CN116453292A - Chemical enterprise production safety risk classification and early warning method - Google Patents
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Abstract
The invention provides a method for classifying and early warning production safety risks of chemical enterprises, which belongs to the technical field of safety management and control and specifically comprises the following steps: obtaining a personnel risk coefficient based on the proportion of workers who do not use the safety tool correctly; acquiring monitoring risk coefficients of different risk sources based on environment monitoring data of the risk source region, and acquiring risk source risk coefficients according to the proportion of the risk sources of which the risk sources are larger than a monitoring risk design value; obtaining an operation risk coefficient based on operation data of chemical production equipment, and obtaining an equipment risk coefficient based on the operation risk coefficient and the proportion of hydraulics abnormal fire hydraulics; based on the personnel risk coefficient, the equipment risk coefficient and the risk source risk coefficient, an evaluation model is constructed to obtain a safety risk coefficient, the production safety risk level is divided based on the safety risk coefficient, whether early warning is carried out or not is determined according to the production safety risk level, and therefore the production safety is further improved.
Description
Technical Field
The invention belongs to the technical field of safety control, and particularly relates to a method for classifying and early warning production safety risks of chemical enterprises.
Background
In order to realize the management and control of the production safety risk of chemical enterprises, a monitoring area is divided according to the number, the type and the area of risk sources in an emergency method and an emergency system of a chemical industry park of the invention patent grant bulletin No. CN 112164208B; setting environment monitoring equipment corresponding to a risk source in a monitoring area and safety rescue equipment associated with the environment monitoring equipment in the monitoring area; setting alarm parameters corresponding to risk sources in a monitoring area on environmental monitoring equipment; the environment monitoring equipment alarms, and the corresponding safety rescue equipment position, the use information and the emergency replay video are called, but the following technical problems exist:
1. the safety states of the chemical production equipment with safety risks and the fire hydrant are not considered to be monitored in real time and alarm analysis is carried out, specifically, in the chemical production process, the temperature and leakage conditions of toxic gas or liquid conveying pipelines with safety risks or chemical production equipment such as a reaction tank and a reaction kettle and the water pressure of the fire hydrant are improved, if the real-time monitoring cannot be carried out, any one of the safety risks can possibly cause the occurrence or expansion of safety accidents, and if the real-time monitoring cannot be carried out, the possibility of the safety accidents is increased, and meanwhile, the accurate division of the safety risk level cannot be accurately realized.
2. The real-time monitoring and alarm analysis of the use condition of the safety tools of the staff are not considered, and particularly, if the staff does not wear the safety tools, certain potential safety hazards exist, so that if the real-time monitoring cannot be carried out, the accurate division of the safety risk level cannot be accurately and comprehensively realized, and the potential safety hazards are large.
Aiming at the technical problems, the invention provides a method for classifying and early warning production safety risks of chemical enterprises.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the invention, a method for classifying and early warning production safety risks of chemical enterprises is provided.
The method for classifying and early warning the production safety risk of the chemical enterprises is characterized by comprising the following steps of:
s11, obtaining a personnel risk coefficient based on the proportion of workers in the enterprise who do not use the safety tools correctly, determining whether risk exists or not based on the personnel risk coefficient, classifying the safety risk level of the enterprise into the highest level if the risk exists, outputting an early warning signal, and entering step S12 if the risk does not exist;
s12, obtaining monitoring risk coefficients of different risk sources based on environment monitoring data of a risk source area inside an enterprise, obtaining risk source risk coefficients based on the proportion of the risk sources with the monitoring risk coefficients being larger than a monitoring risk design value, determining whether risks exist or not based on the risk source risk coefficients, classifying the security risk level of the enterprise into the highest level if the risks exist, outputting an early warning signal, and entering step S13 if the security risk level of the enterprise is not the highest level;
s13, obtaining an operation risk coefficient of chemical production equipment based on operation data of the chemical production equipment with safety risk, obtaining a proportion of fire hydrants with abnormal water pressure based on a water pressure signal of the fire hydrants in an enterprise, obtaining an equipment risk coefficient based on the operation risk coefficient and the proportion of the fire hydrants with abnormal water pressure, determining whether the risk exists based on the equipment risk coefficient, classifying the safety risk level of the enterprise into the highest level if the risk exists, outputting an early warning signal, and entering step S14 if the risk does not exist;
s14, based on the personnel risk coefficient, the equipment risk coefficient and the risk source risk coefficient, adopting an evaluation model based on a machine learning algorithm to obtain the safety risk coefficient of the enterprise, dividing the production safety risk level based on the safety risk coefficient, and determining whether to perform early warning according to the production safety risk level.
The personnel risk coefficient is obtained based on the proportion of workers in the enterprise who do not use the safety tools correctly, so that the personnel risk is quantified and accurately assessed, the safety of the personnel in the whole enterprise is improved, and the possibility of safety risk is reduced.
The risk source risk coefficient is obtained based on the proportion of the risk sources with the monitored risk coefficient larger than the monitored risk design value, so that the construction of the risk source risk coefficient from the proportion of the risk sources with potential safety hazards is realized, and the assessment of the risk source risk coefficient from a more comprehensive angle is realized.
The equipment risk coefficient is evaluated by the proportion of the fire hydrant based on the abnormal water pressure and the operation risk coefficient, so that the quantitative evaluation of the equipment risk is realized, the comprehensiveness of the equipment risk coefficient evaluation is ensured, and the possibility of safety risk is further reduced.
By respectively evaluating personnel risk, risk source risk and equipment risk according to the steps, the risk evaluation is realized from the perspective of the consequences generated after the safety risk occurs, the safety risk of enterprises is further reduced, and the overall safety is ensured.
The safety risk coefficient of the enterprise is evaluated based on the personnel risk coefficient, the equipment risk coefficient and the risk source risk coefficient, so that the safety risk coefficient is evaluated by combining multiple factors, the accuracy and the comprehensiveness of the evaluation are ensured, and the reliability of the division of the safety risk level is also ensured.
In another aspect, embodiments of the present application provide a computer system, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the method for classifying and early warning the production safety risk of the chemical enterprises is characterized in that the processor runs the computer program.
In another aspect, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer, causes the computer to perform the above-described method for classifying and warning the production security risk of a chemical enterprise.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 is a flow chart of a method for grading and pre-warning production security risks of chemical enterprises according to embodiment 1;
FIG. 2 is a flowchart of specific steps for monitoring risk factor construction according to embodiment 1;
FIG. 3 is a flowchart of specific steps for constructing an operational risk factor for a chemical production facility according to example 1;
fig. 4 is a flowchart of specific steps for security risk factor construction according to embodiment 1.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
At present, the assessment of the safety risk of chemical enterprises is often based on the risk source or the running condition of single chemical production equipment, and the availability of fire hydrants, the safety risk of personnel and the like are ignored, so that the safety risk cannot be accurately and comprehensively mastered, the safety risk of the chemical enterprises cannot be accurately and comprehensively identified at the first time, and even serious personal and property loss can be caused.
Example 1
In order to solve the above problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a method for classifying and pre-warning production security risk of chemical enterprises, which is characterized by comprising:
s11, obtaining a personnel risk coefficient based on the proportion of workers in the enterprise who do not use the safety tools correctly, determining whether risk exists or not based on the personnel risk coefficient, classifying the safety risk level of the enterprise into the highest level if the risk exists, outputting an early warning signal, and entering step S12 if the risk does not exist;
for example, the proportion of workers in the enterprise who do not use the safety tools correctly is identified by an imaging device in the enterprise, and specifically, if the time of the workers who do not use the safety tools normally is greater than 2 minutes or a certain time threshold, the workers are determined to use the safety tools incorrectly.
For example, if 60 workers in an enterprise who do not use safety tools correctly are 100 workers, the ratio is 60% and the risk factor of the workers is 0.6.
For example, if the risk factor of the personnel is 0.6 and the first threshold is 0.2, the security risk level of the enterprise is classified as the highest level, and an early warning signal is output.
Specifically, the proportion of the workers in the enterprise who do not use the safety tools correctly is obtained by recognition according to the monitoring camera device in the enterprise, and the proportion of the workers in the enterprise who do not use the safety tools correctly is directly used as a personnel risk coefficient.
The personnel risk coefficient is obtained based on the proportion of workers in the enterprise who do not use the safety tools correctly, so that the personnel risk is quantified and accurately assessed, the safety of the personnel in the whole enterprise is improved, and the possibility of safety risk is reduced.
S12, obtaining monitoring risk coefficients of different risk sources based on environment monitoring data of a risk source area inside an enterprise, obtaining risk source risk coefficients based on the proportion of the risk sources with the monitoring risk coefficients being larger than a monitoring risk design value, determining whether risks exist or not based on the risk source risk coefficients, classifying the security risk level of the enterprise into the highest level if the risks exist, outputting an early warning signal, and entering step S13 if the security risk level of the enterprise is not the highest level;
specifically, as shown in fig. 2, the specific steps of monitoring risk coefficient construction are as follows:
s21, judging whether the environment monitoring data is abnormal or not based on the environment monitoring data of a risk source area in an enterprise and a safety threshold of the environment monitoring data, if so, the monitoring risk coefficient is 1, and if not, entering a step S22;
specifically, for example, the risk source region is determined according to a region where a safety risk exists, and in general, a region where a chemical raw material is placed, a region where a chemical intermediate product is placed, and the like, and a region where a potential safety hazard exists when temperature or humidity is abnormal.
For example, if the wind speed data in the monitored data is 10m/s and the safety threshold of the wind speed data in the monitored data is 8m/s, it is determined that the environment monitored data is abnormal, and the monitoring risk coefficient is 1.
S22, obtaining an environment monitoring ratio based on the ratio of the environment monitoring data to the safety threshold of the environment monitoring data, and constructing an input set based on the environment monitoring ratio;
s23, transmitting the input set to a prediction model based on an ACO-BP neural network algorithm to obtain the monitoring risk coefficient.
For a specific example, the input set is x= { T 1 、H 1 、V 1 、Y 1 }, T therein 1 、H 1 、V 1 、Y 1 The environment monitoring ratio of the temperature data, the environment monitoring ratio of the humidity data, the environment monitoring ratio of the wind speed data and the environment monitoring ratio of the smoke data are respectively.
For example, the ant colony algorithm has good global optimizing capability, when the BP neural network is optimized, the pheromone quantity and the path selection when the ants seek are adjusted according to the comparison result of the output error and the expected error of the training sample, the weight and the threshold initial value of the BP neural network are continuously and repeatedly optimized,
the specific steps of constructing the prediction model based on the ACO-BP neural network algorithm are as follows:
1) The BP neural network is provided with M weights and thresholds to be optimized, each weight and threshold is set as n random nonzero values, and a set I is formed Pi (1≤i≤M)；
2) At the initial moment, the (1 k is not less than M) k-th ants in M ants are selected from the set I Pi Starting from set I according to state transition probabilities Pi Selecting an element j; after the elements are selected from all the sets, a group of weights and thresholds of the BP neural network are formed;
3) When m ants complete circulation, m groups of initial weights and thresholds can be obtained, and a BP neural network is constructed and trained; recording a group of weights and thresholds with the minimum error during network training, comparing the minimum error with the expected error, and executing the step 4) if the minimum error is larger than the expected error, otherwise executing the step 6);
4) For set I Pi The pheromone amount of each element in the formula (1) is more than or equal to i and less than or equal to M) is adjusted, and the adjustment formula is as follows:
5) Repeating the steps 2) and 3) until all ants converge on the same optimal path or the maximum number of iterations is reached.
6) And further training the neural network by utilizing the initial weight and the threshold value of the optimal BP neural network screened by the ant colony algorithm, and exiting after the training condition is met.
For example, the pheromone factor reflects the guiding action degree of the accumulated pheromone concentration on the path in the ant movement process on the subsequent ant colony, and the larger the pheromone factor is, the more easily the algorithm falls into a local extremum; to increase the search efficiency of the algorithm, the state transition probability is improved by dynamically adjusting the parameter pheromone factor.
For example, the calculation formula of the pheromone factor is as follows:
wherein alpha is min For initial value, alpha max Is maximum, N is the maximum iteration number threshold, N C For the current iteration number, rand (1, 1.1) is a random number with a value ranging from 1 to 1.1.
Specifically, the environment monitoring data comprise temperature data, humidity data, wind speed data and smoke data.
The risk source risk coefficient is obtained based on the proportion of the risk sources with the monitored risk coefficient larger than the monitored risk design value, so that the construction of the risk source risk coefficient from the proportion of the risk sources with potential safety hazards is realized, and the assessment of the risk source risk coefficient from a more comprehensive angle is realized.
S13, obtaining an operation risk coefficient of chemical production equipment based on operation data of the chemical production equipment with safety risk, obtaining a proportion of fire hydrants with abnormal water pressure based on a water pressure signal of the fire hydrants in an enterprise, obtaining an equipment risk coefficient based on the operation risk coefficient and the proportion of the fire hydrants with abnormal water pressure, determining whether the risk exists based on the equipment risk coefficient, classifying the safety risk level of the enterprise into the highest level if the risk exists, outputting an early warning signal, and entering step S14 if the risk does not exist;
the operation data of the chemical production equipment include, for example, an operation temperature, an operation humidity, an operation voltage, and an operation current.
Specifically, as shown in fig. 3, the specific steps of construction of the running risk coefficient of the chemical production equipment are as follows:
s31, judging whether the operation data are abnormal or not based on the operation data of the chemical production equipment with safety risk and the safety threshold value of the operation data, if so, the operation risk coefficient is 1, and if not, entering step S32;
for example, if the operation current data of the chemical production equipment is 100A, and the safety threshold of the operation data is 90A, the operation data is abnormal, and the operation risk coefficient is 1.
S32, obtaining an operation risk ratio based on the ratio of the operation data to the safety threshold of the operation data, and constructing an input set based on the operation risk ratio;
s33, transmitting the input set to an evaluation model based on an ACO-BP neural network algorithm to obtain a basic operation risk coefficient of the chemical production equipment;
s34, correcting the basic operation risk coefficient based on the risk coefficient of the chemical production equipment to obtain the operation risk coefficient, wherein the risk coefficient of the chemical production equipment is determined in an expert scoring mode according to the influence area and the hazard degree when the chemical equipment is in failure.
Specifically, the value range of the danger coefficient of the chemical production equipment is between 0 and 1, wherein the greater the danger coefficient is, the higher the hazard degree of the chemical production equipment when the chemical production equipment is in fault is.
For example, when the risk coefficient of the chemical production equipment is 0.9 and the basic operation risk coefficient is 0.6, the corrected operation risk coefficient is 0.6+0.9x0.6/10=0.654.
For example, after the risk coefficient of the chemical production equipment is greater than a certain threshold, the modified operation risk coefficient is obtained by adding the fixed compensation item and the basic operation risk coefficient, and when the risk coefficient is not greater than the threshold, the basic operation risk coefficient is used as the modified operation risk coefficient.
Specifically, when the operation risk coefficient of the chemical production equipment is greater than a first operation risk threshold or the proportion of the hydraulics abnormal fire hydraulics is greater than a first proportion threshold, the equipment risk coefficient is set to be 1.
Specifically, the calculation formula of the equipment risk coefficient is as follows:
wherein P is the proportion of hydraulics abnormal fire hydraulics, P 1 Is a proportional threshold, R 1 And (3) taking min () as a minimum function for the running risk coefficient of chemical production equipment.
By constructing the piecewise function, the influence on equipment risk is fully considered under the proportion of different hydraulics abnormal fire hydraulics, and the accuracy and the comprehensiveness of equipment risk coefficient construction are further ensured.
The equipment risk coefficient is evaluated by the proportion of the fire hydrant based on the abnormal water pressure and the operation risk coefficient, so that the quantitative evaluation of the equipment risk is realized, the comprehensiveness of the equipment risk coefficient evaluation is ensured, and the possibility of safety risk is further reduced.
S14, based on the personnel risk coefficient, the equipment risk coefficient and the risk source risk coefficient, adopting an evaluation model based on a machine learning algorithm to obtain the safety risk coefficient of the enterprise, dividing the production safety risk level based on the safety risk coefficient, and determining whether to perform early warning according to the production safety risk level.
Specifically, as shown in fig. 4, the specific steps of the security risk factor construction are as follows:
s41, constructing a production safety risk input set based on the equipment risk coefficient and the risk source risk coefficient, and obtaining the production safety risk coefficient by adopting a prediction model based on a BP neural network algorithm based on the production safety risk input set;
s42, determining whether a safety risk exists or not based on the production safety risk coefficient, if so, setting the safety risk coefficient of the enterprise to be 1, and if not, entering step S43;
for example, if the production safety risk factor is 0.8 and the fifth threshold is 0.6, the safety risk factor of the enterprise is set to 1, which indicates that the production safety risk is high.
S43, constructing a risk input set based on the production safety risk coefficient and the personnel risk coefficient, and transmitting the risk input set to a risk assessment model based on an ACO-BP neural network algorithm to obtain the safety risk coefficient of the enterprise.
For a specific example, the risk input set is q1= { P 2 、P 3 }, wherein P 2 、P 3 The production safety risk coefficient and the personnel risk coefficient are respectively.
Specific steps of constructing the risk assessment model based on the ACO-BP neural network algorithm are described above, and will not be described herein.
Specifically, when the security risk coefficient of the enterprise is smaller than or equal to a first risk threshold, the security risk level of the enterprise is classified as no risk, when the security risk coefficient of the enterprise is larger than the first risk threshold, the security risk level of the enterprise is classified as a general level, the monitoring frequency of the operation data and the environment monitoring data is improved, when the security risk coefficient of the enterprise is larger than a second risk threshold, the security risk level of the enterprise is classified as a highest level, and an early warning signal is output, and the second risk threshold is larger than the first risk threshold.
By respectively evaluating personnel risk, risk source risk and equipment risk according to the steps, the risk evaluation is realized from the perspective of the consequences generated after the safety risk occurs, the safety risk of enterprises is further reduced, and the overall safety is ensured.
The safety risk coefficient of the enterprise is evaluated based on the personnel risk coefficient, the equipment risk coefficient and the risk source risk coefficient, so that the safety risk coefficient is evaluated by combining multiple factors, the accuracy and the comprehensiveness of the evaluation are ensured, and the reliability of the division of the safety risk level is also ensured.
Example 2
In an embodiment of the present application, a computer system is provided, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and the processor executes the method for classifying and early warning the production safety risk of the chemical enterprises when running the computer program.
Specifically, the embodiment also provides a computer system, which comprises a processor, a memory, a network interface and a database which are connected through a system bus; wherein the processor of the computer system is configured to provide computing and control capabilities; the memory of the computer system includes nonvolatile storage medium, internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The computer device network interface is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to realize the method for classifying and early warning the production safety risk of the chemical enterprises.
Example 3
The invention provides a computer storage medium, on which a computer program is stored, which when executed in a computer, causes the computer to execute the method for classifying and early warning the production security risk of chemical enterprises.
In particular, it will be understood by those skilled in the art that implementing all or part of the above-described methods of the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (9)
1. The method for classifying and early warning the production safety risk of the chemical enterprises is characterized by comprising the following steps of:
s11, obtaining a personnel risk coefficient based on the proportion of workers in the enterprise who do not use the safety tools correctly, determining whether risk exists or not based on the personnel risk coefficient, classifying the safety risk level of the enterprise into the highest level if the risk exists, outputting an early warning signal, and entering step S12 if the risk does not exist;
s12, obtaining monitoring risk coefficients of different risk sources based on environment monitoring data of a risk source area inside an enterprise, obtaining risk source risk coefficients based on the proportion of the risk sources with the monitoring risk coefficients being larger than a monitoring risk design value, determining whether risks exist or not based on the risk source risk coefficients, classifying the security risk level of the enterprise into the highest level if the risks exist, outputting an early warning signal, and entering step S13 if the security risk level of the enterprise is not the highest level;
s13, obtaining an operation risk coefficient of chemical production equipment based on operation data of the chemical production equipment with safety risk, obtaining a proportion of fire hydrants with abnormal water pressure based on a water pressure signal of the fire hydrants in an enterprise, obtaining an equipment risk coefficient based on the operation risk coefficient and the proportion of the fire hydrants with abnormal water pressure, determining whether the risk exists based on the equipment risk coefficient, classifying the safety risk level of the enterprise into the highest level if the risk exists, outputting an early warning signal, and entering step S14 if the risk does not exist;
s14, based on the personnel risk coefficient, the equipment risk coefficient and the risk source risk coefficient, adopting an evaluation model based on a machine learning algorithm to obtain the safety risk coefficient of the enterprise, dividing the production safety risk level based on the safety risk coefficient, and determining whether to perform early warning according to the production safety risk level.
2. The method for classifying and pre-warning the production safety risk of the chemical industry enterprise according to claim 1, wherein the proportion of workers who do not use the safety tools correctly in the enterprise is identified according to a monitoring camera device in the enterprise.
3. The method for classifying and pre-warning production safety risk of chemical enterprises according to claim 1, wherein the specific steps of monitoring risk coefficient construction are as follows:
judging whether the environment monitoring data is abnormal or not based on the environment monitoring data of a risk source area in an enterprise and a safety threshold value of the environment monitoring data, if so, the monitoring risk coefficient is 1, and if not, entering the next step;
obtaining an environment monitoring ratio based on the ratio of the environment monitoring data to the safety threshold of the environment monitoring data, and constructing an input set based on the environment monitoring ratio;
and transmitting the input set to a prediction model based on an ACO-BP neural network algorithm to obtain the monitoring risk coefficient.
4. The method for classifying and pre-warning production safety risk of chemical enterprises according to claim 3, wherein the environmental monitoring data comprises temperature data, humidity data, wind speed data and smoke data.
5. The method for classifying and pre-warning production safety risk of chemical enterprises according to claim 1, wherein the specific steps of constructing the running risk coefficient of the chemical production equipment are as follows:
judging whether the operation data are abnormal or not based on the operation data of the chemical production equipment with safety risk and the safety threshold value of the operation data, if so, the operation risk coefficient is 1, and if not, entering the next step;
obtaining an operation risk ratio based on the ratio of the operation data to the safety threshold of the operation data, and constructing an input set based on the operation risk ratio;
transmitting the input set to an evaluation model based on an ACO-BP neural network algorithm to obtain a basic operation risk coefficient of the chemical production equipment;
and correcting the basic operation risk coefficient based on the risk coefficient of the chemical production equipment to obtain the operation risk coefficient, wherein the risk coefficient of the chemical production equipment is determined by adopting an expert scoring mode according to the influence area and the hazard degree when the chemical equipment breaks down.
6. The method for classifying and pre-warning production safety risk of chemical enterprises according to claim 1, wherein the calculation formula of the equipment risk coefficient is as follows:
wherein P is the proportion of hydraulics abnormal fire hydraulics, P 1 Is a proportional threshold, R 1 And (3) taking min () as a minimum function for the running risk coefficient of chemical production equipment.
7. The method for classifying and pre-warning the production safety risk of the chemical enterprises according to claim 1, wherein the specific steps of constructing the safety risk coefficient are as follows:
constructing a production safety risk input set based on the equipment risk coefficient and the risk source risk coefficient, and obtaining the production safety risk coefficient by adopting a prediction model based on a BP neural network algorithm based on the production safety risk input set;
determining whether a safety risk exists or not based on the production safety risk coefficient, if so, setting the safety risk coefficient of the enterprise to be 1, and if not, entering the next step;
and constructing a risk input set based on the production safety risk coefficient and the personnel risk coefficient, and transmitting the risk input set to a risk assessment model based on an ACO-BP neural network algorithm to obtain the safety risk coefficient of the enterprise.
8. A computer system, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor executes the method for classifying and pre-warning the production security risk of the chemical enterprises according to any one of claims 1 to 7 when running the computer program.
9. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a method for grading and pre-warning production safety risk of a chemical enterprise as claimed in any one of claims 1 to 7.
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