A method, system, device and medium for automatically identifying project risks

Abstract

The present invention belongs to the field of construction engineering technology, and in particular, relates to a method, system, equipment and medium for automatically identifying project risks. By analyzing and extracting data from a prediction model, the property information of components in various parts is automatically identified and extracted, the category of the components is determined, and the components are matched with a database to obtain corresponding construction process information and corresponding risk values ​​and risk levels. The risk matching operation is automatically completed with low time consumption. Compared with the manual method, the work efficiency is greatly improved, and the cycle of the entire risk identification work is shortened, so the cost is low, the efficiency is high, and the degree of automation is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction engineering, and in particular relates to a method, system, equipment and medium for automatically identifying project risks. Background Art

[0002] As a large-scale and complex project, construction engineering contains a lot of risks in construction projects. Therefore, it is very necessary to strengthen the research on risk management of construction projects. Combined with the characteristics of the industry's construction, there are complex project construction technologies and many process flows, which makes it difficult to identify risks. At present, the design unit needs to sort out the risk sources, conduct multi-party review, and the construction unit needs to carry out detailed design and plan response. There are problems such as the difficulty of manual sorting design, long cycle, and low degree of automation. Summary of the invention

[0003] In view of the problem in the prior art that it is difficult to identify risks due to the technical complexity of construction projects, the present invention provides a method, system, equipment and medium for automatically identifying project risks.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for automatically identifying project risks, comprising the following steps:

[0006] Obtain construction category information, construction process information and construction risk information, and establish a risk database;

[0007] Obtaining construction category information of each component in the project and establishing a prediction model;

[0008] Lightweight conversion of the prediction model to obtain a model data set;

[0009] Traversing the model data set to identify the construction category to which the component belongs;

[0010] According to the identified construction category to which the component belongs, matching the construction process in the risk database and obtaining the construction risk information of each component;

[0011] The construction risk information of each component is summarized to obtain the project construction risk information.

[0012] Furthermore, the establishment of a risk database specifically includes:

[0013] Establishing a mapping relationship between the construction category information and the construction process information, and obtaining a construction process category mapping relationship database;

[0014] A construction process risk relationship database is established based on the construction process information in the construction process category mapping relationship database and combined with the construction risk information.

[0015] Furthermore, it also includes: a multi-level tree storage structure is provided for storing the information of the component, wherein the nodes of the multi-level tree represent the components, and the information of the corresponding components is mounted on the nodes, and the information of the component is construction category information, construction process information or construction risk information.

[0016] Furthermore, the traversing the model data set includes:

[0017] The components that are not identified to belong to a construction category are grouped into an unidentified construction category set;

[0018] The components that are not matched to the construction process are grouped as an unmatched construction process set;

[0019] Summarizing the unidentified construction category set and the unmatched construction process set to obtain an unidentified set;

[0020] Components for which no risk information has been obtained are summarized as an unidentified risk set.

[0021] Furthermore, after obtaining the unrecognized set, the method further includes:

[0022] For the component whose construction category is not identified, attach a construction category;

[0023] For the components that are not matched to the construction process, the construction process is linked.

[0024] Furthermore, for the components of the unidentified risk set for which construction risk information has not been obtained, the construction risk information is supplemented.

[0025] Furthermore, after obtaining the project construction risk information, the project construction risk information is displayed in a floating window in the form of a statistical table; components with construction risks are marked with anchor points on the prediction model to display the construction risk information associated with the components with construction risks.

[0026] In a second aspect, the present invention provides a project risk automatic identification system, comprising the following modules:

[0027] Risk database module: used to obtain construction category information, construction process information and construction risk information, and establish a risk database;

[0028] Prediction model module: used to obtain the construction category information of each component in the project and establish a prediction model;

[0029] Lightweight conversion module: used for lightweight conversion of the prediction model to obtain a model data set;

[0030] Construction category identification module: used to traverse the model data set and identify the construction category to which the component belongs;

[0031] Process risk module: used to match the construction process in the risk database and obtain the construction risk information of each component according to the identified construction category to which the component belongs;

[0032] Summary module: used to summarize the construction risk information of each component and obtain project construction risk information.

[0033] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for automatically identifying project risks when executing the computer program.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for automatically identifying project risks is implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] First, the present invention automatically identifies and extracts the attribute information of components in various parts through analysis and data extraction of the prediction model, determines the category to which the components belong, matches them with the database, obtains the corresponding construction process information and corresponding risk values ​​and risk levels, and automatically completes the risk matching operation with low time consumption. Compared with the manual method, it greatly improves the work efficiency and shortens the cycle of the entire risk identification work, thus having low cost, high efficiency and high degree of automation.

[0037] Second, the present invention displays the identification results based on a three-dimensional model, which is intuitive and clear, reduces the complexity of engineering risk management and improves the convenience of construction risk management and supervision, and intuitively presents relevant information such as possible risks, possible consequences of risks, key factors in risk control, and preventive measures during the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 A schematic diagram of a process flow of a method for automatically identifying project risks according to the present invention;

[0040] Figure 2 This is a data classification diagram of a project risk automatic identification method of the present invention;

[0041] Figure 3 This is a schematic diagram of a project risk automatic identification system according to the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0043] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0044] Example 1

[0045] A method for automatically identifying project risks, such as Figure 1 As shown, the following steps are included:

[0046] S1: Obtain construction category information, construction process information and construction risk information, establish a risk database, obtain the construction category information of each component in the project, and establish a prediction model;

[0047] S2: lightweight conversion of the prediction model to obtain a model data set;

[0048] S3: traversing the model data set, identifying the construction category to which the components in the construction project belong, and obtaining a construction category set;

[0049] S4: Matching construction procedures according to the construction category set to obtain a construction procedure set;

[0050] S5: Identify risk information according to the construction process set;

[0051] S6: After the risk identification action is completed, the result information is processed and displayed in combination with the three-dimensional model.

[0052] A risk database has been established, which includes a construction process risk relationship database and a construction process category mapping relationship database. Specifically, the construction process risk relationship database includes data such as construction process information and the risk types, risk levels, possible consequences of risks, key risk control factors, and related preventive measures corresponding to the construction process; the construction process category mapping relationship database includes mapping relationship data between construction processes and construction categories, such as the construction category "wall" associated with the construction process "rebar installation", "formwork installation", etc.

[0053] In step S1, the prediction model is a three-dimensional GIM prediction model. When modeling, the attribute information of each component in the model is accurately preset. According to the construction characteristics of the construction project, the standardized construction category setting is mounted on the type attribute of the model component.

[0054] In step S2, a lightweight conversion is performed through a file converter to generate a lightweight three-dimensional model file in a pmodel format, and the GIM model is processed into a model data set, which includes a geometric data set and an attribute data set.

[0055] Step S3 specifically includes:

[0056] S31: Read the lightweight pmodel file and render it on the front-end page;

[0057] S32: Initiate a preprocessing task, read the geometry data set and the attribute data set, traverse the model data set, identify the construction category to which the component belongs, and group the components whose construction categories are identified as the identified construction category set, and group the components whose construction categories are not identified as the unidentified construction category set, such as Figure 2 As shown;

[0058] The construction process set in step S4 includes a matched construction process set and an unmatched construction process set. According to the identified construction category set, the construction processes that are matched are summarized as the matched construction process set, and the construction processes that cannot be matched are summarized as the unmatched construction process set.

[0059] Further, the unidentified construction category set and the unmatched construction process set are extracted as unidentified sets and stored in an unidentified database;

[0060] Obtain a set of identified construction categories and a set of unidentified construction categories from the database, and render them on the front-end page in a multi-level tree format;

[0061] The user can view the preprocessing results and unidentified sets of the model after preprocessing, perform manual assistance to supplement the unidentified sets, and further link the unidentified target components, construction categories and construction processes.

[0062] Step S5 specifically includes:

[0063] S51: Initiate an identification task, read the matched construction process set and the risk database, and identify and match the construction process in the matched construction process set with the data in the risk database by traversal and regular matching:

[0064] ① If the identification is successful, the corresponding risk information in the risk database will be mounted on the corresponding node of the multi-level tree on the front-end page;

[0065] ② If the identification is unsuccessful, the corresponding node information in the multi-level tree of the front-end page is extracted and stored in the unidentified risk set;

[0066] S52: Storing the risk identification information result set and the unidentified risk set in a database.

[0067] S6: Display risk information:

[0068] S61: Display the final project risk information on the front-end page:

[0069] ① The complete risk information is displayed in the form of a statistical table in the page floating window, which includes risk data related information, such as: risk type, risk level, possible consequences of the risk, preventive measures, and the number of components associated with the risk;

[0070] ② In the prediction model, components with construction risks are marked with anchor points, and the highest risk level among the associated construction risks is displayed; click the anchor point, and the corresponding construction risk information will be automatically filtered out in the floating window table on the page.

[0071] Furthermore, it also includes supplementary risk information.

[0072] The risk identification information result set stored in the database is read and formatted and statistically processed by the front end, and then displayed on the page in a multi-level tree structure. Users can intuitively view the risks currently existing in the project and how many components are associated with the specified risk through the multi-level tree. By clicking on the specified risk tree node, the risk-associated components will be highlighted on the prediction model at the same time.

[0073] The system obtains the component information of unidentified risks and highlights them on the model. The user can select a component with unidentified risks on the model and provide manual assistance to attach the target component to the corresponding risk.

[0074] If there are project risks that are not reflected in the prediction model, or there are component types that are not included in the risk database, and the construction procedures or risks corresponding to the components cannot be identified, the risk information can be supplemented with manual assistance: select the risk information on the display page, and choose to attach the risk to the target component or directly add the risk to the project risk result without attaching the component.

[0075] Example 2

[0076] A project risk automatic identification system includes the following modules:

[0077] Risk database module: used to obtain construction category information, construction process information and construction risk information, and establish a risk database;

[0078] Prediction model module: used to obtain the construction category information of each component in the project and establish a prediction model;

[0079] Lightweight conversion module: used for lightweight conversion of the prediction model to obtain a model data set;

[0080] Identification and matching module: used to traverse the model data set, identify the construction category to which the component belongs according to the risk database, and then match the construction process to obtain the construction risk information of each component;

[0081] Summary module: used to summarize the construction risk information of each component and obtain project construction risk information.

[0082] Example 3

[0083] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for automatically identifying project risks described in Example 1 when executing the computer program.

[0084] Example 4

[0085] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for automatically identifying project risks described in Example 1 is implemented.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for automatically identifying project risks, characterized in that: The steps include: Obtain construction category information, construction process information and construction risk information, and establish a risk database; Obtaining construction category information of each component in the project and establishing a prediction model; Lightweight conversion of the prediction model to obtain a model data set; Traversing the model data set to identify the construction category to which the component belongs; According to the identified construction category to which the component belongs, matching the construction process in the risk database and obtaining the construction risk information of each component; Summarize the construction risk information of each component to obtain project construction risk information; The establishment of the risk database specifically includes: Establishing a mapping relationship between the construction category information and the construction process information, and obtaining a construction process category mapping relationship database; Establishing a construction process risk relationship database based on the construction process information in the construction process category mapping relationship database and in combination with the construction risk information; The prediction model is a three-dimensional GIM prediction model. When modeling, the attribute information of each model component in the prediction model is preset, and the standardized construction category setting is mounted on the type attribute of the model component.

2. The method for automatically identifying project risks according to claim 1, characterized in that: Also includes: A multi-level tree storage structure is provided for storing the information of the components, wherein the nodes of the multi-level tree represent components, and the information of the corresponding components is mounted on the nodes, and the information of the components is construction category information, construction process information or construction risk information.

3. The method for automatically identifying project risks according to claim 1, characterized in that: The traversing of the model data set includes: The components that are not identified to belong to a construction category are grouped into an unidentified construction category set; The components that are not matched to the construction process are grouped as an unmatched construction process set; Summarizing the unidentified construction category set and the unmatched construction process set to obtain an unidentified set; Components for which no risk information has been obtained are summarized as an unidentified risk set.

4. The method for automatically identifying project risks according to claim 3 is characterized in that: After obtaining the unrecognized set, the method further includes: For the component whose construction category is not identified, attach a construction category; For the components that are not matched to the construction process, the construction process is linked.

5. The method for automatically identifying project risks according to claim 3 is characterized in that: For the unidentified risk set, for components for which construction risk information has not been obtained, the construction risk information is supplemented.

6. The method for automatically identifying project risks according to claim 1, characterized in that: After obtaining the project construction risk information, the project construction risk information is displayed in a floating window in the form of a statistical table; components with construction risks are marked with anchor points on the prediction model to display the construction risk information associated with the components with construction risks.

7. A project risk automatic identification system, comprising the following modules: Risk database module: used to obtain construction category information, construction process information and construction risk information, and establish a risk database; Prediction model module: used to obtain the construction category information of each component in the project and establish a prediction model; Lightweight conversion module: used for lightweight conversion of the prediction model to obtain a model data set; Construction category identification module: used to traverse the model data set and identify the construction category to which the component belongs; Process risk module: used to match the construction process in the risk database and obtain the construction risk information of each component according to the identified construction category to which the component belongs; Summarizing module: used to summarize the construction risk information of each component and obtain project construction risk information; The establishment of the risk database specifically includes: Establishing a mapping relationship between the construction category information and the construction process information, and obtaining a construction process category mapping relationship database; Establishing a construction process risk relationship database based on the construction process information in the construction process category mapping relationship database and in combination with the construction risk information; The prediction model is a three-dimensional GIM prediction model. When modeling, the attribute information of each model component in the prediction model is preset, and the standardized construction category setting is mounted on the type attribute of the model component.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for automatically identifying project risks according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for automatically identifying project risks according to any one of claims 1 to 6 is implemented.

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