CN117575329A - Safety production risk monitoring method, storage medium and equipment based on explosion index - Google Patents
Safety production risk monitoring method, storage medium and equipment based on explosion index Download PDFInfo
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Abstract
The invention discloses a safety production risk monitoring method, a storage medium and equipment based on a frying index, wherein the method analyzes enterprise event information in a time sequence through preprocessing and library establishment of enterprise full life cycle basic data, extracts keywords related to safety production and constructs a label system; and establishing a label scoring mechanism through the scoring card model to obtain a label system model. Matching sub-tags by combining the current enterprise and the associated risk characteristics to form an enterprise risk tag; and after scoring the label system model, calculating the comprehensive risk related to the safety production of the monitored enterprise by using the frying index model. According to the method, the enterprise safety production risk is judged from multiple dimensions by combining data with enterprise Internet of things equipment monitoring data with some public properties, the risk early warning model is built, the data is subjected to association analysis by using the model, early warning is achieved, and safety accidents are avoided.
Description
Technical Field
The invention relates to the technical field of enterprise safety production monitoring, in particular to a safety production risk monitoring method, storage medium and equipment based on a frying index.
Background
Five key links of sensing, monitoring, early warning, disposing and evaluating the safety of industrial production are important points in the traditional safety production, and the current method for evaluating the safety production risk mainly analyzes the safety production risk of enterprises from a certain aspect, but is not comprehensive. The main disadvantages are as follows: firstly, the accuracy is not enough: because there are many factors that affect the risk, and some factors may have uncertainty, the current risk value calculation method may not accurately reflect the actual situation. Secondly, the intelligent degree is not high: the existing risk value calculation process often needs a great deal of manual intervention, is easy to make mistakes and has low efficiency. Third, lack of standardization and transparency: different evaluation institutions may have different risk value calculation methods and flows, resulting in possible differences in evaluation results. Meanwhile, the whole process and specific details of risk value calculation are often not completely disclosed, so that the public is hard to know and monitor the reliability of the evaluation result. Fourth, data sources are limited: the current risk value calculation mainly depends on historical data and existing data, and the data may have certain limitations and cannot fully reflect actual conditions. Fifth, risk of difficulty in quantification: some factors may be difficult to accurately quantify, and thus risk values may not be calculated exactly accurately.
Disclosure of Invention
In order to overcome the technical defects, the invention provides a safety production risk monitoring method, a storage medium and equipment based on a ringing index, which can judge the safety production risk of enterprises from multiple dimensions.
In order to solve the problems, the invention is realized according to the following technical scheme:
in a first aspect, the present application provides a safety production risk monitoring method based on a ringing index, comprising the steps of:
acquiring basic data in the whole life cycle of an enterprise, preprocessing the basic data, and establishing a database according to the preprocessed basic data, wherein the basic data comprises enterprise event information and enterprise basic information;
extracting basic data of a monitored enterprise in a database as a sample, and performing time sequence analysis on enterprise event information in the sample;
extracting keywords related to the safety production activity from the enterprise event information, and adding sub-labels to the keywords to obtain a label system of the safety production activity;
establishing a scoring mechanism for the label system according to the scoring card model to obtain a label system model;
acquiring the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise, and matching the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise with the sub-tags to obtain an enterprise risk tag of the monitored enterprise;
scoring the enterprise risk labels according to the label system model, and obtaining a frying index model according to the scoring value and the weight of the enterprise risk labels;
and calculating the ring index of the monitored enterprise according to the ring index model.
As an improvement to the above, the ring index model is expressed as:
R＝W 1 ×R 1 +W 2 ×R 2 +W 3 ×R 3 +...+W n ×R n (1)
W n ＝w 1 +w 2 +...+w m (2)
wherein R is a frying index, R i Represents a risk value, W 1 、W 2 、W 3 、W n Represents the weight, a i Represents standard parameters, x i Represents a synthesis system, w i Is W i A weight value of the corresponding second level term; r is (r) i Is R i And the risk value of the corresponding second-stage item, k being an integer.
As an improvement of the scheme, the enterprise event information comprises a safe production special event, an enterprise complaint event, an enterprise administrative penalty event, an enterprise management event and an enterprise public opinion event.
As an improvement of the above scheme, the sub-label includes illegality, severity, openness, characteristic and scale.
As an improvement of the scheme, the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise are matched with the sub-tags through regular expressions.
As an improvement of the scheme, the safety production risk monitoring method based on the ringing index further comprises the step of judging the enterprise safety accident risk early warning level according to the ringing index.
As an improvement of the above scheme, the risk early warning grade is classified into grade 4.
As an improvement of the above scheme, the enterprise risk label of the monitored enterprise includes market risk, credit risk, liquidity risk, operation risk, legal risk, reputation risk, natural disaster risk, environmental risk and human resource risk.
In a second aspect, the present invention provides a computer-readable storage medium having stored therein
At least one instruction, at least one program, code set, or instruction set that is loaded and executed by a processor to implement the safety production risk monitoring method based on a ringing index as described in the first aspect.
In a third aspect, the present invention provides an apparatus comprising a processor and a memory, the memory storing therein
There is at least one instruction, at least one program, set of codes, or set of instructions loaded and executed by the processor to implement the safety production risk monitoring method based on a ringing index as described in the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
the method and the system can acquire some public information of enterprises through Internet public data, government supervision department data and perception data of the Internet of things equipment, and combine supervision data of enterprise supervision departments (emergency authorities) and monitoring data of the enterprise Internet of things equipment to perform data analysis according to the dimensions of enterprise scale, enterprise public opinion, enterprise management, enterprise supervision, associated enterprises, judicial disputes and monitoring and early warning, wherein each dimension performs candidate anomaly extraction according to a threshold value and a weight, and calculates to obtain an index value; and different classifications are integrated according to different weights, so that an overall index value is finally obtained, a frying index model is finally formed, and monitoring and early warning on enterprise safety production are realized by calculating the frying index.
Drawings
The invention is described in further detail below with reference to the attached drawing figures, wherein:
fig. 1 is a schematic flow chart of a safety production risk monitoring method based on a ring index in an embodiment of the application.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
It should be noted that, the numbers mentioned herein, such as S1 and S2 … …, are merely used as distinction between steps and do not represent that the steps must be strictly performed according to the order of the numbers.
The invention provides a safety production risk monitoring method based on a ringing index, which relates to analysis by utilizing data such as enterprise basic information, enterprise management, enterprise supervision, judicial disputes, monitoring and early warning of equipment of an Internet of things and the like, and is suitable for the safety production field such as mine major disaster risk early warning, whole-process risk early warning of dangerous chemicals and the like by collecting public data related to the enterprise in the whole network range, data reported to a supervision department by the enterprise and the equipment data of the Internet of things installed by the enterprise, and establishing the ringing index model according to business characteristics.
In one embodiment, the present application provides a safety production risk monitoring method based on a ring index, as shown in fig. 1, comprising the steps of:
s1: acquiring basic data in the whole life cycle of an enterprise, preprocessing the basic data, and establishing a database according to the preprocessed basic data, wherein the basic data comprises enterprise event information and enterprise basic information;
specifically, basic data in the whole life cycle of the monitored enterprise is obtained from Internet public data in a web crawler mode, and the data are processed to form a database, wherein the basic data comprise enterprise event information and enterprise basic information.
It should be noted that, the basic information of the enterprise generally includes registration information, legal representatives, registered capital, registered address, etc. of the enterprise, where the above information may be obtained from a file submitted by a commercial registration agency, government agency or enterprise; the enterprise event information includes various events in the enterprise life cycle, such as establishment, change, acquisition, merger, bankruptcy, law litigation, etc., and the information can be obtained from enterprise report, news report, judicial file, etc.
In the embodiment of the application, the enterprise event information includes a security production special event, an enterprise complaint event, an enterprise administrative penalty event, an enterprise management event, an enterprise public opinion event and the like.
S2: extracting basic data of a monitored enterprise in a database as a sample, and performing time sequence analysis on enterprise event information in the sample;
specifically, when the risk of safety production of a certain enterprise is required to be monitored, basic data of the monitored enterprise is extracted from a database to serve as a sample, enterprise event information in the sample is time-ordered through time sequence analysis, and the development trend of the enterprise event can be analyzed by a time sequence analysis method, wherein the time sequence is required to be formed by ordering all the events according to the occurrence date, the event can be realized through clauses in a database query language (such as SQL), the analysis time sequence can be realized by using a statistical method, a chart, a trend line and the like to identify the occurrence rule of the event, and the analysis result is visualized by using a visualization tool (such as Matplotlib, seaborn, tableau and the like) to more intuitively understand the time distribution and trend of the event.
S3: extracting keywords related to the safety production activity from the enterprise event information, and adding sub-labels to the keywords to obtain a label system of the safety production activity;
specifically, by extracting and classifying keywords related to the safety production activities, enterprise event information can be better organized, the transactions related to the safety production can be quickly positioned and identified, and the information retrieval efficiency is improved; the label system is helpful for identifying potential safety risks, and enterprises can discover potential problems or risk signals earlier through monitoring safety production activity keywords, and preventive measures are taken to reduce the possibility of accidents. At the same time, through a clear labelling hierarchy, the enterprise management layer can more easily obtain detailed information about the security production activities, which can be used to make and adjust decisions on security policies, improve training plans, optimize the use of security devices, etc.
In the embodiment of the application, illegal, serious, public, characteristic and scale are taken as sub-labels, wherein the illegal labels mainly represent whether related events are events caused by illegal actions or not; the severity labels represent the severity of the event, represented by 1-4, respectively, 1 representing particularly significant, 2 representing significant, 3 representing relatively large, and 4 representing general; the public label represents the degree of awareness of related events by the public or the degree of report by media, and is mainly measured by indexes such as reading number, forwarding number and the like; the characteristics are mainly measured according to concealment, danger, burstiness, causality, continuity, repeatability, accidents, timeliness, specificity and seasonality; the scale is measured mainly by the scale of economic losses caused by the event. The label system for safe production activity is obtained through various labels.
S4: establishing a scoring mechanism for the label system according to the scoring card model to obtain a label system model;
specifically, a scoring mechanism is established for a label system according to a scoring card model related to safety production, so as to obtain a label system model, wherein the label system model mainly comprises market risks, credit risks, liquidity risks, operation risks, legal risks, reputation risks, natural disaster risks, environmental risks, human resource risks and the like and specific gravity of each label system model. The established scoring system is applied to actual safety production management, and the scoring mechanism can help enterprises to better know the importance of safety production events and improve the overall safety level.
S5: acquiring the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise, and matching the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise with the sub-tags to obtain an enterprise risk tag of the monitored enterprise;
specifically, in the embodiment of the application, the data of the current detected enterprise is obtained from a database, risk keywords related to safety production activities or abnormal behaviors of the enterprise in each event are further extracted to serve as the risk features of the current enterprise, meanwhile, knowledge graph association persons and enterprise analysis are utilized to obtain the current enterprise association risk features under the association risk of the monitored enterprise, the risk features are matched with the sub-tags through regular expressions, and finally enterprise risk tags of the current monitored enterprise in multiple dimensions are formed, wherein the enterprise risk tags comprise enterprise scale, enterprise public opinion, enterprise management, enterprise supervision, associated enterprises, judicial disputes, monitoring early warning and basic attributes.
S6: scoring the enterprise risk labels according to the label system model, and obtaining a frying index model according to the scoring value and the weight of the enterprise risk labels;
specifically, in the embodiment of the present application, the enterprise risk tag in step S5 is scored according to the security production scoring card model, and the frying index data is formed by combining multiple dimensions (enterprise scale, enterprise public opinion, enterprise operation, enterprise supervision, associated enterprise, judicial disputes, monitoring and early warning, basic attributes) and corresponding weights.
The formula algorithm corresponding to the ring index model is as follows:
s6.1: the ring index calculation formula: the risk values of different classifications are accumulated by combining the weight values, and the calculation formula is as follows:
R＝W 1 ×R 1 +W 2 ×R 2 +W 3 ×R 3 +...+W n ×R n (1)
wherein R is i Representing risk values, the related formulas are described in step S6.2, W 1 、W 2 、W 3 、W n Represents weight, W n The value of (2) is mainly a weight value depending on the second term, and the calculation formula is as follows:
W n ＝w 1 +w 2 +...+w m (2)
if W is n Without the weight value of the second term, reference may be made to corresponding empirical values, as shown in table 1.
Table 1 tag and weight value
S6.2: calculating a risk value R i : the risk value of each classification in step S6.1 is mainly calculated as follows:
wherein a is i Representing standard parameters, and specifically realizing by S6.3; x is x i Represents a synthesis system, the expression of which is shown in S6.4.
S6.3: standard parameter a i : the standard parameters are mainly obtained according to empirical values, can be properly adjusted according to different industries, and reference data are as follows:
a 1 ＝1.05，a 2 ＝1.001，a 3 =1.1, and so on.
S6.4, calculating a synthetic system value: each classification is synthesized into a system, the formula composition is consistent, the weights are different, and the calculation formula is as follows:
wherein w is i Is W in S6.1 i The corresponding weight value of the second level item, such as the second level item corresponding to the enterprise scale, comprises three items of practitioner, business income and total asset amount; r is (r) i Is R in S6.1 i The risk value of the corresponding second-level term, k, is an integer from 1 to m, representing how many corresponding second-level terms are per category, e.g., three for enterprise-scale, then m=3.
S7: and calculating the ring index of the monitored enterprise according to the ring index model.
Specifically, the explosion index model performs data analysis according to the dimensions of enterprise scale, enterprise public opinion, enterprise operation, enterprise supervision, associated enterprises, judicial disputes and monitoring and early warning, and each dimension performs candidate anomaly extraction according to a threshold value and a weight and calculates to obtain an index value; different classifications are integrated according to different weights, a total index value is finally obtained, a ring index model is finally formed, the ring index score is in a percentage system, the value corresponds to four-color early warning commonly used in safety production, and the score section and the corresponding early warning color are respectively [0, 40 ] blue early warning, [40, 60) yellow early warning, [60, 80) orange early warning, [80, 100) red early warning.
The invention utilizes the basic properties of enterprises, enterprise management, enterprise supervision, judicial disputes, environmental data collected by the equipment of the Internet of things and the like, and combines the ring index model to realize the safety accident risk early warning of the safety production enterprises.
The safety production risk monitoring method based on the explosion index, provided by the invention, comprises the steps that 1, the model is combined with enterprise operation data, and the enterprise state can be judged through the operation abnormal data, the business change data, the employee departure rate, the safety production training times, the safety production examination passing rate and other data of enterprise operation, so that the higher the enterprise operation abnormal value is, the greater the risk is, and the greater the probability of safety production accidents is; 2. the model combines enterprise supervision data, the enterprise supervision data mainly comes from a supervision department (emergency bureau), the less supervision and punishment data are, the lower the risk is, and conversely, the higher the risk is; 3. the model incorporates enterprise base attribute data. The requirement of grading the heavy dangerous source is clearly proposed according to the temporary provision of great dangerous source supervision and management of dangerous chemicals, and a specific grading method is provided. The major sources of dangerous chemicals can be classified into primary, secondary, tertiary, and quaternary according to their regulations. We calculate using these data to derive a risk value; 4. the model combines judicial dispute data, the judicial dispute data are public data, and can be downloaded from related websites such as judicial offices, if the more judicial disputes are related to an enterprise, the more serious the degree is, and the higher the risk is; 5. the model is combined with monitoring and early warning data of enterprises, corresponding data such as temperature, humidity, concentration, pressure, wind direction and wind speed are obtained for some enterprises provided with the Internet of things equipment, and safety production risks of the enterprises are calculated to obtain corresponding risk values; 6. the model is combined with video analysis data, corresponding video analysis results, such as data of non-wearing safety helmet recognition, smoke recognition, face recognition, unsafe object stacking recognition, unsafe personnel behavior recognition and the like, are obtained for some enterprises provided with video monitoring equipment and analysis models, and then calculation is carried out by combining with the model, so that corresponding risk values are obtained. And finally, calculating the combination weight of each dimension risk value of enterprise scale, enterprise public opinion, enterprise operation, enterprise supervision, associated enterprises, judicial disputes, monitoring and early warning, and calculating again to finally form the ringing index.
According to the enterprise safety production risk judging method, various data related to enterprises are collected through various ways, risk calculation is carried out by utilizing a ringing index model after cleaning, the problems that original data are incomplete and inaccurate, calculation results are inaccurate and the like are solved, enterprise safety production risk is judged from multiple dimensions of enterprise scale, enterprise public opinion, enterprise operation, enterprise supervision, related enterprises, judicial disputes, monitoring early warning and the like by combining data with public properties with enterprise internet of things equipment monitoring data, early warning is achieved, safety accidents are avoided, and the method is very important for the enterprises to realize safety production.
In some embodiments, a computer readable storage medium is provided, the computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to implement the safety production risk monitoring method based on the ringing index provided in the first aspect.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer-readable storage media, which may include computer-readable storage media (or non-transitory media) and communication media (or transitory media).
The term computer-readable storage medium includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The computer readable storage medium may be an internal storage unit of the network management device according to the foregoing embodiment, for example, a hard disk or a memory of the network management device. The computer readable storage medium may also be an external storage device of the network management device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the network management device.
In some embodiments, an apparatus is provided that includes a processor and a memory for storing a computer program; the processor is configured to execute the computer program and implement the safety production risk monitoring method based on the ringing index provided in the first aspect of the present invention when the computer program is executed.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A safety production risk monitoring method based on a ringing index, which is characterized by comprising the following steps:
acquiring basic data in the whole life cycle of an enterprise, preprocessing the basic data, and establishing a database according to the preprocessed basic data, wherein the basic data comprises enterprise event information and enterprise basic information;
extracting basic data of a monitored enterprise in a database as a sample, and performing time sequence analysis on enterprise event information in the sample;
extracting keywords related to the safety production activity from the enterprise event information, and adding sub-labels to the keywords to obtain a label system of the safety production activity;
establishing a scoring mechanism for the label system according to the scoring card model to obtain a label system model;
acquiring the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise, and matching the risk characteristics of the current enterprise and the associated risk characteristics of the current enterprise with the sub-tags to obtain an enterprise risk tag of the monitored enterprise;
scoring the enterprise risk labels according to the label system model, and obtaining a frying index model according to the scoring value and the weight of the enterprise risk labels;
and calculating the ring index of the monitored enterprise according to the ring index model.
2. The safety-production-risk monitoring method based on a ringing index of claim 1, wherein the ringing index model is expressed as:
R＝W 1 ×R 1 +W 2 ×R 2 +W 3 ×R 3 +...+W n ×R n (1)
W n ＝w 1 +w 2 +...+w m (2)
wherein R is a frying index, R i Represents a risk value, W 1 、W 2 、W 3 、W n Represents the weight, a i Represents standard parameters, x i Represents a synthesis system, w i Is W i A weight value of the corresponding second level term; r is (r) i Is R i And the risk value of the corresponding second-stage item, k being an integer.
3. The method of claim 1, wherein the enterprise event information comprises a security production specific event, an enterprise complaint event, an enterprise administrative penalty event, an enterprise business event, an enterprise public opinion event.
4. The method of claim 1, wherein the sub-tags comprise illegal, severe, public, characteristic, and large-scale.
5. The safety production risk monitoring method based on the ringing index according to claim 1, wherein the current enterprise own risk feature and the current enterprise associated risk feature are matched with sub-tags through regular expressions.
6. The safety production risk monitoring method based on the ringing index of claim 1, further comprising determining an enterprise safety accident risk early warning level according to the ringing index.
7. The method for monitoring risk of safety production based on a ring index according to claim 5, wherein the risk early warning class is classified into class 4.
8. The method for monitoring the risk of safety production based on the explosion index according to claim 1, wherein the enterprise risk labels of the monitored enterprises comprise market risk, credit risk, liquidity risk, operation risk, legal risk, reputation risk, natural disaster risk, environmental risk and human resource risk.
9. A computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set loaded and executed by a processor to implement the safety production risk monitoring method based on a ringing index as claimed in any one of claims 1 to 8.
10. An apparatus comprising a processor and a memory having stored therein at least one instruction, at least one program, code set, or instruction set loaded and executed by the processor to implement the ringing index-based safety production risk monitoring method of any one of claims 1 to 8.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020037942A1 (en) * | 2018-08-20 | 2020-02-27 | 平安科技（深圳）有限公司 | Risk prediction processing method and apparatus, computer device and medium |
| CN114580916A (en) * | 2022-03-07 | 2022-06-03 | 上海安硕企业征信服务有限公司 | Enterprise risk assessment method and device, electronic equipment and storage medium |
| CN115907568A (en) * | 2023-02-27 | 2023-04-04 | 北京金信网银金融信息服务有限公司 | Illegal financial activity monitoring method and system based on smoking index |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020037942A1 (en) * | 2018-08-20 | 2020-02-27 | 平安科技（深圳）有限公司 | Risk prediction processing method and apparatus, computer device and medium |
| CN114580916A (en) * | 2022-03-07 | 2022-06-03 | 上海安硕企业征信服务有限公司 | Enterprise risk assessment method and device, electronic equipment and storage medium |
| CN115907568A (en) * | 2023-02-27 | 2023-04-04 | 北京金信网银金融信息服务有限公司 | Illegal financial activity monitoring method and system based on smoking index |
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