Hjxs is obtained by summarizing, analyzing and calculating; The environmental coefficient Hjxs is obtained by the following formula: 。 7. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 6, characterized in that: By extracting the gunpowder storage data set based on the collected first data set, after dimensionless processing, a summary analysis and calculation is performed to obtain the gunpowder storage coefficient Ycxs; The gunpowder storage coefficient Ycxs is obtained by the following formula: 。 8. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 7, characterized in that: Then, the electromagnetic coefficient Dcxs, environmental coefficient Hjxs and gunpowder storage coefficient Ycxs obtained from the first data set are summarized and calculated to obtain a comprehensive storage coefficient Cfxs, and then the comprehensive storage coefficient Cfxs is correlated with the obtained harmful gas coefficient Kqxs to obtain a comprehensive safety factor Aqxs; The comprehensive safety factor Aqxs is obtained by the following formula: ； In the formula, b1 and b2 represent the preset proportional coefficients of the comprehensive storage coefficient Cfxs and the harmful gas coefficient Kqxs respectively, and b1≠b2, 0<b1<0.37, 0<b2<0.63, and the specific values ​​are adjusted and set by the user.

9. The method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 1, characterized in that: By presetting the first evaluation threshold Q and the harmful gas coefficient Kqxs obtained by analyzing and calculating the second data set, a preliminary comparative evaluation is performed, and the risk assessment result of the harmful gas inside the gunpowder storage workshop is obtained. The specific risk assessment scheme is as follows; When the harmful gas coefficient Kqxs> the first evaluation threshold Q, it means that the concentration of harmful gases in the current gunpowder storage workshop is in an abnormal state. At this time, it is necessary to increase the power of the ventilation fan in the storage workshop by 50%, and generate an early warning message to intelligently remind nearby staff not to approach, and use appropriate absorbents and chemicals to absorb the leaked substances; When the harmful gas coefficient Kqxs ≤ the first evaluation threshold Q, it means that the harmful gas inside the current gunpowder storage workshop is in a normal state. At this time, there is no need to generate early warning information, and the second evaluation mechanism is started at the same time.

10. A method for identifying and assessing storage safety risks of explosive and dangerous goods according to claim 9, characterized in that: The second evaluation mechanism is triggered by the first evaluation mechanism. The second evaluation mechanism is to conduct a secondary comparative evaluation by presetting the second evaluation threshold E and the obtained comprehensive safety factor Aqxs, which is used to analyze the storage environment of the gunpowder during storage and the environment inside the gunpowder storage tank for real-time evaluation. The specific evaluation scheme is as follows; When the comprehensive safety factor Aqxs> the second evaluation threshold E, it means that the current gunpowder storage environment is abnormal. At this time, an early warning message is generated to notify the staff to improve the environment inside the gunpowder storage workshop. First, use a humidifier to control the humidity. In the dry season, the power of the humidifier is automatically increased by 50%. Conductive carpets are laid in key areas to help release static electricity accumulation. Measures are taken to prevent static electricity accumulation for staff entering the storage workshop. At the same time, the windows of the gunpowder storage workshop are shielded to optimize the light intensity in the storage workshop. When the comprehensive safety factor Aqxs ≤ the second evaluation threshold E, it means that the current gunpowder storage environment is in a normal state. At this time, there is no need to generate early warning information and continue to perform detection.

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