Project risk determination method and device, electronic equipment and storage medium
By inputting data into the project risk prediction model, the problem of insufficient risk awareness among contractors in existing technologies is solved, enabling accurate and timely prediction of project risks and ensuring stable project operation.
Patent Information
- Application Number
- CN202512020192.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-03-31
AI Technical Summary
In the existing project risk assessment system, contractors pay less attention to other risks, which may lead to unknown risks when the project is completed.
By identifying historical procurement data, construction logs, meeting recordings, equipment operation images, and project drawings for the target project, and inputting them into a pre-trained project risk prediction model, project risks are generated.
It enables accurate and timely prediction of project risks, ensuring the stable operation of the project.
Smart Images
Figure CN121766780A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of risk assessment technology, and in particular to a method, apparatus, electronic device, and storage medium for determining project risks. Background Technology
[0002] Existing evaluation systems primarily focus on easily quantifiable and intuitive indicators such as price and schedule. For example, in many project risk management processes, price factors often carry too much weight, while other risks to contractors receive less attention. This can lead to unforeseen risks remaining at the time of project completion. Summary of the Invention
[0003] This invention provides a method, apparatus, electronic device, and storage medium for determining project risks, in order to address the problem of insufficient risk prediction during project forecasting.
[0004] According to one aspect of the present invention, a method for determining project risk is provided, the method comprising:
[0005] Determine the target project data, which includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing;
[0006] The target project data is input into a pre-trained project risk prediction model to generate the project risk of the target project.
[0007] According to another aspect of the present invention, a project risk determination apparatus is provided, the apparatus comprising:
[0008] The project data determination module is used to determine the target project data, which includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing.
[0009] The project risk determination module is used to input the target project data into a pre-trained project risk prediction model to generate the project risk of the target project.
[0010] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:
[0011] At least one processor; and
[0012] A memory communicatively connected to the at least one processor; wherein,
[0013] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the project risk determination method according to any embodiment of the present invention.
[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the project risk determination method according to any embodiment of the present invention.
[0015] The technical solution of this invention determines the target project data, which includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing. The target project data is then input into a pre-trained project risk prediction model to generate the project risk of the target project. This achieves the prediction of the project risk of the target project while ensuring the accuracy and timeliness of the prediction results.
[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of a project risk determination method provided in Embodiment 1 of the present invention;
[0019] Figure 2 This is a flowchart of another project risk determination method provided by Embodiment 2 of the present invention;
[0020] Figure 3 This is a schematic diagram of a project risk determination device according to Embodiment 3 of the present invention;
[0021] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the project risk determination method of this invention. Detailed Implementation
[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0024] Example 1
[0025] Figure 1 This invention provides a flowchart of a project risk determination method according to Embodiment 1. This embodiment is applicable to situations where project risks are predicted during project operation. The method can be executed by a project risk determination device, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:
[0026] S110. Determine the target project data, which includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing.
[0027] The target project can be any project requiring risk assessment. Target project data can be data related to the target project. Target project data includes, but is not limited to, historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operating parameters, and at least one project drawing.
[0028] Historical procurement data can be procurement data for materials used to complete the project at different times. Construction logs can be formal, continuous, and detailed project management documents recorded daily. Meeting recordings can be recordings of meetings related to the project. Equipment operation images can be images taken during equipment operation. Equipment operating parameters can be specific parameters during equipment operation.
[0029] Images of equipment in operation can be taken during maintenance or obtained through drone inspections, surveillance cameras, and other methods.
[0030] S120. Input the target project data into the pre-trained project risk prediction model to generate the project risk of the target project.
[0031] Project risk prediction models can be pre-trained, trained using sample project data, and possess the ability to analyze project risks.
[0032] The target project data is input into a pre-trained project risk prediction model, which then performs risk prediction based on the target project data, thereby generating project risk.
[0033] By adopting the technical solution of this application, the target project data of the target project is determined. The project data includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing. The target project data is then input into a pre-trained project risk prediction model to generate the project risk of the target project. This achieves the prediction of the project risk of the target project while ensuring the accuracy and timeliness of the prediction results.
[0034] Example 2
[0035] Figure 2 This invention provides a flowchart of another project risk determination method. This embodiment further optimizes the process of generating project risks for the target project in the aforementioned embodiments, based on the above embodiments. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the project risk determination method in this embodiment may include the following steps:
[0036] S210. Determine the target project data, which includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing.
[0037] After determining the target project data, the following is also included:
[0038] Convert meeting recordings into text to generate meeting transcripts;
[0039] Modeling was performed on the drawings of each project to obtain several project models;
[0040] By merging the various project models, a project fusion model is obtained;
[0041] Accordingly, the target project data is input into a pre-trained project risk prediction model, including:
[0042] The meeting text, project integration model, historical procurement data, construction logs, equipment operation images, and equipment operation parameters are input into the pre-trained project risk prediction model.
[0043] To improve the computational efficiency of project risk prediction models, the target project data can be adjusted in advance.
[0044] To address this, speech-to-text conversion can be achieved based on MFCC feature extraction and the Transformer acoustic model, combined with a language model for contextual error correction and intent recognition; key information such as meeting resolutions and task assignments can be extracted through entity recognition to automatically generate a to-do list, thereby obtaining the meeting text.
[0045] Furthermore, the IFC (Industrial Foundation Category) standard is adopted to achieve 3D collaborative design of multi-disciplinary models (architecture, structure, MEP, etc.). Parametric objects (such as columns and windows) replace the point, line, and surface representations of traditional 2D drawings, ensuring consistency in geometric and attribute data. This allows for the modeling of various project drawings, resulting in several project models, which are then merged to obtain a unified project model. For example, tools such as Revit support model merging and conflict detection, and combined with 4D / 5D simulation, enable visualized management of construction progress and costs.
[0046] Optionally, after obtaining the target project data, data cleaning can be performed on the target project data.
[0047] Multiple imputation was used to handle missing numerical values; mode or pattern matching was used to fill in categorical variables.
[0048] For images of the device in operation, computer vision algorithms (such as Sobel edge detection) are used to identify blurred areas in the image; the blurred image is restored through super-resolution reconstruction (such as SRGAN).
[0049] S220. Input the target project data into the pre-trained project risk prediction model.
[0050] S230. Input the construction log and project drawings into the construction risk prediction sub-model to generate the target construction risk; wherein, the construction risk prediction sub-model is trained using a spatiotemporal graph convolutional network as the initial model.
[0051] For construction logs and project drawings, the construction risk prediction sub-model is input into the construction risk prediction sub-model. The sub-model analyzes the construction logs and, in conjunction with the project drawings, determines the specific locations where risks exist.
[0052] S240. Input the equipment operation image and equipment operation parameters into the equipment fault prediction sub-model to generate equipment fault risk; wherein, the equipment fault prediction sub-model is trained using the Transformer model as the initial model.
[0053] After obtaining the equipment operation images and parameters, the equipment operation images and parameters are input into the equipment fault prediction sub-model. The equipment operation images and parameters are used to analyze whether there are faults in the equipment during operation, including but not limited to fire, smoke, power failure, etc.
[0054] S250. Based on historical procurement data, determine the procurement quantity and the evaluation score of each supplier.
[0055] Based on historical procurement data and the actual needs of the target project, the required procurement quantity for the project can be determined. Furthermore, based on historical procurement data, the supply prices, market prices, and delivery times of different suppliers can be determined, thus enabling the generation of supplier evaluation scores for each supplier.
[0056] Optionally, based on historical procurement data, determine the procurement quantity and the evaluation scores of each supplier, including:
[0057] Based on historical procurement data, determine the unit price fluctuations of each raw material and the order quantity of each raw material.
[0058] Determine the raw material requirements for the target project;
[0059] The purchase quantity is determined based on the raw material demand of the target project, the unit price fluctuation of each raw material, and the order quantity of each raw material.
[0060] Determine the delivery time and quality of goods from each supplier;
[0061] Each supplier's evaluation score is determined based on their delivery time and the quality of their goods.
[0062] To determine the purchase quantity and the evaluation scores of each supplier, we can first analyze the raw material prices based on historical purchase data to determine the unit price fluctuations of each raw material and the order quantity of each raw material.
[0063] Determine the raw material requirements for the target project, and based on these requirements, unit price fluctuations for each raw material, and order quantities, determine the procurement quantity. Furthermore, determine the evaluation score for each supplier based on their delivery time and product quality. Product quality can refer to the quality of the raw materials provided by the supplier.
[0064] The supplier evaluation score can also incorporate the supplier's service and financial situation for comprehensive calculation.
[0065] Supplier evaluation score = Goods quality × 0.4 + Delivery time × 0.3 + Service × 0.2 + Financial status × 0.1;
[0066] The supplier evaluation score can be calculated using the above formula, where the parameters in the formula can be adjusted according to the actual situation.
[0067] S260. Input the purchase quantity and the evaluation scores of each supplier into the supply chain delay prediction sub-model to obtain the supply chain risk.
[0068] Since the supplier evaluation score includes the time required for the supplier to supply, it can be combined with the purchase quantity to determine whether there are risks in each supply chain through the supply chain delay prediction sub-model.
[0069] The technical solution of this application generates target construction risks by inputting construction logs and project drawings into a construction risk prediction sub-model, which is trained using a spatiotemporal graph convolutional network as the initial model. Equipment operation images and parameters are input into an equipment failure prediction sub-model to generate equipment failure risks, which are trained using a Transformer model as the initial model. Based on historical procurement data, the procurement quantity and supplier evaluation scores are determined. These are then input into a supply chain delay prediction sub-model to obtain supply chain risks. This allows for the prediction of supply chain risks, equipment failure risks, and construction risks, thereby enabling overall prediction of the target project and ensuring its stable operation.
[0070] Example 3
[0071] Figure 3 This invention provides a structural block diagram of a project risk assessment device, applicable to situations where project risks are predicted during project operation. The project risk assessment device can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 3As shown, the project risk determination device of this embodiment may include: a project data determination module 310 and a project risk determination module 320. Wherein:
[0072] The project data determination module 310 is used to determine the target project data of the target project. The project data includes historical procurement data, construction logs, meeting recordings, equipment operation images, equipment operation parameters, and at least one project drawing.
[0073] The project risk determination module 320 is used to input the target project data into a pre-trained project risk prediction model to generate the project risk of the target project.
[0074] Based on the above embodiments, optionally, the project risk prediction model includes a construction risk prediction sub-model, an equipment failure prediction sub-model, and a supply chain delay prediction sub-model.
[0075] Based on the above embodiments, optionally, the project risk of the target project is generated, including:
[0076] The construction logs and project drawings are input into the construction risk prediction sub-model to generate the target construction risk; the construction risk prediction sub-model is trained using a spatiotemporal graph convolutional network as the initial model.
[0077] Based on the above embodiments, optionally, the project risk of the target project is generated, including:
[0078] The equipment operation images and equipment operation parameters are input into the equipment failure prediction sub-model to generate equipment failure risk; the equipment failure prediction sub-model is trained using the Transformer model as the initial model.
[0079] Based on the above embodiments, optionally, the project risk of the target project is generated, including:
[0080] Based on historical procurement data, determine the procurement quantity and the evaluation scores of each supplier.
[0081] By inputting the purchase quantity and the evaluation scores of each supplier into the supply chain delay prediction sub-model, the supply chain risk is obtained.
[0082] Based on the above embodiments, optionally, the purchase quantity and evaluation scores of each supplier are determined based on historical procurement data, including:
[0083] Based on historical procurement data, determine the unit price fluctuations of each raw material and the order quantity of each raw material.
[0084] Determine the raw material requirements for the target project;
[0085] The purchase quantity is determined based on the raw material demand of the target project, the unit price fluctuation of each raw material, and the order quantity of each raw material.
[0086] Determine the delivery time and quality of goods from each supplier;
[0087] Each supplier's evaluation score is determined based on their delivery time and the quality of their goods.
[0088] Based on the above embodiments, optionally, after determining the target project data of the target project, the method further includes:
[0089] Convert meeting recordings into text to generate meeting transcripts;
[0090] Modeling was performed on the drawings of each project to obtain several project models;
[0091] By merging the various project models, a project fusion model is obtained;
[0092] Accordingly, the target project data is input into a pre-trained project risk prediction model, including;
[0093] The meeting text, project integration model, historical procurement data, construction logs, equipment operation images, and equipment operation parameters are input into the pre-trained project risk prediction model.
[0094] The project risk determination device provided in the embodiments of the present invention can execute the project risk determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.
[0095] Example 4
[0096] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0097] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0098] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0099] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as project risk determination methods.
[0100] In some embodiments, the project risk assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the project risk assessment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the project risk assessment method by any other suitable means (e.g., by means of firmware).
[0101] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0102] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0103] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0105] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0106] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0107] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0108] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method of project risk determination, characterized by, The method comprises the following steps: determining target project data of a target project, wherein the project data comprises historical procurement data, construction logs, conference recordings, equipment operation images, equipment operation parameters, and at least one project drawing; inputting the target project data of the target project into a pre-trained project risk prediction model to generate a project risk of the target project.
2. The method of claim 1, wherein, The project risk prediction model comprises a construction risk prediction sub-model, an equipment failure prediction sub-model, and a supply chain delay prediction sub-model.
3. The method of claim 2, wherein, The project risk prediction model comprises a construction risk prediction sub-model, an equipment failure prediction sub-model, and a supply chain delay prediction sub-model. The project risk prediction model comprises a construction risk prediction sub-model, an equipment failure prediction sub-model, and a supply chain delay prediction sub-model.
4. The method of claim 2, wherein, inputting the construction logs and the project drawing into the construction risk prediction sub-model to generate a target construction risk, wherein the construction risk prediction sub-model is trained based on a spatio-temporal graph convolution network. The project risk prediction model comprises a construction risk prediction sub-model, an equipment failure prediction sub-model, and a supply chain delay prediction sub-model.
5. The method of claim 2, wherein, inputting the equipment operation images and the equipment operation parameters into the equipment failure prediction sub-model to generate an equipment failure risk, wherein the equipment failure prediction sub-model is trained based on a Transformer model. The project risk prediction model comprises a construction risk prediction sub-model, an equipment failure prediction sub-model, and a supply chain delay prediction sub-model. determining procurement quantities and supplier evaluation scores based on the historical procurement data; 6. The method of claim 5, wherein, inputting the procurement quantities and the supplier evaluation scores into the supply chain delay prediction sub-model to obtain a supply chain risk. determining procurement quantities and supplier evaluation scores based on the historical procurement data, comprising: determining unit price fluctuations of each raw material and order quantities of each raw material based on the historical procurement data; determining a raw material demand of the target project; determining procurement quantities based on the raw material demand of the target project, the unit price fluctuations of each raw material, and the order quantities of each raw material; determining delivery times and cargo qualities of each supplier; 7. The method of claim 1, wherein, determining supplier evaluation scores based on the delivery times and the cargo qualities of each supplier. After determining the target project data of the target project, the method further comprises the following steps: performing text conversion on the conference recordings to generate conference texts; modeling each of the project drawings to obtain a plurality of project models; fusing the project models to obtain a project fusion model; correspondingly, inputting the target project data of the target project into the pre-trained project risk prediction model comprises:
8. An item risk determination apparatus characterized by comprising: inputting the conference texts, the project fusion model, the historical procurement data, the construction logs, the equipment operation images, and the equipment operation parameters into the pre-trained project risk prediction model. The method comprises the following steps: a project data determination module configured to determine target project data of a target project, wherein the project data comprises historical procurement data, construction logs, conference recordings, equipment operation images, equipment operation parameters, and at least one project drawing; 9. An electronic device, comprising: a project risk determination module configured to input the target project data of the target project into a pre-trained project risk prediction model to generate a project risk of the target project. The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the project risk determination method in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the project risk determination method in any one of claims 1-7 when executed.