Engineering safety risk management method and system

Abstract

The invention provides an engineering safety risk management method and system. The method comprises the steps that target vectors are determined based on field engineering collection data, and the target vectors at least comprise field engineering feature elements and field risk feature elements; and based on the target vector and the safety risk model, determining early warning information related to the engineering safety risk and sending the early warning information to the user. Through the method, causal relationships between various risk factors and accidents can be comprehensively considered, and automatic risk factor comprehensive analysis is realized, so that the generation process of the risk early warning result is faster and more accurate, and related personnel can conveniently and effectively process the risk factors subsequently.

Description

Technical Field

[0001] This specification relates to the field of engineering safety management, and particularly to an engineering safety risk management method and system. Background Art

[0002] In recent years, with the rapid growth of engineering technology complexity and project scale, engineering safety risks have become increasingly prominent. From design oversights to construction hazards, from natural disasters to human errors, risk factors run through the entire project cycle. Once out of control, it may trigger serious accidents and cause severe consequences. Most of the existing engineering safety assessment methods are limited to checking and evaluating safety risks based on inspection results and conducting risk management based on the evaluation results. However, using this method cannot comprehensively consider various factors affecting construction safety, cannot accurately judge the causal relationship between various factors and construction accidents, and is also easily affected by the subjectivity of inspection and evaluation personnel.

[0003] Therefore, it is desirable to provide an engineering safety risk management method and system to accurately and comprehensively identify and analyze risk factors affecting engineering safety, so as to conduct effective risk control. Summary of the Invention

[0004] To solve the problem of how to accurately analyze safety risks and conduct effective management, this specification provides an engineering safety risk management method and system.

[0005] The summary of the invention includes an engineering safety risk management method. The method includes: determining a target vector based on on-site engineering collected data, where the target vector at least includes on-site engineering feature elements and on-site risk feature elements; determining early warning information related to engineering safety risks based on the target vector and a safety risk model and sending it to the user.

[0006] In some embodiments, based on a safety responsibility list, determining risk characteristic data of functional domains under multiple safety risk control types, where the risk characteristic data corresponds to risk hazards; obtaining historical accident data of multiple historical accidents; determining multiple accident vectors corresponding to the multiple historical accidents based on the historical accident data and the risk characteristic data, where the accident vector at least includes engineering feature elements, risk feature elements, and accident feature elements; constructing an accident vector library based on the multiple accident vectors; dividing the accident vector library into multiple sub-vector libraries based on the engineering feature elements; for each sub-vector library, determining the association relationship between multiple risk feature elements and multiple accident feature elements in the sub-vector library; determining the risk levels corresponding to multiple risk feature elements and multiple accident feature elements in the sub-vector library based on the association relationship; constructing a safety risk model based on multiple risk feature elements, multiple accident feature elements, association relationships, and risk levels in the sub-vector library.

[0007] In some embodiments, determining the risk characteristic data of the functional domains under multiple security risk control types includes determining a risk characteristic tree based on a security responsibility list. The risk characteristic tree includes at least three layers. Among them, the first layer includes multiple security risk control types, the second layer includes the functional domains included in each security risk control type, and the third layer includes the risk characteristic data corresponding to each functional domain. The risk characteristic data includes at least a risk characteristic value and the value-taking conditions corresponding to the risk characteristic value.

[0008] In some embodiments, determining the association relationship between multiple risk characteristic elements and multiple accident characteristic elements in a sub-vector library includes, for each risk characteristic element and each accident characteristic element in the sub-vector library, determining the risk characteristic value corresponding to the risk characteristic element and the accident characteristic value corresponding to the accident characteristic element; classifying the accident vectors in the sub-vector library based on the risk characteristic value to determine a first classification result; classifying the accident vectors in the sub-vector library based on the accident characteristic value to determine a second classification result; and determining the association relationship between the risk characteristic element and the accident characteristic element based on the first classification result and the second classification result.

[0009] In some embodiments, determining the association relationship between multiple risk characteristic elements and multiple accident characteristic elements in a sub-vector library includes screening a preset number of risk characteristic elements from the multiple risk characteristic elements in the sub-vector library to form a risk characteristic element combination, and determining the risk characteristic value combination corresponding to the risk characteristic element combination; for each accident characteristic element in the sub-vector library, determining the accident characteristic value corresponding to the accident characteristic element; classifying the accident vectors in the sub-vector library based on the risk characteristic value combination to determine a third classification result; classifying the accident vectors in the sub-vector library based on the accident characteristic value to determine a fourth classification result; and determining the association relationship between the risk characteristic element combination and the accident characteristic element based on the third classification result and the fourth classification result.

[0010] In some embodiments, constructing a security risk model includes, for each risk characteristic element in the accident vector corresponding to each historical accident, determining the contribution parameter of each risk characteristic element to the historical accident; and determining the key risk characteristic elements of the historical accident based on each risk characteristic element and the corresponding contribution parameter.

[0011] In some embodiments, determining the contribution parameter of each risk characteristic element to a historical accident includes, for each risk characteristic element, determining the first association parameter between each risk characteristic element and the historical accident; for each risk characteristic element combination in the accident vector corresponding to the historical accident, determining the second association parameter between the risk characteristic element combination and the corresponding historical accident; and determining the contribution parameter of each risk characteristic element to the historical accident based on the first association parameter and the second association parameter.

[0012] In some embodiments, determining the key risk feature elements of a historical accident includes: determining whether the contribution parameter of each risk feature element to the historical accident meets a first preset condition; in response to the contribution parameter meeting the first preset condition, determining the risk feature element as a key risk feature element.

[0013] In some embodiments, based on a target vector and a safety risk model, determining early warning information related to engineering safety risks and sending it to a user includes: determining a safety risk model based on the on-site engineering feature elements in the target vector; querying the safety risk model based on the on-site risk feature elements in the target vector to determine target accident feature elements whose association relationship with the on-site risk feature elements meets a second preset condition and the risk levels corresponding to the target accident feature elements; determining early warning information based on the target accident feature elements, risk levels, and early warning rules and sending it to the user.

[0014] In some embodiments, when an accident occurs at an engineering site, the on-site of the target vector further includes on-site accident feature elements. Based on the target vector and a safety risk model, determining early warning information related to engineering safety risks and sending it to the user includes: determining a safety risk model based on the on-site engineering feature elements in the target vector; querying the safety risk model based on the target vector to determine the key risk feature elements of historical accidents; determining early warning information based on the key risk feature elements and early warning rules and sending it to the user.

[0015] The invention content includes an engineering safety risk management system. The system includes a processor for executing the method described in any one of the embodiments of this specification.

[0016] The beneficial effects brought by the above-mentioned invention content include but are not limited to: (1) It can comprehensively consider the causal relationships between various risk factors and accidents, determine a safety risk model with as high accuracy as possible, so that the generation process of risk warning results is faster and more accurate, facilitating subsequent effective processing of risk factors by relevant personnel and reducing the occurrence probability of safety accidents; (2) It can efficiently, comprehensively and accurately identify various risk characteristics based on the actual situation of the construction site and conduct risk level assessment. While quickly identifying various risks and their causes on-site, it takes into account the comprehensive consideration of multiple influencing factors, making the risk identification results more accurate and reliable; (3) It can determine comprehensive safety risk control types, functional domains and risk characteristic data based on the safety performance obligations of each unit and each position, and intuitively display them in a tree structure, facilitating subsequent accident attribution based on the risk characteristic data and determining accurate risk elements; (4) It can comprehensively consider the possibilities of all combinations of risk characteristic elements, determine as accurate weight distribution as possible according to various parameters such as the scale of various combinations, the contribution degree of elements under each combination, and the importance of elements themselves, determine the contribution parameters of risk characteristic elements, and then screen out key risk characteristic elements with larger contribution parameters, facilitating subsequent determination of appropriate warning information, timely and effective processing, and improving the overall risk control ability. Compared with simply judging by association relationships, the key characteristic elements obtained by this method are more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] This specification will further illustrate by way of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is a schematic diagram of an application scenario of an exemplary engineering safety risk management system according to some embodiments of this specification; Figure 2 is a block diagram of an exemplary engineering safety risk management system according to some embodiments of this specification; Figure 3A is a flowchart of an exemplary engineering safety risk management method according to some embodiments of this specification; Figure 3B is a flowchart of an exemplary construction of a safety risk model according to some embodiments of this specification; Figure 4 is a schematic diagram of an exemplary risk characteristic tree according to some embodiments of this specification; Figure 5 is a schematic diagram of an exemplary safety risk model according to some embodiments of this specification; Figure 6 is a flowchart of an exemplary determination of association relationships according to some embodiments of this specification; Figure 7A is a schematic diagram of an exemplary accident vector shown in some embodiments of this specification; Figure 7B is a schematic diagram of an exemplary classified accident vector shown in some embodiments of this specification; Figure 8 is a flowchart of an exemplary determination of an association relationship shown in some embodiments of this specification; Figure 9 is a flowchart of an exemplary determination of key risk characteristic elements shown in some embodiments of this specification. Detailed implementation manners

[0018] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0019] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0020] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0021] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0022] Figure 1 is a schematic diagram of an application scenario of an exemplary engineering safety risk management system shown in some embodiments of this specification. As Figure 1As shown, the engineering safety risk management system 100 may include a data acquisition device 110, a server 120, a user terminal 130, a storage device 140, and a network 150.

[0023] The data acquisition device 110 may collect information such as engineering site data within the engineering site. The engineering site refers to a site used for carrying out relevant engineering activities such as building construction. Engineering activities may include one or more of new construction, renovation, maintenance, and demolition of buildings, railways, etc. In some embodiments, the data acquisition device 110 may include a measuring instrument, a sensor, etc. In some embodiments, the engineering site data obtained by the data acquisition device 110 may be transmitted to the server 120, the user terminal 130, the storage device 140, etc. based on the network 150 to achieve data exchange and communication. In some embodiments, the user may be within the engineering site and obtain the engineering site data. The above description of the engineering site is for illustrative purposes only and is not intended to limit the scope of this description.

[0024] The server 120 is used to manage resources and process data and / or information of at least one component or external data source in the engineering safety risk management system 100. In some embodiments, the server may be a single server or a server group. The server group may be centralized or distributed, and may be dedicated or provided with services by other devices or systems at the same time. In some embodiments, the server may be regional or remote. In some embodiments, the server may be implemented on a cloud platform or provided in a virtual manner.

[0025] In some embodiments, the server 120 may include a processor. The processor may be used to process information and / or data related to the engineering safety risk management system 100. In some embodiments, the processor may process data, information, and / or processing results obtained from other device or system components, and execute program instructions based on these data, information, and / or processing results to perform one or more functions described in this specification. For example, the processor may determine a target vector based on the on-site engineering acquisition data. Another example is that the processor may determine early warning information related to engineering safety risks based on the target vector and the safety risk model and send it to the user through the user terminal 130. For more details, please refer to Figures 3A - 3B and its related description.

[0026] In some embodiments, the processor may include one or more sub-processing devices (for example, a single-core processing device or a multi-core multi-chip processing device). Exemplarily, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a reduced instruction set computer (RISC), a microprocessor, etc. or any combination thereof.

[0027] The user terminal 130 refers to one or more terminal devices or software used by the user. The user refers to the user or manager of the engineering safety risk management method and its system. For example, contract personnel, safety personnel, management personnel, construction personnel, etc.

[0028] In some embodiments, the user can interact with other components (such as the server 120, etc.) in the engineering safety risk management system 100 via the user terminal 130 through the network 150. For example, the server 120 can send early warning information related to engineering safety risks to the user terminal 130 through the network 150. For another example, the user can interact with the server 120 based on the user terminal 130 to obtain the risk levels corresponding to multiple risk characteristic elements and multiple accident characteristic elements.

[0029] In some embodiments, the user terminal 130 may include a mobile device 130-1, a computer 130-2, a laptop computer 130-3, etc. or any combination thereof. In some embodiments, the user terminal 130 may further include virtual reality devices, such as VR devices, AR devices, etc. In some embodiments, the user terminal 130 may further include display screens, speakers, etc. disposed in the engineering site to display early warning information, etc. to the users in the engineering site.

[0030] The storage device 140 is used to store data, instructions, and / or any other information. The storage device 140 may include one or more storage components, and each storage component may be an independent device or a part of other devices.

[0031] In some embodiments, the storage device 140 may include a random access memory (RAM), a read-only memory (ROM), a removable memory, etc. or any combination thereof. In some embodiments, the storage device 140 may be connected to the network 150 to enable communication with one or more components in the engineering safety risk management system 100. In some embodiments, the storage device 140 may be a part of the server 120.

[0032] The network 150 may include any suitable network capable of facilitating the exchange of information and / or data of the engineering safety risk management system 100. In some embodiments, one or more components of the engineering safety risk management system 100 (such as the data acquisition device 110, the server 120, the user terminal 130, the storage device 140, etc.) may exchange information and / or data with one or more components of the engineering safety risk management system 100 through the network 150. In some embodiments, the network 150 may include any one or more of any form of wired network or wireless network. In some embodiments, the network 150 may include one or more network access points.

[0033] It should be noted that the engineering safety risk management system 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various modifications or changes can be made according to the description of this specification. For example, the engineering safety risk management system 100 can be implemented on other devices to achieve similar or different functions. However, the changes and modifications will not deviate from the scope of this specification.

[0034] Figure 2 is a block diagram of an exemplary engineering safety risk management system shown in some embodiments of this specification. In some embodiments, the engineering safety risk management system 200 can include a target vector determination module 210 and an early warning module 220. In some embodiments, each module in the engineering safety risk management system 200 can be implemented by a server 120 (e.g., a processor in the server 120).

[0035] The target vector determination module 210 can be used to determine a target vector based on on-site engineering acquisition data. For more information on how to determine the target vector, reference can be made to the relevant description of step 310.

[0036] The early warning module 220 can determine early warning information related to engineering safety risks based on the target vector and the safety risk model and send it to the user. For more information on how to determine the early warning information, reference can be made to the relevant description of step 320.

[0037] In some embodiments, the engineering safety risk management system 200 can further include a safety risk model construction module 230. The safety risk model construction module 230 can construct a safety risk model. For example, the safety risk model construction module 230 can determine risk characteristic data of functional domains under multiple safety risk control types based on a safety responsibility list, and the risk characteristic data corresponds to risk hazards. For another example, the safety risk model construction module 230 can obtain historical accident data of multiple historical accidents and determine multiple accident vectors corresponding to the multiple historical accidents based on the historical accident data and the risk characteristic data. The accident vector at least includes engineering characteristic elements, risk characteristic elements, and accident characteristic elements. For yet another example, the safety risk model construction module 230 can construct an accident vector library based on the multiple accident vectors and divide the accident vector library into multiple sub-vector libraries based on the engineering characteristic elements. For each sub-vector library, the safety risk model construction module 230 can determine the association relationship between the multiple risk characteristic elements and the multiple accident characteristic elements in the sub-vector library; based on the association relationship, determine the risk levels corresponding to the multiple risk characteristic elements and the multiple accident characteristic elements in the sub-vector library; and construct a safety risk model based on the multiple risk characteristic elements, the multiple accident characteristic elements, the association relationship, and the risk levels in the sub-vector library. For more information on how to construct the safety risk model, reference can be made to the relevant description of steps 331 - 335.

[0038] In some embodiments, two or more modules in the engineering safety risk management system 200 may be combined into one module, and this module may implement the functions of the two or more modules. For example, the early warning module 220 and the safety risk model construction module 230 may be combined into one module, and this module may be used to construct a safety risk model and determine early warning information. In some embodiments, one or more modules in the engineering safety risk management system 200 may be deleted, or one or more modules may be added to the engineering safety risk management system 200. For example, the engineering safety risk management system 200 may not include the safety risk model construction module 230, the safety risk model may be provided by a vendor and stored in a storage device, and the early warning module 220 may obtain the safety risk model from the storage device.

[0039] Figure 3A is a flowchart of an exemplary engineering safety risk management method shown in some embodiments of this specification. As Figure 3A shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by an engineering safety risk management system (for example, the server 120 of the engineering safety risk management system 100, each module of the engineering safety risk management system 200).

[0040] Step 310, based on the on-site engineering acquisition data, determine the target vector. In some embodiments, step 310 may be executed by the target vector determination module 210.

[0041] The on-site engineering acquisition data refers to the engineering-related data collected in the on-site engineering site. For example, on-site engineering feature data, on-site risk feature data. The on-site engineering feature data refers to the engineering feature data obtained at the engineering site, and the engineering feature data refers to the engineering-related data information, such as the engineering category (such as traffic engineering, building construction engineering, water conservancy engineering, etc.), the engineering scale (such as 0 million cubic meters - 1 million cubic meters of engineering, 1 million cubic meters - 2 million cubic meters of engineering, etc.). The on-site risk feature data refers to the risk feature data obtained at the engineering site. For more content about the risk feature data, reference may be made to the following related descriptions, such as step 331.

[0042] In some embodiments, the processor may obtain on-site engineering acquisition data in various ways. For example, the processor may determine risk characteristics and their value-taking conditions through a risk characteristic tree, and then directly obtain real-time acquisition data related to the corresponding risk characteristics at the engineering site through a data acquisition device (such as data acquisition device 110). Based on this real-time acquisition data and the corresponding value-taking conditions, it automatically judges and assigns values to the risk characteristics to determine the risk characteristic values, thereby determining the on-site risk characteristic data. For another example, the processor may determine on-site engineering characteristic data through engineering data recorded in a storage device inside or outside the engineering safety risk management system 200. More content about risk characteristic data, value-taking conditions, and risk characteristic values can be found in the related descriptions below.

[0043] The target vector refers to the characteristic vector formed by on-site engineering acquisition data. In some embodiments, the target vector may at least include on-site engineering characteristic elements and on-site risk characteristic elements. The on-site engineering characteristic elements refer to the elements corresponding to on-site engineering characteristic data, and the on-site risk characteristic elements refer to the elements corresponding to on-site risk characteristic data. For example, the target vector may be {[Engineering category A, Engineering scale B], [(Risk characteristic 1: Risk characteristic value 1), (Risk characteristic 2: Risk characteristic value 2), …, (Risk characteristic n: Risk characteristic value n)]} and so on. More content about risk characteristic data and risk characteristic values can be found in the related descriptions below.

[0044] In some embodiments, the processor may determine the target vector based on the accident vectors in the accident vector library and the on-site engineering acquisition data. For example, the processor may determine a sub-vector library in the matching accident vector library based on the on-site engineering characteristic data in the on-site engineering acquisition data; based on the accident vectors in the sub-vector library, obtain the standard format of the accident vectors, and fill the on-site risk characteristic data in the on-site acquisition data into the risk characteristic element part in the standard format of the accident vectors to determine the target vector. More content about the accident vector library, sub-vector library, and accident vectors can be found in the related descriptions below.

[0045] Step 320, based on the target vector and the safety risk model, determine early warning information related to engineering safety risk and send it to the user. In some embodiments, step 320 may be executed by the early warning module 220.

[0046] The safety risk model refers to a related model for determining engineering safety risks. In some embodiments, the safety risk model may include relevant data on accidents and risk characteristics. Figure 5 is a schematic diagram of an exemplary safety risk model shown in some embodiments of this specification, such as Figure 5As shown, the safety risk model may include accident types (such as accident type 1 - accident type 3, etc.), accident severity levels (i.e., accident characteristic elements and their corresponding accident characteristic values. It can be understood that the accident severity level corresponds to the accident type. For example, the accident severity levels corresponding to accident type 1 are 1.1 - 1.2, the accident severity levels corresponding to accident type 2 are 2.1 - 2.3, the accident severity levels corresponding to accident type 3 are 3.1 - 3.3, etc.), risk levels, different preset numbers of risk characteristic elements and their corresponding risk characteristic values, etc. Regarding the construction of the safety risk model, reference can be made to Figure 3B and its related descriptions.

[0047] The warning information refers to the warning information of engineering safety risks. The warning information can be represented in different forms. For example, the warning information can be text, sound, light, etc. An exemplary warning message can be a text message such as "There is a risk of failure in the foundation anchorage of the tower crane, and it may overturn. Please ask the relevant personnel to evacuate the scene", or the siren indicating the occurrence of a fire, etc.

[0048] In some embodiments, the processor can determine the warning information related to engineering safety risks based on the target vector and the safety risk model, and send it to the user through a third preset rule. The third preset rule can be preset based on experience or requirements. An exemplary third preset rule can be to query the safety risk model based on the target vector. If the risk level of the on-site risk characteristic element and a certain accident characteristic element in the target vector exceeds the risk level threshold, the relevant information will be processed to generate warning information and then sent to the user. The risk level threshold can be preset based on experience or requirements. The specific processing and generation method can be a machine learning model, a preset algorithm, etc. The specific sending method can be to send it to the user terminal through the network, etc.

[0049] In some embodiments, the processor can determine the safety risk model based on the on-site engineering characteristic elements in the target vector. It can be understood that the processor can set different safety risk models corresponding to different sub-vector libraries, that is, set the safety risk models corresponding to the corresponding engineering categories and scales based on the accident vectors of different engineering categories and scales. In this way, the complex safety risk data can be classified based on engineering categories and scales, enhancing the pertinence when querying risk information and effectively improving the query efficiency. Exemplarily, the processor can determine the safety risk model corresponding to the corresponding engineering category and scale based on the engineering category and scale in the on-site engineering characteristic elements of the target vector.

[0050] After that, the processor can query the safety risk model based on the on-site risk characteristic elements in the target vector, and determine the target accident characteristic elements whose association relationship with the on-site risk characteristic elements meets the second preset condition and the risk level corresponding to the target accident characteristic elements. The target accident characteristic elements refer to the accident characteristic elements with safety risks that need to be concerned about. The second preset condition refers to the condition for screening the target accident characteristic elements based on the association relationship. The second preset condition can be set based on experience or requirements. Exemplarily, the second preset condition can be to determine the accident characteristic elements in the safety risk model whose accident characteristic values exceed the accident characteristic value threshold and whose association relationship with the on-site risk characteristic elements exceeds the association relationship threshold as the target accident characteristic elements. The association relationship threshold can be set based on experience or requirements.

[0051] Finally, the processor can determine the warning information based on the target accident characteristic elements, the risk level, and the warning rule, and send it to the user. The warning rule refers to the specific rule for generating the warning information. For example, the warning rule can be that when the risk level exceeds the first risk level threshold (set based on experience or requirements), the warning information is determined to be the target accident characteristic elements marked in red and sent to the user. Another example is that the warning rule can be that when the risk level exceeds the second risk level threshold (set based on experience or requirements) but does not exceed the risk level threshold, the warning information is determined to be the target accident characteristic elements marked in yellow and sent to the user.

[0052] In some embodiments of this specification, by querying the safety risk model based on the on-site risk characteristic elements in the target vector, determining the target accident characteristic elements whose association relationship with the on-site risk characteristic elements meets the second preset condition and the risk level corresponding to the target accident characteristic elements; and determining the warning information based on the target accident characteristic elements, the risk level, and the warning rule and sending it to the user, it is possible to accurately and efficiently identify the accident characteristics with engineering safety risks that need to be focused on in the implementation project data, generate appropriate warning information and send it to the user, and reduce potential safety hazards.

[0053] In some embodiments, the processor can determine the safety risk model based on the on-site engineering characteristic elements in the target vector. For more content about determining the safety risk model, reference can be made to the foregoing related descriptions.

[0054] After that, the processor may query the security risk model based on the target vector to determine the key risk characteristic elements of the accident. The key risk characteristic elements refer to the risk characteristic elements that are relatively critical to the impact of the accident. For example, the risk characteristic elements with the strongest association relationship, etc. In some embodiments, the processor may determine the key risk characteristic elements of the accident based on a fourth preset rule. The fourth preset rule may be preset based on experience or requirements. An exemplary fourth preset rule may be to query the security risk model and determine, as the key risk characteristic elements, the risk characteristic elements in the on-site risk characteristic elements of the target vector that have the strongest association relationship with the accident characteristic elements of the accident. For more information about the key risk characteristic elements, reference may be made to Figure 9 and its related descriptions.

[0055] Finally, the processor may determine the warning information based on the key risk characteristic elements and the warning rule and send it to the user. An exemplary warning rule may be to process the relevant information of the key risk characteristic elements to generate warning information and then send it to the user. The specific processing and generation method may be a machine learning model, a preset algorithm, etc. For more information about the warning rule and the warning information, reference may be made to the foregoing related descriptions.

[0056] In some embodiments of this specification, by determining the security risk model based on the on-site engineering characteristic elements in the target vector, querying the security risk model based on the target vector to determine the key risk characteristic elements of the accident, and determining the warning information based on the key risk characteristic elements and the warning rule and sending it to the user, the key risk characteristics that have the greatest impact on the accident can be preferentially identified, which is convenient for subsequent determination of appropriate warning information, timely and effective processing, and improvement of the overall ability of risk control.

[0057] In some embodiments, the processor may monitor the target vector, the security risk model, and their data changes in real time, and dynamically update the warning information related to engineering security risks and send it to the user.

[0058] In some embodiments of this specification, by determining the target vector based on the on-site engineering collected data, determining the warning information related to engineering security risks based on the target vector and the risk level security risk model and sending it to the user, various risk characteristics can be efficiently, comprehensively, and accurately identified based on the actual situation of the construction site and the risk level can be evaluated. In the case of quickly identifying various on-site risks and their causes, the comprehensive consideration of multiple influencing factors is taken into account, making the risk identification result more accurate and reliable.

[0059] Figure 3B is a flowchart of an exemplary construction of a security risk model shown in some embodiments of this specification. As Figure 3BAs shown, process 330 includes the following steps. In some embodiments, process 330 may be executed by an engineering safety risk management system (eg, server 120 of engineering safety risk management system 100, various modules of engineering safety risk management system 200).

[0060] Step 331, based on the security responsibility list, determine the risk characteristic data of the functional domains under multiple security risk control types.

[0061] The safety responsibility list refers to the list of responsibilities related to engineering safety for multiple positions (such as responsible personnel, supervisors, safety personnel, construction personnel, designers, etc.) under multiple engineering units (such as construction units, supervision units, construction units, design units, survey units, etc.). The safety responsibility list can include various industry specifications, corporate systems, job responsibilities, output time limits, etc.

[0062] The security risk control type refers to the type of method for performing security risk management and control. For example, the security risk control type may include self-defense type, external control type, adjustment type, repair type, and improvement type. In some embodiments, the processor may directly obtain the security risk control type from the security responsibility list.

[0063] Functional domain refers to the specific task module divided based on the security risk control type. For example, when the security risk control type is self-defense type, its lower functional domain may include awareness elements, behavior elements, and instruction elements. For another example, when the security risk control type is improvement type, its lower functional domain may include system optimization and technological innovation.

[0064] Risk characteristic data refers to information related to risk characteristics (i.e., characteristics of safety risks). For example, information on uploaded safety education materials, information on wearing safety protective gear, information on regular equipment inspections, etc. In some embodiments, risk characteristic data may correspond to potential risks. For example, information on wearing safety protective gear may correspond to potential risks such as being hit by an object, being cut, or being burned (i.e., potential risks that may be caused by not wearing safety protective gear).

[0065] In some embodiments, the user can mark the security risk control type, functional domain and corresponding risk characteristic data in the security responsibility list based on needs, experience, etc., and the processor can directly obtain the security risk control type, functional domain and risk characteristic data based on the marked content.

[0066] The risk signature tree refers to a tree-like hierarchical structure used to analyze risk signatures. Figure 4 is a schematic diagram of an exemplary risk feature tree according to some embodiments of this specification. Figure 4As shown, the risk feature tree may include at least three layers. Among them, the first layer includes multiple safety risk control types (such as self-prevention type, external control type, adjustment type, repair type, and improvement type), the second layer includes the functional domains included in each safety risk control type (such as awareness elements, behavior elements, instruction elements, organizational measures, management measures, technical measures, economic measures, hidden danger investigation, supervision and inspection, reporting and complaints, hierarchical control, special operations, supervision and rectification, system optimization, technological innovation, etc.), and the third layer includes the risk feature data corresponding to each functional domain (such as risk feature 1 - risk feature n, etc.).

[0067] In some embodiments, the risk feature data may at least include a risk feature value and the corresponding value-taking condition of the risk feature value. The risk feature value refers to the quantified value of the risk feature. For example, when the risk feature is safety education materials, the risk feature value may be 0, 0.5, or 1. Another example is that when the risk feature is regular equipment inspection, the risk feature value may be 0 or 1. The value-taking condition refers to the assignment rule of the risk feature value. For example, when the risk feature is safety education materials, the value-taking condition may be that if the construction workers do not upload safety education materials before the project construction, the risk feature value is 0; if the construction workers supplement and upload safety education materials during the project construction, the risk feature value is 0.5; if the construction workers have uploaded safety education materials before the project construction, the risk feature value is 1. Another example is that when the risk feature is regular equipment inspection, the value-taking condition may be that if the safety personnel conduct regular equipment inspections, the risk feature value is 1; if the safety personnel do not conduct regular equipment inspections, the risk feature value is 0. In some embodiments, the value-taking condition may be preset based on experience or requirements. The processor can automatically collect the relevant data of the risk feature and assign a value to the risk feature based on the value-taking condition judgment to determine the risk feature value corresponding to the risk feature.

[0068] In some embodiments, the processor may determine the risk feature tree based on the safety responsibility list. For example, the user can mark the safety risk control type, functional domain, and corresponding risk feature data in the safety responsibility list based on requirements, experience, etc., and the processor can determine the risk feature tree based on the marked content.

[0069] In some embodiments of this specification, by determining a risk feature tree with a three-layer structure based on the safety responsibility list, it is possible to summarize comprehensive safety risk control types, functional domains, and risk feature data based on the safety performance obligations of each unit and each position and intuitively display them in a tree structure, which is convenient for subsequent accident attribution based on the risk feature data and determination of accurate risk elements.

[0070] Step 332, obtain the historical accident data of multiple historical accidents.

[0071] A historical accident refers to an engineering safety accident that occurred in a historical event. For example, scaffold collapse, construction worker falling from a height, building collapse, etc. Historical accident data refers to the data information related to historical accidents. For example, accident time, accident location, accident cause, accident casualties, etc.

[0072] In some embodiments, the processor may obtain historical accident data based on multiple channels such as government platforms, third-party websites, enterprise internal materials, web crawling, etc.

[0073] Step 333, based on the historical accident data and risk characteristic data, determine multiple accident vectors corresponding to multiple historical accidents.

[0074] An accident vector refers to a characteristic vector related to a historical accident. In some embodiments, the accident vector may at least include an engineering characteristic element, a risk characteristic element, and an accident characteristic element. The engineering characteristic element refers to the element corresponding to the engineering characteristic data, and the risk characteristic element refers to the element corresponding to the risk characteristic data. For more content about the engineering characteristic data and risk characteristic data, reference can be made to the foregoing related descriptions. The accident characteristic element refers to the element corresponding to the accident characteristic data, and the accident characteristic data refers to the data information related to the accident. For example, accident type (such as collapse type, explosion type, object strike type, electric shock type, etc.), accident severity (which can be represented by the national legal grade and corresponding numerical values. For example, general accident, relatively large accident, major accident, especially major accident, etc.). In some embodiments, the accident characteristic data may at least include an accident characteristic value and the value-taking condition corresponding to the accident characteristic value. The accident characteristic value refers to the quantified value of the accident. For example, when the accident is a foundation pit collapse, the accident characteristic value can be 0 or 1. Another example is that when the accident is an explosion, the risk characteristic value can be 1, 2, 3, or 4. The value-taking condition refers to the assignment rule of the accident characteristic value. For example, when the accident is a foundation pit collapse, the value-taking condition can be that if there is no foundation pit collapse accident, the accident characteristic value is 0; if there is a foundation pit collapse accident, the accident characteristic value is 1. Another example is that when the accident is an explosion, the value-taking condition can be that if the explosion accident is a general accident, the accident characteristic value is 1; if the explosion accident is a relatively large accident, the accident characteristic value is 2; if the explosion accident is a major accident, the accident characteristic value is 3; if the explosion accident is an especially major accident, the accident characteristic value is 4. In some embodiments, the value-taking condition can be preset based on experience or requirements. The processor can automatically collect the relevant data of the accident and assign a value to the risk characteristic based on the value-taking condition to determine the accident characteristic value corresponding to the accident. An exemplary accident vector can be {[engineering category C, engineering scale D], [(risk characteristic 1: risk characteristic value 1), (risk characteristic 2: risk characteristic value 2), …, (risk characteristic n: risk characteristic value n)], [(accident 1: accident characteristic value 1), (accident 2: accident characteristic value 2), …, (accident m: accident characteristic value m)]}, etc.

[0075] In some embodiments, the processor may further determine engineering feature elements, risk feature elements, and accident feature elements related to the accident based on the obtained historical accident data and risk feature data, and construct multiple accident vectors corresponding to multiple historical accidents.

[0076] In some embodiments, the processor may unify the number of elements in the accident vectors to ensure that all accident vectors have a unified standard format. For example, the processor may take the union of the risk feature elements and accident feature elements in the standard format of the accident vectors, and fill the missing parts with preset characters. For example, when the missing parts are filled with 0, if accident vector 1 is {[engineering category, engineering scale], [(risk feature 1: risk feature value 1), (risk feature 2: risk feature value 2)], [(accident 1: accident feature value 1)]}, and accident vector 2 is {[engineering category, engineering scale], [(risk feature 4: risk feature value 4), (risk feature 8: risk feature value 8)], [(accident 2: accident feature value 2), (accident m: accident feature value m)]}, then the processor may determine the standard format of the accident vectors as {[engineering category, engineering scale], [(risk feature 1: risk feature value 1), (risk feature 2: risk feature value 2), (risk feature 4: risk feature value 4), (risk feature 8: risk feature value 8)], [(accident 1: accident feature value 1), (accident 2: accident feature value 2), (accident m: accident feature value m)]}. At this time, the accident vector 1 converted to the standard format is {[engineering category, engineering scale], [(risk feature 1: risk feature value 1), (risk feature 2: risk feature value 2), (risk feature 4: 0), (risk feature 8: 0)], [(accident 1: accident feature value 1), (accident 2: 0), (accident m: 0)]}, and the accident vector 2 converted to the standard format is {[engineering category, engineering scale], [(risk feature 1: 0), (risk feature 2: 0), (risk feature 4: risk feature value 4), (risk feature 8: risk feature value 8)], [(accident 1: 0), (accident 2: accident feature value 2), (accident m: accident feature value m)]}.

[0077] Step 334: Construct an accident vector library based on multiple accident vectors.

[0078] The accident vector library refers to a data set composed of multiple accident vectors. In some embodiments, the processor may integrate multiple accident vectors to construct an accident vector library.

[0079] Step 335: Divide the accident vector library into multiple sub-vector libraries based on the engineering feature elements.

[0080] A sub-vector library refers to a data set obtained by further subdividing an accident vector library. In some embodiments, a processor may divide an accident vector library into multiple sub-vector libraries based on engineering feature elements. For example, the processor may divide accident vectors with the same engineering category in the accident vector library into one sub-vector library. As another example, the processor may divide accident vectors with the same engineering scale in the accident vector library into one sub-vector library.

[0081] In some embodiments, for each sub-vector library, the processor may perform Step 3351 - Step 3353 to build a safety risk model.

[0082] Step 3351: Determine the association relationship between multiple risk feature elements and multiple accident feature elements in the sub-vector library.

[0083] An association relationship refers to a specific relationship indicating the degree of association between things. For example, strong association, weak association, no association, etc. The association relationship can be represented by numbers. For example, 100%, 60%, etc. The larger the number, the stronger the degree of association.

[0084] In some embodiments, the processor may determine the association relationship between multiple risk feature elements and multiple accident feature elements in the sub-vector library based on a first preset rule. The first preset rule can be preset based on experience or requirements. An exemplary first preset rule may be that for each accident feature element with a non-zero accident feature value and each risk feature element with a non-zero risk feature value in each accident vector in the sub-vector library, it is determined that there is a strong association relationship between them.

[0085] In some embodiments, for each risk feature element and each accident feature element in the sub-vector library, the processor may determine the risk feature value and the accident feature value, so as to determine the first classification result and the second classification result; based on the first classification result and the second classification result, determine the association relationship between the risk feature element and the accident feature element. For more details, see Figure 6 and its related descriptions.

[0086] In some embodiments, the processor may screen a preset number of risk feature elements from the multiple risk feature elements in the sub-vector library to form a combination of risk feature elements, determine the corresponding combination of risk feature values and accident feature values; thus determine the third classification result and the fourth classification result; based on the third classification result and the fourth classification result, determine the association relationship between the combination of risk feature elements and the accident feature element. For more details, see Figure 8 and its related descriptions.

[0087] Step 3352: Based on the association relationship, determine the risk levels corresponding to multiple risk feature elements and multiple accident feature elements in the sub-vector library.

[0088] The risk level refers to the priority level divided after comprehensively evaluating the occurrence probability and consequence severity of potential risk events in the project. For example, high risk, medium risk, low risk, etc. The risk level can be represented by numbers. For example, 80%, 40%, etc. The larger the number, the higher the risk level.

[0089] In some embodiments, the processor may determine the risk levels corresponding to multiple risk feature elements and multiple accident feature elements in the sub-vector library based on a second preset rule. The second preset rule can be preset based on experience or requirements. An exemplary second preset rule can be to determine the hazard level of an accident based on the accident feature value; determine the risk levels corresponding to the risk feature elements and accident feature elements based on the hazard level and the association relationship. The specific grading rule can be preset based on experience or requirements. For example, the processor may determine that an accident with an accident feature value of 0 has no hazard, an accident with an accident feature value of 1 has a low hazard, an accident with an accident feature value of 2 has a medium hazard, and accidents with accident feature values of 3 and 4 have a high hazard; determine that the risk levels corresponding to the risk feature elements and accident feature elements with a high hazard level and a strong association relationship are high risks, determine that the risk levels corresponding to the risk feature elements and accident feature elements with a medium hazard level and a strong association relationship are medium risks, and determine that the risk levels corresponding to the risk feature elements and accident feature elements with other hazard levels and other association relationships are low risks, etc. The hazard level can be represented by numbers. For example, 100%, 60%, etc. The larger the number, the higher the hazard level. When the hazard level and the association relationship are represented by numbers, the risk level can be determined based on the geometric calculation result between the aforementioned numbers. For example, it can be set that the risk level = a Hazard level + b Association relationship, where a and b are constants, etc.

[0090] Step 3353: Construct a safety risk model based on multiple risk feature elements, multiple accident feature elements, association relationships, and risk levels in the sub-vector library.

[0091] In some embodiments, the processor may respectively obtain each risk feature element, each accident feature element, all their corresponding association relationships, and risk levels in the foregoing manner and integrate them to construct a safety risk model.

[0092] By determining the risk characteristic data of the functional domains under multiple safety risk control types based on the safety responsibility list, the risk characteristic data corresponding to the potential safety hazards; obtaining the historical accident data of multiple historical accidents; determining multiple accident vectors corresponding to the multiple historical accidents based on the historical accident data and the risk characteristic data, the accident vectors at least including engineering characteristic elements, risk characteristic elements, and accident characteristic elements; constructing an accident vector library based on the multiple accident vectors; dividing the accident vector library into multiple sub-vector libraries based on the engineering characteristic elements; for each sub-vector library, determining the association relationship between the multiple risk characteristic elements and the multiple accident characteristic elements in the sub-vector library; determining the risk levels corresponding to the multiple risk characteristic elements and the multiple accident characteristic elements in the sub-vector library based on the association relationship; constructing a safety risk model based on the multiple risk characteristic elements, the multiple accident characteristic elements, the association relationship, and the risk levels in the sub-vector library, which can comprehensively consider the causal relationship between various risk factors and accidents, determine a safety risk model with as high accuracy as possible, so that the generation process of the risk warning result is faster and more accurate, facilitating the subsequent effective processing of risk factors by relevant personnel, reducing the occurrence probability of safety accidents, realizing the comprehensive analysis of risk factors automatically, and reducing the subjective errors caused by manual determination, etc.

[0093] Figure 6 It is a flowchart of an exemplary determination of an association relationship shown according to some embodiments of the present specification. As Figure 6 shown, process 600 includes the following steps. In some embodiments, for each risk characteristic element and each accident characteristic element in the sub-vector library, the processor may execute process 600.

[0094] Step 610, determining the risk characteristic value corresponding to the risk characteristic element and the accident characteristic value corresponding to the accident characteristic element. In some embodiments, step 610 may be executed by the safety risk model construction module 230.

[0095] In some embodiments, the processor may determine the value-taking conditions corresponding to the risk characteristic element based on the risk characteristic tree; determine the risk characteristic value corresponding to the risk characteristic element in the sub-vector library based on the historical accident data and the value-taking conditions judgment. For example, the processor may determine, based on the risk characteristic tree, that the value-taking condition corresponding to the risk characteristic element "special plan" in the sub-vector library with the engineering type of "foundation pit excavation" is that if there is a special plan before construction, the risk characteristic value is 1, if the special plan is supplemented during construction, the risk characteristic value is 0.5, and if there is no special plan after construction, the risk characteristic value is 0. Based on the historical accident data that the foundation pit collapsed and there was no special plan, it is determined that the risk characteristic value corresponding to this risk characteristic element is 0.

[0096] In some embodiments, the processor may determine the value conditions corresponding to the accident feature elements based on user input or other means; and determine the accident feature values corresponding to the accident feature elements in the sub-vector library based on historical accident data and the value conditions. For example, the processor may determine that the value condition corresponding to the accident feature element is a general accident (classified according to national standards, such as an accident causing less than 3 deaths, or less than 10 serious injuries, or a direct economic loss of less than 10 million yuan is a general accident) and assign it a value of 1. Based on historical accident data, a foundation pit collapse causing 1 death is a general accident, and the accident feature value corresponding to this accident feature element is 1.

[0097] For more information about risk feature elements, risk feature values, accident feature elements, accident feature values, risk feature trees, value conditions, historical accident data, etc., reference can be made to Figures 3A - 3B and its related descriptions.

[0098] Step 620: Classify the accident vectors in the sub-vector library based on the risk feature values to determine the first classification result. In some embodiments, step 620 may be executed by the security risk model construction module 230.

[0099] The first classification result refers to the classification result of the accident vectors determined based on one risk feature element. Figure 7A It is a schematic diagram of an exemplary accident vector shown in some embodiments of this specification. As Figure 7A shown, the accident vector can be divided into an engineering part (i.e., the engineering feature element part, not shown in the figure), a feature part (i.e., the risk feature element part), and an accident part (i.e., the accident feature element part). For more information about the accident vector, reference can be made to Figures 3A - 3B and its related descriptions. In some embodiments, the processor may classify all accident vectors based on the number of types of risk feature values of a certain risk feature element to determine the first classification result. For example, if the risk feature values of risk feature element 1 have 3 types (e.g., 0, 0.5, 1), the processor may divide all accident vectors into 3 categories and determine the first classification result 1 corresponding to risk feature element 1, that is, the accident vectors with a risk feature value of 0 for risk feature element 1 are in the first category, the accident vectors with a risk feature value of 0.5 for risk feature element 1 are in the second category, and the accident vectors with a risk feature value of 1 for risk feature element 1 are in the third category. Figure 7B It is a schematic diagram of an exemplary classified accident vector shown in some embodiments of this specification. As Figure 7BAs shown in the figure, if there are a total of 9 accident vectors G1 - G9 in the sub - vector library, for the risk characteristic element "special plan", all accident vectors can be divided into 3 categories. The first category is [G1, G2, G3] with "construction without a plan" (i.e., the risk characteristic value is 0), the second category is [G4, G5, G6] with "construction first and then supplement the plan" (i.e., the risk characteristic value is 0.5), and the third category is [G7, G8, G9] with "construction with a plan" (i.e., the risk characteristic value is 1).

[0100] Step 630: Classify the accident vectors in the sub - vector library based on the accident characteristic values to determine the second classification result. In some embodiments, step 630 can be executed by the safety risk model construction module 230.

[0101] The second classification result refers to the classification result of accident vectors determined based on one accident characteristic element. In some embodiments, the processor can classify all accident vectors based on the number of types of accident characteristic values of a certain accident characteristic element to determine the second classification result. For example, if the risk characteristic values of accident characteristic element 1 have 2 types (e.g., 0, 1), the processor can divide all accident vectors into 2 categories to determine the second classification result 1 corresponding to accident characteristic element 1, that is, the accident vectors with the accident characteristic value of 0 of accident characteristic element 1 are the first category, and the accident vectors with the risk characteristic value of 1 of accident characteristic element 1 are the second category. As Figure 7B As shown in the figure, if there are a total of 9 accident vectors G1 - G9 in the sub - vector library, based on the accident characteristic values, all accident vectors can be divided into 2 categories. The first category is [G1, G2, G3, G4, G5] with "an accident occurred" (i.e., the accident characteristic value is 1), and the second category is [G6, G7, G8, G9] with "no accident occurred" (i.e., the accident characteristic value is 0).

[0102] Step 640: Determine the association relationship between the risk characteristic element and the accident characteristic element based on the first classification result and the second classification result. In some embodiments, step 640 can be executed by the safety risk model construction module 230.

[0103] In some embodiments, the processor can compare the first classification result and the second classification result. If a certain category in the first classification result is completely included in a certain category in the second classification result, it indicates that the risk characteristic element corresponding to the risk characteristic value of this category and the accident characteristic element corresponding to the accident characteristic value of this category have a strong association relationship; if a certain category in the first classification result is not completely included in a certain category in the second classification result, it indicates that the risk characteristic element corresponding to the risk characteristic value of this category and the accident characteristic element corresponding to the accident characteristic value of this category have a weak association relationship. As Figure 7BAs shown, all of [G1, G2, G3] of "construction without a plan" are within [G1, G2, G3, G4, G5] of "an accident occurred", so it can be determined that the risk characteristic element "construction without a plan" and the accident characteristic element "an accident occurred" are strongly correlated; all of [G7, G8, G9] of "construction with a plan" are within [G6, G7, G8, G9] of "no accident occurred", so it can be determined that the risk characteristic element "construction with a plan" and the accident characteristic element "no accident occurred" are strongly correlated; a part of [G4, G5, G6] of "construct first and supplement the plan later" is included in [G1, G2, G3, G4, G5] of "an accident occurred", and a part is included in [G6, G7, G8, G9] of "no accident occurred", so the risk characteristic element "construct first and supplement the plan later" and the accident characteristic element "an accident occurred", as well as the accident characteristic element "no accident occurred", are both weakly correlated.

[0104] In some embodiments, when the correlation relationship is represented by a number, the processor may determine the ratio of the number of a certain category in the first classification result included in a certain category in the second classification result to all the numbers as the correlation relationship between the risk characteristic element corresponding to this category of risk characteristic value and the accident characteristic element corresponding to this category of accident characteristic value. For example, all of [G1, G2, G3] of "construction without a plan" are within [G1, G2, G3, G4, G5] of "an accident occurred", so it can be determined that the correlation relationship between the risk characteristic element "construction without a plan" and the accident characteristic element "an accident occurred" is 100%. Only 2 vectors of [G4, G5, G6] of "construct first and supplement the plan later" are included in [G1, G2, G3, G4, G5] of "an accident occurred", so it can be determined that the correlation relationship between the risk characteristic element "construct first and supplement the plan later" and the accident characteristic element "an accident occurred" is 66.67%.

[0105] In some embodiments, all the risk characteristic elements and all the accident characteristic elements in the sub-vector library are traversed according to the above method to obtain the correlation relationships between all the risk characteristic elements and all the accident characteristic elements.

[0106] In some embodiments of this specification, for each risk characteristic element and each accident characteristic element in the sub-vector library, by determining the risk characteristic value corresponding to the risk characteristic element and the accident characteristic value corresponding to the accident characteristic element; classifying the accident vectors in the sub-vector library based on the risk characteristic value to determine the first classification result; classifying the accident vectors in the sub-vector library based on the accident characteristic value to determine the second classification result; and determining the correlation relationship between the risk characteristic element and the accident characteristic element based on the first classification result and the second classification result, the correlation relationships between different risk characteristic elements and different accident characteristic elements can be obtained clearly and intuitively, the strong correlation factors for risk accidents can be found conveniently and quickly, and it is convenient to process the corresponding strong correlation factors.

[0107] Figure 8 is a flowchart of an exemplary determination of an association relationship shown in some embodiments of this specification. As Figure 8 shown, process 800 includes the following steps. In some embodiments, process 800 may be executed by a processor.

[0108] Step 810, screen a preset number of risk feature elements from multiple risk feature elements in the sub-vector library to form a risk feature element combination, and determine a risk feature value combination corresponding to the risk feature element combination. In some embodiments, step 810 may be executed by the security risk model construction module 230.

[0109] In some embodiments, the processor may screen a preset number (which may be set based on experience or requirements) of risk feature elements from multiple risk feature elements in the sub-vector library, combine these risk feature elements together to determine a risk feature element combination, then respectively determine the risk feature values corresponding to these risk feature elements, and then combine these risk feature values together to determine a risk feature value combination corresponding to the foregoing risk feature element combination. Regarding the specific manner of determining the risk feature value corresponding to a risk feature element, reference may be made to Figures 3A - 3B , Figure 6 and its related descriptions.

[0110] Step 820, for each accident feature element in the sub-vector library, determine an accident feature value corresponding to the accident feature element. In some embodiments, step 820 may be executed by the security risk model construction module 230. Regarding the specific manner of determining the accident feature value corresponding to an accident feature element, reference may be made to Figures 3A - 3B , Figure 6 and its related descriptions.

[0111] Step 830, classify the accident vectors in the sub-vector library based on the risk feature value combination to determine a third classification result. In some embodiments, step 820 may be executed by the security risk model construction module 230.

[0112] The third classification result refers to the accident vector classification result determined based on multiple risk feature elements. Figure 7AIt is a schematic diagram of an exemplary accident vector shown in some embodiments of this specification. In some embodiments, the processor may classify all accident vectors based on the number of types of combinations of risk characteristic values, and determine a third classification result. For example, when the preset number is 2, the processor may determine that the number of types of combinations of risk characteristic values corresponding to the combination of risk characteristic elements composed of risk characteristic element 2 and risk characteristic element 3 is 6 (i.e., (0,0), (0,0.5), (0,1), (1,0), (1,0.5), (1,1)) based on the fact that there are 2 types of risk characteristic values of risk characteristic element 2 (e.g., 0, 1) and 3 types of risk characteristic values of risk characteristic element 3 (e.g., 0, 0.5, 1), and classify all accident vectors into 6 categories based on these 6 types of combinations of risk characteristic values, and determine the third classification result corresponding to the combination of risk characteristic elements.

[0113] Step 840: Classify the accident vectors in the sub-vector library based on the accident characteristic values, and determine a fourth classification result. In some embodiments, step 820 may be executed by the safety risk model construction module 230.

[0114] The fourth classification result refers to the classification result of accident vectors determined based on one accident characteristic element. The determination method of the fourth classification result may refer to the determination method of the foregoing second classification result.

[0115] Step 850: Determine the association relationship between the combination of risk characteristic elements and the accident characteristic elements based on the third classification result and the fourth classification result. In some embodiments, step 850 may be executed by the safety risk model construction module 230.

[0116] In some embodiments, the processor may compare the third classification result and the fourth classification result. If a certain category in the third classification result is completely included in a certain category in the fourth classification result, it indicates that the combination of risk characteristic elements corresponding to this category of risk characteristic value combinations and the accident characteristic elements corresponding to this category of accident characteristic values have a strong association relationship; if a certain category in the third classification result is not completely included in a certain category in the fourth classification result, it indicates that the combination of risk characteristic elements corresponding to this category of risk characteristic value combinations and the accident characteristic elements corresponding to this category of accident characteristic values have a weak association relationship.

[0117] In some embodiments, when the association relationship is represented by a number, the processor may determine the ratio of the number of a certain category in the third classification result included in a certain category in the third classification result to all the numbers as the association relationship between the combination of risk characteristic elements corresponding to this category of risk characteristic value combinations and the accident characteristic elements corresponding to this category of accident characteristic values.

[0118] In some embodiments, all combinations of risk feature elements formed by all preset quantities of risk feature elements and all accident feature elements in the sub-vector library are traversed according to the above method, and the association relationship between all risk feature elements and all accident feature elements is obtained.

[0119] In some embodiments of this specification, by screening a preset quantity of risk feature elements from multiple risk feature elements in the sub-vector library, a combination of risk feature elements is formed, and a corresponding combination of risk feature values of the risk feature element combination is determined; for each accident feature element in the sub-vector library, an accident feature value corresponding to the accident feature element is determined; based on the combination of risk feature values, the accident vectors in the sub-vector library are classified to determine a third classification result; based on the accident feature values, the accident vectors in the sub-vector library are classified to determine a fourth classification result; based on the third classification result and the fourth classification result, the association relationship between the risk feature element combination and the accident feature element is determined. On the basis of determining the association relationship between a single risk feature element and an accident feature element, the association relationship between a combination formed by multiple risk feature elements and an accident feature element can be further determined. It can be understood that the occurrence of an accident often is not only affected by a single factor. Only by comprehensively considering multiple factors and their combined effects can the determination result of the important associated elements corresponding to the accident be more accurate and more in line with the actual situation.

[0120] Figure 9 is a flowchart of an exemplary determination of key risk feature elements shown in some embodiments of this specification. As Figure 9 shown, process 900 includes the following steps. In some embodiments, process 900 can be executed by a processor.

[0121] Step 910, for each risk feature element in the accident vector corresponding to each historical accident, determine the contribution parameter of each risk feature element to the historical accident. In some embodiments, step 910 can be executed by the safety risk model construction module 230.

[0122] The contribution parameter refers to a parameter indicating the degree of influence of a risk feature element on an accident. The larger the contribution parameter, the greater the degree of influence of the risk feature element on the accident.

[0123] In some embodiments, the processor can determine the contribution parameter of each risk feature element to the historical accident based on a fifth preset rule. The fifth preset rule can be preset based on experience or requirements. An exemplary fifth preset rule can be to directly determine the association relationship between the risk feature element and the corresponding accident feature element as the contribution parameter of the risk feature element to the historical accident.

[0124] In some embodiments, for each risk feature element, the processor can determine a first association parameter between each risk feature element and an accident.

[0125] The first correlation parameter refers to a parameter representing the correlation relationship between a risk characteristic element and an accident characteristic element corresponding to a historical accident. In some embodiments, the processor may determine the first correlation parameter in the manner of determining the correlation relationship in the foregoing step 640. For specific content, reference may be made to Figure 6 and its related descriptions.

[0126] After that, for each combination of risk characteristic elements in the accident vector corresponding to the historical accident, the processor may determine a second correlation parameter between the combination of risk characteristic elements and the corresponding historical accident.

[0127] The second correlation parameter refers to a parameter representing the correlation relationship between a certain risk characteristic element and an accident characteristic element corresponding to a historical accident when the risk characteristic element is in a certain combination of risk characteristic elements. In some embodiments, the processor may, in the manner of determining the correlation relationship in the foregoing step 850, determine the first correlation relationship of the combination of risk characteristic elements when the combination of risk characteristic elements includes the risk characteristic element, and then, in the manner of determining the correlation relationship in the foregoing step 640 or the foregoing step 850, determine the second correlation relationship of the risk characteristic element or combination when the combination of risk characteristic elements does not include the risk characteristic element (it can be understood that if the combination only includes two risk characteristic elements, then there is only one risk characteristic element left after removing the risk characteristic element, and the method in step 640 can be used to calculate the correlation relationship; if the combination includes three or more risk characteristic elements, then after removing the risk characteristic element, it is still a combination of risk characteristic elements, and the method in step 850 still needs to be used to calculate the correlation relationship). The difference obtained by subtracting the second correlation relationship from the first correlation relationship is used as the second correlation parameter. Exemplarily, the correlation relationship between the combination of risk characteristic elements 1 (risk characteristic element 1, risk characteristic element 2) and historical accident 1 is 70%, and the correlation relationship between risk characteristic element 2 and historical accident 1 is 20%. Then, when risk characteristic element 1 is in the combination of risk characteristic elements 1, the second correlation parameter for historical accident 1 is 50%. Another example is that the correlation relationship between the combination of risk characteristic elements 2 (risk characteristic element 1, risk characteristic element 5, risk characteristic element 8) and historical accident 1 is 60%, and the correlation relationship between the combination of risk characteristic elements 3 (risk characteristic element 5, risk characteristic element 8) and historical accident 1 is 40%. Then, when risk characteristic element 1 is in the combination of risk characteristic elements 2, the second correlation parameter for historical accident 1 is 20%. It can be understood that the risk characteristic element may appear in multiple combinations of risk characteristic elements, so there may be multiple calculated second correlation parameters.

[0128] Finally, the processor may determine the contribution parameter of each risk feature element to the historical accident based on the first correlation parameter and the second correlation parameter. In some embodiments, the processor may determine a target weight; based on the first correlation parameter, the second correlation parameter, and the target weight, determine the contribution parameter of each risk feature element to the historical accident. For example, the processor may add the sum of the products of each second correlation parameter of the risk feature element and its corresponding target weight to the first correlation parameter to determine the contribution parameter of the risk feature element to the historical accident, that is, contribution parameter = first correlation parameter + second correlation parameter 1 × target weight 1 of risk feature element combination 1 + … + second correlation parameter n × target weight n of risk feature element combination n. The target weight refers to the contribution weight of the risk feature element relative to the risk feature element combination. In some embodiments, the target weight may be related to at least one of the number of elements in the accident vector, the number of elements in the risk feature element combination, the determination time of the risk feature element, and the self-weight of the risk feature element. For example, the target weight may be related to the number of elements in the accident vector and the number of elements in the risk feature element combination. Exemplarily, the target weight may be calculated based on a preset formula. For example, the preset formula may be , where the number of elements refers to the number of risk feature elements. It can be understood that the target weight is to represent the value improvement of the entire risk feature element combination after adding a certain risk feature element. Therefore, when setting the preset formula for the target weight, it is necessary to first consider the possible combination situations of other risk feature elements in the risk feature element combination except this risk feature element, and then add this risk feature element. For example, the numerator in the foregoing preset formula is (the number of permutations and combinations when considering the order of other risk feature elements in the risk feature element combination except this risk feature element) (the number of permutations and combinations when putting this risk feature element as the last element, because there is only 1 kind, so it is omitted in the formula) (the number of permutations and combinations when considering the order of elements in the accident vector that are not in the risk feature element combination). The denominator is (the number of permutations and combinations when considering the order of all elements in the accident vector). Again, for example, the target weight may be related to the determination time of the risk feature element. The closer the determination time of the risk feature element is to the current time, the greater the target weight is set. Further, for example, the target weight may be related to the self-weight of the risk feature element. The greater the self-weight of the risk feature element, the greater the target weight is set. The self-weight of the risk feature element may be set based on experience or requirements. For example, the self-weight of the risk feature element may be related to the correlation relationship between the risk feature element and the accident feature element. The greater the correlation relationship, the higher the self-weight of the risk feature element. The processor may traverse each risk feature element based on the above method to determine the contribution parameter of each risk feature element to the historical accident.

[0129] It is understandable that there are a very large number of permutation and combination possibilities for the combination of risk characteristic elements, and the gain effects generated by the risk characteristic elements in each combination are also different. If the various second correlation parameters are simply and crudely evenly divided and set, the resulting contribution parameters will undoubtedly have large deviations. In some embodiments of the present specification, the contribution parameters are determined based on the first correlation parameter, the second correlation parameter, and the target weight, and the target weight is set to be related to at least one of the number of elements in the accident vector, the number of elements in the risk characteristic element combination, the determination time of the risk characteristic element, and the self-weight of the risk characteristic element. All possibilities of the risk characteristic element combinations can be comprehensively considered, and as accurate as possible weight allocation can be determined according to various parameters such as the scale of various combinations, the contribution degree of elements under each combination, and the importance degree of the elements themselves, thereby helping to improve the accuracy and reliability of the contribution parameters.

[0130] In some embodiments of the present specification, for each risk characteristic element, by determining the first correlation parameter between each risk characteristic element and the historical accident; for each risk characteristic element combination in the accident vector corresponding to the historical accident, determining the second correlation parameter between the risk characteristic element combination and the corresponding historical accident; based on the first correlation parameter and the second correlation parameter, determining the contribution parameter of each risk characteristic element to the historical accident, it is possible to further determine its influence on the accident when it is in the combination on the basis of determining the association relationship between the risk characteristic element and the accident, and comprehensively determine the total influence of the element, making the calculation of the risk characteristic influence parameter more reasonable.

[0131] Step 920, based on each risk characteristic element and the corresponding contribution parameter, determine the key risk characteristic elements of the historical accident. In some embodiments, step 920 may be executed by the security risk model construction module 230. For more content about the key risk characteristic elements, reference can be made to Figures 3A - 8 and its related descriptions.

[0132] In some embodiments, the processor may compare the contribution parameters of each risk characteristic element, and determine the risk characteristic element with the maximum contribution parameter as the key risk characteristic element of the historical accident.

[0133] In some embodiments, the processor may determine whether the contribution parameter of each risk characteristic element to the historical accident meets a first preset condition.

[0134] The first preset condition refers to the condition for screening key risk feature elements based on contribution parameters. The first preset condition can be set based on experience or requirements. Exemplarily, the first preset condition can be to screen risk feature elements with contribution parameters exceeding the contribution parameter threshold. The contribution parameter threshold can be set based on experience or requirements. The first preset condition can also be to sort the risk feature elements in descending order of contribution parameters and screen the risk feature elements with the top x% of contribution parameter rankings, etc.

[0135] In response to the contribution parameter satisfying the first preset condition, the processor can determine that the risk feature element is a key risk feature element.

[0136] In some embodiments of the present specification, by determining whether the contribution parameter of each risk feature element to the historical accident satisfies the first preset condition; in response to the contribution parameter satisfying the first preset condition, determining that the risk feature element is a key risk feature element, key risk feature elements with larger contribution parameters can be accurately screened out, facilitating timely rectification and avoiding safety risk accidents.

[0137] In some embodiments of the present specification, for each risk feature element in the accident vector corresponding to each historical accident, determining the contribution parameter of each risk feature element to the historical accident; based on each risk feature element and the corresponding contribution parameter, determining the key risk feature elements of the historical accident, risk feature elements with greater influence on safety accidents can be determined and used as key feature elements. Compared with simply making a judgment based on the association relationship, the key feature elements obtained in this way are more reasonable.

[0138] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0139] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0140] In addition, unless otherwise specified in the claims, the order of the processing elements and sequences described in this specification, the use of numerical and alphabetical characters, or the use of other names are not used to limit the order of the processes and methods in this specification. Although some currently useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0141] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this method of disclosure does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0142] In some embodiments, numbers are used to describe components and attribute quantities. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximate", or "substantially" in some examples. Unless otherwise specified, "about", "approximate", or "substantially" indicate that the stated number allows a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used to confirm the breadth of the scope in some embodiments of this specification are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0143] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history documents that are inconsistent with or conflict with the content of this specification, and also excludes the documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0144] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly presented and described in this specification.

Claims

1. A method for engineering safety risk management, characterized in that: include: Determine a target vector based on the field engineering collection data, wherein the target vector includes at least a field engineering characteristic element and a field risk characteristic element; as well as, Based on the target vector and the safety risk model, early warning information related to the engineering safety risk is determined and sent to the user.

2. The method according to claim 1, characterized in that Constructing the security risk model includes: Based on the safety responsibility list, determine the risk characteristic data of functional domains under multiple safety risk control types, wherein the risk characteristic data corresponds to the risk hazards; Obtain historical accident data of multiple historical accidents; Based on the historical accident data and the risk characteristic data, determining a plurality of accident vectors corresponding to the plurality of historical accidents, the accident vectors at least including an engineering characteristic element, a risk characteristic element, and an accident characteristic element; Based on the multiple accident vectors, construct an accident vector library; Based on the engineering characteristic elements, the accident vector library is divided into a plurality of sub-vector libraries; For each of the sub-vector libraries, Determining association relationships between a plurality of the risk characteristic elements and a plurality of the accident characteristic elements in the sub-vector library; Based on the association relationship, determining the risk levels corresponding to the multiple risk feature elements and the multiple accident feature elements in the sub-vector library; and The safety risk model is constructed based on the multiple risk characteristic elements and the multiple accident characteristic elements in the sub-vector library, the association relationship and the risk level.

3. The method according to claim 2, characterized in that Determining the risk characteristic data of functional domains under multiple security risk control types includes: Based on the security responsibility list, a risk characteristic tree is determined, and the risk characteristic tree includes at least three layers, wherein the first layer includes the multiple security risk control types, the second layer includes the functional domains contained in each of the security risk control types, and the third layer includes the risk characteristic data corresponding to each of the functional domains, and the risk characteristic data at least includes risk characteristic values ​​and value conditions corresponding to the risk characteristic values.

4. The method according to claim 2, characterized in that: The determining of the association relationship between the plurality of risk characteristic elements and the plurality of accident characteristic elements in the sub-vector library comprises: For each risk feature element and each accident feature element in the sub-vector library, Determine a risk characteristic value corresponding to the risk characteristic element and an accident characteristic value corresponding to the accident characteristic element; Based on the risk feature value, classify the accident vectors in the sub-vector library to determine a first classification result; Based on the accident feature value, classify the accident vector in the sub-vector library to determine a second classification result; and Based on the first classification result and the second classification result, an association relationship between the risk characteristic element and the accident characteristic element is determined.

5. The method according to claim 2, characterized in that: The determining of the association relationship between the plurality of risk characteristic elements and the plurality of accident characteristic elements in the sub-vector library comprises: Selecting a preset number of risk characteristic elements from the plurality of risk characteristic elements in the sub-vector library to form a risk characteristic element combination, and determining a risk characteristic value combination corresponding to the risk characteristic element combination; For each of the accident characteristic elements in the sub-vector library, determining an accident characteristic value corresponding to the accident characteristic element; Based on the risk feature value combination, classifying the accident vectors in the sub-vector library to determine a third classification result; Based on the accident feature value, classify the accident vectors in the sub-vector library to determine a fourth classification result; and Based on the third classification result and the fourth classification result, the association relationship between the risk characteristic element combination and the accident characteristic element is determined.

6. The method according to claim 2, characterized in that The constructing of the security risk model comprises: For each of the risk characteristic elements in the accident vector corresponding to each of the historical accidents, determining a contribution parameter of each of the risk characteristic elements to the historical accident; and Based on each risk characteristic element and the corresponding contribution parameter, a key risk characteristic element of the historical accident is determined.

7. The method according to claim 6, characterized in that Determining the contribution parameter of each risk characteristic element to the historical accident includes: For each of the risk characteristic elements, determining a first association parameter between each of the risk characteristic elements and the historical accident; For each risk feature element combination in the accident vector corresponding to the historical accident, determining a second association parameter between the risk feature element combination and the corresponding historical accident; and The contribution parameter of each risk characteristic element to the historical accident is determined based on the first association parameter and the second association parameter.

8. The method according to claim 7, characterized in that The step of determining the contribution parameter of each risk characteristic element to the historical accident based on the first association parameter and the second association parameter includes: Determining a target weight, the target weight being related to at least one of the number of elements in the accident vector, the number of elements in the risk characteristic element combination, the determination time of the risk characteristic element, and the own weight of the risk characteristic element; The contribution parameter of each risk characteristic element to the historical accident is determined based on the first association parameter, the second association parameter and the target weight.

9. The method according to claim 7, characterized in that: The key risk characteristic elements for determining the historical accidents include: determining whether the contribution parameter of each risk characteristic element to the historical accident satisfies a first preset condition; and, In response to the contribution parameter satisfying the first preset condition, determining that the risk characteristic element is the key risk characteristic element.

10. The method according to claim 1, characterized in that Determining the early warning information related to the engineering safety risk based on the target vector and the safety risk model and sending it to the user includes: Determining the safety risk model based on the on-site engineering characteristic elements in the target vector; Based on the on-site risk characteristic element in the target vector, query the safety risk model to determine a target accident characteristic element whose association relationship with the on-site risk characteristic element satisfies a second preset condition and a risk level corresponding to the target accident characteristic element; and Based on the target accident characteristic elements, the risk level and the warning rules, the warning information is determined and sent to the user.

11. The method according to claim 1, characterized in that: The engineering site has an accident, the target vector site further includes a site accident feature element, and the determining, based on the target vector and the safety risk model, early warning information related to the engineering safety risk and sending it to the user includes: Determining the safety risk model based on the on-site engineering characteristic elements in the target vector; Based on the target vector, query the safety risk model to determine the key risk characteristic elements of the historical accident; and Based on the key risk characteristic elements and warning rules, the warning information is determined and sent to the user.

12. An engineering safety risk management system, characterized in that: The system comprises a processor configured to execute the method according to any one of claims 1-11.

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