Artificial intelligence-based risk identification and intelligent evaluation method for nine small places

Abstract

The present application relates to the field of risk assessment, in particular to a kind of nine small places risk identification and intelligent evaluation method based on artificial intelligence.The method first obtains the evaluation weight of each risk according to the number of occurrence records of each risk of target place and the rectification time after each occurrence, obtains the dynamic evaluation value of each risk according to the number of occurrence records of the same risk of all places at the same time point, adjusts the evaluation weight of each risk of target place according to the difference of monitoring data between each sensor of target place during the occurrence of each risk and the non-occurrence period, obtains the adjusted evaluation weight of each risk at the current time, and then analyzes the occurrence probability of each risk in target place at the current time, and based on the occurrence probability, real-time early warning is carried out for each risk of target place.The present application can improve the accuracy of risk prediction of nine small places.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of risk assessment, in particular to a nine small place risk identification and intelligent evaluation method based on artificial intelligence. BACKGROUND

[0002] Nine small places refer to a general term of primary schools or kindergartens, small hospitals, small shops, small catering places, small hotels, small singing and dancing entertainment places, small Internet bars, small beauty and bath places and small production and processing enterprises. These places have relatively serious safety hazards due to weak safety awareness and inadequate safety measures. Therefore, accurate identification and evaluation of various risks existing in nine small places can avoid further expansion of risks and ensure the safety of nine small places.

[0003] In related technologies, sensors are usually used to monitor various types of places, and the data monitored are used to evaluate and predict the risks that may occur in the places by combining with fixed weights. However, there are usually a large number of different risks in a specific place, each risk has different characteristics in terms of time variation, such as static risks with stable occurrence frequency over time and dynamic risks with unstable occurrence frequency over time. The weights of different types of risks in the evaluation process should be different over time, so that the existing method using fixed weights cannot accurately predict the occurrence of risks in various types of places. SUMMARY

[0004] In order to solve the technical problem that the existing method using fixed weights cannot accurately predict the occurrence of risks in various types of places, the purpose of the present application is to provide a nine small place risk identification and intelligent evaluation method based on artificial intelligence, and the technical solution adopted is as follows:

[0005] The present application provides a nine small place risk identification and intelligent evaluation method based on artificial intelligence, which comprises:

[0006] Obtain the occurrence records of each risk in each type of place at each time point, the rectification time of each risk after each occurrence, and the monitoring data of each sensor in each type of place at each time point in real time;

[0007] Take any type of place as a target place, obtain the evaluation weight of each risk of the target place according to the number of occurrence records of each risk of the target place and the rectification time of each risk after each occurrence, and obtain the dynamic evaluation value of each risk according to the change of the number of occurrence records of the same risk of all places at the same time point;

[0008] According to the difference between the monitoring data of each sensor of the target site during the occurrence of each risk and during the non-occurrence, an association degree between each sensor of the target site and each risk is obtained; according to the difference between the monitoring data of each sensor of the target site at the current moment and during the occurrence of each risk, the association degree between each sensor and each risk, and the dynamic evaluation value of each risk, a weight adjustment coefficient of each risk of the target site at the current moment is obtained; based on the weight adjustment coefficient, the evaluation weight of each risk of the target site is adjusted to obtain an adjusted evaluation weight of each risk of the target site at the current moment;

[0009] According to the difference between the monitoring data of each sensor of the target site at the current moment and the standard safety threshold, the association degree between each sensor and each risk, and the adjusted evaluation weight of each risk of the target site at the current moment, an occurrence probability of each risk in the target site at the current moment is obtained; based on the occurrence probability, a real-time early warning is given to each risk of the target site.

[0010] Further, the obtaining of the evaluation weight of each risk of the target site comprises:

[0011] In the target site, the average value of the rectification time length of each risk after all occurrences is taken as the overall rectification time length of each risk of the target site;

[0012] After the number of occurrence records of each risk of the target site and the overall rectification time length are comprehensively processed, an evaluation weight of each risk of the target site is obtained.

[0013] Further, the obtaining of the dynamic evaluation value of each risk comprises:

[0014] The number of occurrence records of the same risk of all sites at the same time point is taken as the occurrence frequency of each risk at each time point;

[0015] The discrete degree of the occurrence frequency of each risk at all time points is analyzed to obtain a dynamic evaluation value of each risk.

[0016] Further, the obtaining of the association degree between each sensor of the target site and each risk comprises:

[0017] The average value of the monitoring data of each sensor of the target site at all moments during the occurrence of each risk is taken as the first overall monitoring data of each sensor of the target site with respect to each risk;

[0018] obtaining an average value of the monitoring data of each sensor of the target site at all moments during which each risk does not occur as second overall monitoring data of each sensor of the target site with respect to each risk;

[0019] normalizing an absolute value of a difference between the first overall monitoring data and the second overall monitoring data to obtain a correlation degree between each sensor of the target site and each risk.

[0020] Further, the obtaining of the weight adjustment coefficient of each risk of the target site at the current moment comprises:

[0021] obtaining an absolute value of a difference between the monitoring data of each sensor of the target site at the current moment and the second overall monitoring data as an occurrence evaluation value of each sensor of the target site at the current moment with respect to each risk;

[0022] performing a weighted summation on the occurrence evaluation value of each sensor of the target site at the current moment with respect to each risk by using the correlation degree between each sensor of the target site and each risk to obtain an initial weight adjustment factor of each risk of the target site at the current moment;

[0023] obtaining a weight adjustment coefficient of each risk of the target site at the current moment according to the initial weight adjustment factor of each risk of the target site at the current moment and the dynamic evaluation value of each risk.

[0024] Further, the obtaining of the weight adjustment coefficient of each risk of the target site at the current moment according to the initial weight adjustment factor of each risk of the target site at the current moment and the dynamic evaluation value of each risk comprises:

[0025] comprehensively processing the initial weight adjustment factor of each risk of the target site at the current moment and the dynamic evaluation value of each risk and then performing normalization processing to obtain the weight adjustment coefficient of each risk of the target site at the current moment.

[0026] Further, the obtaining of the adjustment evaluation weight of each risk of the target site at the current moment comprises:

[0027] obtaining a product value of the weight adjustment coefficient of each risk of the target site at the current moment and the evaluation weight of each risk of the target site as a weight adjustment amount of each risk of the target site at the current moment;

[0028] performing normalization processing on a sum value of the evaluation weight of each risk of the target site and the weight adjustment amount of each risk of the target site at the current moment to obtain an adjustment evaluation weight of each risk of the target site at the current moment.

[0029] Further, the obtaining the possibility of occurrence of each risk in the target site at the current time comprises:

[0030] normalizing a difference between the monitoring data of each sensor of the target site at the current time and the standard safety threshold of each sensor, to obtain a risk occurrence factor of each sensor of the target site at the current time;

[0031] performing weighted summation on the risk occurrence factor of each sensor of the target site at the current time by using the correlation degree between each sensor of the target site and each risk, to obtain an initial occurrence coefficient of each risk of the target site at the current time;

[0032] adjusting the initial occurrence coefficient based on the adjustment evaluation weight of each risk of the target site at the current time, to obtain the possibility of occurrence of each risk in the target site at the current time.

[0033] Further, the adjusting the initial occurrence coefficient based on the adjustment evaluation weight of each risk of the target site at the current time, to obtain the possibility of occurrence of each risk in the target site at the current time comprises:

[0034] taking a product value of the adjustment evaluation weight of each risk of the target site at the current time and the initial occurrence coefficient as an occurrence adjustment amount of each risk of the target site at the current time;

[0035] normalizing a sum value of the initial occurrence coefficient of each risk of the target site at the current time and the occurrence adjustment amount, to obtain the possibility of occurrence of each risk in the target site at the current time.

[0036] Further, the real-time early warning of each risk of the target site comprises:

[0037] if the possibility of occurrence of each risk in the target site at the current time is greater than a preset possibility threshold, early warning is performed on each risk of the target site.

[0038] The present application has the following beneficial effects:

[0039] The present application considers that the existing method cannot accurately predict the occurrence of risks in various places using fixed weights. First, the evaluation weight obtained reflects the reference of the real occurrence of each risk of the target place, and it is considered that there are not only static risks with relatively stable occurrence frequency changes over time, but also dynamic risks with unstable occurrence frequency changes over time in the target place. In the process of predicting the occurrence of various risks of the target place, the weight of different types of risks should change over time. Therefore, the dynamic evaluation value obtained reflects the degree of change of the occurrence frequency of each risk over time. Considering that the monitoring data of different sensors have different performance characteristics when a certain specific risk occurs, the correlation between the monitoring data of each sensor and the occurrence of each risk can be reflected by the obtained correlation. Then, by real-time acquisition of the weight adjustment coefficient, the evaluation weight of each risk of the target place is adjusted, and the evaluation weight is adjusted in real time to reflect the reference of the real occurrence of each risk of the target place, and then the occurrence probability of each risk in the target place at the current time is accurately analyzed, and each risk of the target place is accurately warned, and the accuracy of risk prediction is improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0041] Figure 1 A flow chart of a risk identification and intelligent evaluation method for nine small places based on artificial intelligence is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purpose, the following will combine the drawings and preferred embodiments to specifically describe the specific implementation, structure, features and effects of the risk identification and intelligent evaluation method for nine small places based on artificial intelligence according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0044] Specifically, the application provides a specific scheme of a nine-small-site risk identification and intelligent evaluation method based on artificial intelligence.

[0045] Please refer to Figure 1 The method comprises the following steps:

[0046] Step S1: acquiring occurrence records of each risk in each type of site at each time point, rectification time lengths after each occurrence of each risk, and monitoring data of each sensor in each type of site at each time point.

[0047] There are a large number of different risk hidden dangers in the nine small sites, for example, including fire access occupation, insufficient fire-fighting equipment, and line aging and damage, and the like. The embodiment of the application first collects a safety hidden danger statistical table of each type of site, and acquires occurrence records of each risk in each type of site at each time point, and rectification time lengths after each occurrence of each risk from the safety hidden danger statistical table.

[0048] Meanwhile, the embodiment of the application also needs to install various sensors in each type of site, for example, smoke sensors, temperature sensors, carbon monoxide sensors, and electrical fire monitoring detectors, and the like, and acquire monitoring data of each sensor in each type of site at each time point.

[0049] It should be noted that different types of data have different dimensions, and therefore the embodiment of the application also needs to perform standardization processing on the collected different types of data to eliminate the influence of the dimensions, wherein the data standardization is a technical means well known by those skilled in the art, and will not be described here.

[0050] Step S2: taking any one type of site as a target site, obtaining an evaluation weight of each risk of the target site according to the number of occurrence records of each risk of the target site and the rectification time length after each occurrence of each risk; and obtaining a dynamic evaluation value of each risk according to the change of the number of occurrence records of the same risk of all sites at the same time point.

[0051] In a subsequent step, it is necessary to predict the possibility of occurrence of each risk in each type of site based on the monitoring data of the sensor, but the reference of the real occurrence of each risk represented by the monitoring data of the sensor in the prediction process is different, for example, the possibility of real occurrence of part of the risks is small, and the possibility of real occurrence of another part of the risks is large, therefore, the embodiments of the present application first analyze any type of site, take any type of site as a target site, the more the number of occurrence records of each risk in the target site, and the longer the rectification time after each occurrence of each risk in the target site, the greater the reference of the real occurrence of each risk, therefore, the evaluation weight of each risk of the target site can be obtained according to the number of occurrence records of each risk of the target site and the rectification time after each occurrence of each risk, and the reference of the real occurrence of each risk of the target site is reflected by the evaluation weight.

[0052] Preferably, in an embodiment of the present application, the method for obtaining the evaluation weight of each risk of the target site specifically comprises:

[0053] In the target site, the average value of the rectification time after all occurrences of each risk is taken as the overall rectification time of each risk of the target site, and the number of occurrence records of each risk of the target site and the overall rectification time are comprehensively processed and normalized, and the calculation result is limited to , thereby obtaining the evaluation weight of each risk of the target site.

[0054] In the embodiments of the present application, the number of occurrence records of each risk of the target site and the overall rectification time can be integrated by calculating the sum or product value of the two, which is not limited herein, and the same method can also be used to realize the comprehensive processing of two or more data in subsequent steps.

[0055] In an embodiment of the present application, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can also be maximum and minimum value normalization processing, and in other embodiments of the present application, other normalization methods can be selected according to the specific range of the value, or the activation function and hyperbolic tangent function can be used to realize the normalization processing, which will not be repeated and limited herein.

[0056] As an example, in an embodiment of the present application, the expression of the evaluation weight of each risk of the target site can be specifically, for example:

[0057]

[0058] Wherein, represents the evaluation weight of the i th risk of the target site; represents the evaluation weight of the i th risk of the target site; represents the evaluation weight of the i th risk of the target site. The number of records of such risks occurring; Indicates the first in the target location The average rectification time after all occurrences of this risk, i.e., the first The overall rectification time for this type of risk; This represents the normalization function, used for normalization processing.

[0059] Since the target location contains not only static risks whose occurrence frequency changes relatively steadily over time, but also dynamic risks whose occurrence frequency changes erratically over time, the weights of different types of risks should change over time during the prediction of various risks in the target location. This is necessary to accurately predict the occurrence of various risks. Therefore, this embodiment of the invention analyzes the changes in the number of occurrence records of the same risk in all locations at the same time point. The obtained dynamic evaluation value reflects the degree to which the occurrence frequency of each risk changes over time. The larger the dynamic evaluation value of a certain risk, the more dynamically the risk changes over time, and the smaller the value, the more stable the risk is over time, and the static risk is the risk. Subsequently, the evaluation weight of each risk can be effectively adjusted based on the dynamic evaluation value of each risk, thereby improving the accuracy of early warning for each risk.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the dynamic assessment value of each risk specifically includes:

[0061] The number of occurrences of the same risk in all locations at the same time point is recorded as the number of occurrences of each risk at each time point. The dispersion of the occurrences of each risk at all time points is analyzed to obtain a dynamic assessment value for each risk.

[0062] In one embodiment of the present invention, the standard deviation of the number of occurrences of each risk at all time points can be used as the dynamic evaluation value of each risk, thereby realizing the analysis of the dispersion of the number of occurrences of each risk at all time points. In other embodiments of the present invention, the variance of the number of occurrences of each risk at all time points can also be used as the dynamic evaluation value of each risk, which is not limited here.

[0063] Step S3: obtaining the correlation between each sensor of the target site and each risk according to the difference between the monitoring data of each sensor of the target site during the occurrence of each risk and during the non-occurrence of each risk; obtaining the weight adjustment coefficient of each risk of the target site at the current moment according to the difference between the monitoring data of each sensor of the target site at the current moment and during the occurrence of each risk, the correlation between each sensor and each risk, and the dynamic evaluation value of each risk; and adjusting the evaluation weight of each risk of the target site based on the weight adjustment coefficient to obtain the adjusted evaluation weight of each risk of the target site at the current moment.

[0064] Since the embodiments of the present application need to predict and warn the risks in the target site according to the monitoring data of each sensor in the target site in the subsequent steps, and the correlation between the monitoring data of different sensors and a certain risk type is different, for example, for the fire risk, the temperature sensor can reflect whether there is a fire risk more than other types of sensors, therefore, the embodiments of the present application also need to analyze the difference between the monitoring data of each sensor of the target site during the occurrence of each risk and during the non-occurrence of each risk, and through the correlation between the monitoring data of each sensor and the occurrence of each risk, the subsequent accurate analysis of the possibility of the occurrence of each risk reflected by each sensor can be performed based on the correlation between the sensor and the risk, and the accuracy of the risk prediction of the nine small sites can be improved.

[0065] Preferably, in an embodiment of the present application, the method for obtaining the correlation between each sensor of the target site and each risk specifically comprises:

[0066] The average value of the monitoring data of each sensor of the target site at all moments during the occurrence of each risk is taken as the first overall monitoring data of each sensor of the target site with respect to each risk.

[0067] The average value of the monitoring data of each sensor of the target site at all moments during the non-occurrence of each risk is taken as the second overall monitoring data of each sensor of the target site with respect to each risk.

[0068] The greater the difference between the monitoring data of a certain sensor during the occurrence of a certain risk and the monitoring data during the non-occurrence of the risk, the stronger the correlation between the sensor and the risk, therefore, the absolute value of the difference between the first overall monitoring data and the second overall monitoring data can be normalized, and the calculation result is limited to , thereby obtaining the correlation between each sensor of the target site and each risk.

[0069] As an example, in one embodiment of the present invention, the expression for the correlation between each sensor and each risk at the target location can be specifically, for example, as follows:

[0070]

[0071] in, The first place representing the target location Type of sensor and the first The degree of correlation between these risks; The first place representing the target location The type of sensor about the first The first overall monitoring data for this type of risk; The first place representing the target location The type of sensor about the first The second overall monitoring data for this type of risk; This represents the normalization function, used for normalization processing.

[0072] Because the occurrence of each risk in the nine small venues exhibits different characteristics over time—namely, there are static and dynamic risks—and each risk exhibits different degrees of dynamism over time, different weight values ​​need to be assigned to different types of risks in order to accurately predict the probability of various risks occurring in the nine small venues. Therefore, this embodiment of the invention obtains the weight adjustment coefficient of each risk in the target venue at the current moment based on the difference between the monitoring data of each sensor in the target venue at the current moment and the monitoring data during the occurrence of each risk, the correlation between each sensor and each risk, and the dynamic evaluation value of each risk. The larger the weight adjustment coefficient, the greater the degree of adjustment of the evaluation weight of each risk at the current moment. Subsequently, the evaluation weight of each risk in the target venue can be adjusted based on the weight adjustment coefficient, thereby adjusting the weight value when predicting each risk in real time and improving the accuracy of predicting various risks in the target venue.

[0073] Preferably, in one embodiment of the present invention, the method for obtaining the weight adjustment coefficient of each risk of the target location at the current moment specifically includes:

[0074] The absolute value of the difference between the monitoring data of each sensor at the target location at the current moment and the second overall monitoring data is used as the occurrence assessment value of each sensor at the target location for each risk at the current moment. Since the second overall monitoring data reflects the data characteristics of each sensor when the risk has not occurred, the larger the occurrence assessment value of a certain sensor for a certain risk at the current moment, the greater the probability of the risk occurring as indicated by the monitoring data of that sensor at the current moment, and the greater the degree of adjustment of the assessment weight of that risk in the subsequent process.

[0075] The greater the correlation between a certain sensor and a risk, the stronger the connection between the monitoring data characteristics of the sensor and the risk, and thus the greater the initial weight adjustment factor of each risk in the target site at the current time can be obtained by weighting and summing the occurrence evaluation values of each sensor of the target site at the current time with respect to each risk according to the correlation between each sensor of the target site and each risk, and the greater the initial adjustment factor, the greater the degree of adjustment of the evaluation weight of each risk at the current time.

[0076] As an example, in an embodiment of the present application, the expression of the initial weight adjustment factor of each risk in the target site at the current time can be specifically, for example:

[0077]

[0078] wherein, represents the initial weight adjustment factor of the jth risk in the target site at the current time; represents the correlation between the ith sensor and the jth risk in the target site; represents the monitoring data of the ith sensor at the current time; represents the second overall monitoring data of the ith sensor with respect to the jth risk; represents the number of sensors in the target site; represents the occurrence evaluation value of the ith sensor with respect to the jth risk at the current time. Meanwhile, the greater the dynamic evaluation value of a certain risk, the stronger the dynamic change characteristics of the occurrence of the risk over time, and thus the greater the degree of real-time adjustment of the weight of the risk at the prediction time, and thus the weight adjustment coefficient of each risk in the target site at the current time can be obtained according to the initial weight adjustment factor of each risk in the target site at the current time and the dynamic evaluation value of each risk. Preferably, in an embodiment of the present application, the method for obtaining the weight adjustment coefficient of each risk in the target site at the current time further comprises: comprehensively processing the initial weight adjustment factor of each risk in the target site at the current time and the dynamic evaluation value of each risk, and performing normalization processing to limit the calculation result to

[0079] Meanwhile, the greater the dynamic evaluation value of a certain risk, the stronger the dynamic change characteristics of the occurrence of the risk over time, and thus the greater the degree of real-time adjustment of the weight of the risk at the prediction time, and thus the weight adjustment coefficient of each risk in the target site at the current time can be obtained according to the initial weight adjustment factor of each risk in the target site at the current time and the dynamic evaluation value of each risk.

[0080] Preferably, in an embodiment of the present application, the method for obtaining the weight adjustment coefficient of each risk in the target site at the current time further comprises:

[0081] comprehensively processing the initial weight adjustment factor of each risk in the target site at the current time and the dynamic evaluation value of each risk, and performing normalization processing to limit the calculation result to ​​​​​Within the range, the weighting adjustment coefficient of each risk in the target location at the current moment is obtained.

[0082] As an example, in one embodiment of the present invention, the expression for the weight adjustment coefficient of each risk of the target location at the current moment can be specifically as follows:

[0083]

[0084] in, The first place representing the target location The weighting adjustment factor for each type of risk at the current moment; The first place representing the target location The initial weighting adjustment factor for each type of risk at the current moment; Indicates the first Dynamic assessment value of various risks; This represents the normalization function, used for normalization processing.

[0085] Since the assessment weights obtained above are fixed for specific risks, and since some risks in the nine small venues are dynamic and change over time, using the assessment weights of each risk to predict various risks in the nine small venues will reduce the accuracy of the prediction. Therefore, the assessment weights of each risk in the target venue can be adjusted based on the weight adjustment coefficient of each risk in the target venue at the current moment, so as to obtain the adjusted assessment weights of each risk in the target venue at the current moment. Subsequently, the adjusted assessment weights can be used to accurately predict the probability of each risk in the target venue occurring at the current moment.

[0086] Preferably, in one embodiment of the present invention, the method for obtaining the adjusted assessment weight of each risk of the target location at the current moment specifically includes:

[0087] The product of the weight adjustment factor for each risk in the target location at the current moment and the assessment weight for each risk in the target location is used as the weight adjustment amount for each risk in the target location at the current moment. The sum of the assessment weight and the weight adjustment amount for each risk in the target location at the current moment is then normalized, limiting the calculation result to... Within this range, the adjusted assessment weight of each risk at the target location at the current moment is obtained.

[0088] As an example, in one embodiment of the present invention, the expression for adjusting the assessment weight of each risk of the target location at the current moment can be specifically as follows:

[0089]

[0090] in, The first place representing the target location an adjustment evaluation weight of each risk in the target site at the current moment; a first adjustment evaluation weight of each risk in the target site at the current moment; a first adjustment evaluation weight of each risk in the target site at the current moment; a first adjustment evaluation weight of each risk in the target site at the current moment; a first adjustment evaluation weight of each risk in the target site at the current moment; a normalization function for normalization processing.

[0091] Step S4: obtaining a possibility of occurrence of each risk in the target site at the current moment according to a difference between monitoring data of each sensor of the target site at the current moment and a standard safety threshold, a correlation degree between each sensor and each risk, and an adjustment evaluation weight of each risk in the target site at the current moment; and performing real-time early warning on each risk in the target site based on the possibility of occurrence.

[0092] After obtaining the adjustment evaluation weight of each risk in the target site at the current moment, the possibility of occurrence of each risk can be predicted in combination with the adjustment evaluation weight. Since each sensor is provided with a known standard safety threshold, when the monitoring data of the sensor exceeds the standard safety threshold, it indicates that a certain risk may occur. Therefore, the possibility of occurrence of each risk in the target site at the current moment can be obtained according to a difference between monitoring data of each sensor of the target site at the current moment and a standard safety threshold, a correlation degree between each sensor and each risk, and an adjustment evaluation weight of each risk in the target site at the current moment.

[0093] Preferably, in one embodiment of the present application, the method for obtaining the possibility of occurrence of each risk in the target site at the current moment specifically comprises:

[0094] normalizing a difference between monitoring data of each sensor of the target site at the current moment and a standard safety threshold of each sensor, and limiting a calculation result in a range of 0 to 1, so as to obtain a risk occurrence factor of each sensor of the target site at the current moment. The greater the risk occurrence factor of a certain sensor at the current moment, the greater the possibility of risk occurrence shown by the data characteristics of the sensor at the current moment.

[0095] performing weighted summation on the risk occurrence factors of each sensor of the target site at the current moment by using the correlation degree between each sensor of the target site and each risk, so as to obtain an initial occurrence coefficient of each risk in the target site at the current moment. The greater the initial occurrence coefficient of a certain risk at the current moment, the greater the possibility of occurrence of the risk at the current moment.

[0096] ​As an example, in an embodiment of the present application, the expression of the initial occurrence coefficient of each risk of the target site at the current time can be specifically, for example:

[0097]

[0098] wherein, represents the initial occurrence coefficient of the i-th risk of the target site at the current time; represents the correlation between the i-th sensor of the target site and the j-th risk; represents the risk occurrence factor of the i-th sensor of the target site at the current time; represents the number of sensors in the target site. Meanwhile, the greater the adjustment evaluation weight of a certain risk at the current time, the greater the possibility of the real occurrence of the risk, and thus the initial occurrence coefficient can be adjusted based on the adjustment evaluation weight of each risk of the target site at the current time to obtain the occurrence possibility of each risk of the target site at the current time. Preferably, in an embodiment of the present application, the method for obtaining the occurrence possibility of each risk of the target site at the current time further comprises:

[0099] multiplying the adjustment evaluation weight of each risk of the target site at the current time and the initial occurrence coefficient to obtain the occurrence adjustment amount of each risk of the target site at the current time, normalizing the sum of the initial occurrence coefficient and the occurrence adjustment amount of each risk of the target site at the current time, and limiting the calculation result to the range of 0 to 1, thereby obtaining the occurrence possibility of each risk of the target site at the current time.

[0100] As an example, in an embodiment of the present application, the expression of the occurrence possibility of each risk of the target site at the current time can be specifically, for example:

[0101]

[0102] wherein, represents the occurrence possibility of the i-th risk of the target site at the current time;

[0103] represents the initial occurrence coefficient of the i-th risk of the target site at the current time; represents the adjustment evaluation weight of the i-th risk of the target site at the current time;

[0104] ​​​​​​​​represents a normalization function used for normalization processing.

[0105] Further, based on the occurrence probability, each risk of the target place is warned in real time, so as to improve the accuracy of the prediction and warning of various risks in the target place.

[0106] Preferably, in one embodiment of the present application, the method of warning each risk of the target place in real time specifically comprises:

[0107] If the occurrence probability of each risk in the target place at the current time is greater than the preset probability threshold, each risk of the target place is warned, wherein the preset probability threshold is in the range of In one embodiment of the present application, the preset probability threshold is set to 0.7, and the specific value of the preset probability threshold can also be set by the implementer according to the specific implementation scene, which is not limited herein.

[0108] Therefore, the above-mentioned same method can be used to realize the risk warning of various places in the nine small places with higher accuracy.

[0109] It should be noted that the above-mentioned order of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0110] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A method for risk identification and intelligent assessment of small venues based on artificial intelligence, characterized in that, The method includes: Acquire records of the occurrence of each risk in each type of location at each point in time, the rectification time after each occurrence of each risk, and acquire monitoring data of each sensor in each type of location at each moment in real time; Using any type of location as the target location, the assessment weight of each risk in the target location is obtained based on the number of occurrence records of each risk in the target location and the rectification time after each occurrence; the dynamic assessment value of each risk is obtained based on the change in the number of occurrence records of the same risk in all locations at the same time point. Based on the differences in monitoring data of each sensor at the target location between the occurrence and non-occurrence of each risk, the correlation degree between each sensor and each risk at the target location is obtained; based on the differences in monitoring data of each sensor at the target location at the current moment and the monitoring data during the occurrence of each risk, the correlation degree between each sensor and each risk, and the dynamic evaluation value of each risk, the weight adjustment coefficient of each risk at the target location at the current moment is obtained; based on the weight adjustment coefficient, the evaluation weight of each risk at the target location is adjusted to obtain the adjusted evaluation weight of each risk at the target location at the current moment. Based on the differences between the monitoring data and standard safety thresholds of each sensor in the target location at the current moment, the correlation between each sensor and each risk, and the adjusted evaluation weight of each risk in the target location at the current moment, the probability of occurrence of each risk in the target location at the current moment is obtained; based on the probability of occurrence, a real-time early warning is issued for each risk in the target location. The dynamic assessment value for each risk is obtained by: The number of times the same risk occurred in all locations at the same time point is recorded as the number of times each risk occurred at each time point; The dispersion of the occurrence frequency of each risk at all time points is analyzed to obtain a dynamic assessment value for each risk; The correlation between each sensor and each risk at the target location is obtained, including: The average value of the monitoring data of each sensor at the target site at all times during each risk occurrence is taken as the first overall monitoring data of each sensor at the target site with respect to each risk. The average value of the monitoring data of each sensor at the target site at all times during the period when each risk has not occurred is taken as the second overall monitoring data of each sensor at the target site with respect to each risk. The absolute value of the difference between the first overall monitoring data and the second overall monitoring data is normalized to obtain the correlation between each sensor and each risk in the target location; The weighting adjustment factor for each risk at the target location at the current moment includes: The absolute value of the difference between the monitoring data of each sensor at the target location at the current moment and the second overall monitoring data is used as the assessment value of the occurrence of each risk for each sensor at the target location at the current moment. By utilizing the correlation between each sensor in the target location and each risk, the occurrence assessment values ​​of each sensor in the target location at the current time for each risk are weighted and summed to obtain the initial weight adjustment factor for each risk in the target location at the current time. Based on the initial weight adjustment factor for each risk of the target location at the current moment and the dynamic assessment value of each risk, obtain the weight adjustment coefficient for each risk of the target location at the current moment; The step of obtaining the weight adjustment coefficient for each risk of the target location at the current moment, based on the initial weight adjustment factor for each risk at the current moment and the dynamic assessment value of each risk, includes: The initial weight adjustment factor and the dynamic assessment value of each risk at the target location at the current moment are combined and normalized to obtain the weight adjustment coefficient of each risk at the target location at the current moment.

2. The method for risk identification and intelligent assessment of small venues based on artificial intelligence according to claim 1, characterized in that, The assessment weights for each risk at the target location include: In the target location, the average of the rectification time for each risk after all occurrences is taken as the overall rectification time for each risk in the target location; The number of occurrence records for each risk at the target location and the overall rectification time are combined and normalized to obtain the assessment weight of each risk at the target location.

3. The method for risk identification and intelligent assessment of small venues based on artificial intelligence according to claim 1, characterized in that, The adjusted assessment weights for each risk at the target location at the current moment include: The product of the weight adjustment factor of each risk in the target location at the current moment and the evaluation weight of each risk in the target location is used as the weight adjustment amount of each risk in the target location at the current moment. The sum of the assessment weights for each risk in the target location and the weight adjustment amounts for each risk in the target location at the current time is normalized to obtain the adjusted assessment weights for each risk in the target location at the current time.

4. The method for risk identification and intelligent assessment of small venues based on artificial intelligence according to claim 1, characterized in that, The probability of each risk occurring in the target location at the current moment includes: The difference between the monitoring data of each sensor at the target location at the current moment and the standard safety threshold of each sensor is normalized to obtain the risk occurrence factor of each sensor at the target location at the current moment. By utilizing the correlation between each sensor and each risk at the target location, the risk occurrence factors of each sensor at the target location at the current time are weighted and summed to obtain the initial occurrence coefficient of each risk at the target location at the current time. Based on the adjusted assessment weight of each risk in the target location at the current moment, the initial occurrence coefficient is adjusted to obtain the probability of occurrence of each risk in the target location at the current moment.

5. The method for risk identification and intelligent assessment of small venues based on artificial intelligence according to claim 4, characterized in that, The adjusted assessment weights for each risk at the target location at the current moment are used to adjust the initial occurrence coefficients, thereby obtaining the probability of occurrence of each risk at the target location at the current moment, including: The product of the adjusted assessment weight and the initial occurrence coefficient for each risk at the target location at the current moment is used as the occurrence adjustment amount for each risk at the target location at the current moment. The sum of the initial occurrence coefficient and the occurrence adjustment amount of each risk in the target location at the current moment is normalized to obtain the probability of occurrence of each risk in the target location at the current moment.

6. The method for risk identification and intelligent assessment of small venues based on artificial intelligence according to claim 1, characterized in that, The real-time early warning for each risk at the target location includes: If the probability of each risk occurring in the target location at the current moment is greater than a preset probability threshold, then an early warning is issued for each risk in the target location.

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