Project evaluation method, device, electronic device and storage medium

Abstract

The present invention discloses a project assessment method, device, electronic device and storage medium. The method comprises: obtaining the initial risk data of the project; obtaining the historical loss distribution of the project within a preset time interval, performing random simulation based on the historical loss distribution within the preset time interval, obtaining loss simulation results obtained by simulating a preset number of simulations, determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations; and determining the risk level of the project based on the initial risk data and the current predicted loss. Through the above technical solution, the problem of low accuracy of project risk level assessment is solved, and the accuracy of project assessment is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a project evaluation method, device, electronic equipment and storage medium. Background Art

[0002] With the development of technologies such as big data and intelligent information management and control, risk monitoring has been widely used in financial institutions at all levels. Through risk monitoring, risk warnings and intelligent management and control of abnormal business situations of financial institutions can be achieved.

[0003] In the prior art, the expert evaluation method is usually used to predict the project risk level. This method is highly subjective and the accuracy of the project risk level assessment is low. Summary of the invention

[0004] The present invention provides a project evaluation method, device, electronic device and storage medium invention name to solve the problem of low accuracy of project risk level evaluation and improve the accuracy of project evaluation.

[0005] According to one aspect of the present invention, there is provided a project evaluation method, comprising:

[0006] Obtain initial risk data for the project;

[0007] Obtaining the historical loss distribution of the project within a preset time interval, performing random simulation based on the historical loss distribution within the preset time interval, obtaining loss simulation results obtained by simulating a preset number of simulations, and determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations;

[0008] A risk level of the project is determined based on the initial risk data and the current predicted loss.

[0009] According to another aspect of the present invention, there is provided a project evaluation device, characterized in that it comprises:

[0010] Data acquisition module, used to obtain initial risk data of the project;

[0011] A loss prediction module, used to obtain the historical loss distribution of the project within a preset time interval, perform random simulation based on the historical loss distribution within the preset time interval, obtain loss simulation results obtained by simulating a preset number of simulations, and determine the current predicted loss of the project based on the loss simulation results of the preset number of simulations;

[0012] A level determination module is used to determine the risk level of the project based on the initial risk data and the current predicted loss.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the project evaluation method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the project evaluation method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention achieves the acquisition of current assessment data by acquiring the initial risk data of the project; further, the historical loss distribution of the project within a preset time interval is acquired to achieve the acquisition of historical data, so as to perform random simulation on the historical loss distribution within the preset time interval to obtain loss simulation results obtained by simulating a preset number of simulations, and the current predicted loss of the project is determined based on the loss simulation results of the preset number of simulations to achieve the prediction of the current loss; and then the risk level of the project is determined through the initial risk data and the current predicted loss, so as to achieve the quantitative assessment of the risk level, so as to solve the problems of strong subjectivity of the current assessment method and low accuracy of the project risk level assessment, and improve the accuracy of project assessment.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flowchart of a project evaluation method provided according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a project evaluation method provided according to Embodiment 2 of the present invention;

[0023] Figure 3is a flowchart of a project evaluation method provided according to Embodiment 3 of the present invention;

[0024] Figure 4 is a schematic diagram of the structure of a project evaluation device provided according to a fourth embodiment of the present invention;

[0025] Figure 5 It is a structural schematic diagram of an electronic device for implementing the project evaluation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "initial", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 This is a flowchart of a project evaluation method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where risk level evaluation is performed based on the initial risk data and historical loss distribution of the project. The method can be executed by the project evaluation device provided in the embodiment of the present invention. The device can be implemented by software and / or hardware. The device can be configured on an electronic computing device, such as a terminal and / or a server. Specifically, the following steps are included:

[0030] S110. Obtain initial risk data for the project.

[0031] Among them, the project refers to a business project to be evaluated for risk level, such as a business project that the company needs to implement or operate. Projects may include but are not limited to main projects and sub-projects. Sub-projects may include multi-level sub-projects, which are used to further divide projects or sub-projects; initial risk data refers to the risk assessment score of the project obtained according to the expert evaluation method, and the initial risk data can be used to evaluate the risk level of the current project. The initial risk data can be directly obtained from the preset storage location, or calculated through the basic data of the project, which is not limited here.

[0032] For example, taking the business project of a financial company as an example, the multi-level sub-projects may include but are not limited to the first-level sub-projects and the second-level sub-projects, where the second-level sub-projects are sub-projects of the first-level sub-projects. The first-level sub-projects may include but are not limited to personnel, internal processes, systems and external environment, and the second-level sub-projects may include but are not limited to employee ethics, professional ability, macro environment and policies, external fraud and theft, etc. The contents of the above sub-projects are only examples, and specific projects can be selected or set in combination with the actual situation of the project, and are not limited.

[0033] Based on the above embodiment, the initial risk data of the project can be obtained by weighted calculation based on the weights of the sub-projects in the project and the risk data of the sub-projects.

[0034] Specifically, by performing a weighted summary calculation on the weights and scores of each sub-project in the project, the initial risk data of the project, that is, the initial project risk score, can be obtained, providing a reference for the risk level assessment of the project.

[0035] On the basis of the above embodiment, after obtaining the initial risk data of the project, the method further includes: judging the initial risk data based on a preset risk threshold to obtain an initial risk level.

[0036] The preset risk threshold may be a judgment threshold set based on experience, and may be used to judge the initial risk data, thereby determining an initial risk level corresponding to the initial risk data.

[0037] In some embodiments, the preset risk threshold may include a first risk threshold and a second risk threshold, and the second risk threshold is less than the first risk threshold, and the risk level may be divided into three levels: low, medium, and high. If the initial risk data is greater than the first risk threshold, the risk level is a high risk level; if the initial risk data is less than the first risk threshold and greater than the second risk threshold, the risk level is a medium risk level; if the initial risk data is less than the second risk threshold, the risk level is a low risk level. In some embodiments, the number of preset risk thresholds may be greater than two, and the risk level may be divided more finely. The manner in which the preset risk threshold is set in the embodiments of the present invention is not limited herein.

[0038] S120, obtaining the historical loss distribution of the project within a preset time interval, performing random simulation based on the historical loss distribution within the preset time interval, obtaining loss simulation results obtained by simulating a preset number of simulations, and determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations.

[0039] In an embodiment of the present invention, historical loss distribution refers to the loss data of a project within a preset time interval, which may include but is not limited to historical risk frequency and historical risk loss. Historical risk frequency refers to the frequency of occurrence of a project, and historical risk loss refers to the loss caused by the occurrence of the project. The loss may include but is not limited to monetary loss, resource loss, etc. The historical loss distribution may be directly obtained by statistical analysis from a preset storage location. For example, the preset storage location may be a database storing historical loss distribution. Random simulation refers to the experimental statistics of historical loss distribution within a preset time interval. The preset number of simulations may be set based on experience. It is understandable that as the number of random simulations increases, the prediction accuracy of the current predicted loss will gradually increase. Each random simulation may estimate the future loss of the project based on the historical loss distribution to obtain the loss simulation result corresponding to the current random simulation. Further, the current predicted loss of the project may be obtained by statistically aggregating and screening the loss simulation results corresponding to each random simulation. The loss simulation result refers to the set of losses generated by multiple random simulations. The current predicted loss refers to the loss value obtained by screening the loss simulation results.

[0040] S130. Determine the risk level of the project based on the initial risk data and the current predicted loss.

[0041] Among them, the initial risk data is the current data of the project, and the current predicted loss is obtained based on the prediction of historical data. This application determines the risk level of the project based on the initial risk data and the current predicted loss, that is, the risk level of the project is determined using the predicted data of current data and historical data, which not only ensures the real-time nature of the data, but also refers to the predicted data based on historical data, thereby improving the accuracy of the risk level.

[0042] An embodiment of the present invention provides a project assessment method, which obtains the initial risk data of the project to obtain current assessment data; further, obtains the historical loss distribution of the project within a preset time interval to obtain historical data, and performs random simulation on the historical loss distribution within the preset time interval to obtain loss simulation results obtained by simulating a preset number of simulations, and determines the current predicted loss of the project based on the loss simulation results of the preset number of simulations to predict the current loss; and then determines the risk level of the project through the initial risk data and the current predicted loss, and achieves quantitative assessment of the risk level, so as to solve the problems of strong subjectivity of current assessment methods and low accuracy of project risk level assessment, and improve the accuracy of project assessment.

[0043] Embodiment 2

[0044] Figure 2 A flowchart diagram of a project evaluation method provided for the second embodiment of the present invention, based on the above embodiment, further refines "obtaining the historical loss distribution of the project within a preset time interval, performing random simulation based on the historical loss distribution within the preset time interval, obtaining loss simulation results obtained by simulating a preset number of simulations, and determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations". For its specific implementation method, please refer to the detailed description of the present technical solution. Among them, the technical terms that are the same as or corresponding to the above embodiment are not repeated here. Figure 2 As shown, the method of the embodiment of the present invention specifically includes the following steps:

[0045] S210. Obtain initial risk data for the project.

[0046] S220. Obtain the historical loss distribution of the project within a preset time interval, fit the historical risk frequency of the randomly simulated corresponding event within the preset time interval to obtain frequency probability distribution data, and fit the historical risk loss of the randomly simulated corresponding event within the preset time interval to obtain risk loss distribution data.

[0047] The event corresponding to the random simulation refers to a historical risk event that can be called by each random simulation. In each preset number of simulations, at least one of the project, historical risk frequency, and historical risk loss of the event corresponding to the random simulation is different to ensure that the frequency probability distribution data and risk loss distribution data generated by each simulation are different. In an embodiment of the present invention, the random simulation can be a Monte Carlo simulation, which can use the historical loss distribution to estimate the future losses of the project and realize the prediction of the current predicted losses of the project, so as to make up for the deficiency of using the initial risk data to evaluate the risk level of the project.

[0048] Specifically, the frequency fitting function can be used to fit the historical risk frequency of the simulated corresponding event within the preset time interval to obtain the frequency probability distribution data; the loss fitting function can be used to fit the historical risk loss of the randomly simulated corresponding event within the preset time interval to obtain the risk loss distribution data. The frequency probability distribution data and the risk loss distribution data can effectively reflect the historical risk frequency of historical events and the distribution of historical risk losses, and provide reliable parameters for the subsequent prediction of current predicted losses.

[0049] On the basis of the above embodiments, the historical risk frequency of the randomly simulated event within the preset time interval is fitted to obtain frequency probability distribution data, and the historical risk loss of the randomly simulated event within the preset time interval is fitted to obtain risk loss distribution data, including: fitting the historical risk frequency of the randomly simulated event within the preset time interval according to the Poisson distribution to obtain frequency probability distribution data, and fitting the historical risk loss of the randomly simulated event within the preset time interval according to the lognormal distribution to obtain risk loss distribution data.

[0050] It should be noted that the frequency probability distribution data calculated by Poisson distribution has a data distribution state of being thick in the middle and thin on both sides, which fits the distribution state of the actual frequency of occurrence of the project; the risk loss distribution data calculated by lognormal distribution has a data distribution state of being thick in the front and thin in the back, which fits the distribution state of the actual losses generated by the project, thereby providing reliable calculation parameters for the follow-up.

[0051] S230, multiplying the frequency probability distribution data and the risk loss distribution data of the event corresponding to the random simulation to obtain a plurality of risk losses, and adding up the risk losses to obtain the cumulative loss of the event corresponding to the random simulation.

[0052] Specifically, a corresponding number of random number samples can be generated based on the frequency probability distribution data. In each random number sample, the frequency probability distribution data of the corresponding event of the random simulation and the risk loss distribution data are multiplied to obtain the risk loss corresponding to the random number sample; further, the multiple risk losses corresponding to each random number sample are added together to obtain the cumulative loss of the corresponding event of the random simulation, and the cumulative loss can be the cumulative loss value of the next time interval of the current random simulation corresponding event.

[0053] S240. Count the accumulated losses of the events corresponding to the random simulations to obtain loss simulation results obtained by simulating a preset number of simulations, and determine the current predicted loss of the project based on the loss simulation results of the preset number of simulations, wherein in each preset number of simulations, at least one of the project, historical risk frequency and historical risk loss of the events corresponding to the random simulations is different.

[0054] Exemplarily, the preset number of simulations may be N times, and the cumulative losses of the N random simulation corresponding events are counted to obtain a loss simulation result including the cumulative losses of the N random simulation corresponding events, and the loss simulation result may be stored in the form of a distribution array.

[0055] Based on the above embodiment, determining the current predicted loss of the project based on the loss simulation results of the preset simulation times includes: determining the loss simulation results of the preset simulation times within a preset confidence interval as the current predicted loss.

[0056] The preset confidence interval can be set according to the screening accuracy. For example, the preset confidence interval can be a 99% confidence interval, and the loss simulation result within the 99% confidence interval is determined as the current predicted loss. The current predicted loss has a high degree of credibility, which can effectively improve the reliability of the risk level of the project.

[0057] S250: Determine the risk level of the project based on the initial risk data and the current predicted loss.

[0058] An embodiment of the present invention provides a project evaluation method, which obtains frequency probability distribution data by fitting the historical risk frequency of the random simulation corresponding event within a preset time interval, and obtains risk loss distribution data by fitting the historical risk loss of the random simulation corresponding event within the preset time interval, thereby providing reliable parameters for the prediction of subsequent cumulative losses; further, the frequency probability distribution data and the risk loss distribution data of the random simulation corresponding event are multiplied to obtain multiple risk losses, and the risk losses are added together to obtain the cumulative loss of the random simulation corresponding event, thereby realizing quantitative evaluation of the cumulative losses and improving the reliability of project evaluation.

[0059] Embodiment 3

[0060] Figure 3 A flowchart of a project evaluation method provided for the third embodiment of the present invention, the embodiment of the present invention can be combined with the various optional schemes in the above embodiments. In the embodiment of the present invention, optionally, the risk level of the project is determined based on the initial risk data and the current predicted loss, including: multiplying the initial risk data corresponding to each monitoring rule and the current predicted loss to obtain a risk assessment score corresponding to each monitoring rule; sorting the risk assessment scores corresponding to each monitoring rule, judging the sorting results based on a preset level threshold, and obtaining the risk level of the project.

[0061] like Figure 3 As shown, the method of the embodiment of the present invention specifically includes the following steps:

[0062] S310. Obtain initial risk data for the project.

[0063] S320, obtaining the historical loss distribution of the project within a preset time interval, performing random simulation based on the historical loss distribution within the preset time interval, obtaining loss simulation results obtained by simulating a preset number of simulations, and determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations.

[0064] S330: Multiply the initial risk data corresponding to each monitoring rule and the current predicted loss to obtain a risk assessment score corresponding to each monitoring rule.

[0065] S340, sorting the risk assessment scores corresponding to the monitoring rules, and judging the sorting results based on a preset level threshold to obtain the risk level of the project.

[0066] Among them, the monitoring rules can be set according to the project and its sub-projects. Specifically, any project can set sub-projects with different levels, types and other indicators, and multiple monitoring rules can be set according to different sub-projects. In the embodiment of the present invention, the project level is evaluated according to different monitoring rules, and multiple evaluation results can be obtained, which improves the diversity of the evaluation results.

[0067] In an embodiment of the present invention, the risk assessment score can be obtained by multiplying the current predicted loss of the initial risk data, thereby realizing the combination of current data and historical predicted data; further, the risk assessment scores corresponding to each monitoring rule are sorted, the maximum or minimum risk assessment score is selected from multiple risk assessment scores, and the risk assessment scores are determined according to a preset level threshold, thereby obtaining the project risk level. This method is simple and effective, can quickly determine the risk level of the project, and improves the efficiency of risk level determination.

[0068] An embodiment of the present invention provides a project evaluation method, which combines the current data with the historical prediction data by multiplying the initial risk data corresponding to each monitoring rule and the current predicted loss, so that the obtained risk evaluation score is more accurate; further, the risk evaluation scores corresponding to each monitoring rule are sorted, and then the sorting results are judged according to a preset level threshold to obtain the risk level of the project, thereby achieving quantitative evaluation of the risk level and improving the reliability of project evaluation.

[0069] Embodiment 4

[0070] Figure 4This is a schematic diagram of the structure of a project evaluation device provided in Embodiment 4 of the present invention. The project evaluation device provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal and / or a server to implement the project evaluation method in the embodiment of the present invention. The device can specifically include: a data acquisition module 410, a loss prediction module 420, and a level determination module 430.

[0071] Among them, the data acquisition module 410 is used to obtain the initial risk data of the project; the loss prediction module 420 is used to obtain the historical loss distribution of the project within a preset time interval, perform random simulation based on the historical loss distribution within the preset time interval, obtain the loss simulation results obtained by simulating a preset number of simulations, and determine the current predicted loss of the project based on the loss simulation results of the preset number of simulations; the level determination module 430 is used to determine the risk level of the project based on the initial risk data and the current predicted loss.

[0072] An embodiment of the present invention provides a project evaluation device, which obtains the initial risk data of the project to obtain the current evaluation data; further, obtains the historical loss distribution of the project within a preset time interval to obtain the historical data, and performs random simulation on the historical loss distribution within the preset time interval to obtain the loss simulation results obtained by simulating a preset number of simulations, and determines the current predicted loss of the project based on the loss simulation results of the preset number of simulations to predict the current loss; and then determines the risk level of the project through the initial risk data and the current predicted loss, and realizes the quantitative evaluation of the risk level, so as to solve the problems that the current evaluation method is highly subjective and the accuracy of the project risk level evaluation is low, thereby improving the accuracy of project evaluation.

[0073] Based on any optional technical solution in the embodiments of the present invention, optionally, the initial risk data of the project is obtained by weighted calculation based on sub-project weights and sub-project risk data in the project.

[0074] Based on any optional technical solution in the embodiments of the present invention, optionally, the device further includes:

[0075] The initial risk level determination module is used to judge the initial risk data based on a preset risk threshold to obtain an initial risk level.

[0076] Based on any optional technical solution in the embodiments of the present invention, optionally, the historical loss distribution includes historical risk frequency and historical risk loss, and the loss prediction module 420 includes:

[0077] A frequency data acquisition unit is used to fit the historical risk frequency of the random simulation corresponding event within a preset time interval to obtain frequency probability distribution data;

[0078] A loss data acquisition unit is used to fit the historical risk losses of the random simulation corresponding events within a preset time interval to obtain risk loss distribution data;

[0079] A cumulative loss calculation unit, used for multiplying the frequency probability distribution data and the risk loss distribution data of the random simulation corresponding event to obtain a plurality of risk losses, and adding the risk losses to obtain the cumulative loss of the random simulation corresponding event;

[0080] The simulation result determination unit is used to count the cumulative losses of each event corresponding to the random simulation, and obtain the loss simulation results obtained by simulating a preset number of simulations, wherein in each preset number of simulations, at least one of the items, historical risk frequency and historical risk losses of the event corresponding to the random simulation is different.

[0081] Based on any optional technical solution in the embodiments of the present invention, optionally, the frequency data acquisition unit may also be used to:

[0082] According to the Poisson distribution, the historical risk frequency of the corresponding random simulation event within the preset time interval is fitted to obtain the frequency probability distribution data.

[0083] Based on any optional technical solution in the embodiments of the present invention, optionally, the loss data acquisition unit may also be used to:

[0084] According to the log-normal distribution, the historical risk losses of the randomly simulated corresponding events within the preset time interval are fitted to obtain the risk loss distribution data.

[0085] Based on any optional technical solution in the embodiments of the present invention, optionally, the loss prediction module 420 may also be used to:

[0086] The loss simulation results of the preset number of simulations that are within the preset confidence interval are determined as the current predicted loss.

[0087] Based on any optional technical solution in the embodiment of the present invention, optionally, the level determination module 430 may also be used to:

[0088] Multiplying the initial risk data corresponding to each monitoring rule and the current predicted loss to obtain a risk assessment score corresponding to each monitoring rule;

[0089] The risk assessment scores corresponding to the monitoring rules are sorted, and the sorting results are judged based on a preset level threshold to obtain the risk level of the project.

[0090] The project evaluation device provided in the embodiment of the present invention can execute the project evaluation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0091] Embodiment 5

[0092] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0093] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0094] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0095] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the project evaluation method.

[0096] In some embodiments, the project evaluation method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the project evaluation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the project evaluation method in any other appropriate manner (e.g., by means of firmware).

[0097] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0098] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0099] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0100] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0101] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0102] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0103] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0104] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A project evaluation method, characterized in that: include: Obtaining initial risk data of the project, wherein the initial risk data of the project is obtained by weighted calculation based on sub-project weights and sub-project risk data in the project; Obtaining the historical loss distribution of the project within a preset time interval, performing random simulation based on the historical loss distribution within the preset time interval, obtaining loss simulation results obtained by simulating a preset number of simulations, and determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations; Determining a risk level of the project based on the initial risk data and the current predicted loss; After obtaining the initial risk data of the project, the method further includes: The initial risk data is judged based on the preset risk threshold to obtain the initial risk level; The preset risk threshold includes a first risk threshold and a second risk threshold, the second risk threshold is less than the first risk threshold, and the initial risk data is judged based on the preset risk threshold to obtain an initial risk level, including: if the initial risk data is greater than the first risk threshold, the risk level is a high risk level; if the initial risk data is less than the first risk threshold and greater than the second risk threshold, the risk level is a medium risk level; if the initial risk data is less than the second risk threshold, the risk level is a low risk level.

2. The method according to claim 1, characterized in that The historical loss distribution includes historical risk frequency and historical risk loss. The random simulation based on the historical loss distribution within a preset time interval to obtain the loss simulation results obtained by simulating a preset number of simulations includes: Fit the historical risk frequency of the random simulation corresponding event within the preset time interval to obtain the frequency probability distribution data; Fit the historical risk losses of the random simulation corresponding events within the preset time interval to obtain the risk loss distribution data; Multiplying the frequency probability distribution data and the risk loss distribution data of the event corresponding to the random simulation to obtain a plurality of risk losses, and adding the risk losses to obtain the cumulative loss of the event corresponding to the random simulation; The accumulated losses of the events corresponding to the random simulations are counted to obtain loss simulation results obtained by simulating a preset number of simulations, wherein in each preset number of simulations, at least one of the items, historical risk frequencies and historical risk losses of the events corresponding to the random simulations is different.

3. The method according to claim 2, characterized in that The historical risk frequency of the random simulation corresponding event within a preset time interval is fitted to obtain frequency probability distribution data; The historical risk losses of the random simulation corresponding events within the preset time interval are fitted to obtain the risk loss distribution data, including: According to the Poisson distribution, the historical risk frequency of the random simulation corresponding event in the preset time interval is fitted to obtain the frequency probability distribution data; According to the log-normal distribution, the historical risk losses of the randomly simulated corresponding events within the preset time interval are fitted to obtain the risk loss distribution data.

4. The method according to claim 1, characterized in that: The determining the current predicted loss of the project based on the loss simulation results of the preset number of simulations includes: The loss simulation results of the preset number of simulations that are within the preset confidence interval are determined as the current predicted loss.

5. The method according to claim 1, characterized in that The determining the risk level of the project based on the initial risk data and the current predicted loss includes: Multiplying the initial risk data corresponding to each monitoring rule and the current predicted loss to obtain a risk assessment score corresponding to each monitoring rule; The risk assessment scores corresponding to the monitoring rules are sorted, and the sorting results are judged based on a preset level threshold to obtain the risk level of the project.

6. A project evaluation device, characterized in that: include: A data acquisition module, used to acquire initial risk data of a project, wherein the initial risk data of the project is obtained by weighted calculation based on sub-project weights and sub-project risk data in the project; A loss prediction module, used to obtain the historical loss distribution of the project within a preset time interval, perform random simulation based on the historical loss distribution within the preset time interval, obtain loss simulation results obtained by simulating a preset number of simulations, and determine the current predicted loss of the project based on the loss simulation results of the preset number of simulations; A level determination module, configured to determine the risk level of the project based on the initial risk data and the current predicted loss; The device also includes: An initial risk level determination module, used to determine the initial risk data based on a preset risk threshold to obtain an initial risk level; the preset risk threshold includes a first risk threshold and a second risk threshold, and the second risk threshold is less than the first risk threshold; The initial risk level determination module is specifically used to determine that if the initial risk data is greater than a first risk threshold, the risk level is a high risk level; if the initial risk data is less than the first risk threshold and greater than a second risk threshold, the risk level is a medium risk level; if the initial risk data is less than the second risk threshold, the risk level is a low risk level.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; 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 so that the at least one processor can perform the project evaluation method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the project evaluation method according to any one of claims 1 to 5 when executed.

Images