Initiating explosive device production line safety risk assessment method based on digital twinning and risk field theory fusion

Abstract

The invention discloses an initiating explosive device production line safety risk assessment method based on digital twinning and risk field theory fusion, and the method comprises the steps: building an initiating explosive device production line twinning model based on the field data of an initiating explosive device production line, and importing the model into a digital twinning platform; based on a risk field theory, an initiating explosive device production line safety risk mathematical model is constructed, and safety risk attribute definition is mathematically performed on each part of the initiating explosive device production line twin model through the mathematical model in a digital twin platform. According to the method, the three-dimensional risk field models of the kinetic energy field, the potential energy field and the behavior field are constructed, various risk sources in the initiating explosive device production line are comprehensively described, and compared with a traditional single fault diagnosis method, the multi-dimensional risk modeling method can more comprehensively and accurately reflect potential safety hazards in the production process; meanwhile, risk assessment is more accurate through risk quantification.

Description

Technical Field

[0001] The present invention relates to the technical field of safety risk assessment and early warning for explosive train production lines, and specifically relates to a safety risk assessment method for explosive train production lines based on the integration of digital twin and risk field theory. Background Technique

[0002] During the production process of explosive trains, due to the flammability and explosiveness of raw materials and the complexity of the production environment, the safety risks caused mainly have the characteristics of suddenness, chain propagation, and serious consequences. The traditional safety risk assessment methods mainly have the following problems: having static limitations: relying on historical data and qualitative analysis (such as fault tree analysis, risk matrix), unable to dynamically reflect the spatio-temporal evolution characteristics of the human-machine-environment coupling risks in the production line; there is a phenomenon of data islands: effective integration of multi-source heterogeneous data (such as equipment status, environmental parameters, personnel behavior, etc.) is lacking, and it is difficult to support real-time risk early warning.

[0003] In recent years, digital twin technology has provided new ideas for safety risk assessment. Currently, there are two paths for digital twin to assess safety risks: one is to conduct fault diagnosis on key components based on machine learning to realize the processing and analysis of various signals in the production process and monitor the health status of components. However, for the complex operation process of production, the fault diagnosis results of a single component can only represent the typical characteristics of some mechanical components, and cannot well reflect the overall situation of the complex system working conditions, and cannot judge the series of chain reactions that faults will generate, there are potential risks, and the ability to evolve risks is poor. The other is to establish a safety risk early warning visualization system, use Internet of Things technology to obtain on-site data, and realize the perception of the production process. However, the basic mathematical modeling method can only express independent production states, and it is difficult to reflect the coupling effect of equipment in space and time and its impact on the overall production process, and there are problems such as poor real-time performance and difficult judgment of spatio-temporal safety risk assessment. Through comprehensive analysis, the following challenges still exist in the application in the field of explosive train production: (1) There are still limitations in the analysis and modeling of complex coupling risks driven by multi-source data, and it is difficult to effectively integrate data on on-site equipment, environment, and human factors; (2) The risk modeling lacks a quantitative expression of spatio-temporal dynamic characteristics, which limits the ability of real-time assessment. Summary of the Invention

[0004] The purpose of the present invention is to provide a safety risk assessment method for explosive train production lines based on the integration of digital twin and risk field theory to solve the technical problems proposed in the background technique.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: S1. Based on the on-site data of the explosive train production line, establish a twin model of the explosive train production line and import it into the digital twin platform; S2. Based on the risk field theory, construct a mathematical model for the safety risks of the initiator production line, and define the safety risk attributes of each component of the twin model of the initiator production line mathematically in the digital twin platform. The mathematical model includes the field domain that constitutes the risk field model , the kinetic field intensity , the potential field intensity , and the behavior field intensity , and the field force; S3. Define the safety risk attributes of each component of the twin model of the initiator production line physically in the digital twin platform to achieve behavior mapping; S4. Build Internet of Things sensing devices to collect the external environment and equipment operation status data of the initiator production line in real time, establish a communication connection with the digital twin platform, and perform real-time transmission; S5. Conduct real-time assessment of the safety risks of the initiator production line on the twin model of the initiator production line with safety risk attributes.

[0006] Furthermore, the field domain in step S2 includes the twin model of the initiator production line and the mapping relationship between the twin model and the site. Among them, the mapping relationship is specifically: map each physical unit in the twin model to the coordinates in the on-site coordinate space one by one.

[0007] Furthermore, the model construction formulas for the kinetic field intensity , the potential field intensity , and the behavior field intensity in step S2 are respectively: , In the above formula, M represents the mass of the equipment, represents its moving speed, represents the attenuation degree of the distance between the risk assessment point and the equipment, is the distance attenuation coefficient representing inverse square attenuation, represents the risk amplification coefficient of the initiator raw materials or equipment functions carried, , represents the initiator raw material equivalent; represents the numerical index of the th environmental factor, B represents the diffusivity influence factor, represents the correction coefficient of the diffusivity influence factor, S represents the sensor sensitivity, represents the correction coefficient of the sensor sensitivity, represents the weighted assessment of operation rationality, , represents that the operation is completely unreasonable and the risk is the highest; Indicates that the operation is completely reasonable and the risk is minimal, which is defined as the risk coefficient; The production operation area representing the safe state, The production operation area representing the dangerous state.

[0008] Furthermore, the establishment of the field force in step S2 is specifically as follows: Use the Bayesian network to calculate the risk weight, and calculate the field force F according to the risk weight, and quantify the field strength by combining the amplification effect of the field force on the field intensity.

[0009] Furthermore, the specific calculation formulas for the field force F and the quantified field strength are as follows: , In the above formula, is the comprehensive field intensity, represents the gradient of the field intensity, is the risk weight obtained through the conditional probability relationship of the Bayesian network; , , respectively represent the quantified kinetic energy field field intensity , potential energy field field intensity and behavior field field intensity , is the risk weight obtained through the conditional probability relationship of the Bayesian network.

[0010] Furthermore, the physical definition in step S3 is specifically as follows: Add relevant rigid bodies and collision components to each component of the twin model of the initiator production line.

[0011] Furthermore, the specific steps of step S5 are as follows: S51. Project the twin model of the initiator production line with safety risk attributes onto a two-dimensional plane, divide the two-dimensional plane into several grid cells, and use the starting point of the initiator production line as the origin . By projecting the twin model onto the two-dimensional plane for grid division, the distribution of the risk field can be accurately defined in space. Compared with the traditional method, it has higher accuracy and flexibility, and can better handle complex production environments and the coupling effects of multiple risk factors; S52. Assign a unique spatial index to each grid cell, and represent the kinetic energy field, potential energy field, and behavior field with matrices respectively according to the spatial index to form independent matrix spaces; S53. Calculate the grid size occupied by each item in the twin model of the initiator production line with safety risk attributes in the two-dimensional plane in the risk-free state, and use it as the standard information of each item, and pre-set it in the digital twin platform; S54. Based on the standard information of each item, determine in real time whether the positions of the items in the twin model of the initiating explosive device production line with safety risk attributes are abnormal, and display the judgment result in the digital twin platform to achieve real-time assessment of the safety risk of the initiating explosive device production line;

[0012] If it is consistent with the standard information, there is no risk, and behavior mapping is performed; If it is inconsistent with the standard information, there is a risk, and calculate the field strength of the kinetic energy field and the field strength of the potential energy field and the field strength of the behavior field as well as the spatial index of the risk area grid, and give a safety warning for the risky area.

[0013] Furthermore, in the step S54, calculate the spatial index of the risk area grid, and the calculation formula is: , In the above formula, (i,j) represents the spatial index of the risk area grid, and are respectively the side lengths of the grid in the x and y axis directions of the two-dimensional plane, represents the coordinates of the item where the risk occurs, represents the coordinate origin of the two-dimensional plane.

[0014] Beneficial effects: 1. By constructing a three-dimensional risk field model of the kinetic energy field, potential energy field and behavior field, the present invention comprehensively describes various risk sources in the initiating explosive device production line. Compared with the traditional single fault diagnosis method, the multi-dimensional risk modeling method can more comprehensively and accurately reflect the potential safety hazards in the production process, and at the same time quantify the risk to make the risk assessment more accurate; 2. Through the amplification effect of the field force on the field strength, the present invention quantifies and processes to improve the accuracy of the risk assessment, making the assessment result more in line with the actual situation. By introducing the field force amplification mechanism, it can dynamically adjust the contribution of different risk sources to the overall safety situation, so that the risk assessment not only reflects the impact of single-point risks, but also can reflect its diffusion effect within the spatial range, thus improving the accuracy and reliability of risk prediction; 3. By transmitting real-time data, the present invention maps the external environment and equipment operation state data to the digital twin model in real time, dynamically calculates the risk intensity, and improves the safety response efficiency of the initiating explosive device production line. Description of the drawings

[0015] To more clearly illustrate the technical solutions implemented in the present invention, the following will briefly introduce the drawings required for describing the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of the safety risk assessment method for the initiator production line of the present invention; Figure 2 It is a schematic diagram of the twin model of the initiator production line after lightweight processing of the present invention; Figure 3 It is a schematic diagram of the twin model of the initiator production line after being imported into the digital twin platform of the present invention; Figure 4 It is a schematic diagram of the twin model of the initiator production line with safety risk attributes of the present invention projected onto a two-dimensional plane; Figure 5 It is an effect diagram when there is no risk in the real-time risk assessment of the present invention; Figure 6 It is an effect diagram when there is a risk in the real-time risk assessment of the present invention; Figure 7 It is a risk assessment result diagram under the influence of different factors of equipment, personnel, and environment of the present invention. Specific Embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] As Figures 1-7 shown, a safety risk assessment method for the initiator production line based on the integration of digital twin and risk field theory provided by the present invention is as follows: S1. Based on the on-site data of the initiator production line, establish a twin model of the initiator production line to realize the spatial modeling of safety risks at the production site, and then import it into the digital twin platform; As Figures 2-3 shown, specifically: randomly select an initiator production line, construct a three-dimensional model to generate a twin model of the initiator production line, perform lightweight processing on it and import it into the digital twin platform.

[0019] S2. Based on the risk field theory, construct a mathematical model for the safety risks of the initiator production line, and define the safety risk attributes of each component of the twin model of the initiator production line mathematically through the mathematical model in the digital twin platform, so that the twin model has the ability to quantify risks; the mathematical model includes the field domain , field strength, and field force that make up the risk field model. The field strength includes the kinetic energy field strength , potential energy field strength , and behavior field strength . Construct a three-dimensional risk field model of the kinetic energy field, potential energy field, and behavior field, which comprehensively describes various risk sources in the initiator production line. The multi-dimensional risk modeling method can more comprehensively and accurately reflect the potential safety hazards in the production process compared with the traditional single fault diagnosis method.

[0020] Field domain : The field domain is used to describe the spatial range of the risk field and defines the coverage area affected by the risk. In the twin model of the initiator production line of the present invention, the field domain includes the twin model of the initiator production line and the mapping relationship between the twin model and the site. Among them, the mapping relationship is specifically: map each physical unit (such as equipment, workstations, sensors, etc.) in the twin model to the coordinates in the on-site coordinate space one by one.

[0021] Field strength: Construct the kinetic energy field strength , potential energy field strength , and behavior field strength models respectively. The construction formulas are as follows: , In the above formula, M represents the mass of the equipment (operating component), represents its moving speed, represents the attenuation degree of the distance between the risk assessment point and the equipment, is the distance attenuation coefficient indicating inverse square attenuation, represents the risk amplification coefficient of the initiator raw materials or equipment functions carried. Generally, the production raw material equivalent is lower than the standard equivalent, that is , represents the initiator raw material equivalent. The larger it is, the more significant the impact on the kinetic energy field; represents the numerical index of the th environmental factor (for example, temperature, humidity, or dust concentration), reflecting the current state of this factor. B represents the diffusivity impact factor. Environmental factors (such as dust) often have diffusivity characteristics, and diffusivity will cause the risk impact to expand in space. It represents the correction coefficient of the diffusivity influence factor, which is used to adjust the linear influence of diffusivity on risk. S represents the sensor sensitivity. The response sensitivity of the sensor to environmental factors directly affects the accuracy of risk data. It represents the correction coefficient of the sensor sensitivity, which describes the influence of the sensor sensitivity on risk assessment. It represents the weighted evaluation of operational rationality. , It represents that the operation is completely unreasonable and the risk is the highest. It represents that the operation is completely reasonable and the risk is the lowest. It is defined as the risk coefficient, which is used to correct the evaluation effect of the model. It represents the production operation area in a safe state. It represents the production operation area in a dangerous state.

[0022] Field force: The Bayesian network is used to calculate the risk weight, and the field force F is calculated according to the risk weight: , In the above formula, is the comprehensive field strength, that is, the kinetic energy field (equipment), potential energy field (environment) and behavior field (personnel). The superposition of the field strengths of the three forms the comprehensive risk field, which is expressed as , It represents the gradient of the field strength, which describes the direction and rate of risk change. , , respectively represent the quantified kinetic energy field strength , potential energy field strength and behavior field strength , is the risk weight obtained through the conditional probability relationship of the Bayesian network. Combining the amplification effect of the field force on the field strength, the field force describes the influence of the field strength on the comprehensive risk. By weighting and calculating the influence factors under specific conditions through the Bayesian network, the conditional probability relationship in the Bayesian network is introduced in the field force calculation. Combining the known risk events and conditional variables, the field force weight is dynamically adjusted.

[0023] The field force describes the influence of the field strength on the comprehensive risk. Combining the amplification effect of the field force on the field strength, the quantization formula of the risk field strength is: , In the above formula, is the risk weight obtained through the conditional probability relationship of the Bayesian network. The risk field, as a field model for expressing risk, is described by the field domain, field strength, and field force, and can be expressed as: , is the comprehensive field strength, and F represents the field force, represents the field domain;

[0024] The accuracy of risk assessment is improved through quantization processing, that is, through the amplification effect of the field force on the field strength, so that the assessment results are more in line with the actual situation. Specifically, the field strength is used to describe the distribution of the safety risks of the initiator production line in space, while the field force characterizes the degree of influence of the field strength on the overall risk environment. By introducing the field force amplification mechanism, the contributions of different risk sources to the overall safety situation can be dynamically adjusted, so that the risk assessment can not only reflect the impact of single-point risks, but also reflect their diffusion effects within the spatial range, thus improving the accuracy and reliability of risk prediction.

[0025] S3. Define the safety risk attributes of each component of the initiator production line twin model physically in the digital twin platform to achieve behavior mapping. Specifically: Add relevant rigid bodies and collision components to each component of the initiator production line twin model, etc., so that it has the ability to dynamically express the on-site behavior of the initiator production line; For example, moving parts, materials, etc., add relevant rigid bodies and collision components to determine the risk range of a single component and the property changes caused by risks, such as the functional failure of sensors, the displacement of conveyor belts, etc.

[0026] S4. Build Internet of Things sensing devices to collect the external environment and equipment operation status data of the initiator production line in real time, establish a communication connection with the digital twin platform side, and perform real-time transmission to update and map the external environment and equipment operation status data of the initiator production line to the initiator production line twin model with safety risk attributes in real time; Establish real-time data transmission to map the external environment and equipment operation status data to the digital twin body model in real time, perform real-time safety risk assessment, dynamically calculate the risk intensity of each grid, and improve the safety response efficiency of the initiator production line.

[0027] S5. Conduct real-time assessment of the safety risks of the initiator production line for the initiator production line twin model with safety risk attributes;

[0028] As Figures 4-7 shown, in this embodiment, in order to illustrate the implementation method of step S5, the specific steps are as follows: S51. Project the initiator production line twin model with safety risk attributes onto a two-dimensional plane, divide the two-dimensional plane into several i*j grid cells, and take the starting point of the initiator production line as the origin , in this step, according to the actual situation, create a grid of m*n. In this embodiment, according to on-site measurement, a grid of 5*3 (unit: meter) is selected. According to the fine degree requirements of on-site equipment, the selected grid granularity is 0.1 meter, that is, a grid of 50*30 is constructed; S52. Assign a unique spatial index to each grid cell , according to the spatial index The kinetic energy field, potential energy field, and behavior field are respectively represented by matrices to form independent matrix spaces, enabling different types of fields (such as kinetic energy field, potential energy field, and behavior field) to be independently stored and calculated, while also being able to be superimposed in subsequent analyses to achieve comprehensive evaluation; S53. Calculate the grid size occupied by each item in the twin model of the initiating explosive device production line with safety risk attributes on the two-dimensional plane, and use it as the standard information of each item, which is preset in the digital twin platform. For example, a processed part occupies one grid size; S54. According to the standard information of each item, determine in real time whether the position occupied by each item in the twin model of the initiating explosive device production line with safety risk attributes is abnormal, and display the judgment result in the digital twin platform, such as Figure 5 and Figure 6 As shown, realize real-time evaluation of the safety risk of the initiating explosive device production line; If it is consistent with the standard information, there is no risk, and behavior mapping is performed; If it is inconsistent with the standard information, there is a risk, and the field strength of the kinetic energy field is calculated through the mathematical model defined on the twin model of the initiating explosive device production line , the field strength of the potential energy field and the field strength of the behavior field , where and are respectively the side lengths of the grid in the x and y directions on the two-dimensional plane, represents the coordinates of the item with risk, represents the coordinate origin of the two-dimensional plane.

[0029] Verify the safety risk assessment method for the initiating explosive device production line based on the fusion of digital twin and risk field theory provided by the present invention. As Figure 5 shown at the moment, the data is normal, and the digital twin platform maps the behavior in real time. As Figure 6 shown at the moment, there is abnormal data. The risk is evaluated by retrieving and positioning the grid, and visual expression is performed on the digital twin platform; as Figure 7 shown, in Figure 7 (a), when in the risk field at rest, the equipment, personnel, and environment have not started running, and the risk has almost no intensity; in Figure 7 (b) and Figure 7In (c), the influence of the device on the risk field strength at different operating speeds can be seen, and it can be seen that the operation of the device will significantly increase the safety risk; in Figure 7 In (d), the influence on the risk field strength when the human behavior enters; in Figure 7 In (e), the influence on the risk field strength when the environmental state changes; in Figure 7 In (f), the distribution of the coupled risks in the complex scenarios of the device, personnel and environment is combined.

[0030] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claimed invention.

[0031] In addition, it should be understood that although this specification is described according to the embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A safety risk assessment method for an initiator production line based on the integration of digital twin and risk field theory, characterized in that, It includes the following steps: S1. Based on the on-site data of the initiator production line, establish a digital twin model of the initiator production line and import it into the digital twin platform; S2. Based on the risk field theory, construct a mathematical model for the safety risks of the initiator production line, and define the safety risk attributes of each component of the twin model of the initiator production line mathematically through the mathematical model in the digital twin platform, where the mathematical model includes the fields that make up the risk field model , the field strength of the kinetic energy field , the field strength of the potential energy field , the field strength of the behavior field and the field force; S3. Physically define the safety risk attributes of each component of the digital twin model of the initiator production line in the digital twin platform; S4. Build Internet of Things sensing devices to collect the external environment and equipment operation status data of the initiator production line in real time, establish a communication connection with the digital twin platform end, and perform real-time transmission; S5. Conduct real-time assessment of the safety risks of the initiator production line on the digital twin model of the initiator production line with safety risk attributes.

2. The safety risk assessment method for an initiating explosive device production line according to claim 1, characterized in that The field in step S2 includes the twin model of the initiator production line and the mapping relationship between the twin model and the site. Specifically, the mapping relationship is as follows: each physical unit in the twin model is mapped one-to-one with the coordinates in the site coordinate space in the coordinates.

3. The safety risk assessment method for the initiator production line according to claim 1, wherein: The kinetic field intensity in step S2 , the potential field intensity , and the behavior field intensity have the following model construction formulas respectively: , In the above formula, M represents the mass of the device, represents its moving speed, represents the attenuation degree of the distance between the risk assessment point and the device, is the distance attenuation coefficient indicating inverse-square attenuation, represents the risk amplification coefficient of the explosive materials carried or the device function, , represents the explosive material equivalent; represents the numerical index of the th environmental factor, B represents the diffusivity influence factor, represents the correction coefficient of the diffusivity influence factor, S represents the sensor sensitivity, represents the weighted assessment of the operation rationality, , represents that the operation is completely unreasonable and the risk is the highest; represents that the operation is completely reasonable and the risk is the lowest, is defined as the risk coefficient; represents the production operation area in a safe state, represents the production operation area in a dangerous state.

4. The safety risk assessment method for the initiator production line according to claim 1, characterized in that: The specific establishment of the field force in step S2 is as follows: Use the Bayesian network to calculate the risk weight, calculate the field force F according to the risk weight, and quantify the field strength by combining the amplification effect of the field force on the field strength.

5. The safety risk assessment method for the initiator production line according to claim 4, characterized in that: The specific calculation formulas of the field force F and the quantified field strength are as follows: , In the above formula, is the comprehensive field strength, represents the gradient of the field strength, is the risk weight obtained through the conditional probability relationship of the Bayesian network; 、 、 respectively represent the field strength of the quantized kinetic energy field 、the field strength of the potential energy field and the field strength of the behavior field , is the risk weight obtained through the conditional probability relationship of the Bayesian network.

6. The safety risk assessment method for the initiator production line according to claim 1, wherein: The physical definition in step S3 is specifically: Add relevant rigid bodies and collision components to each component of the digital twin model of the initiator production line.

7. The safety risk assessment method for the initiator production line according to claim 1, wherein: The specific steps of step S5 are as follows: S51. Project the twin model of the initiating explosive device production line with safety risk attributes onto a two-dimensional plane, divide the two-dimensional plane into several grid cells, and use the starting point of the initiating explosive device production line as the origin ; S52. Assign a unique spatial index to each grid cell, represent the kinetic energy field, potential energy field, and behavior field with matrices respectively according to the spatial index, and form an independent matrix space; S53. Calculate the grid size occupied by each item in the digital twin model of the initiator production line with safety risk attributes in the risk-free state on the two-dimensional plane, use it as the standard information of each item, and preset it in the digital twin platform; S54. According to the standard information of each item, judge in real time whether the positions occupied by each item in the digital twin model of the initiator production line with safety risk attributes are abnormal, and display the judgment result in the digital twin platform to realize real-time assessment of the safety risks of the initiator production line; If it is consistent with the standard information, there is no risk, and behavior mapping is performed; If it is inconsistent with the standard information, there is a risk, and the field strength of the kinetic energy field is calculated. , the field strength of the potential energy field , the field strength of the behavior field and the spatial index of the risk area grid to give a safety warning for the risky area.

8. The safety risk assessment method for the initiator production line according to claim 7, characterized in that: In step S54, calculate the spatial index of the risk area grid, and the calculation formula is: In the above formula, (i, j) represents the spatial index of the grid in the risk area, and are the side lengths of the grid in the x and y axis directions in the two-dimensional plane respectively, represents the coordinates of the item where the risk occurs, represents the coordinate origin of the two-dimensional plane.

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