Elevator accident scene key element classification and grading evaluation method and related device

Abstract

The invention discloses an elevator accident scene key element classification and grading evaluation method and a related device, and relates to the technical field of elevator safety analysis and comprehensive evaluation, and the method comprises the following steps: obtaining an emergency scene accident report; performing emergency scene factor classification on the emergency scene accident report to obtain a to-be-evaluated factor classification list; constructing an evaluation sample database based on the emergency scene accident report; based on the to-be-evaluated element classification list, performing element extraction and classification labeling on the evaluation sample database to obtain an element accident statistical data set; performing element event co-occurrence statistics on the element accident statistical data set to obtain a data set; performing element security deviation calculation on the data set to obtain a security deviation calculation result; and performing classification and grading evaluation according to a safety deviation degree calculation result to obtain a factor classification and grading result. Technical means and judgment methods can be provided for classified management and control of emergency scene elements, and the elevator safety management capability is promoted.

Description

Technical Field

[0001] The present application relates to the technical field of elevator safety analysis and comprehensive evaluation, and in particular to a method for classifying and grading key elements of elevator accident scenarios and related devices. Background Art

[0002] Elevator accidents occur frequently due to factors such as the age of some elevators and improper operation. Learning from accident scenarios and identifying the key factors that influence their occurrence is crucial for improving hazard detection capabilities and reducing the likelihood of accidents.

[0003] Elevator accident reports are a crucial component of the elevator safety management system. They include basic accident information, the process, and an analysis of the causes. These reports, issued by authoritative authorities and leveraging standardized processes and multi-dimensional analysis, provide a basis for preventing similar incidents. Elevator accident causes range from improper operator operation to aging equipment, poor maintenance and management, and non-compliant design and installation. These factors are directly or indirectly linked. For example, insufficient maintenance funding and difficulties applying for public maintenance funds in some residential elevators lead to missed maintenance and untimely addressing of hidden dangers in older elevators. Simply analyzing direct or indirect causes fails to capture the interconnectedness between these factors. Furthermore, technological advancements are enabling the inclusion of more factors, such as behavioral, environmental, and management, within the analysis scope. Traditional risk factor analysis methods primarily rely on causal analysis, using accident or fault tree methods and structural failure mechanisms. However, these methods lack dynamic adaptability, often focusing on a single factor while ignoring other important aspects. Furthermore, they are not highly sensitive to behavioral and management factors. In view of the various elements of elevator accident scenes and the detailed text data materials of accident reports, there is an urgent need for a method to extract key elements from elevator accident reports and classify and grade them scientifically and systematically, so as to improve the ability to use elevator accident reports for element importance analysis and accident prevention. Summary of the Invention

[0004] The purpose of this application is to provide a classification and grading evaluation method for key elements of elevator accident scenarios and related devices, which can improve the ability to use elevator accident reports to analyze the importance of elements and prevent accidents.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for classifying and grading key elements of an elevator accident scenario, the method comprising:

[0007] Obtain emergency scenario accident reports; the emergency scenario accident reports include several years of elevator accident data and several accident events.

[0008] The emergency scenario accident report is classified into emergency scenario factors to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers and disaster-prone environments.

[0009] Based on the emergency scenario accident report, an evaluation sample database is constructed.

[0010] Based on the classification list of elements to be evaluated, the evaluation sample database is subjected to element extraction and classification labeling to obtain an element accident statistical data set; the element accident statistical data set includes: element occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss.

[0011] Performing element event co-occurrence statistics on the element accident statistical data set to obtain a data set; the element event co-occurrence statistics includes: time integration and consequence sub-item data integration.

[0012] Calculating the safety deviation degree of the elements on the data set to obtain a safety deviation degree calculation result.

[0013] A classification and grading evaluation is performed based on the safety deviation calculation result to obtain a factor classification and grading result.

[0014] In a second aspect, the present application provides a device for classifying and grading key elements of an elevator accident scene, which is used to implement the above-mentioned method for classifying and grading key elements of an elevator accident scene. The device includes:

[0015] The emergency scene accident report acquisition module is used to obtain the emergency scene accident report; the emergency scene accident report includes several years of elevator accident data and several accident events.

[0016] The emergency scenario factor classification module is used to classify the emergency scenario factors of the emergency scenario accident report to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers and disaster-prone environments.

[0017] An evaluation sample database construction module is used to construct an evaluation sample database based on the emergency scenario accident report.

[0018] The element extraction and classification labeling module is configured to perform element extraction and classification labeling on the evaluation sample database based on the classification list of elements to be evaluated, and obtain an element accident statistical data set. The element accident statistical data set includes element occurrence frequency and consequence sub-item data. The consequence sub-item data includes death, injury, and economic loss.

[0019] The element event co-occurrence statistical module is configured to perform element event co-occurrence statistics on the element accident statistical data set, and obtain a data set. The element event co-occurrence statistics include time integration and consequence sub-item data integration.

[0020] The element safety deviation degree calculation module is configured to perform element safety deviation degree calculation on the data set, and obtain a safety deviation degree calculation result.

[0021] The classification and grading evaluation module is configured to perform classification and grading evaluation according to the safety deviation degree calculation result, and obtain a factor classification and grading result.

[0022] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the elevator accident scene key element classification and grading evaluation method described above.

[0023] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the elevator accident scene key element classification and grading evaluation method described above.

[0024] In a fifth aspect, the present application provides a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the elevator accident scene key element classification and grading evaluation method described above.

[0025] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0026] The present application provides a classification and grading evaluation method for key elements of elevator accident scenarios and related devices. First, an emergency scenario accident report is obtained; the emergency scenario accident report includes several years of elevator accident data and several accident events; it can cover a large number of different types of elevator accident situations, so that subsequent analysis and evaluation have a more solid data foundation, avoiding one-sided or inaccurate conclusions due to insufficient data, and can more specifically explore the key factors and laws in elevator accidents. Secondly, the emergency scenario accident report is classified into emergency scenario factors to obtain a classification list of elements to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers, and disaster-prone environments; through classification, different categories of factors that play a key role in elevator accident emergency scenarios can be clearly identified. Furthermore, based on the emergency scenario accident report, an evaluation sample database is constructed; based on the classification list of elements to be evaluated, elements of the evaluation sample database are extracted and classified and labeled to obtain an element accident statistical data set; the element accident statistical data set includes: element occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss; an evaluation sample database is constructed to integrate and manage the collected scattered accident data, which is convenient for subsequent unified processing, query and analysis of the data, and improves the utilization efficiency and availability of the data; based on the classification list of elements to be evaluated, elements of the evaluation sample database are extracted and classified and labeled, which can accurately extract elements related to various emergency scenario factors from a large amount of accident data, and associate accident elements with a variety of consequence data, which helps to measure the comprehensive impact of different elements on accident consequences from multiple dimensions, and more comprehensively reflect the severity and harmfulness of the accident. Then, the element event co-occurrence statistics are performed on the element accident statistical data set to obtain a data set; the element event co-occurrence statistics include: time integration and consequence sub-item data integration; the element events can be comprehensively analyzed from the two important dimensions of time and consequence to understand the frequency of occurrence of elements in different time periods and the changes in the consequences related to them, so as to more accurately grasp the development trend of accidents and the potential risks of elements. Finally, the element safety deviation degree is calculated on the data set to obtain a safety deviation degree calculation result; classification and grading evaluation is performed based on the safety deviation degree calculation result to obtain a factor classification and grading result; the degree of deviation between the accident element and the safety standard or normal state can be quantified to obtain a safety deviation degree calculation result, thereby achieving a quantitative assessment of the safety risk of each element in the elevator accident emergency scenario, providing a more intuitive and specific basis for safety decision-making; classification and grading evaluation is performed based on the safety deviation degree calculation result to obtain a factor classification and grading result, which can classify and grade various emergency scenario factors according to their safety risk level, so that different factors can receive attention and management measures that match their risks in safety management, thereby achieving differentiated management. This application can provide technical means and judgment methods for the classification and control of emergency scenario elements, helping to improve elevator safety management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 This is an application environment diagram of a method for classifying and grading key elements of an elevator accident scenario in one embodiment of the present application.

[0029] Figure 2 A flowchart of a method for classifying and grading key elements of an elevator accident scenario provided in one embodiment of the present application.

[0030] Figure 3 A schematic diagram of an elevator emergency scenario element classification architecture based on dual-chain fusion theory provided in one embodiment of the present application.

[0031] Figure 4 A schematic diagram of the overall framework of the classification and grading evaluation method provided in one embodiment of the present application.

[0032] Figure 5 A schematic diagram showing the results of an example of a classification and grading method provided in one embodiment of the present application.

[0033] Figure 6 A schematic diagram of the functional modules of a device for classifying and grading key elements of an elevator accident scenario provided in one embodiment of the present application.

[0034] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0036] To solve the above problems, the application provides a key element classification and grading evaluation method for an elevator accident scene based on the content of an elevator accident report. The key elements are classified by a double-chain fusion idea, and many elements such as equipment elements, management elements, environmental elements, human elements and maintenance elements in the elevator emergency scene are summarized into three categories of disaster-causing elements, bearing elements and carrying environment elements. The three elements are related to and interact with each other to form a whole system that leads to an accident. Based on the risk analysis idea, the frequency and consequence severity of various elements in the accident report are collected by text extraction technology. Not only the direct and indirect causes are considered, but also various elements in the accident occurrence, rescue and disposal process are considered in the classification and grading range. The idea of optimal solution is used to calculate the frequency distance and consequence distance between various elements and the optimal solution of no accident. The safety deviation index is constructed by the Euclidean weighted calculation to evaluate the importance of various elements, so as to realize the grading evaluation of various elements. Through this classification and grading method, the dynamic development needs of the evaluation method can be met, and the importance of the elements to be evaluated can be compared in the same category. The application is helpful to find out how the disaster-causing factors in the elevator emergency scene affect the disaster-bearing body under the action of the disaster-pregnant environment. Through the development of technical means and scientific judgment results, the prevention and control priorities of different categories of elements are determined, so that targeted and adaptive measures can be taken for monitoring, early warning and control, and the prevention and control ability in the elevator safety field is improved.

[0037] To make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the application will be further described in detail below with reference to the drawings and specific embodiments.

[0038] The elevator accident scene key element classification and grading evaluation method provided by the embodiments of the application can be applied to, for example Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the acquired emergency scenario accident report to the server 104, wherein the emergency scenario accident report includes several years of elevator accident data and several accident events; after the server 104 receives the emergency scenario accident report, the server 104 classifies the emergency scenario factors of the emergency scenario accident report to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factor class, carrier class and disaster-prone environment class; based on the emergency scenario accident report, an evaluation sample database is constructed; based on the classification list of factors to be evaluated, the evaluation sample database is subjected to factor extraction and classification labeling to obtain a factor accident statistical data set; the factor accident statistical data set includes: factor occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss; the factor event co-occurrence statistics of the factor accident statistical data set are performed to obtain a data set; the factor event co-occurrence statistics include: time integration and consequence sub-item data integration; the data set is subjected to factor safety deviation calculation to obtain a safety deviation calculation result; classification and grading evaluation is performed based on the safety deviation calculation result to obtain a factor classification and grading result. The server 104 can feed back the obtained factor classification and grading results to the terminal 102. In addition, in some embodiments, the elevator accident scene key element classification and grading evaluation method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform the elevator accident scene key element classification and grading evaluation based on the emergency scene accident report, or the server 104 can obtain the emergency scene accident report from the data storage system and perform the elevator accident scene key element classification and grading evaluation based on the emergency scene accident report.

[0039] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0040] In an exemplary embodiment, Figure 2 As shown, a method for classifying and grading the key elements of an elevator accident scene is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, and can also be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps S1 to S7.

[0041] in:

[0042] S1: Obtain an emergency scenario accident report; the emergency scenario accident report includes several years of elevator accident data and several accident events.

[0043] S2: Classify the emergency scenario factors of the emergency scenario accident report to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers, and disaster-prone environments.

[0044] S3: Based on the emergency scenario accident report, construct an evaluation sample database.

[0045] S4: Based on the classification list of elements to be evaluated, extract and classify the evaluation sample database to obtain an element accident statistical data set; the element accident statistical data set includes: element occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss.

[0046] S5: performing factor event co-occurrence statistics on the factor accident statistical data set to obtain a data set; the factor event co-occurrence statistics include: time integration and consequence sub-item data integration.

[0047] S6: Calculate the element safety deviation degree on the data set to obtain a safety deviation degree calculation result.

[0048] S7: Perform classification and grading evaluation based on the safety deviation calculation result to obtain factor classification and grading results.

[0049] Implementing the above steps S1 to S7, this application starts from the content of accident reports over the years, extracts structured element content through text extraction, and uses the theory of the integration of disaster chain and accident chain to classify many accident elements such as human factors, environmental factors, management factors, equipment factors and maintenance factors according to the three dimensions of disaster-bearing body, disaster-causing factor and disaster-pregnant environment, effectively reducing the types of analysis elements. The disaster chain classification dimension not only covers all relevant elements in the accident process, but also can clearly show the systematic relationship between accident elements, while avoiding the situation in which traditional elements only focus on a single element and ignore other important aspects. Then, based on the idea of ​​data-driven and superior solution algorithm, using the frequency of occurrence of each element and the severity of the accident consequences, construct an evaluation system for the degree of deviation between different categories of elements and safety conditions, and realize the classification and grading of elevator emergency scene elements. Scientific evaluation. This application can meet the development and multi-scale evaluation needs, and can adjust the evaluation weight and scope according to actual conditions, providing an effective scientific basis and technical means for the evaluation of various complex scene elements of elevator emergency.

[0050] As an optional implementation, in step S1, this embodiment conducts a scientific evaluation of elevator emergency scenario elements based on elevator accident reports. First, elevator accident data for no less than 10 years is collected, and the number of accident events is recorded as N.

[0051] As an optional implementation, in step S2, according to the emergency scene evaluation requirements, this application develops a three-dimensional classification method for elevator emergency scene elements based on the dual-chain fusion theory, see Figure 3 As shown in Figure 1, a classification list of elements to be evaluated is determined based on this classification method. This classification list can be iterated based on technologies such as the Internet of Things, and new elements can be included in the evaluation list to meet the dynamic and open requirements of the evaluation method.

[0052] As an optional implementation, in step S4, according to the classification list of elements to be evaluated in step S2, the frequency of element occurrence and the data on the consequences of death, injury and economic loss are extracted from the evaluation sample database constructed in step S3 to obtain the element accident statistical data set S.

[0053] As an optional implementation, in step S5, the time and consequence sub-item data of the element accident statistical data set S are integrated respectively.

[0054] S5.1 Time integration: According to the dynamic weight algorithm, the more recent the accident case, the higher the impact and explanatory power of its data on the current factors. Based on the characteristics of elevator accidents, the data is divided into two subsets S = {S1, S2} according to 5 years.

[0055] Among them, S1: the first subset, data of the past five years, the first time weight α=0.7.

[0056] S2: The second subset, data from the past 5-10 years, the second time weight β = 0.3.

[0057] For any element i, define:

[0058] Occurrences: (number of occurrences of S1), (Number of occurrences of S2).

[0059] Number of events: N1 (number of S1 accident events), N2 (number of S2 accident events).

[0060] Time-weighted total frequency:

[0061]

[0062] According to the dynamic weighting algorithm, since all data comes from accident cases, the more recent the accident case, the greater its impact and explanatory power on the current factors, and thus the higher its weight. Generally, case data from the past five years holds a weight of 60% to 80%, while data older than five years holds a weight of 20% to 40%. When evaluation factors are updated rapidly, the weight of data from the past five years can be increased to 80%. Since most factors in elevator emergency accident scenarios remain relatively stable, a few factors are continuously updated over time. Therefore, a weight of 70% is chosen for data from the past five years, and 30% for data older than five years.

[0063] S5.2 Consequence sub-item data integration, define the consequence death, injury and economic loss sub-item vectors:

[0064] S i =(s i1 , s i2 , s i3 ) (2);

[0065] Among them, S i is the consequence sub-item data vector set; i1 is the death vector; s i2 is the injured subvector; s i3 is the economic loss sub-vector.

[0066] The comprehensive consequence score is:

[0067] H i =d*s i1 +f*s i2 +e*s i3 (3);

[0068] Among them, H i is the comprehensive result of the consequence sub-items; d is the death weight; f is the injury weight; and e is the economic loss weight.

[0069] According to the deaths and injuries in elevator accidents, they are mainly concentrated in the range of general accidents and major accidents. The weight calculation is based on the equivalence relationship between the critical values ​​of general accidents and major accidents in the "Regulations on the Reporting, Investigation and Handling of Production Safety Accidents": 3 deaths ≈ 10 serious injuries ≈ 10 million yuan in economic losses. According to the characteristics of elevator accidents, from general to major accidents, for every additional death, the accident level may cross the level (such as 2→3 deaths), and the sensitivity coefficient needs to be increased to 1.2 times. Similarly, the sensitivity of injuries is weak, and the coefficient is adjusted to 0.8 times. The economic loss range fluctuates greatly, and the sensitivity is adjusted to 1.5 times. The weight calculation after correction is:

[0070] Modified death weight = 1 × 1.2 = 1.2 (4);

[0071] Corrected injury weight = 0.3 × 0.8 = 0.24 (5);

[0072] Modified economic loss weight = 0.03 × 1.5 = 0.045 (6);

[0073] Normalizing the corrected weights yields:

[0074]

[0075] After the above data processing, we get the data set P i ={C i ,H i}.

[0076] As an optional implementation, in step S6, the element safety deviation degree is calculated for the data set to obtain a safety deviation degree calculation result, specifically including:

[0077] S61: performing polarity normalization processing on the data set to obtain normalized data.

[0078] S62: Determine the factor frequency weight and consequence vector weight based on risk assessment theory and the characteristics of elevator accident mortality rate.

[0079] S63: Based on the factor frequency weight and the consequence vector weight, a superior-inferior solution distance algorithm is used to calculate the factor safety deviation degree.

[0080] Based on the processed data set P i ={C i ,H i}Calculate the safety deviation; first normalize the weighted frequency and weighted consequence separately:

[0081]

[0082] Among them, P ni is the normalized data set; P i min is the minimum value in the data set; P i max is the maximum value in the data set; H ni is the comprehensive result of the normalized consequence sub-items; C ni is the normalized time-weighted total frequency.

[0083] Then, based on risk assessment theory and the characteristics of elevator accident mortality in recent years, we define the factor frequency and consequence vector weights: ω = (0.4, 0.6). Based on the superior and inferior solution distance algorithm, we define the following:

[0084] (1) Ideal solution setting: The zero set where no accident occurs is set as the ideal solution.

[0085] (2) Definition of element safety deviation: The weighted Euclidean distance between the element dataset and the ideal solution set is defined as the element safety deviation:

[0086]

[0087] Among them, R i is the safety deviation of the factor; w1 is the factor frequency weight; w2 is the consequence vector weight.

[0088] In risk assessment, the default weighting of frequency and consequence is 1:1. The weighting of probability (likelihood) and severity of consequences is typically determined based on industry standards, academic research, or practical experience in specific scenarios. Due to the significant social impact of accidents in the special equipment sector, particularly elevator accidents, and the recent increase in elevator accident mortality rates, the consequence weighting is slightly higher than the frequency weighting, set at ω = (0.4, 0.6).

[0089] As an optional implementation, in step S7, a classification and grading evaluation is performed based on the safety deviation calculation results. Based on the special equipment risk and hidden danger classification method, elevator accident factors are also divided into three levels according to the safety deviation degree: important, serious, and general, corresponding to different levels of attention that should be paid to monitoring, warning, and control in emergency scenarios.

[0090] The following points need to be explained in this application:

[0091] (1) The extraction of elements to be evaluated is achieved through text extraction technology. There are two methods based on the dynamic characteristics of the extracted content. The first method is to determine the classification of elements, build a list of elements to be classified, and extract the corresponding elements from the report text through regular expressions; the second method is to use the Baidu UIE model to first extract each element from a small number of accident reports based on the element classification list.

[0092] (2) When calculating the frequency of occurrence of elements, deaths, injuries, and economic losses, the elements shall be counted based on the co-occurrence relationship in the accident reports.

[0093] (3) The number of elevator accidents has decreased in recent years, and the death toll has been below the serious level. At the same time, the single fatality rate of elevator accidents increased from 73% to 92% from 2020 to 2023. The weight design of the evaluation method must conform to the actual situation of the elevator emergency scenario.

[0094] (4) The development of technologies such as the Internet of Things has enabled more and more factors to be observed and included in the evaluation scope. Therefore, the evaluation plan can meet development needs and the scope of evaluation factors can be updated according to actual conditions.

[0095] (5) Factor evaluation needs to be based on the same classification dimension. There are many factors classification dimensions, and non-systematic classification methods are not conducive to analyzing the importance and key links of key factors in emergency scenarios.

[0096] The evaluation method of this application needs to fully conform to the actual situation of elevator emergency scenarios. This application sets a theoretically feasible calculation method based on the basic conditions of elevator emergency scenarios. In actual application, the weights should be reasonably adjusted according to specific circumstances, such as differences in demand and influencing factors. This application provides a classification and grading method for emergency scenario elements based on elevator accident reports, which can provide technical means and judgment methods for the classification and management of emergency scenario elements, helping to improve elevator safety management capabilities.

[0097] Compared with the prior art, this application has the following beneficial effects:

[0098] 1. An innovative classification method combining dual-chain fusion theory with elevator emergency scenarios is proposed, which incorporates various factors affecting elevator safety into three system dimensions: disaster-causing factors, carriers, and disaster-prone environments. This is conducive to exploring the key factors that lead to accidents and conducting timely monitoring, early warning, and control.

[0099] 2. Fully consider elevator emergency scenarios, design a weight structure that conforms to actual conditions, combine the superior and inferior solution distance theory, construct a safety deviation index, and evaluate the importance of factors through weighted Euclidean distance.

[0100] 3. This application constructs a factor evaluation method based on dual-chain fusion and security deviation. This classification and grading evaluation method can update the scope of evaluation factors based on actual conditions and the needs of different scales. At the same time, the evaluation results are highly interpretable. This is conducive to comparing the importance of factors within the same classification dimension, systematically grasping the impact of factors and their interrelationships, and is conducive to improving security management and control capabilities and ensuring effective and accurate responses in emergency scenarios.

[0101] Specifically, this application provides a classification and grading evaluation method for key elements of elevator accident scenarios, based on elevator accident reports, to achieve element openness and multi-scale evaluation, and implement a logical framework such as Figure 4 This embodiment classifies and grades 10 emergency scenario elements based on elevator accident reports from 2015 to 2024, and performs a visual comparative analysis of the evaluation results.

[0102] The ten factors to be evaluated include door system failure, false maintenance records, design flaws, illegal operation, traction system failure; aging equipment, wear on the elevator counterweight return rope pulley bearing, lack of maintenance personnel training; extreme environmental impacts, and lack of maintenance funds. Table 1 shows the classification of the ten factors to be evaluated based on the dual-chain fusion classification method.

[0103] Table 1 Classification list of elements to be evaluated

[0104]

[0105] At least 150 elevator accident reports are collected, and the above elements to be evaluated are extracted and standardized. The number of occurrences of each element, death, injury, and economic loss are extracted, and the data statistics results of the elements to be evaluated are obtained, as shown in Table 2.

[0106] Table 2 Accident statistics results of elements to be evaluated from 2015 to 2024

[0107]

[0108]

[0109] According to the above element index results from the accident statistics, the time period and consequence integration is carried out according to step S5, and the processed statistical results are shown in Table 3.

[0110] Table 3 Statistical results of elements to be evaluated after weighted integration

[0111] Serial number elements Weighted frequency Weighted comprehensive consequences 1 Door system failure 11.86 409.6 2 False maintenance records 11.02 1,142.8 3 Design flaws 6.27 226.7 4 Illegal operations 15.59 540.8 5 Traction system failure 3.90 1,189.6 6 Equipment aging 15.93 1,505.4 7 Counterweight counter rope pulley bearing wear 7.63 1,043.2 8 Lack of training for maintenance personnel 15.76 1,045.2 9 Extreme environmental impacts 3.56 62.6 10 Lack of maintenance funds 7.46 581.6

[0112] According to step S6, the safety deviation degree results of each element to be evaluated are calculated, as shown in Table 4.

[0113] Table 4 Calculation results of safety deviation degree of each element

[0114]

[0115]

[0116] According to the classification of elements, different types of elements are evaluated. Here, according to the safety deviation degree distribution interval, it is divided into three levels:

[0117] Important: 0.6≤ safety deviation degree.

[0118] More serious: 0.3≤ safety deviation degree<0.6.

[0119] General: safety deviation degree<0.3.

[0120] Then, the classification and grading evaluation results of 10 elevator emergency scene element categories are as shown in Table 3. Figure 5 Disaster factor class, important level has false maintenance record, more serious level has illegal operation, door system failure, traction system failure, general level has design defect; disaster body class, important level has lack of training and equipment aging, more serious level has bearing wear; disaster environment class, more serious level has lack of maintenance funds, general level has extreme environmental impact.

[0121] This application can analyze the direct causes of accidents, as well as indirect factors such as equipment status and external environment. It can timely grasp the key points of hidden danger inspection from the perspective of disaster-causing factors, strengthen the phased prevention and control priorities from the perspective of disaster-bearing bodies and disaster-prone environments, understand the amplification and transmission of system risks, and improve the emergency prevention and control and safety management capabilities of elevators.

[0122] Based on the same inventive concept, embodiments of the present application also provide an elevator accident scene key element classification and grading evaluation device for implementing the aforementioned elevator accident scene key element classification and grading evaluation method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more elevator accident scene key element classification and grading evaluation device embodiments provided below can be found in the above-mentioned limitations of the elevator accident scene key element classification and grading evaluation method, and will not be repeated here.

[0123] In an exemplary embodiment, Figure 6 As shown, a device for classifying and grading key elements of an elevator accident scene is provided, and the device for classifying and grading key elements of an elevator accident scene comprises:

[0124] The emergency scene accident report acquisition module M1 is used to obtain an emergency scene accident report; the emergency scene accident report includes several years of elevator accident data and several accident events.

[0125] The emergency scenario factor classification module M2 is used to classify the emergency scenario factors of the emergency scenario accident report to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers and disaster-prone environments.

[0126] The evaluation sample database construction module M3 is used to construct an evaluation sample database based on the emergency scenario accident report.

[0127] The element extraction and classification labeling module M4 is used to extract and classify the evaluation sample database based on the classification list of the elements to be evaluated to obtain an element accident statistical data set; the element accident statistical data set includes: element occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss.

[0128] The element-event co-occurrence statistics module M5 is used to perform element-event co-occurrence statistics on the element accident statistical data set to obtain a data set; the element-event co-occurrence statistics include: time integration and consequence sub-item data integration.

[0129] The element safety deviation degree calculation module M6 is used to perform element safety deviation degree calculation on the data set to obtain a safety deviation degree calculation result.

[0130] The classification and grading evaluation module M7 is used to perform classification and grading evaluation based on the safety deviation calculation result to obtain factor classification and grading results.

[0131] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store emergency scenario accident reports. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for classifying and grading key elements of elevator accident scenarios.

[0132] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0133] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method embodiments when executing the computer program.

[0134] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above-mentioned method embodiments when executed by a processor.

[0135] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the above method embodiments are implemented.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0137] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0138] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0139] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A classification and grading evaluation method for key elements of elevator accident scenarios, characterized by: The classification and grading evaluation method for key elements of elevator accident scenarios includes: Obtaining emergency scenario accident reports; the emergency scenario accident reports include several years of elevator accident data and several accident events; Classifying the emergency scenario factors of the emergency scenario accident report to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers, and disaster-prone environments; Building an evaluation sample database based on the emergency scenario accident report; Based on the classification list of factors to be evaluated, extracting and classifying the factors from the evaluation sample database to obtain a factor accident statistical data set; the factor accident statistical data set includes: factor occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss; Performing factor event co-occurrence statistics on the factor accident statistical data set to obtain a data set; the factor event co-occurrence statistics include: time integration and consequence sub-item data integration; Calculating the safety deviation degree of the elements on the data set to obtain a safety deviation degree calculation result; A classification and grading evaluation is performed based on the safety deviation calculation result to obtain a factor classification and grading result.

2. The elevator accident scene key element classification and grading evaluation method according to claim 1 is characterized in that: The expression of the time integration is: Among them, C i is the time-weighted total frequency; α is the first time weight; β is the second time weight; is the number of occurrences in the first subset, which is the data of the past five years; is the number of occurrences in the second subset; the second subset is data from the past 5-10 years; N1 is the number of accident events in the first subset; N2 is the number of accident events in the second subset.

3. The method for classifying and grading key elements of elevator accident scenarios according to claim 1 is characterized in that: The expression for the integration of the consequence sub-item data is: H i =d*s i1 +f*s i2 +e*s i3 ; Among them, H i is the comprehensive result of the consequence sub-item; d is the death weight; f is the injury weight; e is the economic loss weight; s i1 is the death vector; s i2 is the injured subvector; s i3 is the economic loss sub-vector.

4. The method for classifying and grading key elements of elevator accident scenarios according to claim 1 is characterized in that: Calculating the element safety deviation degree on the data set to obtain a safety deviation degree calculation result specifically includes: performing polarity normalization processing on the data set to obtain normalized data; According to the risk assessment theory and the characteristics of elevator accident mortality, the factor frequency weight and consequence vector weight are determined; Based on the factor frequency weight and the consequence vector weight, the factor safety deviation degree is calculated using the superior and inferior solution distance algorithm.

5. The method for classifying and grading key elements of elevator accident scenarios according to claim 4 is characterized in that: The expression of the normalization process is: Among them, P ni is the normalized data set; P imin is the minimum value in the data set; P imax is the maximum value in the data set; H ni is the comprehensive result of the normalized consequence sub-items; C ni is the normalized time-weighted total frequency.

6. The method for classifying and grading key elements of elevator accident scenarios according to claim 5 is characterized in that: The calculation formula of the safety deviation degree of the elements is: Among them, R i is the safety deviation of the factor; w1 is the factor frequency weight; w2 is the consequence vector weight.

7. A device for classifying and grading key elements of elevator accident scenes, characterized in that: The elevator accident scene key element classification and grading evaluation device is used to implement the elevator accident scene key element classification and grading evaluation method according to any one of claims 1 to 6, and the elevator accident scene key element classification and grading evaluation device includes: An emergency scene accident report acquisition module is used to obtain an emergency scene accident report; the emergency scene accident report includes several years of elevator accident data and several accident events; An emergency scenario factor classification module is used to classify the emergency scenario accident report into emergency scenario factors to obtain a classification list of factors to be evaluated; the emergency scenario factors include: disaster-causing factors, carriers, and disaster-prone environments; An evaluation sample database construction module, used to construct an evaluation sample database based on the emergency scenario accident report; An element extraction and classification labeling module is used to extract and classify the elements of the evaluation sample database based on the classification list of elements to be evaluated, so as to obtain an element accident statistical data set; the element accident statistical data set includes: element occurrence frequency and consequence sub-item data; the consequence sub-item data includes: death, injury and economic loss; An element event co-occurrence statistics module is used to perform element event co-occurrence statistics on the element accident statistical data set to obtain a data set; the element event co-occurrence statistics include: time integration and consequence sub-item data integration; An element safety deviation degree calculation module is used to perform element safety deviation degree calculation on the data set to obtain a safety deviation degree calculation result; The classification and grading evaluation module is used to perform classification and grading evaluation based on the safety deviation calculation result to obtain factor classification and grading results.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for classifying and grading key elements of elevator accident scenarios according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for classifying and grading key elements of an elevator accident scenario according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for classifying and grading key elements of an elevator accident scenario according to any one of claims 1 to 6 is implemented.

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