Multi-risk parameter enterprise risk assessment method and system based on artificial intelligence

Abstract

The present invention discloses a multi-risk parameter enterprise risk assessment method and system based on artificial intelligence, which relates to the field of risk assessment and early warning. It mainly includes: according to the comparison results of the values ​​of each risk parameter at different times and the standard value, the fluctuation degree of each risk parameter at different times is obtained, and the risk state index at different times is obtained by combining the constructed weight vector, and the optimal weight vector that maximizes the aggregation degree and dispersion degree of the risk state index within the preset time is obtained, so as to obtain the optimal risk state index corresponding to the optimal weight vector, and when the optimal risk state index is greater than the preset threshold, it is determined that there is a safety hazard and an alarm is issued. By obtaining the optimal weight corresponding to each risk parameter, the accuracy of the risk assessment result of the enterprise is improved, and the subjectivity caused by human factors is avoided.

Description

Technical Field

[0001] The present application relates to the field of risk assessment and early warning, and specifically to an artificial intelligence-based multi-risk parameter enterprise risk assessment method and system. Background Art

[0002] There are many risk factors in the various production and development processes of chemical companies. These risk factors threaten the lives and property safety of people around them at all times. If management is not strengthened, accidents will occur.

[0003] Taking into account that the hidden risks in chemical sites are caused by multiple risk factors, there are many uncertainties in the analysis of multi-dimensional safety hazard influencing factors. In addition, for multiple risk factors, the weights between risk parameters are often obtained through hierarchical analysis method or organizing experts to score in the existing technology.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that the background technology has at least the following defects:

[0005] The risk assessment results of chemical enterprises in the prior art are too subjective due to excessive human interference factors, and the weights between the risk parameters cannot be adjusted in a timely manner according to the actual situation on site, which makes the risk assessment results of the enterprise unable to match the actual situation. Summary of the invention

[0006] In response to the above technical problems, the present invention provides a multi-risk parameter enterprise risk assessment method and system based on artificial intelligence, which can improve the accuracy of the risk assessment results of the enterprise by obtaining the optimal weights corresponding to each risk parameter and avoid the subjectivity caused by human factors.

[0007] In the first aspect, the embodiments of the present invention provide a multi-risk parameter enterprise risk assessment method based on artificial intelligence, including:

[0008] The data containing various risk parameters within a preset time period in the production process are obtained and normalized.

[0009] Compare the values ​​of each risk parameter at different times with the corresponding standard values ​​to obtain the volatility indicators of different risk parameters at different times.

[0010] A weight vector is constructed, wherein each element in the weight vector corresponds to each risk parameter, and each element in the weight vector is multiplied by the volatility index of each risk parameter at the same moment to obtain the risk status index at each moment.

[0011] The concentration degree and dispersion degree of the risk status indicator within a preset time period are obtained, and the product of the concentration degree and the dispersion degree is used as the objective function, and the weight vector that makes the objective function obtain the maximum is used as the optimal weight vector.

[0012] Each element in the optimal weight vector is multiplied by the volatility index of each risk parameter at the same moment to obtain the optimal risk status index at each moment. When the optimal risk status index is greater than a preset threshold, it is determined that there is a safety hazard and an alarm is issued.

[0013] In a feasible embodiment, obtaining the concentration degree and dispersion degree of the risk status indicator within a preset time period includes:

[0014] Obtain the concentration level of the risk status indicators within a preset time period, including:

[0015]

[0016] in, Weight vector Corresponding preset duration The degree of aggregation within For preset duration Neidi Risk status indicators at all times, For preset duration Neidi Risk status indicators at all times, is a unit step function, and W is a preset parameter.

[0017] The degree of dispersion is obtained by the variance of the risk status indicator within a preset time period.

[0018] In a feasible embodiment, the data containing each risk parameter is normalized, including:

[0019] For risk parameters that are positively correlated with the risk status, the value of the risk parameter is subtracted from the minimum value within the preset time length, and then divided by the difference between the maximum value within the preset time length and the minimum value within the preset time length to obtain the normalized value of the risk parameter at each moment.

[0020] For risk parameters that are negatively correlated with the risk status, subtract the value of the risk parameter from the maximum value of the risk parameter within the preset time period, and divide the result by the difference between the maximum value within the preset time period and the minimum value within the preset time period to obtain the normalized value of the risk parameter at each moment.

[0021] In a feasible embodiment, after multiplying each element in the optimal weight vector by the volatility index of each risk parameter at the same time to obtain the optimal risk state index at each time, the method further includes:

[0022] The optimal risk status index at each moment and the optimal risk status index at the previous moment are weighted averaged, and the weighted average results are used as the optimal risk status index at each moment.

[0023] In a feasible embodiment, the weight of the optimal risk status indicator at the previous moment of each moment in the weighted averaging process is 0.05.

[0024] In a feasible embodiment, the standard value corresponding to each risk parameter is obtained by statistically analyzing the historical data of each risk parameter.

[0025] In a feasible embodiment, the values ​​of each risk parameter at different times are compared with the corresponding standard values ​​to obtain the volatility indexes of different risk parameters at different times, including:

[0026]

[0027] In the formula, For the Moment risk parameters The volatility index, For the Moment risk parameters The value of is the risk parameter The standard value of is a natural constant.

[0028] In a feasible embodiment, the risk status index at each moment is obtained according to the volatility index of each element in the weight vector and each risk parameter at the same moment, including:

[0029] Each element in the weight vector is multiplied by the volatility index of each risk parameter at the same moment to obtain the risk status index at each moment.

[0030] In a feasible embodiment, according to the volatility index of each element in the optimal weight vector and each risk parameter at the same time, the optimal risk state index at each time is obtained respectively, including:

[0031] Multiply each element in the optimal weight vector with the volatility index of each risk parameter at the same moment to obtain the optimal risk status index at each moment.

[0032] In the second aspect, an embodiment of the present invention proposes a multi-risk parameter enterprise risk assessment system based on artificial intelligence, comprising: a memory and a processor, characterized in that the processor executes a computer program stored in the memory to implement the multi-risk parameter enterprise risk assessment method based on artificial intelligence in the embodiment of the present invention.

[0033] The present invention provides a multi-risk parameter enterprise risk assessment method and system based on artificial intelligence, which can obtain the degree of fluctuation of each risk parameter at different times according to the comparison results of the value of each risk parameter at different times with the standard value, and obtain the risk status indicators at different times in combination with the constructed weight vector, and obtain the optimal weight vector that maximizes the comprehensive aggregation and dispersion of the risk status indicators within a preset time period, so as to obtain the optimal risk status indicator corresponding to the optimal weight vector. When the optimal risk status indicator is greater than the preset threshold, it is determined that there is a safety hazard and an alarm is issued.

[0034] Compared with the prior art, the beneficial effect of the embodiments of the present invention is that it is possible to improve the accuracy of the risk assessment results for the enterprise by obtaining the optimal weight corresponding to each risk parameter, and avoid the subjectivity caused by human factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 It is a flowchart of a multi-risk parameter enterprise risk assessment method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0039] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features; in the description of this embodiment, unless otherwise specified, "plurality" means two or more.

[0040] The embodiment of the present invention provides a multi-risk parameter enterprise risk assessment method based on artificial intelligence, such as Figure 1 As shown, including:

[0041] Step S101: Acquire data containing various risk parameters within a preset time period in the production process and perform normalization processing.

[0042] Step S102: Compare the values ​​of each risk parameter at different times with the corresponding standard values ​​to obtain volatility indicators of different risk parameters at different times.

[0043] Step S103: construct a weight vector, wherein each element in the weight vector corresponds to each risk parameter, and multiply each element in the weight vector by the volatility index of each risk parameter at the same moment to obtain the risk status index at each moment.

[0044] Step S104: Obtain the degree of aggregation and dispersion of the risk status indicators within a preset time period, and use the product of the degree of aggregation and the degree of dispersion as the objective function, and use the weight vector that maximizes the objective function as the optimal weight vector.

[0045] Step S105: multiply each element in the optimal weight vector by the volatility index of each risk parameter at the same time, and obtain the optimal risk status index at each time. When the optimal risk status index is greater than a preset threshold, it is determined that there is a safety hazard and an alarm is issued.

[0046] The embodiment of the present invention will collect data on the risk parameters that need to be monitored by selecting and arranging reasonable data collection equipment, and analyze the collected multiple risk parameters to achieve an assessment of the comprehensive risk situation of the chemical site.

[0047] The embodiments of the present invention are mainly applicable to risk assessment and prediction of chemical enterprises and chemical sites. Based on the risk entropy described in the embodiments of the present invention, risk judgment is made on chemical sites, and early warning prompts are issued for monitoring parameters with abnormal risk entropy, reminding relevant management personnel to promptly monitor equipment and instruments related to such parameters in the monitoring area to prevent the occurrence of major risk accidents.

[0048] Furthermore, step S101, data containing various risk parameters within a preset time period in the production process is obtained and normalized. Specifically, it includes:

[0049] First, data on risk parameters within a preset time period are collected through data sensing devices such as sensors and monitors. In this embodiment, the risk parameters include temperature parameter data, combustible gas parameters, toxic gas parameters, meteorological monitoring parameters, tank pressure parameters, etc.

[0050] It should be noted that according to the actual situation in the chemical site, the implementer can select the risk parameters that need to be collected and analyzed by himself, and the embodiment of the present invention does not limit the specific parameter types collected.

[0051] As an example, the preset duration in the embodiment of the present invention is 5 minutes.

[0052] Secondly, the collected risk parameters are normalized. The normalization process includes:

[0053] For risk parameters that are positively correlated with the risk status, the value of the risk parameter is subtracted from the minimum value within the preset time length, and then divided by the difference between the maximum value within the preset time length and the minimum value within the preset time length to obtain the normalized value of the risk parameter at each moment.

[0054] For risk parameters that are negatively correlated with the risk status, subtract the value of the risk parameter from the maximum value of the risk parameter within the preset time period, and divide the result by the difference between the maximum value within the preset time period and the minimum value within the preset time period to obtain the normalized value of the risk parameter at each moment.

[0055] In this way, the normalized values ​​of each risk parameter at different times can be positively correlated with the risk level, thus avoiding contradictions between parameters.

[0056] Furthermore, in step S102, the values ​​of each risk parameter at different times are compared with the corresponding standard values ​​to obtain the volatility indexes of different risk parameters at different times. Specifically, it includes:

[0057] It should be noted that the standard value corresponding to each risk parameter is obtained by statistically analyzing the historical data of each risk parameter.

[0058] The process of obtaining volatility indicators of different risk parameters at different times includes: , where For the Moment risk parameters The volatility index, For the Moment risk parameters The value of is the risk parameter The standard value of is a natural constant. It should be noted that, in the embodiment of the present invention, the greater the volatility index of the risk parameter, the smaller the stability in the region corresponding to the risk parameter. The standard value corresponding to each risk parameter is obtained by statistically analyzing the historical data of each risk parameter.

[0059] This makes it easier to further analyze the stability of the data based on the volatility indicators of each risk parameter at different times.

[0060] Further, step S103, construct a weight vector, each element in the weight vector corresponds to each risk parameter, and each element in the weight vector is multiplied by the volatility index of each risk parameter at the same time, to obtain the risk status index at each time. Specifically including:

[0061] It should be noted that, since the impact of multidimensional parameters is difficult to accurately measure during the risk evolution process, and when the comprehensive evaluation is performed by setting fixed weight values ​​in advance, there will be problems such as insufficient theoretical basis and greater human subjective interference, which will lead to inaccurate prediction results. Therefore, in order to improve the accuracy of risk assessment prediction of chemical sites, the embodiment of the present invention sets a multidimensional risk parameter fusion analysis model to detect the degree of influence of each risk parameter on the comprehensive risk situation of the chemical site, that is, accurately obtain the degree of adverse influence of each risk parameter on the comprehensive risk of the chemical enterprise, so as to more accurately evaluate the risk situation of the chemical enterprise.

[0062] First, a weight vector is constructed. Each element in the vector reflects the degree of adverse impact of each risk parameter on the overall risk of the chemical enterprise. Each element in the weight vector corresponds to each risk parameter. Those skilled in the art should know that the sum of each element in the weight vector is 1.

[0063] Secondly, each element in the weight vector is multiplied by the volatility index of each risk parameter at the same time, and the risk status index at each time is obtained. In this way, the risk status index at different times is obtained, which is convenient for finding the optimal weight vector later.

[0064] Further, step S104, obtain the concentration and dispersion of the risk status indicators within a preset time, and use the product of the concentration and dispersion as the objective function, and use the weight vector that maximizes the objective function as the optimal weight vector. Specifically including:

[0065] First, the aggregation and dispersion levels of risk status indicators within a preset time period are obtained.

[0066] Specifically, the content of obtaining the concentration degree of risk status indicators within a preset time period includes:

[0067] ,in, Weight vector Corresponding preset duration The degree of aggregation within For preset duration Neidi Risk status indicators at all times, For preset duration Neidi Risk status indicators at all times, is a unit step function, and W is a preset parameter that can be set by the implementer as needed. A unit step function means that when the value in the brackets is less than 0, the function takes a value of 0, otherwise, the function takes a value of 1.

[0068] It should be noted that the degree of dispersion is obtained through the variance of the risk status indicators within a preset period of time, including:

[0069] ,in, is the degree of dispersion, is the average value of the risk status indicator at different times within the preset time period, For the preset duration, For preset duration Neidi Risk status indicator at any given moment.

[0070] Secondly, the product of the degree of aggregation and the degree of dispersion is used as the objective function, that is, the objective function , the weight vector that maximizes the objective function is taken as the optimal weight vector.

[0071] It should be noted that the objective function can be solved by an optimization algorithm. In the embodiment of the present invention, the optimization algorithm can be: genetic algorithm, particle swarm optimization algorithm, gradient descent method, greedy algorithm, etc. The implementer can select the optimization algorithm at will, and the embodiment of the present invention does not limit the specific algorithm of the optimization algorithm.

[0072] In this way, the elements in the obtained optimal weight vector respectively reflect the degree of adverse impact of each risk parameter on the chemical enterprise, which is convenient for the subsequent evaluation of the comprehensive risk status of the chemical enterprise.

[0073] Furthermore, in step S105, each element in the optimal weight vector is multiplied by the volatility index of each risk parameter at the same time, and the optimal risk state index at each time is obtained. When the optimal risk state index is greater than a preset threshold, it is determined that there is a safety hazard and an alarm is issued. Specifically, it includes:

[0074] First, each element in the optimal weight vector is multiplied by the volatility index of each risk parameter at the same moment to obtain the optimal risk status index at each moment.

[0075] Optionally, the optimal risk status index at each moment and the optimal risk status index at the previous moment may be weighted averaged, and the weighted average results may be used as the optimal risk status index at each moment. In this way, the optimal risk status index at each moment may be more useful for reference.

[0076] As an example, the weight of the optimal risk status indicator at the previous moment in the weighted average process is 0.05.

[0077] It should be noted that the higher the optimal risk state index at each moment in the chemical site, the more the overall risk of the chemical site at that moment develops towards a disordered state, and the higher the possibility of a risk accident. In order to intuitively evaluate the comprehensive risk situation of the chemical site, in the embodiment of the present invention, when the optimal risk state index at any moment in the chemical site is greater than the preset threshold T, an alarm needs to be issued at this time, so as to promptly prompt the relevant management personnel to conduct further inspections and investigations on the monitoring area as soon as possible, and take corresponding measures to avoid the occurrence of major dangerous accidents.

[0078] As an example, in the embodiment of the present invention, the preset threshold T=0.6.

[0079] Based on the same inventive concept as the above method, this embodiment also provides a multi-risk parameter enterprise risk assessment system based on artificial intelligence. In this embodiment, the multi-risk parameter enterprise risk assessment system based on artificial intelligence includes a memory and a processor. The processor executes the computer program stored in the memory to implement the assessment of enterprise risks using risk parameters as described in the embodiment of the multi-risk parameter enterprise risk assessment method based on artificial intelligence.

[0080] Since the method of evaluating the risks of an enterprise using risk parameters has been described in the embodiment of the multi-risk parameter enterprise risk assessment method based on artificial intelligence, it will not be repeated here.

[0081] In summary, the embodiments of the present invention provide a multi-risk parameter enterprise risk assessment method and system based on artificial intelligence, which can obtain the degree of fluctuation of each risk parameter at different times according to the comparison results of the value of each risk parameter at different times with the standard value, and obtain the risk status indicators at different times in combination with the constructed weight vector, and obtain the optimal weight vector that maximizes the comprehensive degree of aggregation and dispersion of the risk status indicators within a preset time period, so as to obtain the optimal risk status indicator corresponding to the optimal weight vector. When the optimal risk status indicator is greater than the preset threshold, it is determined that there is a safety hazard and an alarm is issued.

[0082] Compared with the prior art, the beneficial effect of the embodiments of the present invention is that it is possible to improve the accuracy of the risk assessment results for the enterprise by obtaining the optimal weight corresponding to each risk parameter, and avoid the subjectivity caused by human factors.

[0083] The words "including", "comprising", "having" and the like in the present invention are open words, meaning "including but not limited to", and can be used interchangeably therewith. The words "or" and "and" used herein mean the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably therewith.

[0084] It should also be noted that in the method and system of the present invention, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0085] The above embodiments are merely examples for the purpose of clear explanation and do not constitute a limitation on the protection scope of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description, and it is not necessary and impossible to list all the implementation methods here. All designs that are the same or similar to the present invention belong to the protection scope of the present invention.

Claims

1. A multi-risk parameter chemical enterprise risk assessment method based on artificial intelligence, characterized in that: include: Acquire data containing various risk parameters within a preset time period in the production process and perform normalization processing; wherein the risk parameters include temperature parameter data, combustible gas parameters, toxic gas parameters, meteorological monitoring parameters and tank pressure parameters; perform normalization processing on the data containing various risk parameters, including: For risk parameters that are positively correlated with the risk status, the value of the risk parameter minus the minimum value within the preset time period is divided by the difference between the maximum value within the preset time period and the minimum value within the preset time period to obtain the normalized value of the risk parameter at each moment; For risk parameters that are negatively correlated with the risk status, the maximum value of the risk parameter within the preset time period minus the value of the risk parameter is divided by the difference between the maximum value within the preset time period and the minimum value within the preset time period to obtain the normalized value of the risk parameter at each moment; Compare the values ​​of each risk parameter at different times with the corresponding standard values ​​to obtain the volatility index of different risk parameters at different times; Constructing a weight vector, wherein each risk parameter corresponds to each element in the weight vector, and obtaining risk status indicators at each moment according to volatility indicators of each element in the weight vector and each risk parameter at the same moment; Obtaining the degree of aggregation and the degree of dispersion of the risk status indicator within a preset time period, and taking the product of the degree of aggregation and the degree of dispersion as the objective function, and taking the weight vector that maximizes the objective function as the optimal weight vector; Multiply each element in the optimal weight vector by the volatility index of each risk parameter at the same time, and obtain the optimal risk status index at each time. When the optimal risk status index is greater than a preset threshold, it is determined that there is a safety hazard and an alarm is issued; Among them, the standard value corresponding to each risk parameter is obtained by statistically analyzing the historical data of each risk parameter; Among them, the values ​​of each risk parameter at different times are compared with the corresponding standard values ​​to obtain the volatility indicators of different risk parameters at different times, including: In the formula, r ab is the volatility index of risk parameter b at the ath moment, x ab is the value of risk parameter b at the ath moment, x 0b is the standard value of risk parameter b, and e is a natural constant; According to the volatility index of each element in the weight vector and each risk parameter at the same time, the risk status index at each time is obtained respectively, including: Each element in the weight vector is multiplied by the volatility index of each risk parameter at the same moment to obtain the risk status index at each moment.

2. The multi-risk parameter chemical enterprise risk assessment method based on artificial intelligence according to claim 1 is characterized in that: Obtain the concentration and dispersion of the risk status indicators within a preset time period, including: Obtain the concentration level of the risk status indicators within a preset time period, including: in, is the weight vector The corresponding aggregation degree within the preset time length m, Z i is the risk status indicator at the i-th moment within the preset time length m, Z t is the risk status indicator at the tth moment within the preset time length m, h() is the unit step function, and W is the preset parameter; The degree of dispersion is obtained by the variance of the risk status indicator within a preset time period.

3. The multi-risk parameter chemical enterprise risk assessment method based on artificial intelligence according to claim 1 is characterized in that: After correspondingly multiplying each element in the optimal weight vector with the volatility index of each risk parameter at the same time to obtain the optimal risk state index at each time, the method further includes: The optimal risk status index at each moment and the optimal risk status index at the previous moment are weighted averaged, and the weighted average results are used as the optimal risk status index at each moment.

4. The multi-risk parameter chemical enterprise risk assessment method based on artificial intelligence according to claim 3 is characterized in that: The weight of the optimal risk status indicator at the previous moment in the weighted average process is 0.

05.

5. An artificial intelligence-based multi-risk parameter enterprise chemical risk assessment system, including: A memory and a processor, characterized in that the processor executes a computer program stored in the memory to implement the multi-risk parameter chemical enterprise risk assessment method based on artificial intelligence as described in any one of claims 1-4.

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