Industrial risk source positioning system and method based on data analysis

Abstract

The invention relates to the technical field of industrial risk source positioning, and discloses an industrial risk source positioning system and method based on data analysis, and the system comprises a data collection module, a data processing module, a data analysis module, a data display module and a positioning module. The severity, criticality, possibility, influence, pollution discharge and waste treatment, energy utilization, industrial layout, environment management level and natural factor index data of the industry are extracted through a feature extraction function, so that the efficiency of a data analysis module is improved; according to the industrial risk source positioning system and the industrial risk source positioning method, the industrial risk is evaluated through multiple groups of indexes, and the industrial risk source with risks is quickly positioned through the positioning module, so that the industrial risk source positioning system is more accurate and higher in efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial risk source positioning, and in particular to an industrial risk source positioning system and method based on data analysis. Background Art

[0002] Industrial risk sources mainly include the following categories: production equipment failure, environmental factors, operational errors, natural disasters, chemical leaks, market risks, technical risks, supply chain risks, environmental risks, financial risks and policy risks;

[0003] The existing industrial risk source positioning system first needs to evaluate the industrial risks and locate the risky industrial equipment based on the evaluation results so that decision makers can formulate corresponding management strategies. However, the existing industrial risk source positioning system has a relatively simple industrial assessment process, which leads to large errors in the assessment results. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an industrial risk source positioning system and method based on data analysis, which solves the problems raised in the above background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, an industrial risk source positioning system and method based on data analysis, including a data collection module, a data processing module, a data analysis module, a data display module and a positioning module, characterized in that: the data collection module: is used to collect data related to industrial equipment from internal systems, external systems, sensors and the Internet of Things;

[0006] Data processing module: receives data from the data collection module, performs data cleaning and preprocessing to ensure data quality and accuracy;

[0007] Data analysis module: Analyze the processed data through statistical and machine learning methods;

[0008] Data display module: intuitively presents the discovery of potential risk sources through charts, graphs and interactive dashboards;

[0009] Positioning module: Based on data analysis and combined with the actual situation of the industrial site, locate specific risk sources.

[0010] Furthermore, the following steps are included:

[0011] S1, receiving data from internal systems, external systems, sensors, and industrial equipment related to the Internet of Things through a data collection module;

[0012] S2. Receive data from the data receiving module, delete duplicate data records to avoid interference with the analysis results, and process missing data and outliers to ensure data accuracy;

[0013] S3. Calculate the risk level of industrial equipment through the analysis algorithm within the data analysis module. The risk level is divided into normal level, major risk and severe risk;

[0014] S4. Visually present potential risk sources of industrial equipment through charts, graphs and interactive dashboards to help decision makers understand the relationship between risk factors and support the formulation of corresponding risk management strategies;

[0015] S5. Based on data analysis and combined with the actual situation of the industrial site, locate specific risk sources.

[0016] Furthermore, the data received by the data collection module are: internal systems, external systems, sensors and the Internet of Things;

[0017] Among them, internal system data: through internal system data, the production, operation and financial data of industrial equipment can be obtained to evaluate the impact of industry and the level of environmental management;

[0018] External system data: collect market trends and industry data information to identify the impact of external environmental changes on industrial risks and evaluate industrial possibilities and natural factors;

[0019] Sensors and the Internet of Things: monitor equipment status, production progress, and material consumption indicators, provide real-time data support, and evaluate the severity and criticality of industries, pollution and waste treatment, energy utilization, and industrial layout.

[0020] Furthermore, the analysis algorithm indicators set in the data analysis module are: severity, criticality, possibility, impact, pollution discharge and waste treatment, energy utilization, industrial layout, environmental management level and natural factors;

[0021] Among them, severity (Z) refers to the severity of industrial pollution and threats;

[0022] Criticality (G): refers to the criticality of industrial equipment;

[0023] Likelihood (K): refers to the possibility of industrial risk occurring;

[0024] Impact (Y): refers to the impact caused by the occurrence of industrial risks;

[0025] Pollution and waste treatment indicators (P): A large amount of wastewater, waste gas and waste residue will be generated during the industrial production process. If they are not properly treated or discharged directly without treatment, they will cause serious pollution and damage to the environment;

[0026] Energy utilization index (N): Industrial development has a large demand for energy. If the energy utilization method is unreasonable, it will increase energy consumption and emissions, thereby exacerbating environmental risks;

[0027] Industrial layout index (B): Irrational industrial layout is likely to cause greater threats to the environment and human health;

[0028] Environmental management level index (H): The level of industrial environmental management directly affects the effectiveness of its environmental protection work;

[0029] Natural factor indicator (R): Natural disasters such as earthquakes and floods may cause damage to the production facilities and environment of industrial enterprises, thereby causing environmental risks.

[0030] Furthermore, the analysis algorithm imported into the data analysis module is:

[0031] F=Z+(G×20%)+(K×5%)+(Y×10%)+(P×10%)+(N×5%)+(B×10%)+(H×5%)+(R×5%).

[0032] Furthermore, the data processing module has data cleaning, data processing and feature extraction functions;

[0033] Among them, the data cleaning function: deletes duplicate data records to avoid interference with analysis results;

[0034] Data processing functions include: processing missing data: filling missing values ​​through interpolation and regression methods, and at the same time, selectively deleting records with more missing values ​​based on data characteristics;

[0035] Handling outliers: Identify and handle outliers caused by data entry errors and equipment failures;

[0036] Feature extraction function: used to extract the data required by the data analysis module to increase the efficiency of the data analysis module.

[0037] Furthermore, the positioning module obtains the specific location based on the data provided by the industrial risk source positioning system and the positioning solution formula.

[0038] Furthermore, the positioning solution formula set in the positioning module is:

[0039]

[0040] Where, θ is the positioning center point; (x, y, z) is the position of the positioning ground station; (x s ,y s 、z s) is the positioning error value; c is the speed of light; Δt is the time difference between the satellite and the ground station; ρ is the distance from the ground station to the satellite; I, T and ∈ are the delay caused by the atmospheric ionosphere and troposphere and the satellite clock error respectively.

[0041] The beneficial effects of the industrial risk source positioning system and method based on data analysis of the present invention are:

[0042] The present invention processes missing data and outliers through a data processing module to ensure data accuracy, and extracts industrial severity, criticality, possibility, impact, pollution discharge and waste treatment, energy utilization, industrial layout, environmental management level and natural factor indicator data through a feature extraction function to increase the efficiency of the data analysis module, and evaluates the extracted data using the analysis algorithm imported into the data analysis module. The present invention evaluates industrial risks through multiple groups of indicators, and quickly locates risky industrial risk sources through a positioning module, making the industrial risk source positioning system more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0044] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0046] like Figure 1 As shown, according to one aspect of the present invention, a system and method for locating industrial risk sources based on data analysis are provided, including a data collection module, a data processing module, a data analysis module, a data display module and a positioning module, characterized in that: the data collection module: is used to collect data related to industrial equipment from internal systems, external systems, sensors and the Internet of Things;

[0047] Data processing module: receives data from the data collection module, performs data cleaning and preprocessing to ensure data quality and accuracy;

[0048] Data analysis module: Analyze the processed data through statistical and machine learning methods;

[0049] Data display module: intuitively presents the discovery of potential risk sources through charts, graphs and interactive dashboards;

[0050] Positioning module: Based on data analysis and combined with the actual situation of the industrial site, locate specific risk sources.

[0051] In this embodiment, the following steps are included:

[0052] S1, receiving data from internal systems, external systems, sensors, and industrial equipment related to the Internet of Things through a data collection module;

[0053] S2. Receive data from the data receiving module, delete duplicate data records to avoid interference with the analysis results, and process missing data and outliers to ensure data accuracy;

[0054] S3. Calculate the risk level of industrial equipment through the analysis algorithm within the data analysis module. The risk level is divided into normal level, major risk and severe risk;

[0055] S4. Visually present potential risk sources of industrial equipment through charts, graphs and interactive dashboards to help decision makers understand the relationship between risk factors and support the formulation of corresponding risk management strategies;

[0056] S5. Based on data analysis and combined with the actual situation of the industrial site, locate specific risk sources.

[0057] In this embodiment, the data received by the data collection module are: internal system, external system, sensor and Internet of Things;

[0058] Among them, internal system data: through internal system data, the production, operation and financial data of industrial equipment can be obtained to evaluate the impact of industry and the level of environmental management;

[0059] External system data: collect market trends and industry data information to identify the impact of external environmental changes on industrial risks and evaluate industrial possibilities and natural factors;

[0060] Sensors and the Internet of Things: monitor equipment status, production progress, and material consumption indicators, provide real-time data support, and evaluate the severity and criticality of industries, pollution and waste treatment, energy utilization, and industrial layout.

[0061] In this embodiment, the analysis algorithm indicators set in the data analysis module are: severity, criticality, possibility, impact, pollution discharge and waste treatment, energy utilization, industrial layout, environmental management level and natural factors;

[0062] Among them, severity (Z) refers to the severity of industrial pollution and threats, with a score range of 20 to 40;

[0063] Criticality (G): refers to the criticality of industrial equipment, with a scoring range of 20 to 50 and a weight of 20%;

[0064] Likelihood (K): refers to the possibility of industrial risk occurring, with a scoring range of 10 to 30 and a weight of 5%;

[0065] Impact (Y): refers to the impact caused by the occurrence of industrial risks, with a score range of 30 to 50 and a weight of 10%;

[0066] Pollution and waste treatment index (P): A large amount of wastewater, waste gas and waste residue will be generated during industrial production. If they are not properly treated or discharged directly without treatment, they will cause serious pollution and damage to the environment. The score range is 40-60, with a weight of 10%;

[0067] Energy utilization index (N): Industrial development has a large demand for energy. If the energy utilization method is unreasonable, it will increase energy consumption and emissions, thereby exacerbating environmental risks. The score range is 20-30, with a weight of 5%;

[0068] Industrial layout index (B): unreasonable industrial layout is likely to cause greater threats to the environment and human health, with a score range of 50 to 70 and a weight of 10%;

[0069] Environmental management level index (H): The level of industrial environmental management directly affects the effectiveness of its environmental protection work, with a score range of 40 to 60 and a weight of 5%;

[0070] Natural factor index (R): Natural disasters such as earthquakes and floods may cause damage to the production facilities and environment of industrial enterprises, thereby causing environmental risks. The score range is 30-50, with a weight of 5%.

[0071] In this embodiment, the analysis algorithm imported into the data analysis module is:

[0072] F=Z+(G×20%)+(K×5%)+(Y×10%)+(P×10%)+(N×5%)+(B×10%)+(H×5%)+(R×5%);

[0073] When F (risk value) is below 50, industrial equipment is at a normal level;

[0074] When F (risk value) is between 50.1 and 60, industrial equipment is at significant risk;

[0075] When F (risk value) is above 60, industrial equipment is at serious risk;

[0076] When F is higher than 50, the industrial equipment data is transmitted to the data display module and the position of the industrial equipment is located.

[0077] In this embodiment, the data processing module has data cleaning, data processing and feature extraction functions;

[0078] Among them, the data cleaning function: deletes duplicate data records to avoid interference with analysis results;

[0079] Data processing functions include: processing missing data: filling missing values ​​through interpolation and regression methods, and at the same time, selectively deleting records with more missing values ​​based on data characteristics;

[0080] Handling outliers: Identify and handle outliers caused by data entry errors and equipment failures;

[0081] Feature extraction function: used to extract the data required by the data analysis module to increase the efficiency of the data analysis module.

[0082] In this embodiment, the positioning module obtains the specific location based on the data provided by the industrial risk source positioning system and the positioning solution formula.

[0083] In this embodiment, the positioning solution formula set in the positioning module is:

[0084]

[0085] Where, θ is the positioning center point; (x, y, z) is the position of the positioning ground station; (x s ,y s 、z s ) is the positioning error value; c is the speed of light; Δt is the time difference between the satellite and the ground station; ρ is the distance from the ground station to the satellite; I, T and ∈ are the delay caused by the atmospheric ionosphere and troposphere and the satellite clock error respectively.

[0086] Embodiment 1

[0087] According to the data received by the data collection module:

[0088] The score of severity (Z) is 20; the score of criticality (G) is 20; the score of possibility (K) is 20; the score of impact (Y) is 30; the score of pollution discharge and waste treatment index (P) is 40; the score of energy utilization index (N) is 20; the score of industrial layout index (B) is 50; the score of environmental management level index (H) is 40; the score of natural factor index (R) is 30. The obtained data is imported into the analysis algorithm to obtain:

[0089] 20+(20×20%)+(10×5%)+(30×10%)+(40×10%)

[0090] +(20×5%)+(50×10%)+(40×5%)+(30×5%)

[0091] =41

[0092] It is known that 41<50, and this group of industrial equipment is at a normal level.

[0093] Embodiment 2

[0094] According to the data received by the data collection module:

[0095] The score of severity (Z) is 40; the score of criticality (G) is 50; the score of possibility (K) is 30; the score of impact (Y) is 50; the score of pollution discharge and waste treatment index (P) is 60; the score of energy utilization index (N) is 30; the score of industrial layout index (B) is 70; the score of environmental management level index (H) is 60; the score of natural factor index (R) is 50. The obtained data is imported into the analysis algorithm to obtain: 40+(50×20%)+(30×5%)+(50×10%)+(60×10%)

[0096] +(30×5%)+(70×10%)+(60×5%)+(50×5%)

[0097] =76.5

[0098] It is known that 76.5>60, and this group of industrial equipment is at serious risk. The industrial risk source positioning system transmits the industrial equipment data to the data display module and locates the position of the industrial equipment.

[0099] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention also fall within the protection scope of the present invention.

Claims

1. An industrial risk source positioning system based on data analysis, comprising a data collection module, a data processing module, a data analysis module, a data display module and a positioning module, characterized in that: The data collection module is used to collect data from internal systems, external systems, sensors, and industrial equipment related to the Internet of Things; Data processing module: receives data from the data collection module, performs data cleaning and preprocessing to ensure data quality and accuracy; Data analysis module: Analyze the processed data through statistical and machine learning methods; Data display module: intuitively presents the discovery of potential risk sources through charts, graphs and interactive dashboards; Positioning module: Based on data analysis and combined with the actual situation of the industrial site, locate specific risk sources.

2. The method for locating industrial risk sources based on data analysis according to claim 1 is characterized in that: The following steps are involved: S1, receiving data from internal systems, external systems, sensors, and industrial equipment related to the Internet of Things through a data collection module; S2. Receive data from the data receiving module, delete duplicate data records to avoid interference with the analysis results, and process missing data and outliers to ensure data accuracy; S3. Calculate the risk level of industrial equipment through the analysis algorithm within the data analysis module. The risk level is divided into normal level, major risk and severe risk; S4. Visually present potential risk sources of industrial equipment through charts, graphs and interactive dashboards to help decision makers understand the relationship between risk factors and support the formulation of corresponding risk management strategies; S5. Based on data analysis and combined with the actual situation of the industrial site, locate specific risk sources.

3. The industrial risk source positioning system based on data analysis according to claim 1 is characterized in that: The data received by the data collection module are: internal system, external system, sensor and Internet of Things; Among them, internal system data: through internal system data, the production, operation and financial data of industrial equipment can be obtained to evaluate the impact of industry and the level of environmental management; External system data: collect market trends and industry data information to identify the impact of external environmental changes on industrial risks and evaluate industrial possibilities and natural factors; Sensors and the Internet of Things: monitor equipment status, production progress, and material consumption indicators, provide real-time data support, and evaluate the severity and criticality of industries, pollution and waste treatment, energy utilization, and industrial layout.

4. The industrial risk source positioning system based on data analysis according to claim 3 is characterized in that: The analysis algorithm indicators set in the data analysis module are: severity, criticality, possibility, impact, pollution discharge and waste treatment, energy utilization, industrial layout, environmental management level and natural factors; Among them, severity (Z) refers to the severity of industrial pollution and threats; Criticality (G): refers to the criticality of industrial equipment; Likelihood (K): refers to the possibility of industrial risk occurring; Impact (Y): refers to the impact caused by the occurrence of industrial risks; Pollution and waste treatment indicators (P): A large amount of wastewater, waste gas and waste residue will be generated during the industrial production process. If they are not properly treated or discharged directly without treatment, they will cause serious pollution and damage to the environment; Energy utilization index (N): Industrial development has a large demand for energy. If the energy utilization method is unreasonable, it will increase energy consumption and emissions, thereby exacerbating environmental risks; Industrial layout index (B): Irrational industrial layout is likely to cause greater threats to the environment and human health; Environmental management level index (H): The level of industrial environmental management directly affects the effectiveness of its environmental protection work; Natural factor indicator (R): Natural disasters such as earthquakes and floods may cause damage to the production facilities and environment of industrial enterprises, thereby causing environmental risks.

5. The industrial risk source positioning system based on data analysis according to claim 4 is characterized in that: The analysis algorithm imported into the data analysis module is: F=Z+(G×20%)+(K×5%)+(Y×10%)+(P×10%)+(N×5%)+(B×10%)+(H×5%)+(R×5%).

6. The industrial risk source positioning system based on data analysis according to claim 1 is characterized in that: The data processing module has data cleaning, data processing and feature extraction functions; Among them, the data cleaning function: deletes duplicate data records to avoid interference with analysis results; Data processing functions include: processing missing data: filling missing values ​​through interpolation and regression methods, and at the same time, selectively deleting records with more missing values ​​based on data characteristics; Handling outliers: Identify and handle outliers caused by data entry errors and equipment failures; Feature extraction function: used to extract the data required by the data analysis module to increase the efficiency of the data analysis module.

7. The industrial risk source positioning system based on data analysis according to claim 1 is characterized in that: The positioning module obtains the specific location based on the data provided by the industrial risk source positioning system and the positioning solution formula.

8. The industrial risk source positioning system based on data analysis according to claim 7 is characterized in that: The positioning solution formula set in the positioning module is: Where, θ is the positioning center point; (x, y, z) is the position of the positioning ground station; (x s ,y s 、z s ) is the positioning error value; c is the speed of light; Δt is the time difference between the satellite and the ground station; ρ is the distance from the ground station to the satellite; I, T and ∈ are the delay caused by the atmospheric ionosphere and troposphere and the satellite clock error, respectively.

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